Query: Deep neural network ensemble architecture for eye movements classification

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Very Deep Convolutional Networks for Large-Scale Image Recognition <=SEP=> In this work we investigate the effect of the convolutional n etwork depth on its accuracy in the large-scale image recognition setting. Our main contribution is a thorough evaluation of networks of increasing depth, which shows that a significant improvement on the prior-art configurations can be achi eved by pushing the depth to 16–19 weight layers. These findings were the basis of our ImageNet Challenge 2014 submission, where our team secured the first a nd he second places in the localisation and classification tracks respec tively. We also show that our representations generalise well to other datasets, whe re t y achieve the stateof-the-art results. Importantly, we have made our two bestp rforming ConvNet models publicly available to facilitate further research o n the use of deep visual representations in computer vision.

The Pascal Visual Object Classes (VOC) Challenge <=SEP=> The Pascal Visual Object Classes (VOC) challenge is a benchmark in visual object category recognition and detection, providing the vision and machine learning communities with a standard dataset of images and annotation, and standard evaluation procedures. Organised annually from 2005 to present, the challenge and its associated dataset has become accepted as the benchmark for object detection. This paper describes the dataset and evaluation procedure. We review the state-of-the-art in evaluated methods for both classification and detection, analyse whether the methods are statistically different, what they are learning from the images (e.g. the object or its context), and what the methods find easy or confuse. The paper concludes with lessons learnt in the three year history of the challenge, and proposes directions for future improvement and extension.

Deep Residual Learning for Image Recognition <=SEP=> Deeper neural networks are more difficult to train. We present a residual learning framework to ease the training of networks that are substantially deeper than those used previously. We explicitly reformulate the layers as learning residual functions with reference to the layer inputs, instead of learning unreferenced functions. We provide comprehensive empirical evidence showing that these residual networks are easier to optimize, and can gain accuracy from considerably increased depth. On the ImageNet dataset we evaluate residual nets with a depth of up to 152 layers - 8× deeper than VGG nets [40] but still having lower complexity. An ensemble of these residual nets achieves 3.57% error on the ImageNet test set. This result won the 1st place on the ILSVRC 2015 classification task. We also present analysis on CIFAR-10 with 100 and 1000 layers. The depth of representations is of central importance for many visual recognition tasks. Solely due to our extremely deep representations, we obtain a 28% relative improvement on the COCO object detection dataset. Deep residual nets are foundations of our submissions to ILSVRC & COCO 2015 competitions1, where we also won the 1st places on the tasks of ImageNet detection, ImageNet localization, COCO detection, and COCO segmentation.

Detecting readers with dyslexia using machine learning with eye tracking measures <=SEP=> Worldwide, around 10% of the population has dyslexia, a specific learning disorder. Most of previous eye tracking experiments with people with and without dyslexia have found differences between populations suggesting that eye movements reflect the difficulties of individuals with dyslexia. In this paper, we present the first statistical model to predict readers with and without dyslexia using eye tracking measures. The model is trained and evaluated in a 10-fold cross experiment with a dataset composed of 1,135 readings of people with and without dyslexia that were recorded with an eye tracker. Our model, based on a Support Vector Machine binary classifier, reaches 80.18% accuracy using the most informative features. To the best of our knowledge, this is the first time that eye tracking measures are used to predict automatically readers with dyslexia using machine learning.

ImageNet Large Scale Visual Recognition Challenge <=SEP=> 

Disassortative Degree Mixing and Information Diffusion for Overlapping Community Detection in Social Networks (DMID) <=SEP=> In this paper we propose a new two-phase algorithm for overlapping community detection (OCD) in social networks. In the first phase, called disassortative degree mixing, we identify nodes with high degrees through a random walk process on the row-normalized disassortative matrix representation of the network. In the second phase, we calculate how closely each node of the network is bound to the leaders via a cascading process called network coordination game. We implemented the algorithm and four additional ones as a Web service on a federated peer-to-peer infrastructure. Comparative test results for small and big real world networks demonstrated the correct identification of leaders, high precision and good time complexity. The Web service is available as open source software.

SustData: A Public Dataset for ICT4S Electric Energy Research <=SEP=> Energy and environmental sustainability can benefit a lot from advances in data mining and machine learning techniques. However, those advances rely on the availability of relevant datasets required to develop, improve and validate new techniques. Only recently the first datasets were made publicly available for the energy and sustainability research community. In this paper we present a freely available dataset containing power usage and related information from 50 homes. Here we describe our dataset, the hardware and software setups used when collecting the data and how others can access it. We then discuss potential uses of this data in the future of energy ecofeedback and demand side management research.

BlueSky: a cloud-backed file system for the enterprise <=SEP=> We present BlueSky, a network file system backed by cloud storage. BlueSky stores data persistently in a cloud storage provider such as Amazon S3 or Windows Azure, allowing users to take advantage of the reliability and large storage capacity of cloud providers and avoid the need for dedicated server hardware. Clients access the storage through a proxy running on-site, which caches data to provide lower-latency responses and additional opportunities for optimization. We describe some of the optimizations which are necessary to achieve good performance and low cost, including a log-structured design and a secure in-cloud log cleaner. BlueSky supports multiple protocols—both NFS and CIFS—and is portable to different providers.

Extreme learning machine: Theory and applications <=SEP=> It is clear that the learning speed of feedforward neural networks is in general far slower than required and it has been a major bottleneck in their applications for past decades. Two key reasons behind may be: (1) the slow gradient-based learning algorithms are extensively used to train neural networks, and (2) all the parameters of the networks are tuned iteratively by using such learning algorithms. Unlike these conventional implementations, this paper proposes a new learning algorithm called extreme learning machine (ELM) for single-hidden layer feedforward neural networks (SLFNs) which randomly chooses hidden nodes and analytically determines the output weights of SLFNs. In theory, this algorithm tends to provide good generalization performance at extremely fast learning speed. The experimental results based on a few artificial and real benchmark function approximation and classification problems including very large complex applications show that the new algorithm can produce good generalization performance in most cases and can learn thousands of times faster than conventional popular learning algorithms for feedforward neural networks. r 2006 Elsevier B.V. All rights reserved.

Training of Airport Security Screeners <=SEP=> scientifically based training system that is effective and efficient and allows achieving an excellent level of detection performance. Object recognition is a very complex process but essentially it means to compare visual information to object representations stored in visual memory. The ability to recognize an object class depends on whether itself or similar instance has been stored previously in visual memory. In other words, you can only recognize what you have learned. This explains why training is so important. Identifying the threat items in Fig. 1a and 1b is difficult without training because the objects are depicted in a view that is rather unusual in everyday life. Detecting a bomb such as in Fig. 1c is difficult for untrained people because usually we do not encounter bombs in everyday life. Therefore, a good training system must contain many forbidden objects in many viewpoints in order to train screeners to detect them reliably. Indeed, several studies from our lab and many others worldwide have found that object recognition is often dependent on viewpoint. Moreover, there are numerous studies from neuroscience suggesting that objects are stored in a view-based format in the brain. As you can see in Fig. 2 the hammer, dirk, grenade and gun, which are visible in the bags of Fig. 1a and 1b are indeed much easier to recognize if they are shown in a view that is more often encountered in real life. Because you never know how terrorists place their threat items in a bag, airport security screeners should be trained to detect prohibited items from all kinds of different viewpoints. In a close collaboration with Zurich State Police, Airport division we have Current x-ray machines provide high resolution images, many image processing features and even automatic explosive detection. But the machine is only one half of the whole system. The last and most important decision is always taken by the human operator. In fact, the best and most expensive equipment is of limited use, if a screener finally fails to recognize a threat in the x-ray image. This is of special importance because according to several aviation security experts the human operator is currently the weakest link in airport security. This matter is being realized more and more and several authorities as well as airports are planning to increase investments into a very important element of aviation security: Effective and efficient training of screeners. Indeed, …

Chord-PKI: A distributed trust infrastructure based on P2P networks <=SEP=> Many P2P applications require security services such as privacy, anonymity, authentication, and non-repudiation. Such services could be provided through a hierarchical Public Key Infrastructure. However, P2P networks are usually Internet-scale distributed systems comprised of nodes with undetermined trust level, thus making hierarchical solutions unrealistic. In this paper, we propose Chord-PKI, a distributed PKI architecture which is build upon the Chord overlay network, in order to provide security services for P2P applications. Our solution distributes the functionality of a PKI across the peers, by using threshold cryptography and proactive updating. We analyze the security of the proposed infrastructure and through simulations, we evaluate its performance for various scenarios of untrusted node distributions.

Ip = Pspace <=SEP=> In this paper, it is proven that when both randomization and interaction are allowed, the proofs that can be verified in polynomial time are exactly those proofs that can be generated with polynomial space.

The International Exascale Software Project roadmap <=SEP=> Over the last 20 years, the open-source community has provided more and more software on which the world’s highperformance computing systems depend for performance and productivity. The community has invested millions of dollars and years of effort to build key components. However, although the investments in these separate software elements have been tremendously valuable, a great deal of productivity has also been lost because of the lack of planning, coordination, and key integration of technologies necessary to make them work together smoothly and efficiently, both within individual petascale systems and between different systems. It seems clear that this completely uncoordinated development model will not provide the software needed to support the unprecedented parallelism required for peta/ exascale computation on millions of cores, or the flexibility required to exploit new hardware models and features, such as transactional memory, speculative execution, and graphics processing units. This report describes the work of the community to prepare for the challenges of exascale computing, ultimately combing their efforts in a coordinated International Exascale Software Project.

Steering Behaviors For Autonomous Characters <=SEP=> This paper presents solutions for one requirement of autonomous characters in animation and games: the ability to navigate around their world in a life-like and improvisational manner. These “steering behaviors” are largely independent of the particulars of the character’s means of locomotion. Combinations of steering behaviors can be used to achieve higher level goals (For example: get from here to there while avoiding obstacles, follow this corridor, join that group of characters...) This paper divides motion behavior into three levels. It will focus on the middle level of steering behaviors, briefly describe the lower level of locomotion, and touch lightly on the higher level of goal setting and strategy.

Compositional Distributional Semantics with Compact Closed Categories and Frobenius Algebras <=SEP=> The provision of compositionality in distributional models of meaning, where a word is represented as a vector of co-occurrence counts with every other word in the vocabulary, offers a solution to the fact that no text corpus, regardless of its size, is capable of providing reliable co-occurrence statistics for anything but very short text constituents. The purpose of a compositional distributional model is to provide a function that composes the vectors for the words within a sentence, in order to create a vectorial representation that reflects its meaning. Using the abstract mathematical framework of category theory, Coecke, Sadrzadeh and Clark showed that this function can directly depend on the grammatical structure of the sentence, providing an elegant mathematical counterpart of the formal semantics view. The framework is general and compositional but stays abstract to a large extent. This thesis contributes to ongoing research related to the above categorical model in three ways: Firstly, I propose a concrete instantiation of the abstract framework based on Frobenius algebras (joint work with Sadrzadeh). The theory improves shortcomings of previous proposals, extends the coverage of the language, and is supported by experimental work that improves existing results. The proposed framework describes a new class of compositional models that find intuitive interpretations for a number of linguistic phenomena. Secondly, I propose and evaluate in practice a new compositional methodology which explicitly deals with the different levels of lexical ambiguity (joint work with Pulman). A concrete algorithm is presented, based on the separation of vector disambiguation from composition in an explicit prior step. Extensive experimental work shows that the proposed methodology indeed results in more accurate composite representations for the framework of Coecke et al. in particular and every other class of compositional models in general. As a last contribution, I formalize the explicit treatment of lexical ambiguity in the context of the categorical framework by resorting to categorical quantum mechanics (joint work with Coecke). In the proposed extension, the concept of a distributional vector is replaced with that of a density matrix, which compactly represents a probability distribution over the potential different meanings of the specific word. Composition takes the form of quantum measurements, leading to interesting analogies between quantum physics and linguistics.

Tibetan-Chinese Cross Language Text Similarity Calculation Based onLDA Topic Model <=SEP=> Topic model building is the basis and the most critical module of cross-language topic detection and tracking. Topic model also can be applied to cross-language text similarity calculation. It can improve the efficiency and the speed of calculation by reducing the texts’ dimensionality. In this paper, we use the LDA model in cross-language text similarity computation to obtain Tibetan-Chinese comparable corpora: (1) Extending Tibetan-Chinese dictionary by extracting Tibetan-Chinese entities from Wikipedia. (2) Using topic model to make the texts mapped to the feature space of topics. (3) Calculating the similarity of two texts in different language according to the characteristics of the news text. The method for text similarity calculation based on LDA model reduces the dimensions of text space vector, and enhances the understanding of the text’s semantics. It also improves the speed and efficiency of calculation.

The emotional brain <=SEP=> The discipline of affective neuroscience is concerned with the neural bases of emotion and mood. The past 30 years have witnessed an explosion of research in affective neuroscience that has addressed questions such as: which brain systems underlie emotions? How do differences in these systems relate to differences in the emotional experience of individuals? Do different regions underlie different emotions, or are all emotions a function of the same basic brain circuitry? How does emotion processing in the brain relate to bodily changes associated with emotion? And, how does emotion processing in the brain interact with cognition, motor behaviour, language and motivation?

Fast in-memory transaction processing using RDMA and HTM <=SEP=> We present DrTM, a fast in-memory transaction processing system that exploits advanced hardware features (i.e., RDMA and HTM) to improve latency and throughput by over one order of magnitude compared to state-of-the-art distributed transaction systems. The high performance of DrTM are enabled by mostly offloading concurrency control within a local machine into HTM and leveraging the strong consistency between RDMA and HTM to ensure serializability among concurrent transactions across machines. We further build an efficient hash table for DrTM by leveraging HTM and RDMA to simplify the design and notably improve the performance. We describe how DrTM supports common database features like read-only transactions and logging for durability. Evaluation using typical OLTP workloads including TPC-C and SmallBank show that DrTM scales well on a 6-node cluster and achieves over 5.52 and 138 million transactions per second for TPC-C and SmallBank Respectively. This number outperforms a state-of-the-art distributed transaction system (namely Calvin) by at least 17.9X for TPC-C.

Teaching theory of mind: a new approach to social skills training for individuals with autism. <=SEP=> This study examined the effectiveness of a social skills training program for normal-IQ adolescents with autism. Five boys participated in the 4 1/2-month treatment condition; four boys matched on age, IQ, and severity of autism constituted the no-treatment control group. In addition to teaching specific interactional and conversational skills, the training program provided explicit and systematic instruction in the underlying social-cognitive principles necessary to infer the mental states of others (i.e., theory of mind). Pre- and post-intervention assessment demonstrated meaningful change in the treatment group's performance on several false belief tasks, but no improvement in the control sample. No changes, however, were demonstrated on general parent and teacher ratings of social competence for either group.

A GA based approach for task scheduling in multi-cloud environment <=SEP=> In multi-cloud environment, task scheduling has attracted a lot of attention due to NP-Complete nature of the problem. Moreover, it is very challenging due to heterogeneity of the cloud resources with varying capacities and functionalities. Therefore, minimizing the makespan for task scheduling is a challenging issue. In this paper, we propose a genetic algorithm (GA) based approach for solving task scheduling problem. The algorithm is described with innovative idea of fitness function derivation and mutation. The proposed algorithm is exposed to rigorous testing using various benchmark datasets and its performance is evaluated in terms of total makespan.

Kernel auto-encoder for semi-supervised hashing <=SEP=> Hashing-based approaches have gained popularity for large-scale image retrieval in recent years. It has been shown that semi-supervised hashing, which incorporates similarity/dissimilarity information into hash function learning could improve the hashing quality. In this paper, we present a novel kernel-based semi-supervised binary hashing model for image retrieval by taking into account auxiliary information, i.e., similar and dissimilar data pairs in achieving high quality hashing. The main idea is to map the data points into a highly non-linear feature space and then map the non-linear features into compact binary codes such that similar/dissimilar data points have similar/dissimilar hash codes. Empirical evaluations on three benchmark datasets demonstrate the superiority of the proposed method over several existing unsupervised and semi-supervised hash function learning methods.

Abnormal use of facial information in high-functioning autism. <=SEP=> Altered visual exploration of faces likely contributes to social cognition deficits seen in autism. To investigate the relationship between face gaze and social cognition in autism, we measured both face gaze and how facial regions were actually used during emotion judgments from faces. Compared to IQ-matched healthy controls, nine high-functioning adults with autism failed to make use of information from the eye region of faces, instead relying primarily on information from the mouth. Face gaze accounted for the increased reliance on the mouth, and partially accounted for the deficit in using information from the eyes. These findings provide a novel quantitative assessment of how people with autism utilize information in faces when making social judgments.

Tracking the Evolution of the Internet of Things Concept Across Different Application Domains <=SEP=> Both the idea and technology for connecting sensors and actuators to a network to remotely monitor and control physical systems have been known for many years and developed accordingly. However, a little more than a decade ago the concept of the Internet of Things (IoT) was coined and used to integrate such approaches into a common framework. Technology has been constantly evolving and so has the concept of the Internet of Things, incorporating new terminology appropriate to technological advances and different application domains. This paper presents the changes that the IoT has undertaken since its conception and research on how technological advances have shaped it and fostered the arising of derived names suitable to specific domains. A two-step literature review through major publishers and indexing databases was conducted; first by searching for proposals on the Internet of Things concept and analyzing them to find similarities, differences, and technological features that allow us to create a timeline showing its development; in the second step the most mentioned names given to the IoT for specific domains, as well as closely related concepts were identified and briefly analyzed. The study confirms the claim that a consensus on the IoT definition has not yet been reached, as enabling technology keeps evolving and new application domains are being proposed. However, recent changes have been relatively moderated, and its variations on application domains are clearly differentiated, with data and data technologies playing an important role in the IoT landscape.

Online sketching hashing <=SEP=> Recently, hashing based approximate nearest neighbor (ANN) search has attracted much attention. Extensive new algorithms have been developed and successfully applied to different applications. However, two critical problems are rarely mentioned. First, in real-world applications, the data often comes in a streaming fashion but most of existing hashing methods are batch based models. Second, when the dataset becomes huge, it is almost impossible to load all the data into memory to train hashing models. In this paper, we propose a novel approach to handle these two problems simultaneously based on the idea of data sketching. A sketch of one dataset preserves its major characters but with significantly smaller size. With a small size sketch, our method can learn hash functions in an online fashion, while needs rather low computational complexity and storage space. Extensive experiments on two large scale benchmarks and one synthetic dataset demonstrate the efficacy of the proposed method.

4 RFID-enabled Supply Chain Traceability : Existing Methods , Applications and Challenges <=SEP=> Radio Frequency Identification (RFID) technology promises to revolutionize various areas in supply chain. Recently, many researchers have investigated on how to improve the ability to track and trace a specific product along the supply chain in terms of both effectiveness and efficiency with the help of this technology. To enable traceability over the entire lifecycle a robust and seamless traceability system has to be constructed. This requires for the following three elements: (1) data model and storage scheme that allows unique identification and scalable database, (2) system framework which enables to share the traceability data between trading partners while maintaining a sovereignty over what is shared and with whom, and (3) a tracing mechanism in order to achieve end-to-end traceability and to provide the history information of products in question. Along with the studies addressing the requirements and design of traceability system architecture, applications in the real environment have also been reported. Due to the strong regulation in EU which states that food business operators shall be able to identify any person who supplied them and any business which takes food from them, RFID-enabled traceability systems are well implemented in the food supply chain. However, there exist other industries adopting RFID to enhance traceability such as pharmaceutical and aviation and even in the continuous process industry like iron ore refining. Despite the promising nature of RFID in tracking, there are several challenges to be addressed. Since an RFID tag does not require line-of-sight, multiple tags can be read simultaneously but also tag collisions may occur. Therefore there is no guarantee that a tag will be continuously detected on consecutive scans. Moreover, the use of RFID tags can be of serious threat to the privacy of information. This may facilitate the espionage of unauthorized personnel. In this chapter, we analyze the main issues of RFID-enabled traceability along the supply chain mentioned above: existing methods, applications and future challenges. Section 2 starts with pointing out the characteristics of RFID data and the requirements for RFIDenabled traceability. Subsequently, we introduce data types, storage schemes and system frameworks proposed in the existing literatures. Then, we discuss tracing methods based on the traceability system architecture. Section 3 presents current applications in real settings of both discrete and continuous production. We also discuss challenges that are preventing companies from adopting RFID for their traceability solutions in section 4. Finally, we conclude our study in section 5.

A Survey on Image Classification Approaches and Techniques <=SEP=> Object Classification is an important task within the field of computer vision. Image classification refers to the labelling of images into one of a number of predefined categories. Classification includes image sensors, image pre-processing, object detection, object segmentation, feature extraction and object classification.Many classification techniques have been developed for image classification. In this survey various classification techniques are considered; Artificial Neural Network(ANN), Decision Tree(DT), Support Vector Machine(SVM) and Fuzzy Classification

Unite the People : Closing the loop between 3 D and 2 D Human Representations Supplementary Material <=SEP=> We have obtained human segmentation labels to integrate shape information into the SMPLify 3D fitting procedure and for the evaluation of methods introduced in the main paper. The labels consist of foreground segmentation for multiple human pose datasets and six body part segmentation for the LSP dataset. Whereas we discuss their use in the context of the UP dataset in the main paper, we discuss the annotation tool that we used for the collection (see Sec. 2.1) as well as the direct use of the human labels for model training (see Sec. 2.2) in this document.

Mass Cytometry: Single Cells, Many Features <=SEP=> Technology development in biological research often aims to either increase the number of cellular features that can be surveyed simultaneously or enhance the resolution at which such observations are possible. For decades, flow cytometry has balanced these goals to fill a critical need by enabling the measurement of multiple features in single cells, commonly to examine complex or hierarchical cellular systems. Recently, a format for flow cytometry has been developed that leverages the precision of mass spectrometry. This fusion of the two technologies, termed mass cytometry, provides measurement of over 40 simultaneous cellular parameters at single-cell resolution, significantly augmenting the ability of cytometry to evaluate complex cellular systems and processes. In this Primer, we review the current state of mass cytometry, providing an overview of the instrumentation, its present capabilities, and methods of data analysis, as well as thoughts on future developments and applications.

Manga content analysis using physiological signals <=SEP=> Recently, the physiological signals have been analyzed more and more, especially in the context of everyday life activities such as watching video or looking at pictures. Tracking these signals gives access to the mental state of the user (interest, tiredness, stress) but also to his emotions (sadness, fright, happiness). The analysis of the reader's physiological signals during reading can provide a better understanding of the reader's feelings but also a better understanding of the documents. Our main research direction is to find the relationship between a change in the reader's physiological signal and the content of the reading. As a first step, we investigate whether it is possible to distinguish a manga (Japanese comic book) from another by analyzing the physiological signals of the reader. We use 3 different manga genres (horror, romance, comedy) and try to predict which one is read by analyzing the features extracted from the physiological signals of the reader. Our method uses the blood volume pulse, the electrodermal activity and the skin temperature of the reader while reading. We show that by using these physiological signals with a support vector machine we can retrieve which manga has been read with a 90% average accuracy.

Blood monocytes: development, heterogeneity, and relationship with dendritic cells. <=SEP=> Monocytes are circulating blood leukocytes that play important roles in the inflammatory response, which is essential for the innate response to pathogens. But inflammation and monocytes are also involved in the pathogenesis of inflammatory diseases, including atherosclerosis. In adult mice, monocytes originate in the bone marrow in a Csf-1R (MCSF-R, CD115)-dependent manner from a hematopoietic precursor common for monocytes and several subsets of macrophages and dendritic cells (DCs). Monocyte heterogeneity has long been recognized, but in recent years investigators have identified three functional subsets of human monocytes and two subsets of mouse monocytes that exert specific roles in homeostasis and inflammation in vivo, reminiscent of those of the previously described classically and alternatively activated macrophages. Functional characterization of monocytes is in progress in humans and rodents and will provide a better understanding of the pathophysiology of inflammation.