Reviewerly/exHarmony huggingface dataset:
Use of service robots in an event setting: Understanding the role of social presence, eeriness, and identity threat

With the increased use of robots in the event industry, most of the literature centers on factors that influence robot adoption, with less attention being paid to the understanding of how customers interact and connect with anthropomorphic robots. With the growing research in this field, questions remain about the event attendees' perceptions and evaluations of humanoid service robots (HSRs) and the interactive effects that shape customers' thinking and behavior towards the use of this technology. This study proposes a conceptual model to examine the role of social presence, while explaining the moderating roles of eeriness and identity threat through two experimental studies, using photo scenarios as stimuli in an online survey, with closed-ended questions. Study 1 was a single-factor between-subject experiment that manipulated a conference registration task in an event setting. Results revealed that event attendees were more satisfied with service employees (SE) than with HSR; and social presence mediated the relationship between type of service provider (HSR vs. SE) and satisfaction. Study 2 examined eeriness and identity threat as moderators and results indicated that eeriness and identity threat significantly moderate the indirect effect of the type of service provider (HSR vs. SE) on satisfaction through social presence. The study provides valuable insight about the evaluation of humanoid service robots by event attendees by deepening our understanding of how event attendees engage with service robots to form stronger connections with such a technology. Taken together, the findings of this study offer managerial implications by balancing event industry employees’ affinity for and the acceptance of the service robots.
Single Image Deraining Network with Rain Embedding Consistency and Layered LSTM

Single image deraining is typically addressed as residual learning to predict the rain layer from an input rainy image. For this purpose, an encoder-decoder network draws wide attention, where the encoder is required to encode a high-quality rain embedding which determines the performance of the subsequent decoding stage to reconstruct the rain layer. However, most of existing studies ignore the significance of rain embedding quality, thus leading to limited performance with over/under-deraining. In this paper, with our observation of the high rain layer reconstruction performance by an rain-to-rain autoencoder, we introduce the idea of "Rain Embedding Consistency" by regarding the encoded embedding by the autoencoder as an ideal rain embedding and aim at enhancing the deraining performance by improving the consistency between the ideal rain embedding and the rain embedding derived by the encoder of the deraining network. To achieve this, a Rain Embedding Loss is applied to directly supervise the encoding process, with a Rectified Local Contrast Normalization (RLCN) as the guide that effectively extracts the candidate rain pixels. We also propose Layered LSTM for recurrent deraining and fine-grained encoder feature refinement considering different scales. Qualitative and quantitative experiments demonstrate that our proposed method outperforms previous state-of-the-art methods particularly on a real-world dataset. Our source code is available at http://www.ok.sc.e.titech.ac.jp/res/SIR/.
Subpopulation Data Poisoning Attacks

Machine learning systems are deployed in critical settings, but they might fail in unexpected ways, impacting the accuracy of their predictions. Poisoning attacks against machine learning induce adversarial modification of data used by a machine learning algorithm to selectively change its output when it is deployed. In this work, we introduce a novel data poisoning attack called a subpopulation attack, which is particularly relevant when datasets are large and diverse. We design a modular framework for subpopulation attacks, instantiate it with different building blocks, and show that the attacks are effective for a variety of datasets and machine learning models. We further optimize the attacks in continuous domains using influence functions and gradient optimization methods. Compared to existing backdoor poisoning attacks, subpopulation attacks have the advantage of inducing misclassification in naturally distributed data points at inference time, making the attacks extremely stealthy. We also show that our attack strategy can be used to improve upon existing targeted attacks. We prove that, under some assumptions, subpopulation attacks are impossible to defend against, and empirically demonstrate the limitations of existing defenses against our attacks, highlighting the difficulty of protecting machine learning against this threat.
A systematic survey on security concerns in cryptocurrencies: State-of-the-art and perspectives

Cryptocurrencies are emerging as virtual financial systems based on their innovative decentralized and distributed strategy in order to provide astonishing capabilities across a range of applications. In contrast to the conventional currencies that exacerbate security threats, these threats have been implicitly addressed by the notion of cryptocurrencies, which have a distinguished impact on their adoption rate among stakeholders. However, this potential adoption bubble may get diminished by the uncontrolled fragility in market conditions and persuasive increase in potential contemporary security concerns. In this survey, we systematically examine the state-of-the-art associated with security concerns in cryptocurrencies from various perspectives. Firstly, we investigate state-of-the-art consensus mechanisms, as they are one of the primary underlying concepts of cryptocurrencies. Thereafter, we thoroughly investigate various applications of cryptocurrencies. Subsequently, we present a detailed examination of various contributions from the literature addressing security aspects in cryptocurrencies. We also shed light on some of the well-known ongoing cryptocurrency-related projects across the globe. Furthermore, we outline open challenges and draw insights for perspective directions of research in the domain.
Adjacent slices feature transformer network for single anisotropic 3D brain MRI image super-resolution

Magnetic resonance imaging (MRI) is widely used in clinical applications. However, due to the limitations in signal-to-noise ratio, physical properties of the scanner and scanning time, MRI images are usually acquired in low resolution, which restrains the accuracy of segmentation and recognition tasks. Recently, convolutional neural network (CNN) super-resolution methods have shown great potential in improving the resolution of MRI. Unfortunately, current methods neglect the data continuity and prior information of MRI images. In this paper, we handle the anisotropic 3D brain MRI images SR task as the problem of inserting new slices between adjacent in-plane slices. Then, we propose a novel adjacent slices feature transformer (ASFT) network to utilize the similarity of adjacent slices. Specifically, the backbone of the ASFT network consists of a series of stacked multi-branch features transformation and extraction (MFTE) blocks. In each MFTE block, we construct new spatial attention to focus on features from specific areas in reference branches and use channel attention to enhance the most valuable information. In addition, we propose a hybrid loss function with content and gradient information to refine the pixels and structures of the reconstructed image. We also introduce global residual learning to reduce the difficulty of network training. Experimental results show that the ASFT network gains the PSNR of 43.68 dB, 40.96 dB, and 41.22 dB with the scale factor of ×2 on the public Kirby21, ANVIL-adult, and MSSEG datasets, respectively. When compared with state-of-the-art MRI SR methods, the ASFT network achieves superior quantitative and qualitative performance.
Dual semi-supervised convex nonnegative matrix factorization for data representation

Semi-supervised nonnegative matrix factorization (NMF) has received considerable attention in machine learning and data mining. A new semi-supervised NMF method, called dual semi-supervised convex nonnegative matrix factorization (DCNMF), is proposed in this paper for fully using the limited label information. Specifically, DCNMF simultaneously incorporates the pointwise and pairwise constraints of labeled samples as dual supervisory information into convex NMF, which results in a better low-dimensional data representation. Moreover, DCNMF imposes the nonnegative constraint only on the coefficient matrix but not on the base matrix. Consequently, DCNMF can process mixed-sign data, and hence enlarge the range of applications. We derive an efficient alternating iterative algorithm for DCNMF to solve the optimization, and analyze the proposed DCNMF method in terms of the convergence and computational complexity. We also discuss the relationships between DCNMF and several typical NMF based methods. Experimental results illustrate that DCNMF outperforms the related state-of-the-art NMF methods on nonnegative and mixed-sign datasets for clustering applications.
Watermarking Images via Counting-Based Secret Sharing for Lightweight Semi-Complete Authentication

Watermarking is the process of embedding specific data to prove ownership copyright authentication. It is needed whenever media-files are used without proper permission is granted. The current watermarking challenge comes from the ownership proof especially as slight tampering occurs on the multimedia-file which injure the watermarking causing difficulty in its copyright proof. This paper proposes utilizing counting-based secret sharing strategy to allow validation of ownership RGB watermarking even if some of the image-file is interfered. We validate image watermarking partially as lightweight semi-complete verification, which is not possible in the normal random-stream watermarking schemes. Although the work is still in its early stage, it is promising research showing real attractive contribution opportunities. The work is tested and compared to other strategies in aspects of robustness, capacity, and security, providing attractive remarks worth noting. The study evaluation results showed interesting contribution believed to be pioneering.
Comparing the Accuracy and Precision of SteamVR Tracking 2.0 and Oculus Quest 2 in a Room Scale Setup

Real walking is the most intuitive navigation means to explore large virtual environments. For such a free walking Virtual Reality (VR) experience, large tracking spaces are required as well as dedicated motion tracking systems covering them. In the past, the coverage of large tracking spaces could only be achieved by professional-grade motion tracking systems. Recently, low-cost, consumer-grade motion tracking systems, such as SteamVR Tracking and Oculus Insight, have arisen, which also allow for room scale setups. However, the capability, limitation, and reliability of consumer-grade VR motion tracking systems is not fully understood yet. In this paper, we aim to fill the gap by comparing SteamVR Tracking and Oculus Insight in a 5m × 5m room scale setup, using state of the art hardware (i.e. the Oculus Quest 2, SteamVR base stations 2.0 and High Tech Computer Corporation (HTC) Vive Trackers Version 2018). The results reveal a significantly higher accuracy for the Oculus Quest 2 compared to SteamVR Tracking in the height of a tracked object. Furthermore, the Oculus Quest 2 tracks its position with substantially higher precision than SteamVR Tracking. Based on the results, we conclude that the Oculus Quest 2 is suitable for a wide range of applications in research and industry, particularly considering its lower acquisition costs, higher mobility and easier setup compared to SteamVR Tracking. However, as this work marks an initial step, more research is needed to fully understand the capabilities and limitations through other metrics (e.g. latency) and other setups (e.g. rooms with fewer landmarks or different lighting conditions).
Structured Multimodal Attentions for TextVQA

Text based Visual Question Answering (TextVQA) is a recently raised challenge requiring models to read text in images and answer natural language questions by jointly reasoning over the question, textual information and visual content. Introduction of this new modality - Optical Character Recognition (OCR) tokens ushers in demanding reasoning requirements. Most of the state-of-the-art (SoTA) VQA methods fail when answer these questions because of three reasons: (1) poor text reading ability; (2) lack of textual-visual reasoning capacity; and (3) choosing discriminative answering mechanism over generative couterpart (although this has been further addressed by M4C). In this paper, we propose an end-to-end structured multimodal attention (SMA) neural network to mainly solve the first two issues above. SMA first uses a structural graph representation to encode the object-object, object-text and text-text relationships appearing in the image, and then designs a multimodal graph attention network to reason over it. Finally, the outputs from the above modules are processed by a global-local attentional answering module to produce an answer splicing together tokens from both OCR and general vocabulary iteratively by following M4C. Our proposed model outperforms the SoTA models on TextVQA dataset and two tasks of ST-VQA dataset among all models except pre-training based TAP. Demonstrating strong reasoning ability, it also won first place in TextVQA Challenge 2020. We extensively test different OCR methods on several reasoning models and investigate the impact of gradually increased OCR performance on TextVQA benchmark. With better OCR results, different models share dramatic improvement over the VQA accuracy, but our model benefits most blessed by strong textual-visual reasoning ability. To grant our method an upper bound and make a fair testing base available for further works, we also provide human-annotated ground-truth OCR annotations for the TextVQA dataset, which were not given in the original release. The code and ground-truth OCR annotations for the TextVQA dataset are available at https://github.com/ChenyuGAO-CS/SMA.
Multi-car paint shop optimization with quantum annealing

We present a generalization of the binary paint shop problem (BPSP) to tackle an automotive industry application, the multi-car paint shop (MCPS) problem. The objective of the optimization is to minimize the number of color switches between cars in a paint shop queue during manufacturing, a known NP-hard problem. We distinguish between different sub-classes of paint shop problems, and show how to formulate the basic MCPS problem as an Ising model. The problem instances used in this study are generated using real-world data from a factory in Wolfsburg, Germany. We compare the performance of the D-Wave 2000Q and Advantage quantum processors to other classical solvers and a hybrid quantum-classical algorithm offered by D-Wave Systems. We observe that the quantum processors are well-suited for smaller problems, and the hybrid algorithm for intermediate sizes. However, we find that the performance of these algorithms quickly approaches that of a simple greedy algorithm in the large size limit.
Neural Architecture Search for 1D CNNs—Different Approaches Tests and Measurements

In the field of sensors, in areas such as industrial, clinical, or environment, it is common to find one dimensional (1D) formatted data (e.g., electrocardiogram, temperature, power consumption). A very promising technique for modelling this information is the use of One Dimensional Convolutional Neural Networks (1D CNN), which introduces a new challenge, namely how to define the best architecture for a 1D CNN. This manuscript addresses the concept of One Dimensional Neural Architecture Search (1D NAS), an approach that automates the search for the best combination of Neuronal Networks hyperparameters (model architecture), including both structural and training hyperparameters, for optimising 1D CNNs. This work includes the implementation of search processes for 1D CNN architectures based on five strategies: greedy, random, Bayesian, hyperband, and genetic approaches to perform, collect, and analyse the results obtained by each strategy scenario. For the analysis, we conducted 125 experiments, followed by a thorough evaluation from multiple perspectives, including the best-performing model in terms of accuracy, consistency, variability, total running time, and computational resource consumption. Finally, by presenting the optimised 1D CNN architecture, the results for the manuscript's research question (a real-life clinical case) were provided.
BeFaaS: An Application-Centric Benchmarking Framework for FaaS Platforms

Following the increasing interest and adoption of FaaS systems, benchmarking frameworks for determining nonfunctional properties have also emerged. While existing (microbenchmark) frameworks only evaluate single aspects of FaaS platforms, a more holistic, application-driven approach is still missing. In this paper, we design and present BeFaaS, an extensible application-centric benchmarking framework for FaaS environments that focuses on the evaluation of FaaS platforms through realistic and typical examples of FaaS applications. BeFaaS includes a built-in e-commerce benchmark, is extensible for new workload profiles and new platforms, supports federated benchmark runs in which the benchmark application is distributed over multiple providers, and supports a fine-grained result analysis. Our evaluation compares three major FaaS providers in single cloud provider setups and shows that BeFaaS is capable of running each benchmark automatically with minimal configuration effort and providing detailed insights for each interaction.
PHANTOM GHOSTDAG

In 2008 Satoshi Nakamoto invented the basis for blockchain-based distributed ledgers. The core concept of this system is an open and anonymous network of nodes, or miners, which together maintain a public ledger of transactions. The ledger takes the form of a chain of blocks, the blockchain, where each block is a batch of new transactions collected from users. One primary problem with Satoshi's blockchain is its highly limited scalability. The security of Satoshi's longest chain rule, more generally known as the Bitcoin protocol, requires that all honest nodes be aware of each other's blocks very soon after the block's creation. To this end, the throughput of the system is artificially suppressed so that each block fully propagates before the next one is created, and that very few "orphan blocks" that fork the chain be created spontaneously.
Telelife: The Future of Remote Living

In recent years, everyday activities such as work and socialization have steadily shifted to more remote and virtual settings. With the COVID-19 pandemic, the switch from physical to virtual has been accelerated, which has substantially affected almost all aspects of our lives, including business, education, commerce, healthcare, and personal life. This rapid and large-scale switch from in-person to remote interactions has exacerbated the fact that our current technologies lack functionality and are limited in their ability to recreate interpersonal interactions. To help address these limitations in the future, we introduce “Telelife,” a vision for the near and far future that depicts the potential means to improve remote living and better align it with how we interact, live and work in the physical world. Telelife encompasses novel synergies of technologies and concepts such as digital twins, virtual/physical rapid prototyping, and attention and context-aware user interfaces with innovative hardware that can support ultrarealistic graphics and haptic feedback, user state detection, and more. These ideas will guide the transformation of our daily lives and routines soon, targeting the year 2035. In addition, we identify opportunities across high-impact applications in domains related to this vision of Telelife. Along with a recent survey of relevant fields such as human-computer interaction, pervasive computing, and virtual reality, we provide a meta-synthesis in this paper that will guide future research on remote living.
Blockchain-based verifiable privacy-preserving data classification protocol for medical data

Massive IoT devices are used for data collection with the fast development of the Internet-of-things (IoT). For example, wearable devices are used to collect users' health data and conduct health monitoring. However, many private information are involved in medical data. Privacy-preserving data classification scheme (PPDC) provided an effective approach to balance the utility and the privacy of data. In the PPDC schemes, a fully trusted auditor is employed to validate the result of data classification. To reduce trust on the auditor, we provide a simplified version of the PPDC scheme. We propose blockchain-based verifiable privacy-preserving data classification protocol (VeriDC) for medical data. It makes the data center check the classification result without involving an auditor. We provide the system model of the verifiable privacy-preserving data classification protocol in blockchain setting and formalize its security model. By using verifiable oblivious pseudorandom function(verifiable OPRF), we can generate a verifiable proof and post it on blockchain to guarantee the transparency of data classification. We present a concrete construction and prove its security. Finally, we compare VeriDC with the related schemes to evaluate the effectiveness of VeriDC.
Facial expression recognition using densely connected convolutional neural network and hierarchical spatial attention

This paper is dedicated to eliminating the impact of redundant information from emotional-unrelated regions on facial expression recognition (FER). To this end, a densely connected convolutional neural network with hierarchical spatial attention is proposed. Specifically, it can adaptively locate salient regions and focus on the emotional related features so that the facial expressions can be represented more efficiently. This superior performance is also verified by some experiments. Experimental results reveal that the proposed method can distinguish facial expression more accurately than existing state-of-the-art methods.
Optimized ensemble deep learning framework for scalable forecasting of dynamics containing extreme events

The remarkable flexibility and adaptability of both deep learning models and ensemble methods have led to the proliferation for their application in understanding many physical phenomena. Traditionally, these two techniques have largely been treated as independent methodologies in practical applications. This study develops an optimized ensemble deep learning framework wherein these two machine learning techniques are jointly used to achieve synergistic improvements in model accuracy, stability, scalability, and reproducibility, prompting a new wave of applications in the forecasting of dynamics. Unpredictability is considered one of the key features of chaotic dynamics; therefore, forecasting such dynamics of nonlinear systems is a relevant issue in the scientific community. It becomes more challenging when the prediction of extreme events is the focus issue for us. In this circumstance, the proposed optimized ensemble deep learning (OEDL) model based on a best convex combination of feed-forward neural networks, reservoir computing, and long short-term memory can play a key role in advancing predictions of dynamics consisting of extreme events. The combined framework can generate the best out-of-sample performance than the individual deep learners and standard ensemble framework for both numerically simulated and real-world data sets. We exhibit the outstanding performance of the OEDL framework for forecasting extreme events generated from a Liénard-type system, prediction of COVID-19 cases in Brazil, dengue cases in San Juan, and sea surface temperature in the Niño 3.4 region.