Reviewerly/exHarmony huggingface dataset:
Blockchain Use Cases Against Climate Destruction

Based on the current measures, it is unlikely that the targets of the Paris Agreement on climate change are to be achieved within the given time. Therefore, new solutions are needed to get climate change under control. Emerging technologies like blockchain allow for new ways to approach climate change. The blockchain serves only as an enabling technology for cryptocurrencies but is a stand-alone tool applicable for various purposes. This paper aims to shed light on the overlap between the areas of blockchain and climate change. Research in this area was examined for potential blockchain use cases to support climate action using a systematic literature review. The found applications can be grouped into the main categories of Emissions Trading and Green Certificates, Sustainable Energy, Sustainable Mobility, and Green Financing. Within these applications, blockchains are being used as supporting technology. Especially transparency, traceability, and immutability are particularly beneficial in blockchain-based applications against climate change. As a downside of the technology, controversial aspects of the blockchain are considered as the energy consumption of the technology.
Asymmetric Orientation Combination for Reversible and Authenticable Data Hiding of Dual Stego-images

A dual-image-based reversible data hiding (RDH) scheme can conceal secret data into a cover image by creating two steganographic images. These two stego-images can cooperate to extract secret data and restore the cover image. This paper describes a generalization of the orientation combination technology for dual-image-based RDH. We first propose a full search algorithm to find the optimal set of the reversible orientation combinations. Based on the algorithm, the modification range of pixel values can be dynamically enlarged, and thus the embedding capacity becomes adjustable. In addition, an authentication algorithm is provided to detect tampered shadows based on a faithful one. Experimental results confirm that the proposed scheme can produce dual stego-images with a good visual quality. Furthermore, our method provides an adjustable payload. A lot more secret data can be embedded than with state-of-the-art methods, with a satisfactory image quality. Two steganalysis tools are applied to demonstrate the security level of the proposed scheme.
Jamming in MR: Towards Real-Time Music Collaboration in Mixed Reality

Recent pandemic-related contact restrictions have made it difficult for musicians to meet in person to make music.As a result, there has been an increased demand for applications that enable remote and real-time music collaboration.One desirable goal here is to give musicians a sense of social presence, to make them feel that they are "on site" with their musical partners.We conducted a focus group study to investigate the impact of remote jamming on users' affect.Further, we gathered user requirements for a Mixed Reality system that enables real-time jamming and developed a prototype based on these findings.
An Integrated Success Model of Internet of Things (IoT)-Based Services in Facilities Management for Public Sector

The rapid growth of the Internet of Things (IoT) has vigorously affected government by enhancing quality and efficiency of public services. However, the application of IoT-based services in public sectors is slow, despite its benefits to citizens. Research is needed to deepen understanding of the factors that influence the successful implementation of facilities management as the Internet-of-Things-based services in public sectors. An integrated model is developed and validated to extend the DeLone and McLean IS success model by including technology readiness and other identified factors which impact the use of facilities management of IoT-based services in public sectors from the perspective of employees. An online questionnaire was developed and distributed to employees from all local authorities throughout Malaysia, and 187 usable responses were collected. The partial least squares structural equation modelling approach was used to test the model, with 90.8% of the variance in IoT-based services, suggesting an acceptable model fit with seven out of nine hypotheses were supported. Thus, the empirical evidence exerts significant effects of technology readiness towards the success of IoT-based facility management in the public sector.
Weather Classification by Utilizing Synthetic Data

Weather prediction from real-world images can be termed a complex task when targeting classification using neural networks. Moreover, the number of images throughout the available datasets can contain a huge amount of variance when comparing locations with the weather those images are representing. In this article, the capabilities of a custom built driver simulator are explored specifically to simulate a wide range of weather conditions. Moreover, the performance of a new synthetic dataset generated by the above simulator is also assessed. The results indicate that the use of synthetic datasets in conjunction with real-world datasets can increase the training efficiency of the CNNs by as much as 74%. The article paves a way forward to tackle the persistent problem of bias in vision-based datasets.
Image Reconstruction of Multibranch Feature Multiplexing Fusion Network with Mixed Multilayer Attention

Image super-resolution reconstruction achieves better results than traditional methods with the help of the powerful nonlinear representation ability of convolution neural network. However, some existing algorithms also have some problems, such as insufficient utilization of phased features, ignoring the importance of early phased feature fusion to improve network performance, and the inability of the network to pay more attention to high-frequency information in the reconstruction process. To solve these problems, we propose a multibranch feature multiplexing fusion network with mixed multilayer attention (MBMFN), which realizes the multiple utilization of features and the multistage fusion of different levels of features. To further improve the network’s performance, we propose a lightweight enhanced residual channel attention (LERCA), which can not only effectively avoid the loss of channel information but also make the network pay more attention to the key channel information and benefit from it. Finally, the attention mechanism is introduced into the reconstruction process to strengthen the restoration of edge texture and other details. A large number of experiments on several benchmark sets show that, compared with other advanced reconstruction algorithms, our algorithm produces highly competitive objective indicators and restores more image detail texture information.
A novel 2-phase residual U-net algorithm combined with optimal mass transportation for 3D brain tumor detection and segmentation

Abstract Utilizing the optimal mass transportation (OMT) technique to convert an irregular 3D brain image into a cube, a required input format for a U-net algorithm, is a brand new idea for medical imaging research. We develop a cubic volume-measure-preserving OMT (V-OMT) model for the implementation of this conversion. The contrast-enhanced histogram equalization grayscale of fluid-attenuated inversion recovery (FLAIR) in a brain image creates the corresponding density function. We then propose an effective two-phase residual U-net algorithm combined with the V-OMT algorithm for training and validation. First, we use the residual U-net and V-OMT algorithms to precisely predict the whole tumor (WT) region. Second, we expand this predicted WT region with dilation and create a smooth function by convolving the step-like function associated with the WT region in the brain image with a $$5\times 5\times 5$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mn>5</mml:mn> <mml:mo>×</mml:mo> <mml:mn>5</mml:mn> <mml:mo>×</mml:mo> <mml:mn>5</mml:mn> </mml:mrow> </mml:math> blur tensor. Then, a new V-OMT algorithm with mesh refinement is constructed to allow the residual U-net algorithm to effectively train Net1–Net3 models. Finally, we propose ensemble voting postprocessing to validate the final labels of brain images. We randomly chose 1000 and 251 brain samples from the Brain Tumor Segmentation (BraTS) 2021 training dataset, which contains 1251 samples, for training and validation, respectively. The Dice scores of the WT, tumor core (TC) and enhanced tumor (ET) regions for validation computed by Net1–Net3 were 0.93705, 0.90617 and 0.87470, respectively. A significant improvement in brain tumor detection and segmentation with higher accuracy is achieved.
Progressive Multi-Stage Neural Audio Coding with Guided References

In this paper, we propose an effective multi-stage neural audio coding algorithm that encodes full-band audio signals (up to 20 kHz) using an end-to-end training criterion. By predefining several dyadic subband signals as training targets, we progressively encode input audio signals in each stage such that deeper stages of the network encode the residual error terms from the previous encoding stage. Our proposed audio codec successfully decodes full-band audio signals by using an effective multi-stage vector quantization scheme to represent key encoding features extracted in the latent space. Subjective listening tests show that the decoded outputs of the proposed audio codec achieve almost transparent quality at an average bitrate of 132 kbps.
Responsible Language Technologies: Foreseeing and Mitigating Harms

As increasingly powerful natural language generation, representation, and understanding models are developed, made available and deployed across numerous downstream applications, many researchers and practitioners have warned about possible adverse impacts. Harmful impacts include but are not limited to disparities in quality-of-service, unequal distribution of resources, erasure, stereotyping and misrepresentation of groups and individuals, they might limit people's agency or affect their well-being. Given that language tasks are often complex, open-ended, and incorporated across a diversity of applications; effectively foreseeing and mitigating such harms has remained an elusive goal. Towards this goal, Natural Language Processing (NLP) literature has only recently started to engage with human-centered perspectives and methods—that are often central to HCI research. In this panel, we bring together researchers with expertise in both NLP and HCI, as well as in issues that pertain to the fairness, transparency, justice, and ethics of computational systems. Our main goals are to explore 1) how to leverage HCI perspectives and methodologies to help foresee potential harms of language technologies and inform their mitigation, 2) synergies between the HCI and the responsible NLP research that can help build common ground, and 3) complement existing efforts to facilitate conversations between the HCI and NLP communities.
A Spiral into the Mind

Comparing mobile eye tracking data from multiple participants without information about areas of interest (AOIs) is challenging because of individual timing and coordinate systems. We present a technique, the gaze spiral, that visualizes individual recordings based on image content of the stimulus. The spiral layout of the slitscan visualization is used to create a compact representation of scanpaths. The visualization provides an overview of multiple recordings even for long time spans and helps identify and annotate recurring patterns within recordings. The gaze spirals can also serve as glyphs that can be projected to 2D space based on established scanpath metrics in order to interpret the metrics and identify groups of similar viewing behavior. We present examples based on two egocentric datasets to demonstrate the effectiveness of our approach for annotation and comparison tasks. Our examples show that the technique has the potential to let users compare even long-term recordings of pervasive scenarios without manual annotation.
PVAE-TTS: Adaptive Text-to-Speech via Progressive Style Adaptation

Adaptive text-to-speech (TTS) has attracted increasing interests for the purpose of training TTS systems without tons of high quality data. Nevertheless, existing adaptive TTS systems still show low adaptation quality for novel speakers, since it is hard to learn an extensive speaking style with limited data. To address this issue, we propose progressive variational autoencoder (PVAE) which generates data with adapting to style gradually. PVAE learns a progressively style-normalized representation, which is a key component of progressive style adaptation. We extend PVAE to PVAE-TTS, a multi-speaker adaptive TTS model which generates natural speech with high adaptation quality for novel speakers. To further improve the adaptation quality, we also propose dynamic style layer normalization (DSLN) which utilizes a convolution operation. The experimental results demonstrate the superiority of PVAE-TTS in terms of both subjective and objective evaluations.
Identifying Barriers in the Implementation of Agile Methodologies in Automotive Industry

Projects have grown into more dynamic and complex endeavors, and agile project management should be considered as a way to deal with them. This is a novel study in this field, because the implementation of agile project management in the automotive industry was not explored so far, thus, this work intended to fill this gap, by identifying barriers in the implementation of agile methodologies in project management regarding the automotive industry. This was conducted through a questionnaire survey, which was developed and distributed to 148 manufacturing companies of components for the automotive industry, out of a total number of companies of 240, and 56 complete answers were obtained (23.33%). Statistical analyses were performed using a Kruskal–Wallis test, a Mann–Whitney test, and Spearmen’s correlation. A real picture of the implementation of agile project management in the Portuguese automotive industry is depicted through this work. ‘Organizational’, ‘Knowledge and Technology’, ‘Institutional’, and ‘Financial’ barriers are found to be the most important. However, in overcoming these barriers, companies can be more sustainable in economic, environmental, and social terms. Recommendations on how to overcome these barriers were presented, and a framework sequencing these recommendations was presented, leading to an effective implementation of agile methodologies. It starts with the willingness of the company, and all collaborators, to adopt the agile methodologies, looking for the agile values as an input in order to achieve a competitive advantage. It is followed by an initial investment, which intends to attain the deployment of an agile team, composed of highly skilled collaborators with a clear understanding of the agile objectives, who disseminate knowledge about agile methodologies to the other collaborators, increasing their ability to implement agile methodologies in project management. This team should work and develop frameworks and workflows, according to each company’s characteristics and environment. The studied aspects can be replicated in other countries, and allow a comparison of the situation between countries, trying to correlate the culture of each country with the ability to implement agile methodologies, among other aspects, such as economical level of the companies, type of production, and the commitment of the collaborators to improve processes and create competitive advantages with which to face competitors.
Curvature-Adaptive Meta-Learning for Fast Adaptation to Manifold Data

Meta-learning methods are shown to be effective in quickly adapting a model to novel tasks. Most existing meta-learning methods represent data and carry out fast adaptation in euclidean space. In fact, data of real-world applications usually resides in complex and various Riemannian manifolds. In this paper, we propose a curvature-adaptive meta-learning method that achieves fast adaptation to manifold data by producing suitable curvature. Specifically, we represent data in the product manifold of multiple constant curvature spaces and build a product manifold neural network as the base-learner. In this way, our method is capable of encoding complex manifold data into discriminative and generic representations. Then, we introduce curvature generation and curvature updating schemes, through which suitable product manifolds for various forms of data manifolds are constructed via few optimization steps. The curvature generation scheme identifies task-specific curvature initialization, leading to a shorter optimization trajectory. The curvature updating scheme automatically produces appropriate learning rate and search direction for curvature, making a faster and more adaptive optimization paradigm compared to hand-designed optimization schemes. We evaluate our method on a broad set of problems including few-shot classification, few-shot regression, and reinforcement learning tasks. Experimental results show that our method achieves substantial improvements as compared to meta-learning methods ignoring the geometry of the underlying space.
BIDAC: Blockchain-enabled Identity-Based Data Access Control in IoT

Considering massive device nodes, strong dynamics, and limited resources of the Internet of Things, mainstream data access control solutions have plaintext transmission problems and may cause single point of failure. In this paper, we propose a blockchain-enabled identity-based data access control in IoT named BIDAC. By using Identity-Based Encryption (IBE) algorithm, the security and trustworthiness of the data can be ensured during the authorization and data-sharing process. With the designed access control smart contracts, access rights can be obtained by gaining private keys, providing fine-grained data access control for the IoT systems. Security analysis of this paper shows that BIDAC can resist passive attack, active attack, and physical attack. By implementing BIDAC on the blockchain, the access control performance shows that the proposed BIDAC has good availability.
A simple method for unsupervised anomaly detection: An application to Web time series data

We propose a simple anomaly detection method that is applicable to unlabeled time series data and is sufficiently tractable, even for non-technical entities, by using the density ratio estimation based on the state space model. Our detection rule is based on the ratio of log-likelihoods estimated by the dynamic linear model, i.e. the ratio of log-likelihood in our model to that in an over-dispersed model that we will call the NULL model. Using the Yahoo S5 data set and the Numenta Anomaly Benchmark data set, publicly available and commonly used benchmark data sets, we find that our method achieves better or comparable performance compared to the existing methods. The result implies that it is essential in time series anomaly detection to incorporate the specific information on time series data into the model. In addition, we apply the proposed method to unlabeled Web time series data, specifically, daily page view and average session duration data on an electronic commerce site that deals in insurance goods to show the applicability of our method to unlabeled real-world data. We find that the increase in page view caused by e-mail newsletter deliveries is less likely to contribute to completing an insurance contract. The result also suggests the importance of the simultaneous monitoring of more than one time series.
Efficient Monte Carlo algorithm of time-dependent particle transport problem in multi-physics coupling calculation

Multi-physics coupling calculation has applications in many important research fields. If particle transport process is included in this calculation, Monte Carlo method is often used to simulate this process and usually a large amount of calculation time is needed. So, efficient Monte Carlo algorithm for time-dependent particle transport problem is important for an efficiently coupling calculation, which inevitably relies on large-scale parallel calculation. Based on the characteristic of time-dependent particle transport problem, two methods are proposed in this paper to achieve high- efficiency calculation. One is a tally-reducing algorithm which is used in the coupling of transport simulation and burnup calculation. By reducing the quantity of data which should be reduced necessarily, this method can reduce the calculation time largely. It can be seen that a new coupling mode for these two processes in MPI environment has a larger value when model scale is larger than the sample size. The other method is an adaptive method of setting the sample size of Monte Carlo simulation. The law of large number assures that the Monte Carlo method will obtain an exact solution when the sample scale tends to infinity. But generally, no one knows which sample scale is big enough for obtaining a solution with target precision in advance. So, the common strategy is to set a huge-enough sample scale by experience and conduct the posterior check for all results. Apparently, this way cannot be efficient because the calculation will go on after the precision of solution has reached an object value. Another popular method is to set the sample size to rely on the relative error of some single calculation. The sample size is enlarged without a break until the relative error is less than some presetting value. This method is not suitable either, because Monte Carlo particle transport simulation will gives feedbacks to other process which is composed of many tallies. It is inappropriate to adjust the sample size according to the relative error of any calculation. Relying on the generalization of the Shannon entropy concept and an on-the-fly diagnosis rule for a entropy value sequence, the adaptive method proposed in this paper can reduce the original huge sample scale to a reasonable level. By numerically testing some non-trivial examples, both algorithms can reduce the calculation time largely, with the results kept almost unchanged, so the efficiency is high in these cases.
Recent Generative Adversarial Approach in Face Aging and Dataset Review

Many studies have been conducted in the field of face aging, from approaches that use pure image-processing algorithms, to those that use generative adversarial networks.In this study, we review a classic approach that uses a generative adversarial network.The structure, formulation, learning algorithm, challenges, advantages, and disadvantages of the algorithms contained in each proposed algorithm are discussed systematically.Generative Adversarial Networks are an approach that obtains the status of the art in the field of face aging by adding an aging module, paying special attention to the face part, and using an identity-preserving module to preserve identity.In this paper, we also discuss the database used for facial aging, along with its characteristics.The dataset used in the face aging process must have the following criteria: (1) a sufficiently large age group in the dataset, each age group must have a small range, (2) a balanced distribution of each age group, and (3) has enough number of face images.