OpenRaiser/Intern-Atlas huggingface dataset:
AI-Based and Mobility-Aware Energy Efficient Resource Allocation and Trajectory Design for NFV Enabled Aerial Networks
Venue: arXiv cs.NI 2021
Year: 2021

In this paper, we propose a novel joint intelligent trajectory design and resource allocation algorithm based on user's mobility and their requested services for unmanned aerial vehicles (UAVs) assisted networks, where UAVs act as nodes of a network function virtualization (NFV) enabled network. Our objective is to maximize energy efficiency and minimize the average delay on all services by allocating the limited radio and NFV resources. In addition, due to the traffic conditions and mobility of users, we let some Virtual Network Functions (VNFs) to migrate from their current locations to other locations to satisfy the Quality of Service requirements. We formulate our problem to find near-optimal locations of UAVs, transmit power, subcarrier assignment, placement, and scheduling the requested service's functions over the UAVs and perform suitable VNF migration. Then we propose a novel Hierarchical Hybrid Continuous and Discrete Action (HHCDA) deep reinforcement learning method to solve our problem. Finally, the convergence and computational complexity of the proposed algorithm and its performance analyzed for different parameters. Simulation results show that our proposed HHCDA method decreases the request reject rate and average delay by 31.5% and 20% and increases the energy efficiency by 40% compared to DDPG method.
Analysis of voxel-based 3D object detection methods efficiency for real-time embedded systems
Venue: arXiv cs.CV 2021
Year: 2021

Real-time detection of objects in the 3D scene is one of the tasks an autonomous agent needs to perform for understanding its surroundings. While recent Deep Learning-based solutions achieve satisfactory performance, their high computational cost renders their application in real-life settings in which computations need to be performed on embedded platforms intractable. In this paper, we analyze the efficiency of two popular voxel-based 3D object detection methods providing a good compromise between high performance and speed based on two aspects, their ability to detect objects located at large distances from the agent and their ability to operate in real time on embedded platforms equipped with high-performance GPUs. Our experiments show that these methods mostly fail to detect distant small objects due to the sparsity of the input point clouds at large distances. Moreover, models trained on near objects achieve similar or better performance compared to those trained on all objects in the scene. This means that the models learn object appearance representations mostly from near objects. Our findings suggest that a considerable part of the computations of existing methods is focused on locations of the scene that do not contribute with successful detection. This means that the methods can achieve a speed-up of $40$-$60\%$ by restricting operation to near objects while not sacrificing much in performance.
Infection in a Confined Space using an Agent-Based Model
Venue: arXiv cs.MA 2021
Year: 2021

This study examined a simulated confined space modelled as a hospital waiting area, where people who could have underlying conditions congregate and mix with potentially infectious individuals. It further investigated the impact of the volume of the waiting area, the number of people in the room, the placement of them as well as their weight. The simulation is an agent-based model (ABM).
An Algorithm for Grant-Free Random Access in Cell-Free Massive MIMO
Venue: arXiv cs.IT 2021
Year: 2021

Massive access is one of the main use cases of beyond 5G (B5G) wireless networks and massive MIMO is a key technology for supporting it. Prior works studied massive access in the co-located massive MIMO framework. In this paper, we investigate the activity detection in grant-free random access for massive machine type communications (mMTC) in cell-free massive MIMO network. Each active device transmits a pre-assigned non-orthogonal pilot sequence to the APs and the APs send the received signals to a central processing unit (CPU) for joint activity detection. We formulate the maximum likelihood device activity detection problem and provide an algorithm based on coordinate descent method having affordable complexity. We show that the cell-free massive MIMO network can support low-powered mMTC devices and provide a broad coverage.
Object-Process Methodology for Intelligent System Development
Venue: arXiv cs.SE 2021
Year: 2021

Development of the new artificial systems with unique characteristics is very challenging task. In this paper the application of the hybrid super intelligence concept with object-process methodology to develop unique high-performance computational systems is considered. The methodological approach how to design new intelligent components for existing high-performance computing development systems is proposed on the example of system requirements creation for "MicroAI" and "Artificial Electronic" systems.
Language Understanding for Field and Service Robots in a Priori Unknown Environments
Venue: arXiv cs.RO 2021
Year: 2021

Contemporary approaches to perception, planning, estimation, and control have allowed robots to operate robustly as our remote surrogates in uncertain, unstructured environments. This progress now creates an opportunity for robots to operate not only in isolation, but also with and alongside humans in our complex environments. Realizing this opportunity requires an efficient and flexible medium through which humans can communicate with collaborative robots. Natural language provides one such medium, and through significant progress in statistical methods for natural-language understanding, robots are now able to interpret a diverse array of free-form commands. However, most contemporary approaches require a detailed, prior spatial-semantic map of the robot's environment that models the space of possible referents of an utterance. Consequently, these methods fail when robots are deployed in new, previously unknown, or partially-observed environments, particularly when mental models of the environment differ between the human operator and the robot. This paper provides a comprehensive description of a novel learning framework that allows field and service robots to interpret and correctly execute natural-language instructions in a priori unknown, unstructured environments. Integral to our approach is its use of language as a "sensor" -- inferring spatial, topological, and semantic information implicit in the utterance and then exploiting this information to learn a distribution over a latent environment model. We incorporate this distribution in a probabilistic, language grounding model and infer a distribution over a symbolic representation of the robot's action space. We use imitation learning to identify a belief-space policy that reasons over the environment and behavior distributions. We evaluate our framework through a variety navigation and mobile-manipulation experiments.
Pravuil: Global Consensus for a United World
Venue: arXiv cs.CR 2021
Year: 2021

Pravuil is a robust, secure, and scalable consensus protocol for a permissionless blockchain suitable for deployment in an adversarial environment such as the Internet. Pravuil circumvents previous shortcomings of other blockchains: - Bitcoin's limited adoption problem: as transaction demand grows, payment confirmation times grow much lower than other PoW blockchains - higher transaction security at a lower cost - more decentralisation than other permissionless blockchains - impossibility of full decentralisation and the blockchain scalability trilemma: decentralisation, scalability, and security can be achieved simultaneously - Sybil-resistance for free implementing the social optimum - Pravuil goes beyond the economic limits of Bitcoin or other PoW/PoS blockchains, leading to a more valuable and stable crypto-currency
Towards Realization of Augmented Intelligence in Dermatology: Advances and Future Directions
Venue: arXiv cs.CV 2021
Year: 2021

Artificial intelligence (AI) algorithms using deep learning have advanced the classification of skin disease images; however these algorithms have been mostly applied "in silico" and not validated clinically. Most dermatology AI algorithms perform binary classification tasks (e.g. malignancy versus benign lesions), but this task is not representative of dermatologists' diagnostic range. The American Academy of Dermatology Task Force on Augmented Intelligence published a position statement emphasizing the importance of clinical validation to create human-computer synergy, termed augmented intelligence (AuI). Liu et al's recent paper, "A deep learning system for differential diagnosis of skin diseases" represents a significant advancement of AI in dermatology, bringing it closer to clinical impact. However, significant issues must be addressed before this algorithm can be integrated into clinical workflow. These issues include accurate and equitable model development, defining and assessing appropriate clinical outcomes, and real-world integration.
Bringing A Robot Simulator to the SCAMP Vision System
Venue: arXiv cs.RO 2021
Year: 2021

This work develops and demonstrates the integration of the SCAMP-5d vision system into the CoppeliaSim robot simulator, creating a semi-simulated environment. By configuring a camera in the simulator and setting up communication with the SCAMP python host through remote API, sensor images from the simulator can be transferred to the SCAMP vision sensor, where on-sensor image processing such as CNN inference can be performed. SCAMP output is then fed back into CoppeliaSim. This proposed platform integration enables rapid prototyping validations of SCAMP algorithms for robotic systems. We demonstrate a car localisation and tracking task using this proposed semi-simulated platform, with a CNN inference on SCAMP to command the motion of a robot. We made this platform available online.
Feature Encoding with AutoEncoders for Weakly-supervised Anomaly Detection
Venue: arXiv cs.LG 2021
Year: 2021

Weakly-supervised anomaly detection aims at learning an anomaly detector from a limited amount of labeled data and abundant unlabeled data. Recent works build deep neural networks for anomaly detection by discriminatively mapping the normal samples and abnormal samples to different regions in the feature space or fitting different distributions. However, due to the limited number of annotated anomaly samples, directly training networks with the discriminative loss may not be sufficient. To overcome this issue, this paper proposes a novel strategy to transform the input data into a more meaningful representation that could be used for anomaly detection. Specifically, we leverage an autoencoder to encode the input data and utilize three factors, hidden representation, reconstruction residual vector, and reconstruction error, as the new representation for the input data. This representation amounts to encode a test sample with its projection on the training data manifold, its direction to its projection and its distance to its projection. In addition to this encoding, we also propose a novel network architecture to seamlessly incorporate those three factors. From our extensive experiments, the benefits of the proposed strategy are clearly demonstrated by its superior performance over the competitive methods.
SmartPatch: Improving Handwritten Word Imitation with Patch Discriminators
Venue: arXiv cs.CV 2021
Year: 2021

As of recent generative adversarial networks have allowed for big leaps in the realism of generated images in diverse domains, not the least of which being handwritten text generation. The generation of realistic-looking hand-written text is important because it can be used for data augmentation in handwritten text recognition (HTR) systems or human-computer interaction. We propose SmartPatch, a new technique increasing the performance of current state-of-the-art methods by augmenting the training feedback with a tailored solution to mitigate pen-level artifacts. We combine the well-known patch loss with information gathered from the parallel trained handwritten text recognition system and the separate characters of the word. This leads to a more enhanced local discriminator and results in more realistic and higher-quality generated handwritten words.
Semi-Supervised Few-Shot Classification with Deep Invertible Hybrid Models
Venue: arXiv cs.CV 2021
Year: 2021

In this paper, we propose a deep invertible hybrid model which integrates discriminative and generative learning at a latent space level for semi-supervised few-shot classification. Various tasks for classifying new species from image data can be modeled as a semi-supervised few-shot classification, which assumes a labeled and unlabeled training examples and a small support set of the target classes. Predicting target classes with a few support examples per class makes the learning task difficult for existing semi-supervised classification methods, including selftraining, which iteratively estimates class labels of unlabeled training examples to learn a classifier for the training classes. To exploit unlabeled training examples effectively, we adopt as the objective function the composite likelihood, which integrates discriminative and generative learning and suits better with deep neural networks than the parameter coupling prior, the other popular integrated learning approach. In our proposed model, the discriminative and generative models are respectively Prototypical Networks, which have shown excellent performance in various kinds of few-shot learning, and Normalizing Flow a deep invertible model which returns the exact marginal likelihood unlike the other three major methods, i.e., VAE, GAN, and autoregressive model. Our main originality lies in our integration of these components at a latent space level, which is effective in preventing overfitting. Experiments using mini-ImageNet and VGG-Face datasets show that our method outperforms selftraining based Prototypical Networks.
SOK: Fake News Outbreak 2021: Can We Stop the Viral Spread?
Venue: arXiv cs.SI 2021
Year: 2021

Social Networks' omnipresence and ease of use has revolutionized the generation and distribution of information in today's world. However, easy access to information does not equal an increased level of public knowledge. Unlike traditional media channels, social networks also facilitate faster and wider spread of disinformation and misinformation. Viral spread of false information has serious implications on the behaviors, attitudes and beliefs of the public, and ultimately can seriously endanger the democratic processes. Limiting false information's negative impact through early detection and control of extensive spread presents the main challenge facing researchers today. In this survey paper, we extensively analyze a wide range of different solutions for the early detection of fake news in the existing literature. More precisely, we examine Machine Learning (ML) models for the identification and classification of fake news, online fake news detection competitions, statistical outputs as well as the advantages and disadvantages of some of the available data sets. Finally, we evaluate the online web browsing tools available for detecting and mitigating fake news and present some open research challenges.
Normalising Lustre Preserves Security
Venue: arXiv cs.PL 2021
Year: 2021

The synchronous reactive data flow language LUSTRE is an expressive language, equipped with a suite of tools for modelling, simulating and model-checking a wide variety of safety-critical systems. A critical intermediate step in the formally certified compilation of LUSTRE involves translation to a well-behaved sub-language called "Normalised LUSTRE" (NLUSTRE). Recently, we proposed a simple Denning-style lattice-based secure information flow type system for NLUSTRE, and proved its soundness by establishing that security-typed programs are non-interfering with respect to the co-inductive stream semantics. In this paper, we propose a similar security type system for unrestricted LUSTRE, and show that Bourke et al.'s semantics-preserving normalisation transformations from LUSTRE to NLUSTRE are security-preserving as well. A novelty is the use of refinement security types for node calls. The main result is the preservation of security types by the normalisation transformations. The soundness of our security typing rules is shown by establishing that well-security-typed programs are non-interfering, via a reduction to type-preservation (here), semantics-preservation (Bourke et al.) and our previous result of non-interference for NLUSTRE.
Inclusion of Domain-Knowledge into GNNs using Mode-Directed Inverse Entailment
Venue: arXiv cs.LG 2021
Year: 2021

We present a general technique for constructing Graph Neural Networks (GNNs) capable of using multi-relational domain knowledge. The technique is based on mode-directed inverse entailment (MDIE) developed in Inductive Logic Programming (ILP). Given a data instance $e$ and background knowledge $B$, MDIE identifies a most-specific logical formula $\bot_B(e)$ that contains all the relational information in $B$ that is related to $e$. We represent $\bot_B(e)$ by a "bottom-graph" that can be converted into a form suitable for GNN implementations. This transformation allows a principled way of incorporating generic background knowledge into GNNs: we use the term `BotGNN' for this form of graph neural networks. For several GNN variants, using real-world datasets with substantial background knowledge, we show that BotGNNs perform significantly better than both GNNs without background knowledge and a recently proposed simplified technique for including domain knowledge into GNNs. We also provide experimental evidence comparing BotGNNs favourably to multi-layer perceptrons (MLPs) that use features representing a "propositionalised" form of the background knowledge; and BotGNNs to a standard ILP based on the use of most-specific clauses. Taken together, these results point to BotGNNs as capable of combining the computational efficacy of GNNs with the representational versatility of ILP.
Crawling Twitter data through API: A technical/legal perspective
Venue: arXiv cs.SI 2021
Year: 2021

The popularity of the online media-driven social network relation is proven in today's digital era. The many challenges that these emergence has created include a huge growing network of social relations, and the large amount of data which is continuously been generated via the different platform of social networking sites, viz. Facebook, Twitter, LinkedIn, Instagram, etc. These data are Personally Identifiable Information (PII) of the users which are also publicly available for some platform, and others allow with some restricted permission to download it for research purposes. The users' accessible data help in providing with better recommendation services to users, however, the PII can be used to embezzle the users and cause severe detriment to them. Hence, it is crucial to maintain the users' privacy while providing their PII accessible for various services. Therefore, it is a burning issue to come up with an approach that can help the users in getting better recommendation services without their privacy being harmed. In this paper, a framework is suggested for the same. Further, how data through Twitter API can be crawled and used has been extensively discussed. In addition to this, various security and legal perspectives regarding PII while crawling the data is highlighted. We believe the presented approach in this paper can serve as a benchmark for future research in the field of data privacy.
Ankle Joints Are Beneficial When Optimizing Supported Real-world Bipedal Robot Gaits
Venue: arXiv cs.RO 2021
Year: 2021

Legged robots promise higher versatility and the ability to traverse much more difficult terrains than their wheeled counterparts. Even though the use of legged robots have increased drastically in the last few years, they are still not close to the performance seen from legged animals in nature. Robotic legs are typically fairly simple mechanically, and few feature an ankle joint, even though most land mammals have one. The ankle could be a key to better performance and stability for legged robots, and in this paper we investigate how the use of an ankle in a bipedal robot could improve its performance when combined with evolutionary techniques for gait optimization. Our study shows, both in simulation and physical experiments, that the addition of an ankle joint results in greater walking speeds for a supported bipedal robot.
Whole-Body Control on Non-holonomic Mobile Manipulation for Grapevine Winter Pruning Automation
Venue: arXiv cs.RO 2021
Year: 2021

Mobile manipulators that combine mobility and manipulability, are increasingly being used for various unstructured application scenarios in the field, e.g. vineyards. Therefore, the coordinated motion of the mobile base and manipulator is an essential feature of the overall performance. In this paper, we explore a whole-body motion controller of a robot which is composed of a 2-DoFs non-holonomic wheeled mobile base with a 7-DoFs manipulator (non-holonomic wheeled mobile manipulator, NWMM) This robotic platform is designed to efficiently undertake complex grapevine pruning tasks. In the control framework, a task priority coordinated motion of the NWMM is guaranteed. Lower-priority tasks are projected into the null space of the top-priority tasks so that higher-priority tasks are completed without interruption from lower-priority tasks. The proposed controller was evaluated in a grapevine spur pruning experiment scenario.
Generation and Analysis of Feature-Dependent Pseudo Noise for Training Deep Neural Networks
Venue: arXiv cs.LG 2021
Year: 2021

Training Deep neural networks (DNNs) on noisy labeled datasets is a challenging problem, because learning on mislabeled examples deteriorates the performance of the network. As the ground truth availability is limited with real-world noisy datasets, previous papers created synthetic noisy datasets by randomly modifying the labels of training examples of clean datasets. However, no final conclusions can be derived by just using this random noise, since it excludes feature-dependent noise. Thus, it is imperative to generate feature-dependent noisy datasets that additionally provide ground truth. Therefore, we propose an intuitive approach to creating feature-dependent noisy datasets by utilizing the training predictions of DNNs on clean datasets that also retain true label information. We refer to these datasets as "Pseudo Noisy datasets". We conduct several experiments to establish that Pseudo noisy datasets resemble feature-dependent noisy datasets across different conditions. We further randomly generate synthetic noisy datasets with the same noise distribution as that of Pseudo noise (referred as "Randomized Noise") to empirically show that i) learning is easier with feature-dependent label noise compared to random noise, ii) irrespective of noise distribution, Pseudo noisy datasets mimic feature-dependent label noise and iii) current training methods are not generalizable to feature-dependent label noise. Therefore, we believe that Pseudo noisy datasets will be quite helpful to study and develop robust training methods.