Reviewerly/exHarmony huggingface dataset:
Investigating the Potential of Solar Trackers in Renewable Energy Integration to Grid

Over the years, there has been a continued effort aimed at enhancing the energy output of PV panels. One of such innovations is in the use of solar trackers to improve the overall performance and efficiency of solar systems. This paper presents a critical review of various types of solar tracking systems, highlighting their key characteristics, merit, and demerits. In addition, a prototype double axis solar tracker was developed and tested. The results show the power output of the prototype double axis solar tracker to be 30.5% higher than that of a fixed PV system, also an increase in efficiency from 35.91% to 45.45% was observed. Finally, the research reveals that a double-axis solar tracked PV system in Nigeria will deliver an estimated 823, 150, 000Wh more energy than PV system without the use of solar tracker in the long term, which further justifies the importance of solar trackers in renewable energy integration to grid.
View-normalized Skeleton Generation for Action Recognition

Skeleton-based action recognition has attracted great interest due to low cost of skeleton data acquisition and high robustness to external conditions. A challenging problem of skeleton-based action recognition is the large intra-class gap caused by various viewpoints of skeleton data, which makes the action modeling difficult for network. To alleviate this problem, a feasible solution is to utilize label supervised methods to learn a view-normalization model. However, since the skeleton data in real scenes is acquired from diverse viewpoints, it is difficult to obtain the corresponding view-normalized skeleton as label. Therefore, how to learn a view-normalization model without the supervised label is the key to solving view-variance problem. To this end, we propose a view normalization-based action recognition framework, which is composed of view-normalization generative adversarial network (VN-GAN) and classification network. For VN-GAN, the model is designed to learn the mapping from diverse-view distribution to normalized-view distribution. In detail, it is implemented by graph convolution, where the generator predicts the transformation angles for view normalization and discriminator classifies the real input samples from the generated ones. For classification network, view-normalized data is processed to predict the action class. Without the interference of view variances, classification network can extract more discriminative feature of action. Furthermore, by combining the joint and bone modalities, the proposed method reaches the state-of-the-art performance on NTU RGB+D and NTU-120 RGB+D datasets. Especially in NTU-120 RGB+D, the accuracy is improved by 3.2% and 2.3% under cross-subject and cross-set criteria, respectively.
New Materialisms, Material Methods, and the Research Process: A Creative Experiment in Cutting Together-Apart

New materialisms call for a (re)thinking of matter as agentic and lively. With the aim to recognize the vitality of matter within qualitative research, scholars are developing material methods and creative processes to explore the intimate connections between humans and nonhumans. Herein we use Barad's understanding of diffraction to revisit the research process as an entanglement of theory, method, and analysis. More specifically, we take up the concept of cutting together-apart, adapting it in a series of creative practices focused on women's exercising bodies and relationships with activewear. In each activity, we experiment with cutting various materials from the project (i.e., digital data, activewear clothing) with each other, feminist colleagues, and project participants. Across these creative experiments, we came to 'new' ways of knowing the activewear phenomenon evoked through the materiality and affective processes of 'cutting,' as well as new ethical considerations about human and nonhuman co-participants.
Interpretation of Emergent Communication in Heterogeneous Collaborative Embodied Agents

Communication between embodied AI agents has received increasing attention in recent years. Despite its use, it is still unclear whether the learned communication is interpretable and grounded in perception. To study the grounding of emergent forms of communication, we first introduce the collaborative multi-object navigation task ‘CoMON.' In this task, an ‘oracle agent' has detailed environment information in the form of a map. It communicates with a ‘navigator agent' that perceives the environment visually and is tasked to find a sequence of goals. To succeed at the task, effective communication is essential. CoMON hence serves as a basis to study different communication mechanisms between heterogeneous agents, that is, agents with different capabilities and roles. We study two common communication mechanisms and analyze their communication patterns through an egocentric and spatial lens. We show that the emergent communication can be grounded to the agent observations and the spatial structure of the 3D environment.
Improving Streaming Graph Processing Performance using Input Knowledge

Streaming graphs are ubiquitous in today’s big data era. Prior work has improved the performance of streaming graph workloads without taking input characteristics into account. In this work, we demonstrate that input knowledge-driven software and hardware co-design is critical to optimize the performance of streaming graph processing. To improve graph update efficiency, we first characterize the performance trade-offs of input-oblivious batch reordering. Guided by our findings, we propose input-aware batch reordering to adaptively reorder input batches based on their degree distributions. To complement adaptive batch reordering, we propose updating graphs dynamically, based on their input characteristics, either in software (via update search coalescing) or in hardware (via acceleration support). To improve graph computation efficiency, we present input-aware work aggregation which adaptively modulates the computation granularity based on inter-batch locality characteristics. Evaluated across 260 workloads, our input-aware techniques provide on average 4.55 × and 2.6 × improvement in graph update performance for different input types (on top of eliminating the performance degradation from input-oblivious batch reordering). The graph compute performance is improved by 1.26 × (up to 2.7 ×).
Cell separation algorithm with enhanced search behaviour in miRNA feature selection for cancer diagnosis

Feature (biomarker) selection of microRNA data is an important step for the cancer classification task. In this paper, the cell separation meta-heuristic algorithm (CSA) which imitates the cell separation action using differential centrifugation process is improved. Enhanced search behaviour embedded in movement of virtual cells in this algorithm is provided. This enhancement contributes to an effective trade-off between global exploration and local exploitation of the search space which is the key factor to improve the performance of any meta-heuristic. In order to perform feature selection, different levels and periods to handle the number of features in the selected subset is automatically used during the search process. This process is set to avoid the effect of the dimension of data on computational time and effort. To examine the improved CSA (I-CSA), 25 test functions were initially used. Next, six benchmark classification problems, including four biological and two other datasets, were employed to test the I-CSA. Furthermore, an experiment for feature selection from microRNA data is conducted. The classification accuracy of the selected features obtained by applying I-CSA on microRNA data is compared with the accuracy of 22 well-known classifiers on a real-world dataset. This dataset contains genomic information of 8,129 patients for 29 different types of cancer with 1,046 gene expressions. The classification accuracy of each cancer type is also ranked with the results of 77 classifiers reported in previous works. As a result, the proposed approach achieved 100% accuracy in 25 out of 29 classes. In seven cases out of 29, the method achieved 100% accuracy, which no classifier in other studies has reached.
On union and intersection of type-2 fuzzy sets not expressible by the sup-t-norm extension principle

This paper constructs a conjunction and a disjunction operator for type-2 fuzzy sets that cannot be expressed by the sup-t-norm extension principle applied to conjunction and disjunction of type-1 fuzzy sets by means of t-norms and t-conorms on the unit interval. This result answers affirmatively an open problem posed by Hernández et al. in 2015. The duality between the set-theoretic operators for type-2 fuzzy sets is obtained from the duality of set-theoretic operations on type-1 fuzzy sets.
Explainable Machine Learning for Improving Logistic Regression Models

Model explainability has become an important objective when developing machine learning algorithms, especially in highly regulated industries. However, it is difficult to achieve both prediction accuracy and intrinsic explainability as the two objectives usually conflict with each other. Recent development regarding Explainable Neural Network, or xNN, shed some lights on resolving the trade-off between accuracy and explainability for neural network. In this paper, we propose an xNN approach to develop or improve logistic regressions, which can be useful in credit risk modeling and money-laundering or fraud detection. Our data experiment shows that the proposed xNN model keeps the flexibility of pursuing high prediction accuracy while attaining improved explainability.
Fast and Reliable Formal Verification of Smart Contracts with the Move Prover

Abstract The Move Prover () is a formal verifier for smart contracts written in the Move programming language. has an expressive specification language, and is fast and reliable enough that it can be run routinely by developers and in integration testing. Besides the simplicity of smart contracts and the Move language, three implementation approaches are responsible for the practicality of : (1) an alias-free memory model, (2) fine-grained invariant checking, and (3) monomorphization. The entirety of the Move code for the Diem blockchain has been extensively specified and can be completely verified by in a few minutes. Changes in the Diem framework must be successfully verified before being integrated into the open source repository on GitHub.
Actuating Myself: Designing Hand-Games Incorporating Electrical Muscle Stimulation

Motor movements are performed while playing hand-games such as Rock-paper-scissors or Thumb-war. These games are believed to benefit both physical and mental health and are considered cultural assets. Electrical Muscle Stimulation (EMS) is a technology that can actuate muscles, triggering motor movements and hence offers an opportunity for novel play experiences based on these traditional hand-games. However, there is only limited understanding of the design of EMS games. We present the design and evaluation of two games inspired by traditional hand-games, ”Slap-me-if-you-can” and ”3-4-5”, which incorporate EMS and can be played alone, unlike traditional games. A thematic analysis of the data collected revealed three themes: 1) Gameplay experiences and influence of EMS hardware, 2) Interaction with EMS and the calibration process and, 3) Shared control and its effect on playing EMS games. We hope that an enhanced understanding of the potential of EMS to support hand-games can aid the advancement of movement-based games as a whole.
Fast Momentum Contrast Learning for Unsupervised Person Re-Identification

Person re-identification (re-ID) aims to identify the same persons' images across different cameras by deploying a trained re-ID model. Nevertheless, the domain bias between different datasets can lead to considerably low identification accuracy when directly applying a trained re-ID model from one dataset to another. Therefore, in order to develop a kind of more transferable re-ID methods with strong robustness, this paper proposes a fast momentum contrast learning framework, which consists of a teacher encoder and a fast momentum encoder, from a reinforcing visual representation learning point of view. The fast momentum encoder produced by the teacher encoder is employed to build a dynamic dictionary and then design the contrast learning loss function together with the teacher encoder. In addition, both the individual loss and the clustering loss are adopted for obtaining the optimal performances in unsupervised person re-ID models. The proposed framework achieves one stage of end-to-end training and experimental results demonstrate that our identification accuracy outperforms existing advanced methods.
Accessing Smart City Services in Untrustworthy Environments via Decentralized Privacy-Preserving Overlay Networks

For managing sensor-based data and services in advanced smart city environments, citizen participation in open private and public network environments plays an increasingly important role. Various approaches allow citizens to share, e.g., local sensors, urban data, and services to increase environmental awareness. Although privacy and (data and service) sovereignty is essential in citizen participation, most of these approaches rely on centralized trusted third parties or are unsuitable to be operated in untrustworthy (e.g., public) network environments. This paper presents drasyl, a fully decentralized transport overlay network that also accommodates related trust and reliability issues. Consequently, drasyl allows citizens to access services and applications in public untrustworthy IP-based networks without nedding a central trusted third-party service. drasyl provides means to strengthen citizens' privacy by end-to-end encrypted communication and utilizes methods to hide communication and service access between citizens from unauthorized third parties. Finally, the paper demonstrates how drasyl can be used as a basis for a peer-to-peer middleware that helps develop distributed applications.
GitOps: The Evolution of DevOps?

GitOps is a new concept that is supposed to be the evolution of DevOps, and we analyze whether that is so. Since neither DevOps nor GitOps has a standard definition, we synthesize one for each and compare them by their key elements.
EDFace-Celeb-1 M: Benchmarking Face Hallucination With a Million-Scale Dataset

Recent deep face hallucination methods show stunning performance in super-resolving severely degraded facial images, even surpassing human ability. However, these algorithms are mainly evaluated on non-public synthetic datasets. It is thus unclear how these algorithms perform on public face hallucination datasets. Meanwhile, most of the existing datasets do not well consider the distribution of races, which makes face hallucination methods trained on these datasets biased toward some specific races. To address the above two problems, in this paper, we build a public Ethnically Diverse Face dataset, EDFace-Celeb-1 M, and design a benchmark task for face hallucination. Our dataset includes 1.7 million photos that cover different countries, with relatively balanced race composition. To the best of our knowledge, it is the largest-scale and publicly available face hallucination dataset in the wild. Associated with this dataset, this paper also contributes various evaluation protocols and provides comprehensive analysis to benchmark the existing state-of-the-art methods. The benchmark evaluations demonstrate the performance and limitations of state-of-the-art algorithms. https://github.com/HDCVLab/EDFace-Celeb-1M.
Bridging Fluency Disparity between Native and Nonnative Speakers in Multilingual Multiparty Collaboration Using a Clarification Agent

Multiparty collaboration using a common language is often challenging for nonnative speakers (NNS). Conversation can move forward rapidly, with terms and references unfamiliar to NNS often going unexplained because NNS do not request clarification due to cognitive overload or face concerns. Language difficulties may further lead to NNS having a low level of participation in a conversation, which could be a loss for multilingual teams. To help NNS resolve potential confusions due to unfamiliar language use without risking face concerns, we created a conversation agent that asked clarification questions intended to help NNS follow and participate in multiparty conversations. We conducted a within-subjects laboratory experiment with 17 triads of 2 NS and 1 NNS, who performed a series of collaborative tasks under three conditions: a) no agent, b) a high-level agent that resembles a NNS with good command of English, and c) a low-level agent that resembles a NNS with poor English skills. Results suggest that NS made significantly more clarifications in both agent conditions than without an agent. In the high-level agent condition, NNS reported an increase in understanding after the agent's interruption and spoke significantly more. Further, NNS evaluated their communication competence in English highest in the low-level agent condition and lowest in the control condition. Our findings suggest several directions to improve the tool to better facilitate multilingual multiparty communication.
Generalized Real-World Super-Resolution through Adversarial Robustness

Real-world Super-Resolution (SR) has been traditionally tackled by first learning a specific degradation model that resembles the noise and corruption artifacts in low- resolution imagery. Thus, current methods lack generalization and lose their accuracy when tested on unseen types of corruption. In contrast to the traditional proposal, we present Robust Super-Resolution (RSR), a method that leverages the generalization capability of adversarial attacks to tackle real-world SR. Our novel framework poses a paradigm shift in the development of real-world SR methods. Instead of learning a dataset-specific degradation, we employ adversarial attacks to create difficult examples that target the model’s weaknesses. Afterward, we use these adversarial examples during training to improve our model’s capacity to process noisy inputs. We perform extensive experimentation on synthetic and real-world images and empirically demonstrate that our RSR method generalizes well across datasets without re-training for specific noise priors. By using a single robust model, we outperform state-of-the- art specialized methods on real-world benchmarks.
“Is your font racist?” Metapragmatic online discourses on the use of typographic mimicry and its appropriateness

Typographic mimicry is the wrapping of writing in a “foreign dress,” i.e. the use of typefaces in which one’s script (e.g. Latin) is made to visually resemble a different script (e.g. Chinese) with the goal of evoking associations with a “foreign” culture. First, this paper addresses the formal aspects of this practice, specifically the choice of visual features to be mimicked. The core part then focuses on typographic mimicry as a social practice and includes a discussion of both the typographic knowledge that different actors – both lay and expert producers and recipients – must apply to establish and recognise the associated cultural indexicality and the typographic ideologies (i.e. beliefs and attitudes) these actors hold. The central question being investigated is how typographic mimicry is discursively negotiated. An exemplary metapragmatic discourse analysis of online reactions to a food ad and comments to two articles covering the topic catered at readers with different knowledge backgrounds highlights that typographic mimicry is not a “neutral” practice. It shows that central aspects being debated are the (re)appropriation of cultural stereotypes by users both outside and within the respective cultures and the related question of whether using typographic mimicry is generally (in)appropriate (or even racist).
LightFEC

Nowadays, the interest of real-time video streaming reaches a peak. To deal with the problem of packet loss and optimize users' Quality of Experience (QoE), Forward error correction (FEC) has been studied and applied extensively. The performance of FEC depends on whether the future loss pattern is precisely predicted, while the previous researches have not provided a robust packet loss prediction method. In this work, we propose LightFEC to make accurate and fast prediction of packet loss pattern. By applying long short-term memory (LSTM) networks, clustering algorithms and model compression methods, LightFEC is able to accurately predict packet loss in various network conditions without consuming too much time. According to the results of well-designed experiments, we find out that LightFEC outperforms other schemes on prediction accuracy, which improves the packet recovery ratio while keeping the redundancy ratio at a low level.
DLA-Net for FG-SBIR

Fine-grained sketch-based image retrieval is considered as an ideal alternative to keyword-based image retrieval and image search by image due to the rich and easily accessible characteristics of sketches. Previous works always follow a paradigm that first extracting image global feature with convolution neural network and then optimizing the model with triplet loss. Many efforts on narrowing the domain gap and extracting discriminating features are made by these works. However, they ignored that the global feature is not good at capturing fine-grained details. In this paper, we emphasize the local features are more discriminating than global feature in FG-SBIR and explore an effective way to utilize local features. Specifically, Local Aligned Network (LA-Net) is proposed first, which solves FG-SBIR by directly aligning the mid-level local features. Experiment manifests it can beat all previous baselines and is easy to implement. LA-Net is hoped to be a new strong baseline for FG-SBIR. Next, Dynamic Local Aligned Network (DLA-Net) is proposed to enhance LA-Net. The question of spatial misalignment caused by the abstraction of the sketch is not considered by LA-Net. To solve this question, a dynamic alignment mechanism is introduced into LA-Net. This new mechanism makes the sketch interact with the photo and dynamically decide where to align according to the different photos. The Experiment indicates DLA-Net successfully addresses the question of spatial misalignment. It gains a significant performance boost over LA-Net and outperforms the state-of-the-art in FG-SBIR. To the best of our knowledge, DLA-Net is the first model that beats humans on all datasets---QMUL FG-SBIR, QMUL Handbag, and Sketchy.
Improving NSGA-II by a Local Search Strategy with Gaussian Mutation

An improved non-dominated sorting genetic algorithm NSGA-II-GLS is proposed in this paper, which intends to enhance the local search ability of the original NSGA-II and reduce the likelihood of falling into local optimal. The proposed algorithm NSGA-II-GLS introduces a Gaussian mutation operator into the genetic operation in the algorithm, which makes the individuals focus on exploiting the space near themselves. Also, the improved algorithm adopts a jiggling local search strategy in order to jump out of non-global Pareto fronts. The performance of NSGA-II-GLS is compared with NSGA-II and NSGA-II-DE through five DTLZ test problems. The simulation results indicate that the NSGA-II-GLS has a good local search capability and the convergence and diversity of Pareto optimal solutions from NSGA-II-GLS are better than the original NSGA-II. In addition, solutions generated by NSGA-II-GLS suggest that NSGA-II-GLS is more versatile than NSGA-II-DE.