Reviewerly/exHarmony huggingface dataset:
#OurPlace2020: Blurring Boundaries of Learning Spaces

The move to online learning has blurred the distinction between the Foucauldian conceptualisation of university as a 'Heterotopia' with the real world of contingent alternative learning spaces students create. In this paper, we highlight the need for a broader and more holistic approach to educational design which requires a re-conceptualisation of the learning environment to include the students' real-world spaces and their socio-cultural surroundings through a postdigital paradigm. We seek to understand how Covid-19 has accelerated postdigital disruption of the concept of university as heterotopia, where learning is traditionally highly structured and segmented in slices of time for seminars, lectures, and workshops. In April 2020, the University of Sydney Business School invited students to share stories of their remote learning experience using the #OurPlace2020 hashtag. Using Actor Network Theory (ANT), we analyse 37 digital stories to provide examples of how the boundaries of the traditional learning spaces of university campus are blurred with real-life learning spaces when students are studying remotely. We argue for the need to adopt a broader analytical approach that can elucidate the complexity of heterogenous networks of interacting digital and non-digital entities through which learning spaces are constructed.
New Instances of Quadratic APN Functions

In a recent work, Beierle, Brinkmann and Leander presented a recursive tree search for finding APN permutations with linear self-equivalences in small dimensions. In this paper, we describe how this search can be adapted to find many new instances of quadratic APN functions. In particular, we found 12,921 new quadratic APN functions in dimension eight, 35 new quadratic APN functions in dimension nine and five new quadratic APN functions in dimension ten up to CCZ-equivalence. Remarkably, two of the 35 new APN functions in dimension nine are APN permutations. Among the 8-bit APN functions, there are three extended Walsh spectra that do not correspond to any of the previously-known quadratic 8-bit APN functions and, surprisingly, there exist at least four CCZ-inequivalent 8-bit APN functions with linearity 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">7</sup> , i.e., the highest possible non-trivial linearity for quadratic functions in dimension eight.
Using Trajectory Compression Rate to Predict Changes in Cybersickness in Virtual Reality Games

Identifying cybersickness in virtual reality (VR) applications such as games in a fast, precise, non-intrusive, and non-disruptive way remains challenging. Several factors can cause cybersickness, and their identification will help find its origins and prevent or minimize it. One such factor is virtual movement. Movement, whether physical or virtual, can be represented in different forms. One way to represent and store it is with a temporally annotated point sequence. Because a sequence is memory-consuming, it is often preferable to save it in a compressed form. Compression allows redundant data to be eliminated while still preserving changes in speed and direction. Since changes in direction and velocity in VR can be associated with cybersickness, changes in compression rate can likely indicate changes in cybersickness levels. In this research, we explore whether quantifying changes in virtual movement can be used to estimate variation in cybersickness levels of VR users. We investigate the correlation between changes in the compression rate of movement data in two VR games with changes in players’ cybersickness levels captured during gameplay. Our results show (1) a clear correlation between changes in compression rate and cybersickness, and (2) that a machine learning approach can be used to identify these changes. Finally, results from a second experiment show that our approach is feasible for cybersickness inference in games and other VR applications that involve movement.
AdaDC: Adaptive Deep Clustering for Unsupervised Domain Adaptation in Person Re-Identification

Unsupervised domain adaptation (UDA) in person re-identification (re-ID) is a challenging task, aiming to learn a model with labeled source data and unlabeled target data to recognize the same person in the target domain across different cameras. Recently, a lot of popular and promising methods based on clustering are proposed for this task and achieve a sizable progress. However, in those methods, without target labels, the clustering algorithms will inevitably produce noisy pseudo-labels. Overfitting on these noisy labels is severely harmful to the performance and generalization of models. In order to address the above issues, we propose a novel framework, Adaptive Deep Clustering (AdaDC), to reduce the negative impact of noisy pseudo-labels. On one hand, the proposed approach employs different clustering methods adaptively and alternately to fully exploit their complementary information and avoid overfitting noisy pseudo-labels. On the other hand, there is a progressive sample selection strategy for reducing noisy label ratio in pseudo-labels, which is achieved by integrating different clustering results. Experiments present that the proposed approach can achieve state-of-the-art performance compared to the other recent UDA person re-ID methods on widely-used datasets. Moreover, there are some other analysis experiments conducted for verifying the effectiveness of the proposed approach.
Presence, Absence, and Alterity: Fire Space and Goffman’s Selves in Postdigital Education

Abstract The literature on space in higher education has arguably been dominated by the concept of ‘learning spaces’. In this paper, I will argue that this construct, while appearing student-focused and creative, is ideologically circumscribed by an underlying social constructivism. Following Bayne et al. (2014), I draw on science and technology studies to consider social topologies, in particular regional space, network space , and their proposed fluid space , and the work of Law and colleagues on the category of fire space , derived from Bachelard’s ( The Psychoanalysis of Fire , 1964) disquisition on the nature of fire. I work with this construct in an analysis of postdigital education, in particular looking at synchronous interaction via video conferencing software such as Zoom. Linking this analysis to the work of Goffman and his concept of the lecturer selves (Goffman in Forms of Talk , 1981), I argue that the concept of fire space may allow for a more nuanced and accurate account of the flickering, contingent nature of (co) presence, absence, and alterity, allowing for a more immanent account of digital interaction in ‘distance’ or ‘online’ education.
Learning to Rank Proposals for Siamese Visual Tracking

Recently, Siamese network based trackers with region proposal networks(RPN) decompose the visual tracking task into classification and regression, and have drawn much attention. However, previous Siamese trackers process all the training samples equally to learn the desired network, and only take the classification scores of proposals to locate the tracked target at the inference stage. To address the above issues, we propose a simple, yet effective strategy to rank the importance of training samples, and pay more attention to the important samples, which can facilitate the classification optimization. Moreover, we propose a lightweight ranking network to generate the ranking scores for proposals. Higher scores are assigned to proposals whose Intersection over Union(IoU) with the ground-truth are larger. The combination of classification and ranking scores serves as a new proposal selection criterion for online tracking, and can boost the tracking performance significantly. Our proposed method could be easily integrated into existing RPN-based Siamese networks in an end-to-end fashion. Extensive experiments are conducted on 10 tracking benchmarks, including NFS, UAV123, OTB2015, Temple-Color, VOT2016, VOT2017, VOT2019, TrackingNet, GOT-10K and LaSOT. The proposed method achieves a state-of-the-art tracking accuracy with a real-time speed.
Blockchain technology and its applications in agriculture and supply chain management: a retrospective overview and analysis

The paper undertakes a bibliometric study to analyse and identify emerging themes for future research in blockchain technology, focusing on agriculture and supply chain management domains. A sample of 1322 articles from Web of Science for 2015–2020 is the basis of the study. The publications are grouped into five clusters, of which Cluster 1 is consistently dominant in the Information Systems publication landscape. Clusters 2, 3, and 4 are evolving, and topics with scant coverage are primarily in Cluster 5, indicating saturation in the area of interdisciplinary studies. The results provide valuable insights for potential contributors and global audiences.
A Fast Consensus for Permissioned Wireless Blockchains

With the wide deployment of Internet of Things (IoT), blockchain systems have been playing a crucial role to establish a trusted computing environment among potentially mistrusting agents without depending on a centralized server. Different from previous blockchain consensus protocols adopted in IoT, which rely on efficient and stable transmissions, in this article, we consider how to reach blockchain consensus in wireless networks without reliable network support. Specifically, a realistic signal to interference plus noise ratio (SINR) model is adopted to depict the unreliable transmissions in wireless channels. Based on the SINR model, a distributed and randomized consensus algorithm is proposed to reach <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> -times consensus among <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula> devices within <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$O(k+\log n)$ </tex-math></inline-formula> time steps with high probability. Note that the time complexity of our algorithm is asymptotically optimal since <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\Omega (k+\log n)$ </tex-math></inline-formula> is a lower bound to achieve <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> -times consensus in a distributed environment. We conduct both rigorous theoretical analysis and extensive simulations to validate our method. It is believed that our work can facilitate the implementation of blockchains in many wireless scenarios in which the reliable and fast transmissions cannot be guaranteed.
Demonstration of entanglement and coherence in GHZ-like state when exposed to classical environments with power-law noise

We investigate the dynamics and protection of entanglement and coherence in three non-interacting qubits that are initially prepared as mixed entangled GHZ-like state when coupled with external classical fields. A type of Gaussian noise, namely power-law (PL) noise, controls the external local fields in three different configurations with single, double, and triple noise sources. When influenced by a single PL noise source, the GHZ-class state remains partially entangled and coherent indefinitely, in contrast to the multiple PL noise sources. However, long-term non-local correlations and coherence are still possible in the presence of multiple noise sources. In classical fluctuating environments, the PL noise restricts the entanglement sudden death and birth phenomenon, thereby preventing the conversion of free states to resource states. In addition to the noise phase, parameter optimization in local fields regulates disorders, noise effects, and memory properties. Unlike the bipartite and tripartite W-type states, the GHZ-like state has shown positive traits of preserving entanglement and coherence in the presence of PL noise.
Randomized Benchmarking as Convolution: Fourier Analysis of Gate Dependent Errors

We show that the Randomized Benchmarking (RB) protocol is a convolution amenable to Fourier space analysis. By adopting the mathematical framework of Fourier transforms of matrix-valued functions on groups established in recent work from Gowers and Hatami [Sbornik: Mathematics 208, 1784 (2017)], we provide an alternative proof of Wallman's [Quantum 2, 47 (2018)] and Proctor's [Phys. Rev. Lett. 119, 130502 (2017)] bounds on the effect of gate-dependent noise on randomized benchmarking. We show explicitly that as long as our faulty gate-set is close to the targeted representation of the Clifford group, an RB sequence is described by the exponential decay of a process that has exactly two eigenvalues close to one and the rest close to zero. This framework also allows us to construct a gauge in which the average gate-set error is a depolarizing channel parameterized by the RB decay rates, as well as a gauge which maximizes the fidelity with respect to the ideal gate-set.
Affordances of augmented reality technology for science education: Views of secondary school students and science teachers

Using augmented reality (AR) in science education is one of the popular topics that have been researched in recent years. However, more qualitative studies are needed as the contributions of using AR in science education to the learning experience and knowledge construction can be better understood through qualitative research. Therefore, it is important to examine the opinions of teachers and students using AR on their experiences. This study aims to reveal their views on AR technology in terms of their perceived learning, interest, attitude, and experience. A case study design was employed. The research was conducted with 40 students in 5th, 6th, 7th, and 8th grades in a secondary school and 10 science teachers. In the research, the SPACE 4D application was used for 3 weeks within the scope of “Sun, Earth, and Moon”, “Solar System and Eclipses,” and “Solar System and Beyond” subjects. The interviews were held with all participants after the 12-h lesson. Students stated that AR increases their interest and motivation, facilitates learning, and prevents boredom. In addition, teachers stated that AR increases attention, the permanence of knowledge and curiosity, as well as concretizes the abstract concepts and is useful. In this regard, this technology can be used in science lessons to motivate students to the lesson, to help students overcome their prejudices, to change students' attitudes, and to provide more effective results.
Decolonizing Philosophy of Technology: Learning from Bottom-Up and Top-Down Approaches to Decolonial Technical Design

Abstract The decolonial theory understands that Western Modernity keeps imposing itself through a triple mutually reinforcing and shaping imprisonment: coloniality of power, coloniality of knowledge, and coloniality of being. Technical design has an essential role in either maintaining or overcoming coloniality. In this article, two main approaches to decolonizing the technical design are presented. First is Yuk Hui’s and Ahmed Ansari’s proposals that, revisiting or recovering the different histories and philosophies of technology produced by humankind, intend to decolonize the minds of philosophers and engineers/architects/designers as a pre-condition for such decolonial designs to take place. I call them top-down approaches. Second is some technical design initiatives that, being developed alongside marginalized/subalternate people, intend to co-construct decolonial sociotechnical solutions through a committed, decolonizing, and careful dialog of knowledge. I call them bottom-up approaches. Once that is done, the article’s second half derives ontological, epistemological, and political consequences from the conjugation of top-down and bottom-up approaches. Such consequences challenge some established or not yet entirely overcome understandings in the philosophy of technology (PT) and, in so doing, are meant to represent some steps in PT’s decolonization. Even though both top-down and bottom-up approaches are considered, the article’s main contributions are associated with (bottom-up) decolonial technical design practices, whose methodologies and outcomes are important study cases for PT and whose practitioners (i.e., decolonial designers) can be taken as inspiring examples for philosophers who want to decolonize/enlarge PT or make it decolonial (that is, a way of fostering decoloniality).
Shorter and Faster Post-Quantum Designated-Verifier zkSNARKs from Lattices

Zero-knowledge succinct arguments of knowledge (zkSNARKs) enable efficient privacy-preserving proofs of membership for general NP languages. Our focus in this work is on post-quantum zkSNARKs, with a focus on minimizing proof size. Currently, there is a 1000x gap in the proof size between the best pre-quantum constructions and the best post-quantum ones. Here, we develop and implement new lattice-based zkSNARKs in the designated-verifier preprocessing model. With our construction, after an initial preprocessing step, a proof for an NP relation of size 2^20 is just over 16 KB. Our proofs are 10.3x shorter than previous post-quantum zkSNARKs for general NP languages. Compared to previous lattice-based zkSNARKs (also in the designated-verifier preprocessing model), we obtain a 42x reduction in proof size and a 60x reduction in the prover's running time, all while achieving a much higher level of soundness. Compared to the shortest pre-quantum zkSNARKs by Groth (Eurocrypt 2016), the proof size in our lattice-based construction is 131x longer, but both the prover and the verifier are faster (by 1.2x and 2.8x, respectively). Our construction follows the general blueprint of Bitansky et al. (TCC 2013) and Boneh et al. (Eurocrypt 2017) of combining a linear probabilistically checkable proof (linear PCP) together with a linear-only vector encryption scheme. We develop a concretely-efficient lattice-based instantiation of this compiler by considering quadratic extension fields of moderate characteristic and using linear-only vector encryption over rank-2 module lattices.
A novel hybrid fuzzy–metaheuristic approach for multimodal single and multi-objective optimization problems

In this paper, we propose a novel hybrid fuzzy–metaheuristic approach with the aim of overcoming premature convergence when solving multimodal single and multi-objective optimization problems. The metaheuristic algorithm used in our proposed approach is based on the imperialist competitive algorithm (ICA), a population-based method for optimization. The ICA divides its population into sub-populations, known as empires. Each empire is composed of a high fitness solution—the imperialist—and some lower fitness solutions—the colonies. Colonies move towards their associated imperialist to achieve better status (higher fitness). The most powerful empire tends to attract weaker colonies. These competitions and movements can be enhanced for better algorithm performance. In our hybrid approach, a global learning strategy is devised for each colony to learn from its best-known position, its associated imperialist and the global best imperialist. A fast-evolutionary elitism local search is used to enhance the collaborative search mechanism (competition) in each empire, and thus the overall optimization performance may be improved. Other main evolutionary operators include velocity adaptation and velocity divergence. To address parameterization and computational cost evaluation issues, two fuzzy inferencing mechanisms are designed and used in parallel: one is a learning strategy adaptor in each run, and the other is a smart evolution selector in each running window. For Pareto front approximation, fast-elitism non-dominated sorting is applied to the solutions, and a novel penalized sigma diversity index is designed to estimate the diversity (power) of solutions in the same rank. Comprehensive experimental results based on 22 single-objective and 25 multi-objective benchmark instances clearly show that our proposed approach provides better solutions compared with other popular metaheuristics and state-of-the-art ICA variants. The proposed approach can be used as an optimization module in any intelligent decision-making systems to enhance efficiency and accuracy. The designed fuzzy inferencing mechanisms can also be incorporated into any single- or multi-objective optimizers for parameter tuning purposes, to make the optimizers more adaptive to new problems or environments.
Time-aware Graph Neural Network for Entity Alignment between Temporal Knowledge Graphs

Entity alignment aims to identify equivalent entity pairs between different knowledge graphs (KGs). Recently, the availability of temporal KGs (TKGs) that contain time information created the need for reasoning over time in such TKGs. Existing embedding-based entity alignment approaches disregard time information that commonly exists in many large-scale KGs, leaving much room for improvement. In this paper, we focus on the task of aligning entity pairs between TKGs and propose a novel Time-aware Entity Alignment approach based on Graph Neural Networks (TEA-GNN). We embed entities, relations and timestamps of different KGs into a vector space and use GNNs to learn entity representations. To incorporate both relation and time information into the GNN structure of our model, we use a time-aware attention mechanism which assigns different weights to different nodes with orthogonal transformation matrices computed from embeddings of the relevant relations and timestamps in a neighborhood. Experimental results on multiple real-world TKG datasets show that our method significantly outperforms the state-of-the-art methods due to the inclusion of time information.
Dataflow Mirroring: Architectural Support for Highly Efficient Fine-Grained Spatial Multitasking on Systolic-Array NPUs

We present dataflow mirroring, architectural support for low-overhead fine-grained systolic array allocation which overcomes the limitations of prior coarse-grained spatial-multitasking Neural Processing Unit (NPU) architectures. The key idea of dataflow mirroring is to reverse the dataflows of co-located Neural Networks (NNs) in horizontal and/or vertical directions, allowing allocation boundaries to be set between any adjacent rows and columns of a systolic array and supporting up to four-way spatial multitasking. Our detailed experiments using MLPerf NNs and a dataflow-mirroring-augmented NPU prototype which extends Google’s TPU with dataflow mirroring shows that dataflow mirroring can significantly improve the multitasking performance by up to 46.4%.