OpenRaiser/Intern-Atlas huggingface dataset:
MIDMod-OSN: A Microscopic-level Information Diffusion Model for Online Social Networks
Venue: arXiv cs.SI 2020
Year: 2020

As online social networks continue to be commonly used for the dissemination of information to the public, understanding the phenomena that govern information diffusion is crucial for many security and safety-related applications, such as maximizing information spread and misinformation containment during crises and natural disasters. In this study, we hypothesize that the features that contribute to information diffusion in online social networks are significantly influenced by the type of event being studied. We classify Twitter events as either informative or trending and then explore the node-to-node influence dynamics associated with information spread. We build a model based on Bayesian Logistic Regression for learning and prediction and Random Forests for feature selection. Experimental results from real-world data sets show that the proposed model outperforms state-of-the-art diffusion prediction models, achieving 93% accuracy in informative events and 86% in trending events. We observed that the models for informative and trending events differ significantly, both in the diffusion process and in the user features that govern the diffusion. Our findings show that followers play an important role in the diffusion process and it is possible to use the diffusion and OSN behavior of users for predicting the trending character of a message without having to count the number of reactions.
Cry Wolf: Toward an Experimentation Platform and Dataset for Human Factors in Cyber Security Analysis
Venue: arXiv cs.CR 2020
Year: 2020

Computer network defense is a partnership between automated systems and human cyber security analysts. The system behaviors, for example raising a high proportion of false alarms, likely impact cyber analyst performance. Experimentation in the analyst-system domain is challenging due to lack of access to security experts, the usability of attack datasets, and the training required to use security analysis tools. This paper describes Cry Wolf, an open source web application for user studies of cyber security analysis tasks. This paper also provides an open-access dataset of 73 true and false Intrusion Detection System (IDS) alarms derived from real-world examples of "impossible travel" scenarios. Cry Wolf and the impossible travel dataset were used in an experiment on the impact of IDS false alarm rate on analysts' abilities to correctly classify IDS alerts as true or false alarms. Results from that experiment are used to evaluate the quality of the dataset using difficulty and discrimination index measures drawn from classical test theory. Many alerts in the dataset provide good discrimination for participants' overall task performance.
Evaluating Registration Without Ground Truth
Venue: arXiv cs.CV 2020
Year: 2020

We present a generic method for assessing the quality of non-rigid registration (NRR) algorithms, that does not depend on the existence of any ground truth, but depends solely on the data itself. The data is a set of images. The output of any NRR of such a set of images is a dense correspondence across the whole set. Given such a dense correspondence, it is possible to build various generative statistical models of appearance variation across the set. We show that evaluating the quality of the registration can be mapped to the problem of evaluating the quality of the resultant statistical model. The quality of the model entails a comparison between the model and the image data that was used to construct it. It should be noted that this approach does not depend on the specifics of the registration algorithm used (i.e., whether a groupwise or pairwise algorithm was used to register the set of images), or on the specifics of the modelling approach used. We derive an index of image model specificity that can be used to assess image model quality, and hence the quality of registration. This approach is validated by comparing our assessment of registration quality with that derived from ground truth anatomical labeling. We demonstrate that our approach is capable of assessing NRR reliably without ground truth. Finally, to demonstrate the practicality of our method, different NRR algorithms -- both pairwise and groupwise -- are compared in terms of their performance on 3D MR brain data.
Backpropamine: training self-modifying neural networks with differentiable neuromodulated plasticity
Venue: arXiv cs.NE 2020
Year: 2020

The impressive lifelong learning in animal brains is primarily enabled by plastic changes in synaptic connectivity. Importantly, these changes are not passive, but are actively controlled by neuromodulation, which is itself under the control of the brain. The resulting self-modifying abilities of the brain play an important role in learning and adaptation, and are a major basis for biological reinforcement learning. Here we show for the first time that artificial neural networks with such neuromodulated plasticity can be trained with gradient descent. Extending previous work on differentiable Hebbian plasticity, we propose a differentiable formulation for the neuromodulation of plasticity. We show that neuromodulated plasticity improves the performance of neural networks on both reinforcement learning and supervised learning tasks. In one task, neuromodulated plastic LSTMs with millions of parameters outperform standard LSTMs on a benchmark language modeling task (controlling for the number of parameters). We conclude that differentiable neuromodulation of plasticity offers a powerful new framework for training neural networks.
Declarative Memory-based Structure for the Representation of Text Data
Venue: arXiv cs.AI 2020
Year: 2020

In the era of intelligent computing, computational progress in text processing is an essential consideration. Many systems have been developed to process text over different languages. Though, there is considerable development, they still lack in understanding of the text, i.e., instead of keeping text as knowledge, many treat text as a data. In this work we introduce a text representation scheme which is influenced by human memory infrastructure. Since texts are declarative in nature, a structural organization would foster efficient computation over text. We exploit long term episodic memory to keep text information observed over time. This not only keep fragments of text in an organized fashion but also reduces redundancy and stores the temporal relation among them. Wordnet has been used to imitate semantic memory, which works at word level to facilitate the understanding about individual words within text. Experimental results of various operation performed over episodic memory and growth of knowledge infrastructure over time is reported.
CybORG: An Autonomous Cyber Operations Research Gym
Venue: arXiv cs.CR 2020
Year: 2020

Autonomous Cyber Operations (ACO) involves the consideration of blue team (defender) and red team (attacker) decision-making models in adversarial scenarios. To support the application of machine learning algorithms to solve this problem, and to encourage such practitioners to attend to problems in the ACO setting, a suitable gym (toolkit for experiments) is necessary. We introduce CybORG, a work-in-progress gym for ACO research. Driven by the need to efficiently support reinforcement learning to train adversarial decision-making models through simulation and emulation, our design differs from prior related work. Our early evaluation provides some evidence that CybORG is appropriate for our purpose and may provide a basis for advancing ACO research towards practical applications.
Exploring BERT Parameter Efficiency on the Stanford Question Answering Dataset v2.0
Venue: arXiv cs.CL 2020
Year: 2020

In this paper we explore the parameter efficiency of BERT arXiv:1810.04805 on version 2.0 of the Stanford Question Answering dataset (SQuAD2.0). We evaluate the parameter efficiency of BERT while freezing a varying number of final transformer layers as well as including the adapter layers proposed in arXiv:1902.00751. Additionally, we experiment with the use of context-aware convolutional (CACNN) filters, as described in arXiv:1709.08294v3, as a final augmentation layer for the SQuAD2.0 tasks. This exploration is motivated in part by arXiv:1907.10597, which made a compelling case for broadening the evaluation criteria of artificial intelligence models to include various measures of resource efficiency. While we do not evaluate these models based on their floating point operation efficiency as proposed in arXiv:1907.10597, we examine efficiency with respect to training time, inference time, and total number of model parameters. Our results largely corroborate those of arXiv:1902.00751 for adapter modules, while also demonstrating that gains in F1 score from adding context-aware convolutional filters are not practical due to the increase in training and inference time.
Towards Better Surgical Instrument Segmentation in Endoscopic Vision: Multi-Angle Feature Aggregation and Contour Supervision
Venue: arXiv cs.CV 2020
Year: 2020

Accurate and real-time surgical instrument segmentation is important in the endoscopic vision of robot-assisted surgery, and significant challenges are posed by frequent instrument-tissue contacts and continuous change of observation perspective. For these challenging tasks more and more deep neural networks (DNN) models are designed in recent years. We are motivated to propose a general embeddable approach to improve these current DNN segmentation models without increasing the model parameter number. Firstly, observing the limited rotation-invariance performance of DNN, we proposed the Multi-Angle Feature Aggregation (MAFA) method, leveraging active image rotation to gain richer visual cues and make the prediction more robust to instrument orientation changes. Secondly, in the end-to-end training stage, the auxiliary contour supervision is utilized to guide the model to learn the boundary awareness, so that the contour shape of segmentation mask is more precise. The proposed method is validated with ablation experiments on the novel Sinus-Surgery datasets collected from surgeons' operations, and is compared to the existing methods on a public dataset collected with a da Vinci Xi Robot.
Novel Change of Measure Inequalities with Applications to PAC-Bayesian Bounds and Monte Carlo Estimation
Venue: arXiv cs.LG 2020
Year: 2020

We introduce several novel change of measure inequalities for two families of divergences: $f$-divergences and $α$-divergences. We show how the variational representation for $f$-divergences leads to novel change of measure inequalities. We also present a multiplicative change of measure inequality for $α$-divergences and a generalized version of Hammersley-Chapman-Robbins inequality. Finally, we present several applications of our change of measure inequalities, including PAC-Bayesian bounds for various classes of losses and non-asymptotic intervals for Monte Carlo estimates.
Multimodal Transformer with Pointer Network for the DSTC8 AVSD Challenge
Venue: arXiv cs.CL 2020
Year: 2020

Audio-Visual Scene-Aware Dialog (AVSD) is an extension from Video Question Answering (QA) whereby the dialogue agent is required to generate natural language responses to address user queries and carry on conversations. This is a challenging task as it consists of video features of multiple modalities, including text, visual, and audio features. The agent also needs to learn semantic dependencies among user utterances and system responses to make coherent conversations with humans. In this work, we describe our submission to the AVSD track of the 8th Dialogue System Technology Challenge. We adopt dot-product attention to combine text and non-text features of input video. We further enhance the generation capability of the dialogue agent by adopting pointer networks to point to tokens from multiple source sequences in each generation step. Our systems achieve high performance in automatic metrics and obtain 5th and 6th place in human evaluation among all submissions.
Human Perception-Optimized Planning for Comfortable VR-Based Telepresence
Venue: arXiv cs.RO 2020
Year: 2020

This paper introduces an emerging motion planning problem by considering a human that is immersed into the viewing perspective of a remote robot. The challenge is to make the experience both effective (such as delivering a sense of presence) and comfortable (such as avoiding adverse sickness symptoms, including nausea). We refer to this challenging new area as human perception-optimized planning and propose a general multiobjective optimization framework that can be instantiated in many envisioned scenarios. We then consider a specific VR telepresence task as a case of human perception-optimized planning, in which we simulate a robot that sends 360 video to a remote user to be viewed through a head-mounted display. In this particular task, we plan trajectories that minimize VR sickness (and thereby maximize comfort). An A* type method is used to create a Pareto-optimal collection of piecewise linear trajectories while taking into account criteria that improve comfort. We conducted a study with human subjects touring a virtual museum, in which paths computed by our algorithm are compared against a reference RRT-based trajectory. Generally, users suffered less from VR sickness and preferred the paths created by the presented algorithm.
Measuring Basic Load-Balancing and Fail-Over Setups for Email Delivery via DNS MX Records
Venue: arXiv cs.NI 2020
Year: 2020

The domain name system (DNS) has long provided means to assure basic load-balancing and fail-over (BLBFO) for email delivery. A traditional method uses multiple mail exchanger (MX) records to distribute the load across multiple email servers. Round-robin DNS is the common alternative to this MX-based balancing. Despite the classical nature of these two solutions, neither one has received particular attention in Internet measurement research. To patch this gap, this paper examines BLBFO configurations with an active measurement study covering over 2.7 million domains from which about 2.1 million have MX records. Of these MX-enabled domains, about 60% are observed to use BLBFO, and MX-based balancing seems more common than round-robin DNS. Email hosting services offer one explanation for this adoption rate. Many domains seem to also prefer fine-tuned configurations instead of relying on randomization assumptions. Furthermore, about 27% of the domains have at least one exchanger with a valid IPv6 address. Finally, some misconfigurations and related oddities are visible.
Edge-Enhanced Graph Convolution Networks for Event Detection with Syntactic Relation
Venue: arXiv cs.CL 2020
Year: 2020

Event detection (ED), a key subtask of information extraction, aims to recognize instances of specific event types in text. Previous studies on the task have verified the effectiveness of integrating syntactic dependency into graph convolutional networks. However, these methods usually ignore dependency label information, which conveys rich and useful linguistic knowledge for ED. In this paper, we propose a novel architecture named Edge-Enhanced Graph Convolution Networks (EE-GCN), which simultaneously exploits syntactic structure and typed dependency label information to perform ED. Specifically, an edge-aware node update module is designed to generate expressive word representations by aggregating syntactically-connected words through specific dependency types. Furthermore, to fully explore clues hidden in dependency edges, a node-aware edge update module is introduced, which refines the relation representations with contextual information. These two modules are complementary to each other and work in a mutual promotion way. We conduct experiments on the widely used ACE2005 dataset and the results show significant improvement over competitive baseline methods.
MuST-Cinema: a Speech-to-Subtitles corpus
Venue: arXiv cs.CL 2020
Year: 2020

Growing needs in localising audiovisual content in multiple languages through subtitles call for the development of automatic solutions for human subtitling. Neural Machine Translation (NMT) can contribute to the automatisation of subtitling, facilitating the work of human subtitlers and reducing turn-around times and related costs. NMT requires high-quality, large, task-specific training data. The existing subtitling corpora, however, are missing both alignments to the source language audio and important information about subtitle breaks. This poses a significant limitation for developing efficient automatic approaches for subtitling, since the length and form of a subtitle directly depends on the duration of the utterance. In this work, we present MuST-Cinema, a multilingual speech translation corpus built from TED subtitles. The corpus is comprised of (audio, transcription, translation) triplets. Subtitle breaks are preserved by inserting special symbols. We show that the corpus can be used to build models that efficiently segment sentences into subtitles and propose a method for annotating existing subtitling corpora with subtitle breaks, conforming to the constraint of length.
Software Engineering und Software Engineering Forschung im Zeitalter der Digitalisierung
Venue: arXiv cs.SE 2020
Year: 2020

Digitization not only affects society, it also requires a redefinition of the location of computer science and computer scientists, as the science journalist Yogeshwar suggests. Since all official aspects of digitalization are based on software, this article is intended to attempt to redefine the role of software engineering and its research. Software-based products, systems or services are influencing all areas of life and are a critical component and central innovation driver of digitization in all areas of life. Scientifically, there are new opportunities and challenges for software engineering as a driving discipline in the development of any technical innovation. However, the chances must not be sacrificed to the competition for bibliometric numbers as an end in themselves.
What do crowd workers think about creative work?
Venue: arXiv cs.HC 2020
Year: 2020

Crowdsourcing platforms are a powerful and convenient means for recruiting participants in online studies and collecting data from the crowd. As information work is being more and more automated by Machine Learning algorithms, creativity $-$ that is, a human's ability for divergent and convergent thinking $-$ will play an increasingly important role on online crowdsourcing platforms. However, we lack insights into what crowd workers think about creative work. In studies in Human-Computer Interaction (HCI), the ability and willingness of the crowd to participate in creative work seems to be largely unquestioned. Insights into the workers' perspective are rare, but important, as they may inform the design of studies with higher validity. Given that creativity will play an increasingly important role in crowdsourcing, it is imperative to develop an understanding of how workers perceive creative work. In this paper, we summarize our recent worker-centered study of creative work on two general-purpose crowdsourcing platforms (Amazon Mechanical Turk and Prolific). Our study illuminates what creative work is like for crowd workers on these two crowdsourcing platforms. The work identifies several archetypal types of workers with different attitudes towards creative work, and discusses common pitfalls with creative work on crowdsourcing platforms.
Diversity-Based Generalization for Unsupervised Text Classification under Domain Shift
Venue: arXiv cs.LG 2020
Year: 2020

Domain adaptation approaches seek to learn from a source domain and generalize it to an unseen target domain. At present, the state-of-the-art unsupervised domain adaptation approaches for subjective text classification problems leverage unlabeled target data along with labeled source data. In this paper, we propose a novel method for domain adaptation of single-task text classification problems based on a simple but effective idea of diversity-based generalization that does not require unlabeled target data but still matches the state-of-the-art in performance. Diversity plays the role of promoting the model to better generalize and be indiscriminate towards domain shift by forcing the model not to rely on same features for prediction. We apply this concept on the most explainable component of neural networks, the attention layer. To generate sufficient diversity, we create a multi-head attention model and infuse a diversity constraint between the attention heads such that each head will learn differently. We further expand upon our model by tri-training and designing a procedure with an additional diversity constraint between the attention heads of the tri-trained classifiers. Extensive evaluation using the standard benchmark dataset of Amazon reviews and a newly constructed dataset of Crisis events shows that our fully unsupervised method matches with the competing baselines that uses unlabeled target data. Our results demonstrate that machine learning architectures that ensure sufficient diversity can generalize better; encouraging future research to design ubiquitously usable learning models without using unlabeled target data.
Applying Rule-Based Context Knowledge to Build Abstract Semantic Maps of Indoor Environments
Venue: arXiv cs.CV 2020
Year: 2020

In this paper, we propose a generalizable method that systematically combines data driven MCMC samplingand inference using rule-based context knowledge for data abstraction. In particular, we demonstrate the usefulness of our method in the scenario of building abstract semantic maps for indoor environments. The product of our system is a parametric abstract model of the perceived environment that not only accurately represents the geometry of the environment but also provides valuable abstract information which benefits high-level robotic applications. Based on predefined abstract terms,such as type and relation, we define task-specific context knowledge as descriptive rules in Markov Logic Networks. The corresponding inference results are used to construct a priordistribution that aims to add reasonable constraints to the solution space of semantic maps. In addition, by applying a semantically annotated sensor model, we explicitly use context information to interpret the sensor data. Experiments on real world data show promising results and thus confirm the usefulness of our system.
A$^3$: Accelerating Attention Mechanisms in Neural Networks with Approximation
Venue: arXiv cs.DC 2020
Year: 2020

With the increasing computational demands of neural networks, many hardware accelerators for the neural networks have been proposed. Such existing neural network accelerators often focus on popular neural network types such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs); however, not much attention has been paid to attention mechanisms, an emerging neural network primitive that enables neural networks to retrieve most relevant information from a knowledge-base, external memory, or past states. The attention mechanism is widely adopted by many state-of-the-art neural networks for computer vision, natural language processing, and machine translation, and accounts for a large portion of total execution time. We observe today's practice of implementing this mechanism using matrix-vector multiplication is suboptimal as the attention mechanism is semantically a content-based search where a large portion of computations ends up not being used. Based on this observation, we design and architect A3, which accelerates attention mechanisms in neural networks with algorithmic approximation and hardware specialization. Our proposed accelerator achieves multiple orders of magnitude improvement in energy efficiency (performance/watt) as well as substantial speedup over the state-of-the-art conventional hardware.