Reviewerly/exHarmony huggingface dataset:
Game Theory Based Opportunistic Computation Offloading in Cloud-Enabled IoV

With the growing popularity of the fifth-generation (5G) wireless systems and cloud-enabled Internet of Vehicles, vehicular cloud has been introduced as a novel mobile device computing mode, which enables vehicles to offload their computation-intensive tasks to neighbors. In this paper, we first present a 5G cloud-enabled scenario of vehicular cloud computing where a vehicular terminal works either as a service provider with idle computation resources or a requestor who has a computation-intensive task that can be executed either locally or offloaded to nearby providers via opportunistic vehicle-to-vehicle communications. Then, we study the following issues: 1) how to determine the appropriate offloading rate of requestors; 2) how to select the most appropriate computation service provider; 3) how to identify the ideal pricing strategy for each service provider. We address the above-mentioned problems by developing a two-player Stackelberg-game-based opportunistic computation offloading scheme under situations involving complete and incomplete information that primarily considers task completion duration and service price. We simplify the former case into a common resource assignment problem with mathematical solutions. For the latter case, Stackelberg equilibriums of the offloading game are derived, and the corresponding existence conditions are concretely discussed. Finally, a Monte-Carlo simulation-based performance evaluation shows that the proposed methods can significantly reduce the task completion duration while ensuring the profit of service providers, thus achieving mutually satisfactory computation offloading decisions.
Comparison of nature-inspired population-based algorithms on continuous optimisation problems

Eleven swarm-intelligence-based (SI) and bio-inspired (BI) algorithms are compared with four advanced adaptive differential evolution (DE) variants, the classic DE and the blind random search on two benchmark sets. One of the benchmark sets is the CEC 2011 collection of 22 real-world optimisation problems, the latter is the suite of 30 artificial optimisation problems defined for the competition of the algorithms within CEC 2014. The results of the experiments demonstrate the superiority of the adaptive DE variants both on real-world problems and the artificial CEC 2014 test suite at all the levels of dimension (10, 30, and 50). Some of the SI and BI algorithms perform even worse than the blind random search. The efficiency of the classic DE is comparable with the better performing SI and BI methods. The results entitle to form a recommendation for practitioners: Do not propose a pseudo-new algorithm but select from the optimisation algorithms supported by thorough research and good ranking at international competitions of optimisation algorithms.
Large-Margin Label-Calibrated Support Vector Machines for Positive and Unlabeled Learning

Positive and unlabeled learning (PU learning) aims to train a binary classifier based on only PU data. Existing methods usually cast PU learning as a label noise learning problem or a cost-sensitive learning problem. However, none of them fully take the data distribution information into consideration when designing the model, which hinders them from acquiring more encouraging performance. In this paper, we argue that the clusters formed by positive examples and potential negative examples in the feature space should be critically utilized to establish the PU learning model, especially when the negative data are not explicitly available. To this end, we introduce a hat loss to discover the margin between data clusters, a label calibration regularizer to amend the biased decision boundary to the potentially correct one, and propose a novel discriminative PU classifier termed "Large-margin Label-calibrated Support Vector Machines" (LLSVM). Our LLSVM classifier can work properly in the absence of negative training examples and effectively achieve the max-margin effect between positive and negative classes. Theoretically, we derived the generalization error bound of LLSVM which reveals that the introduction of PU data does help to enhance the algorithm performance. Empirically, we compared LLSVM with state-of-the-art PU methods on various synthetic and practical data sets, and the results confirm that the proposed LLSVM is more effective than other compared methods on dealing with PU learning tasks.
Predicate constraints based question answering over knowledge graph

Generally, QA systems suffer from the structural difference where a question is composed of unstructured data, while its answer is made up of structured data in a Knowledge Graph (KG). To bridge this gap, most approaches use lexicons to cover data that are represented differently. However, the existing lexicons merely deal with representations for entity and relation mentions rather than consulting the comprehensive meaning of the question. To resolve this, we design a novel predicate constraints lexicon which restricts subject and object types for a predicate. It facilitates a comprehensive validation of a subject, predicate and object simultaneously. In this paper, we propose Predicate Constraints based Question Answering (PCQA). Our method prunes inappropriate entity/relation matchings to reduce search space, thus leading to an improvement of accuracy. Unlike the existing QA systems, we do not use any templates but generates query graphs to cover diverse types of questions. In query graph generation, we put more focus on matching relations rather than linking entities. This is well-suited to the use of predicate constraints. Our experimental results prove the validity of our approach and demonstrate a reasonable performance compared to other methods which target WebQuestions and Free917 benchmarks.
3D-ESPNet with Pyramidal Refinement for Volumetric Brain Tumor Image Segmentation

Automatic quantitative analysis of structural magnetic resonance (MR) images of brain tumors is critical to the clinical care of glioma patients, and for the future of advanced MR imaging research. In particular, automatic brain tumor segmentation can provide volumes of interest (VOIs) to scale the analysis of advanced MR imaging modalities such as perfusion-weighted imaging (PWI), diffusion-weighted imaging (DTI), and MR spectroscopy (MRS), which is currently hindered by the prohibitive cost and time of manual segmentations. However, automatic brain tumor segmentation is complicated by the high heterogeneity and dimensionality of MR data, and the relatively small size of available datasets. This paper extends ESPNet, a fast and efficient network designed for vanilla 2D semantic segmentation, to challenging 3D data in the medical imaging domain [11]. Even without substantive pre- and post-processing, our model achieves respectable brain tumor segmentation results, while learning only 3.8 million parameters. 3D-ESPNet achieves dice scores of 0.850, 0.665, and 0.782 on whole tumor, enhancing tumor, and tumor core classes on the test set of the 2018 BraTS challenge [1–4, 12]. Our source code is open-source and available at https://github.com/sacmehta/3D-ESPNet .
VisDrone-VDT2018: The Vision Meets Drone Video Detection and Tracking Challenge Results

Drones equipped with cameras have been fast deployed to a wide range of applications, such as agriculture, aerial photography, fast delivery, and surveillance. As the core steps in those applications, video object detection and tracking attracts much research effort in recent years. However, the current video object detection and tracking algorithms are not usually optimal for dealing with video sequences captured by drones, due to various challenges, such as viewpoint change and scales. To promote and track the development of the detection and tracking algorithms with drones, we organized the Vision Meets Drone Video Detection and Tracking (VisDrone-VDT2018) challenge, which is a subtrack of the Vision Meets Drone 2018 challenge workshop in conjunctiohe 15th European Conference on Computer Vision (ECCV 2018). Specifically, this workshop challenge consists of two tasks, (1) video object detection, and (2) multi-object tracking. We present a large-scale video object detection and tracking dataset, which consists of 79 video clips with about 1.5 million annotated bounding boxes in 33, 366 frames. We also provide rich annotations, including object categories, occlusion, and truncation ratios for better data usage. Being the largest such dataset ever published, the challenge enables extensive evaluation, investigation and tracking the progress of object detection and tracking algorithms on the drone platform. We present the evaluation protocol of the VisDrone-VDT2018 challenge and the results of the algorithms on the benchmark dataset, which are publicly available on the challenge website: http://www.aiskyeye.com/ . We hope the challenge largely boost the research and development in related fields.
Dense semantic embedding network for image captioning

Recently, attributes that contain high-level semantic information of image are always used as a complementary knowledge to improve image captioning performance. However, the use of attributes in prior works cannot excavate the latent visual concepts effectively. At each time step, the semantic information which is sensitive to the predicted word could be different. In this paper, we propose a Dense Semantic Embedding Network (DSEN) for this task. The distinct operation of this network is to densely embed the attributes with the multi-modal of image and text at each step of word generation. The discriminative semantic information hidden in these attributes is formatted in form of global likelihood probabilities. As a result, this dense embedding can modulate the feature distributions of the image, text modals and the hidden states to explicit semantic representation. Furthermore, to improve the discrimination of attributes, a Threshold ReLU (TReLU) is proposed. In addition, a bidirectional LSTM structure is incorporated into the DSEN to capture both the previous and future contexts. Extensive experiments on the COCO and Flickr30K datasets achieve superior results when compared with the state-of-the-art models for the tasks of both image captioning and image-text cross modal retrieval. Most remarkably, our method obtains outstanding performance on the retrieval task, compared with the state-of-the-art models.
Improved Dynamic Multi-Party Quantum Private Comparison for Next-Generation Mobile Network

The advent of next-generation networks, such as fifth-generation cellular wireless (5G), has transformed every aspect of our lives and promised improvement for various real-life applications. Recently, Liu and Wang proposed a dynamic quantum private comparison protocol that utilizes the property of single photon, in both polarization and spatial-mode degrees of freedom. The protocol is intended to compare the private information of any two parties in <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula> parties with the support of the other <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula> -2 parties. However, we show that their protocol is not secure against a particular strategy of collusion attacks that leads to the problem of information leakage. Therefore, this paper suggests a security enhancement against the proposed attack strategy trying to overcome the security limitation of Liu and Wang’s work. The security analysis of the suggested improvement proved that the modified protocol is secure against both the internal and external attacks, which could be used to control the various auction models for 5G services as wireless network virtualization in a secure way.
Usability evaluation of an instructional application based on Google Glass for mobile phone disassembly tasks

This study aims at comparing a Google Glass-based instructional application with a printed manual in mobile phone disassembly tasks. Thirty participants (15 males and 15 females) were recruited to perform tasks via three types of instructional methods (Google Glass-based augmented reality, Google Glass-based video, and a printed manual). The objective measures of performance (task completion time, error counts), and the subjective measures (NASA-Task Load Index, system usability scale) were considered. Additionally, visual fatigue was assessed by critical flicker fusion frequency and near point accommodation. According to the findings, although the printed manual method produced better results regarding mental workload and system usability, the augmented reality method improved error reduction and efficiency. Furthermore, the participants indicated that the displayed animations were helpful in locating the exact work-pieces. The augmented reality method is believed to be a potential alternative to printed manuals in mobile disassembly tasks.
Mapping geochemical anomalies related to Fe–polymetallic mineralization using the maximum margin metric learning method

Geochemical anomaly identification is an important task in mineral exploration targeting. This task can be regarded as a binary classification problem whereby the aim is to discriminate between anomalous and not anomalous (i.e., background). We can analyze geochemical data from the aspects of frequency distributions, correlations and variances, geometrical properties of geochemical anomalies, and scale independence of geochemical patterns. In this study, we recognize geochemical anomalies based on the intrinsic relationship between geochemical elements which can be addressed by measuring their separation distance. As a machine learning method, metric learning aims at exploiting the statistical information between the geochemical features in limited training samples, and developing a more suitable distance to evaluate the similarity of samples without priori distribution assumptions. Accordingly, a metric learning method based on the maximum margin frame was applied to identify multivariate geochemical anomalies related to Fe-polymetallic mineralization in the southwestern Fujian Province of China. The geochemical exploration data were firstly translated into a dimensional reduced subspace with the help of maximum margin metric learning (MMML). The adaptive coherence estimator (ACE) detector, from the field of target detection, was then employed to identify geochemical anomalies. The anomaly results obtained by this integrated procedure using a combination of MMML and ACE were compared to those obtained by ACE without metric learning using a receiver operating characteristic (ROC) curve. The area under the curve value implied superior performance using the combination of MMML and ACE, suggesting that this hybrid method can be effectively applied to recognize geochemical anomalies linked to mineralization.
On normalization and algorithm selection for unsupervised outlier detection

This paper demonstrates that the performance of various outlier detection methods is sensitive to both the characteristics of the dataset, and the data normalization scheme employed. To understand these dependencies, we formally prove that normalization affects the nearest neighbor structure, and density of the dataset; hence, affecting which observations could be considered outliers. Then, we perform an instance space analysis of combinations of normalization and detection methods. Such analysis enables the visualization of the strengths and weaknesses of these combinations. Moreover, we gain insights into which method combination might obtain the best performance for a given dataset.
The MOM of context-aware systems: A survey

Context-aware computing enriches the capabilities of intelligent devices complemented with smart applications and helps establish smart ecosystems in fields such as Ambient Intelligence, Internet of Things, Mobile Computing, and Pervasive and Ubiquitous Computing. Though the literature has many surveys that outline existing systems, it still remains critical to elucidate the basics of actually building an effective context-aware ecosystem. We outline the basic components required and essential for the same. We believe that a context-aware ecosystem becomes effective when these components are designed and implemented effectively. We call it the MOM of context-aware systems: generic and effective context Modeling, an efficient context Organization, and a robust context Middleware. Context modeling affords a syntax to the raw pieces of relevant information, the organization mechanism furnishes semantic import to the information and relationships, and the middleware compiles and integrates the information, enabling sharing of context. We discuss various context-aware ecosystems and middleware from the literature and highlight how the three building components function in each case. This paper will benefit newcomers to the field who are looking to learn about and build context-aware ecosystems.
An intelligent fuzzy rule-based e-learning recommendation system for dynamic user interests

A relevant and suitable content recommendation is an important and challenging task in e-learning. Relevant terms are retrieved in a recommender system that should also cope with varying user preferences over time. This paper proposes a novel recommendation system which provides suitable contents by refining the final frequent item patterns evolving from frequent pattern mining technique and then classifying the final contents using fuzzy logic into three levels. This is achieved by generating frequent item patterns after consolidating the user interest changes with an extended error margin quotient. Moreover, fuzzy rules are used in this work to enable the rule mining constraints for accommodating all types of learners while applying rules on the pattern tables. This method aims at mining the data stream preferences into equal-sized windows and caters to the varying user interest ratings over time. Experiments prove its efficiency and accuracy over existing conventional methods.
Deep gradient prior network for DEM super-resolution: Transfer learning from image to DEM

Digital elevation model (DEM) super-resolution (SR) aims to increase the spatial resolution of a DEM through data processing, rather than using sensors with higher accuracy. Inspired by the success of convolutional neural networks (CNNs) in image SR, this study introduces the CNN into DEM SR. However, directly training a robust network for DEM SR has remained a challenge as it has been difficult to obtain sufficient high-resolution DEM samples. Therefore, we proposed a novel method that includes two measures to address this issue. The first is to design a deep CNN for acquiring gradient prior knowledge. Based on this prior, high-resolution gradient maps of the studied DEM can be estimated. The second measure is to introduce transfer learning, which applies the knowledge learned from the natural image to the DEM SR problem. Therefore, a CNN is pretrained by the gradient of numerous high-resolution natural images and then fine-tuned with the gradient maps of some DEM training data. Finally, the high-resolution DEM is reconstructed under the constraints of the estimated gradient maps and the original low-resolution DEM. Overall, the experiments indicate that the proposed framework is superior to current state-of-the-art methods.
Improved Crowd Counting Method Based on Scale-Adaptive Convolutional Neural Network

Crowd counting is a challenging task due to the influence of various factors, such as scene transformation, complex crowd distribution, uneven illumination, and occlusion. To overcome such problems, scale-adaptive convolutional neural network (SaCNN) used a convolutional neural network to obtain high-quality crowd density map estimation and integrate the density map to get the estimated headcount. To obtain better performance on crowd counting, an improved crowd counting method based on SaCNN was proposed in this paper. The spread parameter, i.e., the standard variance, of geometry-adaptive Gaussian kernel used in SaCNN was optimized to generate a higher quality ground truth density map for training. The absolute count loss with weight 4e-5 was used to jointly optimize with the density map loss to improve the network generalization ability for crowd scenes with few pedestrians. Also, a random cropping method was applied to improve the diversity of training samples to enhance network generalization ability. The experimental results upon ShanghaiTech public dataset showed that the proposed method can obtain more accurate and more robust results on crowd counting than those of SaCNN.
Extended adaptive Lasso for multi-class and multi-label feature selection

Feature selection is a basic step and important task in applications of pattern recognition and machine learning. In this paper, we propose a new Extended Adaptive Least absolute shrinkage and selection operator (EALasso) feature selection method for multi-class and multi-label learning problems. It preserves the oracle properties of identifying the correct subset model and having the optimal estimation accuracy. An iterative optimization algorithm, with theoretical proof of convergence, is proposed to solve the optimization problem. Experimental results on several real multi-class and multi-label data sets show that the proposed EALasso method outperforms many state-of-the-art feature selection methods for multi-class single-label classification as well as multi-class multi-label classification.
An effective security alert mechanism for real-time phishing tweet detection on Twitter

Phishing is a form of social engineering crime uses to deceive victims by directing them to a fraudulent website where their private and confidential information are collected for further illegal actions. Phishing attacks have now targeted users at Online Social Networks (OSN)s such as Twitter, Facebook, Myspace, etc. which traditionally, targeting email users. Twitter has become so prevalent to phishers to spread phishing attacks nowadays due to its vast information dissemination and difficult to be detected unlike email. As such, the effectiveness of security alert to prompt Twitter users for the tweet containing phishing Uniform Resource Locator (URL) in real-time is crucial. Many solutions have been proposed but their effectiveness are inadequate and doubtful. In this paper, we propose an effective security alert mechanism making use of a classification model derived from a supervised machine learning technique of Random Forest (RF) and the identified 11 best classification features yielded 94.75% accuracy higher than 94.56% yielded by other researchers who used more than 11 features trained on the same dataset collected from Twitter. To determine its effectiveness, we used 200 phishing URLs collected from Twitter and PhishTank respectively. From our experiment, we are able to justify that such proposed security alert mechanism managed to prompt 97.50% effectively the security alert to Twitter users in real-time.