Reviewerly/exHarmony huggingface dataset:
Real-Time Capture of Holistic Tangible Interactions

When digital applications aim to blend virtual and real worlds, understanding the actual physical actions of users becomes an important task; the precise timing of these tangible interaction events is needed, along with the identity, and possibly location and history, of all involved actors/objects. With multiple actors or objects, it is difficult to identify who touches which object and when. Instrumenting objects for Body Channel Communication (BCC) allows message exchange around the human body between instrumented objects and the user themselves. In this paper we show how BCC can be utilized to perform under real-time conditions so that we can directly notice touch events (and the identity of actors). TangibleID is a framework that unifies tangible interaction capture for objects and users based on wearable BCC. TangibleID provides identification and communication with tagged objects/users in less than 120 ms and supports a variety of tangible interactions, without the need to restrict user (hand) movements or to maintain line-of-sight connection to cameras. When an AR application is combined with TangibleID, a new tangible mixed reality experience is achieved, as demonstrated in the "Haunted Castle" showcase. The paper presents an end-to-end technical evaluation including trade-offs regarding robustness and speed of touch recognition, outlines the breadth of interaction modalities, and reports on an initial user assessment.
Ensuring the Visibility and Accessibility of European Creative Content on the World Market: The Need for Copyright Data Improvement in the Light of New Technologies

In the European Strategy for Data, the European Commission highlighted the EU’s ambition to acquire a leading role in the data economy. At the same time, the Commission conceded that the EU would have to increase its pools of quality data available for use and re-use. In the creative industries, this need for enhanced data quality and interoperability is particularly strong. Without data improvement, unprecedented opportunities for monetising the wide variety of EU creative content and making this content available for new technologies, such as artificial intelligence training systems, will most probably be lost. The problem has a worldwide dimension. While the US have already taken steps to provide an integrated data space for music as of 1 January 2021, the EU is facing major obstacles not only in the field of music but also in other creative industry sectors. Weighing costs and benefits, there can be little doubt that new data improvement initiatives and sufficient investment in a better copyright data infrastructure should play a central role in EU copyright policy. Creating a work-related data flow from copyright owners to online content platforms, Article 17 of the Directive on Copyright in the Digital Single Market could play a central role in this respect. Tapping into this data flow, it becomes possible to establish an EU copyright data repository offering more comprehensive, accurate and interoperable information on works and rightholders than existing data silos.
Resolving automatic high availability operations and users commands to virtual machines on OpenStack

In recent years, server virtualization technology has become more and more popular. Many companies tend to build services on virtual machines (VMs) on cloud computing platforms. Therefore, high-availability (HA) technologies that improve system availability by reducing downtime have become critical. In previous research, we proposed a HA technology called High Availability Service System (HASS) for OpenStack. However, the operation of HASS conflicts with the operation of the VM user or administrator. For example, HASS incorrectly judges a user's operation (such as live migrating a VM) as a fault and conducts the corresponding failover. There is no existing method that can be applied to OpenStack to solve this problem. Therefore, this paper aims to propose a new method to resolve the conflict between automatic operation of HA technology and VM operation. The proposed method collects VM operation information and automatically changing the fault detection strategy of OpenStack HA technology. According to the experimental results, even if the user operates the VM, the OpenStack HA technology using the proposed method can accurately detect the VM fault and failover.
Tensor Action Spaces for Multi-agent Robot Transfer Learning

We explore using reinforcement learning on single and multi-agent systems such that after learning is finished we can apply a policy zero-shot to new environment sizes, as well as different number of agents and entities. Building off previous work, we show how to map back and forth between the state and action space of a standard Markov Decision Process (MDP) and multi-dimensional tensors such that zero-shot transfer in these cases is possible. Like in previous work, we use a special network architecture designed to work well with the tensor representation, known as the Fully Convolutional Q-Network (FCQN). We show simulation results that this tensor state and action space combined with the FCQN architecture can learn faster than traditional representations in our environments. We also show that the performance of a transferred policy is comparable to the performance of policy trained from scratch in the modified environment sizes and with modified number of agents and entities. We also show that the zero- shot transfer performance across team sizes and environment sizes remains comparable to the performance of training from scratch specific policies in the transferred environments. Finally, we demonstrate that our simulation trained policies can be applied to real robots and real sensor data with comparable performance to our simulation results. Using such policies we can run variable sized teams of robots in a variable sized operating environment with no changes to the policy and no additional learning necessary.
Artificial Intelligence and Big Data in The Healthcare Sector. The Revolution of Traditional Medicine and The Birth of Precision Therapy. Analysis and Evolution of Big Players in Healthcare and New Start-Ups, Growth Assumptions, and Investment Prospects. National and European Legislative and Regulatory Limits

The advent of artificial intelligence will change society as we know it, producing significant implications in different fields of human interaction, making some professions and entire traditional production processes obsolete. Given the trends of the new technology based on the computational calculation in a predictive key, a substantial reflection of the social model conceived to date is absolutely pivotal. In fact, traditional regulatory concepts i.e. privacy, the protection of personal data, intellectual property, laws and professional orders, appear incompatible with artificial intelligence and the big data chain in general and therefore it is considered necessary to modernize the existing technical-legal and jurisdictional framework in a way that is able to keep up with the times. The research analysed one of the possible uses of the A.I.: the healthcare field, oriented more and more towards the personalisation of the precision medicine cure and the prediction of the disease.
Poster: Exploiting Data Heterogeneity for Performance and Reliability in Federated Learning

Federated Learning [1] enables distributed devices to learn a shared machine learning model together, without uploading their private training data. It has received significant attention recently and has been used in mobile applications such as search suggestion [2] and object detection [3]. Federated Learning is different from distributed machine learning due to the following reasons: 1) System heterogeneity: federated learning is usually performed on devices having highly dynamic and heterogeneous network, compute, and power availability. 2) Data heterogeneity (or statistical heterogeneity): data is produced by different users on different devices, and therefore may have different statistical distribution (non-IID).
Ethics and Secure Gestures

chapter Share on Ethics and Secure Gestures Author: David McMenemy Search about this author Authors Info & Claims Intelligent Computing for Interactive System Design: Statistics, Digital Signal Processing, and Machine Learning in PracticeFebruary 2021 Pages 339–340https://doi.org/10.1145/3447404.3447423Online:23 February 2021Publication History 0citation3DownloadsMetricsTotal Citations0Total Downloads3Last 12 Months3Last 6 weeks0 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
On one Method for Solving the Multi-class Classification Problem

The paper considers one of the main tasks of data mining the problem of pattern recognition for some classes of objects. The problem of multiclass classification was solved based on the solution of special systems of inequalities, and the corresponding algorithm for the decision rule of multiclass classification was constructed.
Enforcement Key Feature Mining for Task Specific Salient Region Detection

Object detection problems also expect fast and accurate location of target objects. But most approaches following convolutional neural networks (CNN) framework generate a large number of redundant candidate regions, wasting a large percent of computation time for candidate classification. Human visual system is able to quickly find the parts or features belonging to objects of interest. Hence, it is believed that by detecting salient regions associated with key classification features for specific objects can quickly provide rough location of objects to be detected. A feature mining method by adding an enforcement learning block on top of CNN framework is proposed. Experiment results showed that the proposed method is able to generate accurate task specific salient regions both for localization and classification. This study attempts to make object localization and classification in one shot based on salient region detection and puts forward a new idea for key feature mining and utilization.
Datacenter Reliability Pursuance with Optimized Load Balancers

Cloud computing can be defined as the provisioning of different computing resources, such as data storage, and computing capabilities from the cloud service providers. These resources are made available through the datacenter to many tenants over the Internet. The cloud datacenter faces few challenges, which brings reliability and performance retardation into the system. The datacenter reliability is a very important aspect as the incoming request to a datacenter is highly scalable. An optimized load balancing strategy can bring reliability into a datacenter. This chapter focuses on the importance of an optimized load balancer, equipped with an efficient load balancing strategy, toward the achievement of reliability pursuance of the datacenter. This chapter will help readers to understand about the different load balancing approaches in a cloud environment, and how these strategies help the cloud service providers in delivering a high-performing and reliable resource provisioning to tenants.
Updating Confidence in Beliefs

This paper develops a belief update rule under ambiguity, motivated by the maxim: in the face of new information, retain those conditional beliefs in which you are more confident, and relinquish only those in which you have less confidence. We provide a preference-based axiomatisation, drawing on the account of confidence in beliefs developed in Hill (2013). The proposed rule constitutes a general framework of which several existing rules for multiple priors (Full Bayesian, Maximum Likelihood) are special cases, but avoids the problems that these rules have with updating on complete ignorance. Moreover, it can handle surprising and null events, such as crises or reasoning in games, recovering traditional approaches, such as conditional probability systems, as special cases.
Arithmetic Evaluation System Based on MixNet-YOLOv3 and CRNN Neural Networks

In the traditional teaching procedure, the repetitive labor of correcting arithmetic exercise brings huge human costs. To reduce these costs and improve the given teaching efficiency, we propose a novel intelligent arithmetic evaluation system, which can automatically identify the meaning of each arithmetic question and make a reasonable judgment or decision. The designed evaluation system can be divided into two modules with detection and identification. In the detection module, due to the intensive distribution and various formats of arithmetic questions in the test papers, we adopt the MixNet-YOLOv3 network with scale balance and lightweight to achieve speed-accuracy trade-off with the mAP being up to 0.989; In the recognition module, considering the formats of each arithmetic problem are mostly fixed, we employ the CRNN network based on the CTC decoding mechanism to achieve an accuracy being up to 0.971. By the incorporation of two networks, the proposed system is capable of intelligently evaluating arithmetic exercise in mobile devices.
Building Adaptive Touch Interfaces—Case Study 6

chapter Share on Building Adaptive Touch Interfaces—Case Study 6 Authors: Daniel Buschek Search about this author , Florian Alt Search about this author Authors Info & Claims Intelligent Computing for Interactive System Design: Statistics, Digital Signal Processing, and Machine Learning in PracticeFebruary 2021 Pages 379–406https://doi.org/10.1145/3447404.3447426Online:23 February 2021Publication History 0citation3DownloadsMetricsTotal Citations0Total Downloads3Last 12 Months0Last 6 weeks0 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
A Preliminary Framework to Fight Tax Evasion in the Home Renovation Market

This chapter presents a preliminary framework to tackle tax evasion in the field of residential renovation. This industry plays a major role in economic development and employment growth. Tax evasion and fraud are extremely difficult to combat in the industry since it is characterized by a large number of stakeholders (manufacturers, retailers, tradesmen, and households) generating complex transactional dynamics that often defy attempts to deploy transactional analytics to detect anomalies, fraud, and tax evasion. This chapter proposes a framework to apply transactional analytics and data mining to develop standard measures and predictive models to detect fraud and tax evasion. Combining big data sets, cross-referencing, and predictive modeling (i.e., anomaly detection, artificial neural network support vector machines, Bayesian network, and association rules) can assist government agencies to combat highly stealth tax evasion and fraud in the residential renovation.
Handwriting Classification of Byzantine Codices via Geometric Transformations Induced by Curvature Deformations

In the present paper, we propose a methodology of general applicability for matching, comparing and grouping planar shapes, under a unified framework. This is achieved by interpreting shapes’ grouping as a result of the hypothesis that shapes of the same class come from the same implicit family of curves. In order to render the analysis independent of the functional form of the curves families’ implicit function, we have formalized the shapes’ comparison in terms of the implicit curvature function. The implementation of the methodology targets towards automatic writer identification and the corresponding information system has been applied to the identification of the writer of Byzantine codices that preserve Iliad. The shapes in hand are the alphabet symbols appearing in the documents’ images to be classified. The realizations of each alphabet symbol are compared pairwise, modulo affine transformations. The statistical compatibility of these comparisons inside the same document and between different documents determines the likelihood of attributing different documents to the same hand. By maximizing the joint likelihood for all alphabet symbols, common in all documents we determine the most probable classification of the given documents into writing hands. Application of the methodology to 25 images of Byzantine codices’ pages indicated that these pages have been written by 4 hands in full accordance with experts’ opinion and knowledge.
Multiview Similarity Learning for Robust Visual Clustering

Multiview similarity learning aims to measure the neighbor relationship between each pair of samples, which has been widely used in data mining and presents encouraging performance on lots of applications. Nevertheless, the recent existing multiview similarity learning methods have two main drawbacks. On one hand, the comprehensive consensus similarity is learned based on previous fixed graphs learned from all views separately, which ignores the latent cues hidden in graphs from different views. On the other hand, when the data are contaminated with noise or outlier, the performance of existing methods will decline greatly because the original true data distribution is destroyed. To address the two problems, a Robust Multiview Similarity Learning (RMvSL) method is proposed in this paper. The contributions of RMvSL includes three aspects. Firstly, the recent low-rank representation shows some advantage in removing noise and outliers, which motivates us to introduce the data representation via low-rank constraint in order to generate clean reconstructed data for robust graph learning in each view. Secondly, a multiview scheme is established to learn the consensus similarity by dynamically learned graphs from all views. Meanwhile, the consensus similarity can be used to propagate the latent relationship information from other views to learn each view graph in turn. Finally, the above two processes are put into a unified objective function to optimize the data reconstruction, view graphs learning and consensus similarity graph learning alternatingly, which can help to obtain overall optimal solutions. Experimental results on several visual data clustering demonstrates that RMvSL outperforms the most existing methods on similarity learning and presents great robustness on noisy data.
Multiple Projections Learning for Dimensional Reduction

Locality Preserving Projection (LPP) is a dimensional reduction method that has been widely used in various fields. While traditional LPP only uses a single projection matrix to reduce the dimension and preserve the locality structure of data, it may cause the single matrix may not handle these two tasks well at the same time. Therefore, in this paper, we proposed relaxed sparse locality presenting projection (RSLPP) which introduces two different projection matrices to better accomplish the two tasks. The addition of another projection matrix can help the original projection matrix has more freedom to select the appropriate feature for preserving the local structure of data. The experimental results on two data sets prove the effectiveness of the method.
ODANet: Online Deep Appearance Network for Identity-Consistent Multi-person Tracking

The analysis of affective states through time in multi-person scenarii is very challenging, because it requires to consistently track all persons over time. This requires a robust visual appearance model capable of re-identifying people already tracked in the past, as well as spotting newcomers. In real-world applications, the appearance of the persons to be tracked is unknown in advance, and therefore one must devise methods that are both discriminative and flexible. Previous work in the literature proposed different tracking methods with fixed appearance models. These models allowed, up to a certain extend, to discriminate between appearance samples of two different people. We propose online deep appearance network (ODANet), a method able to track people, and simultaneously update the appearance model with the newly gathered annotation-less images. Since this task is specially relevant for autonomous systems, we also describe a platform-independent robotic implementation of ODANet. Our experiments show the superiority of the proposed method with respect to the state of the art, and demonstrate the ability of ODANet to adapt to sudden changes in appearance, to integrate new appearances in the tracking system and to provide more identity-consistent tracks.
Power Grid Fault Diagnosis Based on Immune RBF Neural Network

Radial basis function (RBF) has achieved good application in power grid fault diagnosis because of its uncomplicated network structure, strong self-learning ability and good generalization ability. The selection of RBF center point is always one of the main problems of RBF neural network algorithm. The existing selection methods of RBF center point are mainly based on K-means algorithm, but K-means clustering suffers from the low performance due to the low accurate initial value. In order to get the RBF neural network with better diagnosis effect, immune algorithm is used to calculate RBF center point, and then immune RBF neural network is obtained and used in power grid fault diagnosis. The numerical results verify the effectiveness and accuracy of the proposed algorithm for diagnosing the fault location of power grid.
Nominal Unification with Letrec and Environment-Variables

Unification algorithms of nominal expressions with letrec and atom- and expression-variables are already described in the literature. However, only explicit environments could be treated in nominal unification and the use of abstract environments was restricted to nominal matching. This severely restricts the use of algorithms in applications. The following two restrictions permit a step forward and strongly improve the coverage of the application cases: expression- and environment-variables are restricted to occur at most once in the input equations. A terminating and complete nominal unification algorithm is described that computes complete sets of constrained unifiers. Since the set of ground instances of a complete set may be empty due to constraints, we also provide a decision algorithm for inputs which do not contain permutation-variables and show that then nominal unifiability is NP-complete. For input without an occurrence-restriction for expression-variables and w.r.t. garbage-free ground expressions, we sketch an adapted unification algorithm that produces a complete set of unifiers in NP time. For the decision problem we conjecture that it is harder in this case. We believe that lifting the linearity restrictions for environment-variables leads to a prohibitively high computational complexity.
The evaluation of voting rules based on voting networks: Weber problem with support domain

This study focuses on improving the Weber model to obtain a more optimised solution, by which we can measure effectively the quality of the voting rules. By introducing the voter’s supporting domain and the candidate’s supported degree to depict the emotional factors of voters and the real voting network, we propose the concept of validity for a candidate. Upon the traditional Weber model, two improved models are presented and the corresponding global optimal candidate is used as the evaluation benchmark for the voting rules. The experiments show that the optimal solution of the two models has better robustness in complex voting networks, can be used as a standard to evaluate the voting rules. When the support degree of voter is the main factor, the Condorcet rule is optimal in most cases, and when the validity of the candidate is taken as the main factor, the Approval rule must be the best.