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Unlike traditional time series, the action sequences of human decision making usually involve many cognitive processes such as beliefs, desires, intentions, and theory of mind, i.e., what others are thinking. This makes predicting human decision-making challenging to be treated agnostically to the underlying psychological mechanisms. We propose here to use a recurrent neural network architecture based on long short-term memory networks (LSTM) to predict the time series of the actions taken by human subjects engaged in gaming activity, the first application of such methods in this research domain. In this study, we collate the human data from 8 published literature of the Iterated Prisoner's Dilemma comprising 168,386 individual decisions and post-process them into 8,257 behavioral trajectories of 9 actions each for both players. Similarly, we collate 617 trajectories of 95 actions from 10 different published studies of Iowa Gambling Task experiments with healthy human subjects. We train our prediction networks on the behavioral data and demonstrate a clear advantage over the state-of-the-art methods in predicting human decision-making trajectories in both the single-agent scenario of the Iowa Gambling Task and the multi-agent scenario of the Iterated Prisoner's Dilemma. Moreover, we observe that the weights of the LSTM networks modeling the top performers tend to have a wider distribution compared to poor performers, as well as a larger bias, which suggest possible interpretations for the distribution of strategies adopted by each group.

In the coming years, quantum networks will allow quantum applications to thrive thanks to the new opportunities offered by end-to-end entanglement of qubits on remote hosts via quantum repeaters. On a geographical scale, this will lead to the dawn of the Quantum Internet. While a full-blown deployment is yet to come, the research community is already working on a variety of individual enabling technologies and solutions. In this paper, with the guidance of extensive simulations, we take a broader view and investigate the problems of Quality of Service (QoS) and provisioning in the context of quantum networks, which are very different from their counterparts in classical data networks due to some of their fundamental properties. Our work leads the way towards a new class of studies that will allow the research community to better understand the challenges of quantum networks and their potential commercial exploitation.

The approximate uniform sampling of graph realizations with a given degree sequence is an everyday task in several social science, computer science, engineering etc. projects. One approach is using Markov chains. The best available current result about the well-studied switch Markov chain is that it is rapidly mixing on P-stable degree sequences (see DOI:10.1016/j.ejc.2021.103421). The switch Markov chain does not change any degree sequence. However, there are cases where degree intervals are specified rather than a single degree sequence. (A natural scenario where this problem arises is in hypothesis testing on social networks that are only partially observed.) Rechner, Strowick, and M\"uller-Hannemann introduced in 2018 the notion of degree interval Markov chain which uses three (separately well-studied) local operations (switch, hinge-flip and toggle), and employing on degree sequence realizations where any two sequences under scrutiny have very small coordinate-wise distance. Recently Amanatidis and Kleer published a beautiful paper (arXiv:2110.09068), showing that the degree interval Markov chain is rapidly mixing if the sequences are coming from a system of very thin intervals which are centered not far from a regular degree sequence. In this paper we extend substantially their result, showing that the degree interval Markov chain is rapidly mixing if the intervals are centred at P-stable degree sequences.

The advancements in peer-to-peer wireless power transfer (P2P-WPT) have empowered the portable and mobile devices to wirelessly replenish their battery by directly interacting with other nearby devices. The existing works unrealistically assume the users to exchange energy with any of the users and at every such opportunity. However, due to the users' mobility, the inter-node meetings in such opportunistic mobile networks vary, and P2P energy exchange in such scenarios remains uncertain. Additionally, the social interests and interactions of the users influence their mobility as well as the energy exchange between them. The existing P2P-WPT methods did not consider the joint problem for energy exchange due to user's inevitable mobility, and the influence of sociality on the latter. As a result of computing with imprecise information, the energy balance achieved by these works at a slower rate as well as impaired by energy loss for the crowd. Motivated by this problem scenario, in this work, we present a wireless crowd charging method, namely MoSaBa, which leverages mobility prediction and social information for improved energy balancing. MoSaBa incorporates two dimensions of social information, namely social context and social relationships, as additional features for predicting contact opportunities. In this method, we explore the different pairs of peers such that the energy balancing is achieved at a faster rate as well as the energy balance quality improves in terms of maintaining low energy loss for the crowd. We justify the peer selection method in MoSaBa by detailed performance evaluation. Compared to the existing state-of-the-art, the proposed method achieves better performance trade-offs between energy-efficiency, energy balance quality and convergence time.

Silicon-photonic neural networks (SPNNs) have emerged as promising successors to electronic artificial intelligence (AI) accelerators by offering orders of magnitude lower latency and higher energy efficiency. Nevertheless, the underlying silicon photonic devices in SPNNs are sensitive to inevitable fabrication-process variations (FPVs) stemming from optical lithography imperfections. Consequently, the inferencing accuracy in an SPNN can be highly impacted by FPVs -- e.g., can drop to below 10% -- the impact of which is yet to be fully studied. In this paper, we, for the first time, model and explore the impact of FPVs in the waveguide width and silicon-on-insulator (SOI) thickness in coherent SPNNs that use Mach-Zehnder Interferometers (MZIs). Leveraging such models, we propose a novel variation-aware, design-time optimization solution to improve MZI tolerance to different FPVs in SPNNs. Simulation results for two example SPNNs of different scales under realistic and correlated FPVs indicate that the optimized MZIs can improve the inferencing accuracy by up to 93.95% for the MNIST handwritten digit dataset -- considered as an example in this paper -- which corresponds to a <0.5% accuracy loss compared to the variation-free case. The proposed one-time optimization method imposes low area overhead, and hence is applicable even to resource-constrained designs

In the upcoming 6G era, existing terrestrial networks have evolved toward space-air-ground integrated networks (SAGIN), providing ultra-high data rates, seamless network coverage, and ubiquitous intelligence for communications of applications and services. However, conventional communications in SAGIN still face data confidentiality issues. Fortunately, the concept of Quantum Key Distribution (QKD) over SAGIN is able to provide information-theoretic security for secure communications in SAGIN with quantum cryptography. Therefore, in this paper, we propose the quantum-secured SAGIN which is feasible to achieve proven secure communications using quantum mechanics to protect data channels between space, air, and ground nodes. Moreover, we propose a universal QKD service provisioning framework to minimize the cost of QKD services under the uncertainty and dynamics of communications in quantum-secured SAGIN. In this framework, fiber-based QKD services are deployed in passive optical networks with the advantages of low loss and high stability. Moreover, the widely covered and flexible satellite- and UAV-based QKD services are provisioned as a supplement during the real-time data transmission phase. Finally, to examine the effectiveness of the proposed concept and framework, a case study of quantum-secured SAGIN in the Metaverse is conducted where uncertain and dynamic factors of the secure communications in Metaverse applications are effectively resolved in the proposed framework.

Fog computing is introduced by shifting cloud resources towards the users' proximity to mitigate the limitations possessed by cloud computing. Fog environment made its limited resource available to a large number of users to deploy their serverless applications, composed of several serverless functions. One of the primary intentions behind introducing the fog environment is to fulfil the demand of latency and location-sensitive serverless applications through its limited resources. The recent research mainly focuses on assigning maximum resources to such applications from the fog node and not taking full advantage of the cloud environment. This introduces a negative impact in providing the resources to a maximum number of connected users. To address this issue, in this paper, we investigated the optimum percentage of a user's request that should be fulfilled by fog and cloud. As a result, we proposed DeF-DReL, a Systematic Deployment of Serverless Functions in Fog and Cloud environments using Deep Reinforcement Learning, using several real-life parameters, such as distance and latency of the users from nearby fog node, user's priority, the priority of the serverless applications and their resource demand, etc. The performance of the DeF-DReL algorithm is further compared with recent related algorithms. From the simulation and comparison results, its superiority over other algorithms and its applicability to the real-life scenario can be clearly observed.

With the advent of open source software, a veritable treasure trove of previously proprietary software development data was made available. This opened the field of empirical software engineering research to anyone in academia. Data that is mined from software projects, however, requires extensive processing and needs to be handled with utmost care to ensure valid conclusions. Since the software development practices and tools have changed over two decades, we aim to understand the state-of-the-art research workflows and to highlight potential challenges. We employ a systematic literature review by sampling over one thousand papers from leading conferences and by analyzing the 286 most relevant papers from the perspective of data workflows, methodologies, reproducibility, and tools. We found that an important part of the research workflow involving dataset selection was particularly problematic, which raises questions about the generality of the results in existing literature. Furthermore, we found a considerable number of papers provide little or no reproducibility instructions -- a substantial deficiency for a data-intensive field. In fact, 33% of papers provide no information on how their data was retrieved. Based on these findings, we propose ways to address these shortcomings via existing tools and also provide recommendations to improve research workflows and the reproducibility of research.

This paper presents GoPose, a 3D skeleton-based human pose estimation system that uses WiFi devices at home. Our system leverages the WiFi signals reflected off the human body for 3D pose estimation. In contrast to prior systems that need specialized hardware or dedicated sensors, our system does not require a user to wear or carry any sensors and can reuse the WiFi devices that already exist in a home environment for mass adoption. To realize such a system, we leverage the 2D AoA spectrum of the signals reflected from the human body and the deep learning techniques. In particular, the 2D AoA spectrum is proposed to locate different parts of the human body as well as to enable environment-independent pose estimation. Deep learning is incorporated to model the complex relationship between the 2D AoA spectrums and the 3D skeletons of the human body for pose tracking. Our evaluation results show GoPose achieves around 4.7cm of accuracy under various scenarios including tracking unseen activities and under NLoS scenarios.

We demonstrate that merely analog transmissions and match filtering can realize the function of an edge server in federated learning (FL). Therefore, a network with massively distributed user equipments (UEs) can achieve large-scale FL without an edge server. We also develop a training algorithm that allows UEs to continuously perform local computing without being interrupted by the global parameter uploading, which exploits the full potential of UEs' processing power. We derive convergence rates for the proposed schemes to quantify their training efficiency. The analyses reveal that when the interference obeys a Gaussian distribution, the proposed algorithm retrieves the convergence rate of a server-based FL. But if the interference distribution is heavy-tailed, then the heavier the tail, the slower the algorithm converges. Nonetheless, the system run time can be largely reduced by enabling computation in parallel with communication, whereas the gain is particularly pronounced when communication latency is high. These findings are corroborated via excessive simulations.