Miracle-dz/UniFAR-FTUs huggingface dataset:
Robust Traffic Rules and Knowledge Representation for Conflict Resolution in Autonomous Driving

Rules and world knowledge are important priors in decision making in our daily lives, and autonomous driving (AD) systems can exploit them in decision-making and conflict resolution. In this thesis, we investigate the representation and formalization of traffic rules and regulations in natural language text in order to integrate them with a machine learning-based trajectory prediction module for conflict resolution among road users. As a result, the vehicle’s long-term trajectory (3 to 5 seconds horizon) can be predicted using hybrid learning, which incorporates both rules and data into the ML models. Only rule-based systems confront difficulties in depicting complicated interactions among multiple traffic scene participants. Learning-based techniques are capable of representing complicated interactions. However, they require a large amount of data, and in many circumstances, generating corner case data (e.g., accident and rule violation for exceptions) is not feasible. Recent research combining rules with data (neurosymbolic model) is an exciting research direction to leverage the best of both worlds for reasoning over traffic scenes. Apart from investigating the impact of the knowledge integration, we will work towards finding optimal traffic rules representation for the hybrid learning tasks and leverage large language models for the automated representation of traffic rules and regulation needed for downstream AD task. The hybrid learning approach will reduce the data dependency, generate the vehicle’s traffic rule complaint trajectory, and make the model more generalizable even for corner cases or less representative cases in the datasets due to reasoning capability.
Towards Comparing Recommendation to Multiple-Query Search Sessions for Talent Search

Query-level evaluation metrics such as nDCG that originate from field of Information Retrieval (IR) have seen widespread adoption in the Recommender Systems (RS) community for comparing the quality of different ranked lists of recommendations with different levels of relevance to the user. However, the traditional (offline) RS evaluation paradigm is typically restricted to evaluating a single results list. In contrast, IR researchers have also developed evaluation metrics over the past decade for the session-based evaluation of more complex search tasks. Here, the sessions consist of multiple queries and multi-round search interactions, and the metrics evaluate the quality of the session as a whole. Despite the popularity of the more traditional single-list evaluation paradigm, RS can also be used to assist users with complex information access tasks. In this paper, we explore the usefulness of session-level evaluation metrics for evaluating and comparing the performance of both recommender systems and search engines. We show that, despite possible misconceptions that comparing both scenarios is akin to comparing apples to oranges, it is indeed possible to compare recommendation results from a single ranked list to the results from a whole search session. In doing so, we address the following questions: (1) how can we fairly and realistically compare the quality of an individual list of recommended items to the quality of an entire manual search session; (2) how can we measure the contribution that the RS is making to the entire search session. We contextualize our claims by focusing on a particular complex search scenario: the problem of talent search . An example of professional search, talent search involves recruiters searching for relevant candidates given a specific job posting by issuing multiple queries in the course of a search session. We show that it is possible to compare the search behavior and success of recruiters to that of a matchmaking recommender system that generates a single ranked list of relevant candidates for a given job posting. In particular, we adopt a session-based metric from IR and motivate how it can be used to perform valid and realistic comparisons of recommendation lists to multiple-query search sessions.
This paper is included in the Proceedings of the 2022 USENIX Annual Technical Conference.

Modern data center fabrics open the possibility of microsec-ond distributed applications, such as data stores and message queues. A challenging aspect of their development is to ensure that, besides being fast in the common case, these applications react fast to changes in their membership, e.g., due to recon-ﬁguration and failures. This is especially important as they form the backbone of numerous cloud-powered services, such as analytics and trading systems, trying to meet ever-stringent tail latency requirements. As the microservices-oriented architecture is the de facto standard for building cloud services, a single user request translates to a wide fan-out of microser-vices interactions sitting on the critical path. The outcome is implacable: the traditionally uncommon events of reconﬁg-uration and failures are exacerbated by the fan-out of communication, making user requests commonly experience such events and quickly impacting the tail latency of the service. We present uKharon, a microsecond-scale membership service that detects changes in the membership of applications and lets them failover in as little as 50 µ s. uKharon consists of (1) a multi-level failure detector, (2) a consensus engine that relies on one-sided RDMA CAS, and (3) minimal-overhead membership leases, all exploiting RDMA to operate at the microsecond scale. We showcase the power of uKharon by building uKharon-KV, a replicated Key-Value cache based on HERD [24]. uKharon-KV processes PUT requests as fast as the state-of-the-art and improves upon it by (1) removing the need for replicating GET requests and (2) bringing the end-to-end failover down to 53 µ s, a 10 × improvement.
Exploring Diversity in Neural Architectures for Safety

Apart from the predominant convolutional neural networks (CNNs), several new architectures like Vision Transformers (ViTs) and MLP-Mixers have recently been proposed. Research also shows that these architectures learn differently. Ensembles based on different state-of-the-art neural architectures thus provide diversity, an important characteristic in designing safety-critical systems. To quantify the benefit of ensembles, we investigate different metrics like error consistency and diversity metric that have been proposed in the literature. We observe that with comparable individual performance, an ensemble of diverse architectures performs not only more accurately than an ensemble of one architecture, but also more robustly to diverse input
CaMeLS: Cooperative Meta-Learning Service for Recommender Systems

We present CaMeLS, a proof of concept of a cooperative meta-learning service for recommender systems. CaMeLS leverages the computing power of recommender systems users by uploading their metadata and algorithm evaluation scores to a centralized environment. Through the resulting database, CaMeLS then offers meta-learning services for everyone. Additionally, users may access evaluations of common data sets immediately to know the best-performing algorithms for those data sets. The metadata table may also be used for other purposes, e.g., to perform benchmarks. In the initial version discussed in this paper, CaMeLS implements automatic algorithm selection through meta-learning over two recommender systems libraries. Automatic algorithm selection saves users time and computing power and does not require expertise, as the best algorithm is automatically found over multiple libraries. The CaMeLS database contains 20 metadata sets by default. We show that the automatic algorithm selection service is already on par with the single best algorithm in this default scenario. CaMeLS only requires a few seconds to predict a suitable algorithm, rather than potentially hours or days if performed manually, depending on the data set. The code is publicly available on our GitHub https://camels.recommender-systems.com.
From Dynamic Loading to Extensible Transformation: An Infrastructure for Dynamic Library Transformation

The dynamic linker and loader has been one of the fundamental software, and more than 99% of binaries are dynamically linked on Ubuntu. On one hand, vendors are going to break production software into more and more dynamic libraries to lower the maintenance cost. On the other hand, customers require the dynamic loader to provide rich functionalities to serve their isolation, security, and performance demands. However, existing dynamic loaders are implemented in a monolithic fashion, so they are difﬁcult to extend, conﬁgure and optimize. This paper presents iFed , an infrastructure for extensible and ﬂexible dynamic library transformation. We design iFed in a pass-based architecture to compose various functional and optimization passes. iFed uses a runnable in-memory format to represent libraries and coordinate among multiple transformation passes. We further implement two optimization passes in iFed , which efﬁciently leverages hugepages and eliminates relocation overhead. iFed is implemented as a drop-in replacement of the current system default dynamic loader. We evaluate iFed and its optimization passes with a wide range of applications on different hardware platforms. Compared to the default glibc dynamic loader, iFed reduces an order of magnitude of TLB miss. We improve the throughput of a dynamic website by 13.3%, along with a 12.5% reduction of tail latency without any modiﬁcations to the applications.
Are We Forgetting Something? Correctly Evaluate a Recommender System With an Optimal Training Window

Recommender systems are deployed in dynamic environments with constantly changing interests and availability of items, articles and products. The hyperparameter optimisation of such systems usually happens on a static dataset, extracted from a live system Although it is well known that the quality of a computed model highly depends on the quality of the data it was trained on, this is largely neglected in these optimisations. For example, when concept drift occurs in the data, the model is likely to learn patterns that are not aligned with the target prediction data. Interestingly, most scientific articles on recommender systems typically perform their evaluation on entire datasets, without considering their intrinsic quality or that of their parts. First, we show that using only the more recent parts of a dataset can drastically improve the performance of a recommendation system, and we pose that it should be a standard hyperparameter to be tuned prior to evaluation and deployment. Second, we find that comparing the performance of well-known baseline algorithms before and after optimising the training data window significantly changes the performance ranking.
FlatFS: Flatten Hierarchical File System Namespace on Non-volatile Memories

The conventional file system provides a hierarchical namespace by structuring it as a directory tree. Tree-based namespace structure leads to inefficient file path walk and expensive namespace tree traversal, underutilizing ultra-low access latency and good sequential performance provided by non-volatile memory systems. This paper proposes FlatFS, a NVM file system that features a flat namespace architecture while provides a compatible hierarchical namespace view. FlatFS incorporates three novel techniques: coordinated file path walk model, range-optimized NVM-friendly Br tree, and write-optimized compressed index key layout, to fully exploit flat namespace structure to improve file system namespace performance on high-performance NVMs. Evaluation results demonstrate that FlatFS achieves significant performance improvements for metadata-intensive benchmarks and real-world applications compared to other file systems.
Propagation of ion-acoustic solitary waves in a relativistic electron-positron-ion plasma

The propagation of large amplitude ion-acoustic solitary waves (IASWs) in a fully relativistic plasma consisting of cold ions and ultra-relativistic hot electrons and positrons is investigated using the Sagdeev pseudopotential method in a relativistic hydrodynamics model. The effects of streaming speed of the plasma fluid, thermal energy, positron density, and positron temperature on large amplitude IASWs are studied by analysis of the pseudopotential structure. It is found that in regions in which the streaming speed of the plasma fluid is larger than that of the solitary wave, by increasing the streaming speed of the plasma fluid, the depth and width of the potential well increase, resulting in narrower solitons with larger amplitude. This behavior is opposite to the case where the streaming speed of the plasma fluid is less than that of the solitary wave. On the other hand, an increase in the thermal energy results in wider solitons with smaller amplitude, because the depth and width of the potential well decrease in that case. Additionally, the maximum soliton amplitude in- creases and the width becomes narrower as a result of an increase in positron density. It is shown that varying the positron temperature does not have a considerable effect on the width and amplitude of IASWs. The existence of stationary soli- ton-like arbitary amplitude waves is also predicted in fully relativistic electron-positron-ion (EPI) plasmas. The effects of streaming speed of the plasma fluid, thermal energy, positron density, and positron temperature on these kinds of solitons are the same for large amplitude IASWs.
CAP-VMs: Capability-Based Isolation and Sharing in the Cloud

Cloud stacks must isolate application components, while permitting efficient data sharing between components deployed on the same physical host. Traditionally, the MMU enforces isolation and permits sharing at page granularity. MMU approaches, however, lead to cloud stacks with large TCBs in kernel space, and page granularity requires inefficient OS interfaces for data sharing. Forthcoming CPUs with hardware support for memory capabilities offer new opportunities to implement isolation and sharing at a finer granularity. We describe cVMs, a new VM-like abstraction that uses memory capabilities to isolate application components while supporting efficient data sharing, all without mandating application code to be capability-aware. cVMs share a single virtual address space safely, each having only capabilities to access its own memory. A cVM may include a library OS, thus minimizing its dependency on the cloud environment. cVMs efficiently exchange data through two capability-based primitives assisted by a small trusted monitor: (i) an asynchronous read-write interface to buffers shared between cVMs; and (ii) a call interface to transfer control between cVMs. Using these two primitives, we build more expressive mechanisms for efficient cross-cVM communication. Our prototype implementation using CHERI RISC-V capabilities shows that cVMs isolate services (Redis and Python) with low overhead while improving data sharing.
Knowledge Graph (R)Evolution and the Web of Data

When querying knowledge over the Web we typically consider the Web of Data to be a static point of reference that is always available and that never changes. However, when actually running queries "in the wild", we encounter a broad range of problems; spanning from the (un)availability of entire knowledge graphs (and their SPARQL endpoints) to outdated references between knowledge graphs and beyond that, we are almost entirely missing out on the availability of previous versions of knowledge graphs and provenance metadata about them. This position paper discusses these issues in context and sketches some of the solutions to mitigate them. In particular, this paper ﬁrst discusses approaches to keep knowledge graphs available for continuous and scalable querying and afterwards presents an approach that enables community-driven updates so that mistakes can be corrected or missing information can be added. Then, the paper highlights what we can learn from RDF archiving solutions to better support evolving knowledge graphs. And ﬁnally, the paper puts these aspects into perspective and provides an outlook to open challenges and future work.
Intrinsically Interpretable Document Classification via Concept Lattices

Explanations for the predictions made by Machine Learning (ML) models are best framed in terms of abstract, high-level concepts that are easily comprehensible to human beings. The use of such concepts constitutes a subfield of interpretability methods known as concept-based explanations. This work uses concept-based explanations to build an intrinsically interpretable document classifier using a combination of Formal Concept Analysis (FCA) and approaches from applied graph theory. FCA is used to formalize the vague notion of concepts in terms of the formal concepts found in the concept lattices of various document classes. The graph of the lattice covering relation helps to utilize the topological information present in the document-class concept lattices for classifying documents. Finally, the formal concepts that made the strongest contributions to the predictions of the document classifier are revealed, along with their intents; thereby making their contribution more comprehensible to human beings.
Hardening Hypervisors with Ombro

This paper presents Ombro, a low-level virtual instruction set architecture (vISA) which enforces compiler-based security policies on real-world commodity hypervisors. We extend the Secure Virtual Architecture (which itself extends the LLVM compiler’s Intermediate Representation) to support the full set of hardware operations needed to run an x86 commodity hypervisor used in some of the world’s largest public clouds, namely, the Xen 4.12 hypervisor, running in full hardware-accelerated mode using Intel’s Virtual Machine Extensions (VMX). We have ported Xen 4.12 to the Ombro vISA and demonstrated that it can run unmodiﬁed guest VMs of real-world relevance (namely, Linux guests under Xen’s HVM and PVH modes). Furthermore, to demonstrate Ombro’s ability to harden hypervisors from attack, Ombro implements control ﬂow integrity and the ﬁrst protected shadow (split) stack for x86 hypervisors. Our performance results show that Ombro achieves this protection without imposing measurable overheads on most application benchmarks.
DAVIS: Driver's Audio-Visual Speech recognition

DAVIS is a driver’s audio-visual assistive system intended to improve accuracy and robustness of speech recognition of the most frequent drivers’ requests in natural driving conditions. Since speech recognition in driving condition is highly challenging due to acoustic noises, active head turns, pose variation, distance to recording devices, lightning conditions, etc. We rely on multimodal information and use both automatic lip-reading system for visual stream and ASR for audio stream processing. We have trained audio and video models on own RUSAVIC dataset containing in-the-wild audio and video recordings of 20 drivers. The recognition application comprises a graphical user interface and modules for audio and video signal acquisition, analysis, and recognition. The obtained results demonstrate rather high performance of DAVIS and also the fundamental possibility of recognizing speech commands by using video modality, even in such difficult natural conditions as driving.
SpellInk: Interactive Correction of Spelling Mistakes in Handwritten Text

Despite the current dominance of typed text, writing by hand remains the most natural mean of written communication and information keeping. Still, digital pen input provides limited user experience and lacks ﬂexibility, as most of the manipulations are performed on a digitalized version of the text. In this paper, we present our prototype that enables spellchecking for handwritten text: it allows users to inter-actively correct misspellings directly in a handwritten script. We plan to study the usability of the proposed user interface and its acceptance by users. Also, we aim to investigate how user feedback can be used to incrementally improve the underlying recognition models.
Firebolt: Finding Bugs in Programmable Data Plane Generators

Programmable data planes (DP) enable flexible customization of packet processing logic with domain-specific languages such as P4. To relieve developers from lengthy codes and tedious hardware details, many researches propose DP program generators that take high-level intents as input and automatically convert intents into DP programs. Generators must be correct, otherwise they may produce buggy programs or DP logic that is inconsistent with intents. Nevertheless, existing verification tools are designed to verify individual DP programs, not generators. They either cannot achieve high bug coverage or cannot debug generators with high scalability. This paper presents Firebolt , a blackbox testing tool designed to dig out faults in DP program generators, including security vulnerabilities, intent violations, and generator crash. Firebolt achieves high bug coverage by using syntax-guided intent generation to construct a comprehensive, syntactically correct, and semantically valid intent set. To avoid intent explosion, Firebolt designs an intent space pruning approach that eliminates redundant intents while preserving representative ones. For high scalability, Firebolt automatically formalizes DP programs and intents for verification. We apply Firebolt to three popular open-source DP generators. Evaluation results demonstrate that Firebolt can detect 2 × bugs with 0.1% to 0.01% human efforts compared to existing tools.
Estimation of effective temperatures in quantum annealers for sampling applications: A case study with possible applications in deep learning

An increase in the efficiency of sampling from Boltzmann distributions would have a significant impact on deep learning and other machine-learning applications. Recently, quantum annealers have been proposed as a potential candidate to speed up this task, but several limitations still bar these state-of-the-art technologies from being used effectively. One of the main limitations is that, while the device may indeed sample from a Boltzmann-like distribution, quantum dynamical arguments suggest it will do so with an {\it instance-dependent} effective temperature, different from its physical temperature. Unless this unknown temperature can be unveiled, it might not be possible to effectively use a quantum annealer for Boltzmann sampling. In this work, we propose a strategy to overcome this challenge with a simple effective-temperature estimation algorithm. We provide a systematic study assessing the impact of the effective temperatures in the learning of a special class of a restricted Boltzmann machine embedded on quantum hardware, which can serve as a building block for deep-learning architectures. We also provide a comparison to $k$-step contrastive divergence (CD-$k$) with $k$ up to 100. Although assuming a suitable fixed effective temperature also allows us to outperform one step contrastive divergence (CD-1), only when using an instance-dependent effective temperature do we find a performance close to that of CD-100 for the case studied here.