Reviewerly/exHarmony huggingface dataset:
Semiconductor Emitters in Entropy Sources for Quantum Random Number Generation

Abstract Random number generation (RNG) is needed for a myriad of applications ranging from secure communication encryption to numerical simulations to sports and games. However, generating truly random numbers can be elusive. Pseudorandom bit generation using computer algorithms provides a high random bit generation rate. Nevertheless, the reliance on predefined algorithms makes it deterministic and predictable once initial conditions are known. Relying on physical phenomena (such as measuring electrical noise or even rolling dice) can achieve a less predictable sequence of bits. Furthermore, if the physical phenomena originate from quantum effects, they can be truly random and completely unpredictable due to quantum indeterminacy. Traditionally, physical RNG is significantly slower than pseudorandom techniques. To meet the demand for high‐speed RNG with perfect unpredictability, semiconductor light sources are adopted as parts of the sources of randomness, i.e., entropy sources, in quantum RNG (QRNG) systems. The high speed of their noise, the high efficiency, and the small scale of these devices make them ideal for chip‐scale QRNG. Here, the applications and recent advances of QRNG are reviewed using semiconductor emitters. Finally, the performance of these emitters is compared and discuss their potential in future technologies.
Microseismic source location based on improved artificial bee colony algorithm: Performance analysis and case study

Artificial bee colony algorithm Underground engineeringHighly accurate microseismic (MS) localization is the basis for rock damage assessment and disaster warning.The engineering background noise mixed in the MS signal seriously affects the subsequent analysis of the MS signal.A noise reduction method of singular spectral analysis-complementary ensemble empirical mode decomposition-wavelet threshold (CEEMD-WT-SSA) is proposed.The complementary ensemble empirical mode decomposition (CEEMD), complementary ensemble empirical mode decomposition-wavelet threshold (CEEMD-WT) and the proposed method are respectively used for denoise the noisy Ricker wavelet.The signal-to-noise ratio of the denoised signal by the proposed method is 56.77% and 37.88% higher than that of CEEMD and CEEMD-WT methods.Moreover, an adaptive artificial bee colony (ABC) algorithm is applied for MS source location.The time to quantile difference is introduced as the objective function.The blast positioning test results prove that the proposed method improves the positioning accuracy by 44.12% and 47.64% particle swarm optimization (PSO) algorithm and simulated annealing particle swarm optimization (SA-PSO) algorithm, respectively.The MS positions of underground caverns reveal that the calculated cluster of MS events using the adaptive ABC algorithm are more concentrated at the structural plane and appearance deformation failure location and in good agreement with field survey and routine monitoring data.
Tangible interventions for office work well-being: approaches, classification, and design considerations

Office well-being aims to explore and support a healthy, balanced and active work style in office environments. Recent work on tangible user interfaces has started to explore the role of physical, tangible interfaces as active interventions to explore how to tackle problems such as inactive work and lifestyles, and increasingly sedentary behaviours. We identify a fragmented research landscape on tangible Office well-being interventions, missing the relationship between interventions, data, design strategies, and outcomes, and behaviour change techniques. Based on the analysis of 40 papers, we identify 7 classifications in tangible Office well-being interventions and analyse the intervention based on their role and foundation in behaviour change. Based on the analysis, we present design considerations for the development of future tangible Office well-being design interventions and present an overview of the current field and future research into tangible Office well-being interventions to design for a healthier and active office environment.
Future Workshops as Postdigital Research Method

After the Second World War, many social critics thought technology had taken over all forms of modern thought and activity. To these thinkers, the arms industry, the cold war, nuclear threat, atomic science, overpopulation, lonely crowds, computing, famine, and ecological catastrophe were signs of technological determinism. The critics were utterly worried about the state of democracy in a technological world. The postdigital era faces similar challenges and even more pressing problems with the progress and inventions in digitalization. German-born Robert Jungk and Norbert Müllert addressed these problems from the 1960s onwards and wanted to search for solutions to technological determinism. Their answer was to invent a future workshop method that allowed ordinary people to participate in imagining the future and solve small and large-scale social problems. This chapter describes the future workshop as a viable postdigital research method that allows scholars methodological experimentation and switches from what is to what is not yet, but what could be.
Sociomateriality, Postdigital Thinking, and Learning Spaces Research

A central assumption of the postdigital is that digital technologies are firmly established within our everyday surroundings, which naturally includes those settings across and beyond the university campus where teaching and learning happen. It makes sense, therefore, that postdigital thinking has recently become a feature of research that seeks to understand the changing and complex nature of our higher education learning spaces. This work has often also looked towards sociomateriality, which shares with the postdigital an interest in the more-than-human, as well as the conceptual devices of assemblage and entanglement. In this chapter I will use the example of emergent learning spaces research to argue that while they work in complement, postdigital thinking goes beyond sociomateriality in pushing us to more resolutely examine the presence and influence of digital technologies within our learning spaces. I will also suggest that, rather than necessarily existing as a discrete method or theory, the postdigital exists as a kind of research sensibility, which shapes how we understand our educational surroundings, and also our researcher selves.
Negotiating Mnemotechnic Re-presentation

This chapter extends work of Bernard Stiegler on the capacity of the mnemotechnic tool to exteriorize, retain, and transmit re-presentations of knowledge—either automatically overtaking the human capacity for knowledge production or conserving and augmenting it. To conserve or augment knowledge, mnemotechnics requires careful negotiation with social organizations and biological individuals. These interrelations are changing through the massive development of Artificial Intelligence (AI) technologies operating on a global scale as well as ubiquitous computing and a shrinking number of ‘smart’ and individually controlled network terminals. As mnemotechnic tools become increasingly abstracted from the social body and individual human subjects, potentially including researchers themselves, this raises the question of who or what the postdigital research observer is, and what it means to re-present knowledge about the world in the ongoing pursuit of learning.
Images of Incoming: A Critical Account of a (Mostly) Postdigital Photovoice Project with Rural Migrant Women in Northern Ireland and Canada

We offer a critical account of a postdigital, arts based, co- research project with rural migrant women in Canada and Northern Ireland. The project is entitled: ‘Images of Incoming: A Photovoice project exploring belonging and exclusion with newcomer and migrant women in rural areas’. Recognizing that there are still barriers for some migrant women and some local women in accessing digital mediums, the project also deployed face-to-face engagement. Our research positioning challenges neoliberal concepts of ownership and privatization of knowledge with a knowledge socialism ethos that research should not just be for the common good, but, should be empowering, democratic, and agentic. The collaborative research aimed to address one of the Grand Challenges of our time within the imperatives of the Sustainable Development Goals. We used photovoice methodologies to explore belonging and alienation with some 70 women and, from the outset, the women became agentic in choosing their own images and then commenting and discussing their images in workshops. The participants collaborated also in a metanalysis. In addition to academic partners, Queen’s University, Belfast and the University of the Fraser Valley, British Columbia, we partnered with the University of Atypical, a disability arts organization in Northern Ireland, who curated the project exhibition, including sign-language description, constructed a proto-website and co-created an evaluation film documentary.
Understanding Children’s Participation Rights Through a Postdigital Epistemology of Silence

Underpinning all young people’s participation rights is the right to accurate information that builds their capacity to claim their rights. In the postdigital world, access to information is increasingly mediated by technology, as are knowledge exchanges involving children. These knowledge exchanges are understood in social epistemology as testimony. In this chapter I conceptualise the right to information in postdigital research through an epistemology of silence to better understand the misinformation, disinformation, and deceit that pervades young people’s information sources. This chapter will focus upon belief based on testimony because it is pertinent to children’s right to information, which they have a right to seek, receive and impart, and which therefore includes testimony. The chapter closes with a consideration of what this means for postdigital research methodology, arguing that children and young people should take a central role, given that they are growing up in the postdigital world.
SweetCoat-2D: Two-Dimensional Bangla Spelling Correction and Suggestion Using Levenshtein Edit Distance and String Matching Algorithm

Autonomous spelling correction and suggestion generation for any language is a tough task since the system must grasp context or it will create a negative impression. Our goal is to reduce this difficulty and provide a more accurate spelling check and suggestions for the Bangla language. Recently, the Bangla language has entered a more advanced phase of its procedure for automatically repairing misspelled words. However, just a few works have been undertaken on this subject that is adequate. In this study, we have developed an English-coated two-dimensional (2D) encoding mechanism in terms of Bangla Grammer Book, dubbed SweetCoat-2D that is utilized in conjunction with Levenshtein Edit Distance and several String matching approaches to evaluate more precisely. Our encoding method outperforms the rest of the process, thus the outcomes are encouraging. Also, we have constructed a corpus of 15,000 misspelled words to which we apply our SweetCoat-2D encoding algorithm in conjunction with the Levenshtein Distance and String Matching algorithms, yielding improved spelling suggestion results of 92.404%, where accuracy is calculated only for correct suggestions which are provided at the first index. Therefore, there is a high probability that the presented model will be useful and have a substantial impact on the process of correcting misspellings in Bangla.
Improving Few-Shot Performance of DST Model Through Multitask to Better Serve Language-Impaired People

Artificial intelligence-based virtual assistants make people’s daily life more convenient. However, the utterances of language-impaired people are limited and different in characteristics and domains from that of ordinary people. So it is difficult for language-impaired people to benefit from standard data-driven artificial intelligence algorithms. In this paper, we propose a multi-task training method for the dialogue state tracking (DST) task in dialogue systems that make up virtual assistants, improving the performance of T5 on the few-shot cross-domain DST task. Specifically, we consider two ways of handling DST task: predicting the dialogue state from the beginning or updating the dialogue state every turn, and accordingly design the main task and auxiliary task for the model. Experiments show that our method outperforms most previous works on the MultiWOZ 2.0 and 2.1 datasets for the few-shot cross-domain DST task. For the artificial-crafted language-impaired dataset, our method can effectively improve the few-shot cross-domain performance of the model. Additionally, we analyzed the possible reason why this multitasking approach works well.
A fusion algorithm based on whale and grey wolf optimization algorithm for solving real-world optimization problems

In order to better understand and analyze population-based meta-heuristic optimization algorithms, this paper proposed a new hybrid algorithm combined Lévy flight with modified Whale Optimization Algorithm (WOA) and Grey Wolf Optimizer (GWO) , which is called LMWOAGWO to discard the dross and select the essence. Firstly, the population is initialized by using the uniform distribution space combined with the pseudo-reverse learning strategy, which lays the foundation for global search. Then, modifications were made to both WOA and GWO. For WOA algorithm, random adjustment control parameters strategy and different chaotic maps are used to adjust the main parameters of WOA to avoid the algorithm falling into local optimum in the later stage. For GWO algorithm, a new optimal solution is added to the grey wolf population to increase the optimal update position of the algorithm. On this basis, the dynamic weighting strategy is introduced to improve the convergence accuracy and convergence speed of the algorithm. Subsequently, new conditions were added during the WOA exploitation phase to formulate LMWOAGWO and the greedy strategy is used to retain better iteration update locations. Finally, Lévy flight is used to improve the global search ability of the algorithm. Extensive numerical experiments were conducted using 23 standard test benchmark functions, 25 CEC2005 functions, 15 popular benchmark functions and 10 CEC2019 functions to test the performance of LMWOAGWO compared with other well-known swarm optimization algorithms. Experimental and statistical results show that the performance of LMWOAGWO algorithm is better than many state-of-the-art algorithms. Then, 22 real-world optimization problems were used to further study the effectiveness of LMWOAGWO. Winners of CEC2020 Real World Single Objective Constraint Optimization Competition, such as iLSHADEϵ algorithm, sCMAgES algorithm, COLSHADE algorithm and EnMODE algorithm are selected as four comparison algorithms in real world optimization problems. Experimental results show that the proposed LMWOAGWO has the capability to solve real-world optimization problems. Finally, the application efficiency of LMWOAGWO in solving two basic optimization problems in wireless networks is briefly introduced, and compared with the original WOA and GWO. Simulation results show that the performance of the LMWOAGWO is better than WOA and GWO.
Peeling Back the Carbon Curtain: Carbon Optimization Challenges in Cloud Computing

The increasing carbon emissions from cloud computing requires new methods to reduce its environmental impact. We explore extending data center server lifetimes to reduce embodied carbon emissions (from hardware manufacturing), rather than operational (from running hardware). Our experiments are the first to analyze a data center application's end-to-end performance on different server generations, to reveal that older hardware can preserve performance in certain conditions (e.g., low load).
Advantage Constrained Proximal Policy Optimization in Multi-Agent Reinforcement Learning

We investigate the integration of value-based and policy gradient methods in multi-agent reinforcement learning (MARL). The Individual-Global-Max (IGM) principle plays an important role in value-based MARL, as it ensures consistency between joint and local action values. IGM is difficult to guarantee in multi-agent policy gradient methods due to stochastic exploration and conflicting gradient directions. In this paper, we propose a novel multi-agent policy gradient algorithm called Advantage Constrained Proximal Policy Optimization (ACPPO). ACPPO calculates each agent's current local state-action advantage based on their advantage network and estimates the joint state-action advantage based on multi-agent advantage decomposition lemma. According to the consistency of the estimated joint-action advantage and local advantage, the coefficient of each agent constrains the joint-action advantage. ACPPO, unlike previous policy gradient MARL algorithms, does not require an additional sampled baseline to reduce variance or a sequential scheme to improve accuracy. The proposed method is evaluated using the continuous matrix game, the Starcraft Multi-Agent Challenge, and the Multi-Agent MuJoCo task. ACPPO outperforms baselines such as MAPPO, MADDPG, and HATRPO, according to the results.
Robustness of Deep Learning Methods for Occluded Object Detection - A Study Introducing a Novel Occlusion Dataset

A large number of deep learning based object detection algorithms have been proposed and applied in a wide range of domains such as security, autonomous driving and robotics. In practical usage, objects being occluded are common, and can result in reduced accuracy and reliability. To increase the robustness of object detection algorithms under occlusion scenarios, it is necessary to consider the influence of different types of occlusion on the performance of object detection approaches. Our research revealed a gap in benchmarking datasets that could provide exemplars of occlusion that covered a range of occlusion scenarios. In this paper, we present a new benchmarking dataset that includes a range of exemplars providing coverage of different types of occlusion cases. This dataset is designed for object detection of everyday objects in indoor scenarios, and comprises occlusion in three orthogonal atomic factors, namely, the degree of occlusion, the location of occlusion, and classes of occluded object and those occluding other objects. Our dataset is balanced in terms of classes and degrees of occlusion, with a total of 5970 sample images. The effect of these three atomic factors has been investigated on some classic general object detectors. Using this benchmarking dataset, we also present results on the impact of the distribution of the training dataset, in terms of degree of occlusion, on the robustness of several typical object detection algorithms (e.g. Fast RCNN, Faster RCNN, and FCOS, etc). The benchmark is available at “https://drive.google.com/drive/folders/13VkLgbx6t0-vA3vRWrlvHcjra-8BS4aL?usp=sharing”. This dataset is seen as a key contribution to research investigating the influence of occlusion on the performance of object detectors.
Privacy Inference-Empowered Stealthy Backdoor Attack on Federated Learning under Non-IID Scenarios

Federated learning (FL) naturally faces the problem of data heterogeneity in real-world scenarios, but this is often overlooked by studies on FL security and privacy. On the one hand, the effectiveness of backdoor attacks on FL may drop significantly under non-IID scenarios. On the other hand, malicious clients may steal private data through privacy inference attacks. Therefore, it is necessary to have a comprehensive perspective of data heterogeneity, backdoor, and privacy inference. In this paper, we propose a novel privacy inference-empowered stealthy backdoor attack (PI-SBA) scheme for FL under non-IID scenarios. Firstly, a diverse data reconstruction mechanism based on generative adversarial networks (GANs) is proposed to produce a supplementary dataset, which can improve the attacker's local data distribution and support more sophisticated strategies for backdoor attacks. Based on this, we design a source-specified backdoor learning (SSBL) strategy as a demonstration, allowing the adversary to arbitrarily specify which classes are susceptible to the backdoor trigger. Since the PI-SBA has an independent poisoned data synthesis process, it can be integrated into existing backdoor attacks to improve their effectiveness and stealthiness in non-IID scenarios. Extensive experiments based on MNIST, CIFAR10 and Youtube Aligned Face datasets demonstrate that the proposed PI-SBA scheme is effective in non-IID FL and stealthy against state-of-the-art defense methods.
Energy consumption auditing based on a generative adversarial network for anomaly detection of robotic manipulators

This paper presents a generative adversarial network (GAN)-based energy auditing and anomaly detection framework for the health and security monitoring of robotic manipulators. The system features two key components: a side-channel measurement of the energy consumption and a GAN model to learn the data patterns during normal operations only. Anomalies in energy consumption during online testing are then discovered as outliers to the normal data by scrutinizing the anomaly score produced by the discriminator of the GAN model. This semi-supervised network circumvents the need for vast data labeling and exploits the overfitting during the training stage, hence magnifying the difference between the normal patterns learned in training and the unobserved abnormal energy profiles in testing. Moreover, considering the physical aspects of the robotic manipulator, a modified GAN architecture containing multiple discriminators to account for distinct contributions from individual joints/motors. A dynamic thresholding approach that continuously updates the statistical characteristics of the anomaly score is also developed to detect outliers and mitigate the effect of environmental variations during system operation. The proposed framework is tested on the custom dataset for performance evaluation. The results demonstrate the feasibility of the proposed method, especially for detecting physical attacks, which achieves an accuracy of approximately 0.93 in instant-level detection and over 0.84 in event-level detection.
Video Rewrite: Driving Visual Speech with Audio

Video Rewrite uses existing footage to create automatically new video of a person mouthing words that she did not speak in the original footage. This technique is useful in movie dubbing, for example, where the movie sequence can be modified to sync the actors' lip motions to the new soundtrack.