Reviewerly/exHarmony huggingface dataset:
Wavelet extreme learning machine and deep learning for data classification

Recently, the Extreme Learning Machine (ELM) algorithm has been applied to various fields due to its rapidity and significant generalization performance. Traditionally, deep learning (DL) and wavelet neural networks (WNN) methods reach a high classification accuracy in machine learning applications. As a result, a new structure based on WNN, deep architecture and ELM is proposed in this paper. The proposed method is based on Extreme Learning Machine Auto-Encoder with DL structure and a composite wavelet activation function used in the hidden nodes. To evaluate the performance of our approach, we used standard benchmark data-sets, namely COIL-20, Pima Indian Diabetes (PID), MNIST and EMNIST. Experimental results show that our method offers satisfactory results and performance compared to other approaches.
Cloud Services vs. On-Premises Software: Competition Under Security Risk and Product Customization

Because of its on-demand feature and flexible pay-as-you-go mechanism, cloud service dramatically reduces the up-front information technology expenses that may deter many clients from implementing on-premises software. The associated security risks and low customization capability, however, create challenges for the adoption of cloud service. We study the competitive implications of security risks and customization capability on consumer purchase choices and vendors’ pricing and investment strategies. Although cloud services are perceived to be more vulnerable to cyberattack, our results demonstrate that in high-security-loss environments, using cloud service yields a lower average expected loss for consumers as compared with on-premises software. By endogenizing vendors’ investment decisions, our investigation highlights that the cloud vendor does not necessarily economically benefit from investing in addressing cloud security, especially in low-security-loss environments. We also find that the on-premises vendor’s security and customization investments act as strategic substitutes in low-security-loss environments and, under certain conditions, complement in high-security-loss environments. We further examine welfare-maximizing security investments and find that the socially optimal investment requires greater effort to improve cloud security in low-security-loss environments and to improve on-premises software security in high-security-loss environments.
Exploiting Symmetry Reduces the Cost of Training QAOA

A promising approach to the practical application of the Quantum Approximate Optimization Algorithm (QAOA) is finding QAOA parameters classically in simulation and sampling the solutions from QAOA with optimized parameters on a quantum computer. Doing so requires repeated evaluations of QAOA energy in simulation. We propose a novel approach for accelerating the evaluation of QAOA energy by leveraging the symmetry of the problem. We show a connection between classical symmetries of the objective function and the symmetries of the terms of the cost Hamiltonian with respect to the QAOA energy. We show how by considering only the terms that are not connected by symmetry, we can significantly reduce the cost of evaluating the QAOA energy. Our approach is general and applies to any known subgroup of symmetries and is not limited to graph problems. Our results are directly applicable to nonlocal QAOA generalization RQAOA. We outline how available fast graph automorphism solvers can be leveraged for computing the symmetries of the problem in practice. We implement the proposed approach on the MaxCut problem using a state-of-the-art tensor network simulator and a graph automorphism solver on a benchmark of 48 graphs with up to 10,000 nodes. Our approach provides an improvement for $p=1$ on $71.7\%$ of the graphs considered, with a median speedup of $4.06$, on a benchmark where $62.5\%$ of the graphs are known to be hard for automorphism solvers.
Deconstructing search tasks in interactive information retrieval: A systematic review of task dimensions and predictors

Increasingly, users are interacting with information retrieval (IR) systems with the goal of addressing complex, ill-defined search tasks. As a result, the interest in deconstructing and predicting search tasks has grown among IR researchers. Given the multidimensionality of search tasks, researchers usually focus on one or multiple dimensions and study the associations between implicit task dimensions and observable predictors. Synthesizing the knowledge of tasks and predictors learned from individual user studies can clarify the progresses we have made as a research community and provide an intellectual benchmark for further explorations of task-based search interactions. This article presents an overview of 76 task-based interactive IR (IIR) studies published between 2000 and 2020 and systematically coded the papers using features such as task dimensions (definitions and operationalizations), task-predictor associations, as well as task prediction models. Results include 1) data illustrating the growth and interdisciplinarity of IIR studies; 2) a comprehensive typology of task dimensions along with the associated measures; 3) a summary of the statistically significant correlations between task dimensions and predictors; 4) a list of the task dimensions being predicted, ground truth labels, and the feature sets employed. In addition, our bibliography of IIR works can be used by students and junior researchers who are new to the area. The results of our review can facilitate the growth of knowledge in IIR community and serve as the basis for future research on new modalities of user-task interactions.
Blockchain technology: uninvestigated issues emerging from an integrated view within accounting and auditing practices

Purpose The aim of this paper is to explore the effects of adopting and implementing blockchain technology (BT) in accounting and auditing practices in terms of benefits and threats, thus discovering new and upcoming risks and issues. Design/methodology/approach This paper adopts a critical perspective to investigate how the implementation of BT could affect accounting and auditing practices, providing a reflection on the role of accountants and auditors during such a technological revolution. Findings This paper highlights the importance of the unreplaceable professional conscience and experience of accountants and auditors compared to the impersonal and standardised operating system of artificial intelligence provided by BT. The development and diffusion of BT are leading professionals to acquaint themselves with new accounting and auditing systems, such as reinventing old practices and finding new ways of taking advantage of blockchain instead of being overwhelmed. Originality/value Different from the majority of previous literature contributions, this study looks beyond the potential and undeniable benefits that BT can offer to accounting and auditing environments by focussing especially on the threats and risks caused by its implementation.
A Transformer-Based Network for Dynamic Hand Gesture Recognition

Transformer-based neural networks represent a successful self-attention mechanism that achieves state-of-the-art results in language understanding and sequence modeling. However, their application to visual data and, in particular, to the dynamic hand gesture recognition task has not yet been deeply investigated. In this paper, we propose a transformer-based architecture for the dynamic hand gesture recognition task. We show that the employment of a single active depth sensor, specifically the usage of depth maps and the surface normals estimated from them, achieves state-of-the-art results, overcoming all the methods available in the literature on two automotive datasets, namely NVidia Dynamic Hand Gesture and Briareo. Moreover, we test the method with other data types available with common RGB-D devices, such as infrared and color data. We also assess the performance in terms of inference time and number of parameters, showing that the proposed framework is suitable for an online in-car infotainment system.
Hellinger Distance Weighted Ensemble for imbalanced data stream classification

The imbalanced data classification remains a vital problem. The key is to find such methods that classify both the minority and majority class correctly. The paper presents the classifier ensemble for classifying binary, non-stationary and imbalanced data streams where the Hellinger Distance is used to prune the ensemble. The paper includes an experimental evaluation of the method based on the conducted experiments. The first one checks the impact of the base classifier type on the quality of the classification. In the second experiment, the Hellinger Distance Weighted Ensemble (HDWE) method is compared to selected state-of-the-art methods using a statistical test with two base classifiers. The method was profoundly tested based on many imbalanced data streams and obtained results proved the HDWE method's usefulness.
A Novel Training Protocol for Performance Predictors of Evolutionary Neural Architecture Search Algorithms

Evolutionary neural architecture search (ENAS) can automatically design the architectures of deep neural networks (DNNs) using evolutionary computation algorithms. However, most ENAS algorithms require an intensive computational resource, which is not necessarily available to the users interested. Performance predictors are a type of regression models which can assist to accomplish the search, while without exerting much computational resource. Despite various performance predictors have been designed, they employ the same training protocol to build the regression models: 1) sampling a set of DNNs with performance as the training dataset; 2) training the model with the mean square error criterion; and 3) predicting the performance of DNNs newly generated during the ENAS. In this article, we point out that the three steps constituting the training protocol are not well thought-out through intuitive and illustrative examples. Furthermore, we propose a new training protocol to address these issues, consisting of designing a pairwise ranking indicator to construct the training target, proposing to use the logistic regression to fit the training samples, and developing a differential method to build the training instances. To verify the effectiveness of the proposed training protocol, four widely used regression models in the field of machine learning have been chosen to perform the comparisons on two benchmark datasets. The experimental results of all the comparisons demonstrate that the proposed training protocol can significantly improve the performance prediction accuracy against the traditional training protocols.
Human versus Computer Partner in the Paired Oral Discussion Test

Abstract A challenge of large-scale oral communication assessments is to feasibly assess a broad construct that includes interactional competence. One possible approach in addressing this challenge is to use a spoken dialog system (SDS), with the computer acting as a peer to elicit a ratable speech sample. With this aim, an SDS was built and four trained human raters assessed the discourse elicited from 40 test takers that completed a paired oral task with both a human and a computer partner. The test takers were evaluated based on the analytic operational oral communication rating scales which included interactional competence, fluency, pronunciation, and grammar/vocabulary. Repeated-measures ANOVA indicated that fluency, pronunciation, and grammar and vocabulary were scored similarly across the two conditions, while interactional competence was scored substantially higher in the human partner condition. A g-study indicated that the computer partner was more reliable in assessing interactional competence, and rater questionnaire and interview data suggested the computer provided a more standardized assessment. Conversely, raters generally favored the human partner, in part because of its perceived authenticity and naturalness.
Chaos-Based Synchronized Dynamic Keys and Their Application to Image Encryption with an Improved AES Algorithm

This study aimed to design chaos-based synchronized dynamic keys and develop an improved chaos-based advanced encryption standard (AES) algorithm with the proposed synchronized random keys. First, based on sliding mode control (SMC) technology, a rippling control scheme was introduced to guarantee the synchronization between master–slave discrete chaotic systems. Under the synchronization, the same dynamic random chaos signals could be simultaneously obtained at the transmitter and receiver in communication systems. Then, a novel modified AES cryptosystem with dynamic random keys based on chaos synchronization was presented. In a traditional AES cryptosystem, a static key is used, and it must be exchanged in advance and confirmed to be safely kept. However, in the proposed design, by introducing the synchronization technology of chaotic systems, the static key becomes dynamic and random, and it does not need to be kept or transmitted in open channels. Consequently, the disadvantage of key storage could be eliminated and the security of encryption could be improved. Finally, the developed chaos-based AES (CAES) algorithm has been applied to construct a novel image encryption algorithm. The statistical analysis, histogram, information entropy, and correlation indexes have been calculated and analyzed through simulation experiments in order to demonstrate the capability and improvement of this presented CAES cryptosystem.
Mixed reality as a novel tool for diagnostic and surgical navigation in orthopaedics

Abstract Purpose This study presents a novel surgical navigation tool developed in mixed reality environment for orthopaedic surgery. Joint and skeletal deformities affect all age groups and greatly reduce the range of motion of the joints. These deformities are notoriously difficult to diagnose and to correct through surgery. Method We have developed a surgical tool which integrates surgical instrument tracking and augmented reality through a head mounted display. This allows the surgeon to visualise bones with the illusion of possessing “X-ray” vision. The studies presented below aim to assess the accuracy of the surgical navigation tool in tracking a location at the tip of the surgical instrument in holographic space. Results Results show that the average accuracy provided by the navigation tool is around 8 mm, and qualitative assessment by the orthopaedic surgeons provided positive feedback in terms of the capabilities for diagnostic use. Conclusions More improvements are necessary for the navigation tool to be accurate enough for surgical applications, however, this new tool has the potential to improve diagnostic accuracy and allow for safer and more precise surgeries, as well as provide for better learning conditions for orthopaedic surgeons in training.
Trrack: A Library for Provenance-Tracking in Web-Based Visualizations

Provenance-tracking is widely acknowledged as an important feature of visualization systems. By tracking provenance data, visualization designers can provide a wide variety of functionality, ranging from action recovery (undo/redo), reproducibility, collaboration and sharing, to logging in support of quantitative and longitudinal evaluation. However, no widely used library that can provide that functionality is current available. As a consequence, visualization designers either develop ad hoc solutions that are rarely comprehensive, or do not track provenance at all. In this paper, we introduce a web-based software library - Trrack - that is designed for easy integration in existing or future visualization systems. Trrack supports a wide range of use cases, from simple action recovery, to capturing intent and reasoning, and can be used to share states with collaborators and store provenance on a server. Trrack also includes an optional provenance visualization component that supports annotation of states and aggregation of events.
Scene Flow from Point Clouds with or without Learning

Scene flow is the three-dimensional (3D) motion field of a scene. It provides information about the spatial arrangement and rate of change of objects in dynamic environments. Current learning-based approaches seek to estimate the scene flow directly from point clouds and have achieved state-of-the-art performance. However, supervised learning methods are inherently domain specific and require a large amount of labeled data. Annotation of scene flow on real-world point clouds is expensive and challenging, and the lack of such datasets has recently sparked interest in self-supervised learning methods. How to accurately and robustly learn scene flow representations without labeled real-world data is still an open problem. Here we present a simple and interpretable objective function to recover the scene flow from point clouds. We use the graph Laplacian of a point cloud to regularize the scene flow to be “asrigid-as-possible”. Our proposed objective function can be used with or without learning-as a self-supervisory signal to learn scene flow representations, or as a non-learning-based method in which the scene flow is optimized during runtime. Our approach outperforms related works in many datasets. We also show the immediate applications of our proposed method for two applications: motion segmentation and point cloud densification.
Cognitive data augmentation for adversarial defense via pixel masking

The vulnerability of deep networks towards adversarial perturbations has motivated the researchers to design detection and mitigation algorithms. Inspired by the dropout and dropconnect algorithms as well as augmentation techniques, this paper presents "PixelMask" based data augmentation as an efficient method of reducing the sensitivity of convolutional neural networks (CNNs) towards adversarial attacks. In the proposed approach, samples generated using PixelMask are used as augmented data, which helps in learning robust CNN models. Experiments performed with multiple databases and architectures show that the proposed PixelMask based data augmentation approach improves the classification performance on adversarially perturbed images. The proposed defense mechanism can be applied effectively for different adversarial attacks and can easily be combined with any deep neural network (DNN) architecture to increase the robustness. The effectiveness of the proposed defense is demonstrated in gray-box, white-box, and unseen train-test attack scenarios. For example, on the CIFAR-10 database under adaptive attack (i.e., projected gradient descent), the proposed PixelMask is able to improve the recognition performance of CNN by at-least 22.69%. Another advantage of the proposed algorithm over several existing defense algorithms is that the proposed defense either is able to retain or increase the classification accuracy of clean examples.
ScoolAR: An Educational Platform to Improve Students’ Learning Through Virtual Reality

Augmented Reality (AR) and Virtual Reality (VR) applications has been investigated in several domains. As such, their application in educational settings has witnessed to a growing interest by the research community. Teachers can be assisted by AR/VR, in a way that students can strength the learning outcomes, gained during the classroom lecture. However, despite their potential has been widely assessed, there still exists a bottleneck preventing a widespread adoption in the education domain: the lack of easy to use platforms enabling teachers and students to become producers of AR/VR experiences. This paper fills this gap, by proposing a novel platform named ScoolAR, developed for didactic purposes. ScoolAR allows to create AR/VR applications without any programming skills. Up to now, there is no evidence in the state-of-art of a didactic tool that allows to create AR/VR applications without programming skills. From such premises, ScoolAR has been developed to overcome these limitations and to enable an autonomous content creation system and thus boosting more engagement and awareness in the exploitation of AR and VR applications in everyday educational scenarios. Beside describing the architectural framework of the proposed platform, this paper presents the results of experiments conducted in a real didactic scenario. Considering two group of students, the first group was assisted with the ScoolAR framework, the second one conducted the study phase with frontal lecture. The test performed proved that the first group outperformed the second one on all metrics of evaluation. Thus, the combined effort between common didactic activities and technological innovation permits to achieve superior results in terms of both knowledge and competences, especially for those disciplines (e.g. Cultural Heritage and History of Architecture and more) where the transversal learning is fundamental.
Spatial context-aware network for salient object detection

Salient Object Detection (SOD) is a fundamental problem in the field of computer vision. This paper presents a novel Spatial Context-Aware Network (SCA-Net) for SOD in images. Compared with other recent deep learning based SOD algorithms, SCA-Net can more effectively aggregate multi-level deep features. A Long-Path Context Module (LPCM) is employed to grant better discrimination ability to feature maps that incorporate coarse global information. Consequently, a more accurate initial saliency map can be obtained to facilitate subsequent predictions. SCA-Net also adopts a Short-Path Context Module (SPCM) to progressively enforce the interaction between local contextual cues and global features. Extensive experiments on five large-scale benchmarks demonstrate that SCA-Net achieves favorable performance against very recent state-of-the-art algorithms.
Reliable Traffic Monitoring Mechanisms Based on Blockchain in Vehicular Networks

The real-time traffic monitoring is a fundamental mission in a smart city to understand traffic conditions and avoid dangerous incidents. In this paper, we propose a reliable and efficient traffic monitoring system that integrates blockchain and the Internet of vehicles technologies effectively. It can crowdsource its tasks of traffic information collection to vehicles that run on the road instead of installing cameras in every corner. First, we design a lightweight blockchain-based information trading framework to model the interactions between traffic administration and vehicles. It guarantees reliability, efficiency, and security during executing trading. Second, we define the utility functions for the entities in this system and come up with a budgeted auction mechanism that motivates vehicles to undertake the collection tasks actively. In our algorithm, it not only ensures that the total payment to the selected vehicles does not exceed a given budget, but also maintains the truthfulness of auction process that avoids some vehicles to offer unreal bids for getting greater utilities. Finally, we conduct a group of numerical simulations to evaluate the reliability of our trading framework and performance of our algorithms, whose results demonstrate their correctness and efficiency perfectly.