Miracle-dz/UniFAR-FTUs huggingface dataset:
Improving the Forecast Accuracy of Protected Data Using Time Series Features

Forecasters often utilize large amounts of personal or commercially sensitive data. This data can be protected using privacy methods that mitigate privacy concerns and enable compliance with privacy laws and regulations. However, existing privacy methods degrade forecast accuracy to unusable levels. To overcome this problem, we propose a matrix-based privacy method called k-nearest time series + (k-nTS+) swapping that preserves time series features to maintain forecast accuracy. We apply our privacy method to a forecasting competition data set and find that k-nTS+ swapping maintains forecast accuracy and preserves the distribution of time series features much better than competitor methods at similar privacy levels. The k-nTS+ protected time series are also more representative of the original data, potentially leading to increased trust between data owners and forecasters. Using our approach, data owners can share representative time series data with useful features and significantly reduced privacy concerns. This can increase data availability to the forecasting community and give privacy-conscious data owners a competitive advantage.
Development of a Human-Robot Control System Based on Virtual Reality for Swarms of Unmanned Aerial Vehicles (UAVs)

This thesis presents a system for controlling real, simulated or hybrid swarms of unmanned aerial vehicles using a virtual reality headset. It deals with designing the human-swarm interaction system for virtual reality, implementation of the distributed software, challenges of using artiﬁcial force ﬁelds and static meshes to inﬂuence drones in the real-world environment, experimental veriﬁcation of the system in a simulation and the real world, and discussing and comparing the results to existing solutions.
Dawn‐dusk asymmetries in rotating magnetospheres: Lessons from modeling Saturn

Spacecraft measurements reveal perplexing dawn‐dusk asymmetries of field and plasma properties in the magnetospheres of Saturn and Jupiter. Here we describe a previously unrecognized source of dawn‐dusk asymmetry in a rapidly rotating magnetosphere. We analyze two magnetohydrodynamic simulations, focusing on how flows along and across the field vary with local time in Saturn's dayside magnetosphere. As plasma rotates from dawn to noon on a dipolarizing flux tube, it flows away from the equator along the flux tube at roughly half of the sound speed (Cs), the maximum speed at which a bulk plasma can flow along a flux tube into a lower pressure region. As plasma rotates from noon to dusk on a stretching flux tube, the field‐aligned component of its centripetal acceleration decreases and it flows back toward the equator at speeds typically smaller than 12Cs . Correspondingly, the plasma sheet remains far thicker and the field less stretched in the afternoon than in the morning. Different radial force balance in the morning and afternoon sectors produce asymmetry in the plasma sheet thickness and a net dusk‐to‐dawn flow inside of L = 15 or equivalently, a large‐scale electric field (E) oriented from postnoon to premidnight, as reported from observations. Morning‐afternoon asymmetry analogous to that found at Saturn has been observed at Jupiter, and a noon‐midnight component of E cannot be ruled out.
High-throughput and low-latency 60GHz small-cell network architectures over radio-over-fiber technologies

Future broadband access networks in the 5G framework will need to be bilateral, exploiting both optical and wireless technologies. This paper deals with new approaches and synergies on radio-over-fiber (RoF) technologies and how those can be leveraged to seamlessly converge wireless technology for agility and mobility with passive optical networks (PON)-based backhauling. The proposed convergence paradigm is based upon a holistic network architecture mixing mm-wave wireless access with photonic integration, dynamic capacity allocation and network coding schemes to enable high bandwidth and low-latency fixed and 60GHz wireless personal area communications for gigabit rate per user, proposing and deploying on top a Medium-Transparent MAC (MT-MAC) protocol as a low-latency bandwidth allocation mechanism. We have evaluated alternative network topologies between the central office (CO) and the access point module (APM) for data rates up to 2.5 Gb/s and SC frequencies up to 60 GHz. Optical network coding is demonstrated for SCM-based signaling to enhance bandwidth utilization and facilitate optical-wireless convergence in 5G applications, reporting medium-transparent network coding directly at the physical layer between end-users communicating over a RoF infrastructure. Towards equipping the physical layer with the appropriate agility to support MT-MAC protocols, a monolithic InP-based Remote Antenna Unit optoelectronic PIC interface is shown that ensures control over the optical resource allocation assisting at the same time broadband wireless service. Finally, the MT-MAC protocol is analysed and simulation and analytical theoretical results are presented that are found to be in good agreement confirming latency values lower than 1msec for small- to mid-load conditions.
IIF-SAS Final Report for the Project Titled: "Forecasting in Unknown Territory: Towards Physically Motivated Learning for Local Wind Fields"

Very-short-term wind forecasts (i.e., wind speed and power predictions issued at sub-hourly forecast horizons) are indispensable to the reliable operation of wind energy systems. The dominant consensus in the wind forecasting literature and practice is that data-driven approaches are, arguably, the “right” tools for such short-term horizons. This is in contrast to numerical weather predictions (NWP), or hybrid methods thereof, for which the value is typically substantiated as the forecast horizon becomes longer (> 1-3 hours). In this work, we propose a probabilistic machine learning (ML) model that leverages NWP information—which is typically available to the farm or power system operator at the time of forecast—in order to make very-short-term wind speed and power forecasting (up to an hour ahead). Instead of directly using NWPs as input regressors as in most hybrid approaches, we indirectly invoke NWP information in selecting key hyperparameters within the ML model, thereby guiding it to adhere to certain physical principles related to local wind field formation and propagation. Using real-world data from the U.S. NY/NJ Bight where several offshore wind projects are currently in-development, we show that such indirect integration of NWPs within an ML approach outperforms several prevalent forecasting methods, including persistence forecasts, which are known to be highly competitive at ultra-short-term horizons. We envision this work to serve as an exemplar for leveraging the rich, yet coarser-resolution information of NWPs in improving ML-based ultra-short-term wind forecasting models.
CogALex 2.0: Impact of Data Quality on Lexical-Semantic Relation Prediction

Predicting lexical-semantic relations between word pairs has successfully been accomplished by pre-trained neural language models. An XLM-RoBERTa-based approach, for instance, achieved the best performance differentiating between hypernymy, synonymy, antonymy, and random relations in four languages in the CogALex-VI 2020 shared task. However, the results also revealed strong performance divergences between languages and confusions of speciﬁc relations, especially hypernymy and synonymy. Upon inspection, a difference in data quality across languages and relations could be observed. Thus, we provide a manually improved dataset for lexical-semantic relation prediction and evaluate its impact across three pre-trained neural language models.
Poster: Privacy Preserving Collaborative Clustering with Hyper Parameter Recommendation

—Clustering is an unsupervised machine learning technique that creates groups of data that are similar. The collected data may contain sensitive or confidential information and privacy concerns may arise when the data is shared among multiple parties. In this paper, we investigated various clustering algorithms in the context of a collaborative data mining model, in which two data owners performed the clustering algorithm on their shared data. We attempted to address the issue of determining optimal clustering algorithm input parameters when two parties want to perform collaborative clustering on their combined data set with the assistance of a third party (server)
Boosting Outdoor Vision-and-Language Navigation with On-the-route Objects

Outdoor Vision-and-Language Navigation (VLN) is a challenging task that requires an agent to navigate using real-world urban environment data and natural language instructions. Current outdoor VLN models tend to overlook crucial navigation roles, such as objects that serve as landmarks for accurate turn and stop locations. This occurs because they primarily focus on panoramas and instructions, while disregarding objects that provide essential information for accurate decisions, such as identifying correct turn and stop locations, which humans naturally use as landmarks in unfamiliar places. In this paper, we propose the Object-Attention VLN (OAVLN) model, inspired by human navigation, which focuses on relevant on-the-route objects. Our model outperforms previous methods across all evaluation metrics on two benchmark datasets, Touchdown and map2seq.
Automating Counterspeech in Dialogue Systems

The proliferation of online hate speech poses a major problem for society. Counterspeech offers a promising solution for combating hate speech, without invoking freedom of speech concerns, by directly responding to the hate speech in such a way as to challenge the hate narratives. In this thesis, we investigate the important task of automating counterspeech in dialogue systems. The core modelling approach we take is through fine-tuning DialoGPT, a large language model Transformer-based open-domain dialogue system, on an expert-based counterspeech dataset produced under the supervision of trained NGO operators from StopHateUK. To guide system development, we construct an automatic counterspeech evaluation framework that provides insight into how dialogue systems respond to hate speech according to various properties. We then run a series of experiments that demonstrate that counterspeechenhanced fine-tuned dialogue systems produce better counterspeech than baseline approaches according to automatic metrics (increasing BLEU and BERTScore by 1.5% and 6.3% absolute respectively), and show that large-quantity crowd-sourced counterspeech data can be leveraged to supplement expert-based data by improving model generalization and robustness. However, we also observe that the system-generated responses tend to suffer from a lack of diversity, and that the improved counterspeech ability of the fine-tuned systems comes at the cost of a negative impact on general conversational ability. Finally, we validate our results by running a human evaluation study, where we observe that human evaluators consider the counterspeech produced by our best-performing system to generally be close to human-level quality, although the system is prone to occasionally producing inappropriate responses. On the whole, our results show strong promise for the use of automated dialogue systems in the fight against online hate speech.
Improving the Compatibility and Manufacturability of Digital Architectures for Processing Using Resistive Memory *

Many modern applications require frequent data movement between the CPU and the main memory, which consumes a significant amount of energy. To mitigate the cost of data movement, recent works propose a new computing paradigm called processing-using-memory (PUM), which leverages electrical interactions between memory cells to realize useful computation within the memory arrays, without the need for additional CMOS logic. Compared to adding discrete logic near memory arrays, PUM eliminates significantly more data movement and can enable thousands to millions of ways of data-level parallelism [3, 10]. A PUM-capable memory cell can be used as either (1) a multi-bit device capable of analog operations such as multiplication (e.g., [1,2]), or (2) a single-bit device capable of digital operations such as Boolean algebra (e.g., [7, 9, 13]). While PUM can be implemented using both conventional (e.g., DRAM, SRAM) and emerging (e.g., MRAM, PCM, ReRAM)1 memories, prior works build PUM architectures around a specific memory technology. Unfortunately, this creates a significant barrier to the development of PUM-based systems. Due to DRAM scaling issues [8, 11], manufacturers are hesitant to alter the design of DRAM arrays in fear of hurting yield, and while SRAM-based PUM avoids such concerns, it significantly compromises storage density compared to other memory technologies. Emerging memory technologies offer the promise of high-density PUM, but issues remain with the at-scale production of each of them. Given the uncertainty of which technology will emerge as the dominant replacement for DRAM, it becomes unclear which of the previously-proposed PUM architectures will be viable, which in turn prevents systems developers from solving software-level challenges holding back the adoption of PUM [3]. In our JETCAS 2022 paper, we aim to decouple PUM architectures as much as possible from (1) a specific memory technology, as well as (2) a specific logic family (e.g., the MAGIC logic family [6] provides driving voltages and circuitlevel support for either NOR or NAND, while the FELIX logic family [4] provides this for NOR, NAND, and NOT in the same array). For this work, we build upon our prior work on RACER [13], a highly-scalable PUM architecture that efficiently enables MAGIC-NOR-based processing inside small memory arrays. We start by developing interface circuits that allow the RACER architecture to be compatible with any PUM logic family. These circuits ensure that most of RACER’s circuitry can stay the same as we adapt the architecture to new logic families and to other resistive memory technologies (including both 1S1R-based crossbars and 1T1R-based arrays). Building upon our technology-agnostic version of RACER, we seek to design a new logic family that is practical for the types of memory devices that we can manufacture today. Unfortunately, the MAGIC and FELIX proposals require con-
FORWARD PHYSICS FACILITY FPF Working Groups Working Group Conveners :

Working Group Conveners: Luis A. Anchordoqui,1 Akitaka Ariga,2, 3 Tomoko Ariga,4 Alan J. Barr,5 Brian Batell,6 Jianming Bian,7 Jamie Boyd,8 Matthew Citron,9 Giovanni De Lellis,10, 11 Albert De Roeck,8 Antonia Di Crescenzo,8, 10, 11 Milind V. Diwan,12 Jonathan L. Feng,7 Christopher S. Hill,13 Felix Kling,14 Steven Linden,15 Josh McFayden,16 Hidetoshi Otono,4 Mary Hall Reno,17 Juan Rojo,18, 19 Dennis Soldin,20 Sebastian Trojanowski,21, 22 and Wenjie Wu 7
Wall-modeled LES of the Boeing speed bump using a non-Boussinesq modeling framework

In the limit of increasingly large Reynolds numbers, the feasibility of direct numerical simulations (DNS) of the Navier-Stokes equations becomes impractical. A widely used tool aimed at alleviating this issue is the large-eddy simulation (LES) methodology, wherein the simulations are coarse-grained relative to a DNS. The large eddies in the flow are resolved numerically, but the unresolved eddies are modeled using physics-inspired phenomenological models (Smagorinsky 1963; Germano et al. 1991; Nicoud et al. 2011; Vreman et al. 1994). A commonly utilized framework for subgrid-scale (SGS) modeling is the eddy-viscosity formulation using both static (Smagorinsky 1963; Vreman 2004; Rozema et al. 2015) and dynamic methods (Germano et al. 1991; Ghosal et al. 1995; Meneveau et al. 1996). The dynamic procedure (based on the idea of test filtering) was introduced to alleviate the ad hoc prescription of a model coefficient to the numerous SGS models depending on the flow. However, in all of these formulations, the SGS stresses are assumed to be locally aligned and scaled with the strain rates of the resolved eddies, thus acting as a further diffusive source of momentum in addition to the molecular viscosity. Recently, Agrawal et al. (2022) showed that in both a-priori and a-posteriori calculations, a non-Boussinesq formulation of the SGS stresses, or the dynamic tensor-coefficient Smagorinsky model (DTCSM), outperforms the classical eddy-viscosity models. For wall-bounded turbulence, as the Reynolds number increases, the size of the largest energy-containing eddies in the outer part of the boundary layer becomes increasingly large compared to the near-wall eddies. This means that significant grid refinement close to the wall is required to accurately resolve the near-wall eddies, even within the LES paradigm. Choi & Moin (2012) suggest that the grid requirements for such calculations scale with a super-linear power of the Reynolds number, thus making the numerical simulations expensive. To reduce the issues related to this intractability, the effect of the subgrid, near-wall flow within the turbulent boundary layer is modeled, and this method is referred as wall-modeled LES (WMLES). Traditionally, wall-modeling practices include wall-stress models which supply a flux (or wall stress) to the outer LES solution in the form of a boundary condition. These wall models are principally motivated by the Reynolds-averaged Navier-Stokes (RANS) form of the thin-boundary-layer equations (see Bose & Park (2018) for more details). One of the most commonly used wall models is the equilibrium wall model (EQWM), in which the near-wall eddies are assumed to be in local equilibrium. In this framework, a non-zero flux is supplied to the outer LES in the streamwise and spanwise directions, with the non-penetrating Dirichlet boundary condition. This model has been successfully applied in both canonical flows (Kawai & Larsson 2012; Lozano-Durán & Bae 2019) and
This paper is included in the Proceedings of the 32nd USENIX Security Symposium.

Fuzzing reliably and efficiently finds bugs in software, including operating system kernels. In general, higher code coverage leads to the discovery of more bugs. This is why most existing kernel fuzzers adopt strategies to generate a series of inputs that attempt to greedily maximize the amount of code that they exercise. However, simply executing code may not be sufficient to reveal bugs that require specific sequences of actions. Synthesizing inputs to trigger such bugs depends on two aspects: (i) the actions the executed code takes, and (ii) the order in which those actions are taken. An action is a high-level operation, such as a heap allocation, that is performed by the executed code and has a specific semantic meaning. A CTOR , our action-guided kernel fuzzing framework, deviates from traditional methods. Instead of focusing on code coverage optimization, our approach generates fuzzer programs (inputs) that leverage our understanding of triggered actions and their temporal relationships. Specifically, we first capture actions that potentially operate on shared data structures at different times. Then, we synthesize programs using those actions as building blocks, guided by bug templates expressed in our domain-specific language. We evaluated A CTOR on four different versions of the Linux kernel, including two well-tested and frequently updated long-term (5 . 4 . 206, 5 . 10 . 131) versions, a stable (5 . 19), and the latest (6 . 2-rc5) release. Our evaluation revealed a total of 41 previously unknown bugs, of which 9 have already been fixed. Interestingly, 15 (36 . 59%) of them were discovered in less than a day
Ontolabeling: Re-Thinking Data Labeling For Computer Vision

Over the last decade, developments in computer vision tasks have been driven by image, video, and multimodal benchmark datasets fueling the growth of machine learning methods for object detection, classiﬁcation, and scene understanding. Such advances have, however, created static, goal-speciﬁc and heterogeneous datasets, with little to none emphasis on the used taxonomies and semantics behind the class deﬁnitions, making them ill-deﬁned, and hardly mappable to each others. This approach hinders and limits the long-term usability of datasets, their intercompatibility, extensibility, and the ability to repurpose them. In this work we propose a new methodology for data labeling, which we call Ontolabeling 1 , that detaches data structure from semantics, creating two data model layers. The ﬁrst layer organizes spatio-temporal labels for multi-sensor data, while the second layer makes use of ontologies to structure, organize, maintain, extend and repurpose the semantics of the annotations. Our approach is supported by an open source toolkit that enables label management (create, read, update, and delete) following the proposed Ontolabeling principles.
Deep Anomaly Detection for In-Vehicle Monitoring

Deep learning approaches to the detection of visual data instances that markedly digress from regular sequences have been mostly focusing on outdoor video-surveillance scenarios, mainly regarding abnormal behaviour and suspicious or abandoned object detection. However, with the increasing importance of public and shared transportation for urban mobility, it becomes imperative to provide autonomous intelligent systems capable of detecting abnormal behaviour that threatens passenger safety. In-vehicle monitoring becomes particularly relevant for Shared Autonomous Vehicles, which do not have a driver responsible for assuring the well-being and safety of passengers; such vehicles must be accompanied by reliable autonomous in-vehicle surveillance systems.
Hiding in Plain Sight: An Empirical Study of Web Application Abuse in Malware

Web applications provide a wide array of utilities that are abused by malware as a replacement for traditional attacker-controlled servers. Thwarting these Web App-Engaged (WAE) malware requires rapid collaboration between incident responders and web app providers. Unfortunately, our research found that delays in this collaboration allow WAE malware to thrive. We developed Marsea, an automated malware analysis pipeline that studies WAE malware and enables rapid remediation. Given 10K malware samples, Marsea revealed 893 WAE malware in 97 families abusing 29 web apps. Our research uncovered a 226% increase in the number of WAE malware since 2020 and that malware authors are beginning to reduce their reliance on attacker-controlled servers. In fact, we found a 13.7% decrease in WAE malware relying on attacker-controlled servers. To date, we have used Marsea to collaborate with the web app providers to take down 50% of the malicious web app content.
Secrets Revealed in Container Images: An Internet-wide Study on Occurrence and Impact

Containerization allows bundling applications and their dependencies into a single image. The containerization framework Docker eases the use of this concept and enables sharing images publicly, gaining high momentum. However, it can lead to users creating and sharing images that include private keys or API secrets—either by mistake or out of negligence. This leakage impairs the creator’s security and that of everyone using the image. Yet, the extent of this practice and how to counteract it remains unclear. In this paper, we analyze 337,171 images from Docker Hub and 8,076 other private registries unveiling that 8.5% of images indeed include secrets. Specifically, we find 52,107 private keys and 3,158 leaked API secrets, both opening a large attack surface, i.e., putting authentication and confidentiality of privacy-sensitive data at stake and even allow active attacks. We further document that those leaked keys are used in the wild: While we discovered 1,060 certificates relying on compromised keys being issued by public certificate authorities, based on further active Internet measurements, we find 275,269 TLS and SSH hosts using leaked private keys for authentication. To counteract this issue, we discuss how our methodology can be used to prevent secret leakage and reuse.