Reviewerly/exHarmony huggingface dataset:
Non-blind optical degradation correction via frequency self-adaptive and finetune tactics

In mobile photography applications, limited volume constraints the diversity of optical design. In addition to the narrow space, the deviations introduced in mass production cause random bias to the real camera. In consequence, these factors introduce spatially varying aberration and stochastic degradation into the physical formation of an image. Many existing methods obtain excellent performance on one specific device but are not able to quickly adapt to mass production. To address this issue, we propose a frequency self-adaptive model to restore realistic features of the latent image. The restoration is mainly performed in the Fourier domain and two attention mechanisms are introduced to match the feature between Fourier and spatial domain. Our method applies a lightweight network, without requiring modification when the fields of view (FoV) changes. Considering the manufacturing deviations of a specific camera, we first pre-train a simulation-based model, then finetune it with additional manufacturing error, which greatly decreases the time and computational overhead consumption in implementation. Extensive results verify the promising applications of our technique for being integrated with the existing post-processing systems.
SchedTune: A Heterogeneity-Aware GPU Scheduler for Deep Learning

Modern cluster management systems, such as Kubernetes, support heterogeneous workloads and resources. However, existing resource schedulers in these systems do not differentiate between heterogeneous G PU resources-which are becoming a norm-and do not support GPU sharing-which is necessary to support emerging collocation of jobs and multi-tenant applications. Thus the systems suffer from low GPU resource utilization, higher queuing delays, and an increase in application makespan, i.e., the duration between the arrival of the first job and the completion of the last job of a workflow. This is especially a problem in supporting crucial deep learning (DL) applications. To this end, in this paper, we profile and analyze DL jobs on heterogeneous GPUs, investigate the interference caused by collocating jobs on GPUs, and use this information to predict the GPU memory demand and job completion times. We propose SCHEDTUNE, a machine-learning-based heterogeneity-aware scheduler that ensures higher GPU memory utilization and reduced out-of-memory (OOM) failures, while supporting improved makespan. Our evaluation shows that SCHEDTUNE GPU memory predictors and scheduler outperform the state-of-the-art predictors by achieving 81% higher GPU memory utilization, 100% detection and avoidance of OOM errors, and 17.5% reduction in makespan compared to the default Kubernetes scheduler.
What Do We Know About Readability of Test Code? - A Systematic Mapping Study

The readability of software code is a key success criterion for understanding and maintaining software systems and tests. In industry practice, a limited number of guidelines aim for improving and assessing the readability of software (test) code. Although several studies focus on investigating the readability of software code, we observed limited research work that focuses on the readability of software test code. In this paper we focus on systematically investigating the characteristics, factors, and assessment criteria that have an impact on the readability of test code. We build on a Systematic Mapping Study (SMS) to identify key characteristics, factors, and assessment criteria that have an impact on test code readability, legibility, and understandability to support and improve maintenance tasks. The result set includes 16 studies for further analysis. The majority of publications focuses on readability investigations of automatically generated test code (88&#x0025;), often evaluated with surveys to access the readability of test code (44 &#x0025;). Although several approaches aim at assessing the readability with focus on isolated factors, a combination of different readability aspects within an assessment framework can help to better assess and justify the readability of test code with focus on improving software and system test maintenance.
Developing powerpoint-based interactive multimedia of mathematics learning multiples and factors materials for elementary school

This research aims to describe PowerPoint-based interactive multimedia products in learning multiple material mathematics and class IV factors of the Karangtengah Baru, Imogiri State Elementary School, and describe the feasibility of the product. This research uses a development method or Research and Development (R&amp;D), which includes six stages of development. The results of this development study show that the product developed meets valid and practical criteria. The validity can be categorized as "Very Good", with an average score of 4.6 and a percentage of 92%. The assessment results from media experts are categorized as "Good", with an average score of 3.9 and a percentage of 78.7%. The assessment results from media experts are categorized as "Good", with an average score of 3.9 and a percentage of 78.7%. Assessment from class teachers is categorized as "Very Good", with an average score of 4.7 and a percentage of 95.7%. Practicality seen from student response assessments can be categorized as "Very Good", with an average score of 4.7 and a percentage of 94% of 10 students as respondents. Based on the above exposure, it can be concluded that PowerPoint-based interactive multimedia products are developed and feasible for use in Class IV elementary mathematics learning multiple materials and factors.
Mutual-Guidance Transformer-Embedding Network for Video Salient Object Detection

Video salient object detection (VSOD) aims at locating the most attractive objects presented in video sequences by exploiting spatial and temporal cues. Previous methods mainly utilize convolutional neural networks (CNNs) to fuse or complement across RGB and optical flow cues via simple strategies. To take full advantage of CNNs and recently emerged Transformers, this letter proposes a novel mutual-guidance Transformer-embedding network, called MGT-Net, where a mutual-guidance multi-head attention mechanism (MGMA) explores more sophisticated long-range cross-modal interactions. Such a mechanism is designed into a new mutual-guidance Transformer (MGTrans) module that can propagate long-range contextual dependencies based on information of the other modality. To the best of our knowledge, MGT-Net is the first VSOD model that embeds Transformers as modules into CNNs for improved performance. Prior to MGTrans, we also propose and deploy a feature purification module (FPM) to purify noisy backbone features. Experimental results on five benchmark datasets demonstrate the state-of-the-art performance of MGT-Net.
A Semi-Fragile Reversible Watermarking for Authenticating 3D Models in Dual Domains Based on Variable Direction Double Modulation

Aiming at reducing the large distortion of the existing reversible watermarking for three-dimensional(3D) mesh models and meeting the needs of authentication in cloud-based data storage, a semi-fragile reversible watermarking for authenticating 3D models in dual domains is proposed based on variable direction double modulation. A 3D model is first transformed into the spherical coordinate system, and then the direction of quantization modulation is changed under specific circumstances to achieve less distortion. Watermarks are determined by the relative position and vertices number in the one-ring neighbourhood of all vertices, so the watermark generation is independent of the coordinate system and robust to RST(rotation, scaling, and translation). In addition, the watermarks are generated and embedded in both plaintext domain and encrypted domain, and the authentication can be accomplished in both domains. Experimental results and analysis show that the proposed scheme can effectively detect malicious tampering in two domains and reduce the distortion caused by watermark embedding. It has potential application in the content authentication of 3D models in cloud-based data storage.
Multilayer Degradation Representation-Guided Blind Super-Resolution for Remote Sensing Images

Remote sensing image super-resolution aims to boost the image resolution while recovering rich high-frequency details. Currently, most of super-resolution methods are based on an assumption that the degradation kernel is a specific downsampler. However, the degradation kernel is unknown and sophisticated for real remote sensing scenes, leading to a severe performance drop. To alleviate this problem, we propose a multi-layer degradation representation-guided blind super-resolution method for remote sensing images, which mainly consists of three key steps. First, an unsupervised representation learning is exploited to learn the degradation representation from low-resolution images. Then, a degradation-guided deep residual module is designed to model high-order features across different scales from original images. Finally, a multi-layer degradation-aware feature fusion mechanism is proposed to restore the finer details. Experiments on synthetic and real datasets demonstrate that the proposed method can achieve promising performance with respect to other state-of-the-art super-resolution approaches.
Machine Learning Algorithms for 6G Wireless Networks

Over the past decade, in view of minimizing network expenditures, optimizing network performance, and building new revenue streams, wireless technology has been integrated with artificial intelligence/machine learning (AI/ML). Further, there occurs dramatic minimization of power consumption and improvement in system performance when traditional algorithms are replaced with deep learning-based AI techniques. Implementation of ML algorithms enables wireless networks to advance in terms of offering high automation levels from distributed AI/ML architectures applicable at network edge and implementing application-based traffic steering across access networks. This has enabled dynamic network slicing for addressing different scenarios with varying quality of service requirements and has provided ubiquitous connectivity across various 6G communication platforms. Keeping a view of the aforementioned, in this chapter, the authors present a survey of various ML techniques that are applicable to 6G wireless networks. They also list open problems of research that require timely solutions.
Deterministic measurement of a Rydberg superatom qubit via cavity-enhanced single-photon emission

With Rydberg dipole interactions, a mesoscopic atomic ensemble becomes a superatom that behaves like a single atom but couples collectively with photons. It is potentially a strong candidate as a qubit in quantum information science, especially for quantum networks. In this paper, we report a cavity-enhanced single-photon interface for a Rydberg superatom and demonstrate deterministic qubit readout via a photon burst. We make use of a low-finesse ring cavity to enhance the atom–photon interaction and obtain in-fiber single-photon generation efficiency of 44%. Harnessing the dipole interaction between two Rydberg levels, we may create either a sequence of multiple single photons or nothing, conditioned on an initial qubit state. We achieve a measurement fidelity of 93.0% in 4.8 µs. Our work complements the experimental toolbox of harnessing Rydberg superatoms for quantum information applications.
Efficient high-end video data privacy preservation with integrity verification in cloud storage

Cloud data protection intends to secure data stored in the cloud. Security and integrity are still major concerns regarding the outsourced data to the cloud. This work focuses to preserve the privacy and integrity of videos stored in the cloud. A stream cipher suite involving key generation, encryption, and authentication phases is designed. The Elliptic Curve Diffie Hellman approach is used to generate asymmetric keys. HECV compressed videos are fast processed by Apache Flink and are encrypted by XSalsa20 stream cipher. An ASCON-based SHA-3 authentication mechanism is proposed to generate an authentication code for the uploaded videos to guarantee tamper-proof. The proposed method scrambles the Spatio-temporal resolution of videos with better encryption time compared to the traditional approaches. Experimental result shows that the HEVC videos are authenticated and encrypted using the proposed cipher suite is fast, safe, and resists several forms of attacks resulting in improved information sensitivity and cost benefits.
An Explainable Evolving Fuzzy Neural Network to Predict the k Barriers for Intrusion Detection Using a Wireless Sensor Network

Evolving fuzzy neural networks have the adaptive capacity to solve complex problems by interpreting them. This is due to the fact that this type of approach provides valuable insights that facilitate understanding the behavior of the problem being analyzed, because they can extract knowledge from a set of investigated data. Thus, this work proposes applying an evolving fuzzy neural network capable of solving data stream regression problems with considerable interpretability. The dataset is based on a necessary prediction of k barriers with wireless sensors to identify unauthorized persons entering a protected territory. Our method was empirically compared with state-of-the-art evolving methods, showing significantly lower RMSE values for separate test data sets and also lower accumulated mean absolute errors (MAEs) when evaluating the methods in a stream-based interleaved-predict-and-then-update procedure. In addition, the model could offer relevant information in terms of interpretable fuzzy rules, allowing an explainable evaluation of the regression problems contained in the data streams.
Instant Neural Radiance Fields

We extend our instant NeRF implementation [Müller et al. 2022] to allow training from an incremental stream of images and camera poses, provided by a realtime Simultaneous Localization And Mapping (SLAM) system. Camera poses are refined end-to-end by back-propagating the gradients from NeRF training. Reconstruction quality is further improved by compensating for various camera properties, such as rolling shutter, non-linear lens distortion, and variable exposure typical of digital cameras.
Privacy Protection in 5G Positioning and Location-based Services Based on SGX

As the sensitivity of position, the privacy protection in both 5G positioning and its further application in location-based services (LBSs) has been paid special attention and studied. Solutions based on k-anonymity, homomorphic encryption, and secure multi-party computation have been proposed. However, these solutions either require a trusted third party or incur heavy overheads. Besides, there still lacks an integrated solution that can protect privacy for both positioning and LBS provision. Based on Intel SGX, this article proposes a novel light-weight scheme that can protect privacy in both 5G positioning and its further applications in LBS provision in an integrated way. Through secret sharing, the proposed scheme can also support multiple location-based service providers without frequent key exchange. We seriously analyze the security of our scheme. Based on scheme implementation, its efficiency is proved through the performance evaluation conducted over a real-world database.
Will memecoins’ surge trigger a crypto crash? Evidence from the connectedness between leading cryptocurrencies and memecoins

Understanding the crash of cryptocurrencies in 2021 requires analyses of the microstructure of leading cryptocurrencies and memecoins. Using 4-hourly data, we reveal how leading cryptocurrencies and memecoins influence each other with the Granger-causality test and dynamic connectedness. We find that leading cryptocurrencies spillovers dominate memecoins by falling while memecoins spillovers affect leading cryptocurrencies by rising. Moreover, we conduct regression analysis on a daily and 4-hourly basis, and the results confirm the findings above. In most periods, leading cryptocurrencies drive memecoins; however, when memecoins show a positive net spillover, it tends to trigger the leading cryptocurrencies to crash.
Multimodal Brain Tumor Segmentation Using a 3D ResUNet in BraTS 2021

In this paper, we propose a multimodal brain tumor segmentation using a 3D ResUNet deep neural network architecture. Deep neural network has been applying in many domains, including computer vision, natural language processing, etc. It has also been used for semantic segmentation in medical imaging segmentation, including brain tumor segmentation. In this work, we utilize a 3D ResUNet to segment tumors in brain magnetic resonance image (MRI). Multimodal MRI is prevailing in brain tumor analysis due to providing rich tumor information. We apply the proposed method to the Multimodal Brain Tumor Segmentation Challenge (BraTS) 2021 validation dataset for tumor segmentation. The online evaluation of brain tumor segmentation using the proposed method offers the dice score coefficient (DSC) of 0.8196, 0.9195, and 0.8503 for enhancing tumor (ET), whole tumor (WT), and tumor core (TC), respectively.
Density-Based Machine Learning Scheme for Outlier Detection in Smart Forest Fire Monitoring Sensor Cloud

Sensor Cloud is an integration of sensor networks with cloud where sensed data is stored and processed in the cloud. The applications of sensor cloud can be seen in forest fire monitoring, healthcare system, and other Internet-of-Things systems. Outliers may present within this data due to malicious activities, low-quality sensors, or node deployment in harsh environments. Such outliers must be detected timely for effective decision making. Many clustering-based machine learning schemes for outlier detection have been devised. However, accuracy of these techniques can be further improved. This paper proposes a density-based machine learning scheme (DBS) for outlier detection which is implemented in Python and executed on the two datasets of different forest fire monitoring networks. DBS makes density-based clusters of all data points where outliers lie in low-density region. The use of a density-based model in the proposed approach improves precision, throughput, and accuracy. DBS outperforms the existing Mean Shift and K Means based clustering schemes with maximum accuracy 98.40%.
Federated low-rank tensor projections for sequential recommendation

Recommender systems have achieved great success in many fields, benefiting from the accessibility of massive amounts of user behavior data. However, the increasing concern about privacy issues brings new research challenges to traditional data-centralized recommender systems. As a privacy-preserving machine learning technique, federated learning provides potential solutions to these research challenges, by jointly training perceptive global recommendation models based on the decentralized user behavior data. Although existing federated recommendation methods have achieved promising results, they still suffer the following limitations. Firstly, they mainly focus on the horizontal federated recommendation scenarios where the participants are individual users, without considering the scenarios where participants are organizations. Secondly, they tend to model static user preferences and cannot learn the preference evolution in users’ interaction sequences. To address these limitations, we propose a horizontal Federated recommendation framework for Sequential Recommendation (FedSeqRec), where the participants are organizations. As the data in different organizations are usually not independent and identically distributed (Non-IID), federated training tends to yield suboptimal results. To alleviate this problem, we propose a federated re-average algorithm, which takes the consistency of the client’s data distribution with the overall data distribution into consideration. Moreover, we also leverage a low-rank tensor projection to model user long-term preferences. Experimental results on two real-world datasets demonstrate that FedSeqRec outperforms state-of-the-art federated recommendation methods. The implementation code of FedSeqRec is available at https://github.com/MuziLee-x/FedSeqRec.
Residual Dense Attention Networks for COVID-19 Computed Tomography Images Super Resolution

The coronavirus disease 2019 (COVID-19) is highly contagious and pathogenic, posing a serious threat to the public safety of the people. Owing to the low resolution of computed tomography (CT) images, it is essential to use super resolution (SR) reconstruction technology to improve the resolution of COVID-19 medical images. Aiming at the problems of the limited receptive field, low resolution, high complexity, and loss of edge information in the SR reconstruction method of residual learning, we present a residual dense attention network (RDAN) for COVID-19 CT image SR. First, to better extract features and reduce the number of parameters, we design the residual dense network module to extract detailed information from images. Second, we add the channel attention mechanism to enable the network to have adequate high-frequency information with larger weights to reduce the computational cost of the model. Finally, we filter and reorganize the multilayer image information by skip connections so that the network model can allow extensive use of image information of different depths. Comprehensive benchmark evaluation shows that our RDAN method dramatically improves the convergence speed of the network, solves the problem of missing information, and makes the reconstructed COVID-19 CT images have more apparent textures, richer details, and better visual effects that can effectively assist experts in diagnosis.
SonifyIt: Towards Transformative Sound for All Robots

Transformative robot sound yields perceptual, functional, and social benefits in human-robot interactions, but broader research and implementation related to this topic is impeded by the lack of a common sound generation system for robots. Such a system could enable a wide array of situated robot sound studies, smoother collaborations with sound designers than current state of the art methods, and broader adoption of transformative robot sound. Based on other successful open-source projects in the robotics community, we integrated Robot Operating System, a popular robotics middleware, and Pure Data, a visual programming language for multimedia, to enable live sound synthesis and sample playback for robots. This sound generation system synthesized sound in an in-the-wild pilot study with positive qualitative results. Furthermore, an online within-subjects survey study with <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$N=96$</tex-math></inline-formula> showed that the proposed sound system made the robot seem warmer, happier, and more energetic. This work benefits robotics researchers by providing the current sound system as a validated artifact and demonstrating its potential impact on broader robotics applications. We plan to develop this software into an open-source package: SonifyIt.