Reviewerly/exHarmony huggingface dataset:
A Multilevel Fuzzy Transform Method for High Resolution Image Compression

The Multilevel Fuzzy Transform technique (MF-tr) is a hierarchical image compression method based on Fuzzy Transform, which is successfully used to compress images and manage the information loss of the reconstructed image. Unlike other lossy image compression methods, it ensures that the quality of the reconstructed image is not lower than a prefixed threshold. However, this method is not suitable for compressing massive images due to the high processing times and memory usage. In this paper, we propose a variation of MF-tr for the compression of massive images. The image is divided into tiles, each of which is individually compressed using MF-tr; thereafter, the image is reconstructed by merging the decompressed tiles. Comparative tests performed on remote sensing images show that the proposed method provides better performance than MF-tr in terms of compression rate and CPU time. Moreover, comparison tests show that our method reconstructs the image with CPU times that are at least two times less than those obtained using the MF-tr algorithm.
Anchor-Free Object Detection with Scale-Aware Networks for Autonomous Driving

Current anchor-free object detectors do not rely on anchors and obtain comparable accuracy with anchor-based detectors. However, anchor-free object detectors that adopt a single-level feature map and lack a feature pyramid network (FPN) prior information about an object’s scale; thus, they insufficiently adapt to large object scale variation, especially for autonomous driving in complex road scenes. To address this problem, we propose a divide-and-conquer solution and attempt to introduce some prior information about object scale variation into the model when maintaining a streamlined network structure. Specifically, for small-scale objects, we add some dense layer jump connections between the shallow high-resolution feature layers and the deep high-semantic feature layers. For large-scale objects, dilated convolution is used as an ingredient to cover the features of large-scale objects. Based on this, a scale adaptation module is proposed. In this module, different dilated convolution expansion rates are utilized to change the network’s receptive field size, which can adapt to changes from small-scale to large-scale. The experimental results show that the proposed model has better detection performance with different object scales than existing detectors.
An efficient credit card fraud detection approach using cost‐sensitive weak learner with imbalanced dataset

Abstract The usage of credit cards is increasing in the digitization era to purchase goods online or offline, and the number of fraudulent credit card transactions has also increased. Fraudulent transaction detection is a big issue in the credit card domain because the data set is highly imbalanced. The performance of traditional classification techniques has decreased due to this issue. Therefore, a dynamic fraud detection system is required to overcome this issue. This article proposes an efficient cost‐sensitive weak learner approach with a bagging and random forest classifier (CSWLB) to minimize misclassification problems and overcome the class imbalance issue. The proposed CSWLB approach is included in an adaptive algorithmic method (cost‐sensitive) with two weak learner ensembles (bagging‐random forest), which assigns high weight to the fraudulent transactions using the cost‐sensitive learning classifier and generates weight bags using a bagging ensemble classifier. Therefore, the random forest classifier has been implemented on the weight bags to improve classification accuracy. The proposed approach's effectiveness and efficiency have been computed on the Brazilian credit card data set. Its results have been compared with sampling, cost‐sensitive, and machine learning techniques. The proposed CSWLB approach obtained 765 total costs and 97.361% accuracy. The experimental results show that the proposed approach has outperformed compared to other techniques.
A survey on visual quality assessment methods for light fields

Unlike traditional cameras, Light Field (LF) cameras are able to capture both the intensity and direction of light rays from the scene. This rich information demands a certain amount of memory and bandwidth for storage and transmission, and to alleviate this requirement, LF content is processed and compressed. These operations often add degradations to the LF content that can affect its visual quality, requiring the use of methods that can measure visual quality as perceived by the end consumer. For this reason, the area of visual quality of immersive media has been gaining a lot of attention. This paper provides a comprehensive survey of state-of-the-art visual quality assessment methods for LF contents. First, we discuss the LF quality datasets that have been introduced, which contain subjective quality scores. Second, we discuss subjective quality assessment methods followed by objective quality assessment methods. Then we present a statistical comparison of a set of objective visual quality assessment methods. Finally, we summarize this survey and present future research demands for LF contents.
Electrolaryngeal speech enhancement based on a two stage framework with bottleneck feature refinement and voice conversion

An electrolarynx (EL) is a medical device that generates speech for people who lost their biological larynx. However, EL speech signals are unnatural and unintelligible due to the monotonous pitch and the mechanical excitation of the EL device. This paper proposes an end-to-end voice conversion method to enhance EL speech. We adopt a speaker-independent automatic speech recognition model to extract bottleneck features as the intermediate phonetic features for enhancement. Our system includes two stages: the bottleneck feature vectors of the EL speech are mapped by a parallel non-autoregressive model to the corresponding feature vectors of the normal speech in stage one. Then another voice conversion model maps normal speech’s bottleneck feature vectors directly to normal speech’s Mel-spectrogram in stage two, followed by a MelGAN-based vocoder to convert the Mel-spectrogram into waveform. In addition, we incorporate data augmentation and transfer learning to improve conversion performance. Experimental results show that the proposed method outperforms our baseline methods and performs well in terms of naturalness and intelligibility. The audio samples are available online.2
Comparison between handcraft feature extraction and methods based on Recurrent Neural Network models for gesture recognition by instrumented gloves: A case for Brazilian Sign Language Alphabet

Sign language is the communication mode used by hearing-impaired people. The development of tools for recognition of sign language is important to improve the interaction of these people in the society. This work proposes a comparison between models of gesture recognition based on instrumented glove with and without handcrafted feature extraction approach, which is a classical method for pattern recognition. For manual feature extraction approach, Forward Sequential Feature Selection (SFS) method was employed to select the best features and different classifiers were analyzed. For the non-manual feature extraction approach, Recurrent Neural Networks as Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU) models and, additionally, a proposed Ensemble GRU model were employed. The proposed Ensemble GRU is composed of three layers, and each layer is responsible to acquire knowledge of gestures’ periods: initial, gesture, and final periods. The classifiers LSTM and GRU can receive the data segments of the gestures directly, without manual feature extraction. A dataset composed by 26 gestures from alphabet of Brazilian Sign Language was explored. In the handcrafted feature extraction approach, the best result was of 88.5% accuracy, obtained with K-Nearest Neighbors model. On the opposite model, GRU and Ensemble GRU models reached 94.6% and 96.8% accuracy. It can be observed that non-manual feature extraction approach can be implemented for gesture recognition based on instrumented glove and the results can be better than conventional methods. Furthermore, the proposed Ensemble GRU model presented better result than the standard models of GRU and LSTM.
Influence of age and industry experience on learning experiences and outcomes in virtual reality mines rescue training

This study examined the effects of age and industry expertise on trainees’ state of mind before, learning experiences during, and outcomes following virtual reality (VR) mines rescue training. The trainees were 284 mine rescue brigadesmen attending group VR training sessions run by Coal Services NSW. They were aged between 24 and 64 years and had up to 40 years of mines rescue experience. Questionnaire data and learning outcome measures showed that these miners were able to effectively engage with, and learn from, this VR training regardless of their age or mining experience. While the older trainees initially reported higher levels of stress and had less gaming experience, their experiences during VR training were very similar (although reports that the VR technology sometimes did not meet the task requirements did increase with age). Crucially, the perceived learning outcomes of this VR training were unaffected by age or field experience.
Beyond Sentiment: The Value and Measurement of Consumer Certainty in Language

Sentiment analysis has fundamentally changed marketers’ ability to assess consumer opinion. Indeed, the measurement of attitudes via natural language has influenced how marketing is practiced on a day-to-day basis. Yet recent findings suggest that sentiment analysis's current emphasis on measuring valence (i.e., positivity or negativity) can produce incomplete, inaccurate, and even misleading insights. Conceptually, the current work challenges sentiment analysis to move beyond valence. The authors identify the certainty or confidence of consumers’ sentiment as a particularly potent facet to assess. Empirically, they develop a new computational measure of certainty in language—the Certainty Lexicon—and validate its use with sentiment analysis. To construct and validate this measure, the authors use text from 11.6 million people who generated billions of words, millions of online reviews, and hundreds of thousands of entries in an online prediction market. Across social media data sets, in-lab experiments, and online reviews, the authors find that the Certainty Lexicon is more comprehensive, generalizable, and accurate in its measurement compared with other tools. The authors also demonstrate the value of measuring sentiment certainty for marketers: certainty predicted the real-world success of commercials where traditional sentiment analysis did not. The Certainty Lexicon is available at www.CertaintyLexicon.com .
The Effects of Attributes of Non-Immersive Virtual Reality on Customers’ Experience of Video Tours under Social Distancing for COVID-19

This study explores the role of virtual reality (VR) tours in influencing customers' experiences and attitudes as well as their behavioral intentions toward VR tours. A research model that combines the technology acceptance model, flow theory, and social distancing is proposed to demonstrate the interrelationships among attributes of VR tours, online customer experience, customer attitude, and behavioral intention. Social distancing plays a situational role in the model. We collected 545 usable online questionnaires and used SPSS and SmartPLS to analyze the measurement model and proposed hypotheses. Attributes of the VR tour had a positive influence on the extrinsic values and intrinsic values of the customer experience. Furthermore, online customer experience was positively related to customer attitude and intention toward VR tours. In addition, social distancing influenced customer attitudes but not behavioral intention. Moreover, the results of a multigroup analysis based on country of residence suggested relevant differences between the America and Asia groups. This research extends understanding of the online customer experience of VR tours and provides empirical evidence that VR attributes enhance the extrinsic and intrinsic values of online customer experiences. It provides an integrated view of the technology acceptance model, flow theory, and social distancing and suggests that social distancing has an impact on customers' attitude toward VR tours but no effects on their intention. The implications of these findings can help scholars and practitioners better understand consumers' motivations and behavioral intentions to use VR tours, which will help the tourism industry boost revenue and create additional sales channels.
Identifying Users by Their Hand Tracking Data in Augmented and Virtual Reality

Nowadays, Augmented and Virtual Reality devices are widely available and are often shared among users due to their high cost. Thus, distinguishing users to offer personalized experiences is essential. However, currently used explicit user authentication (e.g., entering a password) is tedious and vulnerable to attack. Therefore, this work investigates the feasibility of implicitly identifying users by their hand tracking data. In particular, we identify users by their uni- and bimanual finger behavior gathered from their interaction with eight different universal interface elements, such as buttons and sliders. In two sessions, we recorded the tracking data of 16 participants while they interacted with various interface elements in Augmented and Virtual Reality. We found that user identification is possible with up to 95% accuracy across sessions using an explainable machine learning approach. We conclude our work by discussing differences between interface elements, and feature importance to provide implications for behavioral biometric systems.A video abstract of this work is available online at: https://identifying-users-by-hand-tracking-data.hcigroup.de
AutoCert: Automated TOCTOU-secure digital certification for IoT with combined authentication and assurance

The Internet of Things (IoT) network is comprised of heterogeneous devices which are part of critical infrastructures throughout the world. To enable end-to-end security, the Public Key Infrastructure (PKI) is undergoing advancements to incorporate IoT devices globally which primarily provides device authentication. In addition to this, integrity of the software-state is vital, where Remote Attestation (RA) and Integrity Certificates play an important role. Though, Integrity Certificate verifies the software-state integrity of the device at the time of execution of the remote attestation process, it does not provide mechanisms to validate that the current software-state corresponds to the attested state. This issue is referred to as the Time-Of-Check to Time-Of-Use (TOCTOU) problem and remains unsolved in the context of Integrity Certificates. In this paper, we propose AutoCert, the first TOCTOU-secure mechanism to combine software-state integrity with PKI for IoT which resolves the TOCTOU problem in RA and Integrity Certificates. To this end, we utilize the IETF Remote Attestation Procedures architecture and standard X509 IoT profile certificates to ensure both device authentication and software assurance for IoT. We implement and evaluate the performance of the AutoCert proof-of-concept on a real IoT device, the OPTIGA TPM Evaluation Kit, to show its practicality and usability. AutoCert can validate the attested state of an IoT device in approximately 4746 milliseconds, with a minimal network overhead of 350 bytes.
Which type of ecosystem for distributed ledger technology?

Distributed Ledger Technology (DLT), or the blockchain, enables new ways of inter-firm interaction in a networked environment. The economic community consisted of organizations, that although independent, are connected and interact through this seminal technology form the DLT ecosystem. DLT may be applicable in many ecosystem types. However, little research has been done on how DLT works in the ecosystem types. We acknowledge specific ecosystem characteristics in a DLT network of actors and argue that there are similarities between various ecosystem types. Our study extends DLT and ecosystem literature by answering the question "How do ecosystem types make fit to DLT?". We build upon the software, technological, digital, innovation, product and service ecosystem literature and examine how do they match to DLT ecosystem. We find that these ecosystems can be part or subset of the DLT ecosystem, but it is the business ecosystem type that fits better to DLT ecosystem conceptualization. We further analyse the characteristics, definitions and enablers of business ecosystem and identify the analogies between these two structures. In this way we provide academics and practitioners alike insight into what characteristics shape DLT business ecosystems and what features it adopts from other ecosystem types.
twoaxistracking – a python package for simulating self-shading of two-axis tracking solar collectors

Self-shading in fields of two-axis tracking collectors typically ranges from 1% to 6% of the annual incident irradiation. It is thus essential to account for shading in order to obtain accurate yield estimates and financing for such solar projects. The present study presents the free and open-source Python package twoaxistracking for simulating self-shading in fields of two-axis tracking collectors. The package is freely available at: https://github.com/pvlib/twoaxistracking. The main steps of the method and mathematical formulation are described. Additionally, a demonstration of how to use the package is presented. The shading calculation method excels over previous methods found in the literature in that it can:•Handle arbitrary aperture geometries and distinguish between the total and active areas.•Account for sloped ground and collectors with different heights within the same field.•Reduce computation time by skipping calculations at high solar elevation angles.
A chaotic image encryption scheme based on multi-directional confusion and diffusion operations

A careful perusal of the literature regarding the image encryption algorithms indicates that many research articles based on pixel swapping operations in the horizontal and vertical orientations, have been published. This study ventures to develop a yet another sophisticated algorithm by extending the swapping operations along the diagonal–antidiagonal directions. Besides, unlike the previous studies, the confusion and diffusion operations over the pixels of the given input plain image have been crafted in a parallel fashion. This work has employed a 4D chaotic system to spawn the streams of random numbers. The initial values and the system parameters have been made dependent over the given plain image in order to inject the plaintext sensitivity in the proposed cipher. As a modus operandi of the proposed encryption algorithm, a non-intersecting pair of row and column is selected randomly with the same arbitrary length and swapped/XORed according to the value dictated by the random numbers. The same operations have been carried out for the non-intersecting pair of diagonal and anti-diagonal. The reason for selecting the non-intersecting pair stems from the fact that intersecting pairs cause to lose the precious pixels data which questions the potential invertibility feature of the proposed algorithm. To demonstrate the improved efficiency, ability to withstand the varied attacks by the cryptanalysis savvy, and prospects for some real-world application, the algorithm has been subjected to a comprehensive set of evaluation metrics.
Authentication for drone delivery through a novel way of using face biometrics

Drone delivery, which makes use of unmanned aerial vehicles (UAVs) to deliver or pick up packages, is an emerging service. To ensure that a package is picked up by a legitimate drone and delivered to the correct user, mutual authentication between drones and users is critical. As delivery drones are expensive and may carry important packages, drones should keep a distance from users until the authentication succeeds. Thus, authentication approaches that require human-drone physical contact cannot be applied. Face recognition does not need human-drone contact. However, it has major limitations: (1) it needs users to enroll their face information, (2) it is vulnerable to attacks, such as 3D-printed masks and adversarial examples, and (3) it only supports a drone to authenticate a user (rather than mutual authentication). We propose a novel way of using face biometrics, without these limitations, and apply it to building an authentication system for drone delivery, named Smile2Auth. The evaluation shows that Smile2Auth is highly accurate, secure and usable.
Tutti

Mobile edge computing (MEC), as a key ingredient of the 5G ecosystem, is envisioned to support demanding applications with stringent latency requirements. The basic idea is to deploy servers close to end-users, e.g., on the network edge-side instead of the remote cloud. While conceptually reasonable, we find that the operational 5G is not coordinated with MEC and thus suffers from intolerable long response latency. In this work, we propose Tutti, which couples 5G RAN and MEC at the user space to assure the performance of latency-critical video analytics. To enable such capacity, Tutti precisely customizes the application service demand by fusing instantaneous wireless dynamics from the 5G RAN and application-layer content changes from edge servers. Tutti then enforces a deadline-sensitive resource provision for meeting the application service demand by real-time interaction between 5G RAN and edge servers in a lightweight and standard-compatible way. We prototype and evaluate Tutti on a software-defined platform, which shows that Tutti reduces the response latency by an average of 61.69% compared with the existing 5G MEC system, as well as negligible interaction costs.
MoiréPose

Camera tracking has become a key technology for various application scenarios, especially for AR-based camera-to-screen interaction. Demand for subtle motion detection in camera tracking makes it essential to explore the six degrees of freedom (6-DoF) pose detection with ultra-high precision. In this paper, we propose a novel sensing method MoiréPose to achieve ultra-high precision on the camera's 6-DoF pose estimation. The purpose of MoiréPose is to derive the camera's 3-DoF position and 3-DoF posture relative to the screen according to the captured moiré pattern, which is produced by the superposition of the camera's Color Filter Array (CFA) and the screen raster projected onto the CFA layer. Based on moiré pattern's high sensitivity to 6-DoF pose movement and robustness to the environmental interference in the frequency domain, we propose a spectrogram-based method to realize the camera's 6-DoF detection with ultra-high precision. Moreover, we propose a thumbnail-based method to effectively extend the working range of MoiréPose, so as to realize pervasive camera-to-screen interaction. We have implemented a prototype system and evaluate the performance in real-world environments. Extensive experiment results show that MoiréPose achieves an average position error of 7.5mm and an overall posture error of 1.66°.
On Kripke completeness of modal predicate logics around quantified K5

The paper studies completeness and incompleteness of modal predicate logics in Kripke semantics, especially for logics of the form QΛ, minimal predicate extensions of modal propositional logics. We show that QΛ is incomplete for a continual family of logics Λ above K+□(□p→p), in particular for well-known K5 and K45. On the other hand, in some cases we find completions of QΛ; they are obtained by adding a single extra axiom. Completeness proofs use canonical models, with some modifications, and the case of QK5 is the most interesting from the technical side. We also introduce the “boxing” operation for modal predicate logics and prove transfer results for Kripke and Kripke sheaf completeness with respect to this operation.