OpenRaiser/Intern-Atlas huggingface dataset:
A Review & Framework for Modeling Complex Engineered System Development Processes
Venue: arXiv cs.MA 2021
Year: 2021

Developing complex engineered systems (CES) poses significant challenges for engineers, managers, designers, and businesspeople alike due to the inherent complexity of the systems and contexts involved. Furthermore, experts have expressed great interest in filling the gap in theory about how CES develop. This article begins to address that gap in two ways. First, it reviews the numerous definitions of CES along with existing theory and methods on CES development processes. Then, it proposes the ComplEx System Integrated Utilities Model (CESIUM), a novel framework for exploring how numerous system and development process characteristics may affect the performance of CES. CESIUM creates simulated representations of a system architecture, the corresponding engineering organization, and the new product development process through which the organization designs the system. It does so by representing the system as a network of interdependent artifacts designed by agents. Agents iteratively design their artifacts through optimization and share information with other agents, thereby advancing the CES toward a solution. This paper describes the model, conducts a sensitivity analysis, provides validation, and suggests directions for future study.
Ground Truths for the Humanities
Venue: arXiv cs.DL 2021
Year: 2021

Ensuring a faithful interaction with data and its representation for humanities can and should depend on expert-constructed ground truths.
Actionable Cognitive Twins for Decision Making in Manufacturing
Venue: arXiv cs.AI 2021
Year: 2021

Actionable Cognitive Twins are the next generation Digital Twins enhanced with cognitive capabilities through a knowledge graph and artificial intelligence models that provide insights and decision-making options to the users. The knowledge graph describes the domain-specific knowledge regarding entities and interrelationships related to a manufacturing setting. It also contains information on possible decision-making options that can assist decision-makers, such as planners or logisticians. In this paper, we propose a knowledge graph modeling approach to construct actionable cognitive twins for capturing specific knowledge related to demand forecasting and production planning in a manufacturing plant. The knowledge graph provides semantic descriptions and contextualization of the production lines and processes, including data identification and simulation or artificial intelligence algorithms and forecasts used to support them. Such semantics provide ground for inferencing, relating different knowledge types: creative, deductive, definitional, and inductive. To develop the knowledge graph models for describing the use case completely, systems thinking approach is proposed to design and verify the ontology, develop a knowledge graph and build an actionable cognitive twin. Finally, we evaluate our approach in two use cases developed for a European original equipment manufacturer related to the automotive industry as part of the European Horizon 2020 project FACTLOG.
Fully-echoed Q-routing with Simulated Annealing Inference for Flying Adhoc Networks
Venue: arXiv cs.NI 2021
Year: 2021

Current networking protocols deem inefficient in accommodating the two key challenges of Unmanned Aerial Vehicle (UAV) networks, namely the network connectivity loss and energy limitations. One approach to solve these issues is using learning-based routing protocols to make close-to-optimal local decisions by the network nodes, and Q-routing is a bold example of such protocols. However, the performance of the current implementations of Q-routing algorithms is not yet satisfactory, mainly due to the lack of adaptability to continued topology changes. In this paper, we propose a full-echo Q-routing algorithm with a self-adaptive learning rate that utilizes Simulated Annealing (SA) optimization to control the exploration rate of the algorithm through the temperature decline rate, which in turn is regulated by the experienced variation rate of the Q-values. Our results show that our method adapts to the network dynamicity without the need for manual re-initialization at transition points (abrupt network topology changes). Our method exhibits a reduction in the energy consumption ranging from 7% up to 82%, as well as a 2.6 fold gain in successful packet delivery rate}, compared to the state of the art Q-routing protocols
Using Meta-learning to Recommend Process Discovery Methods
Venue: arXiv cs.LG 2021
Year: 2021

Process discovery methods have obtained remarkable achievements in Process Mining, delivering comprehensible process models to enhance management capabilities. However, selecting the suitable method for a specific event log highly relies on human expertise, hindering its broad application. Solutions based on Meta-learning (MtL) have been promising for creating systems with reduced human assistance. This paper presents a MtL solution for recommending process discovery methods that maximize model quality according to complementary dimensions. Thanks to our MtL pipeline, it was possible to recommend a discovery method with 92% of accuracy using light-weight features that describe the event log. Our experimental analysis also provided significant insights on the importance of log features in generating recommendations, paving the way to a deeper understanding of the discovery algorithms.
Complex Factoid Question Answering with a Free-Text Knowledge Graph
Venue: arXiv cs.CL 2021
Year: 2021

We introduce DELFT, a factoid question answering system which combines the nuance and depth of knowledge graph question answering approaches with the broader coverage of free-text. DELFT builds a free-text knowledge graph from Wikipedia, with entities as nodes and sentences in which entities co-occur as edges. For each question, DELFT finds the subgraph linking question entity nodes to candidates using text sentences as edges, creating a dense and high coverage semantic graph. A novel graph neural network reasons over the free-text graph-combining evidence on the nodes via information along edge sentences-to select a final answer. Experiments on three question answering datasets show DELFT can answer entity-rich questions better than machine reading based models, bert-based answer ranking and memory networks. DELFT's advantage comes from both the high coverage of its free-text knowledge graph-more than double that of dbpedia relations-and the novel graph neural network which reasons on the rich but noisy free-text evidence.
CSFCube -- A Test Collection of Computer Science Research Articles for Faceted Query by Example
Venue: arXiv cs.IR 2021
Year: 2021

Query by Example is a well-known information retrieval task in which a document is chosen by the user as the search query and the goal is to retrieve relevant documents from a large collection. However, a document often covers multiple aspects of a topic. To address this scenario we introduce the task of faceted Query by Example in which users can also specify a finer grained aspect in addition to the input query document. We focus on the application of this task in scientific literature search. We envision models which are able to retrieve scientific papers analogous to a query scientific paper along specifically chosen rhetorical structure elements as one solution to this problem. In this work, the rhetorical structure elements, which we refer to as facets, indicate objectives, methods, or results of a scientific paper. We introduce and describe an expert annotated test collection to evaluate models trained to perform this task. Our test collection consists of a diverse set of 50 query documents in English, drawn from computational linguistics and machine learning venues. We carefully follow the annotation guideline used by TREC for depth-k pooling (k = 100 or 250) and the resulting data collection consists of graded relevance scores with high annotation agreement. State of the art models evaluated on our dataset show a significant gap to be closed in further work. Our dataset may be accessed here: https://github.com/iesl/CSFCube
Improved Estimation of Concentration Under $\ell_p$-Norm Distance Metrics Using Half Spaces
Venue: arXiv cs.LG 2021
Year: 2021

Concentration of measure has been argued to be the fundamental cause of adversarial vulnerability. Mahloujifar et al. presented an empirical way to measure the concentration of a data distribution using samples, and employed it to find lower bounds on intrinsic robustness for several benchmark datasets. However, it remains unclear whether these lower bounds are tight enough to provide a useful approximation for the intrinsic robustness of a dataset. To gain a deeper understanding of the concentration of measure phenomenon, we first extend the Gaussian Isoperimetric Inequality to non-spherical Gaussian measures and arbitrary $\ell_p$-norms ($p \geq 2$). We leverage these theoretical insights to design a method that uses half-spaces to estimate the concentration of any empirical dataset under $\ell_p$-norm distance metrics. Our proposed algorithm is more efficient than Mahloujifar et al.'s, and our experiments on synthetic datasets and image benchmarks demonstrate that it is able to find much tighter intrinsic robustness bounds. These tighter estimates provide further evidence that rules out intrinsic dataset concentration as a possible explanation for the adversarial vulnerability of state-of-the-art classifiers.
Benchmarking Deep Trackers on Aerial Videos
Venue: arXiv cs.CV 2021
Year: 2021

In recent years, deep learning-based visual object trackers have achieved state-of-the-art performance on several visual object tracking benchmarks. However, most tracking benchmarks are focused on ground level videos, whereas aerial tracking presents a new set of challenges. In this paper, we compare ten trackers based on deep learning techniques on four aerial datasets. We choose top performing trackers utilizing different approaches, specifically tracking by detection, discriminative correlation filters, Siamese networks and reinforcement learning. In our experiments, we use a subset of OTB2015 dataset with aerial style videos; the UAV123 dataset without synthetic sequences; the UAV20L dataset, which contains 20 long sequences; and DTB70 dataset as our benchmark datasets. We compare the advantages and disadvantages of different trackers in different tracking situations encountered in aerial data. Our findings indicate that the trackers perform significantly worse in aerial datasets compared to standard ground level videos. We attribute this effect to smaller target size, camera motion, significant camera rotation with respect to the target, out of view movement, and clutter in the form of occlusions or similar looking distractors near tracked object.
Volumetric Propagation Network: Stereo-LiDAR Fusion for Long-Range Depth Estimation
Venue: arXiv cs.CV 2021
Year: 2021

Stereo-LiDAR fusion is a promising task in that we can utilize two different types of 3D perceptions for practical usage -- dense 3D information (stereo cameras) and highly-accurate sparse point clouds (LiDAR). However, due to their different modalities and structures, the method of aligning sensor data is the key for successful sensor fusion. To this end, we propose a geometry-aware stereo-LiDAR fusion network for long-range depth estimation, called volumetric propagation network. The key idea of our network is to exploit sparse and accurate point clouds as a cue for guiding correspondences of stereo images in a unified 3D volume space. Unlike existing fusion strategies, we directly embed point clouds into the volume, which enables us to propagate valid information into nearby voxels in the volume, and to reduce the uncertainty of correspondences. Thus, it allows us to fuse two different input modalities seamlessly and regress a long-range depth map. Our fusion is further enhanced by a newly proposed feature extraction layer for point clouds guided by images: FusionConv. FusionConv extracts point cloud features that consider both semantic (2D image domain) and geometric (3D domain) relations and aid fusion at the volume. Our network achieves state-of-the-art performance on the KITTI and the Virtual-KITTI datasets among recent stereo-LiDAR fusion methods.
On a realization of motion and similarity group equivalence classes of labeled points in $\mathbb R^k$ with applications to computer vision
Venue: arXiv cs.CV 2021
Year: 2021

We study a realization of motion and similarity group equivalence classes of $n\geq 1$ labeled points in $\mathbb R^k,\, k\geq 1$ as a metric space with a computable metric. Our study is motivated by applications in computer vision.
From Semantic Retrieval to Pairwise Ranking: Applying Deep Learning in E-commerce Search
Venue: arXiv cs.IR 2021
Year: 2021

We introduce deep learning models to the two most important stages in product search at JD.com, one of the largest e-commerce platforms in the world. Specifically, we outline the design of a deep learning system that retrieves semantically relevant items to a query within milliseconds, and a pairwise deep re-ranking system, which learns subtle user preferences. Compared to traditional search systems, the proposed approaches are better at semantic retrieval and personalized ranking, achieving significant improvements.
Analysis of QoS in Heterogeneous Networks with Clustered Deployment and Caching Aware Capacity Allocation
Venue: arXiv cs.IT 2021
Year: 2021

In cellular networks, the densification of connected devices and base stations engender the ever-growing traffic intensity, and caching popular contents with smart management is a promising way to alleviate such consequences. Our research extends the previously proposed analysis of three-tier cache enabled Heterogeneous Networks (HetNets). The main contributions are threefold. We consider the more realistic assumption; that is, the distribution of small base stations is following Poisson-Poisson cluster processes, which reflects the real situations of geographic restriction, user dense areas, and coverage-holes. We propose the allocation of downlink data transmission capacity according to the cases of requested contents which are either cached or non-cached in nearby nodes and elucidate the traffic efficiency of the allocation under the effect of clustered deployment of small base stations. The throughput and delay of the allocation system are derived based on the approximated sojourn time of the Discriminatory Processor Sharing (DPS) queue. We present the results of achievable efficiency and such a system's performance for a better caching solution to the challenges of future cellular networks.
Can Vision Transformers Learn without Natural Images?
Venue: arXiv cs.CV 2021
Year: 2021

Can we complete pre-training of Vision Transformers (ViT) without natural images and human-annotated labels? Although a pre-trained ViT seems to heavily rely on a large-scale dataset and human-annotated labels, recent large-scale datasets contain several problems in terms of privacy violations, inadequate fairness protection, and labor-intensive annotation. In the present paper, we pre-train ViT without any image collections and annotation labor. We experimentally verify that our proposed framework partially outperforms sophisticated Self-Supervised Learning (SSL) methods like SimCLRv2 and MoCov2 without using any natural images in the pre-training phase. Moreover, although the ViT pre-trained without natural images produces some different visualizations from ImageNet pre-trained ViT, it can interpret natural image datasets to a large extent. For example, the performance rates on the CIFAR-10 dataset are as follows: our proposal 97.6 vs. SimCLRv2 97.4 vs. ImageNet 98.0.
An Exploration of Geo-temporal Characteristics of Users' Reactions on Social Media During the Pandemic
Venue: arXiv cs.SI 2021
Year: 2021

During the outbreak of the COVID-19 pandemic, social networks become the preeminent medium for communication, social discussion, and entertainment. Social network users are regularly expressing their opinions about the impacts of the coronavirus pandemic. Therefore, social networks serve as a reliable source for studying the topics, emotions, and attitudes of users that are discussed during the pandemic. In this paper, we investigate the reactions and attitudes of people towards topics raised on social media platforms. We collected data of two large-scale COVID-19 datasets from Twitter and Instagram for six and three months, respectively. The paper analyzes the reaction of social network users on different aspects including sentiment analysis, topics detection, emotions, and geo-temporal characteristics of our dataset. We show that the dominant sentiment reactions on social media are neutral while the most discussed topics by social network users are about health issues. The paper examines the countries that attracted more posts and reactions from people, as well as the distribution of health-related topics discussed in the most mentioned countries. We shed light on the temporal shift of topics over countries. Our results show that posts from the top-mentioned countries influence and attract more reaction worldwide than posts from other parts of the world.
Finetuning Pretrained Transformers into RNNs
Venue: arXiv cs.CL 2021
Year: 2021

Transformers have outperformed recurrent neural networks (RNNs) in natural language generation. But this comes with a significant computational cost, as the attention mechanism's complexity scales quadratically with sequence length. Efficient transformer variants have received increasing interest in recent works. Among them, a linear-complexity recurrent variant has proven well suited for autoregressive generation. It approximates the softmax attention with randomized or heuristic feature maps, but can be difficult to train and may yield suboptimal accuracy. This work aims to convert a pretrained transformer into its efficient recurrent counterpart, improving efficiency while maintaining accuracy. Specifically, we propose a swap-then-finetune procedure: in an off-the-shelf pretrained transformer, we replace the softmax attention with its linear-complexity recurrent alternative and then finetune. With a learned feature map, our approach provides an improved tradeoff between efficiency and accuracy over the standard transformer and other recurrent variants. We also show that the finetuning process has lower training cost relative to training these recurrent variants from scratch. As many models for natural language tasks are increasingly dependent on large-scale pretrained transformers, this work presents a viable approach to improving inference efficiency without repeating the expensive pretraining process.
Learning Graph Representation by Aggregating Subgraphs via Mutual Information Maximization
Venue: arXiv cs.LG 2021
Year: 2021

In this paper, we introduce a self-supervised learning method to enhance the graph-level representations with the help of a set of subgraphs. For this purpose, we propose a universal framework to generate subgraphs in an auto-regressive way and then using these subgraphs to guide the learning of graph representation by Graph Neural Networks. Under this framework, we can get a comprehensive understanding of the graph structure in a learnable way. And to fully capture enough information of original graphs, we design three information aggregators: \textbf{attribute-conv}, \textbf{layer-conv} and \textbf{subgraph-conv} to gather information from different aspects. And to achieve efficient and effective contrastive learning, a Head-Tail contrastive construction is proposed to provide abundant negative samples. Under all proposed components which can be generalized to any Graph Neural Networks, in the unsupervised case, we achieve new state-of-the-art results in several benchmarks. We also evaluate our model on semi-supervised learning tasks and make a fair comparison to state-of-the-art semi-supervised methods.
Vulnerability of Appearance-based Gaze Estimation
Venue: arXiv cs.CV 2021
Year: 2021

Appearance-based gaze estimation has achieved significant improvement by using deep learning. However, many deep learning-based methods suffer from the vulnerability property, i.e., perturbing the raw image using noise confuses the gaze estimation models. Although the perturbed image visually looks similar to the original image, the gaze estimation models output the wrong gaze direction. In this paper, we investigate the vulnerability of appearance-based gaze estimation. To our knowledge, this is the first time that the vulnerability of gaze estimation to be found. We systematically characterized the vulnerability property from multiple aspects, the pixel-based adversarial attack, the patch-based adversarial attack and the defense strategy. Our experimental results demonstrate that the CA-Net shows superior performance against attack among the four popular appearance-based gaze estimation networks, Full-Face, Gaze-Net, CA-Net and RT-GENE. This study draws the attention of researchers in the appearance-based gaze estimation community to defense from adversarial attacks.
The Influence of Social Networks on Human Society
Venue: arXiv cs.SI 2021
Year: 2021

This report gives a brief overview of the origin of social networks and their most popular manifestation in the modern era - the Online Social Networks (OSNs) or social media. It further discusses the positive and negative implications of OSNs on human society. The coupling of Data Science and social media (social media mining) is then put forward as a powerful tool to overcome the current challenges and pave the path for futuristic advancements
A Message Passing based Adaptive PDA Algorithm for Robust Radio-based Localization and Tracking
Venue: arXiv cs.IT 2021
Year: 2021

We present a message passing algorithm for localization and tracking in multipath-prone environments that implicitly considers obstructed line-of-sight situations. The proposed adaptive probabilistic data association algorithm infers the position of a mobile agent using multiple anchors by utilizing delay and amplitude of the multipath components (MPCs) as well as their respective uncertainties. By employing a nonuniform clutter model, we enable the algorithm to facilitate the position information contained in the MPCs to support the estimation of the agent position without exact knowledge about the environment geometry. Our algorithm adapts in an online manner to both, the time-varying signal-to-noise-ratio and line-of-sight (LOS) existence probability of each anchor. In a numerical analysis we show that the algorithm is able to operate reliably in environments characterized by strong multipath propagation, even if a temporary obstruction of all anchors occurs simultaneously.
Improving the interaction of Older Adults with Socially Assistive Robots for Table setting
Venue: arXiv cs.RO 2021
Year: 2021

This study provides user-studies aimed at exploring factors influencing the interaction between older adults and a robotic table setting assistant. The in-fluence of the level of automation (LOA) and level of transparency (LOT) on the quality of the interaction was considered. Results revealed that the interaction effect of LOA and LOT significantly influenced the interaction. A lower LOA which required the user to control some of the actions of the robot influenced the older adults to participate more in the interaction when the LOT was low com-pared to situations with higher LOT (more information) and higher LOA (more robot autonomy). Even though the higher LOA influenced more fluency in the interaction, the lower LOA encouraged a more collaborative form of interaction which is a priority in the design of robotic aids for older adult users. The results provide some insights into shared control designs which accommodates the preferences of the older adult users as they interact with robotic aids such as the table setting robot used in this study.