Reviewerly/exHarmony huggingface dataset:
DeepEmLAN: Deep embedding learning for attributed networks

Network embedding aims to learn the low-dimensional representations for the components in the network while maximally preserving the structure and inherent properties. Its efficiency has been proved in various real-world applications. However, most existing studies on attributed networks cannot explore both the multi-typed attributes and the semantic relationships flexibly. To address the above problem, we propose a deep model based embedding learning method for attributed networks, named DeepEmLAN. It can smoothly project different types of attributed information into the same semantic space through a deep attention model, while maintaining the topological structures. Furthermore, we design a heuristic combining strategy to generate the final embeddings, which makes the nodes sharing more neighbors, similar text-enriched or labeled attributes closer in the representational space. To demonstrate the potential of the proposed DeepEmLAN, we evaluate its performance on the challenging tasks of node classification and network reconstruction. The experimental results on several real datasets have shown that DeepEmLAN outperforms competitive state-of-the-art methods significantly.
1RMA

Remote Direct Memory Access (RDMA) plays a key role in supporting performance-hungry datacenter applications. However, existing RDMA technologies are ill-suited to multi-tenant datacenters, where applications run at massive scales, tenants require isolation and security, and the workload mix changes over time. Our experiences seeking to operationalize RDMA at scale indicate that these ills are rooted in standard RDMA's basic design attributes: connectionorientedness and complex policies baked into hardware.
GC-YOLOv3: You Only Look Once with Global Context Block

In order to make the classification and regression of single-stage detectors more accurate, an object detection algorithm named Global Context You-Only-Look-Once v3 (GC-YOLOv3) is proposed based on the You-Only-Look-Once (YOLO) in this paper. Firstly, a better cascading model with learnable semantic fusion between a feature extraction network and a feature pyramid network is designed to improve detection accuracy using a global context block. Secondly, the information to be retained is screened by combining three different scaling feature maps together. Finally, a global self-attention mechanism is used to highlight the useful information of feature maps while suppressing irrelevant information. Experiments show that our GC-YOLOv3 reaches a maximum of 55.5 object detection mean Average Precision (mAP)@0.5 on Common Objects in Context (COCO) 2017 test-dev and that the mAP is 5.1% higher than that of the YOLOv3 algorithm on Pascal Visual Object Classes (PASCAL VOC) 2007 test set. Therefore, experiments indicate that the proposed GC-YOLOv3 model exhibits optimal performance on the PASCAL VOC and COCO datasets.
Efficient Biomedical Image Segmentation on EdgeTPUs at Point of Care

The U-Net architecture is a state-of-the-art neural network for semantic image segmentation that is widely used in biomedical research. It is based on an encoder-decoder framework and its vanilla version shows already high performance in terms of segmentation quality. Due to its large parameter space, however, it has high computational costs on both, CPUs and GPUs. In a research setting, inference time is relevant, but not crucial for the results. However, especially in mobile, clinical applications a light and fast variant would allow deep-learning assisted, objective diagnosis at the point of care. In this work, we suggest an optimized, tiny-weight U-Net for an inexpensive hardware accelerator. We first mined the U-Net architecture to reduce computational complexity to increase runtime performance by simultaneously keeping the accuracy on a high level. Using an open, biomedical dataset for high-speed videoendoscopy (BAGLS), we show that we can dramatically reduce the parameter space and computations by over 99.8% while keeping the segmentation performance at 95% of our baseline. Using a custom upscaling routine, we further successfully deployed our optimized U-Net to an EdgeTPU hardware accelerator to gain cost-effective speed improvements on conventional computers and to showcase the applicability of EdgeTPUs for biomedical imaging processing of large images on portable devices. Combining the optimized architecture and the EdgeTPU, we gain a speedup of >79-times compared to our initial baseline while keeping high accuracy. This combination allows to provide immediate results to the clinician, especially in constrained computational environments, and an objective diagnosis at the point of care.
Multi-label learning for improving discretely-modulated continuous-variable quantum key distribution

Abstract We propose a novel scheme for discretely-modulated continuous-variable quantum key distribution (CVQKD) using machine learning technologies, which called multi-label learning-based CVQKD (ML-CVQKD). In particular, the proposed scheme divides the whole quantum system into state learning process and state prediction process. The former is used for training and estimating classifier, and the latter is used for generating final secret key. Meanwhile, a multi-label classification algorithm (MLCA) is also designed as an embedded classifier for distinguishing coherent state. Feature extraction for coherent state and related machine learning-based metrics for the quantum classifier are successively suggested. Security analysis based on the linear bosonic channel assumption shows that MLCA-embedded ML-CVQKD outperforms other existing discretely-modulated CVQKD protocols, such as four-state protocol and eight-state protocol, as well as the original Gaussian-modulated CVQKD protocol, and it will be further enhanced with the increase of modulation variance.
SRNPU: An Energy-Efficient CNN-Based Super-Resolution Processor With Tile-Based Selective Super-Resolution in Mobile Devices

In this article, we propose an energy-efficient convolutional neural network (CNN) based super-resolution (SR) processor, super-resolution neural processing unit (SRNPU), for mobile applications. Traditionally, it is hard to realize real-time CNN-based SR on resource-limited platforms like mobile devices due to its massive amount of computation workload and communication bandwidth with external memory. The SRNPU can support the tile-based selective super-resolution (TSSR) which dynamically selects the proper sized CNN in a tile-by-tile manner. The TSSR reduces the computational workload of CNN-based SR by 31.1 % while maintaining image restoration performance. Moreover, a proposed selective caching based convolutional layer fusion (SC <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> LF) can reduce 78.8 % of external memory bandwidth with 93.2 % smaller on-chip memory footprint compared with previous layer fusion methods, by only caching short reuse distance intermediate feature maps. Additionally, reconfigurable cyclic ring architecture in the SRNPU enables maintaining high PE utilization by amortizing the reloading process caused by SC <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> LF operation under various convolutional layer configurations. The SRNPU is fabricated in 65 nm CMOS technology and occupies 4×4 mm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> die area. The SRNPU has a peak power efficiency of 1.9 TOPS/W at 0.75 V, 50 MHz. The SRNPU achieves 31.8 fps ×2 scale Full-HD generation and 88.3 fps ×4 scale Full-HD generation with higher restoration performance and power efficiency than previous SR hardware implementations. To the best of our knowledge, the SRNPU is the first ASIC implementation of the CNN-based SR algorithm which supports real-time Full-HD up-scaling.
Multi-Replica and Multi-Cloud Data Public Audit Scheme Based on Blockchain

Cloud storage services provide convenient data storage services for individuals and enterprises. Data owners can remotely access and update outsourcing data. But there are still many security problems, such as data integrity. Although the public audit schemes allow users to authorize third-party auditors (TPA) to verify the integrity of cloud data, there are still a series of problems in the existing public audit schemes. First of all, most of the existing schemes are based on the traditional or identity public key infrastructure. There is a problem of certificate management or key escrow. And they do not support dynamic data update and user identity tracking for group users. Then, existing multi-replica data public audit schemes store all replicas on a cloud storage server. Once the cloud server fails, all replicas will be damaged. Finally, most existing schemes require TPA to be trusted. In practice, TPA may deviate from the public audit protocol or collude with cloud servers to deceive users. To solve these problems, we propose a certificateless multi-replica and multi-cloud data public audit scheme based on blockchain technology. In our scheme, the dynamic hash table and modification record table are introduced to achieve dynamic update of group user data and identity tracking. All replicas are stored in different cloud servers, and their integrity can be audited at the same time. In addition, we use the unpredictability of blocks in the blockchain to construct fair challenge information, thereby preventing malicious TPA and cloud servers from colluding to deceive users. Each audit result is written into the blockchain, which is convenient for users to audit the behavior of TPA. The analysis results show that our proposed scheme is secure in the random oracle model and has higher efficiency in communication and computation cost compared with similar schemes.
Time series forecasting based on kernel mapping and high-order fuzzy cognitive maps

Fuzzy cognitive maps (FCMs) have emerged as a powerful tool for dealing with the task of time series prediction. Most existing research devoted to designing an effective method to extract feature time series from the original time series, which are used to construct FCMs and predict the time series. However, in existing methods, all extracted feature time series, including the redundant feature time series, were used to develop FCMs instead of selecting the key feature time series (KFTS) to construct FCMs, which limits the generalization and prediction accuracy of the models. In this paper, we propose a framework based on kernel mapping and high-order FCMs (HFCM) to forecast time series inspired by the kernel methods and support vector regression (SVR). The model is termed as Kernel-HFCM. Kernel mapping is designed to map the original one-dimensional time series into multidimensional feature time series, and then the feature selection algorithm is proposed to select the KFTS from the multidimensional feature time series to develop the HFCM. Finally, reverse kernel mapping is used to map the feature time series back to the predicted one-dimensional time series. In comparison to the existing methods, the experimental results on seven benchmark datasets demonstrate the effectiveness of Kernel-HFCM in time series prediction.
An Empirical Evaluation of Mutation Operators for Deep Learning Systems

Deep Learning (DL) is increasingly adopted to solve complex tasks such as image recognition or autonomous driving. Companies are considering the inclusion of DL components in production systems, but one of their main concerns is how to assess the quality of such systems. Mutation testing is a technique to inject artificial faults into a system, under the assumption that the capability to expose (kilt) such artificial faults translates into the capability to expose also real faults. Researchers have proposed approaches and tools (e.g., Deep-Mutation and MuNN) that make mutation testing applicable to deep learning systems. However, existing definitions of mutation killing, based on accuracy drop, do not take into account the stochastic nature of the training process (accuracy may drop even when re-training the un-mutated system). Moreover, the same mutation operator might be effective or might be trivial/impossible to kill, depending on its hyper-parameter configuration. We conducted an empirical evaluation of existing operators, showing that mutation killing requires a stochastic definition and identifying the subset of effective mutation operators together with the associated most effective configurations.
Candidate sentence selection for extractive text summarization

Text summarization is a process of generating a brief version of documents by preserving the fundamental information of documents as much as possible. Although most of the text summarization research has been focused on supervised learning solutions, there are a few datasets indeed generated for summarization tasks, and most of the existing summarization datasets do not have human-generated goal summaries which are vital for both summary generation and evaluation. Therefore, a new dataset was presented for abstractive and extractive summarization tasks in this study. This dataset contains academic publications, the abstracts written by the authors, and extracts in two sizes, which were generated by human readers in this research. Then, the resulting extracts were evaluated to ensure the validity of the human extract production process. Moreover, the extractive summarization problem was reinvestigated on the proposed summarization dataset. Here the main point taken into account was to analyze the feature vector to generate more informative summaries. To that end, a comprehensive syntactic feature space was generated for the proposed dataset, and the impact of these features on the informativeness of the resulting summary was investigated. Besides, the summarization capability of semantic features was experienced by using GloVe and word2vec embeddings. Finally, the use of ensembled feature space, which corresponds to the joint use of syntactic and semantic features, was proposed on a long short-term memory-based neural network model. ROUGE metrics evaluated the model summaries, and the results of these evaluations showed that the use of the proposed ensemble feature space remarkably improved the single-use of syntactic or semantic features. Additionally, the resulting summaries of the proposed approach on ensembled features prominently outperformed or provided comparable performance than summaries obtained by state-of-the-art models for extractive summarization.
Analysis of RGB-D camera technologies for supporting different facial usage scenarios

Abstract Recently a wide variety of applications has been developed integrating 3D functionalities. Advantages given by the possibility of relying on depth information allows the developers to design new algorithms and to improve the existing ones. In particular, for what concerns face morphology, 3D has led to the possibility to obtain face depth maps highly close to reality and consequently an improvement of the starting point for further analysis such as Face Detection, Face Authentication, Face Identification and Face Expression Recognition. The development of the aforementioned applications would have been impossible without the progress of sensor technologies for obtaining 3D information. Several solutions have been adopted over time. In this paper, emphasis is put on passive stereoscopy, structured light, time-of-flight (ToF) and active stereoscopy, namely the most used technologies for the cameras design and fulfilment according to the literature. The aim of this article is to investigate facial applications and to examine 3D camera technologies to suggest some guidelines for addressing the correct choice of a 3D sensor according to the application that has to be developed.
Simultaneous learning coefficient matrix and affinity graph for multiple kernel clustering

Due to the effectiveness of handling non-linear data and avoiding kernel customization, multiple kernel clustering (MKC) has been widely investigated and achieved promising results for challenging non-linear clustering tasks. Generally, the existing MKC methods mainly consist of first learning a coefficient matrix by leveraging multiple kernel learning (MKL) and sample self-expressiveness property, and then constructing an affinity graph relying on this coefficient matrix to accomplish spectral clustering. However, the quality of the affinity matrix (graph) is largely determined by the learned coefficient matrix, thus the two independent steps are not conducive to learn an optimal affinity graph. To tackle this problem, in this paper, we propose a new MKC method that uses one-step learning scheme rather than two-step learning scheme to learn an affinity graph, termed SLMKC. Specifically, SLMKC bridges the relationship between the affinity matrix and coefficient matrix by an adaptive local structure learning strategy, so it can simultaneously learn both in a mutual promotion manner. Furthermore, a self-weighted MKL strategy is introduced to learn an optimal consensus kernel, which can avoid selecting a specific kernel function and tuning its associated parameters. Extensive experiments validate that our SLMKC outperforms the state-of-the-art MKC competitors significantly.
Imparting Materials Science Knowledge in the Field of the Crystal Structure of Metals in Times of Online Teaching: A Novel Online Laboratory Teaching Concept with an Augmented Reality Application

During the COVID-19 pandemic, many educators have been required to offer their courses online. A particular challenge is the implementation of practical laboratory experiments in the field of materials science. The central questions are the following: How can students carry out laboratory experiments at home? How are applications on mobile devices helpful in this context? How can experiments be organized to make students approach the topic with self-motivation and excitement? The concept presented in this paper combines the idea of practical work and the use of an augmented reality app. Guided by a well-structured online learning platform, video tutorials, and lab handouts, students were able to carry out their experiments from home. The results of the evaluation of this new laboratory experiment concept suggest that students very positively welcomed this form of education.
An Extensive Study of Flexible Design Methods for the Number Theoretic Transform

Efficient lattice-based cryptosystems operate with polynomial rings with the Number Theoretic Transform (NTT) to reduce the computational complexity of polynomial multiplication. NTT has therefore become a major arithmetic component (thus computational bottleneck) in various cryptographic constructions like hash functions, key-encapsulation mechanisms, digital signatures, and homomorphic encryption. Although there exist several hardware designs in prior work for NTT, they all are isolated design instances fixed for specific NTT parameters or parallelization level. This article provides an extensive study of flexible design methods for NTT implementation. To that end, we evaluate three cases: (1) parametric hardware design, (2) high-level synthesis (HLS) design approach, and (3) design for software implementation compiled on soft-core processors, where all are targeted on reconfigurable hardware devices. We evaluate the designs that implement multiple NTT parameters and/or processing elements, demonstrate the design details for each case, and provide a fair comparison with each other and prior work. On a Xilinx Virtex-7 FPGA, compared to HLS and processor-based methods, the results show that the parametric hardware design is on average <inline-formula><tex-math notation="LaTeX">$4.4\times$</tex-math></inline-formula> and <inline-formula><tex-math notation="LaTeX">$73.9\times$</tex-math></inline-formula> smaller and <inline-formula><tex-math notation="LaTeX">$22.5\times$</tex-math></inline-formula> and <inline-formula><tex-math notation="LaTeX">$19.3\times$</tex-math></inline-formula> faster, respectively. Surprisingly, HLS tools can yield less efficient solutions than processor-based approaches in some cases.
Cottontail DB: An Open Source Database System for Multimedia Retrieval and Analysis

Multimedia retrieval and analysis are two important areas in "Big data" research. They have in common that they work with feature vectors as proxies for the media objects themselves. Together with metadata such as textual descriptions or numbers, these vectors describe a media object in its entirety, and must therefore be considered jointly for both storage and retrieval.
Virtual training leads to physical, cognitive and neural benefits in healthy adults

Physical activity, such as high-intensity intermittent aerobic exercise (HIE), can improve executive functions. Although performing strength or aerobic training might be problematic or not feasible for someone. An experimental situation where there is no actual movement, but the body shows physiological reactions, is during the illusion through immersive virtual reality (IVR). We aimed to demonstrate whether a virtual HIE-based intervention (vHIE) performed exclusively by the own virtual body has physical, cognitive, and neural benefits on the real body. 45 healthy young adults (cross-over design) experienced HIE training in IVR (i.e., the virtual body performed eight sets of 30 s of running followed by 30 s of slow walking, while the subject is completely still) in two random-ordered conditions (administered in two sessions one week apart): the virtual body is displayed in first-person perspective (1PP) or third-person perspective (3PP). During the vHIE, we recorded the heart rate and subjective questionnaires to confirm the effectiveness of the illusion; before and after vHIE, we measured cortical hemodynamic changes in the participants’ left dorsolateral prefrontal cortex (lDLPFC) using the fNIRS device during the Stroop task to test our main hypothesis. Preliminary, we confirmed that the illusion was effective: during the vHIE in 1PP, subjects’ heart rate increased coherently with the virtual movements, and they reported subjective feelings of ownership and agency. Primarily, subjects were faster in executing the Stroop task after the vHIE in 1PP; also, the lDLPFC activity increased coherently. Clinically, these results might be exploited to train cognition and body simultaneously. Theoretically, we proved that the sense of body ownership and agency can affect other parameters, even in the absence of actual movements.