Attributed network embedding has attracted plenty of interests in recent years. It aims to learn task-independent, low-dimension, and continuous vectors for nodes preserving both topology and attribute information. Most existing methods, such as GCN and its variations, mainly focus on the local information, i.e., the attributes of the neighbors. Thus, they have been well studied for assortative networks but ignored disassortative networks, which are common in real scenes. To address this issue, we propose a block-based generative model for attributed network embedding on a probability perspective inspired by the stochastic block model (SBM). Specifically, the nodes are assigned to several blocks wherein the nodes in the same block share the similar link patterns. These patterns can define assortative networks containing communities or disassortative networks with the multipartite, hub, or any hybrid structures. Concerning the attribute information, we assume that each node has a hidden embedding related to its assigned block, and then we use a neural network to characterize the nonlinearity between the node embedding and its attribute. We perform extensive experiments on real-world and synthetic attributed networks, and the experimental results show that our proposed method remarkably outperforms state-of-the-art embedding methods for both clustering and classification tasks, especially on disassortative networks.

Trajectory owner prediction is the basis for many applications such as personalized recommendation, urban planning. Although much effort has been put on this topic, the results archived are still not good enough. Existing methods mainly employ RNNs to model trajectories semantically due to the inherent sequential attribute of trajectories. However, these approaches are weak at Point of Interest (POI) representation learning and trajectory feature detection. Thus, the performance of existing solutions is far from the requirements of practical applications. In this paper, we propose a novel CNN-based Trajectory Owner Prediction (CNNTOP) method. Firstly, we connect all POI according to trajectories from all users. The result is a connected graph that can be used to generate more informative POI sequences than other approaches. Secondly, we employ the Node2Vec algorithm to encode each POI into a low-dimensional real value vector. Then, we transform each trajectory into a fixed-dimensional matrix, which is similar to an image. Finally, a CNN is designed to detect features and predict the owner of a given trajectory. The CNN can extract informative features from the matrix representations of trajectories by convolutional operations, Batch normalization, and $K$-max pooling operations. Extensive experiments on real datasets demonstrate that CNNTOP substantially outperforms existing solutions in terms of macro-Precision, macro-Recall, macro-F1, and accuracy.

Top accomplishments include release of Akida intellectual property for licensing to ASIC suppliers, the development of a neural network converter for CNN to event-based CNN and native SNN translation, and an agreement with Socionext, formerly known as the Fujitsu Semiconductor business, for Akida development and manufacturing. In November 2019, BrainChip was granted a U.S. patent for its dynamic neural networks which are a valuable feature of its Akida AI processing chip used to power biometric and AI applications on devices at the network edge. Unlike other solutions, Akida does not need a host processor, external memory and a math accelerator device. It will be available in a Flip-Chip Ball Grid Array (FCBGA324) that is 15mm x 15mm. In 2019, the Akida logic and layout designs have been finalized.

A deep neural network (DNN) with piecewise linear activatio ns can partition the input space into numerous small linear regions, where diffe rent linear functions are fitted. It is believed that the number of these regions rep resents the expressivity of the DNN. This paper provides a novel and meticulous perspe ctive to look into DNNs: Instead of just counting the number of the linear regio ns, we study their local properties, such as the inspheres, the directions of t he corresponding hyper-planes, the decision boundaries, and the relevance of the su rrounding regions. W e empirically observed that different optimization techniq ues lead to completely different linear regions, even though they result in similar cl assification accuracies. W e hope our study can inspire the design of novel optimizatio n techniques, and help discover and analyze the behaviors of DNNs. In the past few decades, deep neural networks (DNNs) have ach ieved remarkable success in various difficult tasks of machine learning (Krizhevsky et al., 2012; Graves et al., 2013; Goodfellow et al., 2014; He et al., 2016; Silver et al., 2017; Devlin et al., 2019). Albeit the great progress DNNs have made, there are still many problems which have not been thoro ughly studied, such as the expressivity and optimization of DNNs. High expressivity is believed to be one of the most important reasons for the success of DNNs. It is well known that a standard deep feedforward network with pie cewise linear activations can partition the input space into many linear regions, where different li near functions are fitted (Pascanu et al., 2014; Montufar et al., 2014). More specifically, the activat ion states are in one-to-one correspondence with the linear regions, i.e., all points in the same li near region activate the same nodes of the DNN, and hence the hidden layers serve as a series of affine transformations of these points.

We describe a method for training accurate Transformer machine-translation models to run inference using 8-bit integer (INT8) hardware matrix multipliers, as opposed to the more costly single-precision floating-point (FP32) hardware. Unlike previous work, which converted only 85 Transformer matrix multiplications to INT8, leaving 48 out of 133 of them in FP32 because of unacceptable accuracy loss, we convert them all to INT8 without compromising accuracy. Tested on the new-stest2014 English-to-German translation task, our INT8 Transformer Base and Transformer Big models yield BLEU scores that are 99.3% to 100% relative to those of the corresponding FP32 models. Our approach converts all matrix-multiplication tensors from an existing FP32 model into INT8 tensors by automatically making range-precision tradeoffs during training. To demonstrate the robustness of this approach, we also include results from INT6 Transformer models. 1 Introduction We report a method for training accurate yet compact Transformer machine-translation models [ V aswaniet al., 2017 ] . Specifically, we aim these models at hardware with 8-bit integer (INT8) matrix multipliers. Compared to single-precision floating-point (FP32) matrix multiplications, INT8 matrix multiplications not only reduce both storage and bandwidth four times, but they also consume 15 times less energy [ Horowitz, 2014 ] .

Tourism information is scattered around nowadays. To search for the information, it is usually time consuming to browse through the results from search engine, select and view the details of each accommodation. In this paper, we present a methodology to extract particular information from full text returned from the search engine to facilitate the users. Then, the users can specifically look to the desired relevant information. The approach can be used for the same task in other domains. The main steps are 1) building training data and 2) building recognition model. First, the tourism data is gathered and the vocabularies are built. The raw corpus is used to train for creating vocabulary embedding. Also, it is used for creating annotated data. The process of creating named entity annotation is presented. Then, the recognition model of a given entity type can be built. From the experiments, given hotel description, the model can extract the desired entity,i.e, name, location, facility. The extracted data can further be stored as a structured information, e.g., in the ontology format, for future querying and inference. The model for automatic named entity identification, based on machine learning, yields the error ranging 8%-25%.

Robotic Process Automation (RP A) is a technology for automating repetitive routines consisting of sequences of user interactions with one or more applications. In order to fully exploit the opportunities opened by RP A, companies need to discover which specific routines may be automated, and how. In this setting, this paper addresses the problem of analyzing User Interaction (UI) logs in order to discover routines where a user transfers data from one spreadsheet or (Web) form to another. The paper maps this problem to that of discovering data transformations by example - a problem for which several techniques are available. The paper shows that a naive application of a state-of-the-art technique for data transformation discovery is computationally inefficient. Accordingly, the paper proposes two optimizations that take advantage of the information in the UI log and the fact that data transfers across applications typically involve copying alphabetic and numeric tokens separately. The proposed approach and its optimizations are evaluated using UI logs that replicate a real-life repetitive data transfer routine.

Robotic Process Automation (RPA) is a fast-emerging automation technology that sits between the fields of Business Process Management (BPM) and Artificial Intelligence (AI), and allows organizations to automate high volume routines. RPA tools are able to capture the execution of such routines previously performed by a human users on the interface of a computer system, and then emulate their enactment in place of the user by means of a software robot. Nowadays, in the BPM domain, only simple, predictable business processes involving routine work can be automated by RPA tools in situations where there is no room for interpretation, while more sophisticated work is still left to human experts. In this paper, starting from an in-depth experimentation of the RPA tools available on the market, we provide a classification framework to categorize them on the basis of some key dimensions. Then, based on this analysis, we derive four research challenges and discuss prospective approaches necessary to inject intelligence into current RPA technology, in order to achieve more widespread adoption of RPA in the BPM domain.

The history of the government of people is superficially a history of the tensions between personal liberty and social responsibility for the greater good. This is what we're taught about as children and yet I personally believe the major purpose of education is not literacy or numeracy but social conditioning to this mythical golden thread that runs through the polite fabric of societies. At a deeper level, this tension between liberty and responsibility realizes itself in the western economic model as a race for acquiring capital. If we have a lot of money we will be free to do what we want. Similarly to Turing who thought in 1950 that the Turing Test would already be passed, Keynes's view in the 1930s that automation would liberate us from work in 2030 has not come to pass.

-- Multiple sclerosis (MS) affects the central nervous system with a wide range of symptoms. MS can, for example, cause pain, changes in mood and fatigue, and may impair a person's movement, speech and visual functions. Diagnosis of MS typically involves a combination of complex clinical assessments and tests to rule out other diseases with similar symptoms. New technologies, such as smartphone monitoring in free-living conditions, could potentially aid in objectively assessing the symptoms of MS by quantifying symptom presence and intensity over long periods of time. Here, we present a deep-learning approach to diagnosing MS from smartphone-derived digital biomarkers that uses a novel combination of a multilayer perceptron with neural soft attention to improve learning of patterns in long-term smartphone monitoring data. Using data from a cohort of 774 participants, we demonstrate that our deep-learning models are able to distinguish between people with and without MS with an area under the receiver operating characteristic curve of 0.88 (95% CI: 0.70, 0.88). Our experimental results indicate that digital biomarkers derived from smartphone data could in the future be used as additional diagnostic criteria for MS. UL TIPLE sclerosis (MS) is a neurological disease that affects around 2 million people worldwide [1].