Reviewerly/exHarmony huggingface dataset:
Opinion Mining From Social Media Short Texts: Does Collective Intelligence Beat Deep Learning?

The era of big data has, among others, three characteristics: the huge amounts of data created every day and in every form by everyday people, artificial intelligence tools to mine information of those data and effective algorithms that allow this data mining in real or close to real time. On the other hand, opinion mining in social media is nowadays an important parameter of social media marketing. Digital media giants such as Google and Facebook developed and employed their own tools for that purpose. These tools are based on publicly available software libraries and tools such as Word2Vec (or Doc2Vec) and fasttext which, basically, emphasise on topic modelling and extract low level features using deep learning approaches. So far, researchers have focused their efforts on opinion mining and especially sentiment analysis of tweets. This trend reflects the availability of Twitter API that simplifies automatic data (tweet) collection and testing of the proposed algorithms in real situations. However, if we really interested in realistic opinion mining we should consider mining opinions form social media platforms such as Facebook and Instagram which are far more popular among everyday people. The basic purpose of this paper is to compare various kind of low-level features, including those extracted through deep learning, as in fasttext and Doc2Vec, and keywords suggested by the crowd, called crowd lexicon herein, through a crowdsourcing platform. The application area is sentiment analysis of tweets and facebook comments on commercial products. We also compare several machine learning methods for the creation of sentiment analysis models and concluded that, even in the era of big data letting people to annotate (a small portion of) data would allow effective artificial intelligence tools to be developed using the learning by example paradigm.
An insurance theory based optimal cyber-insurance contract against moral hazard

As an important method of risk control in information systems and networks, cyber-insurance has attracted particular attention from both industry and academia. However, two prominent problems hamper the further growth of cyber-insurance. The correlated and interdependent properties of cyber-risks increase the economic risk of insurance companies considerably ; risk pooling can be impeded by these two properties. Further, this situation can be aggravated because cyber-insurance affects the investment for self-protection negatively. This phenomenon is regarded as the ex ante moral hazard. In this study, we establish a mathematical model based on a classic insurance theory to address the abovementioned problems, and propose an optimal cyber-insurance contract scheme that maximizes the expected utility of users. We also propose two personalized contract schemes to incentivize users to invest in self-protection under the no moral hazard and ex ante moral hazard conditions. Extensive experiments are conducted to evaluate the proposed approach, and the experimental results demonstrate the effectiveness and efficiency of the approach.
Towards Optimizing WLANs Power Saving: Novel Context-Aware Network Traffic Classification Based on a Machine Learning Approach

Energy is a vital resource in wireless computing systems. Despite the increasing popularity of wireless local area networks (WLANs), one of the most important outstanding issues remains the power consumption caused by wireless network interface controller. To save this energy and reduce the overall power consumption of wireless devices, most approaches proposed to-date are focused on static and adaptive power saving modes. Existing literature has highlighted several issues and limitations in regards to their power consumption and performance degradation, warranting the need for further enhancements. In this paper, we propose a novel context-aware network traffic classification approach based on machine learning (ML) classifiers for optimizing WLAN power saving. The levels of traffic interaction in the background are contextually exploited for application of ML classifiers. Finally, the classified output traffic is used to optimize our proposed context-aware listen interval power saving modes. A real-world dataset is recorded, based on nine smartphone applications’ network traffic, reflecting different types of network behavior and interaction. This is used to evaluate the performance of eight ML classifiers in this initial study. The comparative results show that more than 99% of accuracy can be achieved. This paper indicates that ML classifiers are suited for classifying smartphone applications’ network traffic based on the levels of interaction in the background.
A Robust Parameter-Free Thresholding Method for Image Segmentation

In this work we presented a new parameter-free thresholding method for image segmentation. In separating an image into two classes, the method employs an objective function that not only maximizes the between-class variance but also the distance between the mean of each class and the global mean of the image. The design of the objective function aims to circumvent the challenge that many existing techniques encounter when the underlying two classes have very different sizes or variances. Advantages of the new method are two-fold. First, it is parameter-free, meaning that it can generate consistent results. Second, the new method has a simple form that makes it easy to adapt to different applications. We tested and compared the new method with the standard Otsu method, the maximum entropy method, and the 2D Otsu method on simulated and real biomedical and photographic images and found the new method can achieve a more accurate and robust performance.
Identifying the Reusable Components from Component-Based System: Proposed Metrics and Model

Reusability is the key component from the software development prospective. This job describes a measurement which is popularly called as reprocess measurement to discover and investigate the static activities of the module. This paper proposed a set of reusability metrics especially partly adaptable, completely changeable and moderately capable modules. The process of reusability can be measured to degree of module in component-based system. This paper provides the novel model as well as the proposed metrics. We propose reusability-metric for all categories of components including partially modifiable, fully modifiable as well as for off-the-shelf components. Using reusability-metric, we draw a reusability-matrix containing the reusability ratios of all the different classes of components. This paper introduces selection criteria for components by using the reusability features of component-based software.
A Deep Similarity Metric Method Based on Incomplete Data for Traffic Anomaly Detection in IoT

In recent years, with the development of the Internet of Things (IoT) technology, a large amount of data can be captured from sensors for real-time analysis. By monitoring the traffic video data from the IoT, we can detect the anomalies that may occur and evaluate the security. However, the number of traffic anomalies is extremely limited, so there is a severe over-fitting problem when using traditional deep learning methods. In order to solve the problem above, we propose a similarity metric Convolutional Neural Network (CNN) based on a channel attention model for traffic anomaly detection task. The method mainly includes (1) A Siamese network with a hierarchical attention model by word embedding so that it can selectively measure similarities between anomalies and the templates. (2) A deep transfer learning method can automatically annotate an unlabeled set while fine-tuning the network. (3) A background modeling method combining spatial and temporal information for anomaly extraction. Experiments show that the proposed method is three percentage points higher than deep convolutional generative adversarial network (DCGAN) and five percentage points higher than AutoEncoder on the accuracy. No more time consumption is needed for the annotation process. The extracted candidates can be classified correctly through the proposed method.
Spatio-temporal hazard assessment of a monogenetic volcanic field, near México City

An eruption in the Younger Chichinautzin Monogenetic Field (YCMF) represents a major volcanic hazard to México City, one of the most important cultural and financial centres in Latin America where >20 million people live. The birth of a new volcano in this field could severely impact the economic and social activity of the country. To identify where and when the next eruption in the YCMF could occur, the temporal and spatial distribution of past events were analyzed, assuming that future activity will follow a similar tendency. We reconstructed ages for the non-dated monogenetic volcanoes based on cone morphometric analyses correlated with published radiometric dates (R2 = 0.901). A Grid Probability Analysis (GPA) was applied for the first time in a monogenetic volcanic field to calculate the spatio-temporal probability of a new volcano forming. The field was divided into 96 cells of 5 × 5 km, which is the maximum observed distance between volcanoes. The recurrence rate and probability of a new volcano forming was then calculated for each cell using the estimated ages and a spatio-temporal probability map was constructed for the field. There are at least three areas with higher probability, concentrated in the central and northwestern parts of the study area. These areas are close to the volcanoes with the most recent eruptions and an eruption in these zones could seriously impact México City and nearby areas as did Xitle and Chichinautzin volcanoes ~2000 years ago.
CoReRank

Twitter's popularity has fostered the emergence of various illegal user activities - one such activity is to artificially bolster visibility of tweets by gaining large number of retweets within a short time span. The natural way to gain visibility is time-consuming. Therefore, users who want their tweets to get quick visibility try to explore shortcuts - one such shortcut is to approach the blackmarket services, and gain retweets for their own tweets by retweeting other customers' tweets. Thus the users intrinsically become a part of a collusive ecosystem controlled by these services. In this paper, we propose CoReRank, an unsupervised framework to detect collusive users (who are involved in producing artificial retweets), and suspicious tweets (which are submitted to the blackmarket services) simultaneously. CoReRank leverages the retweeting (or quoting) patterns of users, and measures two scores - the 'credibility' of a user and the 'merit' of a tweet. We propose a set of axioms to derive the interdependency between these two scores, and update them in a recursive manner. The formulation is further extended to handle the cold start problem. CoReRank is guaranteed to converge in a finite number of iterations and has linear time complexity. We also propose a semi-supervised version of CoReRank (called CoReRank+) which leverages a partial ground-truth labeling of users and tweets. Extensive experiments are conducted to show the superiority of CoReRank compared to six baselines on a novel dataset we collected and annotated. CoReRank beats the best unsupervised baseline method by 269% (20%) (relative) average precision and 300% (22.22%) (relative) average recall in detecting collusive (genuine) users. CoReRank+ beats the best supervised baseline method by 33.18% AUC. CoReRank also detects suspicious tweets with 0.85 (0.60) average precision (recall). To our knowledge, CoReRank is the first unsupervised method to detect collusive users and suspicious tweets simultaneously with theoretical guarantees.
Multi-scale Pyramid Pooling Network for salient object detection

In recent years, visual saliency has witnessed tremendous progress through using deep convolutional neural networks (CNNs). For effective salient object detection, contextual information has been widely employed since the global context can tell different objects apart while the local context can distinguish salient ones from the background. Inspired by this, in this paper we propose a novel Multi-scale Pyramid Pooling Network (MPPNet) by exploiting global and local context in a unified way. This is achieved by incorporating hierarchical local information and global pyramid pooling representation. Particularly, the integration of multi-scale pyramid pooling proves its capacity to produce high-quality prediction map through the use of multiple pooling variables. Quantitative and qualitative experiments demonstrate the effectiveness of the proposed framework. Our method can significantly improve the performance based on four popular benchmark datasets.
Learning from On-Line User Feedback in Neural Question Answering on the Web

Question answering promises a means of efficiently searching web-based content repositories such as Wikipedia. However, the systems of this type most prevalent today merely conduct their learning once in an offline training phase while, afterwards, all parameters remain static. Thus, the possibility of improvement over time is precluded. As a consequence of this shortcoming, question answering is not currently taking advantage of the wealth of feedback mechanisms that have become prominent on the web (e. g., buttons for liking, voting, or sharing).
The Role of Deep Learning in Breast Screening

To review research on deep learning models and their potential application within breast screening. The greatest issue in breast screening is a workforce crisis across the UK, much of Europe and even Japan. Traditional computer-aided detection (CAD) for mammography decision-support could not reach the level of an independent reader. Deep learning (DL) outperforms CAD and is close to surpassing human performance. DL is already capable of decision support and density assessment for 2D full-field digital mammography (FFDM), and is on the cusp of providing consistent, accurate and interpretable mammography reading as an independent reader. A bold vision for the future of breast cancer screening is required if programmes are to maintain double reading standards. DL provides the potential for single reading programmes, such as in the USA, to reach EU double reading accuracy, as well as providing practical support for adoption of the emerging modality of digital breast tomosynthesis.
An Approach for Pulmonary Vascular Extraction from Chest CT Images

Pulmonary vascular extraction from chest CT images plays an important role in the diagnosis of lung disease. To improve the accuracy rate of pulmonary vascular segmentation, a new pulmonary vascular extraction approach is proposed in this study. First, the lung tissue is extracted from chest CT images by region-growing and maximum between-class variance methods. Then the holes of the extracted region are filled by morphological operations to obtain complete lung region. Second, the points of the pulmonary vascular of the middle slice of the chest CT images are extracted as the original seed points. Finally, the seed points are spread throughout the lung region based on the fast marching method to extract the pulmonary vascular in the gradient image. Results of pulmonary vascular extraction from chest CT image datasets provided by the introduced approach are presented and discussed. Based on the ground truth pixels and the resulting quality measures, it can be concluded that the average accuracy of this approach is about 90%. Extensive experiments demonstrate that the proposed method has achieved the best performance in pulmonary vascular extraction compared with other two widely used methods.
eMaintenance in railways: Issues and challenges in cybersecurity

The convergence of information technology and operation technology and the associated paradigm shift toward Industry 4.0 in complex systems, such as railways has brought significant benefits in reliability, maintainability, operational efficiency, capacity, as well as improvements in passenger experience. However, with the adoption of information and communications technologies in railway maintenance, vulnerability to cyber threats has increased. It is essential that organizations move toward security analytics and automation to improve and prevent security breaches and to quickly identify and respond to security events. This paper provides a statistical review of cybersecurity incidents in the transportation sector with a focus on railways. It uses a web-based search for data collection in popular databases. The overall objective is to identify cybersecurity challenges in the railway sector.
A Hybrid Obstacle-Avoidance Method of Spatial Hyper-Redundant Manipulators for Servicing in Confined Space

Summary Due to a large number of redundant degrees of freedom (DOFs), the hyper-redundant manipulator shows outstanding dexterity and adaptability in avoiding the obstacles in confined space. In this paper, a hybrid obstacle-avoidance method of spatial hyper-redundant manipulators is proposed, with both efficiency and accuracy considered. The space around an obstacle is classified into safe, warning, and dangerous zones. A two-level protection strategy is then addressed to handle the obstacle-avoidance problem from qualitative (i.e., pseudo-distance based on super-quadric function) and quantitative (i.e., Euclidean distance based on practical geometry function) perspectives, respectively. The only condition for switching between the two-level protections is the value of pseudo-distance. Then, a modified modal method, which is a trajectory planning method, is presented to plan the collision-free trajectory of the manipulator by maximizing the minimum pseudo-distance or Euclidean distance in different zones. Some parameters, including the arm-angle parameters and the equivalent link length parameters, are defined to represent the manipulator configuration. They are adjusted to avoid the obstacle, singularity, and joint limit. The simulations of 12-DOF manipulator and an experiment of 18-DOF manipulator verify the proposed methods.
Recurrent Neural Network Model: A New Strategy to Solve Fuzzy Matrix Games

This paper aims to investigate the fuzzy constrained matrix game (MG) problems using the concepts of recurrent neural networks (RNNs). To the best of our knowledge, this paper is the first in attempting to find a solution for fuzzy game problems using RNN models. For this purpose, a fuzzy game problem is reformulated into a weighting problem. Then, the Karush-Kuhn-Tucker (KKT) optimality conditions are provided for the weighting problem. The KKT conditions are used to propose the RNN model. Moreover, the Lyapunov stability and the global convergence of the RNN model are also confirmed. Finally, three illustrative examples are presented to demonstrate the effectiveness of this approach. The obtained results are compared with the results obtained by the previous approaches for solving fuzzy constrained MG.
Thinning of convolutional neural network with mixed pruning

Deep learning has achieved state-of-the-art performance in accuracy of many computer vision tasks. However, convolutional neural network is difficult to deploy on resource constrained devices due to their limited computation power and memory space. Thus, it is necessary to prune the redundant weights and filters rationally and effectively. Considering that the pruned model still exists, redundancy after weight pruning or filter pruning alone, a method of combining weight pruning and filter pruning is proposed. First, filter pruning is performed, which is to remove filters with least importance and using fine-tuning to recover the model's accuracy. Then, all connection weights below a threshold are set to zero. Finally, the pruned model obtained by the first two steps is fine-tuned to recover its predictive accuracy. Experiments on MNIST and CIFAR-10 datasets demonstrate that the proposed approach is effective and feasible. Compared with only weight pruning or filter pruning, the mixed pruning can achieve higher compression ratio of the model parameters. For LeNet-5, the proposed approach can achieve a compression rate of 13.01×, with 1% drop in accuracy. For VGG-16, it can achieve a compression rate of 19.20×, incurring 1.56% accuracy loss.
Tweeting in Support of LGBT?

This paper explores the Twitter deliberation as a response to the Supreme Court of India's verdict regarding the decriminalization of homosexuality. We have collected 0.58 million tweets to gauge the societal perception about the LGBT community whether social media users are supportive or skeptic about sexual minorities. Our deep learning algorithms (accuracy in the range of 85%) have outperformed machine learning algorithms (accuracy in the range of 60%). Furthermore, we note that dominant aspects of supporting tweets are revolving around equality, justice, human rights of the LGBT community. On the contrary, opposing tweets are perceiving this verdict as a threat to the Indian culture, tradition and the family system.
Efficient live virtual machine migration for memory write-intensive workloads

Live migration of virtual machines (VMs) is an indispensable feature in cloud computing systems due to the use of load balancing, system maintenance, power management, etc. However, it is usually an intractable problem to migrate the VMs running memory write-intensive workloads, because they rapidly generate plenty of dirty pages, which need to be resent many times. Migration failures or compromises with large downtimes usually result in severe consequences such as the violation of service level agreements, abortion of TCP connections, etc. To solve this challenging problem, we first demonstrate a novel observation: during the pre-copy migrations, some workloads have a large portion of "fake dirty" pages, which are unnecessary and wasteful to be resent. After exploring how they are generated, we propose to leverage the secure hash method to avoid resending them. Besides, to guarantee the successful accomplishment of migration, we further propose the intelligent hybrid migration. Our scheme leverages heuristic and automatically switches from pre-copy to post-copy at the near-optimal moment to obtain a short post-copy duration, thus alleviating post-copy's inherent weaknesses. Evaluations show that our proposed scheme gets a significant performance improvement for VM migration. The workloads which fail to be migrated with pre-copy now accomplish migration quickly, with a total migration time from 27 s to 98 s. Besides, the intelligent hybrid migration remarkably shortens the post-copy duration by an extent from 43% to 60%, compared with the traditional hybrid method.
3D multi-robot patrolling with a two-level coordination strategy

Teams of UGVs patrolling harsh and complex 3D environments can experience interference and spatial conflicts with one another. Neglecting the occurrence of these events crucially hinders both soundness and reliability of a patrolling process. This work presents a distributed multi-robot patrolling technique, which uses a two-level coordination strategy to minimize and explicitly manage the occurrence of conflicts and interference. The first level guides the agents to single out exclusive target nodes on a topological map. This target selection relies on a shared idleness representation and a coordination mechanism preventing topological conflicts. The second level hosts coordination strategies based on a metric representation of space and is supported by a 3D SLAM system. Here, each robot path planner negotiates spatial conflicts by applying a multi-robot traversability function. Continuous interactions between these two levels ensure coordination and conflicts resolution. Both simulations and real-world experiments are presented to validate the performances of the proposed patrolling strategy in 3D environments. Results show this is a promising solution for managing spatial conflicts and preventing deadlocks.