OpenRaiser/Intern-Atlas huggingface dataset:
ConfMix: Unsupervised Domain Adaptation for Object Detection via Confidence-based Mixing
Venue: arXiv cs.CV 2022
Year: 2022

Unsupervised Domain Adaptation (UDA) for object detection aims to adapt a model trained on a source domain to detect instances from a new target domain for which annotations are not available. Different from traditional approaches, we propose ConfMix, the first method that introduces a sample mixing strategy based on region-level detection confidence for adaptive object detector learning. We mix the local region of the target sample that corresponds to the most confident pseudo detections with a source image, and apply an additional consistency loss term to gradually adapt towards the target data distribution. In order to robustly define a confidence score for a region, we exploit the confidence score per pseudo detection that accounts for both the detector-dependent confidence and the bounding box uncertainty. Moreover, we propose a novel pseudo labelling scheme that progressively filters the pseudo target detections using the confidence metric that varies from a loose to strict manner along the training. We perform extensive experiments with three datasets, achieving state-of-the-art performance in two of them and approaching the supervised target model performance in the other. Code is available at: https://github.com/giuliomattolin/ConfMix.
Transferring learned patterns from ground-based field imagery to predict UAV-based imagery for crop and weed semantic segmentation in precision crop farming
Venue: arXiv cs.CV 2022
Year: 2022

Weed and crop segmentation is becoming an increasingly integral part of precision farming that leverages the current computer vision and deep learning technologies. Research has been extensively carried out based on images captured with a camera from various platforms. Unmanned aerial vehicles (UAVs) and ground-based vehicles including agricultural robots are the two popular platforms for data collection in fields. They all contribute to site-specific weed management (SSWM) to maintain crop yield. Currently, the data from these two platforms is processed separately, though sharing the same semantic objects (weed and crop). In our paper, we have developed a deep convolutional network that enables to predict both field and aerial images from UAVs for weed segmentation and mapping with only field images provided in the training phase. The network learning process is visualized by feature maps at shallow and deep layers. The results show that the mean intersection of union (IOU) values of the segmentation for the crop (maize), weeds, and soil background in the developed model for the field dataset are 0.744, 0.577, 0.979, respectively, and the performance of aerial images from an UAV with the same model, the IOU values of the segmentation for the crop (maize), weeds and soil background are 0.596, 0.407, and 0.875, respectively. To estimate the effect on the use of plant protection agents, we quantify the relationship between herbicide spraying saving rate and grid size (spraying resolution) based on the predicted weed map. The spraying saving rate is up to 90% when the spraying resolution is at 1.78 x 1.78 cm2. The study shows that the developed deep convolutional neural network could be used to classify weeds from both field and aerial images and delivers satisfactory results.
Identifying Human Strategies for Generating Word-Level Adversarial Examples
Venue: arXiv cs.CL 2022
Year: 2022

Adversarial examples in NLP are receiving increasing research attention. One line of investigation is the generation of word-level adversarial examples against fine-tuned Transformer models that preserve naturalness and grammaticality. Previous work found that human- and machine-generated adversarial examples are comparable in their naturalness and grammatical correctness. Most notably, humans were able to generate adversarial examples much more effortlessly than automated attacks. In this paper, we provide a detailed analysis of exactly how humans create these adversarial examples. By exploring the behavioural patterns of human workers during the generation process, we identify statistically significant tendencies based on which words humans prefer to select for adversarial replacement (e.g., word frequencies, word saliencies, sentiment) as well as where and when words are replaced in an input sequence. With our findings, we seek to inspire efforts that harness human strategies for more robust NLP models.
Two-stage Stochastic Matching and Pricing with Applications to Ride Hailing
Venue: arXiv cs.DS 2022
Year: 2022

Matching and pricing are two critical levers in two-sided marketplaces to connect demand and supply. The platform can produce more efficient matching and pricing decisions by batching the demand requests. We initiate the study of the two-stage stochastic matching problem, with or without pricing, to enable the platform to make improved decisions in a batch with an eye toward the imminent future demand requests. This problem is motivated in part by applications in online marketplaces such as ride hailing platforms. We design online competitive algorithms for vertex-weighted (or unweighted) two-stage stochastic matching for maximizing supply efficiency, and two-stage joint matching and pricing for maximizing market efficiency. In the former problem, using a randomized primal-dual algorithm applied to a family of ``balancing'' convex programs, we obtain the optimal $3/4$ competitive ratio against the optimum offline benchmark. Using a factor revealing program and connections to submodular optimization, we improve this ratio against the optimum online benchmark to $(1-1/e+1/e^2)\approx 0.767$ for the unweighted and $0.761$ for the weighted case. In the latter problem, we design optimal $1/2$-competitive joint pricing and matching algorithm by borrowing ideas from the ex-ante prophet inequality literature. We also show an improved $(1-1/e)$-competitive algorithm for the special case of demand efficiency objective using the correlation gap of submodular functions. Finally, we complement our theoretical study by using DiDi's ride-sharing dataset for Chengdu city and numerically evaluating the performance of our proposed algorithms in practical instances of this problem.
Safety-aware time-optimal motion planning with uncertain human state estimation
Venue: arXiv cs.RO 2022
Year: 2022

Human awareness in robot motion planning is crucial for seamless interaction with humans. Many existing techniques slow down, stop, or change the robot's trajectory locally to avoid collisions with humans. Although using the information on the human's state in the path planning phase could reduce future interference with the human's movements and make safety stops less frequent, such an approach is less widespread. This paper proposes a novel approach to embedding a human model in the robot's path planner. The method explicitly addresses the problem of minimizing the path execution time, including slowdowns and stops owed to the proximity of humans. For this purpose, it converts safety speed limits into configuration-space cost functions that drive the path's optimization. The costmap can be updated based on the observed or predicted state of the human. The method can handle deterministic and probabilistic representations of the human state and is independent of the prediction algorithm. Numerical and experimental results on an industrial collaborative cell demonstrate that the proposed approach consistently reduces the robot's execution time and avoids unnecessary safety speed reductions.
Promoting Rigour in Blockchains Energy & Environmental Footprint Research: A Systematic Literature Review
Venue: arXiv cs.CY 2022
Year: 2022

There is a growing interest in understanding the energy and environmental footprint of digital currencies, specifically in cryptocurrencies such as Bitcoin and Ethereum. These cryptocurrencies are operated by a geographically distributed network of computing nodes, making it hard to accurately estimate their energy consumption. Existing studies, both in academia and industry, attempt to model the cryptocurrencies energy consumption often based on a number of assumptions for instance about the hardware in use or geographic distribution of the computing nodes. A number of these studies has already been widely criticized for their design choices and subsequent over or under-estimation of the energy use. In this study, we evaluate the reliability of prior models and estimates by leveraging existing scientific literature from fields cognizant of blockchain such as social energy sciences and information systems. We first design a quality assessment framework based on existing research, we then conduct a systematic literature review examining scientific and non-academic literature demonstrating common issues and potential avenues of addressing these issues. Our goal with this article is to to advance the field by promoting scientific rigor in studies focusing on Blockchain's energy footprint. To that end, we provide a novel set of codes of conduct for the five most widely used research methodologies: quantitative energy modeling, literature reviews, data analysis \& statistics, case studies, and experiments. We envision that these codes of conduct would assist in standardizing the design and assessment of studies focusing on blockchain-based systems' energy and environmental footprint.
Empathetic Dialogue Generation via Sensitive Emotion Recognition and Sensible Knowledge Selection
Venue: arXiv cs.CL 2022
Year: 2022

Empathy, which is widely used in psychological counselling, is a key trait of everyday human conversations. Equipped with commonsense knowledge, current approaches to empathetic response generation focus on capturing implicit emotion within dialogue context, where the emotions are treated as a static variable throughout the conversations. However, emotions change dynamically between utterances, which makes previous works difficult to perceive the emotion flow and predict the correct emotion of the target response, leading to inappropriate response. Furthermore, simply importing commonsense knowledge without harmonization may trigger the conflicts between knowledge and emotion, which confuse the model to choose incorrect information to guide the generation process. To address the above problems, we propose a Serial Encoding and Emotion-Knowledge interaction (SEEK) method for empathetic dialogue generation. We use a fine-grained encoding strategy which is more sensitive to the emotion dynamics (emotion flow) in the conversations to predict the emotion-intent characteristic of response. Besides, we design a novel framework to model the interaction between knowledge and emotion to generate more sensible response. Extensive experiments on EmpatheticDialogues demonstrate that SEEK outperforms the strong baselines in both automatic and manual evaluations.
BC-IoDT: Blockchain-based Framework for Authentication in Internet of Drone Things
Venue: arXiv cs.CR 2022
Year: 2022

We leverage blockchain technology for drone node authentication in internet of drone things (IoDT). During the authentication procedure, the credentials of drone nodes are examined to remove malicious nodes from the system. In IoDT, drones are responsible for gathering data and transmitting it to cluster heads (CHs) for further processing. The CH collects and organizes data. Due to computational load, their energy levels rapidly deplete. To overcome this problem, we present a low-energy adaptive clustering hierarchy (R2D) protocol based on distance, degree, and residual energy. R2D is used to replace CHs with normal nodes based on the biggest residual energy, the degree, and the shortest distance from BS. The cost of keeping a big volume of data on the blockchain is high. We employ the Interplanetary File System (IPFS), to address this issue. Moreover, IPFS protects user data using the industry-standard encryption technique AES-128. This standard compares well to other current encryption methods. Using a consensus mechanism based on proof of work requires a high amount of computing resources for transaction verification. The suggested approach leverages a consensus mechanism known as proof of authority (PoA) to address this problem . The results of the simulations indicate that the suggested system model functions effectively and efficiently. A formal security analysis is conducted to assess the smart contract's resistance to attacks.
TransLIST: A Transformer-Based Linguistically Informed Sanskrit Tokenizer
Venue: arXiv cs.CL 2022
Year: 2022

Sanskrit Word Segmentation (SWS) is essential in making digitized texts available and in deploying downstream tasks. It is, however, non-trivial because of the sandhi phenomenon that modifies the characters at the word boundaries, and needs special treatment. Existing lexicon driven approaches for SWS make use of Sanskrit Heritage Reader, a lexicon-driven shallow parser, to generate the complete candidate solution space, over which various methods are applied to produce the most valid solution. However, these approaches fail while encountering out-of-vocabulary tokens. On the other hand, purely engineering methods for SWS have made use of recent advances in deep learning, but cannot make use of the latent word information on availability. To mitigate the shortcomings of both families of approaches, we propose Transformer based Linguistically Informed Sanskrit Tokenizer (TransLIST) consisting of (1) a module that encodes the character input along with latent-word information, which takes into account the sandhi phenomenon specific to SWS and is apt to work with partial or no candidate solutions, (2) a novel soft-masked attention to prioritize potential candidate words and (3) a novel path ranking algorithm to rectify the corrupted predictions. Experiments on the benchmark datasets for SWS show that TransLIST outperforms the current state-of-the-art system by an average 7.2 points absolute gain in terms of perfect match (PM) metric. The codebase and datasets are publicly available at https://github.com/rsingha108/TransLIST
A Simple Deterministic Distributed Low-Diameter Clustering
Venue: arXiv cs.DS 2022
Year: 2022

We give a simple, local process for nodes in an undirected graph to form non-adjacent clusters that (1) have at most a polylogarithmic diameter and (2) contain at least half of all vertices. Efficient deterministic distributed clustering algorithms for computing strong-diameter network decompositions and other key tools follow immediately. Overall, our process is a direct and drastically simplified way for computing these fundamental objects.
A drag-and-drop proof tactic
Venue: arXiv cs.HC 2022
Year: 2022

We explore the features of a user interface where formal proofs can be built through gestural actions. In particular, we show how proof construction steps can be associated to drag-and-drop actions. We argue that this can provide quick and intuitive proof construction steps. This work builds on theoretical tools coming from deep inference. It also resumes and integrates some ideas of the former proof-by-pointing project.
Integrating Policy Summaries with Reward Decomposition for Explaining Reinforcement Learning Agents
Venue: arXiv cs.LG 2022
Year: 2022

Explaining the behavior of reinforcement learning agents operating in sequential decision-making settings is challenging, as their behavior is affected by a dynamic environment and delayed rewards. Methods that help users understand the behavior of such agents can roughly be divided into local explanations that analyze specific decisions of the agents and global explanations that convey the general strategy of the agents. In this work, we study a novel combination of local and global explanations for reinforcement learning agents. Specifically, we combine reward decomposition, a local explanation method that exposes which components of the reward function influenced a specific decision, and HIGHLIGHTS, a global explanation method that shows a summary of the agent's behavior in decisive states. We conducted two user studies to evaluate the integration of these explanation methods and their respective benefits. Our results show significant benefits for both methods. In general, we found that the local reward decomposition was more useful for identifying the agents' priorities. However, when there was only a minor difference between the agents' preferences, then the global information provided by HIGHLIGHTS additionally improved participants' understanding.
Management of Machine Learning Lifecycle Artifacts: A Survey
Venue: arXiv cs.DB 2022
Year: 2022

The explorative and iterative nature of developing and operating machine learning (ML) applications leads to a variety of artifacts, such as datasets, features, models, hyperparameters, metrics, software, configurations, and logs. In order to enable comparability, reproducibility, and traceability of these artifacts across the ML lifecycle steps and iterations, systems and tools have been developed to support their collection, storage, and management. It is often not obvious what precise functional scope such systems offer so that the comparison and the estimation of synergy effects between candidates are quite challenging. In this paper, we aim to give an overview of systems and platforms which support the management of ML lifecycle artifacts. Based on a systematic literature review, we derive assessment criteria and apply them to a representative selection of more than 60 systems and platforms.
Low-Resources Project-Specific Code Summarization
Venue: arXiv cs.SE 2022
Year: 2022

Code summarization generates brief natural language descriptions of source code pieces, which can assist developers in understanding code and reduce documentation workload. Recent neural models on code summarization are trained and evaluated on large-scale multi-project datasets consisting of independent code-summary pairs. Despite the technical advances, their effectiveness on a specific project is rarely explored. In practical scenarios, however, developers are more concerned with generating high-quality summaries for their working projects. And these projects may not maintain sufficient documentation, hence having few historical code-summary pairs. To this end, we investigate low-resource project-specific code summarization, a novel task more consistent with the developers' requirements. To better characterize project-specific knowledge with limited training samples, we propose a meta transfer learning method by incorporating a lightweight fine-tuning mechanism into a meta-learning framework. Experimental results on nine real-world projects verify the superiority of our method over alternative ones and reveal how the project-specific knowledge is learned.
$m^4Adapter$: Multilingual Multi-Domain Adaptation for Machine Translation with a Meta-Adapter
Venue: arXiv cs.CL 2022
Year: 2022

Multilingual neural machine translation models (MNMT) yield state-of-the-art performance when evaluated on data from a domain and language pair seen at training time. However, when a MNMT model is used to translate under domain shift or to a new language pair, performance drops dramatically. We consider a very challenging scenario: adapting the MNMT model both to a new domain and to a new language pair at the same time. In this paper, we propose $m^4Adapter$ (Multilingual Multi-Domain Adaptation for Machine Translation with a Meta-Adapter), which combines domain and language knowledge using meta-learning with adapters. We present results showing that our approach is a parameter-efficient solution which effectively adapts a model to both a new language pair and a new domain, while outperforming other adapter methods. An ablation study also shows that our approach more effectively transfers domain knowledge across different languages and language information across different domains.
NEREL-BIO: A Dataset of Biomedical Abstracts Annotated with Nested Named Entities
Venue: arXiv cs.CL 2022
Year: 2022

This paper describes NEREL-BIO -- an annotation scheme and corpus of PubMed abstracts in Russian and smaller number of abstracts in English. NEREL-BIO extends the general domain dataset NEREL by introducing domain-specific entity types. NEREL-BIO annotation scheme covers both general and biomedical domains making it suitable for domain transfer experiments. NEREL-BIO provides annotation for nested named entities as an extension of the scheme employed for NEREL. Nested named entities may cross entity boundaries to connect to shorter entities nested within longer entities, making them harder to detect. NEREL-BIO contains annotations for 700+ Russian and 100+ English abstracts. All English PubMed annotations have corresponding Russian counterparts. Thus, NEREL-BIO comprises the following specific features: annotation of nested named entities, it can be used as a benchmark for cross-domain (NEREL -> NEREL-BIO) and cross-language (English -> Russian) transfer. We experiment with both transformer-based sequence models and machine reading comprehension (MRC) models and report their results. The dataset is freely available at https://github.com/nerel-ds/NEREL-BIO.
RollBack: A New Time-Agnostic Replay Attack Against the Automotive Remote Keyless Entry Systems
Venue: arXiv cs.CR 2022
Year: 2022

Today's RKE systems implement disposable rolling codes, making every key fob button press unique, effectively preventing simple replay attacks. However, a prior attack called RollJam was proven to break all rolling code-based systems in general. By a careful sequence of signal jamming, capturing, and replaying, an attacker can become aware of the subsequent valid unlock signal that has not been used yet. RollJam, however, requires continuous deployment indefinitely until it is exploited. Otherwise, the captured signals become invalid if the key fob is used again without RollJam in place. We introduce RollBack, a new replay-and-resynchronize attack against most of today's RKE systems. In particular, we show that even though the one-time code becomes invalid in rolling code systems, replaying a few previously captured signals consecutively can trigger a rollback-like mechanism in the RKE system. Put differently, the rolling codes become resynchronized back to a previous code used in the past from where all subsequent yet already used signals work again. Moreover, the victim can still use the key fob without noticing any difference before and after the attack. Unlike RollJam, RollBack does not necessitate jamming at all. Furthermore, it requires signal capturing only once and can be exploited at any time in the future as many times as desired. This time-agnostic property is particularly attractive to attackers, especially in car-sharing/renting scenarios where accessing the key fob is straightforward. However, while RollJam defeats virtually any rolling code-based system, vehicles might have additional anti-theft measures against malfunctioning key fobs, hence against RollBack. Our ongoing analysis (covering Asian vehicle manufacturers for the time being) against different vehicle makes and models has revealed that ~70% of them are vulnerable to RollBack.