Query: Weakly Supervised Semantic Image Segmentation with Self-correcting Networks

Relevant passages:
Learning with Noisy Labels <=SEP=> In this paper, we theoretically study the problem of binary classification in the presence of random classification noise — the learner, instead of seeing the true labels, sees labels that have independently been flipped with some small probability. Moreover, random label noise is class-conditional — the flip probability depends on the class. We provide two approaches to suitably modify any given surrogate loss function. First, we provide a simple unbiased estimator of any loss, and obtain performance bounds for empirical risk minimization in the presence of iid data with noisy labels. If the loss function satisfies a simple symmetry condition, we show that the method leads to an efficient algorithm for empirical minimization. Second, by leveraging a reduction of risk minimization under noisy labels to classification with weighted 0-1 loss, we suggest the use of a simple weighted surrogate loss, for which we are able to obtain strong empirical risk bounds. This approach has a very remarkable consequence — methods used in practice such as biased SVM and weighted logistic regression are provably noise-tolerant. On a synthetic non-separable dataset, our methods achieve over 88% accuracy even when 40% of the labels are corrupted, and are competitive with respect to recently proposed methods for dealing with label noise in several benchmark datasets.

Efficient Piecewise Training of Deep Structured Models for Semantic Segmentation <=SEP=> Recent advances in semantic image segmentation have mostly been achieved by training deep convolutional neural networks (CNNs). We show how to improve semantic segmentation through the use of contextual information, specifically, we explore 'patch-patch' context between image regions, and 'patch-background' context. For learning from the patch-patch context, we formulate Conditional Random Fields (CRFs) with CNN-based pairwise potential functions to capture semantic correlations between neighboring patches. Efficient piecewise training of the proposed deep structured model is then applied to avoid repeated expensive CRF inference for back propagation. For capturing the patch-background context, we show that a network design with traditional multi-scale image input and sliding pyramid pooling is effective for improving performance. Our experimental results set new state-of-the-art performance on a number of popular semantic segmentation datasets, including NYUDv2, PASCAL VOC 2012, PASCAL-Context, and SIFT-flow. In particular, we achieve an intersection-overunion score of 78:0 on the challenging PASCAL VOC 2012 dataset.

The Cityscapes Dataset for Semantic Urban Scene Understanding <=SEP=> Visual understanding of complex urban street scenes is an enabling factor for a wide range of applications. Object detection has benefited enormously from large-scale datasets, especially in the context of deep learning. For semantic urban scene understanding, however, no current dataset adequately captures the complexity of real-world urban scenes. To address this, we introduce Cityscapes, a benchmark suite and large-scale dataset to train and test approaches for pixel-level and instance-level semantic labeling. Cityscapes is comprised of a large, diverse set of stereo video sequences recorded in streets from 50 different cities. 5000 of these images have high quality pixel-level annotations, 20 000 additional images have coarse annotations to enable methods that leverage large volumes of weakly-labeled data. Crucially, our effort exceeds previous attempts in terms of dataset size, annotation richness, scene variability, and complexity. Our accompanying empirical study provides an in-depth analysis of the dataset characteristics, as well as a performance evaluation of several state-of-the-art approaches based on our benchmark.

SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation <=SEP=> We present a novel and practical deep fully convolutional neural network architecture for semantic pixel-wise segmentation termed SegNet. This core trainable segmentation engine consists of an encoder network, a corresponding decoder network followed by a pixel-wise classification layer. The architecture of the encoder network is topologically identical to the 13 convolutional layers in the VGG16 network [1] . The role of the decoder network is to map the low resolution encoder feature maps to full input resolution feature maps for pixel-wise classification. The novelty of SegNet lies is in the manner in which the decoder upsamples its lower resolution input feature map(s). Specifically, the decoder uses pooling indices computed in the max-pooling step of the corresponding encoder to perform non-linear upsampling. This eliminates the need for learning to upsample. The upsampled maps are sparse and are then convolved with trainable filters to produce dense feature maps. We compare our proposed architecture with the widely adopted FCN [2] and also with the well known DeepLab-LargeFOV [3] , DeconvNet [4] architectures. This comparison reveals the memory versus accuracy trade-off involved in achieving good segmentation performance. SegNet was primarily motivated by scene understanding applications. Hence, it is designed to be efficient both in terms of memory and computational time during inference. It is also significantly smaller in the number of trainable parameters than other competing architectures and can be trained end-to-end using stochastic gradient descent. We also performed a controlled benchmark of SegNet and other architectures on both road scenes and SUN RGB-D indoor scene segmentation tasks. These quantitative assessments show that SegNet provides good performance with competitive inference time and most efficient inference memory-wise as compared to other architectures. We also provide a Caffe implementation of SegNet and a web demo at http://mi.eng.cam.ac.uk/projects/segnet/.

U-Net: Convolutional Networks for Biomedical Image Segmentation <=SEP=> 

Long-Time Exposure to Violent Video Games Does Not Show Desensitization on Empathy for Pain: An fMRI Study <=SEP=> As a typical form of empathy, empathy for pain refers to the perception and appraisal of others' pain, as well as the corresponding affective responses. Numerous studies investigated the factors affecting the empathy for pain, in which the exposure to violent video games (VVGs) could change players' empathic responses to painful situations. However, it remains unclear whether exposure to VVG influences the empathy for pain. In the present study, in terms of the exposure experience to VVG, two groups of participants (18 in VVG group, VG; 17 in non-VVG group, NG) were screened from nearly 200 video game experience questionnaires. And then, the functional magnetic resonance imaging data were recorded when they were viewing painful and non-painful stimuli. The results showed that the perception of others' pain were not significantly different in brain regions between groups, from which we could infer that the desensitization effect of VVGs was overrated.

An Augmented Reality System for Astronomical Observations <=SEP=> Anyone who has gazed through the eyepiece of an astronomical telescope knows that, with the exception of the Moon and the planets, extra-solar astronomical objects are disappointing to observe visually. This is mainly due to their low surface brightness, but also depends on the visibility, sky brightness and telescope aperture. We propose a system which projects images of astronomical objects (with focus on nebulae and galaxies), animations and additional information directly into the eyepiece view of an astronomical telescope. As the telescope orientation is queried continuously, the projected image is adapted in real-time to the currently visible field of view. For projection, a custom-built video projection module with high contrast and low maximum luminance value was developed. With this technology visitors to public observatories have the option to experience the richness of faint astronomical objects while directly looking at them through a telescope.

A gateway system for an automotive system: LIN, CAN, and FlexRay <=SEP=> In the automotive industry, the usage of microcontrollers has been increasing rapidly. Many mechanical parts of automobiles are being replaced with microcontrollers, and these microcontrollers are controlling various parts of the automobile, so communications between microcontrollers must be reliable. Until now, several protocols for automotive communications have been introduced, and LIN, CAN, and FlexRay are the most widely known protocols. Different vendors of automobiles use different protocols and each protocol possess different features. This paper presents a network gateway system between LIN, low-speed CAN, high-speed CAN, and FlexRay.

ECG-Cryptography and Authentication in Body Area Networks <=SEP=> Wireless body area networks (BANs) have drawn much attention from research community and industry in recent years. Multimedia healthcare services provided by BANs can be available to anyone, anywhere, and anytime seamlessly. A critical issue in BANs is how to preserve the integrity and privacy of a person's medical data over wireless environments in a resource efficient manner. This paper presents a novel key agreement scheme that allows neighboring nodes in BANs to share a common key generated by electrocardiogram (ECG) signals. The improved Jules Sudan (IJS) algorithm is proposed to set up the key agreement for the message authentication. The proposed ECG-IJS key agreement can secure data commnications over BANs in a plug-n-play manner without any key distribution overheads. Both the simulation and experimental results are presented, which demonstrate that the proposed ECG-IJS scheme can achieve better security performance in terms of serval performance metrics such as false acceptance rate (FAR) and false rejection rate (FRR) than other existing approaches. In addition, the power consumption analysis also shows that the proposed ECG-IJS scheme can achieve energy efficiency for BANs.

Realistic and Fast Cloud Rendering <=SEP=> Clouds are an important aspect of rendering outdoor scenes. Thi paper describes a cloud system that extends texture splatting on par ticles to model a dozen cloud types (e.g., stratus, cumulus congestus, cumulonimb us), an improvement over earlier systems that modeled only one type of cumulus. W e also achieve fast real-time rendering, even for scenes of dense overcast cove rage, which was a limitation for previous systems. We present a new shading model that uses artist-driven contr ols rather than a programmatic approach to approximate lighting. This is sui table when fine-grained control over the look-and-feel is necessary, and artistic r esources are available. We also introduce a way to simulate cloud formation and dissipa tion using texture

An anomaly-based network intrusion detection system using Deep learning <=SEP=> Recently, anomaly-based intrusion detection techniques are valuable methodology to detect both known as well as unknown/new attacks, so they can cope with the diversity of the attacks and the constantly changing nature of network attacks. There are many problems need to be considered in anomaly-based network intrusion detection system (NIDS), such as ability to adapt to dynamic network environments, unavailability of labeled data, false positive rate. This paper, we use Deep learning techniques to implement an anomaly-based NIDS. These techniques show the sensitive power of generative models with good classification, capabilities to deduce part of its knowledge from incomplete data and the adaptability. Our experiments with KDDCup99 network traffic connections show that our work is effective to exact detect in anomaly-based NIDS and classify intrusions into five groups with the accuracy based on network data sources.

Smarter City : Smart Energy Grid based on Blockchain Technology <=SEP=> The improvement of the Quality of Life (QoL) and the enhancement of the Quality of Services (QoS) represent the main goal of every city evolutionary process. It is possible making cities smarter promoting innovative solutions by use of Information and Communication Technology (ICT) for collecting and analysing large amounts of data generated by several sources, such as sensor networks, wearable devices, and IoT devices spread among the city. The integration of different technologies and different IT systems, needed to build smart city applications and services, remains the most challenge to overcome. In the Smart City context, this paper intends to investigate the Smart Environment pillar, and in particular the aspect related to the implementation of Smart Energy Grid for citizens in the urban context. The innovative characteristic of the proposed solution consists of using the Blockchain technology to join the Grid, exchanging information, and buy/sell energy between the involved nodes (energy providers and private citizens), using the Blockchain granting ledger. Keywords— Information Technology, Smart City, Digital Revolution, Digital Innovation, Blockchain, Smart Energy Grid, Machine Learning.

Collaging on Internal Representations: An Intuitive Approach for Semantic Transfiguration <=SEP=> We present a novel CNN-based image editing method that allows the user to change the semantic information of an image over a user-specified region. Our method makes this possible by combining the idea of manifold projection with spatial conditional batch normalization (sCBN), a version of conditional batch normalization with userspecifiable spatial weight maps. With sCBN and manifold projection, our method lets the user perform (1) spatial class translation that changes the class of an object over an arbitrary region of user’s choice, and (2) semantic transplantation that transplants semantic information contained in an arbitrary region of the reference image to an arbitrary region in the target image. These two transformations can be used simultaneously, and can realize a complex composite image-editing task like “change the nose of a beagle to that of a bulldog, and open her mouth”. The user can also use our method with intuitive copy-paste-style manipulations. We demonstrate the power of our method on various images. Code will be available at https://github. com/pfnet-research/neural-collage.

Applying data mining techniques in job recommender system for considering candidate job preferences <=SEP=> Job recommender systems are desired to attain a high level of accuracy while making the predictions which are relevant to the customer, as it becomes a very tedious task to explore thousands of jobs, posted on the web, periodically. Although a lot of job recommender systems exist that uses different strategies , here efforts have been put to make the job recommendations on the basis of candidate's profile matching as well as preserving candidate's job behavior or preferences. Firstly, rules predicting the general preferences of the different user groups are mined. Then the job recommendations to the target candidate are made on the basis of content based matching as well as candidate preferences, which are preserved either in the form of mined rules or obtained by candidate's own applied jobs history. Through this technique a significant level of accuracy has been achieved over other basic methods of job recommendations.

Social interaction based video recommendation: Recommending YouTube videos to facebook users <=SEP=> Online videos, e.g., YouTube videos, are important topics for social interactions among users of online social networking sites (OSN), e.g., Facebook. This opens up the possibility of exploiting video-related user social interaction information for better video recommendation. Towards this goal, we conduct a case study of recommending YouTube videos to Facebook users based on their social interactions. We first measure social interactions related to YouTube videos among Facebook users. We observe that the attention a video attracts on Facebook is not always well-aligned with its popularity on YouTube. Unpopular videos on YouTube can become popular on Facebook, while popular videos on YouTube often do not attract proportionally high attentions on Facebook. This finding motivates us to develop a simple top-k video recommendation algorithm that exploits user social interaction information to improve the recommendation accuracy for niche videos, that are globally unpopular, but highly relevant to a specific user or user group. Through experiments on the collected Facebook traces, we demonstrate that our recommendation algorithm significantly outperforms the YouTube-popularity based video recommendation algorithm as well as a collaborative filtering algorithm based on user similarities.

TextLuas: Tracking and Visualizing Document and Term Clusters in Dynamic Text Data <=SEP=> For large volumes of text data collected over time, a key knowledge discovery task is identifying and tracking clusters. These clusters may correspond to emerging themes, popular topics, or breaking news stories in a corpus. Therefore, recently there has been increased interest in the problem of clustering dynamic data. However, there exists little support for the interactive exploration of the output of these analysis techniques, particularly in cases where researchers wish to simultaneously explore both the change in cluster structure over time and the change in the textual content associated with clusters. In this paper, we propose a model for tracking dynamic clusters characterized by the evolutionary events of each cluster. Motivated by this model, the TextLuas system provides an implementation for tracking these dynamic clusters and visualizing their evolution using a metro map metaphor. To provide overviews of cluster content, we adapt the tag cloud representation to the dynamic clustering scenario. We demonstrate the TextLuas system on two different text corpora, where they are shown to elucidate the evolution of key themes. We also describe how TextLuas was applied to a problem in bibliographic network research.

Signal processing of range detection for SFCW radars using Matlab and GNU radio <=SEP=> Development of radar technology is now rapidly. One of them is Step Frequency Continuous Wave Radar (SFCW Radar). SFCW radar can be used for various purposes. SFCW radar consists of antenna, control unit, signal processing unit. The radar advantages compared to the other radar are this radar is easier to implement and more widely within the range of radar. In this research that will be done is design SFCW radar. There are several stages to be performed in this research. At first, the simulation will be done using Matlab®. This simulation will determine the parameters needed to obtain an appropriate resolution. The second, simulations is performed by using GNU radio. This simulation will be adjusted to the parameters that have been previously designed by Matlab®. Both of the results will be analyzed. Input signal is complex. Simulation results for 5kHz-1.285MHz will display in graph. Graph will show location of the object in 5 m, 1km, and 1.1km depend on initial design.

Weather classification with deep convolutional neural networks <=SEP=> In this paper, we study weather classification from images using Convolutional Neural Networks (CNNs). Our approach outperforms the state of the art by a huge margin in the weather classification task. Our approach achieves 82.2% normalized classification accuracy instead of 53.1% for the state of the art (i.e., 54.8% relative improvement). We also studied the behavior of all the layers of the Convolutional Neural Networks, we adopted, and interesting findings are discussed.

Image Deblurring via Enhanced Low-Rank Prior <=SEP=> Low-rank matrix approximation has been successfully applied to numerous vision problems in recent years. In this paper, we propose a novel low-rank prior for blind image deblurring. Our key observation is that directly applying a simple low-rank model to a blurry input image significantly reduces the blur even without using any kernel information, while preserving important edge information. The same model can be used to reduce blur in the gradient map of a blurry input. Based on these properties, we introduce an enhanced prior for image deblurring by combining the low rank prior of similar patches from both the blurry image and its gradient map. We employ a weighted nuclear norm minimization method to further enhance the effectiveness of low-rank prior for image deblurring, by retaining the dominant edges and eliminating fine texture and slight edges in intermediate images, allowing for better kernel estimation. In addition, we evaluate the proposed enhanced low-rank prior for both the uniform and the non-uniform deblurring. Quantitative and qualitative experimental evaluations demonstrate that the proposed algorithm performs favorably against the state-of-the-art deblurring methods.

10 Internet Addiction and Its Cognitive Behavioral Therapy <=SEP=> 

A droop control strategy of parallel-inverter-based microgrid <=SEP=> In this paper, the control strategy for a parallel-inverters-based microgrid is presented. The control strategy includes outer active and reactive power control loop based on frequency and voltage droop method to avoid critical communications among distributed generation units (DGs). The inner inverter control loop is employed to produce the required inverter output voltage provided by the outer power loop. In addition, two inverter control schemes are introduced and compared. This microgrid can operate at both grid-connected and islanded mode with proper power sharing capability between parallel DG systems. Moreover, smooth transition between operating modes is achieved without causing negative effect on the utility and critical loads. The performance of this control strategy is verified in simulation using Matlab/Simulink.

Functional connectivity dynamically evolves on multiple time-scales over a static structural connectome: Models and mechanisms <=SEP=> Over the last decade, we have observed a revolution in brain structural and functional Connectomics. On one hand, we have an ever-more detailed characterization of the brain's white matter structural connectome. On the other, we have a repertoire of consistent functional networks that form and dissipate over time during rest. Despite the evident spatial similarities between structural and functional connectivity, understanding how different time-evolving functional networks spontaneously emerge from a single structural network requires analyzing the problem from the perspective of complex network dynamics and dynamical system's theory. In that direction, bottom-up computational models are useful tools to test theoretical scenarios and depict the mechanisms at the genesis of resting-state activity. Here, we provide an overview of the different mechanistic scenarios proposed over the last decade via computational models. Importantly, we highlight the need of incorporating additional model constraints considering the properties observed at finer temporal scales with MEG and the dynamical properties of FC in order to refresh the list of candidate scenarios.

MDig : Multi-digit Recognition using Convolutional Nerual Network on Mobile <=SEP=> Multi-character recognition in arbitrary photographs on mobile platform is difficult, in terms of both accuracy and real-time performance. In this paper, we focus on the domain of hand-written multi-digit recognition. Convolutional neural network (CNN) is the state-of-the-art solution for object recognition, and presents a workload that is both compute and data intensive. To reduce the workload, we train a shallow CNN offline, achieving 99.07% top-1 accuracy. And we utilize preprocessing and segmentation to reduce input image size fed into CNN. For CNN implementation on the mobile platform, we adopt and modify DeepBeliefSDK to support batching fully-connected layers. On NVIDIA SHIELD tablet, the application processes a frame and extracts 32 digits in approximately 60ms, and batching the fully-connected layers reduces the CNN runtime by another 12%.

Non-psychotropic plant cannabinoids: new therapeutic opportunities from an ancient herb. <=SEP=> Delta(9)-tetrahydrocannabinol binds cannabinoid (CB(1) and CB(2)) receptors, which are activated by endogenous compounds (endocannabinoids) and are involved in a wide range of physiopathological processes (e.g. modulation of neurotransmitter release, regulation of pain perception, and of cardiovascular, gastrointestinal and liver functions). The well-known psychotropic effects of Delta(9)-tetrahydrocannabinol, which are mediated by activation of brain CB(1) receptors, have greatly limited its clinical use. However, the plant Cannabis contains many cannabinoids with weak or no psychoactivity that, therapeutically, might be more promising than Delta(9)-tetrahydrocannabinol. Here, we provide an overview of the recent pharmacological advances, novel mechanisms of action, and potential therapeutic applications of such non-psychotropic plant-derived cannabinoids. Special emphasis is given to cannabidiol, the possible applications of which have recently emerged in inflammation, diabetes, cancer, affective and neurodegenerative diseases, and to Delta(9)-tetrahydrocannabivarin, a novel CB(1) antagonist which exerts potentially useful actions in the treatment of epilepsy and obesity.

Spider Monkey Optimization algorithm for numerical optimization <=SEP=> Swarm intelligence is one of the most promising area for the researchers in the field of numerical optimization. Researchers have developed many algorithms by simulating the swarming behavior of various creatures like ants, honey bees, fish, birds and the findings are very motivating. In this paper, a new approach for numerical optimization is proposed by modeling the foraging behavior of spider monkeys. Spider monkeys have been categorized as fission–fusion social structure based animals. The animals which follow fission– fusion social systems, split themselves from large to smaller groups and vice-versa based on the scarcity or availability of food. The proposed swarm intelligence approach is named as Spider Monkey Optimization (SMO) algorithm and can broadly be classified as an algorithm inspired by intelligent foraging behavior of fission–fusion social structure based animals.

Modelling Domain Relationships for Transfer Learning on Retrieval-based Question Answering Systems in E-commerce <=SEP=> Nowadays, it is a heated topic for many industries to build automatic question-answering (QA) systems. A key solution to these QA systems is to retrieve from a QA knowledge base the most similar question of a given question, which can be reformulated as a paraphrase identification (PI) or a natural language inference (NLI) problem. However, most existing models for PI and NLI have at least two problems: They rely on a large amount of labeled data, which is not always available in real scenarios, and they may not be efficient for industrial applications. In this paper, we study transfer learning for the PI and NLI problems, aiming to propose a general framework, which can effectively and efficiently adapt the shared knowledge learned from a resource-rich source domain to a resource-poor target domain. Specifically, since most existing transfer learning methods only focus on learning a shared feature space across domains while ignoring the relationship between the source and target domains, we propose to simultaneously learn shared representations and domain relationships in a unified framework. Furthermore, we propose an efficient and effective hybrid model by combining a sentence encoding-based method and a sentence interaction-based method as our base model. Extensive experiments on both paraphrase identification and natural language inference demonstrate that our base model is efficient and has promising performance compared to the competing models, and our transfer learning method can help to significantly boost the performance. Further analysis shows that the inter-domain and intra-domain relationship captured by our model are insightful. Last but not least, we deploy our transfer learning model for PI into our online chatbot system, which can bring in significant improvements over our existing system. Finally, we launch our new system on the chatbot platform Eva in our E-commerce site AliExpress.

Generation of circularly polarized conical beam pattern using toroidal helical knot antenna <=SEP=> A novel circularly polarized antenna with a conical beam radiation pattern is presented. It consists of a feeding probe and a (1, 6) helical torus knot as a polarizer. Measured results shows that, the antenna has an impedance bandwidth of 19.67% and axial ratio bandwidth of 15.7%. With the RHCP gain of about 6 dBic at 3.5 GHz. Toroidal helical knot antenna is mechanically simple to fabricate using additive manufacturing technology. The proposed prototype is suitable for mounting on vehicles to facilitate communication with geostationary satellites.

Towards deep learning with segregated dendrites <=SEP=> Deep learning has led to significant advances in artificial intelligence, in part, by adopting strategies motivated by neurophysiology. However, it is unclear whether deep learning could occur in the real brain. Here, we show that a deep learning algorithm that utilizes multi-compartment neurons might help us to understand how the neocortex optimizes cost functions. Like neocortical pyramidal neurons, neurons in our model receive sensory information and higher-order feedback in electrotonically segregated compartments. Thanks to this segregation, neurons in different layers of the network can coordinate synaptic weight updates. As a result, the network learns to categorize images better than a single layer network. Furthermore, we show that our algorithm takes advantage of multilayer architectures to identify useful higher-order representations-the hallmark of deep learning. This work demonstrates that deep learning can be achieved using segregated dendritic compartments, which may help to explain the morphology of neocortical pyramidal neurons.

Introduction to E-commerce Learning Objectives <=SEP=> ● To understand the complexity of e-commerce and its many facets. ● To explore how e-business and e-commerce fit together. ● To identify the impact of e-commerce. ● To recognise the benefits and limitations of e-commerce. ● To use classification frameworks for analysing e-commerce. ● To identify the main barriers to the growth and development of e-commerce in organisations.  Even today, some considerable time after the so called 'dot com/Internet revolution', electronic commerce (e-commerce) remains a relatively new, emerging and constantly changing area of business management and information technology. There has been and continues to be much publicity and discussion about e-commerce. Library catalogues and shelves are filled with books and articles on the subject. However, there remains a sense of confusion, suspicion and misunderstanding surrounding the area, which has been exacerbated by the different contexts in which electronic commerce is used, coupled with the myriad related buzzwords and acronyms. This book aims to consolidate the major themes that have arisen from the new area of electronic commerce and to provide an understanding of its application and importance to management. In order to understand electronic commerce it is important to identify the different terms that are used, and to assess their origin and usage. According to the editor-in-chief of International Journal of Electronic Commerce , Vladimir Zwass, 'Electronic commerce is sharing business information , maintaining business relationships and conducting business transactions by means of telecommunications networks'. 1 He maintains that in its purest form, electronic commerce has existed for over 40 years, originating from the electronic transmission of messages during the Berlin airlift in 1948. 2 From this, electronic data interchange (EDI) was the next stage of e-commerce development. In the 1960s a cooperative effort between industry groups produced a first attempt at common electronic data formats. The formats, however, were only for purchasing, transportation and finance data, and were used primarily for intra-industry transactions. It was not until the late 1970s that work began for national Electronic Data Interchange (EDI) standards, which developed well into the early 1990s. EDI is the electronic transfer of a standardised business transaction between a sender and receiver computer, over some kind of private network or value added network (VAN). Both sides would have to have the same application software and the data would be exchanged in an extremely rigorous format. In sectors such as retail, automotive, defence and heavy manufacturing, EDI was …

The Human Hippocampus and Spatial and Episodic Memory <=SEP=> Finding one's way around an environment and remembering the events that occur within it are crucial cognitive abilities that have been linked to the hippocampus and medial temporal lobes. Our review of neuropsychological, behavioral, and neuroimaging studies of human hippocampal involvement in spatial memory concentrates on three important concepts in this field: spatial frameworks, dimensionality, and orientation and self-motion. We also compare variation in hippocampal structure and function across and within species. We discuss how its spatial role relates to its accepted role in episodic memory. Five related studies use virtual reality to examine these two types of memory in ecologically valid situations. While processing of spatial scenes involves the parahippocampus, the right hippocampus appears particularly involved in memory for locations within an environment, with the left hippocampus more involved in context-dependent episodic or autobiographical memory.