Reviewerly/exHarmony huggingface dataset:
A Survey on Recent Advances in Fall Detection Systems Using Machine Learning Formalisms

As reported by numerous World Health Organization studies, fall incidents are considered one of the leading causes of loss of autonomy, injuries, and even deaths, and this is not only among elderly people but also in other categories such as workers. Fall incidents also have a considerable impact on the budget allocated to the care of people suffering from the effects of falls. This work presents a comprehensive review of state-of-the-art fall detection technologies considering the most powerful machine learning methodologies, both classical formalism (shallow methods), and approaches based on deep learning formalism. The authors reviewed the most recent and effective methods for fall detection and presented the used sensors, cameras, applied pre-treatments, generated attributes, and algorithms used in this field of application. The present work is completed by a discussion presenting some limitations that need to be analyzed and taken into account to further improve the quality of fall detection and reduce their impacts.
Learning a Scalable Algorithm for Improving Betweenness in the Lightning Network

This paper presents a scalable algorithm for solving the Maximum Betweenness Improvement Problem as it appears in the Bitcoin Lightning Network. In this approach, each node is embedded with a feature vector whereby an Advantage Actor-Critic model identifies key nodes in the network that a joining node should open channels with to maximize its own expected routing opportunities. This model is trained using a custom built environment, lightning-gym, which can randomly generate small scale-free networks or import snapshots of the Lightning Network. After 100 training episodes on networks with 128 nodes, this A2C agent can recommend channels in the Lightning Network that consistently outperform recommendations from centrality based heuristics and in less time. This approach gives nodes in the network access to a fast, low resource, algorithm to increase their expected routing opportunities.
Behavioral Biometrics for more (dis) trust and security

Nowadays, the identity of individuals has expanded to the cyber world. Users' virtual identity is exposed to different applications evolving quickly, and interactions with various social networking sites have become essential in our daily lives. Behavioral biometrics have shown to be an effective solution for better security, but on the other hand, technological advancements always establish a pathway to implications and challenges. This article elaborates on behavioral biometrics features and their massive use in various fields. The research is placed in the context of trust and security in nowadays societies. Therefore, we shed light on the challenges and security concerns. We conclude that behavioral biometrics supersede physical biometrics in terms of security assurance. Behavioral biometrics have the potential to grow and provide a state-of-the-art solution for cybersecurity.
The Internet of Musical Stuff (IoMuSt): ubimus perspectives on artificial scarcity, virtual communities and (de)objectification

Part of the recent developments in Ubiquitous Music (ubimus) research involve the proposal of the Internet of Musical Stuff (IoMuSt) as an expansion and complement to the Internet of Musical Things (IoMusT). The transition from IoMusT to IoMuSt entails a critique of blockchain and non-fungible tokens (NFTs) as technologies for allotment, disciplination and regimentation of formerly open and freely accessible artistic web content. In brief, the replacement of the operative concepts constructed around “things” with strategies based on “stuff” highlights the underlying interconnected processes and factors that impact interaction and usage, pointing to resources that become disposable and valueless within an objectified and monetized musical internet. This conceptual and methodological turn allows us to deal with distributed-creativity phenomena in marginalized spaces, highlighting the role of resources that are widely reproducible, fluid and ever-changing. In this paper, we address IoMuSt-based responses to issues such as the artificial production of scarcity associated with the application of NFTs. The selected musical examples showcase the meshwork of dynamic relationships that characterizes ubimus research. In particular, we focus on a comprovisation project involving VOIP visual communication through Skype, Meet and Zoom, a ubimus experience involving a Telegram chatbot and a set of musical experiments enabled by an online tool for remote live patching.
Face reenactment via generative landmark guidance

The identity preserving problem is one of the major obstacles in face reenactment. The problem occurs when the model fails to preserve the detailed information of the source identity, and especially obvious when reenacting different identities. The underlying factors may include the leaking of driver identity, due to the identity mismatch, and unseen large head poses. In this paper, we propose a novel face reenactment approach via generative landmark coordinates. Specifically, a conditional generative adversarial network is developed to estimate reenacted landmark coordinates for the driving image, which successfully excludes its identity information. These generated coordinates are further injected into the subsequent inference style transferal module to increase the realism of face images. We evaluated our method on the VoxCeleb1 dataset for self-reenactment and the CelebV dataset for reenacting different identities. Extensive experiments demonstrate that our method can produce more realistic reenacted face images.
Reproducibility and Gap Control of Superconducting Flux Qubits

Superconducting flux qubits are promising candidates for the physical realization of a scalable quantum processor. Indeed, these circuits may have both a small decoherence rate and a large anharmonicity. These properties enable the application of fast quantum gates with high fidelity and reduce scaling limitations due to frequency crowding. The major difficulty of flux qubits' design consists of controlling precisely their transition energy---the so-called qubit gap---while keeping long and reproducible relaxation times. Solving this problem is challenging and requires extremely good control of e-beam lithography, oxidation parameters of the junctions, and sample surface. Here we present measurements of a large batch of flux qubits and demonstrate a high level of reproducibility and control of qubit gaps ($\ifmmode\pm\else\textpm\fi{}0.6\phantom{\rule{0.2em}{0ex}}\mathrm{GHz}$), relaxation times ($15$--$20\phantom{\rule{0.2em}{0ex}}\text{\ensuremath{\mu}}\mathrm{s}$), and pure echo dephasing times ($15$--$30\phantom{\rule{0.2em}{0ex}}\text{\ensuremath{\mu}}\mathrm{s}$). These results open the way for potential applications in the fields of quantum hybrid circuits and quantum computation.
Discriminative Graph Representation Learning with Distributed Sampling

Graph neural networks (GNNs) have been widely used to accomplish graph classification tasks such as predicting molecular properties and classifying the labels of proteins. Discovering the latent discriminative substructures (e.g., functional groups in molecules) is a vital task to enhance the classification performance. In this paper, this task is addressed as a problem of discriminative graph representation learning. Specifically, a novel node sampling strategy is developed to achieve this goal. To this end, graph-dependent sampling vectors are first learned by a mini-network to exploit various informative substructures on graphs and sample some representative nodes, which could be regarded as performing a distributed sampling on graphs. Then, the sampled nodes are organized together topologically as a subgraph with landing probabilities of random walks. Moreover, a self-adaptive pooling ratio of nodes is obtained via feature smoothness of graphs, eliminating the trouble of manual selection of subgraph size. As a result, these treatments are equivalent to performing the difficult step of down-pooling operation on non-grid graph data. Extensive experiments and ablation studies on multiple benchmark datasets demonstrate the effectiveness and superiority of our proposed approach. Additionally, interpretability studies illustrate the ability of our model to extract discriminative substructures.
The Use of Digital Technology for COVID-19 Detection and Response Management in Indonesia: Mixed Methods Study

The COVID-19 pandemic has triggered a greater use of digital technologies as part of the health care response in many countries, including Indonesia. It is the world's fourth-most populous nation and Southeast Asia's most populous country, with considerable public health pressures.The aim of our study is to identify and review the use of digital health technologies in COVID-19 detection and response management in Indonesia.We conducted a literature review of publicly accessible information in technical and scientific journals, as well as news articles from September 2020 to August 2022 to identify the use case examples of digital technologies in COVID-19 detection and response management in Indonesia.The results are presented in 3 groups, namely (1) big data, artificial intelligence, and machine learning (technologies for the collection or processing of data); (2) health care system technologies (acting at the public health level); and (3) COVID-19 screening, population treatment, and prevention population treatment (acting at the individual patient level). Some of these technologies are the result of government-academia-private sector collaborations during the pandemic, which represent a novel, multisectoral practice in Indonesia within the public health care ecosystem. A small number of the identified technologies pre-existed the pandemic but were upgraded and adapted for current needs.Digital technologies were developed in Indonesia during the pandemic, with a direct impact on supporting COVID-19 management, detection, response, and treatment. They addressed different areas of the technological spectrum and with different levels of adoption, ranging from local to regional to national. The indirect impact of this wave of technological creation and use is a strong foundation for fostering future multisectoral collaboration within the national health care system of Indonesia.
Agents Dealing with Norms and Regulations

Norms influence behaviour in many ways. In situations such as the COVID-19 pandemic where the effect of policies on the spread of the virus is evaluated, this leads to disputes about their effectiveness. In order to build agent-based social simulations that give proper support for this evaluation process we need agents that properly deal with norms. In this paper we present a new agent deliberation architecture that takes more aspects of norms into account than traditional architectures have done. Dealing properly with norms means that agents can reason through the consequences of the norms, that they are used to motivate and not just constrain behaviour, and that the agents can violate the norm as well. For the former we use the ideas of perspectives on norms, while the latter is enabled through the use of values. Within our architecture we can also represent habitual behaviour, context sensitive planning, and through the use of landmarks, reactive planning. We use the example of a restaurant-size based restriction to show how our architecture works.
Enhancing the coherence of superconducting quantum bits with electric fields

Abstract In the endeavor to make quantum computers a reality, integrated superconducting circuits have become a promising architecture. A major challenge of this approach is decoherence originating from spurious atomic tunneling defects at the interfaces of qubit electrodes, which may resonantly absorb energy from the qubit’s oscillating electric field and reduce the qubit’s energy relaxation time T 1 . Here, we show that qubit coherence can be improved by tuning dominating defects away from the qubit resonance using an applied DC-electric field. We demonstrate a method that optimizes the applied field bias and enhances the average qubit T 1 time by 23%. We also discuss how local gate electrodes can be implemented in superconducting quantum processors to enable simultaneous in situ coherence optimization of individual qubits.
Straggler- and Adversary-Tolerant Secure Distributed Matrix Multiplication Using Polynomial Codes

Large matrix multiplications commonly take place in large-scale machine-learning applications. Often, the sheer size of these matrices prevent carrying out the multiplication at a single server. Therefore, these operations are typically offloaded to a distributed computing platform with a master server and a large amount of workers in the cloud, operating in parallel. For such distributed platforms, it has been recently shown that coding over the input data matrices can reduce the computational delay by introducing a tolerance against straggling workers, i.e., workers for which execution time significantly lags with respect to the average. In addition to exact recovery, we impose a security constraint on both matrices to be multiplied. Specifically, we assume that workers can collude and eavesdrop on the content of these matrices. For this problem, we introduce a new class of polynomial codes with fewer non-zero coefficients than the degree +1. We provide closed-form expressions for the recovery threshold and show that our construction improves the recovery threshold of existing schemes in the literature, in particular for larger matrix dimensions and a moderate to large number of colluding workers. In the absence of any security constraints, we show that our construction is optimal in terms of recovery threshold.
Teaching Professional Ethical Knowledge and Teaching Digital Skills in Higher Education

This paper analyzes the professional ethical knowledge of teachers in the use of technologies by teachers in training. It is intended to measure the correct inclusion of technologies in the classroom with the use of the Disciplinary Pedagogical Technological Knowledge model (TPACK). For this, a descriptive exploratory methodological design study was carried out. The instruments used have been the Likert scale questionnaire and the personal interview, organized into four dimensions, which are the self-perception of professional ethical knowledge, ethical knowledge of technology, pedagogical technological ethical knowledge, and disciplinary technological ethical knowledge of the future teachers. The questionnaire has been applied to a sample of 1.051 trainee teachers from a European university in the context of COVID-19 and 822 students participated in the interview. The results show the scarce training in professional ethical knowledge of teachers in training and the importance of addressing this knowledge in the twenty first century, a post-pandemic context. Another of the conclusions is the need to include the ethical component in the TPACK model to achieve correct and ethical use of digital resources in the classroom.
Attitudes and uses of archival materials among science-based anthropologists

While archival user studies have largely focused on humanities (and adjacent) scholars, this paper focuses on anthropologists engaged in scientific research. Based on qualitative results from an open-ended survey, we investigate how science-based anthropologists perceive and use archives in their work. We ask: How are science-based anthropologists and archaeologists reusing archival data in their research? What difficulties or barriers do they encounter in reusing archival data in scientific contexts? What attitudes or understandings about archival research are held by science-based anthropologists and archaeologists? Our findings primarily add to the body of literature about user experience in archives and more broadly to the emerging literature on archival data reuse. Major findings include (1) barriers and gatekeeping legacies that impact archival research and the ability of researchers to reuse data and (2) mixed perceptions about archives among researchers. We also discuss suggestions made by these communities of practice, and the ways that barriers to archival data reuse may stem from a lack of knowledge about core archival and information infrastructures among researcher communities. Together, this research showcases possible (re)uses of important primary source data in archives among scientific communities but highlights that barriers to access and misperceptions create a gap in exploiting that potential. We argue for a "re-imagining" of anthropological archives as relevant to contemporary communities and scientific pursuits toward a richer scientific research environment.
Contextual Instance Decoupling for Instance-Level Human Analysis

One fundamental challenge of instance-level human analysis is to decouple instances in crowded scenes, where multiple persons are overlapped with each other. This paper proposes the Contextual Instance Decoupling (CID), which presents a new pipeline of decoupling persons for multi-person instance-level analysis. Instead of relying on person bounding boxes to spatially differentiate persons, CID decouples persons in an image into multiple instance-aware feature maps. Each of those feature maps is hence adopted to infer instance-level cues for a specific person, e.g., keypoints, instance mask or part segmentation masks. Compared with bounding box detection, CID is differentiable and robust to detection errors. Decoupling persons into different feature maps also allows to isolate distractions from other persons, and explore context cues at scales larger than the bounding box size. Extensive experiments on various tasks including multi-person pose estimation, person foreground segmentation, and part segmentation, show that CID consistently outperforms previous methods in both accuracy and efficiency. For instance, it achieves 71.3% AP on CrowdPose in multi-person pose estimation, outperforming the recent single-stage DEKR by 5.6%, the bottom-up CenterAttention by 3.7%, and the top-down JC-SPPE by 5.3%. This advantage sustains on multi-person segmentation and part segmentation tasks.
Assessing the allocation of attention during visual search using digit-tracking, a calibration-free alternative to eye tracking

Abstract Digit-tracking, a simple, calibration-free technique, has proven to be a good alternative to eye tracking in vision science. Participants view stimuli superimposed by Gaussian blur on a touchscreen interface and slide a finger across the display to locally sharpen an area the size of the foveal region just at the finger's position. Finger movements are recorded as an indicator of eye movements and attentional focus. Because of its simplicity and portability, this system has many potential applications in basic and applied research. Here we used digit-tracking to investigate visual search and replicated several known effects observed using different types of search arrays. Exploration patterns measured with digit-tracking during visual search of natural scenes were comparable to those previously reported for eye-tracking and constrained by similar saliency. Therefore, our results provide further evidence for the validity and relevance of digit-tracking for basic and applied research on vision and attention.
Distributed Traffic Synthesis and Classification in Edge Networks: A Federated Self-supervised Learning Approach

With the rising demand for wireless services and increased awareness of the need for data protection, existing network traffic analysis and management architectures are facing unprecedented challenges in classifying and synthesizing the increasingly diverse services and applications. This paper proposes FS-GAN, a federated self-supervised learning framework to support automatic traffic analysis and synthesis over a large number of heterogeneous datasets. FS-GAN is composed of multiple distributed Generative Adversarial Networks (GANs), with a set of generators, each being designed to generate synthesized data samples following the distribution of an individual service traffic, and each discriminator being trained to differentiate the synthesized data samples and the real data samples of a local dataset. A federated learning-based framework is adopted to coordinate local model training processes of different GANs across different datasets. FS-GAN can classify data of unknown types of service and create synthetic samples that capture the traffic distribution of the unknown types. We prove that FS-GAN can minimize the Jensen-Shannon Divergence (JSD) between the distribution of real data across all the datasets and that of the synthesized data samples. FS-GAN also maximizes the JSD among the distributions of data samples created by different generators, resulting in each generator producing synthetic data samples that follow the same distribution as one particular service type. Extensive simulation results show that the classification accuracy of FS-GAN achieves over <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$20\%$</tex-math></inline-formula> improvement in average compared to the state-of-the-art clustering-based traffic analysis algorithms. FS-GAN also has the capability to synthesize highly complex mixtures of traffic types without requiring any human-labeled data samples.
General Greedy De-Bias Learning

Neural networks often make predictions relying on the spurious correlations from the datasets rather than the intrinsic properties of the task of interest, facing with sharp degradation on out-of-distribution (OOD) test data. Existing de-bias learning frameworks try to capture specific dataset bias by annotations but they fail to handle complicated OOD scenarios. Others implicitly identify the dataset bias by special design low capability biased models or losses, but they degrade when the training and testing data are from the same distribution. In this paper, we propose a General Greedy De-bias learning framework (GGD), which greedily trains the biased models and base model. The base model is encouraged to focus on examples that are hard to solve with biased models, thus remaining robust against spurious correlations in the test stage. GGD largely improves models’ OOD generalization ability on various tasks, but sometimes over-estimates the bias level and degrades on the in-distribution test. We further re-analyze the ensemble process of GGD and introduce the Curriculum Regularization inspired by curriculum learning, which achieves a good trade-off between in-distribution (ID) and out-of-distribution performance. Extensive experiments on image classification, adversarial question answering, and visual question answering demonstrate the effectiveness of our method. GGD can learn a more robust base model under the settings of both task-specific biased models with prior knowledge and self-ensemble biased model without prior knowledge. Codes are available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/GeraldHan/GGD</uri> .
QoS-aware Task Scheduling based on Reinforcement Learning for the Cloud-Fog Continuum

In this paper, we propose three multi-objective task scheduling algorithms for the cloud-fog continuum, that minimize both the makes pan and processing cost of workflows, considering the QoS requirements of the applications. Numerical results show that the scheduler based on Reinforcement Learning outperforms those based on classical optimization.