Reviewerly/exHarmony huggingface dataset:
Best Practices for Quantum Error Mitigation with Digital Zero-Noise Extrapolation

Digital zero-noise extrapolation (dZNE) has emerged as a common approach for quantum error mitigation (QEM) due to its conceptual simplicity, accessibility, and resource efficiency. In practice, however, properly applying dZNE to extend the computational reach of noisy quantum processors is rife with subtleties. Here, based on literature review and original experiments on noisy simulators and real quantum hardware, we define best practices for QEM with dZNE for each step of the workflow, including noise amplification, execution on the quantum device, extrapolation to the zero-noise limit, and composition with other QEM methods. We anticipate that this effort to establish best practices for dZNE will be extended to other QEM methods, leading to more reproducible and rigorous calculations on noisy quantum hardware. The full version of this paper can be found in Ref. [1].
Synchronous Perfectly Secure Message Transmission with Optimal Asynchronous Fallback Guarantees

Secure message transmission (SMT) constitutes a fundamental network-layer building block for distributed protocols over incomplete networks. More specifically, a sender $$\textbf{S}$$ and a receiver $$\textbf{R}$$ are connected via $$\ell $$ disjoint paths, a subset of which are controlled by the adversary. Perfectly-secure SMT protocols in synchronous and asynchronous networks are resilient up to $$\ell /2$$ and $$\ell /3$$ corruptions respectively. In this work, we ask whether it is possible to achieve a perfect SMT protocol that simultaneously tolerates $$t_s < \ell /2$$ corruptions when the network is synchronous, and $$t_a < \ell /3$$ when the network is asynchronous. We completely resolve this question by showing that perfect SMT is possible if and only if $$2t_a + t_s < \ell $$ . In addition, we provide a concretely round-efficient solution for the (slightly worse) trade-off $$t_a + 2t_s < \ell $$ . As a direct application of these results, following the recent work by Appan, Chandramouli, and Choudhury [PODC'22], we obtain an n-party perfectly-secure multi-party computation protocol with asynchronous fallback over any network with connectivity $$\ell $$ , as long as $$t_a + 3t_s <n$$ and $$2t_a + t_s < \ell $$ .
QAVA-DPC: Eye-Tracking Based Quality Assessment and Visual Attention Dataset for Dynamic Point Cloud in 6 DoF

Perceptual quality assessment of Dynamic Point Cloud (DPC) contents plays an important role in various Virtual Reality (VR) applications that involve human beings as the end user, understanding and modeling perceptual quality assessment is greatly enriched by insights from visual attention. However, incorporating aspects of visual attention in DPC quality models is largely unexplored, as ground-truth visual attention data is scarcely available. This paper presents a dataset containing subjective opinion scores and visual attention maps of DPCs, collected in a VR environment using eye-tracking technology. The data was collected during a subjective quality assessment experiment, in which subjects were instructed to watch and rate DPCs at various degradation levels under 6 degrees-of-freedom inspection, using a head-mounted display. The dataset comprises 5 reference DPC contents, with each reference encoded at 3 distortion levels using 3 different codecs, amounting to a total of 9 degraded DPC contents. Moreover, it includes 1,000 gaze trials from 40 participants, resulting in 15,000 visual attention maps in total. The curated dataset can serve as authentic benchmark data for assessing the performance of objective DPC quality metrics. Additionally, it establishes a link between quality assessment and visual attention within the context of DPC. This work deepens our understanding of DPC quality and visual attention, driving progress in the realm of VR experiences and perception.
ProSpect: Prompt Spectrum for Attribute-Aware Personalization of Diffusion Models

Personalizing generative models offers a way to guide image generation with user-provided references. Current personalization methods can invert an object or concept into the textual conditioning space and compose new natural sentences for text-to-image diffusion models. However, representing and editing specific visual attributes such as material, style, and layout remains a challenge, leading to a lack of disentanglement and editability. To address this problem, we propose a novel approach that leverages the step-by-step generation process of diffusion models, which generate images from low to high frequency information, providing a new perspective on representing, generating, and editing images. We develop the Prompt Spectrum Space P*, an expanded textual conditioning space, and a new image representation method called ProSpect. ProSpect represents an image as a collection of inverted textual token embeddings encoded from per-stage prompts, where each prompt corresponds to a specific generation stage (i.e., a group of consecutive steps) of the diffusion model. Experimental results demonstrate that P* and ProSpect offer better disentanglement and controllability compared to existing methods. We apply ProSpect in various personalized attribute-aware image generation applications, such as image-guided or text-driven manipulations of materials, style, and layout, achieving previously unattainable results from a single image input without fine-tuning the diffusion models. Our source code is available at https://github.com/zyxElsa/ProSpect.
Artificial intelligence in compulsory level of education: perspectives from Namibian in-service teachers

Abstract With the push to integrate Artificial Intelligence (AI) from kindergarten to twelfth-grade levels comes a need for equipped teachers. However, there needs to be more initiative in professional learning opportunities, which demands the required effort to ensure teachers learn the AI content they will be teaching. To design an effective professional development program, understanding teachers' existing knowledge, interest and disposition towards AI is crucial to devise strategies that could best support the teachers. As a result, this study aims to investigate in-service teachers' perspectives of teaching AI in schools from the perspective of planned behavior theory. Using a 7-factor scale of AI Anxiety, AI Readiness, AI Relevance, Attitude towards using AI, AI for Social Good, Confidence in AI and Behavioral Intention. This study sampled 159 in-service teachers in Namibia. The teachers' responses were analyzed with SmartPLS using Structural Equation Modelling and various Multigroup Analysis techniques. This study found that teachers' behavioral intention to teach AI depends on a combination of factors, including the relevance of AI, attitude towards using AI, the use of AI for social good and confidence. Meanwhile, AI Anxiety and readiness could not be linked to the intention to teach AI. We discussed our findings, highlighted the study implication, and suggested future directions.
Building segmentation from UAV orthomosaics using unet-resnet-34 optimised with grey wolf optimisation algorithm

Abstract Given the importance and interest of buildings in the urban environment, numerous studies have focused on automatically extracting building outlines by exploiting different datasets and techniques. Recent advancements in unmanned aerial vehicles (UAVs) and their associated sensors have made it possible to obtain high-resolution data to update building information. These detailed, up-to-date geographic data on the built environment are essential and present a practical approach to comprehending how assets and people are exposed to hazards. This paper presents an effective method for extracting building outlines from UAV-derived orthomosaics using a semantic segmentation approach based on a U-Net architecture with a ResNet-34 backbone (UResNet-34). The novelty of this work lies in integrating a grey wolf optimiser (GWO) to fine-tune the hyperparameters of the UResNet-34 model, significantly enhancing building extraction accuracy across various localities. The experimental results, based on testing data from four different localities, demonstrate the robustness and generalisability of the approach. In this study, Locality-1 is well-laid buildings with roads, Locality-2 is dominated by slum buildings in proximity, Locality-3 has few buildings with background vegetation and Locality-4 is a conglomeration of Locality-1 and Locality-2. The proposed GWO-UResNet-34 model produced superior performance, surpassing the U-Net and UResNet-34. Thus, for Locality-1, the GWO-UResNet-34 achieved 94.74% accuracy, 98.11% precision, 84.85% recall, 91.00% F1-score, and 88.16% MIoU. For Locality-2, 90.88% accuracy, 73.23% precision, 75.65% recall, 74.42% F1-score, and 74.06% MioU was obtained.The GWO-UResNet-34 had 99.37% accuracy, 90.97% precision, 88.42% recall, 89.68% F1-score, and 90.21% MIoU for Locality-3, and 95.30% accuracy, 93.03% precision, 89.75% recall, 91.36% F1-score, and 88.92% MIoU for Locality-4.
Sequential Manipulation Planning for Over-Actuated Unmanned Aerial Manipulators

We investigate the sequential manipulation planning problem for unmanned aerial manipulators (UAMs). Unlike prior work that primarily focuses on one-step manipulation tasks, sequential manipulations require coordinated motions of a UAM's floating base, the manipulator, and the object being manipulated, entailing a unified kinematics and dynamics model for motion planning under designated constraints. By leveraging a virtual kinematic chain (VKC)-based motion planning framework that consolidates components' kinematics into one chain, the sequential manipulation task of a UAM can be planned as a whole, yielding more coordinated motions. Integrating the kinematics and dynamics models with a hierarchical control framework, we demonstrate, for the first time, an over-actuated UAM achieves a series of new sequential manipulation capabilities in both simulation and experiment.
Large Language Model Assisted Software Engineering: Prospects, Challenges, and a Case Study

Large language models such as OpenAI's GPT and Google's Bard offer new opportunities for supporting software engineering processes. Large language model assisted software engineering promises to support developers in a conversational way with expert knowledge over the whole software lifecycle. Current applications range from requirements extraction, ambiguity resolution, code and test case generation, code review and translation to verification and repair of software vulnerabilities. In this paper we present our position on the potential benefits and challenges associated with the adoption of language models in software engineering. In particular, we focus on the possible applications of large language models for requirements engineering, system design, code and test generation, code quality reviews, and software process management. We also give a short review of the state-of-the-art of large language model support for software construction and illustrate our position by a case study on the object-oriented development of a simple "search and rescue" scenario.
Model-free mean-field reinforcement learning: Mean-field MDP and mean-field Q-learning

We study infinite horizon discounted mean field control (MFC) problems with common noise through the lens of mean field Markov decision processes (MFMDP). We allow the agents to use actions that are randomized not only at the individual level but also at the level of the population. This common randomization is introduced for the purpose of exploration from a reinforcement learning (RL) paradigm. It also allows us to establish connections between both closed-loop and open-loop policies for MFC and Markov policies for the MFMDP. In particular, we show that there exists an optimal closed-loop policy for the original MFC and we prove dynamic programming principles for the state and state-action value functions. Building on this framework and the notion of state-action value function, we then propose RL methods for such problems, by adapting existing tabular and deep RL methods to the mean-field setting. The main difficulty is the treatment of the population state, which is an input of the policy and the value function. We provide convergence guarantees for the tabular Q-learning algorithm based on discretizations of the simplex. We also show that neural network based deep RL algorithms are more suitable for continuous spaces as they allow us to avoid discretizing the mean field state space. Numerical examples are provided.
Pre-train, Prompt, and Recommendation: A Comprehensive Survey of Language Modeling Paradigm Adaptations in Recommender Systems

Abstract The emergence of Pre-trained Language Models (PLMs) has achieved tremendous success in the field of Natural Language Processing (NLP) by learning universal representations on large corpora in a self-supervised manner. The pre-trained models and the learned representations can be beneficial to a series of downstream NLP tasks. This training paradigm has recently been adapted to the recommendation domain and is considered a promising approach by both academia and industry. In this paper, we systematically investigate how to extract and transfer knowledge from pre-trained models learned by different PLM-related training paradigms to improve recommendation performance from various perspectives, such as generality, sparsity, efficiency and effectiveness. Specifically, we propose a comprehensive taxonomy to divide existing PLM-based recommender systems w.r.t. their training strategies and objectives. Then, we analyze and summarize the connection between PLM-based training paradigms and different input data types for recommender systems. Finally, we elaborate on open issues and future research directions in this vibrant field.
On the Effect of Anticipation on Reading Times

Abstract Over the past two decades, numerous studies have demonstrated how less-predictable (i.e., higher surprisal) words take more time to read. In general, these studies have implicitly assumed the reading process is purely responsive: Readers observe a new word and allocate time to process it as required. We argue that prior results are also compatible with a reading process that is at least partially anticipatory: Readers could make predictions about a future word and allocate time to process it based on their expectation. In this work, we operationalize this anticipation as a word’s contextual entropy. We assess the effect of anticipation on reading by comparing how well surprisal and contextual entropy predict reading times on four naturalistic reading datasets: two self-paced and two eye-tracking. Experimentally, across datasets and analyses, we find substantial evidence for effects of contextual entropy over surprisal on a word’s reading time (RT): In fact, entropy is sometimes better than surprisal in predicting a word’s RT. Spillover effects, however, are generally not captured by entropy, but only by surprisal. Further, we hypothesize four cognitive mechanisms through which contextual entropy could impact RTs—three of which we are able to design experiments to analyze. Overall, our results support a view of reading that is not just responsive, but also anticipatory.1
MLBRSA: Multi-Learning-Based Reptile Search Algorithm for Global Optimization and Software Requirement Prioritization Problems

In the realm of computational problem-solving, the search for efficient algorithms tailored for real-world engineering challenges and software requirement prioritization is relentless. This paper introduces the Multi-Learning-Based Reptile Search Algorithm (MLBRSA), a novel approach that synergistically integrates Q-learning, competitive learning, and adaptive learning techniques. The essence of multi-learning lies in harnessing the strengths of these individual learning paradigms to foster a more robust and versatile search mechanism. Q-learning brings the advantage of reinforcement learning, enabling the algorithm to make informed decisions based on past experiences. On the other hand, competitive learning introduces an element of competition, ensuring that the best solutions are continually evolving and adapting. Lastly, adaptive learning ensures the algorithm remains flexible, adjusting the traditional Reptile Search Algorithm (RSA) parameters. The application of the MLBRSA to numerical benchmarks and a few real-world engineering problems demonstrates its ability to find optimal solutions in complex problem spaces. Furthermore, when applied to the complicated task of software requirement prioritization, MLBRSA showcases its capability to rank requirements effectively, ensuring that critical software functionalities are addressed promptly. Based on the results obtained, the MLBRSA stands as evidence of the potential of multi-learning, offering a promising solution to engineering and software-centric challenges. Its adaptability, competitiveness, and experience-driven approach make it a valuable tool for researchers and practitioners.
Why large language models are poor theories of human linguistic cognition: A reply to Piantadosi

<p xmlns="http://www.ncbi.nlm.nih.gov/JATS1">In a recent manuscript entitled “Modern language models refute Chomsky’s approach to language”, Steven Piantadosi proposes that large language models such as GPT-3 can serve as serious theories of human linguistic cognition. In fact, he maintains that these models are significantly better linguistic theories than proposals emerging from within generative linguistics. The present note explains why this claim is wrong.
Effectiveness of Virtual Reality on Learning Engagement

With its immersive, interactive, and creative capabilities, virtual reality (VR) has been widely used in educational settings to provide students with challenging real-world experiences. As a result, interest in its effectiveness has grown. The goal of this meta-analysis is to examine how VR technology affects students' motivation to study. A comprehensive analysis of the literature up to December 2022 revealed 15 randomized controlled trials or quasi-experimental studies. These studies were also coded to examine the moderating effects of their features, such as types of learner engagement, learner stages, learning domains, types of VR technology, and the moderating effect of knowledge types. The results indicate a large effect of VR on student engagement in learning (g=0.85). Furthermore, the findings of the moderator analysis demonstrate that VR has a more significant impact on cognitive engagement, higher education learners, immersive VR experiences, the field of art education, and procedural knowledge learning.
A Polynomial Time Attack on Instances of M-SIDH and FESTA

The recent devastating attacks on SIDH rely on the fact that the protocol reveals the images $$\varphi (P)$$ and $$\varphi (Q)$$ of the secret isogeny $$\varphi : E_0\rightarrow E$$ on a basis $$\{P, Q\}$$ of the N-torsion subgroup $$E_0[N]$$ where $$N^2 > \deg (\varphi )$$ . To thwart this attack, two recent proposals, M-SIDH and FESTA, proceed by only revealing the images upto unknown scalars $$\lambda _1, \lambda _2 \in \mathbb {Z}_N^\times $$ , i.e. only $$\lambda _1 \varphi (P)$$ and $$\lambda _2 \varphi (Q)$$ are revealed, where $$\lambda _1 = \lambda _2$$ for M-SIDH and $$\lambda _1 = \lambda _2^{-1}$$ for FESTA. Similar information is leaked in CSIDH since $$\varphi $$ maps the eigenspaces of Frobenius on $$E_0$$ to the corresponding eigenspaces on $$E$$ . In this paper, we introduce a new polynomial time attack that generalizes the well known "lollipop" attack and analyze how it applies to M-SIDH, FESTA and CSIDH. We show that M-SIDH can be broken in polynomial time whenever $$E_0$$ or $$E$$ is $$\mathbb {F}_p$$ -rational, even when the endomorphism rings of $$E_0$$ and $$E$$ are unknown. This can be generalized to the case where the starting (or end) curve is not $$\mathbb {F}_p$$ -rational, but is connected to its Frobenius conjugate by an isogeny of small degree. For FESTA, where the curve $$E_0$$ is already $$\mathbb {F}_p$$ -rational, we obtain a polynomial time attack under the added requirement that at least one of the basis points $$P, Q$$ spans an eigenspace of Frobenius, of an endomorphism of low degree, or of a composition of both. We note that the current implementation of FESTA does not choose such a basis. Since it is always possible to construct an endomorphism, typically of large degree, with either $$P, Q$$ an eigenvector, we conclude that FESTA with overstretched parameters is insecure. Although the information leaked in CSIDH is very similar to FESTA, we show that our attack does not reveal any new information about the secret isogeny, i.e. we only learn that it is $$\mathbb {F}_p$$ -rational, which is a priori knowledge. Finally, we analyze if and how it would be possible to backdoor M-SIDH and FESTA by choosing system parameters that look inconspicuous, but in fact reduce to the special cases above via a secret isogeny chosen by the adversary.
Blockchain-Based E-Voting Systems: A Technology Review

The employment of blockchain technology in electronic voting (e-voting) systems is attracting significant attention due to its ability to enhance transparency, security, and integrity in digital voting. This study presents an extensive review of the existing research on e-voting systems that rely on blockchain technology. The study investigates a range of key research concerns, including the benefits, challenges, and impacts of such systems, together with technologies and implementations, and an identification of future directions of research in this domain. We use a hybrid review approach, applying systematic literature review principles to select and categorize scientific papers and reviewing the technology used in these in terms of the above key concerns. In the 252 selected papers, aspects such as security, transparency, and decentralization are frequently emphasized as the main benefits. In contrast, although aspects like privacy, verifiability, efficiency, trustworthiness, and auditability receive significant attention, they are not the primary focus. We observed a relative lack of emphasis on aspects such as accessibility, compatibility, availability, and usability in the reviewed literature. These aspects, although acknowledged, are not as thoroughly discussed as the aforementioned key benefits in the proposed solutions for blockchain-based e-voting systems, whereas the considered studies have proposed well-structured solutions for blockchain-based e-voting systems focusing on how blockchain can strengthen security, transparency, and privacy, in particular, the crucial aspect of scalability needs attention.