OpenRaiser/Intern-Atlas huggingface dataset:
IoT with a Soft Touch: A Modular Remote Sensing Platform for STE(A)M Applications
Venue: arXiv cs.CY 2022
Year: 2022

Besides wide attraction in the industry, IoT is being used to advance STEM and STEAM education across a range of education levels. This work presents a remote sensing platform, named IoT with a Soft Touch, developed to achieve two goals. First, it aims to lower the technicality, stimulating the students to do STE(A)M. Second, the technology is to be used in `softer' applications (e.g., environmental and health care), thereby aiming to attract a more diverse set of student profiles. Students can easily build a wireless sensing device, with a specific application in mind. The modular design of the platform and an intuitive graphical configurator tool allows them to tailor the device's functionality to their needs. The sensor's data is transmitted wirelessly with LoRaWAN. The data can be viewed and analyzed on a dashboard, or the raw data can be extracted for further processing, e.g., as part of the school's STE(A)M curriculum. This work elaborates on the low-power and modular design challenges, and how the platform is used in education.
Optimal Workplace Occupancy Strategies during the COVID-19 Pandemic
Venue: arXiv cs.DS 2022
Year: 2022

During the COVID-19 pandemic, many organizations (e.g. businesses, companies, government facilities, etc.) have attempted to reduce infection risk by employing partial home office strategies. However, working from home can also reduce productivity for certain types of work and particular employees. Over the long term, many organizations therefore face a need to balance infection risk against productivity. Motivated by this trade-off, we model this situation as a bi-objective optimization problem and propose a practical approach to find trade-off (Pareto optimal) solutions. We present a new probabilistic framework to compute the expected number of infected employees as a function of key parameters including: the incidence level in the neighborhood of the organization, the COVID-19 transmission rate, the number of employees, the percentage of vaccinated employees, the testing frequency, and the contact rate among employees. We implement the model and the optimization algorithm and perform several numerical experiments with different parameter settings. Furthermore, we provide an online application based on the models and algorithms developed in this paper, which can be used to identify the optimal workplace occupancy rate for real-world organizations.
Life, the Metaverse and Everything: An Overview of Privacy, Ethics, and Governance in Metaverse
Venue: arXiv cs.CY 2022
Year: 2022

The metaverse is expected to be the next major evolution phase of the internet. The metaverse will have an impact on human society, production, and life. In this work, we analyze the current trends and challenges that building such a virtual environment will face. We focus on three major pillars to guide the development of the metaverse: privacy, governance, and ethical design, to guide the development of the metaverse. Finally, we propose a preliminary modular-based framework for an ethical design of the metaverse.
Feasibility of nowcasting SDG indicators: a comprehensive survey
Venue: arXiv cs.CY 2022
Year: 2022

The 2030 Agenda and accompanying Sustainable Development Goals (SDGs) are vital in guiding national and global policy. However, many of the SDG indicators used to measure progress toward those goals suffer from long publication lags. Nowcasting has the potential to address this problem and generate more timely estimates of those indicators. This paper provides resources for achieving that potential by 1) carrying out a comprehensive nowcasting feasibility survey of all SDG indicators to assess their potential to be nowcast, and 2) performing a case study of indicator 9.4.1 to illustrate and shed light on the process of performing a nowcasting exercise. There exist 231 SDG indicators, but due to only examining Tier 1 indicators and the fact that many indicators have multiple sub-indicators, 362 indicators and sub-indicators were eventually surveyed. Of those 362, 150 were found highly likely to be suitable candidates for nowcasting, 87 were found to be likely, and 125 were found to be unsuitable.
Identifying Security Risks in NFT Platforms
Venue: arXiv cs.CY 2022
Year: 2022

This paper examines the effects of inherent risks in the emerging technology of non-fungible tokens and proposes an actionable set of solutions for stakeholders in this ecosystem and observers. Web3 and NFTs are a fast-growing 300 billion dollar economy with some clear, highly publicized harms that came to light recently. We set out to explore the risks to understand their nature and scope, and if we could find ways to mitigate them. In due course of investigation, we recap the background of the evolution of the web from a client-server model to the rise of Web2.0 tech giants in the early 2000s. We contrast how the Web3 movement is trying to re-establish the independent style of the early web. In our research we discover a primary set of risks and harms relevant to the ecosystem, and classify them into a simple taxonomy while addressing their mitigations with solutions. We arrive at a set of solutions that are a combination of processes to be adopted, and technological changes or improvements to be incorporated into the ecosystem, to implement risk mitigations. By linking mitigations to individual risks, we are confident our recommendations will improve the security maturity of the growing Web3 ecosystem. We are not endorsing, or recommending specifically any particular product or service in our solution set. Nor are we compensated or influenced in any way by these companies to list these products in our research. The evaluations of products in our research have to simply be viewed as suggested improvements.
Matrix Completion with Sparse Noisy Rows
Venue: arXiv cs.LG 2022
Year: 2022

Exact matrix completion and low rank matrix estimation problems has been studied in different underlying conditions. In this work we study exact low-rank completion under non-degenerate noise model. Non-degenerate random noise model has been previously studied by many researchers under given condition that the noise is sparse and existing in some of the columns. In this paper, we assume that each row can receive random noise instead of columns and propose an interactive algorithm that is robust to this noise. We show that we use a parametrization technique to give a condition when the underlying matrix could be recoverable and suggest an algorithm which recovers the underlying matrix.
Internet-of-Things Architectures for Secure Cyber-Physical Spaces: the VISOR Experience Report
Venue: arXiv cs.CR 2022
Year: 2022

Internet of things (IoT) technologies are becoming a more and more widespread part of civilian life in common urban spaces, which are rapidly turning into cyber-physical spaces. Simultaneously, the fear of terrorism and crime in such public spaces is ever-increasing. Due to the resulting increased demand for security, video-based IoT surveillance systems have become an important area for research. Considering the large number of devices involved in the illicit recognition task, we conducted a field study in a Dutch Easter music festival in a national interest project called VISOR to select the most appropriate device configuration in terms of performance and results. We iteratively architected solutions for the security of cyber-physical spaces using IoT devices. We tested the performance of multiple federated devices encompassing drones, closed-circuit television, smart phone cameras, and smart glasses to detect real-case scenarios of potentially malicious activities such as mosh-pits and pick-pocketing. Our results pave the way to select optimal IoT architecture configurations -- i.e., a mix of CCTV, drones, smart glasses, and camera phones in our case -- to make safer cyber-physical spaces' a reality.
Modern Views of Machine Learning for Precision Psychiatry
Venue: arXiv cs.LG 2022
Year: 2022

In light of the NIMH's Research Domain Criteria (RDoC), the advent of functional neuroimaging, novel technologies and methods provide new opportunities to develop precise and personalized prognosis and diagnosis of mental disorders. Machine learning (ML) and artificial intelligence (AI) technologies are playing an increasingly critical role in the new era of precision psychiatry. Combining ML/AI with neuromodulation technologies can potentially provide explainable solutions in clinical practice and effective therapeutic treatment. Advanced wearable and mobile technologies also call for the new role of ML/AI for digital phenotyping in mobile mental health. In this review, we provide a comprehensive review of the ML methodologies and applications by combining neuroimaging, neuromodulation, and advanced mobile technologies in psychiatry practice. Additionally, we review the role of ML in molecular phenotyping and cross-species biomarker identification in precision psychiatry. We further discuss explainable AI (XAI) and causality testing in a closed-human-in-the-loop manner, and highlight the ML potential in multimedia information extraction and multimodal data fusion. Finally, we discuss conceptual and practical challenges in precision psychiatry and highlight ML opportunities in future research.
Deep-Ensemble-Based Uncertainty Quantification in Spatiotemporal Graph Neural Networks for Traffic Forecasting
Venue: arXiv cs.LG 2022
Year: 2022

Deep-learning-based data-driven forecasting methods have produced impressive results for traffic forecasting. A major limitation of these methods, however, is that they provide forecasts without estimates of uncertainty, which are critical for real-time deployments. We focus on a diffusion convolutional recurrent neural network (DCRNN), a state-of-the-art method for short-term traffic forecasting. We develop a scalable deep ensemble approach to quantify uncertainties for DCRNN. Our approach uses a scalable Bayesian optimization method to perform hyperparameter optimization, selects a set of high-performing configurations, fits a generative model to capture the joint distributions of the hyperparameter configurations, and trains an ensemble of models by sampling a new set of hyperparameter configurations from the generative model. We demonstrate the efficacy of the proposed methods by comparing them with other uncertainty estimation techniques. We show that our generic and scalable approach outperforms the current state-of-the-art Bayesian and a number of other commonly used frequentist techniques.
Stuttgart Open Relay Degradation Dataset (SOReDD)
Venue: arXiv cs.LG 2022
Year: 2022

Real-life industrial use cases for machine learning oftentimes involve heterogeneous and dynamic assets, processes and data, resulting in a need to continuously adapt the learning algorithm accordingly. Industrial transfer learning offers to lower the effort of such adaptation by allowing the utilization of previously acquired knowledge in solving new (variants of) tasks. Being data-driven methods, the development of industrial transfer learning algorithms naturally requires appropriate datasets for training. However, open-source datasets suitable for transfer learning training, i.e. spanning different assets, processes and data (variants), are rare. With the Stuttgart Open Relay Degradation Dataset (SOReDD) we want to offer such a dataset. It provides data on the degradation of different electromechanical relays under different operating conditions, allowing for a large number of different transfer scenarios. Although such relays themselves are usually inexpensive standard components, their failure often leads to the failure of a machine as a whole due to their role as the central power switching element of a machine. The main cost factor in the event of a relay defect is therefore not the relay itself, but the reduced machine availability. It is therefore desirable to predict relay degradation as accurately as possible for specific applications in order to be able to replace relays in good time and avoid unplanned machine downtimes. Nevertheless, data-driven failure prediction for electromechanical relays faces the challenge that relay degradation behavior is highly dependent on the operating conditions, high-resolution measurement data on relay degradation behavior is only collected in rare cases, and such data can then only cover a fraction of the possible operating environments. Relays are thus representative of many other central standard components in automation technology.
Reliable Editions from Unreliable Components: Estimating Ebooks from Print Editions Using Profile Hidden Markov Models
Venue: arXiv cs.DL 2022
Year: 2022

A profile hidden Markov model, a popular model in biological sequence analysis, can be used to model related sequences of characters transcribed from books, magazines, and other printed materials. This paper documents one application of a profile HMM: automatically producing an ebook edition from distinct print editions. The resulting ebook has virtually all the desired properties found in a publisher-prepared ebook, including accurate transcription and an absence of print artifacts such as end-of-line hyphenation and running headers. The technique, which has particular benefits for readers and libraries that require books in an accessible format, is demonstrated using seven copies of a nineteenth-century novel.
Predictable Sharing of Last-level Cache Partitions for Multi-core Safety-critical Systems
Venue: arXiv cs.AR 2022
Year: 2022

Last-level cache (LLC) partitioning is a technique to provide temporal isolation and low worst-case latency (WCL) bounds when cores access the shared LLC in multicore safety-critical systems. A typical approach to cache partitioning involves allocating a separate partition to a distinct core. A central criticism of this approach is its poor utilization of cache storage. Today's trend of integrating a larger number of cores exacerbates this issue such that we are forced to consider shared LLC partitions for effective deployments. This work presents an approach to share LLC partitions among multiple cores while being able to provide low WCL bounds.
Monitoring social distancing with single image depth estimation
Venue: arXiv cs.CV 2022
Year: 2022

The recent pandemic emergency raised many challenges regarding the countermeasures aimed at containing the virus spread, and constraining the minimum distance between people resulted in one of the most effective strategies. Thus, the implementation of autonomous systems capable of monitoring the so-called social distance gained much interest. In this paper, we aim to address this task leveraging a single RGB frame without additional depth sensors. In contrast to existing single-image alternatives failing when ground localization is not available, we rely on single image depth estimation to perceive the 3D structure of the observed scene and estimate the distance between people. During the setup phase, a straightforward calibration procedure, leveraging a scale-aware SLAM algorithm available even on consumer smartphones, allows us to address the scale ambiguity affecting single image depth estimation. We validate our approach through indoor and outdoor images employing a calibrated LiDAR + RGB camera asset. Experimental results highlight that our proposal enables sufficiently reliable estimation of the inter-personal distance to monitor social distancing effectively. This fact confirms that despite its intrinsic ambiguity, if appropriately driven single image depth estimation can be a viable alternative to other depth perception techniques, more expensive and not always feasible in practical applications. Our evaluation also highlights that our framework can run reasonably fast and comparably to competitors, even on pure CPU systems. Moreover, its practical deployment on low-power systems is around the corner.
Meta-Learning Approaches for a One-Shot Collective-Decision Aggregation: Correctly Choosing how to Choose Correctly
Venue: arXiv cs.LG 2022
Year: 2022

Aggregating successfully the choices regarding a given decision problem made by the multiple collective members into a single solution is essential for exploiting the collective's intelligence and for effective crowdsourcing. There are various aggregation techniques, some of which come down to a simple and sometimes effective deterministic aggregation rule. However, it has been shown that the efficiency of those techniques is unstable under varying conditions and within different domains. Other methods mainly rely on learning from the decision-makers previous responses or the availability of additional information about them. In this study, we present two one-shot machine-learning-based aggregation approaches. The first predicts, given multiple features about the collective's choices, including meta-cognitive ones, which aggregation method will be best for a given case. The second directly predicts which decision is optimal, given, among other things, the selection made by each method. We offer a meta-cognitive feature-engineering approach for characterizing a collective decision-making case in a context-sensitive fashion. In addition, we offer a new aggregation method, the Devil's-Advocate aggregator, to deal with cases in which standard aggregation methods are predicted to fail. Experimental results show that using either of our proposed approaches increases the percentage of successfully aggregated cases (i.e., cases in which the correct answer is returned) significantly, compared to the uniform application of each rule-based aggregation method. We also demonstrate the importance of the Devil's Advocate aggregator.
Signal Propagation: A Framework for Learning and Inference In a Forward Pass
Venue: arXiv cs.LG 2022
Year: 2022

We propose a new learning framework, signal propagation (sigprop), for propagating a learning signal and updating neural network parameters via a forward pass, as an alternative to backpropagation. In sigprop, there is only the forward path for inference and learning. So, there are no structural or computational constraints necessary for learning to take place, beyond the inference model itself, such as feedback connectivity, weight transport, or a backward pass, which exist under backpropagation based approaches. That is, sigprop enables global supervised learning with only a forward path. This is ideal for parallel training of layers or modules. In biology, this explains how neurons without feedback connections can still receive a global learning signal. In hardware, this provides an approach for global supervised learning without backward connectivity. Sigprop by construction has compatibility with models of learning in the brain and in hardware than backpropagation, including alternative approaches relaxing learning constraints. We also demonstrate that sigprop is more efficient in time and memory than they are. To further explain the behavior of sigprop, we provide evidence that sigprop provides useful learning signals in context to backpropagation. To further support relevance to biological and hardware learning, we use sigprop to train continuous time neural networks with Hebbian updates, and train spiking neural networks with only the voltage or with biologically and hardware compatible surrogate functions.
Face Recognition In Children: A Longitudinal Study
Venue: arXiv cs.CV 2022
Year: 2022

The lack of high fidelity and publicly available longitudinal children face datasets is one of the main limiting factors in the development of face recognition systems for children. In this work, we introduce the Young Face Aging (YFA) dataset for analyzing the performance of face recognition systems over short age-gaps in children. We expand previous work by comparing YFA with several publicly available cross-age adult datasets to quantify the effects of short age-gap in adults and children. Our analysis confirms a statistically significant and matcher independent decaying relationship between the match scores of ArcFace-Focal, MagFace, and Facenet matchers and the age-gap between the gallery and probe images in children, even at the short age-gap of 6 months. However, our result indicates that the low verification performance reported in previous work might be due to the intra-class structure of the matcher and the lower quality of the samples. Our experiment using YFA and a state-of-the-art, quality-aware face matcher (MagFace) indicates 98.3% and 94.9% TAR at 0.1% FAR over 6 and 36 Months age-gaps, respectively, suggesting that face recognition may be feasible for children for age-gaps of up to three years.
Robust Fingerprinting of Genomic Databases
Venue: arXiv cs.CR 2022
Year: 2022

Database fingerprinting has been widely used to discourage unauthorized redistribution of data by providing means to identify the source of data leakages. However, there is no fingerprinting scheme aiming at achieving liability guarantees when sharing genomic databases. Thus, we are motivated to fill in this gap by devising a vanilla fingerprinting scheme specifically for genomic databases. Moreover, since malicious genomic database recipients may compromise the embedded fingerprint by launching effective correlation attacks which leverage the intrinsic correlations among genomic data (e.g., Mendel's law and linkage disequilibrium), we also augment the vanilla scheme by developing mitigation techniques to achieve robust fingerprinting of genomic databases against correlation attacks. We first show that correlation attacks against fingerprinting schemes for genomic databases are very powerful. In particular, the correlation attacks can distort more than half of the fingerprint bits by causing a small utility loss (e.g.,database accuracy and consistency of SNP-phenotype associations measured via p-values). Next, we experimentally show that the correlation attacks can be effectively mitigated by our proposed mitigation techniques. We validate that the attacker can hardly compromise a large portion of the fingerprint bits even if it pays a higher cost in terms of degradation of the database utility. For example, with around 24% loss in accuracy and 20% loss in the consistency of SNP-phenotype associations, the attacker can only distort about 30% fingerprint bits, which is insufficient for it to avoid being accused. We also show that the proposed mitigation techniques also preserve the utility of the shared genomic databases.