query: Critical infrastructure interdependency modeling: Using graph models to assess the vulnerability of smart power grid and SCADA networks

positive: Statistical mechanics of complex networks Complex networks describe a wide range of systems in nature and society. Frequently cited examples include the cell, a network of chemicals linked by chemical reactions, and the Internet, a network of routers and computers connected by physical links. While traditionally these systems have been modeled as random graphs, it is increasingly recognized that the topology and evolution of real networks are governed by robust organizing principles. This article reviews the recent advances in the field of complex networks, focusing on the statistical mechanics of network topology and dynamics. After reviewing the empirical data that motivated the recent interest in networks, the authors discuss the main models and analytical tools, covering random graphs, small-world and scale-free networks, the emerging theory of evolving networks, and the interplay between topology and the network’s robustness against failures and attacks.

positive: Emergence of Scaling in Random Networks www.sciencemag.org (this information is current as of September 11, 2008 ): The following resources related to this article are available online at http://www.sciencemag.org/cgi/content/full/286/5439/509 version of this article at: including high-resolution figures, can be found in the online Updated information and services, found at: can be related to this article A list of selected additional articles on the Science Web sites http://www.sciencemag.org/cgi/content/full/286/5439/509#related-content http://www.sciencemag.org/cgi/content/full/286/5439/509#otherarticles , 3 of which can be accessed for free: cites 11 articles This article 2718 article(s) on the ISI Web of Science. cited by This article has been http://www.sciencemag.org/cgi/content/full/286/5439/509#otherarticles 95 articles hosted by HighWire Press; see: cited by This article has been http://www.sciencemag.org/cgi/collection/physics Physics : subject collections This article appears in the following http://www.sciencemag.org/about/permissions.dtl in whole or in part can be found at: this article permission to reproduce of this article or about obtaining reprints Information about obtaining

positive: Collective dynamics of ‘small-world’ networks Networks of coupled dynamical systems have been used to model biological oscillators, Josephson junction arrays,, excitable media, neural networks, spatial games, genetic control networks and many other self-organizing systems. Ordinarily, the connection topology is assumed to be either completely regular or completely random. But many biological, technological and social networks lie somewhere between these two extremes. Here we explore simple models of networks that can be tuned through this middle ground: regular networks ‘rewired’ to introduce increasing amounts of disorder. We find that these systems can be highly clustered, like regular lattices, yet have small characteristic path lengths, like random graphs. We call them ‘small-world’ networks, by analogy with the small-world phenomenon, (popularly known as six degrees of separation). The neural network of the worm Caenorhabditis elegans, the power grid of the western United States, and the collaboration graph of film actors are shown to be small-world networks. Models of dynamical systems with small-world coupling display enhanced signal-propagation speed, computational power, and synchronizability. In particular, infectious diseases spread more easily in small-world networks than in regular lattices.

positive: Human Behaviour and the Principle of Least Effort: an Introduction to Human Ecology Some people may be laughing when looking at you reading in your spare time. Some may be admired of you. And some may want be like you who have reading hobby. What about your own feel? Have you felt right? Reading is a need and a hobby at once. This condition is the on that will make you feel that you must read. If you know are looking for the book enPDFd human behavior and the principle of least effort an introduction to human ecology as the choice of reading, you can find here.

positive: Evolutionary games and spatial chaos MUCH attention has been given to the Prisoners' Dilemma as a metaphor for the problems surrounding the evolution of coopera-tive behaviour1–6. This work has dealt with the relative merits of various strategies (such as tit-for-tat) when players who recognize each other meet repeatedly, and more recently with ensembles of strategies and with the effects of occasional errors. Here we neglect all strategical niceties or memories of past encounters, considering only two simple kinds of players: those who always cooperate and those who always defect. We explore the consequences of placing these players in a two-dimensional spatial array: in each round, every individual 'plays the game' with the immediate neighbours; after this, each site is occupied either by its original owner or by one of the neighbours, depending on who scores the highest total in that round; and so to the next round of the game. This simple, and purely deterministic, spatial version of the Prisoners' Dilemma, with no memories among players and no strategical elaboration, can generate chaotically changing spatial patterns, in which cooperators and defectors both persist indefinitely (in fluctuating proportions about predictable long-term averages). If the starting configurations are sufficiently symmetrical, these ever-changing sequences of spatial patterns—dynamic fractals—can be extraordinarily beautiful, and have interesting mathematical properties. There are potential implications for the dynamics of a wide variety of spatially extended systems in physics and biology.

negative: Disordered speech recognition using acoustic and sEMG signals Parallel isolated word corpora were collected from healthy speakers and individuals with speech impairment due to stroke or cerebral palsy. Surface electromyographic (sEMG) signals were collected for both vocalized and mouthed speech production modes. Pioneering work on disordered speech recognition using the acoustic signal, the sEMG signals, and their fusion are reported. Results indicate that speakerdependent isolated-word recognition from the sEMG signals of articulator muscle groups during vocalized disorderedspeech production was highly effective. However, word recognition accuracy for mouthed speech was much lower, likely related to the fact that some disordered speakers had considerable difficulty producing consistent mouthed speech. Further development of the sEMG-based speech recognition systems is needed to increase usability and robustness.

negative: A 3D Morphable Model Learnt from 10,000 Faces We present Large Scale Facial Model (LSFM) - a 3D Morphable Model (3DMM) automatically constructed from 9,663 distinct facial identities. To the best of our knowledge LSFM is the largest-scale Morphable Model ever constructed, containing statistical information from a huge variety of the human population. To build such a large model we introduce a novel fully automated and robust Morphable Model construction pipeline. The dataset that LSFM is trained on includes rich demographic information about each subject, allowing for the construction of not only a global 3DMM but also models tailored for specific age, gender or ethnicity groups. As an application example, we utilise the proposed model to perform age classification from 3D shape alone. Furthermore, we perform a systematic analysis of the constructed 3DMMs that showcases their quality and descriptive power. The presented extensive qualitative and quantitative evaluations reveal that the proposed 3DMM achieves state-of-the-art results, outperforming existing models by a large margin. Finally, for the benefit of the research community, we make publicly available the source code of the proposed automatic 3DMM construction pipeline. In addition, the constructed global 3DMM and a variety of bespoke models tailored by age, gender and ethnicity are available on application to researchers involved in medically oriented research.

negative: IoT based smart surveillance security system using raspberry Pi Communication mainly is the transfer of whatever thing or exchanging of data, so that the Internet of things is naught but the transferring or exchanging of anything with several other things. The using of internet authorized system or devices roughly calculated as that by 2020 there will be nearly about billions. The purpose of the paper is to define a safekeeping alert device spending little handling power by Internet of things which help out to observer plus alerts when gestures or else motion are there then send images to a cloud server. Besides, internet of things centered use can be used tenuously to observe the action as well as acquire warning when gestures or else indication are there. The images are showed straight to a cloud attendant, when the cloud attendant is not accessible at that time the records are put in storage close by on a Raspberry Pi. A credit card size Raspberry Pi with a advantage of Open Source Computer Vision (Open-CV) software knobs the image processing, control algorithms used for the attentiveness then shows taken images to concern persons email by the use of Wi-Fi module. The system uses ordinary webcam.

negative: The Scalability of Trustless Trust Permission-less blockchains can realise trustless trust, albeit at the cost of limiting the complexity of computation tasks. To explain the implications for scalability, we have implemented a trust model for smart contracts, described as agents in an open multi-agent system. Agent intentions are not necessarily known and autonomous agents have to be able to make decisions under risk. The ramifications of these general conditions for scalability are analysed for Ethereum and then generalised to other current and future platforms.

negative: On Measures of Entropy and Information 2 Mutual information 3 Mutual information . . . . . . . . . . . . . . . . . 3 Multivariate mutual information . . . . . . . . . 4 Interaction information . . . . . . . . . . . . . . . 5 Conditional mutual information . . . . . . . . . 5 Binding information . . . . . . . . . . . . . . . . 6 Residual entropy . . . . . . . . . . . . . . . . . . 6 Total correlation . . . . . . . . . . . . . . . . . . . 6 Lautum information . . . . . . . . . . . . . . . . 6 Uncertainty coefficient . . . . . . . . . . . . . . . 7

negative:
Efficacy of psychotherapeutic interventions to promote forgiveness: a meta-analysis. OBJECTIVE
This meta-analysis addressed the efficacy of psychotherapeutic interventions to help people forgive others and to examine moderators of treatment effects.

METHOD
Eligible studies reported quantitative data on forgiveness of a specific hurt following treatment by a professional with an intervention designed explicitly to promote forgiveness. Random effects meta-analyses were conducted using k = 53 posttreatment effect sizes (N = 2,323) and k = 41 follow-up effect sizes (N = 1,716) from a total of 54 published and unpublished research reports.

RESULTS
Participants receiving explicit forgiveness treatments reported significantly greater forgiveness than participants not receiving treatment (Δ+ = 0.56 [0.43, 0.68]) and participants, receiving alternative treatments (Δ+ = 0.45 [0.21, 0.69]). Also, forgiveness treatments resulted in greater changes in depression, anxiety, and hope than no-treatment conditions. Moderators of treatment efficacy included treatment dosage, offense severity, treatment model, and treatment modality. Multimoderator analyses indicated that treatment dosage (i.e., longer interventions) and modality (individual > group) uniquely predicted change in forgiveness compared with no-treatment controls. Compared with alternative treatment conditions, both modality (individual > group) and offense severity were marginally predictive (ps < .10) of treatment effects.

CONCLUSIONS
It appears that using theoretically grounded forgiveness interventions is a sound choice for helping clients to deal with past offenses and helping them achieve resolution in the form of forgiveness. Differences between treatment approaches disappeared when controlling for other significant moderators; the advantage for individual interventions was most clearly demonstrated for Enright-model interventions, as there have been no studies of individual interventions using the Worthington model.

negative: Mobile application security: malware threats and defenses Due to the quantum leap in functionality, the rate of upgrading traditional mobile phones to smartphones is tremendous. One of the most attractive features of smartphones is the availability of a large number of apps for users to download and install. However, it also means hackers can easily distribute malware to smartphones, launching various attacks. This issue should be addressed by both preventive approaches and effective detection techniques. This article first discusses why smartphones are vulnerable to security attacks. Then it presents malicious behavior and threats of malware. Next, it reviews the existing malware prevention and detection techniques. Besides more research in these directions, it points out efforts from app developers, app store administrators, and users, who are also required to defend against such malware.

negative: Hand rehabilitation based on augmented reality This paper presents an augmented reality based system for hand rehabilitation. In this system, digital data gloves are used to detect the movements of the patients' hands and collect the physical information from the patients. Using augmented reality technology, a highly controllable environment with tasks of different levels of difficulty is provided to the patients for them to perform the rehabilitation exercises gradually. The targets of the exercises can be adjusted dynamically with respect to the physical conditions and progress of the patients. Multimodal feedbacks are provided to facilitate and encourage the patients during the rehabilitation sessions.

negative: Evolutionary Computation and Convergence to a Pareto Front Research into solving multiobjective optimization problems (MOP) has as one of its an overall goals that of developing and defining foundations of an Evolutionary Computation (EC)-based MOP theory. In this paper, we introduce relevant MOP concepts, and the notion of Pareto optimality, in particular. Specific notation is defined and theorems are presented ensuring Paretobased Evolutionary Algorithm (EA) implementations are clearly understood. Then, a specific experiment investigating the convergence of an arbitrary EA to a Pareto front is presented. This experiment gives a basis for a theorem showing a specific multiobjective EA statistically converges to the Pareto front. We conclude by using this work to justify further exploration into the theoretical foundations of EC-based MOP solution methods.

negative: Windows driver memory analysis: A reverse engineering methodology In a digital forensics examination, the capture and analysis of volatile data provides significant information on the state of the computer at the time of seizure. Memory analysis is a premier method of discovering volatile digital forensic information. While much work has been done in extracting forensic artifacts from Windows kernel structures, less focus has been paid to extracting information from Windows drivers. There are two reasons for this: (1) source code for one version of the Windows kernel (but not associated drivers) is available for educational use and (2) drivers are generally called asynchronously and contain no exported functions. Therefore, finding the handful of driver functions of interest out of the thousands of candidates makes reverse code engineering problematic at best. Developing a methodology to minimize the effort of analyzing these drivers, finding the functions of interest, and extracting the data structures of interest is highly desirable. This paper provides two contributions. First, it describes a general methodology for reverse code engineering of Windows drivers memory structures. Second it applies the methodology to tcpip.sys, a Windows driver that controls network connectivity. The result is the extraction from tcpip.sys of the data structures needed to determine current network connections and listeners from the 32 and 64 bit versions of Windows Vista and Windows 7.

negative: A Hierarchical Neural Autoencoder for Paragraphs and Documents Natural language generation of coherent long texts like paragraphs or longer documents is a challenging problem for recurrent networks models. In this paper, we explore an important step toward this generation task: training an LSTM (Longshort term memory) auto-encoder to preserve and reconstruct multi-sentence paragraphs. We introduce an LSTM model that hierarchically builds an embedding for a paragraph from embeddings for sentences and words, then decodes this embedding to reconstruct the original paragraph. We evaluate the reconstructed paragraph using standard metrics like ROUGE and Entity Grid, showing that neural models are able to encode texts in a way that preserve syntactic, semantic, and discourse coherence. While only a first step toward generating coherent text units from neural models, our work has the potential to significantly impact natural language generation and summarization1.

negative: Moving beyond untagging: photo privacy in a tagged world Photo tagging is a popular feature of many social network sites that allows users to annotate uploaded images with those who are in them, explicitly linking the photo to each person's profile. In this paper, we examine privacy concerns and mechanisms surrounding these tagged images. Using a focus group, we explored the needs and concerns of users, resulting in a set of design considerations for tagged photo privacy. We then designed a privacy enhancing mechanism based on our findings, and validated it using a mixed methods approach. Our results identify the social tensions that tagging generates, and the needs of privacy tools to address the social implications of photo privacy management.

negative: A Machine Learning Approach for Product Matching and Categorization Use case : Enriching Product Ads with Semantic Structured Data Consumers today have the option to purchase products from thousands of e-shops. However, the completeness of the product specifications and the taxonomies used for organizing the products differ across different e-shops. To improve the consumer experience, e.g., by allowing for easily comparing offers by different vendors, approaches for product integration on the Web are needed. In this paper, we present an approach that leverages neural language models and deep learning techniques in combination with standard classification approaches for product matching and categorization. In our approach we use structured product data as supervision for training feature extraction models able to extract attribute-value pairs from textual product descriptions. To minimize the need for lots of data for supervision, we use neural language models to produce word embeddings from large quantities of publicly available product data marked up with Microdata, which boost the performance of the feature extraction model, thus leading to better product matching and categorization performances. Furthermore, we use a deep Convolutional Neural Network to produce image embeddings from product images, which further improve the results on both tasks.

negative: Controlling tensegrity robots through evolution Tensegrity structures (built from interconnected rods and cables) have the potential to offer a revolutionary new robotic design that is light-weight, energy-efficient, robust to failures, capable of unique modes of locomotion, impact tolerant, and compliant (reducing damage between the robot and its environment). Unfortunately robots built from tensegrity structures are difficult to control with traditional methods due to their oscillatory nature, nonlinear coupling between components and overall complexity. Fortunately this formidable control challenge can be overcome through the use of evolutionary algorithms. In this paper we show that evolutionary algorithms can be used to efficiently control a ball shaped tensegrity robot. Experimental results performed with a variety of evolutionary algorithms in a detailed soft-body physics simulator show that a centralized evolutionary algorithm performs 400% better than a hand-coded solution, while the multiagent evolution performs 800% better. In addition, evolution is able to discover diverse control solutions (both crawling and rolling) that are robust against structural failures and can be adapted to a wide range of energy and actuation constraints. These successful controls will form the basis for building high-performance tensegrity robots in the near future.

negative: Resource Management for Device-to-Device Underlay Communication Device-to-Device (D2D) communication is a technology component for LTE-A. The existing researches allow D2D as an underlay to the cellular network to increase the spectral efficiency. In this book, D2D communication underlaying cellular networks is studied. Some physical-layer techniques and cross-layer optimization methods on resource management and interference avoidance are proposed and discussed. WINNER II channel models is applied to be the signal and interference model and simulation results show that the performance of D2D link is closely related to the distance between D2D transmitter and receiver and that between interference source and the receiver. Besides, by power control, D2D SINR degrades, which will naturally contribute to low interference to cellular communication. A simple mode selection method of D2D communication is introduced. Based on path-loss (PL) mode selection criterion, D2D gives better performance than traditional cellular system. When D2D pair is farther away from the BS, a better results can be obtained. Game theory, which offers a wide variety of analytical tools to study the complex interactions of players and predict their choices, can be used for power and radio resource management in D2D communication. A reverse iterative combinatorial auction is formulated as a mechanism to allocate the spectrum resources for D2D communications with multiple user pairs sharing the same channel. In addition, a game theoretic approach is developed to implement joint scheduling, power control and channel allocation for D2D communication. Finally, joint power and spectrum resource allocation method is studied under consideration of battery lifetime, which is an important application of D2D communication on increasing user's energy efficiency. The simulation results show that all these methods have beneficial effects on improving the system performance.

negative: Bayesian Compressive Sensing Using Laplace Priors In this paper, we model the components of the compressive sensing (CS) problem, i.e., the signal acquisition process, the unknown signal coefficients and the model parameters for the signal and noise using the Bayesian framework. We utilize a hierarchical form of the Laplace prior to model the sparsity of the unknown signal. We describe the relationship among a number of sparsity priors proposed in the literature, and show the advantages of the proposed model including its high degree of sparsity. Moreover, we show that some of the existing models are special cases of the proposed model. Using our model, we develop a constructive (greedy) algorithm designed for fast reconstruction useful in practical settings. Unlike most existing CS reconstruction methods, the proposed algorithm is fully automated, i.e., the unknown signal coefficients and all necessary parameters are estimated solely from the observation, and, therefore, no user-intervention is needed. Additionally, the proposed algorithm provides estimates of the uncertainty of the reconstructions. We provide experimental results with synthetic 1-D signals and images, and compare with the state-of-the-art CS reconstruction algorithms demonstrating the superior performance of the proposed approach.

negative: Cranial Impalement of a Falling Fence Spike in a Child: A Case Report Cranial impalement injuries are rare. They occur from a variety of objects, and via different mechanisms. We describe the case of a 5-year old boy who suffered cranial impalement injury via a unique mechanism. He presented to our centre with an impacted 17.8cm long metallic rod (a fence spike) in the vertex of his cranium, just off the midline. The spike penetrated his head and broke off its supporting frame as the frame was falling off a collapsing brick fence. He was transported as soon as possible to the hospital by relatives, without any attempt to remove the impaled spike. An urgent cranial computerized tomogram was done, and the object was removed under general anaesthesia in the operating theatre. The patient had complete recovery and was subsequently discharged from the hospital, with no residual neurological deficit. This case demonstrates a rare mechanism of cranial impalement. It also highlights the importance of following basic principles in the management of such injuries.

negative: Selective mutism and anxiety: a review of the current conceptualization of the disorder. Selective mutism (SM) is a rare and interesting condition that has been associated with a wide variety of childhood psychiatric conditions. Historically viewed as more of an oddity than a distinct diagnostic entity, early conceptualizations of the condition were based largely on case studies that tended to link SM with oppositional behavior. More recently, controlled studies have enhanced our understanding of SM. This review summarizes the current conceptualization of SM, highlighting evidence supporting the notion that SM is an anxiety-related condition.

negative: DataSite: Proactive visual data exploration with computation of insight-based recommendations Effective data analysis ideally requires the analyst to have high expertise as well as high knowledge of the data. Even with such familiarity, manually pursuing all potential hypotheses and exploring all possible views is impractical. We present DataSite, a proactive visual analytics system where the burden of selecting and executing appropriate computations is shared by an automatic server-side computation engine. Salient features identified by these automatic background processes are surfaced as notifications in a feed timeline. DataSite effectively turns data analysis into a conversation between analyst and computer, thereby reducing the cognitive load and domain knowledge requirements. We validate the system with a user study comparing it to a recent visualization recommendation system, yielding significant improvement, particularly for complex analyses that existing analytics systems do not support well.

negative: A Blockchain-Based Approach to Health Information Exchange Networks Sharing healthcare data between institutions is challenging. Heterogeneous data structures may preclude compatibility, while disparate use of healthcare terminology limits data comprehension. Even if structure and semantics could be agreed upon, both security and data consistency concerns abound. Centralized data stores and authority providers are attractive targets for cyber attack, and establishing a consistent view of the patient record across a data sharing network is problematic. In this work we present a Blockchain-based approach to sharing patient data. This approach trades a single centralized source of trust in favor of network consensus, and predicates consensus on proof of structural and semantic interoperability.

negative: Text Mining on Player Personality for Game Recommendation Recommender systems have emergedrecently in many aspects due to enormous amount of information or items existed in e-commerce or social network services. Users find it convenient if the service can recommend interesting items to them automatically. Past systems adopted users' preference or social connections for such recommendation. In this work, we will try to identify a game player's personality traits and use them to recommend games that may be interested by the player. The personality traits of a player is identified by applying a text mining process on textual contents related to the player. The same process is also applied on the comments of the games to identified the personality traits of games. Recommendation is then performed based on the similarity between the traits of the user and the game. We performed experiments on 63 players and 2050 games and obtained promising results which may demonstrate the plausibility of personality-based recommender systems.

negative: Complementary and alternative therapies for treating multiple sclerosis symptoms: a systematic review. Multiple sclerosis (MS) is a chronic disease of the central nervous system without a known cure. Thus the role of complementary and alternative therapies (CATs) for the management of symptoms lies in palliative care and this is borne out by the popularity of these treatments amongst MS sufferers. This review is aimed at determining whether this use is supported by evidence of effectiveness from rigorous clinical trials. Database literature searches were performed and papers were extracted in a pre-defined manner. Twelve randomized controlled trials were located that investigated a CAT for MS: nutritional therapy (4), massage (1), Feldenkrais bodywork (1), reflexology (1), magnetic field therapy (2), neural therapy (1) and psychological counselling (2). The evidence is not compelling for any of these therapies, with many trials suffering from significant methodological flaws. There is evidence to suggest some benefit of nutritional therapy for the physical symptoms of MS. Magnetic field therapy and neural therapy appear to have a short-term beneficial effect on the physical symptoms of MS. Massage/bodywork and psychological counselling seem to improve depression, anxiety and self-esteem. The effectiveness for other CATs is unproven at this time. In all the CATs examined further investigations are needed in the form of rigorous large-scale trials.

negative: Building Better Open-Source Tools to Support Fairness in Automated Scoring Automated scoring of written and spoken responses is an NLP application that can significantly impact lives especially when deployed as part of high-stakes tests such as the GRE® and the TOEFL®. Ethical considerations require that automated scoring algorithms treat all testtakers fairly. The educational measurement community has done significant research on fairness in assessments and automated scoring systems must incorporate their recommendations. The best way to do that is by making available automated, non-proprietary tools to NLP researchers that directly incorporate these recommendations and generate the analyses needed to help identify and resolve biases in their scoring systems. In this paper, we attempt to provide such a solution.

negative: Deep HyperNEAT : Evolving the Size and Depth of the Substrate Evolutionary This report describes DeepHyperNEAT, an extension of HyperNEAT to allow it to alter the topology of its indirectlyencoded neural network (called the substrate) so that it can continue to grow and increase in complexity over evolution. This aim is accomplished by augmenting HyperNEAT’s compositional pattern producing networks (CPPNs) with new information that allows them to represent substrate topology, and by adding three novel mutations to HyperNEAT that exploit this new information. The purpose of this report is to detail the method and validate its ability to evolve, which is shown through a simple XOR-based test. Significantly more work will be needed to analyze the algorithmic and scientific implications of these new capabilities, but by releasing this report it becomes possible for others also to explore the opportunities that such an extension opens up. A link to associated source code is also provided at the end of this document. Introduction HyperNEAT [1; 2; 3] (Hypercube-based NeuroEvolution of Augmenting Topologies) is a method for evolving neural networks (NNs) through an indirect encoding based on the NEAT method [4] that came before it. The main idea in HyperNEAT is that a special genetic representation called a compositional pattern producing network (CPPN) [5] that is evolved by NEAT generates a pattern of weights that connect a fixed set of neurons positioned within a two-dimensional or three-dimensional space called the substrate. A longstanding limitation of the conventional HyperNEAT algorithm is that while the CPPN can increase in size, the number of neurons and layers in the substrate remains fixed from the beginning of evolution. While there exists one method called evolvable substrate HyperNEAT (ES-HyperNEAT) [6] that allows HyperNEAT to increase the complexity of its substrate that indeed offers opportunities not available in the original HyperNEAT, the new method proposed here, called Deep HyperNEAT is designed explicitly to evolve substrates reminiscent of architectures seen in deep learning today, with increasing numbers of layers and separate parallel pathways through the network. Deep HyperNEAT achieves its new ability by fitting more information into the standard CPPN of HyperNEAT, which now can mutate to describe new layers and new neurons in the substrate. This paper is a preliminary technical report on the method, released to allow others to experiment with its ideas quickly. Thus many intriguing scientific questions about the opportunities created by this new capability will not be answered here; instead the focus is on introducing the technical components of the method and showing that they work correctly (i.e. perform as expected) in a simple validation experiment on the XOR problem. Rigorous scientific investigation is left for the future. The paper is organized as follows: HyperNEAT and the CPPN indirect encoding are reviewed first. The main extensions to HyperNEAT and CPPNs that constitute Deep HyperNEAT are then described, and demonstrated running in a simple XOR test. The paper concludes with thoughts on implications and future work. A public repository containing all of the code for the method is linked at the end of the paper. Background: HyperNEAT HyperNEAT [1; 2; 3] is a neuroevolution method that utilizes indirect encoding to evolve connectivity patterns in neural networks (NN). HyperNEAT historically has found applications in a wide breadth of domains, including multiagent control [7], checkers [2; 3], quadruped locomotion [8; 9], Robocup Keepaway [10], and simulated driving in traffic [11]. The motivation for HyperNEAT was to capture the ability of natural genetic encoding (DNA) to reuse information to encode massive neural structures with orders of magnitude fewer genes. The key principle in HyperNEAT that enabled this kind of meaningful reuse is that neural geometry should be able to exploit problem structure. (This principle is similar to the inspiration behind incorporating convolution in deep learning, but its realization and implications in HyperNEAT are different.) For example, two hidden neurons that process adjacent parts of an input field (e.g. a visual field) should also be roughly adjacent within the neural geometry. That way, locality within an NN becomes a heuristic for relatedness. To situate neurons in an NN within a geometric context, each neuron in HyperNEAT is assigned a Cartesian coordinate. For example, if the NN is configured as a stack of two-dimensional layers of neurons, then the position of each neuron within a layer is simply (x,y). In HyperNEAT it is conventional that the coordinate system of each layer centers at (0,0) and ranges between [-1, 1] in both x and y, thereby forming a square filter or “sheet” of neurons. That way, each neuron n within a layer l can be identified by its position (x,y). Specifying neurons in the NN as a function of the neural geometry allows for an elegant formalism for encoding the connectivity patterns between layers. The connectivity pattern between one layer and another (e.g. all possible connections and weights between neurons in some layer i and another layer j) can be encoded by a function ConnectLayers that takes the positions of the source and target neurons as input. Instead of evolving the weights of every connection individually as in past approaches to neuroevolution like NEAT [12], HyperNEAT evolves a graphical representation of a function, ConnectLayers, that describes how the neurons are connected. In this way, ConnectLayers acts as a lower-dimensional encoding for how to generate connectivity patterns. For example, we might want to query ConnectLayers for the weight of a connection between a neuron a in layer 1 with coordinates (x1,y1) and a neuron b in layer 2 with coordinates (x2,y2). ConnectLayers then returns the weight wx1,y1,x2,y2 : wx1,y1,x2,y2 = ConnectLayers(x1,y1,x2,y2). (1) The main idea in HyperNEAT is to evolve ConnectLayers. In HyperNEAT, ConnectLayers is represented by a compositional pattern producing network (CPPN) [13] (figure 1). A CPPN is an evolvable directed graph representation for functions. Much like NNs, nodes are functions and connections have weights that are applied to the outputs of nodes before passing into other nodes. Importantly, CPPN node functions are heterogeneous in any given CPPN (they are sampled from a set of possible functions usually containing sigmoid, linear, Gaussian, and cosine) and there is no notion of layers. A thorough introduction or review of CPPNs is in [5]. The CPPN begins with a simple topology (usually just its input nodes and its output node with a sparse number of connections between them) and complexifies via evolution in HyperNEAT. Evolving the CPPN this way, through complexification, allows the CPPN to be biased towards compressed representations of connectivity patterns. This approach leads to increasingly intricate yet organized connectivity patterns. In practice CPPNs have proven effective and popular in part because they are designed to evolve increasing complexity with the original NEAT algorithm, which underlies evolution in HyperNEAT. The predicate “hyper” in HyperNEAT signifies that the CPPN is in effect encoding a pattern within a four-dimensional or six-dimensional hypercube (because ConnectLayers typically takes four or six inputs), which is then painted onto the connectivity of a network. In effect, HyperNEAT provides the unique ability to evolve a compressed function that can encode the connectivity of an arbitrarily large network as a function of its geometry. That way, it is possible to produce NNs whose connections display regular and compositional structure through the reuse of motifs encoded in the CPPN. The

negative: Improved seam carving for video retargeting Video, like images, should support content aware resizing. We present video retargeting using an improved seam carving operator. Instead of removing 1D seams from 2D images we remove 2D seam manifolds from 3D space-time volumes. To achieve this we replace the dynamic programming method of seam carving with graph cuts that are suitable for 3D volumes. In the new formulation, a seam is given by a minimal cut in the graph and we show how to construct a graph such that the resulting cut is a valid seam. That is, the cut is monotonic and connected. In addition, we present a novel energy criterion that improves the visual quality of the retargeted images and videos. The original seam carving operator is focused on removing seams with the least amount of energy, ignoring energy that is introduced into the images and video by applying the operator. To counter this, the new criterion is looking forward in time - removing seams that introduce the least amount of energy into the retargeted result. We show how to encode the improved criterion into graph cuts (for images and video) as well as dynamic programming (for images). We apply our technique to images and videos and present results of various applications.

negative: Demand Side Management in Smart Grids Smart grids are considered to be the next generation electric grids. Drivers for this development are among others: an increased electricity demand, an ageing network infrastructure and an increased share of renewable energy sources. Sustainability and the European 20/20/20 climate and energy targets are also main drivers for this development. Some of the important aspects of smart grids are that they allow two-way communication between the actors in the grid and enable optimisation of electricity use and production and customer participation. Demand side management is associated to smart grids and means adapting the electricity demand to the electricity production and the available electricity in the grid. It is considered that smart grids and demand side management hold potential to facilitate an increased share of renewable energy sources and to reduce the need for the current power reserve, which is based on fossil energy sources. The aim of this study was to compile and analyse some of the work that has been done and is being implemented around the world concerning demand side management in smart grids with a focus on market rules and business models in relation to Swedish conditions. The study includes a review of selected research and demonstration projects. Success factors and knowledge gaps have been identified through an analysis of the project findings and recommendations. A description of the current Nordic market conditions is also given. The conclusions regarding the conditions on the Swedish electricity market is that it appears to be relatively well adjusted to demand side management with the possibility for hourly meter readings and a full deployment of smart meters. The Nord Pool electricity market allows both day-ahead trading and intra-day trading on the elspot and the elbas markets. The review of the projects show that there is a potential for achieving flexible load from private customers. Hourly metering is seen as a prerequisite for demand side management and active customers is also considered crucial. Visualisation of electricity consumption and current costs is shown to be an important tool for encouraging customers to take an active part in their electricity consumption. Some of the other methods for achieving flexibility are for example: different types of pricing models, a signal for renewable energy sources and direct and indirect control of smart appliances and heating systems. The aggregation concept is an example of direct control that have been used and show potential for achieving flexibility in the grid. The concept means that a so called aggregator, which for example could be the supplier or the distribution system operator, compiles and controls the electrical load for several customers. A key challenge that has been identified is the issue of standardisation. Exchange of experiences from various research and demonstration projects is also considered desirable to support the development of smart grid and demand response.

negative: Design Criteria for Electric Vehicle Fast Charge Infrastructure Based on Flemish Mobility Behavior This paper studies the technical design criteria for fast charge infrastructure, covering the mobility needs. The infrastructure supplements the residential and public slow charging infrastructure. Two models are designed. The first determines the charging demand, based on current mobility behavior in Flanders. The second model simulates a charge infrastructure that meets the resulting fast charge demand. The energy management is performed by a rule-based control algorithm, that directs the power flows between the fast chargers, the energy storage system, the grid connection, and the photovoltaic installation. There is a clear trade-off between the size of the energy storage system and the power rating of the grid connection. Finally, the simulations indicate that 99.7% of the vehicles visiting the fast charge infrastructure can start charging within 10 minutes with a configuration limited to 5 charging spots, instead of 9 spots when drivers are not willing to wait.

negative: OPTIMIZING THE PERFORMANCE OF VEHICLE-TO-GRID ( V 2 G ) ENABLED BATTERY ELECTRIC VEHICLES THROUGH A SMART CHARGE SCHEDULING MODEL −A smart charge scheduling model is presented for potential (1) vehicle-to-grid (V2G) enabled battery electric vehicle (BEV) owners who are willing to participate in the grid ancillary services, and (2) grid operators. Unlike most V2G implementations, which are considered from the perspective of power grid systems, this model includes a communication network architecture for connecting system components that supports both BEV owners and grid operators to efficiently monitor and manage the charging and ancillary service activities. This model maximizes the net profit to each BEV participant while simultaneously satisfying energy demands for his/her trips. The performance of BEVs using the scheduling model is validated by estimating optimal annual financial benefits under different scenarios. An analysis of popular BEV models revealed that one of the existing BEVs considered in the study can generate an annual regulation profit of $454, $394 and $318 when the average daily driving distance is 20 miles, 40 miles and 60 miles, respectively. All popular BEV models can completely compensate the energy cost and generate a positive net profit, through the application of the scheduling model presented in this paper, with an annual driving distance of approximately 15,000 miles. Simulation analysis indicated that the extra load distribution from the optimized BEV charging operations were well balanced compared to the unmanaged BEV

negative: Emerging Trends in Smart Home Security, Privacy, and Digital Forensics Technology integration is becoming an impetus to everyday lives. This new interconnected world can be found from our most private spaces to the public ones. Smart homes, which is the use of Internet of Things (IoT) within a home, has become the utmost concern in the security and privacy domain. In this paper, we review the current literature from both research and practice and offer five research trends for security and privacy in smart homes: potential for remote security breaches, risks for smart home devices, privacy violations, infrastructure vulnerabilities, and digital forensics challenges. We integrated these five trends in a conceptual model showcasing their roles in the rapidly changing IoT landscape. Combining the trends and smart forensics, we elucidate the bimodal lenses in security and privacy: preventive and investigative. These discussions offer research directions and practitioners’ implications for further development of a safe and secured smart home.

negative: ABCNN: Attention-Based Convolutional Neural Network for Modeling Sentence Pairs How to model a pair of sentences is a critical issue in many NLP tasks such as answer selection (AS), paraphrase identification (PI) and textual entailment (TE). Most prior work (i) deals with one individual task by fine-tuning a specific system; (ii) models each sentence’s representation separately, rarely considering the impact of the other sentence; or (iii) relies fully on manually designed, task-specific linguistic features. This work presents a general Attention Based Convolutional Neural Network (ABCNN) for modeling a pair of sentences. We make three contributions. (i) The ABCNN can be applied to a wide variety of tasks that require modeling of sentence pairs. (ii) We propose three attention schemes that integrate mutual influence between sentences into CNNs; thus, the representation of each sentence takes into consideration its counterpart. These interdependent sentence pair representations are more powerful than isolated sentence representations. (iii) ABCNNs achieve state-of-the-art performance on AS, PI and TE tasks. We release code at: https://github.com/yinwenpeng/Answer_Selection.

negative: Automated and safe vulnerability assessment As the number of system vulnerabilities multiplies in recent years, vulnerability assessment has emerged as a powerful system security administration tool that can identify vulnerabilities in existing systems before they are exploited. Although there are many commercial vulnerability assessment tools in the market, none of them can formally guarantee that the assessment process never compromises the computer systems being tested. This paper proposes a featherweight virtual machine (FVM) technology to address the safety issue associated with vulnerability testing. Compared with other virtual machine technologies, FVM is designed to facilitate sharing between virtual machines but still provides strong protection between them. The FVM technology allows a vulnerability assessment tool to test an exact replica of a production-mode network service, including both hardware and system software components, while guaranteeing that the production-mode network service is fully isolated from the testing process. In addition to safety, the vulnerability assessment support system described in this paper can also automate the entire process of vulnerability testing and thus for the first time makes it feasible to run vulnerability testing autonomously and frequently. Experiments on a Windows-based prototype show that Nessus assessment results against an FVM virtual machine are identical to those against a real machine. Furthermore, modifications to the file system and registry state made by vulnerability assessment runs are completely isolated from the host machine. Finally, the performance impact of vulnerability assessment runs on production network services is as low as 3%

negative: Implementation of multiport dc-dc converter-based Solid State Transformer in smart grid system A solid-state transformer (SST) would be at least as efficient as a conventional version but would provide other benefits as well, particularly as renewable power sources become more widely used. Among its more notable strong points are on-demand reactive power support for the grid, better power quality, current limiting, management of distributed storage devices and a dc bus. Most of the nation's power grid currently operates one way - power flows from the utility to the consumer - and traditional transformers simply change voltage from one level to another. But smart transformers, based on power semiconductor switches, are more versatile. Not only can they change voltage levels, but also can effectively control the power flow in both directions. The development of a Solid State Transformer (SST) that incorporates a DC-DC multiport converter to integrate both photovoltaic (PV) power generation and battery energy storage is presented in this paper. The DC-DC stage is based on a quad active-bridge (QAB) converter which not only provides isolation for the load, but also for the PV and storage. The AC-DC stage is implemented with a pulse-width-modulated (PWM) single phase rectifier. A novel technique that complements the SISO controller by taking into account the cross coupling characteristics of the QAB converter is also presented herein. Cascaded SISO controllers are designed for the AC-DC stage. The QAB demanded power is calculated at the QAB controls and then fed into the rectifier controls in order to minimize the effect of the interaction between the two SST stages. The dynamic performance of the designed control loops based on the proposed control strategies are verified through extensive simulation of the SST average and switching models.


query: Modular and hierarchical learning systems

positive: Adaptive Mixtures of Local Experts We present a new supervised learning procedure for systems composed of many separate networks, each of which learns to handle a subset of the complete set of training cases. The new procedure can be viewed either as a modular version of a multilayer supervised network, or as an associative version of competitive learning. It therefore provides a new link between these two apparently different approaches. We demonstrate that the learning procedure divides up a vowel discrimination task into appropriate subtasks, each of which can be solved by a very simple expert network.

positive: Bayesian Methods for Mixtures of Experts Tony Robinson Cambridge University Engineering Department Cambridge CB2 1PZ England. Tel: [+44] 1223 332815 ajr@eng.cam.ac.uk We present a Bayesian framework for inferring the parameters of a mixture of experts model based on ensemble learning by variational free energy minimisation. The Bayesian approach avoids the over-fitting and noise level under-estimation problems of traditional maximum likelihood inference. We demonstrate these methods on artificial problems and sunspot time series prediction.

positive: The Evidence Framework Applied to Classification Networks Three Bayesian ideas are presented for supervised adaptive classifiers. First, it is argued that the output of a classifier should be obtained by marginalizing over the posterior distribution of the parameters; a simple approximation to this integral is proposed and demonstrated. This involves a "moderation" of the most probable classifier's outputs, and yields improved performance. Second, it is demonstrated that the Bayesian framework for model comparison described for regression models in MacKay (1992a,b) can also be applied to classification problems. This framework successfully chooses the magnitude of weight decay terms, and ranks solutions found using different numbers of hidden units. Third, an information-based data selection criterion is derived and demonstrated within this framework.

positive: Neural Networks and the Bias/Variance Dilemma Feedforward neural networks trained by error backpropagation are examples of nonparametric regression estimators. We present a tutorial on nonparametric inference and its relation to neural networks, and we use the statistical viewpoint to highlight strengths and weaknesses of neural models. We illustrate the main points with some recognition experiments involving artificial data as well as handwritten numerals. In way of conclusion, we suggest that current-generation feedforward neural networks are largely inadequate for difficult problems in machine perception and machine learning, regardless of parallel-versus-serial hardware or other implementation issues. Furthermore, we suggest that the fundamental challenges in neural modeling are about representation rather than learning per se. This last point is supported by additional experiments with handwritten numerals.

negative: The variational approximation for Bayesian inference The influence of this Thomas Bayes' work was immense. It was from here that "Bayesian" ideas first spread through the mathematical world, as Bayes's own article was ignored until 1780 and played no important role in scientific debate until the 20th century. It was also this article of Laplace's that introduced the mathematical techniques for the asymptotic analysis of posterior distributions that are still employed today. And it was here that the earliest example of optimum estimation can be found, the derivation and characterization of an estimator that minimized a particular measure of posterior expected loss. After more than two centuries, we mathematicians, statisticians cannot only recognize our roots in this masterpiece of our science, we can still learn from it.

negative: A multi-objective artificial bee colony algorithm This work presents a multi-objective optimization method based on the artificial bee colony, called the MOABC, for optimizing problems with multiple objectives. The MOABC uses a grid-based approach to adaptively assess the Pareto frontmaintained in an external archive. The external archive is used to control the flying behaviours of the individuals and structuring the bee colony. The employed bees adjust their trajectories based on the non-dominated solutionsmaintained in the external archive. On the other hand, the onlooker bees select the food sources advertised by the employed bees to update their positions. The qualities of these food sources are computed based on the Pareto dominance notion. The scout bees are used by the MOABC to get rid of food sources with poor qualities. The proposed algorithm was evaluated on a set of standard test problems in comparison with other state-of-the-art algorithms. Experimental results indicate that the proposed approach is competitive compared to other algorithms considered in this work. © 2011 Elsevier B.V. All rights reserved.

negative: Refining Geometry from Depth Sensors using IR Shading Images We propose a method to refine geometry of 3D meshes from a consumer level depth camera, e.g. Kinect, by exploiting shading cues captured from an infrared (IR) camera. A major benefit to using an IR camera instead of an RGB camera is that the IR images captured are narrow band images that filter out most undesired ambient light, which makes our system robust against natural indoor illumination. Moreover, for many natural objects with colorful textures in the visible spectrum, the subjects appear to have a uniform albedo in the IR spectrum. Based on our analyses on the IR projector light of the Kinect, we define a near light source IR shading model that describes the captured intensity as a function of surface normals, albedo, lighting direction, and distance between light source and surface points. To resolve the ambiguity in our model between the normals and distances, we utilize an initial 3D mesh from the Kinect fusion and multi-view information to reliably estimate surface details that were not captured and reconstructed by the Kinect fusion. Our approach directly operates on the mesh model for geometry refinement. We ran experiments on our algorithm for geometries captured by both the Kinect I and Kinect II, as the depth acquisition in Kinect I is based on a structured-light technique and that of the Kinect II is based on a time-of-flight technology. The effectiveness of our approach is demonstrated through several challenging real-world examples. We have also performed a user study to evaluate the quality of the mesh models before and after our refinements.

negative: Receptive fields of single neurones in the cat's striate cortex. In the central nervous system the visual pathway from retina to striate cortex provides an opportunity to observe and compare single unit responses at several distinct levels. Patterns of light stimuli most effective in influencing units at one level may no longer be the most effective at the next. From differences in responses at successive stages in the pathway one may hope to gain some understanding of the part each stage plays in visual perception. By shining small spots of light on the light-adapted cat retina Kuffler (1953) showed that ganglion cells have concentric receptive fields, with an 'on' centre and an 'off ' periphery, or vice versa. The 'on' and 'off' areas within a receptive field were found to be mutually antagonistic, and a spot restricted to the centre of the field was more effective than one covering the whole receptive field (Barlow, FitzHugh & Kuffler, 1957). In the freely moving lightadapted cat it was found that the great majority of cortical cells studied gave little or no response to light stimuli covering most of the animal's visual field, whereas small spots shone in a restricted retinal region often evoked brisk responses (Hubel, 1959). A moving spot of light often produced stronger responses than a stationary one, and sometimes a moving spot gave more activation for one direction than for the opposite. The present investigation, made in acute preparations, includes a study of receptive fields of cells in the cat's striate cortex. Receptive fields of the cells considered in this paper were divided into separate excitatory and inhibitory ('on' and 'off') areas. In this respect they resembled retinal ganglion-cell receptive fields. However, the shape and arrangement of excitatory and inhibitory areas differed strikingly from the concentric pattern found in retinal ganglion cells. An attempt was made to correlate responses to moving stimuli

negative: A handheld mirror simulation We present the design and construction of a handheld mirror simulation device. The perception of the world reflected through a mirror depends on the viewer's position with respect to the mirror and the 3-D geometry of the world. In order to simulate a real mirror on a computer screen, images of the observed world, consistent with the viewer's position, must be synthesized and displayed in realtime. Our system is build around a LCD screen manipulated by the user, a single camera fixed on the screen, and a tracking device. The continuous input video stream and tracker data is used to synthesize, in real-time, a continuous video stream displayed on the LCD screen. The synthesized video stream is a close approximation of what the user would see on the screen surface if it were a real mirror. Our system provides a generic interface for applications involving rich, first-person interaction, such as the Virtual Daguerreotype.

negative: Magic decorator: automatic material suggestion for indoor digital scenes Assigning textures and materials within 3D scenes is a tedious and labor-intensive task. In this paper, we present Magic Decorator, a system that automatically generates material suggestions for 3D indoor scenes. To achieve this goal, we introduce local material rules, which describe typical material patterns for a small group of objects or parts, and global aesthetic rules, which account for the harmony among the entire set of colors in a specific scene. Both rules are obtained from collections of indoor scene images. We cast the problem of material suggestion as a combinatorial optimization considering both local material and global aesthetic rules. We have tested our system on various complex indoor scenes. A user study indicates that our system can automatically and efficiently produce a series of visually plausible material suggestions which are comparable to those produced by artists.

negative: Efficiency Degradation in Wideband Power Amplifiers This paper addresses the efficiency degradation observed in wideband power amplifiers. It starts by presenting a detailed explanation that relates this observed performance degradation with the terminations at baseband. Then, a comparison between two implemented power amplifiers with the same fundamental and harmonic terminations, but with different baseband networks is presented, showing that an optimized bias network design can improve the observed efficiency degradation.

negative: Generating Keyword Queries for Natural Language Queries to Alleviate Lexical Chasm Problem In recent years, the task of reformulating natural language queries has received considerable attention from both industry and academic communities. Because of the lexical chasm problem between natural language queries and web documents, if we directly use natural language queries as inputs for retrieval, the results are usually unsatisfactory. In this work, we formulated the task as a translation problem to convert natural language queries into keyword queries. Since the nature language queries users input are diverse and multi-faceted, general encoder-decoder models cannot effectively handle low-frequency words and out-of-vocabulary words. We propose a novel encoder-decoder method with two decoders: the pointer decoder firstly extracts query terms directly from the source text via copying mechanism, then the generator decoder generates query terms using two attention modules simultaneously considering the source text and extracted query terms. For evaluation and training, we also proposed a semi-automatic method to construct a large-scale dataset about natural language query-keyword query pairs. Experimental results on this dataset demonstrated that our model could achieve better performance than the previous state-of-the-art methods.

negative: Customizing Computational Methods for Visual Analytics with Big Data The volume of available data has been growing exponentially, increasing data problem's complexity and obscurity. In response, visual analytics (VA) has gained attention, yet its solutions haven't scaled well for big data. Computational methods can improve VA's scalability by giving users compact, meaningful information about the input data. However, the significant computation time these methods require hinders real-time interactive visualization of big data. By addressing crucial discrepancies between these methods and VA regarding precision and convergence, researchers have proposed ways to customize them for VA. These approaches, which include low-precision computation and iteration-level interactive visualization, ensure real-time interactive VA for big data.

negative: EW-SHOT L EARNING A recent approach to few-shot classification called matching networks has demonstrated the benefits of coupling metric learning with a training procedure that mimics test. This approach relies on an attention scheme that forms a distribution over all points in the support set, scaling poorly with its size. We propose a more streamlined approach, prototypical networks, that learns a metric space in which few-shot classification can be performed by computing Euclidean distances to prototype representations of each class, rather than individual points. Our method is competitive with state-of-the-art few-shot classification approaches while being much simpler and more scalable with the size of the support set. We empirically demonstrate the performance of our approach on the Omniglot and miniImageNet datasets. We further demonstrate that a similar idea can be used for zero-shot learning, where each class is described by a set of attributes, and achieve state-ofthe-art results on the Caltech UCSD bird dataset.

negative: The exact bound in the Erdös - Ko - Rado theorem

negative: Setting Lower Bounds on Truthfulness We present general techniques for proving inapproximability results for several paradigmatic truthful multidimensional mechanism design problems. In particular, we demonstrate the strength of our techniques by exhibiting a lower bound of 2 − 1 m for the scheduling problem with m unrelated machines (formulated as a mechanism design problem in the seminal paper of Nisan and Ronen on Algorithmic Mechanism Design). Our lower bound applies to truthful randomized mechanisms, regardless of any computational assumptions on the running time of these mechanisms. Moreover, it holds even for the wider class of truthfulness-in-expectation mechanisms. This lower bound nearly matches the known 1.58606 randomized truthful upper bound for the case of two machines (a non-truthful FPTAS exists). Recently, Daskalakis and Weinberg [17] show that there is a polynomial-time 2approximately optimal Bayesian mechanism for makespan minimization for unrelated machines. We complement this result by showing an appropriate lower bound of 1.25 for deterministic incentive compatible Bayesian mechanisms. We then show an application of our techniques to the workload-minimization problem in networks. We prove our lower bounds for this problem in the inter-domain routing setting presented by Feigenbaum, Papadimitriou, Sami, and Shenker. Finally, we discuss several notions of non-utilitarian fairness (Max-Min fairness, Min-Max fairness, and envy minimization) and show how our techniques can be used to prove lower bounds for these notions. No lower bounds for truthful mechanisms in multidimensional probabilistic settings were previously known.1 ∗Computer Science Department, Technion, Haifa, Israel. ahumu@yahoo.com. †School of Computer Science and Engineering, Hebrew University, Jerusalem, Israel. schapiram@huji.ac.il. The current paper supersedes "Setting Lower Bounds on Truthfulness" that appeared as an extended abstract in the Proceedings of the 18th Annual ACM-SIAM Symposium on Discrete Algorithms (SODA07), pages 1143-1152, 2007. The current version includes a new lower bound result for Bayesian Incentive compatible Mechanisms.

negative: A d–q Voltage Droop Control Method With Dynamically Phase-Shifted Phase-Locked Loop for Inverter Paralleling Without Any Communication Between Individual Inverters This paper presents a modified droop control method for equal load sharing of parallel-connected inverters, without any communication between individual inverters. Droop in <italic>d</italic>- and <italic>q</italic>-axis voltages are given depending upon <italic>d</italic>- and <italic>q</italic>-axis currents, respectively. Each inverter works in the voltage control mode, where it controls the filter capacitor voltage. Voltage references of each inverter come from <italic>d</italic>- and <italic>q</italic>-axis voltage droops. This <italic>d</italic>- and <italic>q</italic> -axis voltage droops force the parallel-connected inverters to share equal current and hence equal active and reactive power. A dynamically phase-shifted phase-locked loop (PLL) technique is locally designed for generating phase reference of each inverter. The phase angle between filter capacitor voltage vector and <italic>d</italic>-axis is dynamically adjusted with the change in <italic>q</italic>-axis inverter current to generate the phase reference of each inverter. The strategy has been verified with simulations and experiments and results are presented in this paper.

negative: A Practical Approach to Insertion with Variable Socket Position Using Deep Reinforcement Learning Insertion is a challenging haptic and visual control problem with significant practical value for manufacturing. Existing approaches in the model-based robotics community can be highly effective when task geometry is known, but are complex and cumbersome to implement, and must be tailored to each individual problem by a qualified engineer. Within the learning community there is a long history of insertion research, but existing approaches are typically either too sample-inefficient to run on real robots, or assume access to high-level object features, e.g. socket pose. In this paper we show that relatively minor modifications to an off-the-shelf Deep-RL algorithm (DDPG), combined with a small number of human demonstrations, allows the robot to quickly learn to solve these tasks efficiently and robustly. Our approach requires no modeling or simulation, no parameterized search or alignment behaviors, no vision system aside from raw images, and no reward shaping. We evaluate our approach on a narrowclearance peg-insertion task and a deformable clip-insertion task, both of which include variability in the socket position. Our results show that these tasks can be solved reliably on the real robot in less than 10 minutes of interaction time, and that the resulting policies are robust to variance in the socket position and orientation.

negative: LTE in the sky: trading off propagation benefits with interference costs for aerial nodes The popularity of unmanned aerial vehicles has exploded over the last few years, urgently demanding solutions to transfer large amounts of data from the UAV to the ground. Conversely, a control channel to the UAV is desired, in order to safely operate these vehicles remotely. This article analyzes the use of LTE for realizing this downlink data and uplink control. By means of measurements and simulations, we study the impact of interference and path loss when transmitting data to and from the UAV. Two scenarios are considered in which UAVs act as either base stations transmitting in downlink or UEs transmitting in uplink, and their impact on the respective downlink and uplink performance of an LTE ground network is analyzed. Both measurements and simulations are used to quantify such impact for a range of scenarios with varying altitude, distance from the base station, or UAV density. The measurement sets show that signal-to-interference ratio decreases up to 7 dB for UAVs at 150 m compared to ground users. Simulation results show that a UAV density of 10/km2 gives an average degradation of the signal-to-interference ratio of more than 6 dB. It is concluded that interference is going to be a major limiting factor when LTE enabled UAVs are introduced, and that strong technical solutions will have to be found.

negative: Scattered data modeling A variety of methods for modeling scattered data are discussed, with an emphasis on two types of data: volumetric and spherical. To demonstrate the performance of the various methods, results from an empirical study using trivariate scattered data are presented. The author's objective is to provide information to aid in selecting a type of method or to form the basis for customizing a method for a particular application.<<ETX>>

negative: Benign Osteoblastoma Involving Maxilla: A Case Report and Review of the Literature Background. Osteoblastoma is a rare benign tumor. This tumor is characterized by osteoid and bone formation with the presence of numerous osteoblasts. The lesion is more frequently seen in long bones and rarely involves maxilla and mandible. Due to its clinical and histological similarity with other bone tumors such as osteoid osteoma and fibro-osseous lesions, osteoblastoma presents a diagnostic dilemma. Case Report. Very few cases of osteoblastomas involving maxillofacial region have been reported in the literature. This case report involves osteoblastoma involving right maxilla in an 18-year-old male patient. Following detailed clinical examination, radiological interpretation, and histopathological diagnosis, surgical excision was performed. The patient was followed up for a period of 3 years and was disease free. Summary and Conclusion. Benign osteoblastoma involving jaw bones is a rare tumor. There is a close resemblance of this tumor with other lesions such as fibro-osseous lesions and odontogenic tumors and thus faces a diagnostic challenge. Surgical excision with a long-term follow-up gives good prognosis to this lesion-Benign Osteoblastoma.

negative: Almost Optimal Exploration in Multi-Armed Bandits We study the problem of exploration in stochastic Multi-Armed Bandits. Even in the simplest setting of identifying the best arm, there remains a logarithmic multiplicative gap between the known lower and upper bounds for the number of arm pulls required for the task. This extra logarithmic factor is quite meaningful in nowadays large-scale applications. We present two novel, parameterfree algorithms for identifying the best arm, in two di↵erent settings: given a target confidence and given a target budget of arm pulls, for which we prove upper bounds whose gap from the lower bound is only doublylogarithmic in the problem parameters. We corroborate our theoretical results with experiments demonstrating that our algorithm outperforms the state-of-the-art and scales better as the size of the problem increases.

negative: Buck-boost converter fed BLDC motor drive for solar PV array based water pumping Solar photovoltaic (SPV) array based water pumping is receiving wide attention now a days because the everlasting solar energy is the best alternative to the conventional energy sources. This paper deals with the utilization of a buck-boost converter in solar PV array based water pumping as an intermediate DC-DC converter between a solar PV array and a voltage source inverter (VSI) in order to achieve the maximum efficiency of the solar PV array and the soft starting of the permanent magnet brushless DC (BLDC) motor by proper control. Consisting of a least number of components and single switch, the buck-boost converter exhibits very good conversion efficiency. Moreover, buck-boost DC-DC converter topology is the only one allowing follow-up of the PV array maximum power point (MPP) regardless of temperature, irradiance, and connected load. A BLDC motor is employed to drive a centrifugal type of water pump because its load characteristic is well matched to the maximum power locus of the PV generator. The transient, dynamic and steady state behaviors of the proposed solar PV array powered buck-boost converter fed BLDC motor driven water pumping system are evaluated under the rapid and slowly varying atmospheric conditions using the sim-power-system toolboxes of the MATLAB/Simulink environment.

negative: Event detection from social media: 5W1H analysis on big data Increasing number of social media content shared across the globe in real time creates a fascinating area of research. For most events, social media users act as collective sensors by sharing important information about events of any scale. Due to its real time nature, social media is much quicker to respond to such events relative to conventional news media. This paper proposes an event detection system which provides 5W1H (What, Where, When, Why, Who, How) analysis for each detected event. We make use of a myriad of techniques such as anomaly detection, named entity recognition, automatic summary generation, user link analysis. Our experimental results for the system indicate a faster event detection performance compared to conventional news sources. Event analysis results are also in line with the corresponding news articles about detected events.

negative: Persistence and periodicity in a dynamic proximity network The topology of social networks can be understood as being inherently dynamic, with edges having a distinct position in time. Most characterizations of dynamic networks discretize time by converting temporal information into a sequence of network “snapshots” for further analysis. Here we study a highly resolved data set of a dynamic proximity network of 66 individuals. We show that the topology of this network evolves over a very broad distribution of time scales, that its behavior is characterized by strong periodicities driven by external calendar cycles, and that the conversion of inherently continuous-time data into a sequence of snapshots can produce highly biased estimates of network structure. We suggest that dynamic social networks exhibit a natural time scale ∆nat, and that the best conversion of such dynamic data to a discrete sequence of networks is done at this natural rate.

negative: Metal-oxide-semiconductor field-effect transistor with a vacuum channel. High-speed electronic devices rely on short carrier transport times, which are usually achieved by decreasing the channel length and/or increasing the carrier velocity. Ideally, the carriers enter into a ballistic transport regime in which they are not scattered. However, it is difficult to achieve ballistic transport in a solid-state medium because the high electric fields used to increase the carrier velocity also increase scattering. Vacuum is an ideal medium for ballistic transport, but vacuum electronic devices commonly suffer from low emission currents and high operating voltages. Here, we report the fabrication of a low-voltage field-effect transistor with a vertical vacuum channel (channel length of ~20 nm) etched into a metal-oxide-semiconductor substrate. We measure a transconductance of 20 nS µm(-1), an on/off ratio of 500 and a turn-on gate voltage of 0.5 V under ambient conditions. Coulombic repulsion in the two-dimensional electron system at the interface between the oxide and the metal or the semiconductor reduces the energy barrier to electron emission, leading to a high emission current density (~1 × 10(5) A cm(-2)) under a bias of only 1 V. The emission of two-dimensional electron systems into vacuum channels could enable a new class of low-power, high-speed transistors.

negative: Privacy attitudes and privacy behaviour: A review of current research on the privacy paradox phenomenon Do people really care about their privacy? Surveys show that privacy is a primary concern for citizens in the digital age. On the other hand, individuals reveal personal information for relatively small rewards, often just for drawing the attention of peers in an online social network. This inconsistency of privacy attitudes and privacy behavior is often referred to as the “privacy paradox”. In this paper, we present the results of a review of research literature on the privacy paradox. We analyze studies that provide evidence of a paradoxical dichotomy between attitudes and behavior and studies that challenge the existence of such a phenomenon. The diverse research results are explained by the diversity in research methods, the different contexts and the different conceptualizations of the privacy paradox. We also present several interpretations of the privacy paradox, stemming from social theory, psychology, behavioral economics and, in one case, from quantum theory. We conclude that current research has improved our understanding of the privacy paradox phenomenon. It is, however, a complex phenomenon that requires extensive further research. Thus, we call for synthetic studies to be based on comprehensive theoretical models that take into account the diversity of personal information and the diversity of privacy concerns. We suggest that future studies should use evidence of actual behavior rather than self-reported behavior.

negative: GraphBuilder: scalable graph ETL framework Graph abstraction is essential for many applications from finding a shortest path to executing complex machine learning (ML) algorithms like collaborative filtering. Graph construction from raw data for various applications is becoming challenging, due to exponential growth in data, as well as the need for large scale graph processing. Since graph construction is a data-parallel problem, MapReduce is well-suited for this task. We developed GraphBuilder, a scalable framework for graph Extract-Transform-Load (ETL), to offload many of the complexities of graph construction, including graph formation, tabulation, transformation, partitioning, output formatting, and serialization. GraphBuilder is written in Java, for ease of programming, and it scales using the MapReduce model. In this paper, we describe the motivation for GraphBuilder, its architecture, MapReduce algorithms, and performance evaluation of the framework. Since large graphs should be partitioned over a cluster for storing and processing and partitioning methods have significant performance impacts, we develop several graph partitioning methods and evaluate their performance. We also open source the framework at https://01.org/graphbuilder/.

negative: Convolutional neural networks for SAR image segmentation Segmentation of Synthetic Aperture Radar (SAR) images has several uses, but it is a difficult task due to a number of properties related to SAR images. In this article we show how Convolutional Neural Networks (CNNs) can easily be trained for SAR image segmentation with good results. Besides this contribution we also suggest a new way to do pixel wise annotation of SAR images that replaces a human expert manual segmentation process, which is both slow and troublesome. Our method for annotation relies on 3D CAD models of objects and scene, and converts these to labels for all pixels in a SAR image. Our algorithms are evaluated on the Moving and Stationary Target Acquisition and Recognition (MSTAR) dataset which was released by the Defence Advanced Research Projects Agency during the 1990s. The method is not restricted to the type of targets imaged in MSTAR but can easily be extended to any SAR data where prior information about scene geometries can be estimated.

negative: A customizable pipeline for social media text normalization Social networks are persistently generating text-based data that encapsulate vast amounts of knowledge. However, the presence of non-standard terms and misspellings in texts originating from social networks poses a crucial challenge for natural language processing and machine learning systems that attempt to mine this knowledge. To address this problem, we propose a sequential, modular, and hybrid pipeline for social media text normalization. In the first phase, text preprocessing techniques and social media-specific vocabularies gathered from publicly available sources are used to transform, with high precision, out-of-vocabulary terms into in-vocabulary terms. A sequential language model, generated using the partially normalized texts from the first phase, is then utilized to normalize short, high-frequency, ambiguous terms. A supervised learning module is employed to normalize terms based on a manually annotated training corpus. Finally, a tunable, distributed language model-based backoff module at the end of the pipeline enables further customization of the system to specific domains of text. We performed intrinsic evaluations of the system on a publicly available domain-independent dataset from Twitter, and our system obtained an F-score of 0.836, outperforming other benchmark systems for the task. We further performed brief, task-oriented evaluations of the system to illustrate the customizability of the system to domain-specific tasks and the effects of normalization on downstream applications. The modular design enables the easy customization of the system to distinct types domain-specific social media text, in addition to its off-the-shelf application to generic social media text.

negative: Hierarchical Deep Multiagent Reinforcement Learning Despite deep reinforcement learning has recently achieved great successes, however in multiagent environments, a number of challenges still remain. Multiagent reinforcement learning (MARL) is commonly considered to suffer from the problem of non-stationary environments and exponentially increasing policy space. It would be even more challenging to learn effective policies in circumstances where the rewards are sparse and delayed over long trajectories. In this paper, we study Hierarchical Deep Multiagent Reinforcement Learning (hierarchical deep MARL) in cooperative multiagent problems with sparse and delayed rewards, where efficient multiagent learning methods are desperately needed. We decompose the original MARL problem into hierarchies and investigate how effective policies can be learned hierarchically in synchronous/asynchronous hierarchical MARL frameworks. Several hierarchical deep MARL architectures, i.e., Ind-hDQN, hCom and hQmix, are introduced for different learning paradigms. Moreover, to alleviate the issues of sparse experiences in high-level learning and non-stationarity in multiagent settings, we propose a new experience replay mechanism, named as Augmented Concurrent Experience Replay (ACER). We empirically demonstrate the effects and efficiency of our approaches in several classic Multiagent Trash Collection tasks, as well as in an extremely challenging team sports game, i.e., Fever Basketball Defense.

negative: Enumerating the Non-Isomorphic Assembly Configurations of Modular Robotic Systems A \modular" robotic system consists of joint and link modules that can be assembled in a variety of conngurations to meet diierent or changing task requirements. However, due to typical symmetries in module design, diierent assembly conngurations may lead to robotic structures which are kinematically identical, or isomorphic. This paper considers how to enumerate the non-isomorphic assembly conngurations of a modular robotic system. We introduce an Assembly Incidence Matrix (AIM) to represent a modular robot assembly con-guration. Then we use symmetries of the module geometry and graph isomorphisms to deene an equivalence relation on the AIMs. Equivalent AIMs represent isomorphic robot assembly conngurations. Based on this equivalence relation, we propose an algorithm to generate non-isomorphic assembly conngurations of an n-link tree-like robot with diierent joint and link module types. Examples demonstrate that this method is a signiicant improvement over a brute force enumeration process.

negative: Full virtualization based ARINC 653 partitioning As the number of electronic components of avionics systems is significantly increasing, it is desirable to run several avionics software on a single computing device. In such system, providing a seamless way to integrate separate applications on a computing device is a very critical issue as the Integrated Modular Avionics (IMA) concept addresses. In this context, the ARINC 653 standard defines resource partitioning of avionics application software. The virtualization technology has very high potential of providing an optimal implementation of the partition concept. In this paper, we study supports for full virtualization based ARINC 653 partitioning. The supports include extension of XML-based configuration file format and hierarchical scheduler for temporal partitioning. We show that our implementation can support well-known VMMs, such as VirtualBox and VMware and present basic performance numbers.

negative: A modular control scheme for PMSM speed control with pulsating torque minimization In this paper, a modular control approach is applied to a permanent-magnet synchronous motor (PMSM) speed control. Based on the functioning of the individual module, the modular approach enables the powerfully intelligent and robust control modules to easily replace any existing module which does not perform well, meanwhile retaining other existing modules which are still effective. Property analysis is first conducted for the existing function modules in a conventional PMSM control system: proportional-integral (PI) speed control module, reference current-generating module, and PI current control module. Next, it is shown that the conventional PMSM controller is not able to reject the torque pulsation which is the main hurdle when PMSM is used as a high-performance servo. By virtue of the internal model, to nullify the torque pulsation it is imperative to incorporate an internal model in the feed-through path. This is achieved by replacing the reference current-generating module with an iterative learning control (ILC) module. The ILC module records the cyclic torque and reference current signals over one entire cycle, and then uses those signals to update the reference current for the next cycle. As a consequence, the torque pulsation can be reduced significantly. In order to estimate the torque ripples which may exceed certain bandwidth of a torque transducer, a novel torque estimation module using a gain-shaped sliding-mode observer is further developed to facilitate the implementation of torque learning control. The proposed control system is evaluated through real-time implementation and experimental results validate the effectiveness.

negative: Degree of Modularity in Engineering Systems and Products with Technical and Business Constraints There is consensus that modularity has many benefits from cost savings due to increased commonality to enabling a higher variety of products. Full modularity is, however, not always achievable. How engineering systems and products whose design is heavily influenced by technical constraints, such as weight or size limitations, tend to exhibit rather integral architectures is shown in this study. For this, two metrics are defined on the basis of a binary design structure matrix (DSM) representation of a system or product. The non-zero fraction (NZF) captures the sparsity of the interrelationships between components between zero and one, while the singular value modularity index (SMI) captures the degree of internal coupling, also between zero and one. These metrics are first developed using idealized canonical architectures and are then applied to two different product pairs that are functionally equivalent, but different in terms of technical constraints. Empirical evidence is presented that the lightweight variant of the same product tends to be more integral, presumably to achieve higher mass efficiency. These observations are strengthened by comparing the results to another, previously published, modularity metric as well as by comparing sparsity and modularity of a set of 15 products against a control population of randomly generated architectures of equivalent size and density. The results suggest that, indeed, some products are inherently less modular than others due to technological factors. The main advantage of SMI is that it enables analysis of the degree of modularity of any architecture independent of subjective module choices.

negative: Large-Scale Hierarchical Text Classification with Recursively Regularized Deep Graph-CNN Text classification to a hierarchical taxonomy of topics is a common and practical problem. Traditional approaches simply use bag-of-words and have achieved good results. However, when there are a lot of labels with different topical granularities, bagof-words representation may not be enough. Deep learning models have been proven to be effective to automatically learn different levels of representations for image data. It is interesting to study what is the best way to represent texts. In this paper, we propose a graph-CNN based deep learning model to first convert texts to graph-of-words, and then use graph convolution operations to convolve the word graph. Graph-ofwords representation of texts has the advantage of capturing non-consecutive and long-distance semantics. CNN models have the advantage of learning different level of semantics. To further leverage the hierarchy of labels, we regularize the deep architecture with the dependency among labels. Our results on both RCV1 and NYTimes datasets show that we can significantly improve large-scale hierarchical text classification over traditional hierarchical text classification and existing deep models.