Reviewerly/exHarmony huggingface dataset:
Location Proof Systems for Smart Internet of Things: Requirements, Taxonomy, and Comparative Analysis

In the current hyper-connected, data-driven era, smart devices are providing access to geolocation information, enabling a paradigm shift in diverse domains. Location proof systems utilize smart devices to provide witnessed proof of location to enable secure location-based services (LBS). Applications of location proof systems include safety, asset management and operations monitoring in health care, supply chain tracking, and Internet-of-Things (IoT)-based location intelligence in businesses. In this paper, we investigate the state of the art in location proof systems, examining design challenges and implementation considerations for application in the real world. To frame the analysis, we have developed a taxonomy of location proof systems and performed a comparative analysis over the common attributes, highlighting their strength and weaknesses. Furthermore, we have identified future trends for this increasingly important area of investigation and development.
Blind image quality assessment in the contourlet domain

No-reference/blind image quality assessment (NR-IQA/BIQA) algorithms play an important role in image evaluation, as they can assess the quality of an image automatically, only using the distorted image whose quality is being assessed. Among the existing NR-IQA/BIQA methods, natural scene statistic (NSS) models which can be expressed in different bandpass domains show good consistency with human subjective judgments of quality. In this paper, we create new 'quality-aware' features: the energy differences of the sub-band coefficients across scales via contourlet transform, and propose a new NR-IQA/BIQA model that operates on natural scene statistics in the contourlet domain. Prior to applying the contourlet transform, we apply two preprocessing steps that help to create more information-dense, low-entropy representations. Specifically, we transform the picture into the CIELAB color space and gradient magnitude map. Then, a number of 'quality-aware' features are discovered in the contourlet transform domain: the energy of the sub-band coefficients within scales, and the energy differences between scales, as well as measurements of the statistical relationships of pixels across scales. A detailed analysis is conducted to show how different distortions affect the statistical characteristics of these features, and then features are fed to a support vector regression (SVR) model which learns to predict image quality. Experimental results show that the proposed method has high linearity against human subjective perception, and outperforms the state-of-the-art NR-IQA models.
Differences in the Uncanny Valley between Head-Mounted Displays and Monitors

The uncanny valley describes a relationship between the degree of the emotional response with respect to a character's resemblance to an actual human being. It has been a topic for several decades and has been discussed by scholars of different disciplines and in various aspects such as robotics, 3D computer animations, interactive applications, and even lifelike dolls. With the increasing popularity of photo-realistic computer animation and telepresence applications, we are more and more exposed to characters that might be affected by the uncanny valley effect. Recent research suggests that the output medium, such as a monitor or head-mounted display, can have a significant effect on how we perceive given visualizations. In relation to the uncanny valley, we observe a similar effect in our study: characters appear significantly more eerie as well as humanlike when watched on a head-mounted virtual reality headset instead of a monitor. The amplification we see is similar to the findings that the uncanny valley effect is more pronounced when the respective characters are in motion.
Random Partition Models for Microclustering Tasks

Traditional Bayesian random partition models assume that the size of each cluster grows linearly with the number of data points. While this is appealing for some applications, this assumption is not appropriate for other tasks such as entity resolution (ER), modeling of sparse networks, and DNA sequencing tasks. Such applications require models that yield clusters whose sizes grow sublinearly with the total number of data points—the microclustering property. Motivated by these issues, we propose a general class of random partition models that satisfy the microclustering property with well-characterized theoretical properties. Our proposed models overcome major limitations in the existing literature on microclustering models, namely a lack of interpretability, identifiability, and full characterization of model asymptotic properties. Crucially, we drop the classical assumption of having an exchangeable sequence of data points, and instead assume an exchangeable sequence of clusters. In addition, our framework provides flexibility in terms of the prior distribution of cluster sizes, computational tractability, and applicability to a large number of microclustering tasks. We establish theoretical properties of the resulting class of priors, where we characterize the asymptotic behavior of the number of clusters and of the proportion of clusters of a given size. Our framework allows a simple and efficient Markov chain Monte Carlo algorithm to perform statistical inference. We illustrate our proposed methodology on the microclustering task of ER, where we provide a simulation study and real experiments on survey panel data.
An Intelligent Traffic Classification in SDN-IoT: A Machine Learning Approach

In recent years, there has been a sharp increase in IoT devices. Majority of these IoT devices have strict QoS requirements. This has made it very difficult for network providers to provide good network solutions whiles keeping cost in check. To meet the QoS demands in IoT networks, a new paradigm, SDN-IoT, leveraging the advantages of SDN architecture on IoT networks have been proposed to improve network quality. The programmability of the SDN controller allows the application of Machine learning in networks. This paper proposes a Machine learning model that classifies traffic in SDN-IoT networks for traffic engineering. The classification process compares the random forest algorithm, decision tree algorithm, and the K-nearest neighbors' algorithm. The paper also compares the impact of two feature selection methods, Sequential Feature Selection (SFS) and Shapley additive explanations (SHAP) on the accuracies of the classifiers to reduce the number of features needed for classification. The algorithms are accessed based on their accuracy and F1 score. The best performing algorithm is random forest classifier with SFS which achieves accuracy of 0.833 with six features.
A <scp><i>Canis lupus</i></scp> inspired upgraded Harris hawks optimizer for nonlinear, constrained, continuous, and discrete engineering design problem

Abstract Recently established Harris hawks optimization (HHO) has natural behavior for finding an optimum solution in global search space without getting trapped in previous convergence. However, the exploitation phase of the current Harris hawks optimizer algorithm is poor. In the present research, an improved version of the HHO algorithm, which combines Harris hawks optimizer with Canis lupus inspire grey wolf optimizer (GWO) and named as hHHO‐GWO algorithm, has been proposed to find the solution of various optimization problems such as nonlinear, nonconvex, and highly constrained engineering design problem. In the proposed research, the phase of exploration and exploitation of the existing HHO algorithm has been further improved using GWO algorithm and its performance has been tested for various benchmarks problems including CEC2005 (unimodal, multimodal, and fixed dimensions functions), multimodal functions with variable dimensions, and CEC‐BC‐2017 test functions. Further, the developed hybrid optimizer has been tested for 11 different engineering design and optimization problems and experimental results of hHHO‐GWO have been compared with other optimizer.
ALBUS: A method for efficiently processing SpMV using SIMD and Load balancing

SpMV (Sparse matrix–vector multiplication) is widely used in many fields. Improving the performance of SpMV has been the pursuit of many researchers. Parallel SpMV using multi-core processors has been a standard parallel method used by researchers. In reality, the number of non-zero elements in many sparse matrices is not evenly distributed, so parallelism without preprocessing will cause a large amount of performance loss due to uneven load. In this paper, we propose ALBUS (Absolute Load Balancing Using SIMD (Single Instruction Multiple Data)), a method for efficiently processing SpMV using load balancing and SIMD vectorization. On the one hand, ALBUS can achieve multi-core balanced load processing; on the other hand, it gives full play to the ability of SIMD vectorization parallelism under the CPU. We selected 20 sets of regular matrices and 20 sets of irregular matrices to form the Benchmark suite. We performed SpMV performance comparison tests on ALBUS, CSR5 (Compressed Sparse Row 5), Merge(Merge-based SpMV), and MKL (Math Kernel Library) under the same conditions. On the E5-2670 v3 CPU platform, For 20 sets of regular matrices, ALBUS can achieve an average speedup of 1.59x, 1.32x, 1.48x (up to 2.53x, 2.22x, 2.31x) compared to CSR5, Merge, MKL, respectively. For 20 sets of irregular matrices, ALBUS can achieve an average speedup of 1.38x, 1.42x, 2.44x (up to 2.33x, 2.24x, 5.37x) compared to CSR5, Merge, MKL, respectively.
Research Data in Chemistry – Results of the first NFDI4Chem Community Survey

Good research data management (RDM) requires a constant endeavour of the researchers but – first and foremost – user‐friendly infrastructure and well accepted standards need to be developed for the researchers. The national research data infrastructure initiative for chemistry in Germany NFDI4Chem sets out to embrace the challenge of creating a general research data management portal and hereby connecting already existing infrastructure as well as to foster the cultural change in chemistry towards digitalization by developing general minimum information standards for all methods used and teaching RDM principles to the community. In order to serve the needs of the chemical community at its best, NFDI4Chem accomplishes regular community surveys. The first survey has been performed in 2019 and the results were condensed into the NFDI4Chem proposal. This article summarizes the design of the study and its results. With regard to the project development, this first national survey serves as zero‐point of all upcoming efforts in research data management.
Interaction with Virtual Content using Augmented Reality

Assembly procedures are a common task in several domains of application. Augmented Reality (AR) has been considered as having great potential in assisting users while performing such tasks. However, poor interaction design and lack of studies, often results in complex and hard to use AR systems. This paper considers three different interaction methods for assembly procedures (Touch gestures in a mobile device; Mobile Device movements; 3D Controllers and See-through HMD). It also describes a controlled experiment aimed at comparing acceptance and usability between these methods in an assembly task using Lego blocks. The main conclusions are that participants were faster using the 3D controllers and Video see-through HMD. Participants also preferred the HMD condition, even though some reported light symptoms of nausea, sickness and/or disorientation, probably due to limited resolution of the HMD cameras used in the video see-through setting and some latency issues. In addition, although some research claims that manipulation of virtual objects with movements of the mobile device can be considered as natural, this condition was the least preferred by the participants.
Methodologies for Quantifying (Re-)randomization Security and Timing under JIT-ROP

Just-in-time return-oriented programming (JIT-ROP) allows one to dynamically discover instruction pages and launch code reuse attacks, effectively bypassing most fine-grained address space layout randomization (ASLR) protection. However, in-depth questions regarding the impact of code (re-)randomization on code reuse attacks have not been studied. For example, how would one compute the re-randomization interval effectively by considering the speed of gadget convergence to defeat JIT-ROP attacks? ; how do starting pointers in JIT-ROP impact gadget availability and gadget convergence time? ; what impact do fine-grained code randomizations have on the Turing-complete expressive power of JIT-ROP payloads? We conduct a comprehensive measurement study on the effectiveness of fine-grained code randomization schemes, with 5 tools, 20 applications including 6 browsers, 1 browser engine, and 25 dynamic libraries. We provide methodologies to measure JIT-ROP gadget availability, quality, and their Turing-complete expressiveness, as well as to empirically determine the upper bound of re-randomization intervals in re-randomization schemes using the Turing-complete (TC), priority, MOV TC, and payload gadget sets. Experiments show that the upper bound ranges from 1.5 to 3.5 seconds in our tested applications. Besides, our results show that locations of leaked pointers used in JIT-ROP attacks have no impacts on gadget availability but have an impact on how fast attackers find gadgets. Our results also show that instruction-level single-round randomization thwarts current gadget finding techniques under the JIT-ROP threat model.
Towards Population-Based Structural Health Monitoring, Part IV: Heterogeneous Populations, Transfer and Mapping

Population-based structural health monitoring (PBSHM) involves utilising knowledge from one set of structures in a population and applying it to a different set, such that predictions about the health states of each member in the population can be performed and improved. Central ideas behind PBSHM are those of knowledge transfer and mapping. In the context of PBSHM, knowledge transfer involves using information from a structure, defined as a source domain, where labels are known for a given feature, and mapping these onto the unlabelled feature space of a different, target domain structure. If the mapping is successful, a machine learning classifier trained on the transformed source domain data will generalise to the unlabelled target domain data; i.e. a classifier built on one structure will generalise to another, making Structural Heath Monitoring (SHM) cost-effective and applicable to a wide range of challenging industrial scenarios. This process of mapping features and labels across source and target domains is defined as domain adaptation, a subcategory of transfer learning. However, a key assumption in conventional domain adaptation methods is that there is consistency between the feature and label spaces. This means that the features measured from one structure must be the same dimension as the other (i.e. the same number of spectral lines of a transmissibility), and that labels associated with damage locations, classification and assessment, exist on both structures. These consistency constraints can be restrictive, limiting to which types of population domain adaptation can be applied. This paper, therefore, provides a mathematical underpinning for when domain adaptation is possible in a structural dynamics context, with reference to topology of a graphical representation of structures. By defining when conventional domain adaptation is applicable in a structural dynamics setting, approaches are discussed that could overcome these consistency restrictions. This approach provides a general means for performing transfer learning within a PBSHM context for structural dynamics-based features.
Monitoring File Integrity Using Blockchain and Smart Contracts

The adoption of cloud computing solutions is an established reality in government agencies and in small, medium, and large companies due to procurement easiness and the variety of available services, as well as its low cost compared to the acquisition and management of own infrastructures. Among the most used services is cloud file storage, and the security of this storage has been an essential subject of recent research, particularly customer data integrity. Thus, this article proposes a solution for the monitoring of the integrity of files stored in the cloud, based on the use of smart contracts in Blockchain Networks, symmetric encryption, and computational trust. The proposed solution consists of a protocol that provides confidentiality, decentralization, audit availability, and the secure sharing of file integrity monitoring results, without overloading the services involved, as well as an unabridged reference implementation which was used to validate the proposal. The results obtained during the validation tests have shown that the solution is feasible and faultless in detecting corrupted files. These tests also confirmed that the sharing of integrity monitoring results, coupled with the application of computational trust techniques, significantly increased the efficiency of the proposed solution.
Trustworthy and Transparent Third-party Authority

Recent advances in cryptographic approaches, such as Functional Encryption and Attribute-based Encryption and their variants, have shown significant promise for enabling public clouds to provide secure computation and storage services for users’ sensitive data. A crucial component of these approaches is a third-party authority (TPA) that must be trusted to set up public parameters, provide private key service, and so on. Components of deployed cryptographic mechanisms such as the certificate authorities (CAs) , which are the TPAs of the underlying PKI for the SSL/TLS protocol, have faced several types of attacks (e.g., stealthy targeted and censorship attacks), and certificate mis-issuance problems. Such practical challenges indicate that the successful deployment of newer emerging cryptographic schemes will also significantly depend on the trustworthiness of the TPAs. Furthermore, recently proposed decentralized TPA approaches that lower the threshold on the conditions required for an entity to become an authority can make the trust issue much worse. To address this issue, we propose an authority transparency framework to ensure the trustworthiness of TPAs of recent and emerging advanced cryptographic schemes. The framework includes a formal model and a secure logging -based approach to implement the framework. Further, to address the issues related to privacy, we also present a privacy-preserving authority transparency approach. We present security analysis and performance evaluation to show that authority transparency achieves the security and performance goals.
Task Similarity Estimation Through Adversarial Multitask Neural Network

Multitask learning (MTL) aims at solving the related tasks simultaneously by exploiting shared knowledge to improve performance on individual tasks. Though numerous empirical results supported the notion that such shared knowledge among tasks plays an essential role in MTL, the theoretical understanding of the relationships between tasks and their impact on learning shared knowledge is still an open problem. In this work, we are developing a theoretical perspective of the benefits involved in using information similarity for MTL. To this end, we first propose an upper bound on the generalization error by implementing the Wasserstein distance as the similarity metric. This indicates the practical principles of applying the similarity information to control the generalization errors. Based on those theoretical results, we revisited the adversarial multitask neural network and proposed a new training algorithm to learn the task relation coefficients and neural network parameters automatically. The computer vision benchmarks reveal the abilities of the proposed algorithms to improve the empirical performance. Finally, we test the proposed approach on real medical data sets, showing its advantage for extracting task relations.
Perfusion Quantification from Endoscopic Videos: Learning to Read Tumor Signatures

Intra-operative (this work was partially supported by Disruptive Technologies Innovation Fund, Ireland, project code DTIF2018 240 CA) identification of malignant versus benign or healthy tissue is a major challenge in fluorescence guided cancer surgery. We propose a perfusion quantification method for computer-aided interpretation of subtle differences in dynamic perfusion patterns which can be used to distinguish between normal tissue and benign or malignant tumors intra-operatively by using multispectral endoscopic videos. The method exploits the fact that vasculature arising from cancer angiogenesis gives tumors differing perfusion patterns from the surrounding normal tissues. Experimental evaluation of our method on a cohort of colorectal cancer surgery endoscopic videos suggests that it discriminates between healthy, cancerous and benign tissues with 95% accuracy.
Decision Tree Algorithm-Based Model and Computer Simulation for Evaluating the Effectiveness of Physical Education in Universities

In this paper, the forest algorithm and the decision tree algorithm are mainly used to analyze students’ physical education information, course exam results, and student learning data and relevant feature attributes from the online teaching platform. We aim to generate decision trees using the decision tree algorithm for the purpose of generating classification rules, based on which we can find factors that are important to students’ physical education performance and form data basis for improving teaching quality to help teaching management and teachers improve teaching methods and adjust teaching strategies. We specifically achieved this objective by constructing a model for assessing the effectiveness of student teaching, the steps of which include data collection and preparation, data preprocessing (data cleaning, conversion, integration), model construction (algorithm training), and algorithm optimization, as well as realizing the simulation results of the model. At the same time, the importance of the relevant attributes of the model is analyzed, and some measures are proposed to improve the universities: the standard of physical education teaching and the corresponding strategies for improving teaching methods. The mainstream development environment is chosen to ensure the complete operation of the project system that integrates learning, operation, and evaluation. The sports virtual simulation experimental teaching system realized in this paper has good functionality, stability, and application benefits in operation and use.