OpenRaiser/Intern-Atlas huggingface dataset:
Explaining Inference Queries with Bayesian Optimization
Venue: arXiv cs.DB 2021
Year: 2021

Obtaining an explanation for an SQL query result can enrich the analysis experience, reveal data errors, and provide deeper insight into the data. Inference query explanation seeks to explain unexpected aggregate query results on inference data; such queries are challenging to explain because an explanation may need to be derived from the source, training, or inference data in an ML pipeline. In this paper, we model an objective function as a black-box function and propose BOExplain, a novel framework for explaining inference queries using Bayesian optimization (BO). An explanation is a predicate defining the input tuples that should be removed so that the query result of interest is significantly affected. BO - a technique for finding the global optimum of a black-box function - is used to find the best predicate. We develop two new techniques (individual contribution encoding and warm start) to handle categorical variables. We perform experiments showing that the predicates found by BOExplain have a higher degree of explanation compared to those found by the state-of-the-art query explanation engines. We also show that BOExplain is effective at deriving explanations for inference queries from source and training data on a variety of real-world datasets. BOExplain is open-sourced as a Python package at https://github.com/sfu-db/BOExplain.
The Hessigheim 3D (H3D) Benchmark on Semantic Segmentation of High-Resolution 3D Point Clouds and Textured Meshes from UAV LiDAR and Multi-View-Stereo
Venue: arXiv cs.CV 2021
Year: 2021

Automated semantic segmentation and object detection are of great importance in geospatial data analysis. However, supervised machine learning systems such as convolutional neural networks require large corpora of annotated training data. Especially in the geospatial domain, such datasets are quite scarce. Within this paper, we aim to alleviate this issue by introducing a new annotated 3D dataset that is unique in three ways: i) The dataset consists of both an Unmanned Aerial Vehicle (UAV) laser scanning point cloud and a 3D textured mesh. ii) The point cloud features a mean point density of about 800 pts/sqm and the oblique imagery used for 3D mesh texturing realizes a ground sampling distance of about 2-3 cm. This enables the identification of fine-grained structures and represents the state of the art in UAV-based mapping. iii) Both data modalities will be published for a total of three epochs allowing applications such as change detection. The dataset depicts the village of Hessigheim (Germany), henceforth referred to as H3D. It is designed to promote research in the field of 3D data analysis on one hand and to evaluate and rank existing and emerging approaches for semantic segmentation of both data modalities on the other hand. Ultimately, we hope that H3D will become a widely used benchmark dataset in company with the well-established ISPRS Vaihingen 3D Semantic Labeling Challenge benchmark (V3D). The dataset can be downloaded from https://ifpwww.ifp.uni-stuttgart.de/benchmark/hessigheim/default.aspx.
The effects of citation-based research evaluation schemes on self-citation behavior
Venue: arXiv cs.DL 2021
Year: 2021

We investigate the changes in the self-citation behavior of Italian professors following the introduction of a citation-based incentive scheme, for national accreditation to academic appointments. Previous contributions on self-citation behavior have either focused on small samples or relied on simple models, not controlling for all confounding factors. The present work adopts a complex statistics model implemented on bibliometric individual data for over 15,000 Italian professors. Controlling for a number of covariates (number of citable papers published by the author; presence of international authors; number of co-authors; degree of the professor's specialization), the average increase in self-citation rates following introduction of the ASN is of 9.5%. The increase is common to all disciplines and academic ranks, albeit with diverse magnitude. Moreover, the increase is sensitive to the relative incentive, depending on the status of the scholar with respect to the scientific accreditation. A further analysis shows that there is much heterogeneity in the individual patterns of self-citing behavior, albeit with very few outliers.
Application of Yolo on Mask Detection Task
Venue: arXiv cs.CV 2021
Year: 2021

2020 has been a year marked by the COVID-19 pandemic. This event has caused disruptions to many aspects of normal life. An important aspect in reducing the impact of the pandemic is to control its spread. Studies have shown that one effective method in reducing the transmission of COVID-19 is to wear masks. Strict mask-wearing policies have been met with not only public sensation but also practical difficulty. We cannot hope to manually check if everyone on a street is wearing a mask properly. Existing technology to help automate mask checking uses deep learning models on real-time surveillance camera footages. The current dominant method to perform real-time mask detection uses Mask-RCNN with ResNet as the backbone. While giving good detection results, this method is computationally intensive and its efficiency in real-time face mask detection is not ideal. Our research proposes a new approach to mask detection by replacing Mask-R-CNN with a more efficient model "YOLO" to increase the processing speed of real-time mask detection and not compromise on accuracy. Besides, given the small volume as well as extreme imbalance of the mask detection datasets, we adopt a latest progress made in few-shot visual classification, simple CNAPs, to improve the classification performance.
Non-stationary Reinforcement Learning without Prior Knowledge: An Optimal Black-box Approach
Venue: arXiv cs.LG 2021
Year: 2021

We propose a black-box reduction that turns a certain reinforcement learning algorithm with optimal regret in a (near-)stationary environment into another algorithm with optimal dynamic regret in a non-stationary environment, importantly without any prior knowledge on the degree of non-stationarity. By plugging different algorithms into our black-box, we provide a list of examples showing that our approach not only recovers recent results for (contextual) multi-armed bandits achieved by very specialized algorithms, but also significantly improves the state of the art for (generalized) linear bandits, episodic MDPs, and infinite-horizon MDPs in various ways. Specifically, in most cases our algorithm achieves the optimal dynamic regret $\widetilde{\mathcal{O}}(\min\{\sqrt{LT}, Δ^{1/3}T^{2/3}\})$ where $T$ is the number of rounds and $L$ and $Δ$ are the number and amount of changes of the world respectively, while previous works only obtain suboptimal bounds and/or require the knowledge of $L$ and $Δ$.
NUVA: A Naming Utterance Verifier for Aphasia Treatment
Venue: arXiv cs.CL 2021
Year: 2021

Anomia (word-finding difficulties) is the hallmark of aphasia, an acquired language disorder most commonly caused by stroke. Assessment of speech performance using picture naming tasks is a key method for both diagnosis and monitoring of responses to treatment interventions by people with aphasia (PWA). Currently, this assessment is conducted manually by speech and language therapists (SLT). Surprisingly, despite advancements in automatic speech recognition (ASR) and artificial intelligence with technologies like deep learning, research on developing automated systems for this task has been scarce. Here we present NUVA, an utterance verification system incorporating a deep learning element that classifies 'correct' versus' incorrect' naming attempts from aphasic stroke patients. When tested on eight native British-English speaking PWA the system's performance accuracy ranged between 83.6% to 93.6%, with a 10-fold cross-validation mean of 89.5%. This performance was not only significantly better than a baseline created for this study using one of the leading commercially available ASRs (Google speech-to-text service) but also comparable in some instances with two independent SLT ratings for the same dataset.
Information Extraction From Co-Occurring Similar Entities
Venue: arXiv cs.IR 2021
Year: 2021

Knowledge about entities and their interrelations is a crucial factor of success for tasks like question answering or text summarization. Publicly available knowledge graphs like Wikidata or DBpedia are, however, far from being complete. In this paper, we explore how information extracted from similar entities that co-occur in structures like tables or lists can help to increase the coverage of such knowledge graphs. In contrast to existing approaches, we do not focus on relationships within a listing (e.g., between two entities in a table row) but on the relationship between a listing's subject entities and the context of the listing. To that end, we propose a descriptive rule mining approach that uses distant supervision to derive rules for these relationships based on a listing's context. Extracted from a suitable data corpus, the rules can be used to extend a knowledge graph with novel entities and assertions. In our experiments we demonstrate that the approach is able to extract up to 3M novel entities and 30M additional assertions from listings in Wikipedia. We find that the extracted information is of high quality and thus suitable to extend Wikipedia-based knowledge graphs like DBpedia, YAGO, and CaLiGraph. For the case of DBpedia, this would result in an increase of covered entities by roughly 50%.
Civil Rephrases Of Toxic Texts With Self-Supervised Transformers
Venue: arXiv cs.CL 2021
Year: 2021

Platforms that support online commentary, from social networks to news sites, are increasingly leveraging machine learning to assist their moderation efforts. But this process does not typically provide feedback to the author that would help them contribute according to the community guidelines. This is prohibitively time-consuming for human moderators to do, and computational approaches are still nascent. This work focuses on models that can help suggest rephrasings of toxic comments in a more civil manner. Inspired by recent progress in unpaired sequence-to-sequence tasks, a self-supervised learning model is introduced, called CAE-T5. CAE-T5 employs a pre-trained text-to-text transformer, which is fine tuned with a denoising and cyclic auto-encoder loss. Experimenting with the largest toxicity detection dataset to date (Civil Comments) our model generates sentences that are more fluent and better at preserving the initial content compared to earlier text style transfer systems which we compare with using several scoring systems and human evaluation.
Dynamic $β$-VAEs for quantifying biodiversity by clustering optically recorded insect signals
Venue: arXiv cs.LG 2021
Year: 2021

While insects are the largest and most diverse group of terrestrial animals, constituting ca. 80% of all known species, they are difficult to study due to their small size and similarity between species. Conventional monitoring techniques depend on time consuming trapping methods and tedious microscope-based work by skilled experts in order to identify the caught insect specimen at species, or even family level. Researchers and policy makers are in urgent need of a scalable monitoring tool in order to conserve biodiversity and secure human food production due to the rapid decline in insect numbers. In order to improve upon existing insect clustering methods, we propose an adaptive variant of the variational autoencoder (VAE) which is capable of clustering data by phylogenetic groups. The proposed dynamic beta-VAE dynamically adapts the scaling of the reconstruction and regularization loss terms (beta value) yielding useful latent representations of the input data. We demonstrate the usefulness of the dynamic beta-VAE on optically recorded insect signals from regions of southern Scandinavia to cluster unlabelled targets into possible species. We also demonstrate improved clustering performance in a semi-supervised setting using a small subset of labelled data. These experimental results, in both unsupervised- and semi-supervised settings, with the dynamic beta-VAE are promising and, in the near future, can be deployed to monitor insects and conserve the rapidly declining insect biodiversity.
Interrupting Real-Time IoT Tasks: How Bad Can It Be to Connect Your Critical Embedded System to the Internet?
Venue: arXiv cs.NI 2021
Year: 2021

Embedded systems have been used to control physical environments for decades. Usually, such use cases require low latencies between commands and actions as well as a high predictability of the expected worst-case delay. To achieve this on small, low-powered microcontrollers, Real-Time Operating Systems (RTOSs) are used to manage the different tasks on these machines as deterministically as possible. However, with the advent of the Internet of Things (IoT) in industrial applications, the same embedded systems are now equipped with networking capabilities, possibly endangering critical real-time systems through an open gate to interrupts. This paper presents our initial study of the impact network connections can have on real-time embedded systems. Specifically, we look at three aspects: The impact of network-generated interrupts, the overhead of the related networking tasks, and the feasibility of sharing computing resources between networking and real-time tasks. We conducted experiments on two setups: One treating NICs and drivers as black boxes and one simulating network interrupts on the machines. The preliminary results show that a critical task performance loss of up to 6.67% per received packet per second could be induced where lateness impacts of 1% per packet per second can be attributed exclusively to ISR-generated delays.
All instantiations of the greedy algorithm for the shortest superstring problem are equivalent
Venue: arXiv cs.DS 2021
Year: 2021

In the Shortest Common Superstring problem (SCS), one needs to find the shortest superstring for a set of strings. While SCS is NP-hard and MAX-SNP-hard, the Greedy Algorithm "choose two strings with the largest overlap; merge them; repeat" achieves a constant factor approximation that is known to be at most 3.5 and conjectured to be equal to 2. The Greedy Algorithm is not deterministic, so its instantiations with different tie-breaking rules may have different approximation factors. In this paper, we show that it is not the case: all factors are equal. To prove this, we show how to transform a set of strings so that all overlaps are different whereas their ratios stay roughly the same. We also reveal connections between the original version of SCS and the following one: find a~superstring minimizing the number of occurrences of a given symbol. It turns out that the latter problem is equivalent to the original one.
On the Distribution of the Sum of Double-Nakagami-m Random Vectors and Application in Randomly Reconfigurable Surfaces
Venue: arXiv cs.IT 2021
Year: 2021

Meta-surfaces intend to improve significantly the performance of future wireless networks by controlling the wireless propagation and shaping the radio waves according to the generalized Snell's law. A recent application of meta-surfaces is reconfigurable intelligent surfaces which are practically limited by the requirement for perfect knowledge of the user's position. For the case where the user's position cannot be obtained, we introduce randomly reconfigurable surfaces (RRSs) aiming to diffuse the incoming wave. A RRS is defined as a reconfigurable meta-surface that each of its elements induces a randomly selected time-variant phase shift on the reflected signal. To facilitate the performance analysis of a RRS-assisted system, first, we present novel closed-form expressions for the probability density function, the cumulative distribution function, the moments, and the characteristic function of the distribution of the sum of double-Nakagami-m random vectors, whose amplitudes follow the double-Nakagami-m distribution, i.e., the distribution of the product of two Nakagami-m random variables, and phases follow the circular uniform distribution. We also consider a special case of this distribution, namely the distribution of the sum of Rayleigh-Nakagami-m random vectors. Then, we exploit these expressions to investigate the performance of the RRS-assisted composite channel, assuming that the two links undergo Nakagami-m fading and the equivalent phase follows the circular uniform distribution. Closed-form expressions for the outage probability, the average received signal-to-noise ratio, the ergodic capacity, the bit error probability, the amount of fading, and the channel quality estimation index are provided to evaluate the performance of the considered system. These metrics are also derived for the practical special case where one of the two links undergoes Rayleigh fading.
TDMA-based scheduling for multi-hop wireless sensor networks with 3-egress gateway linear topology
Venue: arXiv cs.NI 2021
Year: 2021

Packet transmission scheduling on multi-hop wireless sensor networks with 3-egress gateway linear topology is studied. Each node generates a data packet in every one cycle period and forwards it bounded for either of gateways at edges. We focus on centrally-managed Time Division Multiple Access (TDMA)-based slot allocations and provide the design of a packet transmission scheduling framework with static time-slot allocation and basic redundant transmission to reduce and recover packet losses. We proposed three general path models, which would represent all possible variations of this topology, and derived a global static time-slot allocation on each of models to maximize the theoretical probability that all packets are successfully delivered to one of the gateways within one cycle period.
Improving Model-Based Reinforcement Learning with Internal State Representations through Self-Supervision
Venue: arXiv cs.LG 2021
Year: 2021

Using a model of the environment, reinforcement learning agents can plan their future moves and achieve superhuman performance in board games like Chess, Shogi, and Go, while remaining relatively sample-efficient. As demonstrated by the MuZero Algorithm, the environment model can even be learned dynamically, generalizing the agent to many more tasks while at the same time achieving state-of-the-art performance. Notably, MuZero uses internal state representations derived from real environment states for its predictions. In this paper, we bind the model's predicted internal state representation to the environment state via two additional terms: a reconstruction model loss and a simpler consistency loss, both of which work independently and unsupervised, acting as constraints to stabilize the learning process. Our experiments show that this new integration of reconstruction model loss and simpler consistency loss provide a significant performance increase in OpenAI Gym environments. Our modifications also enable self-supervised pretraining for MuZero, so the algorithm can learn about environment dynamics before a goal is made available.
A Cognitive and Machine Learning-Based Software Development Paradigm Supported by Context
Venue: arXiv cs.SE 2021
Year: 2021

Advances in the use of cognitive and machine learning (ML) enabled systems fuel the quest for novel approaches and tools to support software developers in executing their tasks. First, as software development is a complex and dynamic activity, these tasks are highly dependent on the characteristics of the software project and its context, and developers need comprehensive support in terms of information and guidance based on the task context. Second, there is a lack of methods based on conversational-guided agents that consider cognitive aspects such as paying attention and remembering. Third, there is also a lack of techniques that make use of historical implicit or tacit data to infer new knowledge about the project tasks such as related tasks, task experts, relevant information needed for task completion and warnings, and navigation aspects of the process such as what tasks to perform next and optimal task sequencing. Based on these challenges, this paper introduces a novel paradigm for human-machine software support based on context, cognitive assistance, and machine learning, and briefly describes ongoing research activities to realize this paradigm. The research takes advantage of the synergy among emergent methods provided in context-aware software processes, cognitive computing such as chatbots, and machine learning such as recommendation systems. These novel paradigms have the potential to transform the way software development currently occurs by allowing developers to receive valuable information and guidance in real-time while they are participating in projects.
Addressing the Topological Defects of Disentanglement via Distributed Operators
Venue: arXiv cs.LG 2021
Year: 2021

A core challenge in Machine Learning is to learn to disentangle natural factors of variation in data (e.g. object shape vs. pose). A popular approach to disentanglement consists in learning to map each of these factors to distinct subspaces of a model's latent representation. However, this approach has shown limited empirical success to date. Here, we show that, for a broad family of transformations acting on images--encompassing simple affine transformations such as rotations and translations--this approach to disentanglement introduces topological defects (i.e. discontinuities in the encoder). Motivated by classical results from group representation theory, we study an alternative, more flexible approach to disentanglement which relies on distributed latent operators, potentially acting on the entire latent space. We theoretically and empirically demonstrate the effectiveness of this approach to disentangle affine transformations. Our work lays a theoretical foundation for the recent success of a new generation of models using distributed operators for disentanglement.
Analyse formelle de concept pour le routage des requêtes dans les systèmes pair-à-pair
Venue: arXiv cs.NI 2021
Year: 2021

The Peer-to-Peer systems (P2P) were led these last years as the major technology of access upon various resources on Internet. These systems build a cluster witch contains a very large number of peers. As the result the selection of peers who can answer for a given query is a very difficult problem. The efficiency of the selection algorithms can be improved by introducing of semantics into the process of queries routing. We present in this paper a novel improved version of our semantic routing algorithm LearningPeerSelection (LPS) presented in CORIA 2009, an incremental strategy of updating knowledge bases and an advanced experimental study. To test the proposed algorithm, we defined a layer of routing on the PeerSim simulator.
UAV Localization Using Autoencoded Satellite Images
Venue: arXiv cs.CV 2021
Year: 2021

We propose and demonstrate a fast, robust method for using satellite images to localize an Unmanned Aerial Vehicle (UAV). Previous work using satellite images has large storage and computation costs and is unable to run in real time. In this work, we collect Google Earth (GE) images for a desired flight path offline and an autoencoder is trained to compress these images to a low-dimensional vector representation while retaining the key features. This trained autoencoder is used to compress a real UAV image, which is then compared to the precollected, nearby, autoencoded GE images using an inner-product kernel. This results in a distribution of weights over the corresponding GE image poses and is used to generate a single localization and associated covariance to represent uncertainty. Our localization is computed in 1% of the time of the current standard and is able to achieve a comparable RMSE of less than 3m in our experiments, where we robustly matched UAV images from six runs spanning the lighting conditions of a single day to the same map of satellite images.