OpenRaiser/Intern-Atlas huggingface dataset:
Bi-Manual Manipulation and Attachment via Sim-to-Real Reinforcement Learning
Venue: arXiv cs.RO 2022
Year: 2022

Most successes in robotic manipulation have been restricted to single-arm robots, which limits the range of solvable tasks to pick-and-place, insertion, and objects rearrangement. In contrast, dual and multi arm robot platforms unlock a rich diversity of problems that can be tackled, such as laundry folding and executing cooking skills. However, developing controllers for multi-arm robots is complexified by a number of unique challenges, such as the need for coordinated bimanual behaviors, and collision avoidance amongst robots. Given these challenges, in this work we study how to solve bi-manual tasks using reinforcement learning (RL) trained in simulation, such that the resulting policies can be executed on real robotic platforms. Our RL approach results in significant simplifications due to using real-time (4Hz) joint-space control and directly passing unfiltered observations to neural networks policies. We also extensively discuss modifications to our simulated environment which lead to effective training of RL policies. In addition to designing control algorithms, a key challenge is how to design fair evaluation tasks for bi-manual robots that stress bimanual coordination, while removing orthogonal complicating factors such as high-level perception. In this work, we design a Connect Task, where the aim is for two robot arms to pick up and attach two blocks with magnetic connection points. We validate our approach with two xArm6 robots and 3D printed blocks with magnetic attachments, and find that our system has 100% success rate at picking up blocks, and 65% success rate at the Connect Task.
Minimizing Trust with Exclusively-Used Physically-Isolated Hardware
Venue: arXiv cs.CR 2022
Year: 2022

Smartphone owners often need to run security-critical programs on the same device as other untrusted and potentially malicious programs. This requires users to trust hardware and system software to correctly sandbox malicious programs, trust that is often misplaced. Our goal is to minimize the number and complexity of hardware and software components that a smartphone owner needs to trust to withstand adversarial inputs. We present a multi-domain hardware design composed of statically-partitioned, physically-isolated trust domains. We introduce a few simple, formally-verified hardware components to enable a program to gain provably exclusive and simultaneous access to both computation and I/O on a temporary basis. To manage this hardware, we present OctopOS, an OS composed of mutually distrustful subsystems. We present a prototype of this machine (hardware and OS) on a CPU-FPGA board and show that it incurs a small hardware cost compared to modern SoCs. For security-critical programs, we show that this machine significantly reduces the required trust compared to mainstream TEEs while achieving decent performance. For normal programs, performance is similar to a legacy machine.
Driving Anomaly Detection Using Conditional Generative Adversarial Network
Venue: arXiv cs.CV 2022
Year: 2022

Anomaly driving detection is an important problem in advanced driver assistance systems (ADAS). It is important to identify potential hazard scenarios as early as possible to avoid potential accidents. This study proposes an unsupervised method to quantify driving anomalies using a conditional generative adversarial network (GAN). The approach predicts upcoming driving scenarios by conditioning the models on the previously observed signals. The system uses the difference of the output from the discriminator between the predicted and actual signals as a metric to quantify the anomaly degree of a driving segment. We take a driver-centric approach, considering physiological signals from the driver and controller area network-Bus (CAN-Bus) signals from the vehicle. The approach is implemented with convolutional neural networks (CNNs) to extract discriminative feature representations, and with long short-term memory (LSTM) cells to capture temporal information. The study is implemented and evaluated with the driving anomaly dataset (DAD), which includes 250 hours of naturalistic recordings manually annotated with driving events. The experimental results reveal that recordings annotated with events that are likely to be anomalous, such as avoiding on-road pedestrians and traffic rule violations, have higher anomaly scores than recordings without any event annotation. The results are validated with perceptual evaluations, where annotators are asked to assess the risk and familiarity of the videos detected with high anomaly scores. The results indicate that the driving segments with higher anomaly scores are more risky and less regularly seen on the road than other driving segments, validating the proposed unsupervised approach.
Self-Distribution Distillation: Efficient Uncertainty Estimation
Venue: arXiv cs.LG 2022
Year: 2022

Deep learning is increasingly being applied in safety-critical domains. For these scenarios it is important to know the level of uncertainty in a model's prediction to ensure appropriate decisions are made by the system. Deep ensembles are the de-facto standard approach to obtaining various measures of uncertainty. However, ensembles often significantly increase the resources required in the training and/or deployment phases. Approaches have been developed that typically address the costs in one of these phases. In this work we propose a novel training approach, self-distribution distillation (S2D), which is able to efficiently train a single model that can estimate uncertainties. Furthermore it is possible to build ensembles of these models and apply hierarchical ensemble distillation approaches. Experiments on CIFAR-100 showed that S2D models outperformed standard models and Monte-Carlo dropout. Additional out-of-distribution detection experiments on LSUN, Tiny ImageNet, SVHN showed that even a standard deep ensemble can be outperformed using S2D based ensembles and novel distilled models.
Two Approaches to Survival Analysis of Open Source Python Projects
Venue: arXiv cs.SE 2022
Year: 2022

A recent study applied frequentist survival analysis methods to a subset of the Software Heritage Graph and determined which attributes of an OSS project contribute to its health. This paper serves as an exact replication of that study. In addition, Bayesian survival analysis methods were applied to the same dataset, and an additional project attribute was studied to serve as a conceptual replication. Both analyses focus on the effects of certain attributes on the survival of open-source software projects as measured by their revision activity. Methods such as the Kaplan-Meier estimator, Cox Proportional-Hazards model, and the visualization of posterior survival functions were used for each of the project attributes. The results show that projects which publish major releases, have repositories on multiple hosting services, possess a large team of developers, and make frequent revisions have a higher likelihood of survival in the long run. The findings were similar to the original study; however, a deeper look revealed quantitative inconsistencies.
Tight Lower Bounds for Approximate & Exact $k$-Center in $\mathbb{R}^d$
Venue: arXiv cs.CG 2022
Year: 2022

In the discrete $k$-center problem, we are given a metric space $(P,\texttt{dist})$ where $|P|=n$ and the goal is to select a set $C\subseteq P$ of $k$ centers which minimizes the maximum distance of a point in $P$ from its nearest center. For any $ε>0$, Agarwal and Procopiuc [SODA '98, Algorithmica '02] designed an $(1+ε)$-approximation algorithm for this problem in $d$-dimensional Euclidean space which runs in $O(dn\log k) + \left(\dfrac{k}ε\right)^{O\left(k^{1-1/d}\right)}\cdot n^{O(1)}$ time. In this paper we show that their algorithm is essentially optimal: if for some $d\geq 2$ and some computable function $f$, there is an $f(k)\cdot \left(\dfrac{1}ε\right)^{o\left(k^{1-1/d}\right)} \cdot n^{o\left(k^{1-1/d}\right)}$ time algorithm for $(1+ε)$-approximating the discrete $k$-center on $n$ points in $d$-dimensional Euclidean space then the Exponential Time Hypothesis (ETH) fails. We obtain our lower bound by designing a gap reduction from a $d$-dimensional constraint satisfaction problem (CSP) defined by Marx and Sidiropoulos [SoCG '14] to discrete $d$-dimensional $k$-center. As a byproduct of our reduction, we also obtain that the exact algorithm of Agarwal and Procopiuc [SODA '98, Algorithmica '02] which runs in $n^{O\left(d\cdot k^{1-1/d}\right)}$ time for discrete $k$-center on $n$ points in $d$-dimensional Euclidean space is asymptotically optimal. Formally, we show that if for some $d\geq 2$ and some computable function $f$, there is an $f(k)\cdot n^{o\left(k^{1-1/d}\right)}$ time exact algorithm for the discrete $k$-center problem on $n$ points in $d$-dimensional Euclidean space then the Exponential Time Hypothesis (ETH) fails. Previously, such a lower bound was only known for $d=2$ and was implicit in the work of Marx [IWPEC '06]. [see paper for full abstract]
Towards Formalizing HRI Data Collection Processes
Venue: arXiv cs.RO 2022
Year: 2022

Within the human-robot interaction (HRI) community, many researchers have focused on the careful design of human-subjects studies. However, other parts of the community, e.g., the technical advances community, also need to do human-subjects studies to collect data to train their models, in ways that require user studies but without a strict experimental design. The design of such data collection is an underexplored area worthy of more attention. In this work, we contribute a clearly defined process to collect data with three steps for machine learning modeling purposes, grounded in recent literature, and detail an use of this process to facilitate the collection of a corpus of referring expressions. Specifically, we discuss our data collection goal and how we worked to encourage well-covered and abundant participant responses, through our design of the task environment, the task itself, and the study procedure. We hope this work would lead to more data collection formalism efforts in the HRI community and a fruitful discussion during the workshop.
Precise Onboard Aircraft Cabin Localization using UWB and ML
Venue: arXiv cs.NI 2022
Year: 2022

Precise indoor positioning systems (IPSs) are key to perform a set of tasks more efficiently during aircraft production, operation and maintenance. For instance, IPSs can overcome the tedious task of configuring (wireless) sensor nodes in an aircraft cabin. Although various solutions based on technologies of established consumer goods, e.g., Bluetooth or WiFi, have been proposed and tested, the published accuracy results fail to make these technologies relevant for practical use cases. This stems from the challenging environments for positioning, especially in aircraft cabins, which is mainly due to the geometries, many obstacles, and highly reflective materials. To address these issues, we propose to evaluate in this work an Ultra-Wideband (UWB)-based IPS via a measurement campaign performed in a real aircraft cabin. We first illustrate the difficulties that an IPS faces in an aircraft cabin, by studying the signal propagation effects which were measured. We then investigate the ranging and localization accuracies of our IPS. Finally, we also introduce various methods based on machine learning (ML) for correcting the ranging measurements and demonstrate that we are able to localize a node with respect to an aircraft seat with a measured likelihood of 97%.
Levenberg-Marquardt Method Based Cooperative Source Localization in SIMO Molecular Communication via Diffusion Systems
Venue: arXiv cs.IT 2022
Year: 2022

Molecular communication underpins nano-scale communications in nanotechnology. The combination of multinanomachines to form nano-networks is one of the main enabling methods. Due to the importance of source localization in establishing nano-networks, this paper proposes a cooperative source localization method for Molecular Communication via Diffusion (MCvD) systems using multiple spherical absorption receivers. Since there is no exact mathematical expression of the channel impulse response for multiple absorbing receivers, we adopt an empirical expression and use Levenberg-Marquardt method to estimate the distance of the transmitter to each receiver, based on which the location of the transmitter is obtained using an iterative scheme where the initial point is obtained using a multi-point localization method. Particle based simulation is carried out to evaluate the performance of the proposed method. Simulation results show that the proposed method can accurately estimate the location of transmitter in short to medium communication ranges.
A Language-Independent Analysis Platform for Source Code
Venue: arXiv cs.CR 2022
Year: 2022

In this paper, we present the CPG analysis platform, which enables the translation of source code into a programming language-independent representation, based on a code property graph. This allows security experts and developers to capture language level semantics for security analyses or identify patterns with respect to code compliance. Through the use of fuzzy parsing, also incomplete or non-compilable code, written in different programming languages, can be analyzed. The platform comprises an analysis library and interfaces to query, interact with or visualize source code graphs. This set of CPG tools allows finding common weaknesses in heterogeneous software environments, independently of the underlying programming language.
A Survey of Machine Learning Algorithms for 6G Wireless Networks
Venue: arXiv cs.NI 2022
Year: 2022

The primary focus of Artificial Intelligence/Machine Learning (AI/ML) integration within the wireless technology is to reduce capital expenditures, optimize network performance, and build new revenue streams. Replacing traditional algorithms with deep learning AI techniques have dramatically reduced the power consumption and improved the system performance. Further, implementation of ML algorithms also enables the wireless network service providers to (i) offer high automation levels from distributed AI/ML architectures applicable at the network edge, (ii) implement application-based traffic steering across the access networks, (iii) enable dynamic network slicing for addressing different scenarios with varying quality of service requirements, and (iv) enable ubiquitous connectivity across the various 6G communication platforms. In this chapter, we review/survey the ML techniques which are applicable to the 6G wireless networks. and also list the open problems of research which require timely solutions.
Open Set Recognition using Vision Transformer with an Additional Detection Head
Venue: arXiv cs.CV 2022
Year: 2022

Deep neural networks have demonstrated prominent capacities for image classification tasks in a closed set setting, where the test data come from the same distribution as the training data. However, in a more realistic open set scenario, traditional classifiers with incomplete knowledge cannot tackle test data that are not from the training classes. Open set recognition (OSR) aims to address this problem by both identifying unknown classes and distinguishing known classes simultaneously. In this paper, we propose a novel approach to OSR that is based on the vision transformer (ViT) technique. Specifically, our approach employs two separate training stages. First, a ViT model is trained to perform closed set classification. Then, an additional detection head is attached to the embedded features extracted by the ViT, trained to force the representations of known data to class-specific clusters compactly. Test examples are identified as known or unknown based on their distance to the cluster centers. To the best of our knowledge, this is the first time to leverage ViT for the purpose of OSR, and our extensive evaluation against several OSR benchmark datasets reveals that our approach significantly outperforms other baseline methods and obtains new state-of-the-art performance.
Playing with blocks: Toward re-usable deep learning models for side-channel profiled attacks
Venue: arXiv cs.CR 2022
Year: 2022

This paper introduces a deep learning modular network for side-channel analysis. Our deep learning approach features the capability to exchange part of it (modules) with others networks. We aim to introduce reusable trained modules into side-channel analysis instead of building architectures for each evaluation, reducing the body of work when conducting those. Our experiments demonstrate that our architecture feasibly assesses a side-channel evaluation suggesting that learning transferability is possible with the network we propose in this paper.
Multi-focus thermal image fusion
Venue: arXiv cs.CV 2022
Year: 2022

This paper proposes a novel algorithm for multi-focus thermal image fusion. The algorithm is based on local activity analysis and advanced pre-selection of images into fusion process. The algorithm improves the object temperature measurement error up to 5 Celsius degrees. The proposed algorithm is evaluated by half total error rate, root mean squared error, cross correlation and visual inspection. To the best of our knowledge, this is the first work devoted to multi-focus thermal image fusion. For testing of proposed algorithm we acquire six thermal image set with objects at different focal depth.
Morphological Reinflection with Multiple Arguments: An Extended Annotation schema and a Georgian Case Study
Venue: arXiv cs.CL 2022
Year: 2022

In recent years, a flurry of morphological datasets had emerged, most notably UniMorph, a multi-lingual repository of inflection tables. However, the flat structure of the current morphological annotation schema makes the treatment of some languages quirky, if not impossible, specifically in cases of polypersonal agreement, where verbs agree with multiple arguments using true affixes. In this paper, we propose to address this phenomenon by expanding the UniMorph annotation schema to a hierarchical feature structure that naturally accommodates complex argument marking. We apply this extended schema to one such language, Georgian, and provide a human-verified, accurate and balanced morphological dataset for Georgian verbs. The dataset has 4 times more tables and 6 times more verb forms compared to the existing UniMorph dataset, covering all possible variants of argument marking, demonstrating the adequacy of our proposed scheme. Experiments with a standard reinflection model show that generalization is easy when the data is split at the form level, but extremely hard when splitting along lemma lines. Expanding the other languages in UniMorph to this schema is expected to improve both the coverage, consistency and interpretability of this benchmark.
SoK: TEE-assisted Confidential Smart Contract
Venue: arXiv cs.CR 2022
Year: 2022

The blockchain-based smart contract lacks privacy since the contract state and instruction code are exposed to the public. Combining smart-contract execution with Trusted Execution Environments (TEEs) provides an efficient solution, called TEE-assisted smart contracts, for protecting the confidentiality of contract states. However, the combination approaches are varied, and a systematic study is absent. Newly released systems may fail to draw upon the experience learned from existing protocols, such as repeating known design mistakes or applying TEE technology in insecure ways. In this paper, we first investigate and categorize the existing systems into two types: the layer-one solution and layer-two solution. Then, we establish an analysis framework to capture their common lights, covering the desired properties (for contract services), threat models, and security considerations (for underlying systems). Based on our taxonomy, we identify their ideal functionalities and uncover the fundamental flaws and reasons for the challenges in each specification design. We believe that this work would provide a guide for the development of TEE-assisted smart contracts, as well as a framework to evaluate future TEE-assisted confidential contract systems.
In-Context Learning for Few-Shot Dialogue State Tracking
Venue: arXiv cs.CL 2022
Year: 2022

Collecting and annotating task-oriented dialogues is time-consuming and costly; thus, zero and few shot learning could greatly benefit dialogue state tracking (DST). In this work, we propose an in-context learning (ICL) framework for zero-shot and few-shot learning DST, where a large pre-trained language model (LM) takes a test instance and a few exemplars as input, and directly decodes the dialogue state without any parameter updates. To better leverage a tabular domain description in the LM prompt, we reformulate DST into a text-to-SQL problem. We also propose a novel approach to retrieve annotated dialogues as exemplars. Empirical results on MultiWOZ show that our method IC-DST substantially outperforms previous fine-tuned state-of-the-art models in few-shot settings. In addition, we test IC-DST in zero-shot settings, in which the model only takes a fixed task instruction as input, finding that it outperforms previous zero-shot methods by a large margin.
NELA-Local: A Dataset of U.S. Local News Articles for the Study of County-level News Ecosystems
Venue: arXiv cs.CY 2022
Year: 2022

In this paper, we present a dataset of over 1.4M online news articles from 313 local U.S. news outlets published over 20 months (between April 4th, 2020 and December 31st, 2021). These outlets cover a geographically diverse set of communities across the United States. In order to estimate characteristics of the local audience, included with this news article data is a wide range of county-level metadata, including demographics, 2020 Presidential Election vote shares, and community resilience estimates from the U.S. Census Bureau. The NELA-Local dataset can be found at: https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/GFE66K.
A Reachability Index for Recursive Label-Concatenated Graph Queries
Venue: arXiv cs.DB 2022
Year: 2022

Reachability queries checking the existence of a path from a source node to a target node are fundamental operators for querying and processing graph data. Current approaches for index-based evaluation of reachability queries either focus on plain reachability or constraint-based reachability with alternation only. In this paper, for the first time we study the problem of index-based processing for recursive label-concatenated reachability queries, referred to as RLC queries. These queries check the existence of a path that can satisfy the constraint defined by a concatenation of at most k edge labels under the Kleene plus. Many practical graph database and network analysis applications exhibit RLC queries. However, their evaluation remains prohibitive in current graph database engines. We introduce the RLC index, the first reachability index to efficiently process RLC queries. The RLC index checks whether the source vertex can reach an intermediate vertex that can also reach the target vertex under a recursive label-concatenated constraint. We propose an indexing algorithm to build the RLC index, which guarantees the soundness and the completeness of query execution and avoids recording redundant index entries. Comprehensive experiments on real-world graphs show that the RLC index can significantly reduce both the offline processing cost and the memory overhead of transitive closure while improving query processing up to six orders of magnitude over online traversals. Finally, our open-source implementation of the RLC index significantly outperforms current mainstream graph engines for evaluating RLC queries.