Reviewerly/exHarmony huggingface dataset:
Self-Supervised Feature Augmentation for Large Image Object Detection

Input scale plays an important role in modern detection frameworks, and an optimal training scale for images exists empirically. However, the optimal one usually cannot be reached in facing extremely large images under the memory constraint. In this study, we explore the scale effect inside the object detection pipeline and find that feature upsampling with the introduction of high-resolution information benefits the detection. Compared with direct input upscaling, feature upsampling trades a small performance loss for a large amount of memory savings. From these observations, we propose a self-supervised feature augmentation network, which takes downsampled images as inputs and aims to generate comparable features with the ones when feeding upscaled images to networks. We present a guided feature upsampling module, which takes downsampled images as inputs, to learn upscaled feature representations with the supervision of real large features acquired from upscaled images. In a self-supervised learning manner, we can introduce detailed information of images to the network. For an efficient feature upsampling, we design a residualized sub-pixel convolution block based on a sub-pixel convolution layer, which involves considerable information in upsampling process. Experiments on Mapillary Vistas Dataset (MVD), Cityscapes, and COCO are conducted to demonstrate the effectiveness of our method. On the MVD and Cityscapes detection benchmarks, in which the images are extremely large, our method surpasses current approaches. On COCO, the proposed method obtains comparable results to existing methods but with higher efficiency.
A trusted medical image super-resolution method based on feedback adaptive weighted dense network

High-resolution (HR) medical images are preferred in clinical diagnoses and subsequent analysis. However, the acquisition of HR medical images is easily affected by hardware devices. As an effective and trusted alternative method, the super-resolution (SR) technology is introduced to improve the image resolution. Compared with traditional SR methods, the deep learning-based SR methods can obtain more clear and trusted HR images. In this paper, we propose a trusted deep convolutional neural network-based SR method named feedback adaptive weighted dense network (FAWDN) for HR medical image reconstruction. Specifically, the proposed FAWDN can transmit the information of the output image to the low-level features by a feedback connection. To explore advanced feature representation and reduce the feature redundancy in dense blocks, an adaptive weighted dense block (AWDB) is introduced to adaptively select the informative features. Experimental results demonstrate that our FAWDN outperforms the state-of-the-art image SR methods and can obtain more clear and trusted medical images than comparative methods.
Privacy-Preserving and Distributed Algorithms for Modular Exponentiation in IoT With Edge Computing Assistance

With the development of Internet of Things (IoT) and 5G, edge computing, as a new computing paradigm, has been widely popularized in academia and industry. Due to the distributed architecture and being close to the user, edge computing can faster respond to the IoT device's request and provide a better quality of service for IoT applications. An important application of edge computing is to outsource the complicated computation task to the nearby edge nodes. Modular exponentiation is widely considered as one of the most common and expensive operations in cryptographic protocols. As far as we know, all secure outsourcing algorithms of modular exponentiation are based on the centralized cloud server, but not based on multiple edge nodes. In this article, we propose the first secure and distributed outsourcing algorithm for modular exponentiation (fixed base and variable exponent) under the multiple noncolluding edge node model. In our algorithm, the exponent is divided into a certain number of parts. In addition, we propose another secure and distributed outsourcing algorithm of modular exponentiation (variable base and variable exponent). The user can protect the privacy in the process of outsourcing and detect the invalid results from edge nodes with high probability. Finally, we provide the experimental evaluation to support that our proposed algorithms are efficient on both the user side and the edge node side.
CoMHisP: A Novel Feature Extractor for Histopathological Image Classification Based on Fuzzy SVM With Within-Class Relative Density

Machine learning (ML) has emerged as a powerful tool for pattern recognition. Traditional ML algorithms have limited ability to reveal the most sophisticated features of cancer histopathological images, but their robustness and fault tolerance can be enhanced by using fuzzy modeling to capture the uncertainty in image data. Therefore, this article proposes a novel CoMHisP framework based on a fuzzy support vector machine with within-class density information (FSVM-WD). It utilizes a novel feature extraction technique by optimizing the block size to extract image micropatterns and computing center of mass (CoM) for each pixel to extract feature vectors. The performance of the proposed framework is evaluated using a CMTHis dataset comprising histopathological images of canine mammary tumor (CMT), a prevalent neoplastic disease in female dogs, and an established model for human breast cancer. Data analysis reveals that stain normalization and magnification influence the performance of the CoMHisP framework, with the best results achieved at lower magnifications after stain normalization. The proposed framework achieves a classification accuracy of 97.25% ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\pm$</tex-math></inline-formula> 1.80%) using a FSVM-WD classifier, outperforming both traditional ML and deep FE-VGGNET16-based feature descriptors. To the best of our knowledge, this is the first time a CoM-based feature descriptor has been proposed for histopathological image analysis of CMTs and its performance was evaluated using a fuzzy SVM-based classifier. The proposed method performs well with datasets of limited size and low-magnification images and, therefore, has the potential to provide rapid and accurate diagnosis in low-cost clinical settings.
Five common pitfalls in eye-tracking research

Eye tracking has become an increasingly popular research methodology among language researchers to examine online cognitive processing of second-language (L2) speakers and bilinguals. As the scope of eye-tracking research expands, there is a need to ensure that the methodology is used properly, so as to safeguard the validity of research findings and the empirical basis upon which theories are built. We present five pitfalls in eye-tracking research that may threaten the internal or external validity of the study. We offer concrete recommendations to avoid each pitfall and demonstrate how these recommendations can be implemented so researchers may take full advantage of the opportunities of eye-tracking research. The five pitfalls occur at all stages of the research cycle. We emphasize (1) adopting a confirmatory (theory-driven, hypothesis-testing) approach to research, (2) selecting and handling eye-movement measures in a principled manner, (3) aligning the research design with the eye tracker properties (spatial accuracy, precision), (4) inspecting raw, sample-level eye-tracking data and their preprocessing by the eye-tracking software. We conclude by inviting eye-tracking researchers (5) to embrace open science practices to enhance the replication and reproducibility of their work.
Image Encryption Based on AES and RSA Algorithms

Cryptography is the science that concern of protecting data by transforming its comprehensible form into incomprehensible one. This study aims to compare between Advanced Encryption Standard (AES) and Rivest-Shamir-Adleman (RSA) encryption algorithms in image encryption using MATLAB. The comparison is done in the terms of testing image encryption quality for each algorithm. In addition, analyzing the histogram and correlation results. The results showed that AES algorithm has a better image encryption quality with a more convergent columns in the histogram. Moreover, AES algorithm correlation coefficient tends to be closer to the zero, thus a stronger correlation. Generally, the outcomes of this study showed that the AES algorithm is better than RSA algorithm in image encryption <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> .
Pattern recognition techniques for provenance classification of archaeological ceramics using ultrasounds

This paper presents a novel application of pattern recognition to the provenance classification of archaeological ceramics. This is a challenging problem for archaeologists, which involves assigning a making location to a fragment of archaeological pottery that was found along with other fragments of pieces made in different distant locations from the find. The pieces look very similar to each other and, often, other contextual information about the use of the pieces cannot be used due to the small size of the fragments. Current standard methods to solve this problem are limited since they are time consuming, require costly equipment, and can lead to the destruction of a part of the pieces. The proposed method overcome those limitations using non-destructive ultrasonic testing and incorporates versatile data analysis through advanced pattern recognition techniques. Those techniques include the following: feature ranking, sample augmentation, semi-supervision based on active learning; and optimal fusion. This latter is based in the concept of alpha integration, which allows optimal fitting of the fusion model parameters. Different provenance classification problems are showcased: provenance classification of terra sigillata ceramic pieces from Aretina, Northern Italy and Sud-Gaul origins; and provenance classification of Iberian ceramic pieces from archaeological sites of Paterna, and Les Jovaes in Valencia, Spain. We demonstrate that the proposed fusion-based method achieves the best results, in terms of balanced classification accuracy and F1 score, compared with competitive methods like linear discriminant analysis, random forest, and support vector machine. Experiments for simulating small sample sizes and uncertainty in labeling of the pieces are included. In addition, the paper provides a design of a practical specialized device that could be used in different applications of archaeological ceramic classification.
MV-GNN: Multi-View Graph Neural Network for Compression Artifacts Reduction

Inevitable compression artifacts in multi-view video (MVV) can clearly degrade the quality of experience in many interaction-oriented 3D visual applications. Under the framework of asymmetric coding, low-quality images can be enhanced with high-quality images from the neighboring viewpoints considering the similarity among different views. However, compression artifacts and warping error cause different cross-view quality gaps for various sequences, and thus the contribution of cross-view priors can hardly be located and considered in previous works. In this paper, we propose a multi-view graph neural network (MV-GNN) to reduce compression artifacts in multi-view compressed images. We dedicate to design a fusion mechanism which can exploit contributions from neighboring viewpoints and meanwhile suppress the misleading information. In our method, a GNN-based fusion mechanism is designed to fuse the cross-view information under the aggregation and update mechanism of GNN. Experiments show that 1.672 dB and 0.0242 average gains on PSNR and SSIM metrics can be obtained, respectively. For the subjective evaluations, blocking effect in the compressed images are clearly suppressed and the damaged object boundary are better recovered. The experimental results demonstrate that our MV-GNN outperforms the state-of-the-art methods.
Intelligent Fault-Prediction Assisted Self-Healing for Embryonic Hardware

This paper proposes novel methods for making embryonic bio-inspired hardware efficient against faults through self-healing, fault prediction, and fault-prediction assisted self-healing. The proposed self-healing recovers a faulty embryonic cell through innovative usage of healthy cells. Through experimentations, it is observed that self-healing is effective, but it takes a considerable amount of time for the hardware to recover from a fault that occurs suddenly without forewarning. To get over this problem of delay, novel deep learning-based formulations are proposed for fault predictions. The proposed self-healing technique is then deployed along with the proposed fault prediction methods to gauge the accuracy and delay of embryonic hardware. The proposed fault prediction and self-healing methods have been implemented in VHDL over FPGA. The proposed fault predictions achieve high accuracy with low training time. The accuracy is up to 99.36% with the training time of 2.16 min. The area overhead of the proposed self-healing method is 34%, and the fault recovery percentage is 75%. To the best of our knowledge, this is the first such work in embryonic hardware, and it is expected to open a new frontier in fault-prediction assisted self-healing for embryonic systems.
WalkingVibe: Reducing Virtual Reality Sickness and Improving Realism while Walking in VR using Unobtrusive Head-mounted Vibrotactile Feedback

Virtual Reality (VR) sickness is common with symptoms such as headaches, nausea, and disorientation, and is a major barrier to using VR. We propose WalkingVibe, which applies unobtrusive vibrotactile feedback for VR walking experiences, and also reduces VR sickness and discomfort while improving realism. Feedback is delivered through two small vibration motors behind the ears at a frequency that strikes a balance in inducing vestibular response while minimizing annoyance. We conducted a 240-person study to explore how visual, audio, and various tactile feedback designs affect the locomotion experience of users walking passively in VR while seated statically in reality. Results showed timing and location for tactile feedback have significant effects on VR sickness and realism. With WalkingVibe, 2-sided step-synchronized design significantly reduces VR sickness and discomfort while significantly improving realism. Furthermore, its unobtrusiveness and ease of integration make WalkingVibe a practical approach for improving VR experiences with new and existing VR headsets.
Choice of Voices

The advancement of text-to-speech (TTS) voices and a rise of commercial TTS platforms allow people to easily experience TTS voices across a variety of technologies, applications, and form factors. As such, we evaluated TTS voices for long-form content: not individual words or sentences, but voices that are pleasant to listen to for several minutes at a time. We introduce a method using a crowdsourcing platform and an online survey to evaluate voices based on listening experience, perception of clarity and quality, and comprehension. We evaluated 18 TTS voices, three human voices, and a text-only control condition. We found that TTS voices are close to rivaling human voices, yet no single voice outperforms the others across all evaluation dimensions. We conclude with considerations for selecting text-to-speech voices for long-form content.
Improved block ordering and frequency selection for reversible data hiding in JPEG images

JPEG images are widely used in daily life. Reversible data hiding (RDH) in JPEG images is useful for many applications. How to avoid image distortion and preserve file size is very important for RDH in JPEG images. In this paper, a novel RDH scheme is proposed for JPEG images based on improved block ordering and frequency selection. The distortion that may be caused by the data embedding in each block is estimated more accurately based on the DCT coefficient distribution, and the blocks that result in less distortion will be firstly used for data embedding. Moreover, the non-zero AC coefficients with the frequencies having higher embedding efficiency will be used preferentially to further reduce image distortion. Consequently, less side information on frequency selection is needed for data extraction and image recovery with our scheme. Extensive experiments are conducted on several typical images and 500 images randomly selected from BOSSbase image database, and the numerical results demonstrate that better performance in terms of visual quality and file size preservation can be achieved with the proposed scheme compared to the state-of-the-art RDH schemes for JPEG images.
SpongeBugs: Automatically generating fix suggestions in response to static code analysis warnings

Static code analysis tools such as FindBugs and SonarQube are widely used on open-source and industrial projects to detect a variety of issues that may negatively affect the quality of software. Despite these tools' popularity and high level of automation, several empirical studies report that developers normally fix only a small fraction (typically, less than 10% (Marcilio et al., 2019) of the reported issues—so-called "warnings". If these analysis tools could also automatically provide suggestions on how to fix the issues that trigger some of the warnings, their feedback would become more actionable and more directly useful to developers. In this work, we investigate whether it is feasible to automatically generate fix suggestions for common warnings issued by static code analysis tools, and to what extent developers are willing to accept such suggestions into the codebases they are maintaining. To this end, we implemented SpongeBugs, a Java program transformation technique that fixes 11 distinct rules checked by two well-known static code analysis tools (SonarQube and SpotBugs). Fix suggestions are generated automatically based on templates, which are instantiated in a way that removes the source of the warnings; templates for some rules are even capable of producing multi-line patches. Based on the suggestions provided by SpongeBugs, we submitted 38 pull requests, including 946 fixes generated automatically by our technique for various open-source Java projects, including Eclipse UI – a core component of the Eclipse IDE – and both SonarQube and SpotBugs. Project maintainers accepted 87% of our fix suggestions (97% of them without any modifications). We further evaluated the applicability of our technique on software written by students and on a curated collection of bugs. All results indicate that our approach to generating fix suggestions is feasible, flexible, and can help increase the applicability of static code analysis tools.
Repulsive photons in a quantum nonlinear medium

The ability to control strongly interacting light quanta (photons) is of central importance in quantum science and engineering1–5. Recently it was shown that such strong interactions can be engineered in specially prepared quantum optical systems6–10. Here, we demonstrate a method for coherent control of strongly interacting photons, extending quantum nonlinear optics into the domain of repulsive photons. This is achieved by coherently coupling photons to several atomic states, including strongly interacting Rydberg levels in a cold Rubidium gas. Using this approach we demonstrate both repulsive and attractive interactions between individual photons and characterize them by the measured two- and three-photon correlation functions. For the repulsive case, we demonstrate signatures of interference and self ordering from three-photon measurements. These observations open a route to study strongly interacting dissipative systems and quantum matter composed of light such as a crystal of individual photons11,12.
The Role of Conversational Grounding in Supporting Symbiosis Between People and Digital Assistants

In "smart speaker'' digital assistant systems such as Google Home, there is no visual user interface, so people must learn about the system's capabilities and limitations by experimenting with different questions and commands. However, many new users give up quickly and limit their use to a few simple tasks. This is a problem for both the user and the system. Users who stop trying out new things cannot learn about new features and functionality, and the system receives less data upon which to base future improvements. Symbiosis---a mutually beneficial relationship---between AI systems like digital assistants and people is an important aspect of developing systems that are partners to humans and not just tools. In order to better understand requirements for symbiosis, we investigated the relationship between the types of digital assistant responses and users' subsequent questions, focusing on identifying interactions that were discouraging to users when speaking with a digital assistant. We conducted a user study with 20 participants who completed a series of information seeking tasks using the Google Home, and analyzed transcripts using a method based on applied conversation analysis. We found that the most common response from the Google Home, a version of "Sorry, I'm not sure how to help'', provided no feedback for participants to build on when forming their next question. However, responses that provided somewhat strange but tangentially related answers were actually more helpful for conversational grounding, which extended the interaction. We discuss the connection between grounding and symbiosis, and present recommendations for requirements for forming partnerships with digital assistants.
A latency compensation technique based on game characteristics to mitigate the influence of delay on cloud gaming quality of experience

Cloud Gaming (CG) is an immersive multimedia service that promises many benefits. In CG, the games are rendered in a cloud server, and the resulted scenes are streamed as a video sequence to the client. Using CG users are not forced to update their gaming hardware frequently, and available games can be played on any operating system or suitable device. However, cloud gaming requires a reliable and low-latency network, which makes it a very challenging service. Transmission latency strongly affects the playability of a cloud game and consequently reduces the users' Quality of Experience (QoE). In this paper, we propose a latency compensation technique using game adaptation that mitigates the influence of delay on QoE. This technique uses five game characteristics for the adaptation. These characteristics, in addition to an Aim-assistance technique, were implemented in four games for evaluation. A subjective study using 194 participants was conducted using a crowdsourcing approach. The results showed that the majority of the proposed adaptation techniques lead to significant improvements in the cloud gaming QoE.
How distance metrics influence missing data imputation with k-nearest neighbours

In missing data contexts, k-nearest neighbours imputation has proven beneficial since it takes advantage of the similarity between patterns to replace missing values. When dealing with heterogeneous data, researchers traditionally apply the HEOM distance, that handles continuous, nominal and missing data. Although other heterogeneous distances have been proposed, they have not yet been investigated and compared for k-nearest neighbours imputation. In this work, we study the effect of several heterogeneous distances on k-nearest neighbours imputation on a large benchmark of publicly-available datasets.