Reviewerly/exHarmony huggingface dataset:
Demand side expectations of cyber insurance

Cyber insurance has attracted much attention from both practitioners, policymakers and academics in the past few years. However, it also faces some challenges before it can reach its full potential as a tool for better cyber risk management. One such challenge is the gap between what customers expect and what insurers really offer. This paper investigates this gap empirically, based on interviews with informant companies in Norway and Sweden considering cyber insurance. The expectations expressed in the interviews are compared to anonymized incident claims reports and claims statistics for 2018 from a global insurance intermediary. The results show no obvious pattern of discrepancies between different domains. However, informant expectations on business interruption coverage are much greater than one would expect from its share of claims. In this respect, informant expectations on business interruption coverage are more aligned with some recently published scenarios on possible major business interruptions.
A Systematic Error Compensation Method Based on an Optimized Extreme Learning Machine for Star Sensor Image Centroid Estimation

As an important error in star centroid location estimation, the systematic error greatly restricts the accuracy of the three-axis attitude supplied by a star sensor. In this paper, an analytical study about the behavior of the systematic error in the center of mass (CoM) centroid estimation method under different Gaussian widths of starlight energy distribution is presented by means of frequency field analysis and numerical simulations. Subsequently, an optimized extreme learning machine (ELM) based on the bat algorithm (BA) is adopted to predict the systematic error of the actual star centroid position and then compensate the systematic error from the CoM method. In the BA-ELM model, the input weights matrix and hidden layer biases parameters are encoded as microbat’s locations and optimized by utilizing the strong global search capacity of BA, which significantly improves the performance of ELM in terms of prediction accuracy. The simulation result indicates that our method can reduce the systematic error to less than 3.0 × 10−7 pixels, and its compensation accuracy is two or three orders of magnitude higher than that of other methods for estimating a star centroid location under a 3 × 3 pixel sampling window.
Surveillance Camera using Face Recognition for automatic Attendance feeder and Energy conservation in classroom

This works gives solution to two most important problems in the universities by equipping a surveillance camera with Artificial Intelligence (AI) technology. The first problem solved is unnecessary time wastage in manual and bio-metric (fingerprint based) attendance marking for students. The second problem solved is the unnecessary electricity wastage in classrooms without occupants. Using the videos getting recorded in surveillance cameras, the number of heads detection and face recognition is done. When there is no occupants in the class, the number of heads detected will be zero. So we can cut-off the electricity supply for that classroom. With the face recognition process the attendance for the students will be get automatically marked. The Intel movidius stick does the work of face recognition and finding the head counts.
Assessment of Shift-Invariant CNN Gaze Mappings for PS-OG Eye Movement Sensors

Photosensor oculography (PS-OG) eye movement sensors offer desirable performance characteristics for integration within wireless head mounted devices (HMDs), including low power consumption and high sampling rates. To address the known performance degradation of these sensors due to HMD shifts, various machine learning techniques have been proposed for mapping sensor outputs to gaze location. This paper advances the understanding of a recently introduced convolutional neural network designed to provide shift invariant gaze mapping within a specified range of sensor translations. Performance is assessed for shift training examples which better reflect the distribution of values that would be generated through manual repositioning of the HMD during a dedicated collection of training data. The network is shown to exhibit comparable accuracy for this realistic shift distribution versus a previously considered rectangular grid, thereby enhancing the feasibility of in-field set-up. In addition, this work further demonstrates the practical viability of the proposed initialization process by demonstrating robust mapping performance versus training data scale. The ability to maintain reasonable accuracy for shifts extending beyond those introduced during training is also demonstrated.
CAM-STITCH

Transforming the sensor data from building systems into meaningful information could be used to build data-driven reference models for occupant behavior and actions using Machine Learning (ML) and Deep Learning (DL) techniques. These models can also be implemented for different notable applications such as - controlling heating, ventilation and air conditioning (HVAC) parameters, automated lighting, safety and security, and efficient space utilization. In this paper, we deployed 3D Stereo Vision Cameras from Xovis in a public building to capture trajectory data for multiple occupants. If there are inconsistencies/a gap in the field of view (FoV) of the cameras, continuous tracking of multiple occupants becomes quite challenging. If the inconsistencies persist, a single occupant is assigned multiple occupant-ID's, which is misleading and inaccurate for both occupancy count and tracking measurements within the monitored area. To mitigate and overcome the inconsistencies in the FoV of the deployed cameras, we propose the CAM-STITCH algorithm, which would enable multi-sensor stitching of the occupant trajectories using a variant of Recurrent Neural Network known as Long Short-Term Memory (LSTM) model. The CAM-STITCH algorithm is evaluated by calculating the Root Mean Squared Error (RMSE) between the measured and predicted position coordinates (x, y), which resulted in an average RMSE of 9.53 centimeters on trajectory 1 and an average RMSE of 12.72 centimeters on trajectory 2 for multiple occupants. CAM-STITCH is also designed to overcome dynamic occlusions posed by the 3D Stereo Vision Cameras. The proposed algorithm CAM-STITCH can ensure that the gathered data from the building is reliable for accurate trajectory measurements and can further assist in performing intelligent building operations.
Cognitive Diversity, Managerial Characteristics and Performance Differences across the Cleantech Firms

Continuous change in the energy sector towards more sustainable solutions has raised the importance of managers' abilities to interpret changes in their environments and translate those perspectives into strategic choices. This study examines managerial interpretation on changing business environment opening the relationship of cognitive diversity, board composition, and performance differences across the cleantech firms. The authors' empirical study employs both indirect and direct cognitive measures for analysis of cognitive maps collected through surveys from the cleantech firms. The study utilizes the hybrid cognitive mapping technique with distance ratio to investigate cognitive diversity and bridges it with the firms' performance. The study also advances and operationalizes the distance ratio as a measure for the analysis of cognitive maps to utilize more information available in the maps. The results indicate that the managerial characteristics showing firm-level economic expertise creates high-level cognitive diversity and high financial volatility in performance among the cleantech firms.
INTELLIGENT DATA ANALYSIS AND CLOUD COMPUTING

In this paper the term of «intelligent data analysis» is discussed, the cloud computing concept is described. The system developed and deployed on the computer cluster by ECM department of BSUIR is shown as an example of the intelligent data analysis by means of cloud computing. Some results of research with the help of this system are given.
Selection of key visual cues in real and virtual environments for assembly tasks

Previous research identified that learning assembly tasks in Virtual Environments (VEs) is more difficult than in Real Environments (REs). This work's objective is to identify the key visual areas for both environments when performing an assembly task for ten consecutive cycles, when following visual instructions and when having visual distractors. Using an eye-tracker, we identified the key visual areas required for an assembly task in both environments. Results indicate that practice allowed participants to reduce their assembly time in both environments. They also indicate that two areas, Assembly Area and Blocks, concentrated a higher proportion of eye-fixations; 59.98% for REs and 81.48% for VEs, with a statistically significant observation difference between environments (t-test value = −14.23, with p-value <0.00001 and Cohen's d = 6.36). We conclude that participants considered the same key visual areas for both environments and that VE interaction has a significant role in observation behavior.
Assessing cybersecurity maturity of organizations: An empirical investigation in the Indian context

The prevailing digital landscape, riddled with increasingly complex and ubiquitous cybersecurity threats, poses overwhelming challenges to the secure operation of Information and Communication Technologies (ICT) and ICT-enabled services. To protect their national interest and maintain public confidence in ICT services, it has become critical for all countries to ensure that organizations under their purview are equipped to counter cyber threats effectively. Therefore, all countries have been taking numerous measures and spending significant resources in improving the cybersecurity readiness of organizations operating in their cyber ecosystem.This study is focused on assessing the cybersecurity preparedness of Indian organizations. With this objective, six workshops and two Table Top eXercises (TTX) were conducted. Data were collected from participating entities (n = 103) in moderator-driven, self-assessment mode in the following six dimensions of cybersecurity:• Initiatives, Challenges, and Priorities• Technical measures• Organizational measures• Legal measures• Capacity-building and Awareness augmentation• Cooperation and Information sharing.It is our hope that this study will lead to answering questions pertaining to the identification of areas of priority with regard to cyber preparedness of Indian organizations, recognition of good practices that can be replicated among other organizations and constraints that restrain organizations from going full-tilt.
Performance Prediction Based on Neural Architecture Features

Nerual Architecture Search (NAS) usually requires to train quantities of candidate neural networks on dataset for choosing high performance network architecture and optimizing hyperparameters, which is very time consuming and computationally expensive. In order to resolve the issue we try to use performance prediction method to predict the model performance with little or even no training steps. We assume that the performance is determined once the architecture and other hyperparameters are chosen. So we firstly extract the sequence features of the chain-structured neural architecture by introducing N-grams model to process architecture textual description. Subsequently, based on the extracted neural architecture features, we use appropriate regression model to predict validation accuracies for modeling learning curve. Through a series of experimental comparisons, we verify the effectiveness of our proposed performance prediction method and the effective acceleration of the NAS process.
Flood-fill-based object segmentation and tracking for intelligent vehicles

In this article, an application for object segmentation and tracking for intelligent vehicles is presented. The proposed object segmentation and tracking method is implemented by combining three stages in each frame. First, based on our previous research on a fast ground segmentation method, the present approach segments three-dimensional point clouds into ground and non-ground points. The ground segmentation is important for clustering each object in subsequent steps. From the non-ground parts, we continue to segment objects using a flood-fill algorithm in the second stage. Finally, object tracking is implemented to determine the same objects over time in the final stage. This stage is performed based on likelihood probability calculated using features of each object. Experimental results demonstrate that the proposed system shows effective, real-time performance.
Book QA: Stories of Challenges and Opportunities

We present a system for answering questions based on the full text of books (BookQA), which first selects book passages given a question at hand, and then uses a memory network to reason and predict an answer.To improve generalization, we pretrain our memory network using artificial questions generated from book sentences.We experiment with the recently published NarrativeQA corpus, on the subset of Who questions, which expect book characters as answers.We experimentally show that BERT-based retrieval and pretraining improve over baseline results significantly.At the same time, we confirm that NarrativeQA is a highly challenging data set, and that there is need for novel research in order to achieve high-precision BookQA results.We analyze some of the bottlenecks of the current approach, and we argue that more research is needed on text representation, retrieval of relevant passages, and reasoning, including commonsense knowledge.
2-Adic Complexity of Two Classes of Generalized Cyclotomic Binary Sequences with Order 4

Pseudo-random sequences with good statistical property, such as low autocorrelation, high linear complexity and 2-adic complexity, have been widely applied to designing reliable stream ciphers. In this paper, we explicitly determine the 2-adic complexities of two classes of generalized cyclotomic binary sequences with order 4. Our results show that the 2-adic complexities of both of the sequences attain the maximum. Thus, they are large enough to resist the attack of the rational approximation algorithm for feedback with carry shift registers. We also present some examples to illustrate the validity of the results by Magma programs.
Generating Summaries Through Unigram and Bigram

This article describes a new method for generating extractive summaries directly via unigram and bigram extraction techniques. The methodology uses the selective part of speech tagging to extract significant unigrams and bigrams from a set of sentences. Extracted unigrams and bigrams along with other features are used to build a final summary. A new selective rule-based part of speech tagging system is developed that concentrates on the most important parts of speech for summarizations: noun, verb, and adjective. Other parts of speech such as prepositions, articles, adverbs, etc., play a lesser role in determining the meaning of sentences; therefore, they are not considered when choosing significant unigrams and bigrams. The proposed method is tested on two problem domains: citations and opinosis data sets. Results show that the proposed method performs better than Text-Rank, LexRank, and Edmundson summarization methods. The proposed method is general enough to summarize texts from any domain.
Otherworld: Ouija Board as a Resource for Design

The Ouija board is a device to contact spirits from the so-called otherworld. Although it is considered paranormal activity, the way it works rests on ideomotor actions and we argue that the Ouija is a resource for design for the following aspects: It is a 1) collective tangible exploration tool operated by two or more people through a physical pointer that moves, seemingly on its own, around the letters to probe meanings by composing messages. It has been used by artists as a medium offering 2) creative stimulation to generate material and develop ideas for their work. The Ouija also enables people to express nonconscious knowledge, as research suggests, and thus can provide 3) access to tacit dimension. In this paper, we present the Otherworld Framework that describes its principal elements and provide speculations on how to exploit them in design for collaborative, engaging and unconventional group interactions.
KSLIC: K-mediods Clustering Based Simple Linear Iterative Clustering

Simple Linear Iterative Clustering (SLIC) is one of the most excellent superpixel segmentation algorithms with the most comprehensive performance and is widely used in various scenes of production and living. As a preprocessing step in image processing, superpixel segmentation should meet various demands in real life as much as possible, but SLIC is highly sensitive to noise. In this paper, a K-mediods clustering based simple linear iterative clustering (KSLIC) is proposed, which replaces the K-means clustering in SLIC with a modified local K-mediods clustering. To evaluate the performance of KSLIC, we test it on BSD500 benchmark dataset. The results show that it outperforms SLIC in terms of different noise environments including Gaussian noise, multiplicative noise and salt and pepper noise.
Upscaling High-Resolution Mineralogical Analyses to Estimate Mineral Abundances in Drill Core Hyperspectral Data

In this paper, we propose a supervised learning method for estimating mineral quantities in drill core hyperspectral data. Our proposed method links the high-resolution mineralogical analyses and hyperspectral data to learn a dictionary. The learned dictionary is then used for linear unmixing and estimating mineral abundances of the entire drill core sample. To evaluate the performance of the proposed method, we use a drill core data set, which is composed of the VNIR-SWIR hyperspectral data and high-resolution mineralogical analyses performed by a Scanning Electron Microscopy (SEM) instrument equipped with the Mineral Liberation Analysis (MLA) software. The quantitative and qualitative analysis of the experimental results shows that the proposed method provides reliable mineral quantity estimates.
A high-confidence model updating correlation filtering tracker with scale adaptation for visual target tracking

In recent years, correlation filters have achieved excellent performance in terms of accuracy and robustness for visual target tracking. However, it is still a challenging task to improve the tracking capability which can handle complex background and deal with target scale changes. In this paper, a new tracker based on the correlation filter framework with scale adaptation for visual target tracking is presented: 1) an effective scale adaptive scheme, which can solve the fixed template size with the basis of traditional correlation filter tracker, is proposed; 2) two powerful features including histogram of oriented gradient and color name, which can improve the overall tracking performance, are integrated together; 3) a high-confidence model updating strategy, which can avoid the model occlusion problem during model updating, is developed. The extensive experimental evaluations on OTB-2013 dataset have demonstrated that the developed novel tracker in our study can successfully outperform the other trackers on the benchmark dataset with 51 sequences. It is very promising for various challenging scenarios.
Improved Q-learning based route planning method for UAVs in unknown environment

Unmanned aerial vehicles (UAVs) have demonstrated their application value in many fields. With the rapidly interest in UAV technology, the body of research on autonomous route planning has enjoyed an accelerating rate of novel proposals and developments. Traditional roadmap-based global route planning algorithms and the local route planning algorithms easily trapped in local optimum are difficult to apply in unknown and complex environments. The reinforcement learning algorithm that perceives information through trial-and-error learning provides a new direction for the route planning of UAV. In this paper, a novel route planning algorithm based on improved Q-learning is proposed for UAV in an unknown environment. The prior knowledge is used to guide the UAV to select the action, and a new convergence method based on single state-action value difference is proposed to speed up the convergence rate. Finally, the simulation experiment results show that the proposed method is more effective than the original method, and the algorithm is feasible and effective for UAV route planning.
Poster

Machine learning algorithms fall prey to adversarial examples. As profiling side-channel attacks are seeing rapid adoption of machine learning-based approaches that can even defeat commonly used side-channel countermeasures, we investigate the potential of adversarial example as a defense mechanism. We show that adversarial examples have the potential to serve as a countermeasure against machine learning-based side-channel attacks. Further, we exploit the transferability property to show that a common adversarial example can act as a countermeasure against a range of machine learning-based side-channel classifiers.
Coherence and probability in legal evidence

Abstract The authors investigate to what extent an evaluation of legal evidence in terms of coherence (suggested by Thagard, Amaya, Van Koppen and others) is reconcilable with a probabilistic (Bayesian) approach to legal evidence. The article is written by one author (Dahlman) with a background in the bayesian approach to legal evidence, and one author (Mackor) with a background in scenario theory. The authors find common ground but partly diverge in their conclusions. Their findings give support to the claim (reductionism) that coherence can be translated into probability without loss. Dahlman therefore concludes that the probabilistic vocabulary is superior to the coherence vocabulary, since it is more precise. Mackor is more agnostic in her conclusions about reductionism. In Mackor's view, the findings of their joint investigation do not imply that the probabilistic approach is superior to the coherentist approach.
Top-K interesting preference rules mining based on MaxClique

In order to fully considered context constraints and eliminate the redundancy of preferences in the personalized queries in the database, a Top-K conditional preference mining framework based on maximal clique is proposed. Firstly, a conditional constraint model i+ ≻ i−∣X is proposed, which means that users prefer i+ to i− under the constraint of X. Based on this model, we design a MPM algorithm to efficiently obtain users contextual preferences by using graphic model and new pruning strategies. Then, we propose an updated belief system and the concept of common belief. The belief system is constructed by using users common preferences, and combined with Bayesian approach to acquire users’ Top-K preference rules. Finally, the experiment result indicates that MPM algorithm is far more efficient than three classic or leading-edge mining algorithms in mining all rules and has comparable performance with 2 state-of-the-art Top-K methods. Moreover, the experiment results on Recall, precision and F1-Measure show that MPM algorithm comprehensively outperforms than six kinds various algorithms.