Reviewerly/exHarmony huggingface dataset:
Modelling Unobserved Heterogeneity in Claim Counts Using Finite Mixture Models

When modelling insurance claim count data, the actuary often observes overdispersion and an excess of zeros that may be caused by unobserved heterogeneity. A common approach to accounting for overdispersion is to consider models with some overdispersed distribution as opposed to Poisson models. Zero-inflated, hurdle and compound frequency models are typically applied to insurance data to account for such a feature of the data. However, a natural way to deal with unobserved heterogeneity is to consider mixtures of a simpler models. In this paper, we consider k-finite mixtures of some typical regression models. This approach has interesting features: first, it allows for overdispersion and the zero-inflated model represents a special case, and second, it allows for an elegant interpretation based on the typical clustering application of finite mixture models. k-finite mixture models are applied to a car insurance claim dataset in order to analyse whether the problem of unobserved heterogeneity requires a richer structure for risk classification. Our results show that the data consist of two subpopulations for which the regression structure is different.
Collective Protection: Preventing Sensitive Inferences via Integrative Transformation

Sharing ubiquitous mobile sensor data, especially physiological data, raises potential risks of leaking physical and demographic information that can be inferred from the time series sensor data. Existing sensitive information protection mechanisms that depend on data transformation are effective only on a particular sensitive attribute, together with usually requiring the labels of sensitive information for training. Considering this gap, we propose a novel user sensitive information protection framework without using a sensitive training dataset or being validated on protecting only one specific sensitive information. The presented approach transforms raw sensor data into a new format that has a "style" (sensitive information) of random noise and a "content" (desired information) of the raw sensor data, thus is free of user sensitive information for training and able to collectively protect all sensitive information at once. Our implementation and experiments on two real-world multisensor human activity datasets demonstrate that the proposed data transformation technique can achieve the protection for all sensitive information at once without requiring the knowledge of users' personal attributes for training, and simultaneously preserve the usability of the new transformed data with regard to inferring human activities with insignificant performance loss.
Scene Understanding: A Survey to See the World at a Single Glance

Humans are extremely proficient at visually perceiving natural scenes and understanding high level scene structures. In recent times, scene understanding is a challenging and most important problem in computer vision. Images are visual, however the visual information can be with various features like shape, edges, texture and color. The main objective behind object detection is to identify what are all objects present in an image and where they all located. Understanding a scene integrates meaningful information at multiple levels to extract semantic relationships and patterns. The interaction between various objects is most intuitive and natural to human being. As compared with object recognition, scene understanding identify the target of objects and also the distribution of targets in a scene. Scene understanding have a significant impact in computer vision to perceive, analyze, and interpret the visual scenes which leads to new research areas. In this paper, we review the scene understanding concept including feature extraction, classification and scene recognition and related datasets. At last, we have concluded some challenges of object detection remain unsolved and can be scope for further research work.
Correlation minimizing replay memory in temporal-difference reinforcement learning

Online reinforcement learning agents are now able to process an increasing amount of data which makes their approximation and compression into value functions a more demanding task. To improve approximation, thus the learning process itself, it has been proposed to select randomly a mini-batch of the past experiences that are stored in the replay memory buffer to be replayed at each learning step. In this work, we present an algorithm that classifies and samples the experiences into separate contextual memory buffers using an unsupervised learning technique. This allows each new experience to be associated to a mini-batch of the past experiences that are not from the same contextual buffer as the current one, thus further reducing the correlation between experiences. Experimental results show that the correlation minimizing sampling improves over Q-learning algorithms with uniform sampling, and that a significant improvement can be observed when coupled with the sampling methods that prioritize on the experience temporal difference error.
Local foodie

Food is an essential part of travel experience. Consumption of locally produced food while traveling has the two-fold benefit of providing insight into the local culture and increasing the sustainability of tourism. However, finding local food often requires motivation and effort, as information about food ingredients' origins and supply chains is not easy to discover. This paper presents the three-phase experience design process of a prototype of a mobile augmented reality (MAR) application "Local Foodie" designed to encourage tourists in Finland to consume local food. Adventure, autonomy, and competence were determined as experience goals for the application, and an interactive MAR prototype was created through iterative design. The results of a user evaluation (n=10) of the prototype suggest that the use of the application was intrinsically motivating, and the MAR elements contributed especially to the fulfillment of adventure and autonomy experience goals. Future work could leverage context-awareness and personalization to further enhance the experience of adventure.
Classification of Spontaneous and Posed Smiles by Photo-reflective Sensors Embedded with Smart Eyewear

Smile is one of the representative emotional expressions which is observed frequently in daily life and essential for various non-verbal communications. People make spontaneous smiles and intentional ones. It is important to guess properly whether a person is making a smile spontaneously or intentionally to understand the meaning of smiles. In this study, we propose a smile classification system with smart eyewear that equips photo-reflective sensors and examines whether we can distinguish two types of smiles; spontaneous smiles caused by funny videos and posed smiles evoked by instructions. We extract geometric features: reflection intensity distribution of sensors and temporal features in a time axis. By applying for Support Vector Machine, we observed 94.6% as the mean accuracy among 12 participants when we used both geometric and temporal features with user-dependent training. The result suggested that we can distinguish between spontaneous and posed smile by the sensors embedded with the smart eyewear.
Improving parameter recovery for conflict drift-diffusion models

Several drift-diffusion models have been developed to account for the performance in conflict tasks. Although a common characteristic of these models is that the drift rate changes within a trial, their architecture is rather different. Comparative studies usually examine which model fits the data best. However, a good fit does not guarantee good parameter recovery, which is a necessary condition for a valid interpretation of any fit. A recent simulation study revealed that recovery performance varies largely between models and individual parameters. Moreover, recovery was generally not very impressive. Therefore, the aim of the present study was to introduce and test an improved fit procedure. It is based on a grid search for determining the initial parameter values and on a specific criterion for assessing the goodness of fit. Simulations show that not only the fit performance but also parameter recovery improved substantially by applying this procedure, compared to the standard one. The improvement was largest for the most complex model.
Explainable Density-Based Approach for Self-Driving Actions Classification

This paper describes a new self-organizing neuro-fuzzy approach to autonomously learn interpretable models by self-driving cars. A new explainable self-organizing architecture and a new density-based feature selection method are proposed. These new approaches are used to classify different action states occurring from different self-driving conditions. The proposed approach is able to provide human understandable IF ... THEN rules representation due to its learning engine which is composed of a massively parallel set of 0-order fuzzy rules. The proposed density-based feature selection method is based on the ranking of the densities of each feature in the data space, and takes advantage of the parallel characteristic of the proposed explainable self-organizing approach to create individualized subsets of features per class. The main goal of both proposed methods is to provide highly accurate models with high transparency, interpretability, and explainability for self-driving vehicles. In order to validate our proposal, experiments were realized using a real dataset provided by Ford Motor Company. The dataset contains different driving states occurring during self-driving performances. Results demonstrate that the proposed approach could surpass its state-of-the-art competitors in terms of accuracy for this challenge multiclass classification problem.
Gendering research on online illegal drug markets

This paper reviews and critiques research on online illegal drug markets, arguing that existing conceptualizations and methodological approaches have resulted in a very limited discussion of women and questions of gender. The first part lays out the stereotypes and unarticulated assumptions that enable questions about women and gender to be side-lined, as follows: (i) that online anonymity rules out knowing about gender in online drug markets; (ii) that online drug markets are male-dominated spaces; and iii) that women are limited to minor or peripheral roles in those markets. Our aim is to make apparent and challenge the marginalization of enquiry about women and gender in existing scholarship about online illegal drug markets. In the second part, we draw on scholarship on women and gender in the drug trade more generally to consider what studying online illegal drug markets might add to our understanding of both women’s participation in these markets and the way in which gender is more widely performed. We consider whether online markets may facilitate women’s participation (due to anonymity, for example), or whether online drug markets replicate gendered stratifications characteristic of offline markets. We also explore the potential significance of women’s participation in online illegal drug markets for harm reduction services. In conclusion, we suggest that future research should challenge the assumption that we can understand online markets without thinking about gender and outline the steps toward building a gendered perspective in this area.
DABGEO: A reusable and usable global energy ontology for the energy domain

The heterogeneity of energy ontologies hinders the interoperability between ontology-based energy management applications to perform a large-scale energy management. Thus, there is the need for a global ontology that provides common vocabularies to represent the energy subdomains. A global energy ontology must provide a balance of reusability–usability to moderate the effort required to reuse it in different applications. This paper presents DABGEO: a reusable and usable global ontology for the energy domain that provides a common representation of energy domains represented by existing energy ontologies. DABGEO can be reused by ontology engineers to develop ontologies for specific energy management applications. In contrast to previous global energy ontologies, it follows a layered structure to provide a balance of reusability–usability. In this work, we provide an overview of the structure of DABGEO and we explain how to reuse it in a particular application case. In addition, the paper includes an evaluation of DABGEO to demonstrate that it provides a balance of reusability–usability.
Training Body Awareness and Control with Technology Probes

Technology probes have been used in co-creative embodied design processes to spur creativity and generate design ideas. We present a range of Training Technology Probes (TTPs), designed to facilitate collocated physical and social training. We deployed them in the context of a physical training course for children with motor challenges, where they were tested and iterated onsite through our participants' (instructors' and children's) creative uses. We report on intended/expected uses as well as improvised ones resulting from creative appropriations that were found useful in our physical activity. We discuss key core properties of the TTPs that supported productive creative appropriation. This work adds to others on technology probes by emphasizing their generative value in goal-oriented somatic explorations and practices, like our training course.
A socialbots analysis-driven graph-based approach for identifying coordinated campaigns in twitter

Twitter is a popular microblogging platform, which facilitates users to express views and thoughts on day-to-day events using short texts limited to a maximum of 280 characters. However, it is generally targeted by socialbots for political astroturfing, advertising, spamming, and other illicit act ivities due to its open and real-time information sharing and dissemination nature. In this paper, we present a socialbots analysis-driven graph-based approach for identifying coordinated campaigns among Twitter users. To this end, we first present statistical insights derived from the analysis of logged data of 98 socialbots, which were injected in Twitter and associated with top-six Twitter using countries. In the analysis, we study and present the impact of socialbots' profile features, such as age and gender on infiltration. We also present a multi-attributed graph-based approach to model the profile attributes and interaction behavior of users as a similarity graph for identifying different groups of synchronized users involved in coordinated campaigns. The proposed approach is experimentally evaluated using four different evaluation parameters over a real dataset containing socialbots' trapped user profiles. The evaluation of identified campaigns in the form of clusters reveals the traces of spammers, botnets, and other malicious users.
Design and Development of an Augmented Reality Tracing Application for Kindergarten Students

Children at the age of 3 to 5 years, start to learn language writing by tracing over alphabets. They start by joining the dots marked on books to create an alphabet. However, this learning process is not very effective in engaging children for active and creative learning. In this paper, we present an Augmented Reality Tracing (ART) application that creates an interactive learning environment for kids. Although the basic idea of tracing remains the same, ART allows children to recognize the mistracing or deviation and encourages them to trace better in the next trial. Therefore, the application would enhance children's language writing experience and motivate them for self-learning.
An Improved GAN Semantic Image Inpainting

Image inpainting is used to fill in missing regions based on remaining image data. Although the existing methods, that use deep generative models to infer the missing content, produce realistic images, sometimes the results are unsatisfactory due to arithmetical issues caused by the use of unbalanced ingredients of the proposed cost functions. In this paper, we propose a loss that generates more plausible results. Experiments on two datasets show that our method predicts information in large missing regions and achieves pixel-level photorealism, significantly outperforming state-of-the-art methods [24] and [25]. Having improved the semantic image inpainting we focus on applying the method to laparoscopic images that suffer from glares. The modified technique again outperforms its rivals. Moreover, it is faster than classical PDE based inpainting techniques and, more importantly, its running time is almost independent on the size of missing area, both critical issues in medical image processing.
Class Imbalance in Software Fault Prediction Data Set

Classification has been the prominent technique in machine learning domain, due to its ability of forecasting and predicts capabilities it is widely used in various domains such as health care, networking, social network, and software engineering with enhancement of different algorithm. The performance of the classifier majorly depends on the quality and amount of data present in the training sample. In real-world scenario, the majority of training samples suffered from class imbalance problem, that is, most of the data samples belong to one particular category, i.e., majority class while very few represent the minority class. In this case, classification techniques tend to be overwhelmed by the majority class and ignore the minority class. To solve class imbalance problem people relay on the different kind of sampling techniques either by generating synthetic data or by concentrating on minority class samples, but those approaches have introduced adverse effect in the learnability. In this paper, we attempt to study different techniques proposed to solve the class imbalance problem.
Representation Learning by Convolutional Neural Network for Smartphone Sensor Based Activity Recognition

Although many researchers have widely investigated activity recognition using smartphone sensing, estimation accuracy can be adversely affected by individual dependence. The result of our survey showed that the process of smartphone sensor based activity recognition that has not been sufficiently discussed, especially using representation learning by Convolutional Neural Network (CNN). The effectiveness of the representation learning model using CNN in activity recognition was verified, as were 10 types of activity recognition models: Deep Neural Network (DNN) using Hand-Crafted (HC) features, simple CNN model, AlexNet, SE-AlexNet, Fully Convolutional Network (FCN), SE-FCN, VGG, SE-VGG, ResNet, and SE-ResNet, using a benchmark dataset for human activity recognition. Finally, the deep learning models were trained a total of 600 times (10 models, 6 types with varying the number of people in training dataset, and 10 trials to reduce the influence of randomness bias). The results indicate that SE-VGG is the most accurate, as many subjects can be comprised in the training data.
MFE-HAR

Human activity recognition plays a key role in the application areas such as fitness tracking, healthcare and aged care support. However, inaccurate recognition results may cause an adverse effect on users or even an unpredictable accident. In order to improve the accuracy of human activity recognition, multi-device and deep learning based approaches have been proposed. However, they are not practical on a daily basis due to the limitations that devices are difficult to wear, and deep learning requires large training dataset and incurs expensive computational costs. To address this problem, we propose a novel approach, multiscale feature engineering for human activity recognition (MFE-HAR), which exploits the properties of arm movement from global and local scales using the accelerometer and gyroscope sensors on a single wearable device. Our method takes advantage of having important features at multiple scales over previous single-scale methods. We evaluated the performance of the proposed method on two public datasets and achieved the mean classification accuracy of 93% and 98% respectively. Our proposed system performs better than the state of the art multi-device based approaches, and is more practical for real-world applications.
A game-theoretic framework for dynamic cyber deception in internet of battlefield things

Cyber deception techniques are crucial to protect networks in battlefield settings and combat malicious cyber attacks. Cyber deception can effectively disrupt the surveillance process outcome of an adversary. In this paper, we propose a novel approach for cyber deception to protect important nodes and trap the adversary. We present a sequential approach of honeypot placement to defend and protect the network vital nodes. We formulate a stochastic game to study the dynamic interactions between the network administrator and the attacker. The defender makes strategic decisions about where to place honeypots to introduce new vulnerabilities to the network. The attacker's goal is to develop an attack strategy to compromise the nodes of the network by exploiting a set of known vulnerabilities. To consider a practical threat model, we assume that the attacker can only observe a noisy version of the network state. To this end, both players solve a partially observable stochastic game (POSG). Finally, we present a discussion on existing techniques to solve the formulated game and possible approaches to reduce the game complexity as part of our ongoing and future research.
An Optimized Face Recognition for Edge Computing

In this paper, we present an optimized face recognition algorithm for edge computing. We replace the original VGG16 with a more compact MobileNet to obtain a much smaller SSD model for face detection, and use a traditional 2-D face key point detection to obtain an aligned face image. In face representation, we re-train the SphereFace using FP16 instead of FP32 to further reduce resource consumption. We then build the face detection and face representation modules on a Movidius NCS2 device for acceleration, and build another face alignment module on a Raspberry Pi 3B+ embedded system. Experimental results show that our optimized face recognition has successfully raised its processing speed for over 60 times, and is able to perform a real-time face recognition at 7.031 FPS with a high accuracy of 93%. Moreover, the whole computing system has a low consumption of 6.7W, 17 times lower than similar solutions using CPU and GPU.