Reviewerly/exHarmony huggingface dataset:
Efficient and Accurate Object 3D Selection With Eye Tracking-Based Progressive Refinement

Selection by progressive refinement allows the accurate acquisition of targets with small visual sizes while keeping the required precision of the task low. Using the eyes as a means to perform 3D selections is naturally hindered by the low accuracy of eye movements. To account for this low accuracy, we propose to use the concept of progressive refinement to allow accurate 3D selection. We designed a novel eye tracking selection technique with progressive refinement–Eye-controlled Sphere-casting refined by QUAD-menu (EyeSQUAD). We propose an approximation method to stabilize the calculated point-of-regard and a space partitioning method to improve computation. We evaluated the performance of EyeSQUAD in comparison to two previous selection techniques–ray-casting and SQUAD–under different target size and distractor density conditions. Results show that EyeSQUAD outperforms previous eye tracking-based selection techniques, is more accurate and can achieve similar selection speed as ray-casting, and is less accurate and slower than SQUAD. We discuss implications of designing eye tracking-based progressive refinement interaction techniques and provide a potential solution for multimodal user interfaces with eye tracking.
Space, a Central Frontier-The Role of Spatial Abilities When Learning the Structure of 3D AR Objects

Learning about three-dimensional (3D) objects is the focus of many augmented reality (AR) applications. Although this underlines the importance of spatiality in AR experiences, learners' spatial abilities should be considered in this context. While spatial abilities may compensate for the lack of spatial information in 2D representations (ability-as-compensator hypothesis), they may also be necessary for learning with 3D representations in the first place (ability-as-enhancer hypothesis). In the current study, we examine the role 3D spatial visualization abilities and 2D spatial memory abilities may play when learning with 3D AR objects. Both variables were measured in an exploratory pilot study in which N = 33 participants learned about the spatial structure of the modules of the International Space Station (ISS) with either an AR or a non-AR mobile application. We found that spatial abilities indeed had moderating effects on achievement in the learning task and knowledge test, although the results are inconclusive concerning an ability-as-enhancer or -compensator hypothesis. We discuss the results and conclude that it is necessary that researchers take a closer look at the role of learners' spatial abilities when learning with 3D representations in AR. With additional insights, practitioners can then make informed decisions on using AR applications.
Cryptanalysis of a Special Case of RSA Large Decryption Exponent Using Lattice Basis Reduction Method

RSA public key cryptosystem is the “de-facto” standard, provides confidentiality and privacy security services over the internet. At Eurocrypt 1999, Boneh and Durfee proposed a polynomial time attacks on RSA small decryption key exponent. Their attacks worked by exploiting the lattice and sub lattice structure using lattice based Coppersmith's method to solve a modular polynomials, when d <; N <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0.284</sup> and d <; N <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0.292</sup> respectively. In this work, we propose a new attack on some special case of Boneh and Durfee's attack method with respect to large decryption exponent (i.e. d = N > e = N <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">α</sup> , where α and ε are the encryption and decryption exponents respectively) for some α ≤ ε. The condition d > φ(N) - N <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ε</sup> satisfies our devised attack and the experimental outcome certifies that an RSA cryptosystem with large decryption exponent successfully revealed the weak keys through lattice basis reduction method.
Human-centred Persona Driven Personalization in Business Data Analytics

The modern business environment is empowered by the abundant availability of data and plethora of sophisticated data analysis tools to identify and quickly address market needs. While these tools have evolved significantly during the last years, offering trailblazing data exploration experiences with stunning multi-modal visualizations, they mistreat the importance of individualized, user-centred delivery of information/insights. As a result, users may require much more effort and time to reach decisions that have implications on both the short-term and long-term success of sustainability of an organization. This paper highlights the need for user-centred/persona-driven data exploration through adaptive data visualizations and personalized support to an end-to-end business process. It proposes an extended human-centred persona and discusses preliminary exploratory results in relation to the formulation of the contextual characteristics of a business environment, i.e., business tasks, visualizations and data.
Hippocampus segmentation in MR images: Multiatlas methods and deep learning methods

The automatic and accurate segmentation of the hippocampus in brain magnetic resonance (MR) images is important to study various neurological diseases. However, it is a challenging task due to the small structure size, the irregular shape, and the blurred boundaries between the hippocampus and its surrounding structures. In this chapter, we review the two most popularly used types of methods for this task: (1) multiatlas-based methods and (2) learning-based methods. We first review various existing patch-based multiatlas label fusion strategies. Then, we present a supervised metric learning-based label fusion in detail. This method learns a distance metric model from the atlases to keep the image patches of the same structure close to each other and those of different structures distant. For the learning-based methods, we present a multiatlas-based deep learning method and an end-to-end deep learning method in detail. Specifically, the multiatlas-based deep learning method applies a deep learning-based confidence estimation method to alleviate the potential effects of the registration errors in the traditional atlas-based methods. The end-to-end deep learning method directly learns the segmentation maps from the input images, by embedding a dilated dense network in the residual U-net. We present a comprehensive evaluation of the discussed methods compared with the state-of-the-art methods using the public datasets. In the end of the chapter, we include promising directions for related future research.
Law Enforcement Priorities in the Era of New Digital Tools

The Innovation Law Enforcement Dialogue practitioners’ network (I-LEAD) builds upon the work of the European Network of Law Enforcement Technology Services (ENLETS) that brings EU law enforcement together to share best practices, activate co-creation and stimulate research for operational purposes. I-LEAD’s key focus is to contribute to the development of new and existing crime fighting capabilities of LEAs across Europe. This will be undertaken via a series of bespoke practitioner workshops that have been exclusively designed to identify and define end-user priorities in 25 subject-specific areas of law enforcement. The findings from the workshops will be used to identify “fit for purpose” solutions within the marketplace and/or direct research and innovation within academia, with small and medium-sized enterprises (SMEs) and/or those organisations within the security industry environment. The overall results of this work will effectively contribute to the development and enhancement, where relevant and required, of the existing policing “crime fighting tool kit”. I-LEAD will also examine and recommend opportunities for standardisation and joint procurement, concentrating on the technical, human, organisational and regulatory elements. In this chapter, we present a summary of the I-LEAD findings based on the successful establishment of five workshops that brings together 78 experts from operational law enforcement across 21 EU Member States. The workshop areas are related to Open Source Intelligence (OSINT), Mobility for Officers, People Trafficking, Intelligence Analysis and Technologies in DNA.
A Method of Load Forecasting Based on Temporal Convolutional Network

With the wide application of RNN and the high accuracy of LSTM and GRU, it is more and more popular in the field of load forecasting. However, the stability of the training is always worrying, and the phenomenon of gradient vanishing often occurs. Aiming at the problems, this paper proposes an intelligent load forecasting method which is suitable for medium and long term and short term. The method is based on temporal convolutional network Based on the residual learning idea of RESNET network model, the extended causal convolution and self-attention mechanism are used to build the intelligent load forecasting model of low-voltage substation area, which reflects the timeliness, accuracy and intelligence.
Distribution slack allocation algorithm for energy aware task scheduling in cloud datacenters

Regarding the ever-increasing development of data and computational centers due to the contribution of high-performance computing systems in such sectors, energy consumption has always been of great importance due to CO2 emissions that can result in adverse effects on the environment. In recent years, the notions such as “energy” and also “Green Computing” have played crucial roles when scheduling parallel tasks in datacenters. The duplication and clustering strategies, as well as Dynamic Voltage and Frequency Scaling (DVFS) techniques, have focused on the reduction of the energy consumption and the optimization of the performance parameters. Concerning scheduling Directed Acyclic Graph (DAG) of a datacenter processors equipped with the technique of DVFS, this paper proposes an energy- and time-aware algorithm based on dual-phase scheduling, called EATSDCDD, to apply the combination of the strategies for duplication and clustering along with the distribution of slack-time among the tasks of a cluster. DVFS and control procedures in the proposed green system are mapped into Petri net-based models, which contribute to designing a multiple decision process. In the first phase, we use an intelligent combined approach of the duplication and clustering strategies to run the immediate tasks of DAG along with monitoring the throughput by concentrating on the reduction of makespan and the energy consumed in the processors. The main idea of the proposed algorithm involves the achievement of a maximum reduction in energy consumption in the second phase. To this end, the slack time was distributed among non-critical dependent tasks. Additionally, we cover the issues of negotiation between consumers and service providers at the rate of μ based on Green Service Level Agreement (GSLA) to achieve a higher saving of the energy. Eventually, a set of data established for conducting the examinations and also different parameters of the constructed random DAG are assessed to examine the efficiency of our proposed algorithm. The obtained results confirms that our algorithm outperforms compared the other algorithms considered in this study.
EdgeGAN: One-way mapping generative adversarial network based on the edge information for unpaired training set

Image conversion has attracted mounting attention due to its practical applications. This paper proposes a lightweight network structure that can implement unpaired training sets to complete one-way image mapping, based on the generative adversarial network (GAN) and a fixed-parameter edge detection convolution kernel. Compared with the cycle consistent adversarial network (CycleGAN), the proposed network features simpler structure, fewer parameters (only 37.48% of the parameters in CycleGAN), and less training cost (only 35.47% of the GPU memory usage and 17.67% of the single iteration time in CycleGAN). Remarkably, the cyclic consistency becomes not mandatory for ensuring the consistency of the content before and after image mapping. This network has achieved significant processing effects in some image translation tasks, and its effectiveness and validity have been well demonstrated through typical experiments. In the quantitative classification evaluation based on VGG-16, the algorithm proposed in this paper has achieved superior performance.
Exploring the Teacher’s Utilization of ICT Resources in Schools and their Student’s Competence in Basic Computer Applications

Abstract This descriptive study explored the Teacher’s Utilization of ICT Resources in Schools and their Student’s Competence in Basic Computer Applications. This involved the fourteen Science teachers and the 245 Grade 10 students in the three selected public secondary schools in Ragay, Camarines Sur, Philippines, for the School Year 2019-2020. It revealed that selected public secondary schools in Ragay Sometimes Utilized various ICT Resources in teaching Science, and the Science teachers of those schools are Skillful in Information and Communications Technology and are Much Ready in Equipment Readiness in integrating ICT in school and at home; their Grade 10 students are Competent in the Basic Computer Applications particularly in the Word Processing, Spreadsheet application, PowerPoint Presentation and the Use of Internet. Thus, it is recommended that computers and the internet be made available for students and teachers and regular training of teachers to utilize various resources in teaching Science to improve the students’ critical thinking, motivation, and higher-order thinking skills.
Object-Aware Ghost Identification and Elimination for Dynamic Scene Mosaic

Composite ghost is a common phenomenon that widely exists in dynamic scene image mosaic and significantly affects the naturalness of mosaic. To remove the ghost effectively and produce visually natural mosaic, we propose a novel image mosaic method by jointly identifying composite ghost and eliminating ghost regions without distorting, splitting, and duplicating objects. Specifically, our main contributions are three-fold: <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">First</i> , we propose the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">motion-aware</i> composite ghost identification to localize the potential composite ghosts in the mosaic region ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e</i> ., overlapping area between two images to be stitched) by detecting the salient-moving objects in two stitched images. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Second</i> , we design the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">object-aware</i> alternative region selection strategy to produce ghostless regions that can replace the localized composite ghosts while avoiding object distortion, object separation, and object repetition. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Third</i> , we realize the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">image interpolation-based</i> composite ghost elimination that can generate natural stitched image by eliminating the composite ghost of the initial blending result with the selected image source. We validate the proposed method on challenging datasets and show that our method outperform the state-of-the-art methods.
Application of Machine Learning Techniques to Predict Breast Cancer Survival

Despite recent significant advances in big data analytics, there is substantial evidence of machine learning techniques that perform poorly when building prediction models. This research aimed to investigate the performance and effectiveness of machine learning techniques including Naive Bayes (NB), PART, Random Forest (RF), Support Vector Machine (SVM), Adaboost, and Bagging in order to advance existing understandings of model behavior with big data. A large dataset of hospital-based breast cancer from the SEER data file with diagnostic information was used from 2005 to 2014. To address outliers and imbalance issues, we used C4.5 and Synthetic Minority Oversampling TEchnique (SMOTE) to eliminate outliers and balance the dataset. Stratified 10-fold cross-validation was used to divide the dataset to reduce bias and variance of experimental results. Accuracy, G-mean (G), F-measure, and Matthews correlation coefficient (MCC) are employed as criteria to present the overall performance of the models. Moreover, sensitivity, specificity, and precision are utilized as criteria to show the insightful performance of the models. The experimental results indicate that RF is superior to Naive Bayes (NB), PART, Support Vector Machine (SVM), Adaboost, and Bagging in all criteria. Also, models generated from datasets with few outliers and balanced data outperform the original dataset in terms of insight and overall performances.
Social Visibility Optimization in OSNs with Anonymity Guarantees: Modeling, Algorithms and Applications

Online social network (OSN) is an ideal venue to enhance one's visibility. This paper considers how a user (called requester) in an OSN selects a small number of available users and invites them as new friends/followers so as to maximize his "social visibility". More importantly, the requester has to do this under the anonymity setting, which means he is not allowed to know the neighborhood information of these available users in the OSN. In this paper, we first develop a mathematical model to quantify the social visibility and formulate the problem of visibility maximization with anonymity guarantee, abbreviated as "VisMAX-A". Then we design an algorithmic framework named as "AdaExp", which adaptively expands the requester's visibility in multiple rounds. In each round of the expansion, AdaExp uses a query oracle with anonymity guarantee to select only one available user. By using probabilistic data structures like the k-minimum values (KMV) sketch, we design an efficient query oracle with anonymity guarantees. We also conduct experiments on real-world social networks and validate the effectiveness of our algorithms.
Workload-aware Materialization for Efficient Variable Elimination on Bayesian Networks

Bayesian networks are general, well-studied probabilistic models that capture dependencies among a set of variables. Variable Elimination is a fundamental algorithm for probabilistic inference over Bayesian networks. In this paper, we propose a novel materialization method, which can lead to significant efficiency gains when processing inference queries using the Variable Elimination algorithm. In particular, we address the problem of choosing a set of intermediate results to precompute and materialize, so as to maximize the expected efficiency gain over a given query workload. For the problem we consider, we provide an optimal polynomial-time algorithm and discuss alternative methods. We validate our technique using real-world Bayesian networks. Our experimental results confirm that a modest amount of materialization can lead to significant improvements in the running time of queries, with an average gain of 70%, and reaching up to a gain of 99%, for a uniform workload of queries. Moreover, in comparison with existing junction tree methods that also rely on materialization, our approach achieves competitive efficiency during inference using significantly lighter materialization.
An Adaptive Clustering Algorithm Based on Circular Units

The traditional K-means algorithm has been widely studied and applied in data analysis due to its simplicity, efficiency and effectiveness. However, the traditional K-means algorithm strongly relies on the pre-defined number of clusters k, which leads to different training iterations and make the clustering results fall into local minimum. In addition, the traditional K-means algorithm is computationally complex and uses a proximity allocation strategy to handle outliers, which results in lower clustering accuracy. To address such issues, we present a novel improved Circular Units-based Adaptive Clustering (CUCA) algorithm in this paper. Our proposed algorithm describes each cluster in terms of circular data units and selects the initial centroids based on the density of each unit to solve the above-mentioned issues, i. e., different training iterations and the possibility of converging to local minimum. The algorithm can exactly find the neighbouring circular data units of each prime centroid based on the tree neighbour cluster search, relationship judgment only between the prime centroids and its neighbour circular data units instead of all units, reducing the time overhead. The algorithm uses predictive affiliation mechanism to determine the relationship between outliers and each cluster, and obtains the result that the outliers belong to a cluster or become a separate cluster, improving clustering accuracy.
Look at It This Way: A Comparison of Metaphors for Directing the User's Gaze in eXtended Reality Training Systems

Interest is raising around eXtended Reality Training Systems (XRTSs), which started to be considered as a credible option to train companies' workforce. Even though there is a growing body of literature on best practices and techniques to be adopted for teaching individuals how to perform a variety of operations (e.g., for assembly and maintenance procedures), there are also training situations which have gone mostly unexplored yet. In this paper, we propose and evaluate three different metaphors to face the key challenges associated with training procedures involving parallax-dependent tasks, i.e., tasks in which the instructor needs to make the trainee reach a target observation point and guide his/her attention towards a given point of interest at the same time. Effects observed through a user study that was run in a testbed environment indicated that metaphors based on 3D avatars and frustum visualization can provide important advantages over video-based techniques.
Applying Machine Learning for Automatic Product Categorization

Abstract Every five years, the U.S. Census Bureau conducts the Economic Census, the official count of US businesses and the most extensive collection of data related to business activity. Businesses, policymakers, governments and communities use Economic Census data for economic development, business decisions, and strategic planning. The Economic Census provides key inputs for economic measures such as the Gross Domestic Product and the Producer Price Index. The Economic Census requires businesses to fill out a lengthy questionnaire, including an extended section about the goods and services provided by the business. To address the challenges of high respondent burden and low survey response rates, we devised a strategy to automatically classify goods and services based on product information provided by the business. We asked several businesses to provide a spreadsheet containing Universal Product Codes and associated text descriptions for the products they sell. We then used natural language processing to classify the products according to the North American Product Classification System. This novel strategy classified text with very high accuracy rates - our best algorithms surpassed over 90%.