OpenRaiser/Intern-Atlas huggingface dataset:
A Flexible FPGA-Based ISA Configurable SoC platform
Venue: arXiv cs.LO 2021
Year: 2021

We proposes a platform which can generate hardware/software description based on flexible in-struction set architectures (ISAs). The platform takes advantage of the flexibility of field pro-grammable gate array (FPGA) to design many micro control units (MCUs) based on different ISAs. The platform can generate many ISAs, MCUs, and Assemblers according to a pre-defined ISA and user applications. Although the MCU performance is not optimized, the FPGA shows a great potential on resource reduction and enough performance at very low system clock rate. The flexible ISA has shown great importance for the design targeted to specific purpose. We also show a case study of the proposed flexible ISA-based FPGA-MCU. It can control many specifi-cally designed hardware IPs and a customized multi-task OS with tasks. Not only the case works correctly, but also the proposed FPGA-MCU of the case is flexible with reduced FPGA resources, low cost, and within time constraints.
On the Advantages of Multiple Stereo Vision Camera Designs for Autonomous Drone Navigation
Venue: arXiv cs.RO 2021
Year: 2021

In this work we showcase the design and assessment of the performance of a multi-camera UAV, when coupled with state-of-the-art planning and mapping algorithms for autonomous navigation. The system leverages state-of-the-art receding horizon exploration techniques for Next-Best-View (NBV) planning with 3D and semantic information, provided by a reconfigurable multi stereo camera system. We employ our approaches in an autonomous drone-based inspection task and evaluate them in an autonomous exploration and mapping scenario. We discuss the advantages and limitations of using multi stereo camera flying systems, and the trade-off between number of cameras and mapping performance.
Contention Resolution with Predictions
Venue: arXiv cs.DC 2021
Year: 2021

In this paper, we consider contention resolution algorithms that are augmented with predictions about the network. We begin by studying the natural setup in which the algorithm is provided a distribution defined over the possible network sizes that predicts the likelihood of each size occurring. The goal is to leverage the predictive power of this distribution to improve on worst-case time complexity bounds. Using a novel connection between contention resolution and information theory, we prove lower bounds on the expected time complexity with respect to the Shannon entropy of the corresponding network size random variable, for both the collision detection and no collision detection assumptions. We then analyze upper bounds for these settings, assuming now that the distribution provided as input might differ from the actual distribution generating network sizes. We express their performance with respect to both entropy and the statistical divergence between the two distributions -- allowing us to quantify the cost of poor predictions. Finally, we turn our attention to the related perfect advice setting, parameterized with a length $b\geq 0$, in which all active processes in a given execution are provided the best possible $b$ bits of information about their network. We provide tight bounds on the speed-up possible with respect to $b$ for deterministic and randomized algorithms, with and without collision detection. These bounds provide a fundamental limit on the maximum power that can be provided by any predictive model with a bounded output size.
Enhance to Read Better: A Multi-Task Adversarial Network for Handwritten Document Image Enhancement
Venue: arXiv cs.CV 2021
Year: 2021

Handwritten document images can be highly affected by degradation for different reasons: Paper ageing, daily-life scenarios (wrinkles, dust, etc.), bad scanning process and so on. These artifacts raise many readability issues for current Handwritten Text Recognition (HTR) algorithms and severely devalue their efficiency. In this paper, we propose an end to end architecture based on Generative Adversarial Networks (GANs) to recover the degraded documents into a clean and readable form. Unlike the most well-known document binarization methods, which try to improve the visual quality of the degraded document, the proposed architecture integrates a handwritten text recognizer that promotes the generated document image to be more readable. To the best of our knowledge, this is the first work to use the text information while binarizing handwritten documents. Extensive experiments conducted on degraded Arabic and Latin handwritten documents demonstrate the usefulness of integrating the recognizer within the GAN architecture, which improves both the visual quality and the readability of the degraded document images. Moreover, we outperform the state of the art in H-DIBCO challenges, after fine tuning our pre-trained model with synthetically degraded Latin handwritten images, on this task.
Personal Internet of Things (PIoT): What is it Exactly?
Venue: arXiv cs.NI 2021
Year: 2021

The use of Internet of Things (IoT) devices in homes and the immediate proximity of an individual communicates to create Personal IoT (PIoT) networks. The exploratory study of PIoT is in its infancy, which will explore the expansion of new use cases, service requirements, and the proliferation of PIoT devices. This article provides a big picture of PIoT architecture, vision, and future research scope.
Task inefficiency patterns for a wave equation solver
Venue: arXiv cs.DC 2021
Year: 2021

The orchestration of complex algorithms demands high levels of automation to use modern hardware efficiently. Task-based programming with OpenMP 5.0 is a prominent candidate to accomplish this goal. We study OpenMP 5.0's tasking in the context of a wave equation solver (ExaHyPE) using three different architectures and runtimes. We describe several task-scheduling flaws present in currently available runtimes, demonstrate how they impact performance and show how to work around them. Finally, we propose extensions to the OpenMP standard.
A data-driven strategy to combine word embeddings in information retrieval
Venue: arXiv cs.IR 2021
Year: 2021

Word embeddings are vital descriptors of words in unigram representations of documents for many tasks in natural language processing and information retrieval. The representation of queries has been one of the most critical challenges in this area because it consists of a few terms and has little descriptive capacity. Strategies such as average word embeddings can enrich the queries' descriptive capacity since they favor the identification of related terms from the continuous vector representations that characterize these approaches. We propose a data-driven strategy to combine word embeddings. We use Idf combinations of embeddings to represent queries, showing that these representations outperform the average word embeddings recently proposed in the literature. Experimental results on benchmark data show that our proposal performs well, suggesting that data-driven combinations of word embeddings are a promising line of research in ad-hoc information retrieval.
UAV-Assisted Communication in Remote Disaster Areas using Imitation Learning
Venue: arXiv cs.NI 2021
Year: 2021

The damage to cellular towers during natural and man-made disasters can disturb the communication services for cellular users. One solution to the problem is using unmanned aerial vehicles to augment the desired communication network. The paper demonstrates the design of a UAV-Assisted Imitation Learning (UnVAIL) communication system that relays the cellular users' information to a neighbor base station. Since the user equipment (UEs) are equipped with buffers with limited capacity to hold packets, UnVAIL alternates between different UEs to reduce the chance of buffer overflow, positions itself optimally close to the selected UE to reduce service time, and uncovers a network pathway by acting as a relay node. UnVAIL utilizes Imitation Learning (IL) as a data-driven behavioral cloning approach to accomplish an optimal scheduling solution. Results demonstrate that UnVAIL performs similar to a human expert knowledge-based planning in communication timeliness, position accuracy, and energy consumption with an accuracy of 97.52% when evaluated on a developed simulator to train the UAV.
Optimizations for Hardware-in-the-Loop-Based V2X Validation Platforms
Venue: arXiv cs.NI 2021
Year: 2021

Connectivity and automation are increasingly getting importance in the automotive industry, which is observing a radical change from vehicles driven by humans to fully automated and remotely controlled ones. The test and validation of all the related devices and applications is thus becoming a crucial aspect; this is raising the interest on hardware-in-the-loop (HiL) platforms which reduce the need for complicated field trials, thus limiting the costs and delay added to the process. With reference to the test and validation of vehicle-to-everything (V2X) communications aspects, and assuming either sidelink LTE/5GV2X or IEEE 802.11p/bd technologies, in this work we focus on the real-time HiL simulation of the information exchanged by one vehicle under test and the surrounding, simulated, objects. Such exchange must be reproduced in a time-efficient manner, with elaborations done fast enough to allow testing the applications in real-time. More precisely, we discuss the simulation of nonideal positioning and channel propagation taking into account current impairments. We also provide details on optimization solutions that allowed us to trade-off minor loss in accuracy with a significant reduction of the computation time burden, reaching up to more than one order of magnitude speed increase in our experiments.
A Review on Applications and Challenges in Internet of Things
Venue: arXiv cs.NI 2021
Year: 2021

World has been introduced with a new way of living by inventing this advanced technology Internet of Things (IOT). By using this technology all the real-world objects can be made to interconnect and communicate with each other which can be done by diagnosing, sensing and networking over the internet to fulfill some objectives. Due to advancements that have taken place in sensors, wireless communication systems and cloud computing has brought steps to invent this innovative technology. This paper deals with the elements, applications and the challenges faced by Internet of Things. This paper mainly concentrates on the current scenario of IoT
On the Exploitation of Neuroevolutionary Information: Analyzing the Past for a More Efficient Future
Venue: arXiv cs.NE 2021
Year: 2021

Neuroevolutionary algorithms, automatic searches of neural network structures by means of evolutionary techniques, are computationally costly procedures. In spite of this, due to the great performance provided by the architectures which are found, these methods are widely applied. The final outcome of neuroevolutionary processes is the best structure found during the search, and the rest of the procedure is commonly omitted in the literature. However, a good amount of residual information consisting of valuable knowledge that can be extracted is also produced during these searches. In this paper, we propose an approach that extracts this information from neuroevolutionary runs, and use it to build a metamodel that could positively impact future neural architecture searches. More specifically, by inspecting the best structures found during neuroevolutionary searches of generative adversarial networks with varying characteristics (e.g., based on dense or convolutional layers), we propose a Bayesian network-based model which can be used to either find strong neural structures right away, conveniently initialize different structural searches for different problems, or help future optimization of structures of any type to keep finding increasingly better structures where uninformed methods get stuck into local optima.
General Game Heuristic Prediction Based on Ludeme Descriptions
Venue: arXiv cs.AI 2021
Year: 2021

This paper investigates the performance of different general-game-playing heuristics for games in the Ludii general game system. Based on these results, we train several regression learning models to predict the performance of these heuristics based on each game's description file. We also provide a condensed analysis of the games available in Ludii, and the different ludemes that define them.
Beyond Algorithmic Bias: A Socio-Computational Interrogation of the Google Search by Image Algorithm
Venue: arXiv cs.CY 2021
Year: 2021

We perform a socio-computational interrogation of the google search by image algorithm, a main component of the google search engine. We audit the algorithm by presenting it with more than 40 thousands faces of all ages and more than four races and collecting and analyzing the assigned labels with the appropriate statistical tools. We find that the algorithm reproduces white male patriarchal structures, often simplifying, stereotyping and discriminating females and non-white individuals, while providing more diverse and positive descriptions of white men. By drawing from Bourdieu's theory of cultural reproduction, we link these results to the attitudes of the algorithm's designers, owners, and the dataset the algorithm was trained on. We further underpin the problematic nature of the algorithm by using the ethnographic practice of studying-up: We show how the algorithm places individuals at the top of the tech industry within the socio-cultural reality that they shaped, many times creating biased representations of them. We claim that the use of social-theoretic frameworks such as the above are able to contribute to improved algorithmic accountability, algorithmic impact assessment and provide additional and more critical depth in algorithmic bias and auditing studies. Based on the analysis, we discuss the scientific and design implications and provide suggestions for alternative ways to design just socioalgorithmic systems.
Benchmarking Scientific Image Forgery Detectors
Venue: arXiv cs.CV 2021
Year: 2021

The scientific image integrity area presents a challenging research bottleneck, the lack of available datasets to design and evaluate forensic techniques. Its data sensitivity creates a legal hurdle that prevents one to rely on real tampered cases to build any sort of accessible forensic benchmark. To mitigate this bottleneck, we present an extendable open-source library that reproduces the most common image forgery operations reported by the research integrity community: duplication, retouching, and cleaning. Using this library and realistic scientific images, we create a large scientific forgery image benchmark (39,423 images) with an enriched ground-truth. In addition, concerned about the high number of retracted papers due to image duplication, this work evaluates the state-of-the-art copy-move detection methods in the proposed dataset, using a new metric that asserts consistent match detection between the source and the copied region. The dataset and source-code will be freely available upon acceptance of the paper.
3D Segmentation Learning from Sparse Annotations and Hierarchical Descriptors
Venue: arXiv cs.CV 2021
Year: 2021

One of the main obstacles to 3D semantic segmentation is the significant amount of endeavor required to generate expensive point-wise annotations for fully supervised training. To alleviate manual efforts, we propose GIDSeg, a novel approach that can simultaneously learn segmentation from sparse annotations via reasoning global-regional structures and individual-vicinal properties. GIDSeg depicts global- and individual- relation via a dynamic edge convolution network coupled with a kernelized identity descriptor. The ensemble effects are obtained by endowing a fine-grained receptive field to a low-resolution voxelized map. In our GIDSeg, an adversarial learning module is also designed to further enhance the conditional constraint of identity descriptors within the joint feature distribution. Despite the apparent simplicity, our proposed approach achieves superior performance over state-of-the-art for inferencing 3D dense segmentation with only sparse annotations. Particularly, with $5\%$ annotations of raw data, GIDSeg outperforms other 3D segmentation methods.
Search Spaces for Neural Model Training
Venue: arXiv cs.LG 2021
Year: 2021

While larger neural models are pushing the boundaries of what deep learning can do, often more weights are needed to train models rather than to run inference for tasks. This paper seeks to understand this behavior using search spaces -- adding weights creates extra degrees of freedom that form new paths for optimization (or wider search spaces) rendering neural model training more effective. We then show how we can augment search spaces to train sparse models attaining competitive scores across dozens of deep learning workloads. They are also are tolerant of structures targeting current hardware, opening avenues for training and inference acceleration. Our work encourages research to explore beyond massive neural models being used today.
Self-Ensembling Contrastive Learning for Semi-Supervised Medical Image Segmentation
Venue: arXiv cs.CV 2021
Year: 2021

Deep learning has demonstrated significant improvements in medical image segmentation using a sufficiently large amount of training data with manual labels. Acquiring well-representative labels requires expert knowledge and exhaustive labors. In this paper, we aim to boost the performance of semi-supervised learning for medical image segmentation with limited labels using a self-ensembling contrastive learning technique. To this end, we propose to train an encoder-decoder network at image-level with small amounts of labeled images, and more importantly, we learn latent representations directly at feature-level by imposing contrastive loss on unlabeled images. This method strengthens intra-class compactness and inter-class separability, so as to get a better pixel classifier. Moreover, we devise a student encoder for online learning and an exponential moving average version of it, called teacher encoder, to improve the performance iteratively in a self-ensembling manner. To construct contrastive samples with unlabeled images, two sampling strategies that exploit structure similarity across medical images and utilize pseudo-labels for construction, termed region-aware and anatomical-aware contrastive sampling, are investigated. We conduct extensive experiments on an MRI and a CT segmentation dataset and demonstrate that in a limited label setting, the proposed method achieves state-of-the-art performance. Moreover, the anatomical-aware strategy that prepares contrastive samples on-the-fly using pseudo-labels realizes better contrastive regularization on feature representations.
Contrastive Fine-tuning Improves Robustness for Neural Rankers
Venue: arXiv cs.IR 2021
Year: 2021

The performance of state-of-the-art neural rankers can deteriorate substantially when exposed to noisy inputs or applied to a new domain. In this paper, we present a novel method for fine-tuning neural rankers that can significantly improve their robustness to out-of-domain data and query perturbations. Specifically, a contrastive loss that compares data points in the representation space is combined with the standard ranking loss during fine-tuning. We use relevance labels to denote similar/dissimilar pairs, which allows the model to learn the underlying matching semantics across different query-document pairs and leads to improved robustness. In experiments with four passage ranking datasets, the proposed contrastive fine-tuning method obtains improvements on robustness to query reformulations, noise perturbations, and zero-shot transfer for both BERT and BART based rankers. Additionally, our experiments show that contrastive fine-tuning outperforms data augmentation for robustifying neural rankers.
SDN-based Runtime Security Enforcement Approach for Privacy Preservation of Dynamic Web Service Composition
Venue: arXiv cs.CR 2021
Year: 2021

Aiming at the privacy preservation of dynamic Web service composition, this paper proposes a SDN-based runtime security enforcement approach for privacy preservation of dynamic Web service composition. The main idea of this approach is that the owner of service composition leverages the security policy model (SPM) to define the access control relationships that service composition must comply with in the application plane, then SPM model is transformed into the low-level security policy model (RSPM) containing the information of SDN data plane, and RSPM model is uploaded into the SDN controller. After uploading, the virtual machine access control algorithm integrated in the SDN controller monitors all of access requests towards service composition at runtime. Only the access requests that meet the definition of RSPM model can be forwarded to the target terminal. Any access requests that do not meet the definition of RSPM model will be automatically blocked by Openflow switches or deleted by SDN controller, Thus, this approach can effectively solve the problems of network-layer illegal accesses, identity theft attacks and service leakages when Web service composition is running. In order to verify the feasibility of this approach, this paper implements an experimental system by using POX controller and Mininet virtual network simulator, and evaluates the effectiveness and performance of this approach by using this system. The final experimental results show that the method is completely effective, and the method can always get the correct calculation results in an acceptable time when the scale of RSPM model is gradually increasing.
Passing Multi-Channel Material Textures to a 3-Channel Loss
Venue: arXiv cs.GR 2021
Year: 2021

Our objective is to compute a textural loss that can be used to train texture generators with multiple material channels typically used for physically based rendering such as albedo, normal, roughness, metalness, ambient occlusion, etc. Neural textural losses often build on top of the feature spaces of pretrained convolutional neural networks. Unfortunately, these pretrained models are only available for 3-channel RGB data and hence limit neural textural losses to this format. To overcome this limitation, we show that passing random triplets to a 3-channel loss provides a multi-channel loss that can be used to generate high-quality material textures.
Put your money where your mouth is: Using deep learning to identify consumer tribes from word usage
Venue: arXiv cs.CL 2021
Year: 2021

Internet and social media offer firms novel ways of managing their marketing strategy and gain competitive advantage. The groups of users expressing themselves on the Internet about a particular topic, product, or brand are frequently called a virtual tribe or E-tribe. However, there are no automatic tools for identifying and studying the characteristics of these virtual tribes. Towards this aim, this paper presents Tribefinder, a system to reveal Twitter users' tribal affiliations, by analyzing their tweets and language use. To show the potential of this instrument, we provide an example considering three specific tribal macro-categories: alternative realities, lifestyle, and recreation. In addition, we discuss the different characteristics of each identified tribe, in terms of use of language and social interaction metrics. Tribefinder illustrates the importance of adopting a new lens for studying virtual tribes, which is crucial for firms to properly design their marketing strategy, and for scholars to extend prior marketing research.
On Morphing 1-Planar Drawings
Venue: arXiv cs.CG 2021
Year: 2021

Computing a morph between two drawings of a graph is a classical problem in computational geometry and graph drawing. While this problem has been widely studied in the context of planar graphs, very little is known about the existence of topology-preserving morphs for pairs of non-planar graph drawings. We make a step towards this problem by showing that a topology-preserving morph always exists for drawings of a meaningful family of $1$-planar graphs. While our proof is constructive, the vertices may follow trajectories of unbounded complexity.