Reviewerly/exHarmony huggingface dataset:
An Isolation-Based Distributed Outlier Detection Framework Using Nearest Neighbor Ensembles for Wireless Sensor Networks

In recent years, wireless sensor networks have been extensively deployed to collect various data.Due to the effect of harsh environments and the limitation of the computing and communication capabilities of sensor nodes, the quality and reliability of sensor data are affected by outliers.Thus, an effective outlier detection method is essential.The existing outlier detection methods have some drawbacks, such as extra resource consumption introduced by the size growth of a local detector, poor performance of combination methods of local detectors, and the weak adaptability of the dynamic changes of the environment, etc.We propose an isolation-based distributed outlier detection framework using nearest-neighbor ensembles (iNNE) to effectively detect outliers in wireless sensor networks.In our proposed framework, local detectors are constructed in each node by the iNNE algorithm.A new combination method taking advantage of the spatial correlation among sensor nodes for local detectors is presented.The method is based on the weighted voting idea.In addition, we introduce a sliding window to update local detectors, which enables the adaption of dynamic changes in the environment.The extensive experiments are conducted on two classic real sensor datasets.The experimental results show our framework significantly improves the detection accuracy and reduces the false alarm rate compared with other outlier detection frameworks.INDEX TERMS Outlier detection, wireless sensor networks (WSN), iforest, local outlier factor (LOF), isolation using nearest neighbor ensembles (iNNE), sliding window.
Sequential Dirichlet process mixtures of multivariate skew $t$-distributions for model-based clustering of flow cytometry data

Flow cytometry is a high-throughput technology used to quantify multiple surface and intracellular markers at the level of a single cell.This enables us to identify cell subtypes and to determine their relative proportions.Improvements of this technology allow us to describe millions of individual cells from a blood sample using multiple markers.This results in high-dimensional datasets, whose manual analysis is highly time-consuming and poorly reproducible.While several methods have been developed to perform automatic recognition of cell populations most of them treat and analyze each sample independently.However, in practice individual samples are rarely independent, especially in longitudinal studies.Here we analyze new longitudinal flow-cytometry data from the DALIA-1 trial, which evaluates a therapeutic vaccine against HIV, by proposing a new Bayesian nonparametric approach with Dirichlet process mixture (DPM) of multivariate skew t-distributions to perform model based clustering of flow-cytometry data.DPM models directly estimate the number of cell populations from the data, avoiding model selection issues, and skew t-distributions provides robustness to outliers and nonelliptical shape of cell populations.To accommodate repeated measurements, we propose a sequential strategy relying on a parametric approximation of the posterior.We illustrate the good performance of our method on simulated data and on an experimental benchmark dataset.This sequential strategy outperforms all other methods evaluated on the benchmark dataset and leads to improved performance on the DALIA-1 data.
Convolutional Auto-encoding of Sentence Topics for Image Paragraph Generation

Image paragraph generation is the task of producing a coherent story (usually a paragraph) that describes the visual content of an image. The problem nevertheless is not trivial especially when there are multiple descriptive and diverse gists to be considered for paragraph generation, which often happens in real images. A valid question is how to encapsulate such gists/topics that are worthy of mention from an image, and then describe the image from one topic to another but holistically with a coherent structure. In this paper, we present a new design --- Convolutional Auto-Encoding (CAE) that purely employs convolutional and deconvolutional auto-encoding framework for topic modeling on the region-level features of an image. Furthermore, we propose an architecture, namely CAE plus Long Short-Term Memory (dubbed as CAE-LSTM), that novelly integrates the learnt topics in support of paragraph generation. Technically, CAE-LSTM capitalizes on a two-level LSTM-based paragraph generation framework with attention mechanism. The paragraph-level LSTM captures the inter-sentence dependency in a paragraph, while sentence-level LSTM is to generate one sentence which is conditioned on each learnt topic. Extensive experiments are conducted on Stanford image paragraph dataset, and superior results are reported when comparing to state-of-the-art approaches. More remarkably, CAE-LSTM increases CIDEr performance from 20.93% to 25.15%.
Transfer Learning for Sequence Labeling Using Source Model and Target Data

In this paper, we propose an approach for transferring the knowledge of a neural model for sequence labeling, learned from the source domain, to a new model trained on a target domain, where new label categories appear. Our transfer learning (TL) techniques enable to adapt the source model using the target data and new categories, without accessing to the source data. Our solution consists in adding new neurons in the output layer of the target model and transferring parameters from the source model, which are then fine-tuned with the target data. Additionally, we propose a neural adapter to learn the difference between the source and the target label distribution, which provides additional important information to the target model. Our experiments on Named Entity Recognition show that (i) the learned knowledge in the source model can be effectively transferred when the target data contains new categories and (ii) our neural adapter further improves such transfer.
Generating Multiple Diverse Responses with Multi-Mapping and Posterior Mapping Selection

In human conversation an input post is open to multiple potential responses, which is typically regarded as a one-to-many problem. Promising approaches mainly incorporate multiple latent mechanisms to build the one-to-many relationship. However, without accurate selection of the latent mechanism corresponding to the target response during training, these methods suffer from a rough optimization of latent mechanisms. In this paper, we propose a multi-mapping mechanism to better capture the one-to-many relationship, where multiple mapping modules are employed as latent mechanisms to model the semantic mappings from an input post to its diverse responses. For accurate optimization of latent mechanisms, a posterior mapping selection module is designed to select the corresponding mapping module according to the target response for further optimization. We also introduce an auxiliary matching loss to facilitate the optimization of posterior mapping selection. Empirical results demonstrate the superiority of our model in generating multiple diverse and informative responses over the state-of-the-art methods.
Heterogeneous Attributed Network Embedding with Graph Convolutional Networks

Network embedding which assigns nodes in networks to lowdimensional representations has received increasing attention in recent years. However, most existing approaches, especially the spectral-based methods, only consider the attributes in homogeneous networks. They are weak for heterogeneous attributed networks that involve different node types as well as rich node attributes and are common in real-world scenarios. In this paper, we propose HANE, a novel network embedding method based on Graph Convolutional Networks, that leverages both the heterogeneity and the node attributes to generate high-quality embeddings. The experiments on the real-world dataset show the effectiveness of our method.
Attribute Aware Pooling for Pedestrian Attribute Recognition

This paper expands the strength of deep convolutional neural networks (CNNs) to the pedestrian attribute recognition problem by devising a novel attribute aware pooling algorithm. Existing vanilla CNNs cannot be straightforwardly applied to handle multi-attribute data because of the larger label space as well as the attribute entanglement and correlations. We tackle these challenges that hampers the development of CNNs for multi-attribute classification by fully exploiting the correlation between different attributes. The multi-branch architecture is adopted for fucusing on attributes at different regions. Besides the prediction based on each branch itself, context information of each branch are employed for decision as well. The attribute aware pooling is developed to integrate both kinds of information. Therefore, attributes which are indistinct or tangled with others can be accurately recognized by exploiting the context information. Experiments on benchmark datasets demonstrate that the proposed pooling method appropriately explores and exploits the correlations between attributes for the pedestrian attribute recognition.
Secure Halftone Image Steganography Based on Pixel Density Transition

Most state-of-the-art halftone image steganographic techniques only consider the flipping distortion according to the human visual system, which are not always secure when they are attacked by steganalyzers. In this paper, we propose a halftone image steganographic scheme that aims to generate stego images with good visual quality and strong statistical security of anti-steganalysis. First, the concept of pixel density is proposed and a novel construction called pixel density histogram (PDH) is proposed to design a “embedding” scheme for halftone images. Then, we optimize density pair selection to select density blocks that can improve visual quality. Finally, the messages are embedded through pixel density transition, where a novel pixel flipping strategy is proposed, which can maintain the structural dependence by optimizing the pixel mesh Markov transition matrix (PMMTM). The experimental results demonstrate that the proposed steganography scheme can achieve strong statistical security of anti-steganalysis with good visual quality without degrading the embedding capacity.
A Span-based Joint Model for Opinion Target Extraction and Target Sentiment Classification

Target-Based Sentiment Analysis aims at extracting opinion targets and classifying the sentiment polarities expressed on each target. Recently, token based sequence tagging methods have been successfully applied to jointly solve the two tasks, which aims to predict a tag for each token. Since they do not treat a target containing several words as a whole, it might be difficult to make use of the global information to identify that opinion target, leading to incorrect extraction. Independently predicting the sentiment for each token may also lead to sentiment inconsistency for different words in an opinion target. In this paper, inspired by span-based methods in NLP, we propose a simple and effective joint model to conduct extraction and classification at span level rather than token level. Our model first emulates spans with one or more tokens and learns their representation based on the tokens inside. And then, a span-aware attention mechanism is designed to compute the sentiment information towards each span. Extensive experiments on three benchmark datasets show that our model consistently outperforms the state-of-the-art methods.
Learning Swarm Behaviors using Grammatical Evolution and Behavior Trees

Algorithms used in networking, operation research and optimization can be created using bio-inspired swarm behaviors, but it is difficult to mimic swarm behaviors that generalize through diverse environments. State-machine-based artificial collective behaviors evolved by standard Grammatical Evolution (GE) provide promise for general swarm behaviors but may not scale to large problems. This paper introduces an algorithm that evolves problem-specific swarm behaviors by combining multi-agent grammatical evolution and Behavior Trees (BTs). We present a BT-based BNF grammar, supported by different fitness function types, which overcomes some of the limitations in using GEs to evolve swarm behavior. Given human-provided, problem-specific fitness-functions, the learned BT programs encode individual agent behaviors that produce desired swarm behaviors. We empirically verify the algorithm's effectiveness on three different problems: single-source foraging, collective transport, and nest maintenance. Agent diversity is key for the evolved behaviors to outperform hand-coded solutions in each task.
Hi-Fi Ark: Deep User Representation via High-Fidelity Archive Network

Deep learning techniques have been widely applied to modern recommendation systems, bringing in flexible and effective ways of user representation. Conventionally, user representations are generated purely in the offline stage. Without referencing to the specific candidate item for recommendation, it is difficult to fully capture user preference from the perspective of interest. More recent algorithms tend to generate user representation at runtime, where user's historical behaviors are attentively summarized w.r.t. the presented candidate item. In spite of the improved efficacy, it is too expensive for many real-world scenarios because of the repetitive access to user's entire history. In this work, a novel user representation framework, Hi-Fi Ark, is proposed. With Hi-Fi Ark, user history is summarized into highly compact and complementary vectors in the offline stage, known as archives. Meanwhile, user preference towards a specific candidate item can be precisely captured via the attentive aggregation of such archives. As a result, both deployment feasibility and superior recommendation efficacy are achieved by Hi-Fi Ark. The effectiveness of Hi-Fi Ark is empirically validated on three real-world datasets, where remarkable and consistent improvements are made over a variety of well-recognized baseline methods.
Learning Network Embedding with Community Structural Information

Network embedding is an effective approach to learn the low-dimensional representations of vertices in networks, aiming to capture and preserve the structure and inherent properties of networks. The vast majority of existing network embedding methods exclusively focus on vertex proximity of networks, while ignoring the network internal community structure. However, the homophily principle indicates that vertices within the same community are more similar to each other than those from different communities, thus vertices within the same community should have similar vertex representations. Motivated by this, we propose a novel network embedding framework NECS to learn the Network Embedding with Community Structural information, which preserves the high-order proximity and incorporates the community structure in vertex representation learning. We formulate the problem into a principled optimization framework and provide an effective alternating algorithm to solve it. Extensive experimental results on several benchmark network datasets demonstrate the effectiveness of the proposed framework in various network analysis tasks including network reconstruction, link prediction and vertex classification.
Positive and Unlabeled Learning with Label Disambiguation

Positive and Unlabeled (PU) learning aims to learn a binary classifier from only positive and unlabeled training data. The state-of-the-art methods usually formulate PU learning as a cost-sensitive learning problem, in which every unlabeled example is simultaneously treated as positive and negative with different class weights. However, the ground-truth label of an unlabeled example should be unique, so the existing models inadvertently introduce the label noise which may lead to the biased classifier and deteriorated performance. To solve this problem, this paper proposes a novel algorithm dubbed as "Positive and Unlabeled learning with Label Disambiguation'' (PULD). We first regard all the unlabeled examples in PU learning as ambiguously labeled as positive and negative, and then employ the margin-based label disambiguation strategy, which enlarges the margin of classifier response between the most likely label and the less likely one, to find the unique ground-truth label of each unlabeled example. Theoretically, we derive the generalization error bound of the proposed method by analyzing its Rademacher complexity. Experimentally, we conduct intensive experiments on both benchmark and real-world datasets, and the results clearly demonstrate the superiority of the proposed PULD to the existing PU learning approaches.
Multi-Agent Visualization for Explaining Federated Learning

As an alternative decentralized training approach, Federated Learning enables distributed agents to collaboratively learn a machine learning model while keeping personal/private information on local devices. However, one significant issue of this framework is the lack of transparency, thus obscuring understanding of the working mechanism of Federated Learning systems. This paper proposes a multi-agent visualization system that illustrates what is Federated Learning and how it supports multi-agents coordination. To be specific, it allows users to participate in the Federated Learning empowered multi-agent coordination. The input and output of Federated Learning are visualized simultaneously, which provides an intuitive explanation of Federated Learning for users in order to help them gain deeper understanding of the technology.
A Novel Privacy Preserving Framework for Large Scale Graph Data Publishing

The need to efficiently store and query large scale graph datasets is evident in the growing number of data-intensive applications, particularly to maximize the mining of intelligence from these data (e.g., to inform decision making). However, directly releasing graph dataset for analysis may leak sensitive information of an individual even if the graph is anonymized, as demonstrated by the re-identification attacks on the DBpedia datasets. A key challenge in the design of graph sanitization methods is scalability, as existing execution models generally have significant memory requirements. In this paper, we propose a novel k-decomposition algorithm and define a new information loss matrix designed for utility measurement in massively large graph datasets. We also propose a novel privacy preserving framework that can be seamlessly integrated with graph storage, anonymization, query processing, and analysis. Our experimental studies show that the proposed solution achieves privacy-preserving, utility, and efficiency.
Graph-Based Meaning Representations: Design and Processing

This tutorial is on representing and processing sentence meaning in the form of labeled directed graphs.The tutorial will (a) briefly review relevant background in formal and linguistic semantics; (b) semi-formally define a unified abstract view on different flavors of semantic graphs and associated terminology; (c) survey common frameworks for graph-based meaning representation and available graph banks; and (d) offer a technical overview of a representative selection of different parsing approaches.
Disjoint Splitting for Multi-Agent Path Finding with Conflict-Based Search

Multi-Agent Path Finding (MAPF) is the planning problem of finding collision-free paths for a team of agents. We focus on Conflict-Based Search (CBS), a two-level tree-search state-of-the-art MAPF algorithm. The standard splitting strategy used by CBS is not disjoint, i.e., when it splits a problem into two subproblems, some solutions are shared by both subproblems, which can create duplication of search effort. In this paper, we demonstrate how to improve CBS with disjoint splitting and how to modify the low-level search of CBS to take maximal advantage of it. Experiments show that disjoint splitting increases the success rates and speeds of CBS and its variants by up to 2 orders of magnitude.
An interpretable natural language processing system for written medical examination assessment

The assessment of written medical examinations is a tedious and expensive process, requiring significant amounts of time from medical experts. Our objective was to develop a natural language processing (NLP) system that can expedite the assessment of unstructured answers in medical examinations by automatically identifying relevant concepts in the examinee responses. Our NLP system, Intelligent Clinical Text Evaluator (INCITE), is semi-supervised in nature. Learning from a limited set of fully annotated examples, it sequentially applies a series of customized text comparison and similarity functions to determine if a text span represents an entry in a given reference standard. Combinations of fuzzy matching and set intersection-based methods capture inexact matches and also fragmented concepts. Customizable, dynamic similarity-based matching thresholds allow the system to be tailored for examinee responses of different lengths. INCITE achieved an average F1-score of 0.89 (precision = 0.87, recall = 0.91) against human annotations over held-out evaluation data. Fuzzy text matching, dynamic thresholding and the incorporation of supervision using annotated data resulted in the biggest jumps in performances. Long and non-standard expressions are difficult for INCITE to detect, but the problem is mitigated by the use of dynamic thresholding (i.e., varying the similarity threshold for a text span to be considered a match). Annotation variations within exams and disagreements between annotators were the primary causes for false positives. Small amounts of annotated data can significantly improve system performance. The high performance and interpretability of INCITE will likely significantly aid the assessment process and also help mitigate the impact of manual assessment inconsistencies.
A brief review: security issues in cloud computing and their solutions

Cloud computing is an Internet-based, emerging technology, tends to be prevailing in our environment especially in the field of computer sciences and information technologies which require network computing on large scale. Cloud Computing is a shared pool of services which is gaining popularity due to its cost, effectiveness, avilability and great production. Along with its numerous benefits, cloud computing brings much more challenging situation regarding data privacy, data protection, authenticated access, Intellectual property rights etc. Due to these issues, adoption of cloud computing is becoming difficult in today’s world. In this review paper, various security issues regarding data privacy and reliability, key factors which are affecting cloud computing, have been addressed and also suggestions on particular areas have been discussed.