Reviewerly/exHarmony huggingface dataset:
Geographic Diversification of Recommended POIs in Frequently Visited Areas

In the personalized Point-Of-Interest (POI) (or venue) recommendation, the diversity of recommended POIs is an important aspect. Diversity is especially important when POIs are recommended in the target users’ frequently visited areas, because users are likely to revisit such areas. In addition to the (POI) category diversity that is a popular diversification objective in recommendation domains, diversification of recommended POI locations is an interesting subject itself. Despite its importance, existing POI recommender studies generally focus on and evaluate prediction accuracy. In this article, geographical diversification ( geo-diversification ), a novel diversification concept that aims to increase recommendation coverage for a target users’ geographic areas of interest, is introduced, from which a method that improves geo-diversity as an addition to existing state-of-the-art POI recommenders is proposed. In experiments with the datasets from two real Location Based Social Networks (LSBNs), we first analyze the performance of four state-of-the-art POI recommenders from various evaluation perspectives including category diversity and geo-diversity that have not been examined previously. The proposed method consistently improves geo-diversity (CPR(geo)@20) by 5 to 12% when combined with four state-of-the-art POI recommenders with negligible prediction accuracy (Recall@20) loss and provides 6 to 18% geo-diversity improvement with tolerable prediction accuracy loss (up to 2.4%).
Obstacle Avoidance Strategy for Quadrotor UAV based on Improved Particle Swarm optimization Algorithm

Rotor unmanned aerial vehicles (UAVs) have been widely used in daily life due to their high flexibility and controllability. To ensure the flight safety of UAVs, this paper proposes a new obstacle avoidance strategy. First, the laser sensor TFmini is used to detect distances and orientations of the obstacles near a quadrotor UAV. Second, after modeling the flight environment, the Dijkstra method is employed to search the feasible paths for the UAV. Third, the particle swarm optimization algorithm (PSO) is presented to obtain a global optimal path, based on the standard PSO algorithm, an improved version is proposed to search a better solution. Simulation results demonstrate that the proposed obstacle avoidance strategy of UAVs can obtain an ideal global optimal path.
Analysis of brain NMR images for age estimation with deep learning

During the last decade, deep learning and Convolutional Neural Networks (CNNs) have produced a devastating impact on computer vision, yielding exceptional results on a variety of problems, including analysis of medical images. Recently, these techniques have been extended to 3D images with the downside of a large increase in the computational load. In particular, state-of-the-art CNNs have been used for brain Nuclear Magnetic Resonance (NMR) imaging, with the aim of estimating the patients’ age. In fact, a large discrepancy between the real and the estimated age is a clear alarm for the onset of neurodegenerative diseases, such as some types of early dementia and Alzheimer’s disease. In this paper, we propose an effective alternative to 3D convolutions that guarantees a significant reduction of the computational requirements for this kind of analysis. The proposed architectures achieve comparable results with the competitor 3D methods, requiring only a fraction of the training time and GPU memory.
Context constraint and pattern memory for long-term correlation tracking

This paper proposes a novel correlation tracking framework with context constraint and pattern memory (CCPM) to address scale variation, heavy occlusion and out-of-view in long-term tracking tasks. Taking the spatial structure into account, we learn multi-channel correlation filters by training the background-aware samples constrained by Gaussian function and utilize the scaling pool technique to estimate the target scale variation. Then a novel online recovery mechanism, including the weighted fern classifier and the pattern-memorized template matcher, is applied to strengthen the ability to re-capture the target in case of tracking failure. The spatio-temporal weights used in the fern classifier can effectively suppress the significant position drift caused by the distortion of the target, whereas the pattern-memorized templates compressed by Gaussian mixture model (GMM) can reduce capacity usage and computational cost. Finally, we focus on the model corruption problem, the average peak-to-correlation energy (APCE) is exploited to identify the reliable parts of the tracking trajectory, and we update the model adaptively in term of the feedback from high-confidence tracking results. Extensive experimental results demonstrate that the proposed algorithm performs favorably in OTB2013 data set against several state-of-the-art methods.
Random dictatorship for privacy-preserving social choice

Abstract Social choice provides methods for collective decisions. They include methods for voting and for aggregating rankings. These methods are used in multiagent systems for similar purposes when decisions are to be made by agents. Votes and rankings are sensitive information. Because of that, privacy mechanisms are needed to avoid the disclosure of sensitive information. Cryptographic techniques can be applied in centralized environments to avoid the disclosure of sensitive information. A trusted third party can then compute the outcome. In distributed environments, we can use a secure multiparty computation approach for implementing a collective decision method. Other privacy models exist. Differential privacy and k -anonymity are two of them. They provide privacy guarantees that are complementary to multiparty computation approaches, and solutions that can be combined with the cryptographic ones, thus providing additional privacy guarantees, e.g., a differentially private multiparty computation model. In this paper, we propose the use of probabilistic social choice methods to achieve differential privacy. We use the method called random dictatorship and prove that under some circumstances differential privacy is satisfied and propose a variation that is always compliant with this privacy model. Our approach can be implemented using a centralized approach and also a decentralized approach. We briefly discuss these implementations.
An efficient Meta-cognitive Fuzzy C-Means clustering approach

This paper presents an efficient Meta-cognitive Fuzzy C-Means (McFCM) clustering approach inspired by human meta-cognitive learning principles. Unlike other clustering approaches that use all the samples for learning, McFCM adopts a human like cognitive learning strategy to select the samples for learning, resulting in the use of lesser samples and faster convergence. McFCM consists of two units namely, a cognitive and a meta-cognitive unit. The cognitive unit is the basic fuzzy clustering unit which clusters the input data. The meta-cognitive unit consists of a replica of the cognitive unit along with performance measures for monitoring and learning strategies used for control actions. The meta-cognitive unit controls the learning process in the cognitive unit by deciding on what-to-learn, when-to-learn and how-to-learn. Detailed performance evaluation of McFCM is presented using seven benchmark classification problems from the UCI repository and the results have been statistically compared with other well known existing clustering methods. The results show a superior performance of McFCM. Performance of McFCM is also presented using a real world Autism Spectrum Disorder Detection problem with better classification accuracy compared to other existing methods.
Formal Security Verification of Industry 4.0 Applications

Without appropriate counter-measures, cyber-attacks can exploit the increased system connectivity provided by Industry 4.0 (I4.0) to cause catastrophic events, by, e.g., injecting or tampering with messages. The solution supported by standards, such as, OPC-UA, is to sign or encrypt messages. However, given the limited resources of devices, instead of encrypting all messages in the network, it is better to encrypt only the messages that if tampered with or injected, could lead to undesired configurations. This paper describes the use of formal verification to analyse the security of I4.0 applications. We formalize in Rewriting Logic, I4.0 applications and systems, i.e., networked sets of devices, and a symbolic intruder model. Our formalization can be executed by the tool Maude to automate such security analysis, e.g., determine which messages are sufficient to sign in order avoid injection and tampering attacks.
Inking Your Insights: Investigating Digital Externalization Behaviors During Data Analysis

Externalizing one's thoughts can be helpful during data analysis, such as which one marks interesting data, notes hypotheses, and draws diagrams. In this paper, we present two exploratory studies conducted to investigate types and use of externalizations during the analysis process. We first studied how people take notes during different stages of data analysis using VoyagerNote, a visualization recommendation system augmented to support text annotations, and coupled with participants' favorite external note-taking tools (e.g., word processor, pen & paper). Externalizations manifested mostly as notes written on paper or in a word processor, with annotations atop views used almost exclusively in the initial phase of analysis. In the second study, we investigated two specific opportunities: (1) integrating digital pen input to facilitate the use of free-form externalizations and (2) providing a more explicit linking between visualizations and externalizations. We conducted the study with VoyagerInk, a visualization system that enabled free-form externalization with a digital pen as well as touch interactions to link externalizations to data. Participants created more graphical externalizations with VoyagerInk and revisited over half of their externalizations via the linking mechanism. Reflecting on the findings from these two studies, we discuss implications for the design of data analysis tools.
Transfer Hierarchical Attention Network for Generative Dialog System

Abstract In generative dialog systems, learning representations for the dialog context is a crucial step in generating high quality responses. The dialog systems are required to capture useful and compact information from mutually dependent sentences such that the generation process can effectively attend to the central semantics. Unfortunately, existing methods may not effectively identify importance distributions for each lower position when computing an upper level feature, which may lead to the loss of information critical to the constitution of the final context representations. To address this issue, we propose a transfer learning based method named transfer hierarchical attention network (THAN). The THAN model can leverage useful prior knowledge from two related auxiliary tasks, i.e., keyword extraction and sentence entailment, to facilitate the dialog representation learning for the main dialog generation task. During the transfer process, the syntactic structure and semantic relationship from the auxiliary tasks are distilled to enhance both the word-level and sentence-level attention mechanisms for the dialog system. Empirically, extensive experiments on the Twitter Dialog Corpus and the PERSONA-CHAT dataset demonstrate the effectiveness of the proposed THAN model compared with the state-of-the-art methods.
Accounting Information Systems and System of Systems

Accounting Information Systems are playing the protagonist role in enterprise. This is because they provide the necessary financial information required by law. At the same time there is an increasingly cyber security risk for AIS, which can be considered part of critical infrastructure, in that sense assessing cybersecurity risks is essential. The contribution of the paper is twofold. Firstly, in this paper the authors apply the attack surface methodology and further develop it to assess such risks. Secondly, the attack surface methodology has been expanded to include related systems to AIS.
Detecting subtle alteration information from ASTER data using a multifractal-based method: A case study from Wuliang Mountain, SW China

Remote sensing has been widely used to interpret hydrothermal alteration zones and it can support the geological field mapping, which is very useful for mineral exploration in inaccessible regions. However, the alteration information in covered terrains is usually not strong enough to be detected using traditional remote sensing methods, so how to efficiently detect subtle alteration information remains a challenging task. In this study, the multifractal theory-based Spectrum-Area (S-A) method is used for the processing of remote sensing data and the new method of “Principal Component Analysis (PCA) + S-A” is proposed as a tool to interpret hydrothermal alteration beneath covered areas. In order to test the efficiency of this method, the “PCA + S-A” method was compared with the traditionally used PCA method based on Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data. The Wuliang Mountain in the Yunnan Province of SW China, a rugged terrain with high altitudes and dense vegetation coverage, was selected as a case study to detect information on chlorite, epidote, biotite, and muscovite alteration, respectively. The following field survey and petrographic studies reveal that: (1) the average detection accuracy of the “PCA + S-A” method is 90.80%, 9.34% higher than that of the PCA method; (2) in the covered areas where the traditional PCA method cannot detect any subtle hydrothermal alteration, the “PCA + S-A” method can be successfully applied; and (3) in those areas where the fractional vegetation cover (FVC) is greater than 0.8, the detection of subtle hydrothermal alteration using the proposed method is inefficient, which requires further studies.
Unsupervised Statistical Text Simplification

Most recent approaches for Text Simplification (TS) have drawn on insights from machine translation to learn simplification rewrites from the monolingual parallel corpus of complex and simple sentences, yet their effectiveness strongly relies on large amounts of parallel sentences. However, there has been a serious problem haunting TS for decades, that is, the availability of parallel TS corpora is scarce or not fit for the learning task. In this paper, we will focus on one especially useful and challenging problem of unsupervised TS without a single parallel sentence. To the best of our knowledge, we present the first unsupervised text simplification system based on phrase-based machine translation system, which leverages a careful initialization of phrase tables and language models. On the widely used WikiLarge and WikiSmall benchmarks, our system respectively obtains 39.08 and 25.12 SARI points, even outperforms some supervised baselines.
Medical Image Classification Algorithm Based on Weight Initialization-Sliding Window Fusion Convolutional Neural Network

Due to the complexity of medical images, traditional medical image classification methods have been unable to meet actual application needs. In recent years, the rapid development of deep learning theory has provided a technical approach for solving medical image classification tasks. However, deep learning has the following problems in medical image classification. First, it is impossible to construct a deep learning model hierarchy for medical image properties; second, the network initialization weights of deep learning models are not well optimized. Therefore, this paper starts from the perspective of network optimization and improves the nonlinear modeling ability of the network through optimization methods. A new network weight initialization method is proposed, which alleviates the problem that existing deep learning model initialization is limited by the type of the nonlinear unit adopted and increases the potential of the neural network to handle different visual tasks. Moreover, through an in-depth study of the multicolumn convolutional neural network framework, this paper finds that the number of features and the convolution kernel size at different levels of the convolutional neural network are different. In contrast, the proposed method can construct different convolutional neural network models that adapt better to the characteristics of the medical images of interest and thus can better train the resulting heterogeneous multicolumn convolutional neural networks. Finally, using the adaptive sliding window fusion mechanism proposed in this paper, both methods jointly complete the classification task of medical images. Based on the above ideas, this paper proposes a medical classification algorithm based on a weight initialization/sliding window fusion for multilevel convolutional neural networks. The methods proposed in this study were applied to breast mass, brain tumor tissue, and medical image database classification experiments. The results show that the proposed method not only achieves a higher average accuracy than that of traditional machine learning and other deep learning methods but also is more stable and more robust.
Compact Scene Graphs for Layout Composition and Patch Retrieval

Structured representations such as scene graphs serve as an efficient and compact representation that can be used for downstream rendering or retrieval tasks. However, existing efforts to generate realistic images from scene graphs perform poorly on scene composition for cluttered or complex scenes. We propose two contributions to improve the scene composition. First, we enhance the scene graph representation with heuristic-based relations, which add minimal storage overhead. Second, we use extreme points representation to supervise the learning of the scene composition network. These methods achieve significantly higher performance over existing work (69.0% vs 51.2% in relation score metric). We additionally demonstrate how scene graphs can be used to retrieve pose-constrained image patches that are semantically similar to the source query. Improving structured scene graph representations for rendering or retrieval are an important step towards realistic image generation.
The Internet of Things: Challenges and Considerations for Cybercrime Investigations and Digital Forensics

The Internet of Things (IoT) represents the seamless merging of the real and digital world, with new devices created that store and pass around data. Processing large quantities of IoT data will proportionately increase workloads of data centres, leaving providers with new security, capacity, and analytics challenges. Handling this data conveniently is a critical challenge, as the overall application performance is highly dependent on the properties of the data management service. This article explores the challenges posed by cybercrime investigations and digital forensics concerning the shifting landscape of crime – the IoT and the evident investigative complexity – moving to the Internet of Anything (IoA)/Internet of Everything (IoE) era. IoT forensics requires a multi-faceted approach where evidence may be collected from a variety of sources such as sensor devices, communication devices, fridges, cars and drones, to smart swarms and intelligent buildings.
Controllable Attention for Structured Layered Video Decomposition

The objective of this paper is to be able to separate a video into its natural layers, and to control which of the separated layers to attend to. For example, to be able to separate reflections, transparency or object motion. We make the following three contributions: (i) we introduce a new structured neural network architecture that explicitly incorporates layers (as spatial masks) into its design. This improves separation performance over previous general purpose networks for this task; (ii) we demonstrate that we can augment the architecture to leverage external cues such as audio for controllability and to help disambiguation; and (iii) we experimentally demonstrate the effectiveness of our approach and training procedure with controlled experiments while also showing that the proposed model can be successfully applied to real-word applications such as reflection removal and action recognition in cluttered scenes.
Detection of Lung Cancer using SVM Classifier and KNN Algorithm

In this computer era we are totally going with the automation of everything, in the same way the medical industry is also automated with the help of image processing and data analytics. The best way to control the death cause by cancer is early detection. The medical image or a CT scan image is pre-processed. The contrast of the image is increased with the CLAHE Equalization technique. Then it is segmented with the help of random walk segmentation method. In segmentation the three process will happen the ROI of image is segmented and then then the border correction is done. As third part the continuous pixel change is segmented. The classification is the major portion where the cancerous and non-cancerous is identified with the pre trained model. All the methods used above deals with the traditional way of image processing and data analytics. In Future this accuracy will be boosted with the modern XGboost algorithm where less data is used to get high accuracy.
Semi-supervised Deep Quantization for Cross-modal Search

The problem of cross-modal similarity search, which aims at making efficient and accurate queries across multiple domains, has become a significant and important research topic. Composite quantization, a compact coding solution superior to hashing techniques, has shown its effectiveness for similarity search. However, most existing works utilizing composite quantization to search multi-domain content only consider either pairwise similarity information or class label information across different domains, which fails to tackle the semi-supervised problem in composite quantization. In this paper, we address the semi-supervised quantization problem by considering: (i) pairwise similarity information (without class label information) across different domains, which captures the intra-document relation, (ii) cross-domain data with class label which can help capture inter-document relation, and (iii) cross-domain data with neither pairwise similarity nor class label which enables the full use of abundant unlabelled information. To the best of our knowledge, we are the first to consider both supervised information (pairwise similarity + class label) and unsupervised information (neither pairwise similarity nor class label) simultaneously in composite quantization. A challenging problem arises: how can we jointly handle these three sorts of information across multiple domains in an efficient way? To tackle this challenge, we propose a novel semi-supervised deep quantization (SSDQ) model that takes both supervised and unsupervised information into account. The proposed SSDQ model is capable of incorporating the above three kinds of information into one single framework when utilizing composite quantization for accurate and efficient queries across different domains. More specifically, we employ a modified deep autoencoder for better latent representation and formulate pairwise similarity loss, supervised quantization loss as well as unsupervised distribution match loss to handle all three types of information. The extensive experiments demonstrate the significant improvement of SSDQ over several state-of-the-art methods on various datasets.