OpenRaiser/Intern-Atlas huggingface dataset:
ColibriDoc: An Eye-in-Hand Autonomous Trocar Docking System
Venue: arXiv cs.RO 2021
Year: 2021

Retinal surgery is a complex medical procedure that requires exceptional expertise and dexterity. For this purpose, several robotic platforms are currently being developed to enable or improve the outcome of microsurgical tasks. Since the control of such robots is often designed for navigation inside the eye in proximity to the retina, successful trocar docking and inserting the instrument into the eye represents an additional cognitive effort, and is, therefore, one of the open challenges in robotic retinal surgery. For this purpose, we present a platform for autonomous trocar docking that combines computer vision and a robotic setup. Inspired by the Cuban Colibri (hummingbird) aligning its beak to a flower using only vision, we mount a camera onto the endeffector of a robotic system. By estimating the position and pose of the trocar, the robot is able to autonomously align and navigate the instrument towards the Trocar's Entry Point (TEP) and finally perform the insertion. Our experiments show that the proposed method is able to accurately estimate the position and pose of the trocar and achieve repeatable autonomous docking. The aim of this work is to reduce the complexity of robotic setup preparation prior to the surgical task and therefore, increase the intuitiveness of the system integration into the clinical workflow.
Minor changes make a difference: a case study on the consistency of UD-based dependency parsers
Venue: arXiv cs.CL 2021
Year: 2021

Many downstream applications are using dependency trees, and are thus relying on dependency parsers producing correct, or at least consistent, output. However, dependency parsers are trained using machine learning, and are therefore susceptible to unwanted inconsistencies due to biases in the training data. This paper explores the effects of such biases in four languages - English, Swedish, Russian, and Ukrainian - though an experiment where we study the effect of replacing numerals in sentences. We show that such seemingly insignificant changes in the input can cause large differences in the output, and suggest that data augmentation can remedy the problems.
TiWS-iForest: Isolation Forest in Weakly Supervised and Tiny ML scenarios
Venue: arXiv cs.LG 2021
Year: 2021

Unsupervised anomaly detection tackles the problem of finding anomalies inside datasets without the labels availability; since data tagging is typically hard or expensive to obtain, such approaches have seen huge applicability in recent years. In this context, Isolation Forest is a popular algorithm able to define an anomaly score by means of an ensemble of peculiar trees called isolation trees. These are built using a random partitioning procedure that is extremely fast and cheap to train. However, we find that the standard algorithm might be improved in terms of memory requirements, latency and performances; this is of particular importance in low resources scenarios and in TinyML implementations on ultra-constrained microprocessors. Moreover, Anomaly Detection approaches currently do not take advantage of weak supervisions: being typically consumed in Decision Support Systems, feedback from the users, even if rare, can be a valuable source of information that is currently unexplored. Beside showing iForest training limitations, we propose here TiWS-iForest, an approach that, by leveraging weak supervision is able to reduce Isolation Forest complexity and to enhance detection performances. We showed the effectiveness of TiWS-iForest on real word datasets and we share the code in a public repository to enhance reproducibility.
Servicing Timed Requests on a Line
Venue: arXiv cs.DS 2021
Year: 2021

We consider an off-line optimisation problem where $k$ robots must service $n$ requests on a single line. A request $i$ has weight $w_i$ and takes place at time $t_i$ at location $d_i$ on the line. A robot can service a request and collect the weight $w_i$, if it is present at $d_i$ at time $t_i$. The objective is to find $k$ robot-schedules that maximize the total weight. The optimisation problem is motivated by a robotics application [Asahiro et al. Discrete Applied Mathematics, 2006] and can be modeled as a minimum cost flow problem with unit capacities in a flow network $\mathcal{N}$. Consequently, we ask for a collection of $k$ node-disjoint paths from the source $s$ to the sink $t$ in $\mathcal{N}$, with minimum total weight. It was shown in [Asahiro et al. Discrete Applied Mathematics, 2006] that the flow network $\mathcal{N}$ can be implicitly represented by $n$ points on the plane which yields to an $O(n \log n)$-time algorithm for $k=1$ and the special case where all requests have the same weight. However, for $k \geq 2$ the problem can be solved in $O(kn^2)$ time with the successive shortest path algorithm which does not use this implicit representation. We consider arbitrary request weights and show a recursive $O(k^{2k}n \log^{2k} n)$-time algorithm which improves the previous bound if $k$ is considered constant. Our result also improves the running time of previous algorithms for other variants of the optimisation problem. Finally, we show problem properties that may be useful within the context of applications that motivate the problem and may yield to more efficient algorithms.
KARL-Trans-NER: Knowledge Aware Representation Learning for Named Entity Recognition using Transformers
Venue: arXiv cs.CL 2021
Year: 2021

The inception of modeling contextual information using models such as BERT, ELMo, and Flair has significantly improved representation learning for words. It has also given SOTA results in almost every NLP task - Machine Translation, Text Summarization and Named Entity Recognition, to name a few. In this work, in addition to using these dominant context-aware representations, we propose a Knowledge Aware Representation Learning (KARL) Network for Named Entity Recognition (NER). We discuss the challenges of using existing methods in incorporating world knowledge for NER and show how our proposed methods could be leveraged to overcome those challenges. KARL is based on a Transformer Encoder that utilizes large knowledge bases represented as fact triplets, converts them to a graph context, and extracts essential entity information residing inside to generate contextualized triplet representation for feature augmentation. Experimental results show that the augmentation done using KARL can considerably boost the performance of our NER system and achieve significantly better results than existing approaches in the literature on three publicly available NER datasets, namely CoNLL 2003, CoNLL++, and OntoNotes v5. We also observe better generalization and application to a real-world setting from KARL on unseen entities.
Energy-Efficient Design for a NOMA assisted STAR-RIS Network with Deep Reinforcement Learning
Venue: arXiv cs.IT 2021
Year: 2021

Simultaneous transmitting and reflecting reconfigurable intelligent surfaces (STAR-RISs) has been considered as a promising auxiliary device to enhance the performance of the wireless network, where users located at the different sides of the surfaces can be simultaneously served by the transmitting and reflecting signals. In this paper, the energy efficiency (EE) maximization problem for a non-orthogonal multiple access (NOMA) assisted STAR-RIS downlink network is investigated. Due to the fractional form of the EE, it is challenging to solve the EE maximization problem by the traditional convex optimization solutions. In this work, a deep deterministic policy gradient (DDPG)-based algorithm is proposed to maximize the EE by jointly optimizing the transmission beamforming vectors at the base station and the coefficients matrices at the STAR-RIS. Simulation results demonstrate that the proposed algorithm can effectively maximize the system EE considering the time-varying channels.
VoIP Can Still Be Exploited -- Badly
Venue: arXiv cs.CR 2021
Year: 2021

VoIP phones are early representatives as well as present enhancers of the IoT. This paper observes that they are still widely used in a traditional, unsecured configuration and demonstrates the Phonejack family of attacks: Phonejack 1 conjectures the exploitation of phone vulnerabilities; Phonejack 2 demonstrates how to mount a denial-of-service attack on a network of phones; Phonejack 3 sniffs calls. It is reassuring, however, that inexpensive devices such as a Raspberry Pi can be configured and programmed as effective countermeasures, thus supporting the approach of integrating both technologies. We demonstrate both attacks and defence measures in a video clip. The concluding evaluations argue that trusting the underlying network security measures may turn out overly optimistic; moreover, VoIP phones really ought to be protected as laptops routinely are today
A Scheme of Channel Prediction Based on Artificial Neural Network
Venue: arXiv cs.IT 2021
Year: 2021

Accurate channel modeling is the foundation of communication system design. However, the traditional measurement-based modeling approach has increasing challenges for the scenarios with insufficient measurement data. To obtain enough data for channel modeling, the Artificial Neural Network (ANN) is used in this paper to predict channel data. The high mobility railway channel is considered, which is a typical scenario where it is challenging to obtain enough data for modeling within a short sampling interval. Three types of ANNs, the Back Propagation Network, Radial Basis Function Neural Network and Extreme Learning Machine, are considered to predict channel path loss and shadow fading. The Root-Mean-Square error is used to evaluate prediction accuracy. The factors that may influence prediction accuracy are compared and discussed, including the type of network, number of neurons and proportion of training data. It is found that a larger number of neurons can significantly reduce prediction error, whereas the influence of proportion of training data is relatively small. The results can be used to improve modeling accuracy of path loss and shadow fading when measurement data is reduced.
Popcorns-Pro: A Cooperative Network-Server Approach for Data Center Energy Optimization
Venue: arXiv cs.AR 2021
Year: 2021

Data centers have become a popular computing platform for various applications, and they account for nearly 2% of total US energy consumption. Therefore, it has become important to optimize data center power, and reduce their energy footprint. Most existing work optimizes power in servers and networks independently and does not address them together in a holistic fashion that has the potential to achieve greater power savings. In this article, we present PopcornsPro, a cooperative server network framework for energy optimization. We present a comprehensive power model for heterogeneous data center switches along with low power mode designs in combination with the server power model. We design job scheduling algorithms that place tasks onto servers in a power-aware manner, such that servers and network switches can take effective advantage of low power state and available network link capacities. Our experimental results show that we are able to achieve significantly higher savings up to 80% compared to the previously well-known server and network power optimization policies.
Regularized directional representations for medical image registration
Venue: arXiv cs.CV 2021
Year: 2021

In image registration, many efforts have been devoted to the development of alternatives to the popular normalized mutual information criterion. Concurrently to these efforts, an increasing number of works have demonstrated that substantial gains in registration accuracy can also be achieved by aligning structural representations of images rather than images themselves. Following this research path, we propose a new method for mono- and multimodal image registration based on the alignment of regularized vector fields derived from structural information such as gradient vector flow fields, a technique we call \textit{vector field similarity}. Our approach can be combined in a straightforward fashion with any existing registration framework by substituting vector field similarity to intensity-based registration. In our experiments, we show that the proposed approach compares favourably with conventional image alignment on several public image datasets using a diversity of imaging modalities and anatomical locations.
A Sketch Based Game Theoretic Approach to Detect Anomalous Dense Sub-Communities in Large Data Streams
Venue: arXiv cs.SI 2021
Year: 2021

Detecting anomalous subgraphs in a dynamic graph in an online or streaming fashion is an important requirement in industrial settings for intrusion detection or denial of service attacks. While only detecting anomalousness in the system by edge frequencies is an optimal approach, many latent information can get unnoticed in the process, since as a characteristic of the network only edge frequencies are considered. We propose a game theoretic approach whereby using the modularity function we try to estimate in a streaming graph \emph{whether addition of a new edge in the current time tick results in a dense subgraph creation, thus indicating possible anomalous score}. Our contributions are as follows: (a) We propose a novel game-theoretic framework for detecting dense subcommunities in an online streaming environment; (b) We detect such subcommunities using constant memory storage. Our results are corroborated with empirical evaluation on real datasets.
NeuSample: Neural Sample Field for Efficient View Synthesis
Venue: arXiv cs.CV 2021
Year: 2021

Neural radiance fields (NeRF) have shown great potentials in representing 3D scenes and synthesizing novel views, but the computational overhead of NeRF at the inference stage is still heavy. To alleviate the burden, we delve into the coarse-to-fine, hierarchical sampling procedure of NeRF and point out that the coarse stage can be replaced by a lightweight module which we name a neural sample field. The proposed sample field maps rays into sample distributions, which can be transformed into point coordinates and fed into radiance fields for volume rendering. The overall framework is named as NeuSample. We perform experiments on Realistic Synthetic 360$^{\circ}$ and Real Forward-Facing, two popular 3D scene sets, and show that NeuSample achieves better rendering quality than NeRF while enjoying a faster inference speed. NeuSample is further compressed with a proposed sample field extraction method towards a better trade-off between quality and speed.
Text Mining Drug/Chemical-Protein Interactions using an Ensemble of BERT and T5 Based Models
Venue: arXiv cs.CL 2021
Year: 2021

In Track-1 of the BioCreative VII Challenge participants are asked to identify interactions between drugs/chemicals and proteins. In-context named entity annotations for each drug/chemical and protein are provided and one of fourteen different interactions must be automatically predicted. For this relation extraction task, we attempt both a BERT-based sentence classification approach, and a more novel text-to-text approach using a T5 model. We find that larger BERT-based models perform better in general, with our BioMegatron-based model achieving the highest scores across all metrics, achieving 0.74 F1 score. Though our novel T5 text-to-text method did not perform as well as most of our BERT-based models, it outperformed those trained on similar data, showing promising results, achieving 0.65 F1 score. We believe a text-to-text approach to relation extraction has some competitive advantages and there is a lot of room for research advancement.
Automatic Extraction of Medication Names in Tweets as Named Entity Recognition
Venue: arXiv cs.CL 2021
Year: 2021

Social media posts contain potentially valuable information about medical conditions and health-related behavior. Biocreative VII Task 3 focuses on mining this information by recognizing mentions of medications and dietary supplements in tweets. We approach this task by fine tuning multiple BERT-style language models to perform token-level classification, and combining them into ensembles to generate final predictions. Our best system consists of five Megatron-BERT-345M models and achieves a strict F1 score of 0.764 on unseen test data.
Attentive Prototypes for Source-free Unsupervised Domain Adaptive 3D Object Detection
Venue: arXiv cs.CV 2021
Year: 2021

3D object detection networks tend to be biased towards the data they are trained on. Evaluation on datasets captured in different locations, conditions or sensors than that of the training (source) data results in a drop in model performance due to the gap in distribution with the test (or target) data. Current methods for domain adaptation either assume access to source data during training, which may not be available due to privacy or memory concerns, or require a sequence of lidar frames as an input. We propose a single-frame approach for source-free, unsupervised domain adaptation of lidar-based 3D object detectors that uses class prototypes to mitigate the effect pseudo-label noise. Addressing the limitations of traditional feature aggregation methods for prototype computation in the presence of noisy labels, we utilize a transformer module to identify outlier ROI's that correspond to incorrect, over-confident annotations, and compute an attentive class prototype. Under an iterative training strategy, the losses associated with noisy pseudo labels are down-weighed and thus refined in the process of self-training. To validate the effectiveness of our proposed approach, we examine the domain shift associated with networks trained on large, label-rich datasets (such as the Waymo Open Dataset and nuScenes) and evaluate on smaller, label-poor datasets (such as KITTI) and vice-versa. We demonstrate our approach on two recent object detectors and achieve results that out-perform the other domain adaptation works.
Task Assignment in Distributed Systems based on PSO Approach
Venue: arXiv cs.DC 2021
Year: 2021

In a distributed system, Task Assignment Problem (TAP) is a key factor for obtaining efficiency. TAP illustrates the appropriate allocation of tasks to the processor of each computer. In this problem, the proposed methods up to now try to minimize Makespan and maximizing CPU utilization. Since this problem is NP-complete, many genetic algorithms have been proposed to search optimal solutions from the entire solution space. Disregarding the techniques which can reduce the complexity of optimization, the existing approaches scan the entire solution space. On the other hand, this approach is time-consuming in scheduling which is considered a shortcoming. Therefore, in this paper, a hybrid genetic algorithm has been proposed to overcome this shortcoming. Particle Swarm Optimization (PSO) has been applied as local search in the proposed genetic algorithm in this paper. The results obtained from simulation can prove that, in terms of CPU utilization and Makespan, the proposed approach outperforms the GA-based approach.
A Comprehensive Survey on the Convergence of Vehicular Social Networks and Fog Computing
Venue: arXiv cs.NI 2021
Year: 2021

In recent years, the number of IoT devices has been growing fast which leads to a challenging task for managing, storing, analyzing, and making decisions about raw data from different IoT devices, especially for delay-sensitive applications. In a vehicular network (VANET) environment, the dynamic nature of vehicles makes the current open research issues even more challenging due to the frequent topology changes that can lead to disconnections between vehicles. To this end, a number of research works have been proposed in the context of cloud and fog computing over the 5G infrastructure. On the other hand, there are a variety of research proposals that aim to extend the connection time between vehicles. Vehicular Social Networks (VSNs) have been defined to decrease the burden of connection time between the vehicles. This survey paper first provides the necessary background information and definitions about fog, cloud and related paradigms such as 5G and SDN. Then, it introduces the reader to Vehicular Social Networks, the different metrics and the main differences between VSNs and Online Social Networks. Finally, this survey investigates the related works in the context of VANETs that have demonstrated different architectures to address the different issues in fog computing. Moreover, it provides a categorization of the different approaches and discusses the required metrics in the context of fog and cloud and compares them to Vehicular social networks. A comparison of the relevant related works is discussed along with new research challenges and trends in the domain of VSNs and fog computing.
Light Field Implicit Representation for Flexible Resolution Reconstruction
Venue: arXiv cs.CV 2021
Year: 2021

Inspired by the recent advances in implicitly representing signals with trained neural networks, we aim to learn a continuous representation for narrow-baseline 4D light fields. We propose an implicit representation model for 4D light fields which is conditioned on a sparse set of input views. Our model is trained to output the light field values for a continuous range of query spatio-angular coordinates. Given a sparse set of input views, our scheme can super-resolve the input in both spatial and angular domains by flexible factors. consists of a feature extractor and a decoder which are trained on a dataset of light field patches. The feature extractor captures per-pixel features from the input views. These features can be resized to a desired spatial resolution and fed to the decoder along with the query coordinates. This formulation enables us to reconstruct light field views at any desired spatial and angular resolution. Additionally, our network can handle scenarios in which input views are either of low-resolution or with missing pixels. Experiments show that our method achieves state-of-the-art performance for the task of view synthesis while being computationally fast.
InfoPrint: Embedding Information into 3D Printed Objects
Venue: arXiv cs.HC 2021
Year: 2021

We present a technique to embed information invisible to the eye inside 3D printed objects. The information is integrated in the object model, and then fabricated using off-the-shelf dual-head FDM (Fused Deposition Modeling) 3D printers. Our process does not require human intervention during or after printing with the integrated model. The information can be arbitrary symbols, such as icons, text,binary, or handwriting. To retrieve the information, we evaluate two different infrared-based imaging devices that are readily available-thermal cameras and near-infrared scanners. Based on our results, we propose design guidelines for a range of use cases to embed and extract hidden information. We demonstrate how our method can be used for different applications, such as interactive thermal displays, hidden board game tokens, tagging functional printed objects, and autographing non-fungible fabrication work.