OpenRaiser/Intern-Atlas huggingface dataset:
Reproducibility Companion Paper: Knowledge Enhanced Neural Fashion Trend Forecasting
Venue: arXiv cs.LG 2021
Year: 2021

This companion paper supports the replication of the fashion trend forecasting experiments with the KERN (Knowledge Enhanced Recurrent Network) method that we presented in the ICMR 2020. We provide an artifact that allows the replication of the experiments using a Python implementation. The artifact is easy to deploy with simple installation, training and evaluation. We reproduce the experiments conducted in the original paper and obtain similar performance as previously reported. The replication results of the experiments support the main claims in the original paper.
CI-dataset and DetDSCI methodology for detecting too small and too large critical infrastructures in satellite images: Airports and electrical substations as case study
Venue: arXiv cs.CV 2021
Year: 2021

The detection of critical infrastructures in large territories represented by aerial and satellite images is of high importance in several fields such as in security, anomaly detection, land use planning and land use change detection. However, the detection of such infrastructures is complex as they have highly variable shapes and sizes, i.e., some infrastructures, such as electrical substations, are too small while others, such as airports, are too large. Besides, airports can have a surface area either small or too large with completely different shapes, which makes its correct detection challenging. As far as we know, these limitations have not been tackled yet in previous works. This paper presents (1) a smart Critical Infrastructure dataset, named CI-dataset, organised into two scales, small and large scales critical infrastructures and (2) a two-level resolution-independent critical infrastructure detection (DetDSCI) methodology that first determines the spatial resolution of the input image using a classification model, then analyses the image using the appropriate detector for that spatial resolution. The present study targets two representative classes, airports and electrical substations. Our experiments show that DetDSCI methodology achieves up to 37,53% F1 improvement with respect to Faster R-CNN, one of the most influential detection models.
A Simulation Framework for Contention-Free Scheduling on WiGig
Venue: arXiv cs.NI 2021
Year: 2021

The latest IEEE 802.11 amendments provide support to directional communications in the Millimeter Wave spectrum and, thanks to the wide bandwidth available at such frequencies, makes it possible to wirelessly approach several emergent use cases, such as virtual and augmented reality, telepresence, and remote control of industrial facilities. However, these applications require stringent Quality of Service, that only contention-free scheduling algorithms can guarantee. In this paper, we propose an end-to-end framework for the joint admission control and scheduling of periodic traffic streams over mmWave Wireless Local Area Networks based on Network Simulator 3, a popular full-stack open-source network simulator. Moreover, we design a baseline algorithm to handle scheduling requests, and we evaluate its performance with a full-stack perspective. The algorithm is tested in three scenarios, where we investigated different configurations and features to highlight the differences and trade-offs between contention-based and contention-free access strategies.
Immersive virtual reality methods in cognitive neuroscience and neuropsychology: Meeting the criteria of the National Academy of Neuropsychology and American Academy of Clinical Neuropsychology
Venue: arXiv cs.HC 2021
Year: 2021

Clinical tools involving immersive virtual reality (VR) may bring several advantages to cognitive neuroscience and neuropsychology. However, there are some technical and methodological pitfalls. The American Academy of Clinical Neuropsychology (AACN) and the National Academy of Neuropsychology (NAN) raised 8 key issues pertaining to Computerized Neuropsychological Assessment Devices. These issues pertain to: (1) the safety and effectivity; (2) the identity of the end-user; (3) the technical hardware and software features; (4) privacy and data security; (5) the psychometric properties; (6) examinee issues; (7) the use of reporting services; and (8) the reliability of the responses and results. The VR Everyday Assessment Lab (VR-EAL) is the first immersive VR neuropsychological battery with enhanced ecological validity for the assessment of everyday cognitive functions by offering a pleasant testing experience without inducing cybersickness. The VR-EAL meets the criteria of the NAN and AACN, addresses the methodological pitfalls, and brings advantages for neuropsychological testing. However, there are still shortcomings of the VR-EAL, which should be addressed. Future iterations should strive to improve the embodiment illusion in VR-EAL and the creation of an open access VR software library should be attempted. The discussed studies demonstrate the utility of VR methods in cognitive neuroscience and neuropsychology.
Finance 4.0: Design principles for a value-sensitive cryptoecnomic system to address sustainability
Venue: arXiv cs.CY 2021
Year: 2021

Cryptoeconomic systems derive their power but can not be controlled by the underlying software systems and the rules they enshrine. This adds a level of complexity to the software design process. At the same time, such systems, when designed with human values in mind, offer new approaches to tackle sustainability challenges, that are plagued by commons dilemmas and negative external effects caused by a one-dimensional monetary system. This paper proposes a design science research methodology with value-sensitive design methods to derive design principles for a value-sensitive socio-ecological cryptoeconomic system that incentivizes actions toward sustainability via multi-dimensional token incentives. These design principles are implemented in a software that is validated in user studies that demonstrate its relevance, usability and impact. Our findings provide new insights on designing cryptoeconomic systems. Moreover, the identified design principles for a value-sensitive socio-ecological financial system indicate opportunities for new research directions and business innovations.
Optimal Sampling Density for Nonparametric Regression
Venue: arXiv cs.LG 2021
Year: 2021

We propose a novel active learning strategy for regression, which is model-agnostic, robust against model mismatch, and interpretable. Assuming that a small number of initial samples are available, we derive the optimal training density that minimizes the generalization error of local polynomial smoothing (LPS) with its kernel bandwidth tuned locally: We adopt the mean integrated squared error (MISE) as a generalization criterion, and use the asymptotic behavior of the MISE as well as the locally optimal bandwidths (LOB) - the bandwidth function that minimizes MISE in the asymptotic limit. The asymptotic expression of our objective then reveals the dependence of the MISE on the training density, enabling analytic minimization. As a result,we obtain the optimal training density in a closed-form. The almost model-free nature of our approach thus helps to encode the essential properties of the target problem, providing a robust and model-agnostic active learning strategy. Furthermore, the obtained training density factorizes the influence of local function complexity, noise level and test density in a transparent and interpretable way. We validate our theory in numerical simulations, and show that the proposed active learning method outperforms the existing state-of-the-art model-agnostic approaches.
Throughput-Fairness Tradeoffs in Mobility Platforms
Venue: arXiv cs.CY 2021
Year: 2021

This paper studies the problem of allocating tasks from different customers to vehicles in mobility platforms, which are used for applications like food and package delivery, ridesharing, and mobile sensing. A mobility platform should allocate tasks to vehicles and schedule them in order to optimize both throughput and fairness across customers. However, existing approaches to scheduling tasks in mobility platforms ignore fairness. We introduce Mobius, a system that uses guided optimization to achieve both high throughput and fairness across customers. Mobius supports spatiotemporally diverse and dynamic customer demands. It provides a principled method to navigate inherent tradeoffs between fairness and throughput caused by shared mobility. Our evaluation demonstrates these properties, along with the versatility and scalability of Mobius, using traces gathered from ridesharing and aerial sensing applications. Our ridesharing case study shows that Mobius can schedule more than 16,000 tasks across 40 customers and 200 vehicles in an online manner.
On Finding Separators in Temporal Split and Permutation Graphs
Venue: arXiv cs.DM 2021
Year: 2021

Removing all connections between two vertices s and z in a graph by removing a minimum number of vertices is a fundamental problem in algorithmic graph theory. This (s,z)-separation problem is well-known to be polynomial solvable and serves as an important primitive in many applications related to network connectivity. We study the NP-hard temporal (s,z)-separation problem on temporal graphs, which are graphs with fixed vertex sets but edge sets that change over discrete time steps. We tackle this problem by restricting the layers (i.e., graphs characterized by edges that are present at a certain point in time) to specific graph classes. We restrict the layers of the temporal graphs to be either all split graphs or all permutation graphs (both being perfect graph classes) and provide both intractability and tractability results. In particular, we show that in general the problem remains NP-hard both on temporal split and temporal permutation graphs, but we also spot promising islands of fixed-parameter tractability particularly based on parameterizations that measure the amount of "change over time".
Photonic extreme learning machine by free-space optical propagation
Venue: arXiv cs.ET 2021
Year: 2021

Photonic brain-inspired platforms are emerging as novel analog computing devices, enabling fast and energy-efficient operations for machine learning. These artificial neural networks generally require tailored optical elements, such as integrated photonic circuits, engineered diffractive layers, nanophotonic materials, or time-delay schemes, which are challenging to train or stabilize. Here we present a neuromorphic photonic scheme - photonic extreme learning machines - that can be implemented simply by using an optical encoder and coherent wave propagation in free space. We realize the concept through spatial light modulation of a laser beam, with the far field that acts as feature mapping space. We experimentally demonstrated learning from data on various classification and regression tasks, achieving accuracies comparable to digital extreme learning machines. Our findings point out an optical machine learning device that is easy-to-train, energetically efficient, scalable and fabrication-constraint free. The scheme can be generalized to a plethora of photonic systems, opening the route to real-time neuromorphic processing of optical data.
Diameter, radius and all eccentricities in linear time for constant-dimension median graphs
Venue: arXiv cs.DS 2021
Year: 2021

Median graphs form the class of graphs which is the most studied in metric graph theory. Recently, Bénéteau et al. [2019] designed a linear-time algorithm computing both the $Θ$-classes and the median set of median graphs. A natural question emerges: is there a linear-time algorithm computing the diameter and the radius for median graphs? We answer positively to this question for median graphs $G$ with constant dimension $d$, i.e. the dimension of the largest induced hypercube of $G$. We propose a combinatorial algorithm computing all eccentricities of median graphs with running time $O(2^{O(d\log d)}n)$. As a consequence, this provides us with a linear-time algorithm determining both the diameter and the radius of median graphs with $d = O(1)$, such as cube-free median graphs. As the hypercube of dimension 4 is not planar, it shows also that all eccentricities of planar median graphs can be computed in $O(n)$.
Self-Calibration of the Offset Between GPS and Semantic Map Frames for Robust Localization
Venue: arXiv cs.RO 2021
Year: 2021

In self-driving, standalone GPS is generally considered to have insufficient positioning accuracy to stay in lane. Instead, many turn to LIDAR localization, but this comes at the expense of building LIDAR maps that can be costly to maintain. Another possibility is to use semantic cues such as lane lines and traffic lights to achieve localization, but these are usually not continuously visible. This issue can be remedied by combining semantic cues with GPS to fill in the gaps. However, due to elapsed time between mapping and localization, the live GPS frame can be offset from the semantic map frame, requiring calibration. In this paper, we propose a robust semantic localization algorithm that self-calibrates for the offset between the live GPS and semantic map frames by exploiting common semantic cues, including traffic lights and lane markings. We formulate the problem using a modified Iterated Extended Kalman Filter, which incorporates GPS and camera images for semantic cue detection via Convolutional Neural Networks. Experimental results show that our proposed algorithm achieves decimetre-level accuracy comparable to typical LIDAR localization performance and is robust against sparse semantic features and frequent GPS dropouts.
INSPIRE: The Entry Point to Europe's Big Geospatial Data Infrastructure
Venue: arXiv cs.DB 2021
Year: 2021

Initiated in 2007, the INSPIRE Directive has set a legal framework to create a European-wide Spatial Data Infrastructure (SDI) to support the European Union (EU) environmental policies. This chapter analyses the INSPIRE infrastructure from a Big Geospatial Data perspective, describing how data is shared in an interoperable way by public sector organisations in the EU Member States and how it is made available in and accessible within the infrastructure. The INSPIRE Geoportal, which is the entry point to the whole infrastructure, is presented in detail. To justify its nature of a Big Geospatial Data infrastructure, the characteristics of INSPIRE data are mapped to those of Big Data's six 'Vs'. Despite many good results achieved in terms of data sharing, some challenges still remain related to data consumption from the user side. The chapter concludes with a dedicated discussion on how INSPIRE, and traditional SDIs in general, should evolve into modern data ecosystems to address these challenges while also embracing the modern practices of data sharing through the web.
Practical Convex Formulation of Robust One-hidden-layer Neural Network Training
Venue: arXiv cs.LG 2021
Year: 2021

Recent work has shown that the training of a one-hidden-layer, scalar-output fully-connected ReLU neural network can be reformulated as a finite-dimensional convex program. Unfortunately, the scale of such a convex program grows exponentially in data size. In this work, we prove that a stochastic procedure with a linear complexity well approximates the exact formulation. Moreover, we derive a convex optimization approach to efficiently solve the "adversarial training" problem, which trains neural networks that are robust to adversarial input perturbations. Our method can be applied to binary classification and regression, and provides an alternative to the current adversarial training methods, such as Fast Gradient Sign Method (FGSM) and Projected Gradient Descent (PGD). We demonstrate in experiments that the proposed method achieves a noticeably better adversarial robustness and performance than the existing methods.
On Secrecy Performance of Mixed α-η-μ and Malaga RF-FSO Variable Gain Relaying Channel
Venue: arXiv cs.IT 2021
Year: 2021

With the completion of standardization of fifth-generation (5G) networks, the researchers have begun visioning sixth-generation (6G) networks that are predicted to be human-centric. Hence, similar to 5G networks, besides high data rate, providing secrecy and privacy will be the center of attention by the wireless research community. To support the visions beyond 5G (B5G) and 6G, in this paper we propose a secure radio frequency (RF)-free-space optical (FSO) mixed framework under the attempt of wiretapping by an eavesdropper at the RF hop. We assume the RF links undergo alpha-eta-mu fading whereas the FSO link exhibits a unified Malaga turbulence model with pointing error. The secrecy performance is evaluated by deducing expressions for three secrecy metrics i.e. average secrecy capacity, secure outage probability, and probability of non-zero secrecy capacity in terms of univariate and bivariate Meijer's G and Fox's H functions. We further capitalize on these expressions to demonstrate the impacts of fading, atmospheric turbulence, and pointing errors and show a comparison between two detection techniques (i.e. heterodyne detection (HD) and intensity modulation with direct detection (IM/DD)) that clearly reveals better secrecy can be achieved with HD technique relative to the IM/DD method. The inclusion of generalized fading models at the RF and FSO hops offers unification of several classical scenarios as special cases thereby exhibiting a more generic nature relative to the existing literature. Finally, all the analytical results are corroborated via Monte-Carlo simulations.
Model Checking Finite-Horizon Markov Chains with Probabilistic Inference
Venue: arXiv cs.LO 2021
Year: 2021

We revisit the symbolic verification of Markov chains with respect to finite horizon reachability properties. The prevalent approach iteratively computes step-bounded state reachability probabilities. By contrast, recent advances in probabilistic inference suggest symbolically representing all horizon-length paths through the Markov chain. We ask whether this perspective advances the state-of-the-art in probabilistic model checking. First, we formally describe both approaches in order to highlight their key differences. Then, using these insights we develop Rubicon, a tool that transpiles Prism models to the probabilistic inference tool Dice. Finally, we demonstrate better scalability compared to probabilistic model checkers on selected benchmarks. All together, our results suggest that probabilistic inference is a valuable addition to the probabilistic model checking portfolio -- with Rubicon as a first step towards integrating both perspectives.
Composition and Application of Current Advanced Driving Assistance System: A Review
Venue: arXiv cs.AI 2021
Year: 2021

Due to the growing awareness of driving safety and the development of sophisticated technologies, advanced driving assistance system (ADAS) has been equipped in more and more vehicles with higher accuracy and lower price. The latest progress in this field has called for a review to sum up the conventional knowledge of ADAS, the state-of-the-art researches, and novel applications in real-world. With the help of this kind of review, newcomers in this field can get basic knowledge easier and other researchers may be inspired with potential future development possibility. This paper makes a general introduction about ADAS by analyzing its hardware support and computation algorithms. Different types of perception sensors are introduced from their interior feature classifications, installation positions, supporting ADAS functions, and pros and cons. The comparisons between different sensors are concluded and illustrated from their inherent characters and specific usages serving for each ADAS function. The current algorithms for ADAS functions are also collected and briefly presented in this paper from both traditional methods and novel ideas. Additionally, discussions about the definition of ADAS from different institutes are reviewed in this paper, and future approaches about ADAS in China are introduced in particular.
Basic and Depression Specific Emotion Identification in Tweets: Multi-label Classification Experiments
Venue: arXiv cs.LG 2021
Year: 2021

In this paper, we present empirical analysis on basic and depression specific multi-emotion mining in Tweets with the help of state of the art multi-label classifiers. We choose our basic emotions from a hybrid emotion model consisting of the common emotions from four highly regarded psychological models of emotions. Moreover, we augment that emotion model with new emotion categories because of their importance in the analysis of depression. Most of those additional emotions have not been used in previous emotion mining research. Our experimental analyses show that a cost sensitive RankSVM algorithm and a Deep Learning model are both robust, measured by both Macro F-measures and Micro F-measures. This suggests that these algorithms are superior in addressing the widely known data imbalance problem in multi-label learning. Moreover, our application of Deep Learning performs the best, giving it an edge in modeling deep semantic features of our extended emotional categories.
Neural Morphology Dataset and Models for Multiple Languages, from the Large to the Endangered
Venue: arXiv cs.CL 2021
Year: 2021

We train neural models for morphological analysis, generation and lemmatization for morphologically rich languages. We present a method for automatically extracting substantially large amount of training data from FSTs for 22 languages, out of which 17 are endangered. The neural models follow the same tagset as the FSTs in order to make it possible to use them as fallback systems together with the FSTs. The source code, models and datasets have been released on Zenodo.
Evaluation of Account Recovery Strategies with FIDO2-based Passwordless Authentication
Venue: arXiv cs.CR 2021
Year: 2021

Threats to passwords are still very relevant due to attacks like phishing or credential stuffing. One way to solve this problem is to remove passwords completely. User studies on passwordless FIDO2 authentication using security tokens demonstrated the potential to replace passwords. However, widespread acceptance of FIDO2 depends, among other things, on how user accounts can be recovered when the security token becomes permanently unavailable. For this reason, we provide a heuristic evaluation of 12 account recovery mechanisms regarding their properties for FIDO2 passwordless authentication. Our results show that the currently used methods have many drawbacks. Some even rely on passwords, taking passwordless authentication ad absurdum. Still, our evaluation identifies promising account recovery solutions and provides recommendations for further studies.