OpenRaiser/Intern-Atlas huggingface dataset:
Circles are like Ellipses, or Ellipses are like Circles? Measuring the Degree of Asymmetry of Static and Contextual Embeddings and the Implications to Representation Learning
Venue: arXiv cs.CL 2020
Year: 2020

Human judgments of word similarity have been a popular method of evaluating the quality of word embedding. But it fails to measure the geometry properties such as asymmetry. For example, it is more natural to say "Ellipses are like Circles" than "Circles are like Ellipses". Such asymmetry has been observed from a psychoanalysis test called word evocation experiment, where one word is used to recall another. Although useful, such experimental data have been significantly understudied for measuring embedding quality. In this paper, we use three well-known evocation datasets to gain insights into asymmetry encoding of embedding. We study both static embedding as well as contextual embedding, such as BERT. Evaluating asymmetry for BERT is generally hard due to the dynamic nature of embedding. Thus, we probe BERT's conditional probabilities (as a language model) using a large number of Wikipedia contexts to derive a theoretically justifiable Bayesian asymmetry score. The result shows that contextual embedding shows randomness than static embedding on similarity judgments while performing well on asymmetry judgment, which aligns with its strong performance on "extrinsic evaluations" such as text classification. The asymmetry judgment and the Bayesian approach provides a new perspective to evaluate contextual embedding on intrinsic evaluation, and its comparison to similarity evaluation concludes our work with a discussion on the current state and the future of representation learning.
What Makes a Star Teacher? A Hierarchical BERT Model for Evaluating Teacher's Performance in Online Education
Venue: arXiv cs.LG 2020
Year: 2020

Education has a significant impact on both society and personal life. With the development of technology, online education has been growing rapidly over the past decade. While there are several online education studies on student behavior analysis, the course concept mining, and course recommendations (Feng, Tang, and Liu 2019; Pan et al. 2017), there is little research on evaluating teachers' performance in online education. In this paper, we conduct a systematic study to understand and effectively predict teachers' performance using the subtitles of 1,085 online courses. Our model-free analysis shows that teachers' verbal cues (e.g., question strategy, emotional appealing, and hedging) and their course structure design are both significantly correlated with teachers' performance evaluation. Based on these insights, we then propose a hierarchical course BERT model to predict teachers' performance in online education. Our proposed model can capture the hierarchical structure within each course as well as the deep semantic features extracted from the course content. Experiment results show that our proposed method achieves significant gain over several state-of-the-art methods. Our study provides a significant social impact in helping teachers improve their teaching style and enhance their instructional material design for more effective online teaching in the future.
Meta-Generating Deep Attentive Metric for Few-shot Classification
Venue: arXiv cs.CV 2020
Year: 2020

Learning to generate a task-aware base learner proves a promising direction to deal with few-shot learning (FSL) problem. Existing methods mainly focus on generating an embedding model utilized with a fixed metric (eg, cosine distance) for nearest neighbour classification or directly generating a linear classier. However, due to the limited discriminative capacity of such a simple metric or classifier, these methods fail to generalize to challenging cases appropriately. To mitigate this problem, we present a novel deep metric meta-generation method that turns to an orthogonal direction, ie, learning to adaptively generate a specific metric for a new FSL task based on the task description (eg, a few labelled samples). In this study, we structure the metric using a three-layer deep attentive network that is flexible enough to produce a discriminative metric for each task. Moreover, different from existing methods that utilize an uni-modal weight distribution conditioned on labelled samples for network generation, the proposed meta-learner establishes a multi-modal weight distribution conditioned on cross-class sample pairs using a tailored variational autoencoder, which can separately capture the specific inter-class discrepancy statistics for each class and jointly embed the statistics for all classes into metric generation. By doing this, the generated metric can be appropriately adapted to a new FSL task with pleasing generalization performance. To demonstrate this, we test the proposed method on four benchmark FSL datasets and gain surprisingly obvious performance improvement over state-of-the-art competitors, especially in the challenging cases, eg, improve the accuracy from 26.14% to 46.69% in the 20-way 1-shot task on miniImageNet, while improve the accuracy from 45.2% to 68.72% in the 5-way 1-shot task on FC100. Code is available: https://github.com/NWPUZhoufei/DAM.
VICToRy: Visual Interactive Consistency Management in Tolerant Rule-based Systems
Venue: arXiv cs.SE 2020
Year: 2020

In the field of Model-Driven Engineering, there exist numerous tools that support various consistency management operations including model transformation, synchronisation and consistency checking. The supported operations, however, typically run completely in the background with only input and output made visible to the user. We argue that this often reduces both understandability and controllability. As a step towards improving this situation, we present VICToRy, a debugger for model generation and transformation based on Triple Graph Grammars, a well-known rule-based approach to bidirectional transformation. In addition to a fine-grained, step-by-step, interactive visualisation, VICToRy enables the user to actively explore and choose between multiple valid rule applications thus improving control and understanding.
An Algebraic Graph Transformation Approach for RDF and SPARQL
Venue: arXiv cs.DB 2020
Year: 2020

We consider the recommendations of the World Wide Web Consortium (W3C) about RDF framework and its associated query language SPARQL. We propose a new formal framework based on category theory which provides clear and concise formal definitions of the main basic features of RDF and SPARQL. We define RDF graphs as well as SPARQL basic graph patterns as objects of some nested categories. This allows one to clarify, in particular, the role of blank nodes. Furthermore, we consider basic SPARQL CONSTRUCT and SELECT queries and formalize their operational semantics following a novel algebraic graph transformation approach called POIM.
State Masking Over a Two-State Compound Channel
Venue: arXiv cs.IT 2020
Year: 2020

We consider fundamental limits for communicating over a compound channel when the state of the channel needs to be masked. Our model is closely related to an area of study known as covert communication that is a setting in which the transmitter wishes to communicate to legitimate receiver(s) while ensuring that the communication is not detected by an adversary. The main contribution in our two-state masking setup is the establishment of bounds on the throughput-key region when the constraint that quantifies how much the states are masked is defined to be the total variation distance between the two channel-induced distributions. For the scenario in which the key length is infinite, we provide sufficient conditions for when the bounds to coincide for the throughput, which follows the square-root law. Numerical examples, including that of a Gaussian channel, are provided to illustrate our results.
A Novel 3D Non-Stationary Multi-Frequency Multi-Link Wideband MIMO Channel Model
Venue: arXiv cs.IT 2020
Year: 2020

In this paper, a multi-frequency multi-link three-dimensional (3D) non-stationary wideband multiple-input multiple-output (MIMO) channel model is proposed. The spatial consistency and multi-frequency correlation are considered in parameters initialization of every single-link and different frequencies, including large scale parameters (LSPs) and small scale parameters (SSPs). Moreover, SSPs are time-variant and updated when scatterers and the receiver (Rx) are moving. The temporal evolution of clusters is modeled by birth and death processes. The single-link channel model which has considered the inter-correlation can be easily extended to multi-link channel model. Statistical properties, including spatial cross-correlation function (CCF), power delay profile (PDP), and correlation matrix collinearity (CMC) are investigated and compared with the 3rd generation partner project (3GPP) TR 38.901 and quasi deterministic radio channel generator (QuaDRiGa) channel models. Besides, the CCF is validated against measurement data.
Identification of Prototypical Task Executions Based on Smoothness as Basis of Human-to-Robot Kinematic Skill Transfer
Venue: arXiv cs.RO 2020
Year: 2020

In this paper we investigate human-to-robot skill transfer based on the identification of prototypical task executions by clustering a set of examples performed by human demonstrators, where smoothness and kinematic features represent skill and task performance, respectively. We exemplify our skill transfer approach with data from an experimental task in which a tool touches a support surface with a target velocity. Prototypical task executions are identified and transferred to a generic robot arm in simulation. The results illustrate how task models based on skill and performance features can provide analysis and design criteria for robotic applications.
Neural Contextual Bandits with Deep Representation and Shallow Exploration
Venue: arXiv cs.LG 2020
Year: 2020

We study a general class of contextual bandits, where each context-action pair is associated with a raw feature vector, but the reward generating function is unknown. We propose a novel learning algorithm that transforms the raw feature vector using the last hidden layer of a deep ReLU neural network (deep representation learning), and uses an upper confidence bound (UCB) approach to explore in the last linear layer (shallow exploration). We prove that under standard assumptions, our proposed algorithm achieves $\tilde{O}(\sqrt{T})$ finite-time regret, where $T$ is the learning time horizon. Compared with existing neural contextual bandit algorithms, our approach is computationally much more efficient since it only needs to explore in the last layer of the deep neural network.
Self-Explaining Structures Improve NLP Models
Venue: arXiv cs.CL 2020
Year: 2020

Existing approaches to explaining deep learning models in NLP usually suffer from two major drawbacks: (1) the main model and the explaining model are decoupled: an additional probing or surrogate model is used to interpret an existing model, and thus existing explaining tools are not self-explainable; (2) the probing model is only able to explain a model's predictions by operating on low-level features by computing saliency scores for individual words but are clumsy at high-level text units such as phrases, sentences, or paragraphs. To deal with these two issues, in this paper, we propose a simple yet general and effective self-explaining framework for deep learning models in NLP. The key point of the proposed framework is to put an additional layer, as is called by the interpretation layer, on top of any existing NLP model. This layer aggregates the information for each text span, which is then associated with a specific weight, and their weighted combination is fed to the softmax function for the final prediction. The proposed model comes with the following merits: (1) span weights make the model self-explainable and do not require an additional probing model for interpretation; (2) the proposed model is general and can be adapted to any existing deep learning structures in NLP; (3) the weight associated with each text span provides direct importance scores for higher-level text units such as phrases and sentences. We for the first time show that interpretability does not come at the cost of performance: a neural model of self-explaining features obtains better performances than its counterpart without the self-explaining nature, achieving a new SOTA performance of 59.1 on SST-5 and a new SOTA performance of 92.3 on SNLI.
On Root Detection Strategies for Android Devices
Venue: arXiv cs.CR 2020
Year: 2020

The Android operating system runs on the majority of smartphones nowadays. Its success is driven by its availability to a variety of smartphone hardware vendors on the one hand, and the customization possibilities given to its users on the other hand. While other big smartphone operating systems restrict user configuration to a given set of functionality, Android users can leverage the whole potential of their devices. This high degree of customization enabled by a process called rooting, where the users escalate their privileges to those of the operating system, introduces security, data integrity and privacy concerns. Several rooting detection mechanisms for Android devices already exist, aimed at different levels of detection. This paper introduces further strategies derived from the Linux ecosystem and outlines their usage on the Android platform. In addition, we present a novel remote rooting detection approach aimed at trust and integrity checks between devices in wireless networks.
Phonetic Posteriorgrams based Many-to-Many Singing Voice Conversion via Adversarial Training
Venue: arXiv cs.SD 2020
Year: 2020

This paper describes an end-to-end adversarial singing voice conversion (EA-SVC) approach. It can directly generate arbitrary singing waveform by given phonetic posteriorgram (PPG) representing content, F0 representing pitch, and speaker embedding representing timbre, respectively. Proposed system is composed of three modules: generator $G$, the audio generation discriminator $D_{A}$, and the feature disentanglement discriminator $D_F$. The generator $G$ encodes the features in parallel and inversely transforms them into the target waveform. In order to make timbre conversion more stable and controllable, speaker embedding is further decomposed to the weighted sum of a group of trainable vectors representing different timbre clusters. Further, to realize more robust and accurate singing conversion, disentanglement discriminator $D_F$ is proposed to remove pitch and timbre related information that remains in the encoded PPG. Finally, a two-stage training is conducted to keep a stable and effective adversarial training process. Subjective evaluation results demonstrate the effectiveness of our proposed methods. Proposed system outperforms conventional cascade approach and the WaveNet based end-to-end approach in terms of both singing quality and singer similarity. Further objective analysis reveals that the model trained with the proposed two-stage training strategy can produce a smoother and sharper formant which leads to higher audio quality.
Competition analysis on the over-the-counter credit default swap market
Venue: arXiv cs.LG 2020
Year: 2020

We study two questions related to competition on the OTC CDS market using data collected as part of the EMIR regulation. First, we study the competition between central counterparties through collateral requirements. We present models that successfully estimate the initial margin requirements. However, our estimations are not precise enough to use them as input to a predictive model for CCP choice by counterparties in the OTC market. Second, we model counterpart choice on the interdealer market using a novel semi-supervised predictive task. We present our methodology as part of the literature on model interpretability before arguing for the use of conditional entropy as the metric of interest to derive knowledge from data through a model-agnostic approach. In particular, we justify the use of deep neural networks to measure conditional entropy on real-world datasets. We create the $\textit{Razor entropy}$ using the framework of algorithmic information theory and derive an explicit formula that is identical to our semi-supervised training objective. Finally, we borrow concepts from game theory to define $\textit{top-k Shapley values}$. This novel method of payoff distribution satisfies most of the properties of Shapley values, and is of particular interest when the value function is monotone submodular. Unlike classical Shapley values, top-k Shapley values can be computed in quadratic time of the number of features instead of exponential. We implement our methodology and report the results on our particular task of counterpart choice. Finally, we present an improvement to the $\textit{node2vec}$ algorithm that could for example be used to further study intermediation. We show that the neighbor sampling used in the generation of biased walks can be performed in logarithmic time with a quasilinear time pre-computation, unlike the current implementations that do not scale well.
Exploration in Algorithm Engineering: Modeling Algorithms
Venue: arXiv cs.SE 2020
Year: 2020

According to some algorithmicists, algorithmics traditionally uses algorithm theory, which stems from mathematics. The growing need for innovative algorithms has caused increasing gaps between theory and practice. Originally, this motivated the development of algorithm engineering, which is viewed as experimental techniques related to software engineering. Currently, algorithm engineering is a methodology for algorithmic research that combines theory with implementation and experimentation in order to produce better algorithms with high practical impact. Still, researchers have questioned whether the notion of algorithms can be defined in a fully generable way and discussed what kinds of entities algorithms actually are. They have also struggled to maintain a view that formulates algorithms mathematically (e.g., Turing machines and finite-state machines [FSMs]) while adapting a more applied view. Answering the question of what algorithms have practical applications in software specifications in particular, this paper proposes a diagrammatical definition of an algorithm based on a new modeling machine called a thinging machine (TM). The machine has five actions (e.g., create, process, release, transfer, and receive) that can form a network of machines. The paper explores the application of the definition in Turing machines and FSMs. The results point to the fact that the proposed definition can serve as a middle-ground representation of algorithms, a definition which is between formal specification and the commonly used informal definition (e.g., set of instructions).
Adaptable Automation with Modular Deep Reinforcement Learning and Policy Transfer
Venue: arXiv cs.LG 2020
Year: 2020

Recent advances in deep Reinforcement Learning (RL) have created unprecedented opportunities for intelligent automation, where a machine can autonomously learn an optimal policy for performing a given task. However, current deep RL algorithms predominantly specialize in a narrow range of tasks, are sample inefficient, and lack sufficient stability, which in turn hinder their industrial adoption. This article tackles this limitation by developing and testing a Hyper-Actor Soft Actor-Critic (HASAC) RL framework based on the notions of task modularization and transfer learning. The goal of the proposed HASAC is to enhance the adaptability of an agent to new tasks by transferring the learned policies of former tasks to the new task via a "hyper-actor". The HASAC framework is tested on a new virtual robotic manipulation benchmark, Meta-World. Numerical experiments show superior performance by HASAC over state-of-the-art deep RL algorithms in terms of reward value, success rate, and task completion time.
Proactive DDoS Attack Mitigation in Cloud-Fog Environment using Moving Target Defense
Venue: arXiv cs.CR 2020
Year: 2020

Distributed Denial of Service (DDoS) attacks are serious cyber attacks and mitigating DDoS attacks in cloud is a topic of ongoing research interest which remains a major security challenge. Fog computing is an extension of cloud computing which has been used to secure cloud. Moving Target Defense (MTD) is a newly recognized, proactive security defense that can be used to mitigate DDoS attacks on cloud. MTD intends to make a system dynamic in nature and uncertain by changing attack surface continuously to confuse attackers. In this paper, a novel DDoS mitigation framework is presented to support Cloud-Fog Platform using MTD technique (CFPM). CFPM applies migration MTD technique at fog layer to mitigate DDoS attacks in cloud. It detects attacker among all the legitimate clients proactively at the fog layer and isolate it from innocent clients. CFPM uses an effective request handling procedure for load balancing and attacker isolation procedure which aims to minimize disruption to cloud server as well as serving fog servers. In addition, effectiveness of CFPM is evaluated by analyzing the behavior of the system before and after attack, considering different possible scenarios. This approach is effective as it uses the advantage of both MTD technique and Fog computing paradigm supporting cloud environment.
IoT DoS and DDoS Attack Detection using ResNet
Venue: arXiv cs.CR 2020
Year: 2020

The network attacks are increasing both in frequency and intensity with the rapid growth of internet of things (IoT) devices. Recently, denial of service (DoS) and distributed denial of service (DDoS) attacks are reported as the most frequent attacks in IoT networks. The traditional security solutions like firewalls, intrusion detection systems, etc., are unable to detect the complex DoS and DDoS attacks since most of them filter the normal and attack traffic based upon the static predefined rules. However, these solutions can become reliable and effective when integrated with artificial intelligence (AI) based techniques. During the last few years, deep learning models especially convolutional neural networks achieved high significance due to their outstanding performance in the image processing field. The potential of these convolutional neural network (CNN) models can be used to efficiently detect the complex DoS and DDoS by converting the network traffic dataset into images. Therefore, in this work, we proposed a methodology to convert the network traffic data into image form and trained a state-of-the-art CNN model, i.e., ResNet over the converted data. The proposed methodology accomplished 99.99\% accuracy for detecting the DoS and DDoS in case of binary classification. Furthermore, the proposed methodology achieved 87\% average precision for recognizing eleven types of DoS and DDoS attack patterns which is 9\% higher as compared to the state-of-the-art.
A small note on variation in segmentation annotations
Venue: arXiv cs.CV 2020
Year: 2020

We report on the results of a small crowdsourcing experiment conducted at a workshop on machine learning for segmentation held at the Danish Bio Imaging network meeting 2020. During the workshop we asked participants to manually segment mitochondria in three 2D patches. The aim of the experiment was to illustrate that manual annotations should not be seen as the ground truth, but as a reference standard that is subject to substantial variation. In this note we show how the large variation we observed in the segmentations can be reduced by removing the annotators with worst pair-wise agreement. Having removed the annotators with worst performance, we illustrate that the remaining variance is semantically meaningful and can be exploited to obtain segmentations of cell boundary and cell interior.