Reviewerly/exHarmony huggingface dataset:
Subgroup analysis of zero-inflated Poisson regression model with applications to insurance data

Customized personal rate offering is of growing importance in the insurance industry. To achieve this, an important step is to identify subgroups of insureds from the corresponding heterogeneous claim frequency data. In this paper, a penalized Poisson regression approach for subgroup analysis in claim frequency data is proposed. Subjects are assumed to follow a zero-inflated Poisson regression model with group-specific intercepts, which capture group characteristics of claim frequency. A penalized likelihood function is derived and optimized to identify the group-specific intercepts and effects of individual covariates. To handle the challenges arising from the optimization of the penalized likelihood function, an alternating direction method of multipliers algorithm is developed and its convergence is established. Simulation studies and real applications are provided for illustrations.
Navigational Control Analysis of Two-Wheeled Self-Balancing Robot in an Unknown Terrain Using Back-Propagation Neural Network Integrated Modified DAYANI Approach

Summary The present paper discusses on development and implementation of back-propagation neural network integrated modified DAYANI method for path control of a two-wheeled self-balancing robot in an obstacle cluttered environment. A five-layered back-propagation neural network has been instigated to find out the intensity of various weight factors considering seven navigational parameters as obtained from the modified DAYANI method. The intensity of weight factors is found out using the neural technique with input parameters such as number of visible intersecting obstacles along the goal direction, minimum visible front obstacle distances as obtained from the sensors, minimum left side obstacle distance within the visible left side range of the robot, average of left side obstacle distances, minimum right side obstacle distance within the visible right side range of the robot, average of right side obstacle distances and goal distance from the robot’s probable next position. Comparison between simulation and experimental exercises is carried out for verifying the robustness of the proposed controller. Also, the authenticity of the proposed controller is verified through a comparative analysis between the results obtained by other existing techniques with the current technique in an exactly similar test scenario and an enhancement of the results is witnessed.
Cross-View Person Identification Based on Confidence-Weighted Human Pose Matching

Cross-view person identification (CVPI) from multiple temporally synchronized videos taken by multiple wearable cameras from different, varying views is a very challenging but important problem, which has attracted more interest recently. Current state-of-the-art performance of CVPI is achieved by matching appearance and motion features across videos, while the matching of pose features does not work effectively given the high inaccuracy of the 3D pose estimation on videos/images collected in the wild. To address this problem, we first introduce a new metric of confidence to the estimated location of each human-body joint in 3D human pose estimation. Then, a mapping function, which can be hand-crafted or learned directly from the datasets, is proposed to combine the inaccurately estimated human pose and the inferred confidence metric to accomplish CVPI. Specifically, the joints with higher confidence are weighted more in the pose matching for CVPI. Finally, the estimated pose information is integrated into the appearance and motion features to boost the CVPI performance. In the experiments, we evaluate the proposed method on three wearable-camera video datasets and compare the performance against several other existing CVPI methods. The experimental results show the effectiveness of the proposed confidence metric, and the integration of pose, appearance, and motion produces a new state-of-the-art CVPI performance.
A multiobjective migration algorithm as a resource consolidation strategy in cloud computing

To flexibly meet users' demands in cloud computing, it is essential for providers to establish the efficient virtual mapping in datacenters. Accordingly, virtualization has become a key aspect of cloud computing. It is possible to consolidate resources based on the single objective of reducing energy consumption. However, it is challenging for the provider to consolidate resources efficiently based on a multiobjective optimization strategy. In this paper, we present a novel migration algorithm to consolidate resources adaptively using a two-level scheduling algorithm. First, we propose the grey relational analysis (GRA) and technique for order preference by similarity to the ideal solution (TOPSIS) policy to simultaneously determine the hotspots by the main selected factors, including the CPU and the memory. Second, a two-level hybrid heuristic algorithm is designed to consolidate resources in order to reduce costs and energy consumption, mainly depending on the PSO and ACO algorithms. The improved PSO can determine the migrating VMs quickly, and the proposed ACO can locate the positions. Extensive experiments demonstrate that the two-level scheduling algorithm performs the consolidation strategy efficiently during the dynamic allocation process.
Risk-Sensitive Learning to Rank with Evolutionary Multi-Objective Feature Selection

Learning to Rank (L2R) is one of the main research lines in Information Retrieval. Risk-sensitive L2R is a sub-area of L2R that tries to learn models that are good on average while at the same time reducing the risk of performing poorly in a few but important queries (e.g., medical or legal queries). One way of reducing risk in learned models is by selecting and removing noisy, redundant features, or features that promote some queries to the detriment of others. This is exacerbated by learning methods that usually maximize an average metric (e.g., mean average precision (MAP) or Normalized Discounted Cumulative Gain (NDCG)). However, historically, feature selection (FS) methods have focused only on effectiveness and feature reduction as the main objectives. Accordingly, in this work, we propose to evaluate FS for L2R with an additional objective in mind, namely risk-sensitiveness . We present novel single and multi-objective criteria to optimize feature reduction, effectiveness, and risk-sensitiveness, all at the same time. We also introduce a new methodology to explore the search space, suggesting effective and efficient extensions of a well-known Evolutionary Algorithm (SPEA2) for FS applied to L2R. Our experiments show that explicitly including risk as an objective criterion is crucial to achieving a more effective and risk-sensitive performance. We also provide a thorough analysis of our methodology and experimental results.
The Value of Software Architecture Recovery for Maintenance

In order to maintain a system, it is beneficial to know its software architecture. In the common case that this architecture is unavailable, architecture recovery provides a way to recover an architectural view of the system. Many different methods and tools exist to provide such a view. While there have been taxonomies of different recovery methods and surveys of their results along with measurements of how these results conform to expert's opinions on the systems, there has not been a survey that goes beyond a simple automatic comparison. Instead, this paper seeks to answer questions about the viability of individual methods in given situations, the quality of their results and whether these results can be used to indicate and measure the quality and quantity of architectural changes. For our case study, we look at the results of recoveries of versions of Android, Apache Hadoop and Apache Chukwa obtained by running PKG, ACDC and ARC.
Cardiac MRI Left Ventricle Segmentation and Quantification: A Framework Combining U-Net and Continuous Max-Flow

Cardiac magnetic resonance imaging (MRI) is routinely used for cardiovascular disease diagnosis and therapy guidance. Left ventricle (LV) segmentation is typically required as a first step to quantify cardiac indices. In this work, we developed an automatic approach for LV segmentation and indices quantification of cardiac MRI. We employed a U-net convolutional neural network to generate LV segmentation probability maps. The initial probability maps were used to provide the labeling cost measurements of a continuous min-cut segmentation model and the final segmentation was regularized using image edge information. The continuous min-cut segmentation model was solved globally and exactly through convex relaxation and dual optimization on a GPU. We applied our approach to a clinical dataset of 45 subjects and achieved a mean DSC of $$89.4\pm 5.0\%$$ and average symmetric surface distance of $$0.81\pm 0.31$$ mm for LV myocardium segmentation. For LV indices quantification, we observed a mean absolute error of 114.8 mm $$^2$$ for LV cavity, 168.6 mm $$^2$$ for LV myocardium, $$\sim $$ 1.8 mm for LV cavity dimensions, and 1.2 $${\sim }$$ 1.6 mm for LV myocardium wall thickness measurements. These results suggest that our framework provide the potential for LV function quantification using cardiac MRI.
Video-guided Camera Control for Target Tracking and Following

This paper considers the problem of controlling a nonholonomic mobile ground robot equipped with an onboard camera characterized by a bounded field-of-view, tasked with detecting and following a potentially moving human target using onboard computing and video processing in real time. Computer vision algorithms have been recently shown highly effective at object detection and classification in images obtained by vision sensors. Existing methods typically assume a stationary camera and/or use pre-recorded image sequences that do not provide a causal relationship with future images. The control method developed in this paper seeks to improve the performance of the computer vision algorithms, by planning the robot/camera trajectory relative to the moving target based on the desired size and position of the target in the image plane, without the need to estimate the target’s range. The method is tested and validated using a highly realistic and interactive game programming environment, known as Unreal Engine™, that allows for closed-loop simulations of the robot-camera system. Results are further validated through physical experiments using a Clearpath™ Jackal robot equipped with a camera which is capable of following a human target for long time periods. Both simulation and experimental results show that the proposed vision-based controller is capable of stabilizing the target object size and position in the image plane for extended periods of time.
Active semi-supervised learning with multiple complementary information

In many practical machine learning problems, the acquisition of labeled data is often expensive and time consuming. To reduce this labeling cost, active learning has been introduced in many scientific fields. This study considers the problem of active learning of a regression model in the context of an optimal experimental design. Classical optimal experimental design approaches are based on the least square errors of labeled samples. Recently, a couple of active learning approaches that take advantage of both labeled and unlabeled data have been developed based on Laplacian regularized regression models with a single criterion. However, these approaches are susceptible to selecting undesirable samples when the number of initially labeled samples is small. To address this susceptibility, this study proposes an active learning method that considers multiple complementary criteria. These criteria include sample representativeness, diversity information, and variance reduction of the Laplacian regularization model. Specifically, we developed novel density and diversity criteria based on a clustering algorithm to identify the samples that are representative of their distributions, while minimizing their redundancy. Experiments were conducted on synthetic and benchmark data to compare the performance of the proposed method with that of existing methods. Experimental results demonstrate that the proposed active learning algorithm outperforms its existing counterparts.
Evaluating Anti-Fingerprinting Privacy Enhancing Technologies

We study how to evaluate Anti-Fingerprinting Privacy Enhancing Technologies (AFPETs). Experimental methods have the advantage of control and precision, and can be applied to new AFPETs that currently lack a user base. Observational methods have the advantage of scale and drawing from the browsers currently in real-world use. We propose a novel combination of these methods, offering the best of both worlds, by applying experimentally created models of a AFPET's behavior to an observational dataset. We apply our evaluation methods to a collection of AFPETs to find the Tor Browser Bundle to be the most effective among them. We further uncover inconsistencies in some AFPETs' behaviors.
ISLES Challenge: U-Shaped Convolution Neural Network with Dilated Convolution for 3D Stroke Lesion Segmentation

In this paper, we propose the algorithm for stroke lesion segmentation based on a deep convolutional neural network (CNN). The model is based on U-shaped CNN, which has been applied successfully to other medical image segmentation tasks. The network architecture was derived from the model presented in Isensee et al. [1] and is capable of processing whole 3D images. The model incorporates the convolution layers through upsampled filters – also known as dilated convolution. This change enlarges filter’s field of the view and allows the net to integrate larger context into the computation. We add the dilated convolution into different parts of network architecture and study the impact on the overall model performance. The best model which uses the dilated convolution in the input of the net outperforms the original architecture in nearly all used evaluation metrics. The code and trained models can be found on the GitHub website: http://github.com/tureckova/ISLES2018/ .
Single Sample Face Recognition in the Last Decade: A Survey

Single sample face recognition (SSFR) is a challenging research problem in which only one face image per person is available for training. Moreover, the face image may have different pose, expression, illumination, occlusion etc. rendering this problem more complex. Several methods have been suggested by various researchers in literature to solve SSFR. Here, we provide a comprehensive review of the methods proposed in the last decade for solving SSFR problem and introduce a novel taxonomy for the same. We divide SSFR methods broadly into five categories viz. (i) feature based, (ii) virtual sample generation based, (iii) generic database based, (iv) Hybrid and (v) other methods. We have also briefly reviewed the face databases used for evaluating single sample face recognition methods. Furthermore, the performance of the methods has been analyzed in terms of classification accuracy as given in literature. At last, we also suggest some future direction to the researchers and practitioners working in this fascinating research area.
Toward any-language zero-shot topic classification of textual documents

In this paper, we present a zero-shot classification approach to document classification in any language into topics which can be described by English keywords. This is done by embedding both labels and documents into a shared semantic space that allows one to compute meaningful semantic similarity between a document and a potential label. The embedding space can be created by either mapping into a Wikipedia-based semantic representation or learning cross-lingual embeddings. But if the Wikipedia in the target language is small or there is not enough training corpus to train a good embedding space for low-resource languages, then performance can suffer. Thus, for low-resource languages, we further use a word-level dictionary to convert documents into a high-resource language, and then perform classification based on the high-resource language. This approach can be applied to thousands of languages, which can be contrasted with machine translation, which is a supervision-heavy approach feasible for about 100 languages. We also develop a ranking algorithm that makes use of language similarity metrics to automatically select a good pivot or bridging high-resource language, and show that this significantly improves classification of low-resource language documents, performing comparably to the best bridge possible.
Improved Cross-Lingual Question Retrieval for Community Question Answering

We perform cross-lingual question retrieval in community question answering (cQA), i.e., we retrieve similar questions for queries that are given in another language. The standard approach to cross-lingual information retrieval, which is to automatically translate the query to the target language and continue with a monolingual retrieval model, typically falls short in cQA due to translation errors. This is even more the case for specialized domains such as in technical cQA, which we explore in this work. To remedy, we propose two extensions to this approach that improve cross-lingual question retrieval: (1) we enhance an NMT model with monolingual cQA data to improve the translation quality, and (2) we improve the robustness of a state-of-the-art neural question retrieval model to common translation errors by adding back-translations during training. Our results show that we achieve substantial improvements over the baseline approach and considerably close the gap to a setup where we have access to an external commercial machine translation service (i.e., Google Translate), which is often not the case in many practical scenarios. Our source code and data is publicly available.1
A Human-Cognitive Perspective of Users’ Password Choices in Recognition-Based Graphical Authentication

Graphical password composition is an important part of graphical user authentication which affects the strength of the chosen password. Considering that graphical authentication is associated with visual search, perception, and information retrieval, in this paper we report on an eye-tracking study (N = 109) that aimed to investigate the effects of users' cognitive styles toward the strength of the created passwords and shed light into whether and how the visual strategy of the users during graphical password composition is associated with the passwords' strength. For doing so, we adopted Witkin's Field Dependence-Independence theory, which underpins individual differences in visual information and cognitive processing, as graphical password composition tasks are associated with visual search. The analysis revealed that users with different cognitive processing characteristics followed different patterns of visual behavior during password composition which affected the strength of the created passwords. The findings underpin the need of considering human-cognitive characteristics as a design factor in graphical password schemes. The paper concludes by discussing implications for improving recognition-based graphical passwords through adaptation and personalization techniques based on individual cognitive characteristics.
Application of remote technologies in education

Purpose At present, the development of information technologies changes the whole system of public functioning. Special attention is paid to the system of higher education, as it forms future highly qualified specialists, who will become the basis of digital economy. According to this, new remote technologies are implemented into the system of education each year, which are a basic prototype of smart technologies. The purpose of this paper is to determine the role of remote technologies in the development of the system of higher education and to form skills with the students. Design/methodology/approach Based on this, it is necessary to solve the following tasks: view the aspects, peculiarities and possibilities of remote education; distinguish the main tools of remote technologies in education; offer electronic final program as a remote method of evaluation of students’ knowledge. Findings In this study, the following methods are used: analysis, synthesis, logical method, abstraction and comparison. Scientific novelty consists in performing research in the sphere of significance and implementation of remote technologies in the system of higher education of the Russian Federation. Originality/value This research could be useful for public officers who form programs of development of higher education and for academic staff of higher educational establishments.
Private Data Acquisition Method Based on System-Level Data Migration and Volatile Memory Forensics for Android Applications

Nowadays, many Android applications enable data encryption to protect the security of private data, making it difficult for investigators to access the clear data even if they have already obtained the application database. Volatile memory dynamically presents the current state of applications and OS, which contains a store of valuable information including the plain text of application data, and it is a significant analysis object in the field of digital forensics. Over the past decade, some forensics researchers have proposed a number of volatile memory acquisition methods for Android mobile devices and have made valuable contributions. However, most of the existing methods are subjecting to severe restrictions in real investigation environment and only can be applied to pre-prepared devices, resulting in these methods are impractical. In order to address this problem, this paper proposes an Android application memory data acquisition method, called PASM, which can be applied to unprepared Android devices. PASM makes use of system-level data migration function provided by Android manufacturers to migrate and load the application private data into an intermediate device. The intermediate device is pre-flashed with a custom kernel providing the function of volatile memory forensics, so that the application private data can be acquired from the volatile memory of the intermediate device. We select thirty privacy-sensitive applications as the test objects and build seven different experiment scenarios to acquire the private data stored in memory image dumped by PASM. The experiment results show that PASM is able to acquire part of private data stored in volatile memory, and more importantly, PASM has the ability to overcome most of the limitations in the real world, which is more practical than existing Android memory acquisition methods.
An intelligent system to detect human suspicious activity using deep neural networks

The importance of the surveillance is increasing every day. Surveillance is monitoring of activities, behavior and other changing information. An intelligent automatic system to detect behavior of the human is very important in public places. For this necessity, a framework is proposed to detect s uspicious human behavior as well as tracking of human who is doing some unusual activity such as fighting and threatening actions and also distinguishing the human normal activities from the suspicious behavior. The human activity is recognized by extracting the features using the convolution neural network (CNN) on the extracted optical flow slices and pre-training the activities based on the real-time activities. The obtained learned feature creates a score for each input which is used to predict the type of activity and it is classified using multi-class support vector machine (MSVM). This improved design will provide better surveillance system than existing. Such system can be used in public places like shopping mall, railway station or in a closed environment such as ATM where security is the prime concern. The performance of the system is evaluated, by using different standard datasets having different objects and achieved 95% performance as explained in experimental analysis.