OpenRaiser/Intern-Atlas huggingface dataset:
Effect of Text Processing Steps on Twitter Sentiment Classification using Word Embedding
Venue: arXiv cs.CL 2020
Year: 2020

Processing of raw text is the crucial first step in text classification and sentiment analysis. However, text processing steps are often performed using off-the-shelf routines and pre-built word dictionaries without optimizing for domain, application, and context. This paper investigates the effect of seven text processing scenarios on a particular text domain (Twitter) and application (sentiment classification). Skip gram-based word embeddings are developed to include Twitter colloquial words, emojis, and hashtag keywords that are often removed for being unavailable in conventional literature corpora. Our experiments reveal negative effects on sentiment classification of two common text processing steps: 1) stop word removal and 2) averaging of word vectors to represent individual tweets. New effective steps for 1) including non-ASCII emoji characters, 2) measuring word importance from word embedding, 3) aggregating word vectors into a tweet embedding, and 4) developing linearly separable feature space have been proposed to optimize the sentiment classification pipeline. The best combination of text processing steps yields the highest average area under the curve (AUC) of 88.4 (+/-0.4) in classifying 14,640 tweets with three sentiment labels. Word selection from context-driven word embedding reveals that only the ten most important words in Tweets cumulatively yield over 98% of the maximum accuracy. Results demonstrate a means for data-driven selection of important words in tweet classification as opposed to using pre-built word dictionaries. The proposed tweet embedding is robust to and alleviates the need for several text processing steps.
A Survey on Complex Question Answering over Knowledge Base: Recent Advances and Challenges
Venue: arXiv cs.CL 2020
Year: 2020

Question Answering (QA) over Knowledge Base (KB) aims to automatically answer natural language questions via well-structured relation information between entities stored in knowledge bases. In order to make KBQA more applicable in actual scenarios, researchers have shifted their attention from simple questions to complex questions, which require more KB triples and constraint inference. In this paper, we introduce the recent advances in complex QA. Besides traditional methods relying on templates and rules, the research is categorized into a taxonomy that contains two main branches, namely Information Retrieval-based and Neural Semantic Parsing-based. After describing the methods of these branches, we analyze directions for future research and introduce the models proposed by the Alime team.
Detecting Relevance during Decision-Making from Eye Movements for UI Adaptation
Venue: arXiv cs.HC 2020
Year: 2020

This paper proposes an approach to detect information relevance during decision-making from eye movements in order to enable user interface adaptation. This is a challenging task because gaze behavior varies greatly across individual users and tasks and groundtruth data is difficult to obtain. Thus, prior work has mostly focused on simpler target-search tasks or on establishing general interest, where gaze behavior is less complex. From the literature, we identify six metrics that capture different aspects of the gaze behavior during decision-making and combine them in a voting scheme. We empirically show, that this accounts for the large variations in gaze behavior and out-performs standalone metrics. Importantly, it offers an intuitive way to control the amount of detected information, which is crucial for different UI adaptation schemes to succeed. We show the applicability of our approach by developing a room-search application that changes the visual saliency of content detected as relevant. In an empirical study, we show that it detects up to 97% of relevant elements with respect to user self-reporting, which allows us to meaningfully adapt the interface, as confirmed by participants. Our approach is fast, does not need any explicit user input and can be applied independent of task and user.
SADet: Learning An Efficient and Accurate Pedestrian Detector
Venue: arXiv cs.CV 2020
Year: 2020

Although the anchor-based detectors have taken a big step forward in pedestrian detection, the overall performance of algorithm still needs further improvement for practical applications, \emph{e.g.}, a good trade-off between the accuracy and efficiency. To this end, this paper proposes a series of systematic optimization strategies for the detection pipeline of one-stage detector, forming a single shot anchor-based detector (SADet) for efficient and accurate pedestrian detection, which includes three main improvements. Firstly, we optimize the sample generation process by assigning soft tags to the outlier samples to generate semi-positive samples with continuous tag value between $0$ and $1$, which not only produces more valid samples, but also strengthens the robustness of the model. Secondly, a novel Center-$IoU$ loss is applied as a new regression loss for bounding box regression, which not only retains the good characteristics of IoU loss, but also solves some defects of it. Thirdly, we also design Cosine-NMS for the postprocess of predicted bounding boxes, and further propose adaptive anchor matching to enable the model to adaptively match the anchor boxes to full or visible bounding boxes according to the degree of occlusion, making the NMS and anchor matching algorithms more suitable for occluded pedestrian detection. Though structurally simple, it presents state-of-the-art result and real-time speed of $20$ FPS for VGA-resolution images ($640 \times 480$) on challenging pedestrian detection benchmarks, i.e., CityPersons, Caltech, and human detection benchmark CrowdHuman, leading to a new attractive pedestrian detector.
Modeling and Predicting Trust Dynamics in Human-Robot Teaming: A Bayesian Inference Approach
Venue: arXiv cs.HC 2020
Year: 2020

Trust in automation, or more recently trust in autonomy, has received extensive research attention in the past two decades. The majority of prior literature adopted a "snapshot" view of trust and typically evaluated trust through questionnaires administered at the end of an experiment. This "snapshot" view, however, does not acknowledge that trust is a time-variant variable that can strengthen or decay over time. To fill the research gap, the present study aims to model trust dynamics when a human interacts with a robotic agent over time. The underlying premise of the study is that by interacting with a robotic agent and observing its performance over time, a rational human agent will update his/her trust in the robotic agent accordingly. Based on this premise, we develop a personalized trust prediction model based on Beta distribution and learn its parameters using Bayesian inference. Our proposed model adheres to three major properties of trust dynamics reported in prior empirical studies. We tested the proposed method using an existing dataset involving 39 human participants interacting with four drones in a simulated surveillance mission. The proposed method obtained a Root Mean Square Error (RMSE) of 0.072, significantly outperforming existing prediction methods. Moreover, we identified three distinctive types of trust dynamics, the Bayesian decision maker, the oscillator, and the disbeliever, respectively. This prediction model can be used for the design of individualized and adaptive technologies.
On the Path-Loss of Reconfigurable Intelligent Surfaces: An Approach Based on Green's Theorem Applied to Vector Fields
Venue: arXiv cs.IT 2020
Year: 2020

In this paper, we introduce a physics-based analytical characterization of the free-space path-loss of a wireless link in the presence of a reconfigurable intelligent surface. The proposed approach is based on the vector generalization of Green's theorem. The obtained path-loss model can be applied to two-dimensional homogenized metasurfaces, which are made of sub-wavelength scattering elements and that operate either in reflection or transmission mode. The path-loss is formulated in terms of a computable integral that depends on the transmission distances, the polarization of the radio waves, the size of the surface, and the desired surface transformation. Closed-form expressions are obtained in two asymptotic regimes that are representative of far-field and near-field deployments. Based on the proposed approach, the impact of several design parameters and operating regimes is unveiled.
Substituting Restorative Benefits of Being Outdoors through Interactive Augmented Spatial Soundscapes
Venue: arXiv cs.CY 2020
Year: 2020

Geriatric depression is a common mental health condition affecting majority of older adults in the US. As per Attention Restoration Theory (ART), participation in outdoor activities is known to reduce depression and provide restorative benefits. However, many older adults, who suffer from depression, especially those who receive care in organizational settings, have less access to sensory experiences of the outdoor natural environment. This is often due to their physical or cognitive limitations and from lack of organizational resources to support outdoor activities. To address this, we plan to study how technology can bring the restorative benefits of outdoors to the indoor environments through augmented spatial natural soundscapes. Thus, we propose an interview and observation-based study at an assisted living facility to evaluate how augmented soundscapes substitute for outdoor restorative, social, and experiential benefits. We aim to integrate these findings into a minimally intrusive and intuitive design of an interactive augmented soundscape, for indoor organizational care settings.
Demo: A Proof-of-Concept Implementation of Guard Secure Routing Protocol
Venue: arXiv cs.DC 2020
Year: 2020

Skip Graphs belong to the family of Distributed Hash Table (DHT) structures that are utilized as routing overlays in various peer-to-peer applications including blockchains, cloud storage, and social networks. In a Skip Graph overlay, any misbehavior of peers during the routing of a query compromises the system functionality. Guard is the first authenticated search mechanism for Skip Graphs, enables reliable search operation in a fully decentralized manner. In this demo paper, we present a proof-of-concept implementation of Guard on Skip Graph nodes as well as a deployment demo scenario.
Beyond the Worst-Case Analysis of Algorithms (Introduction)
Venue: arXiv cs.DS 2020
Year: 2020

One of the primary goals of the mathematical analysis of algorithms is to provide guidance about which algorithm is the "best" for solving a given computational problem. Worst-case analysis summarizes the performance profile of an algorithm by its worst performance on any input of a given size, implicitly advocating for the algorithm with the best-possible worst-case performance. Strong worst-case guarantees are the holy grail of algorithm design, providing an application-agnostic certification of an algorithm's robustly good performance. However, for many fundamental problems and performance measures, such guarantees are impossible and a more nuanced analysis approach is called for. This chapter surveys several alternatives to worst-case analysis that are discussed in detail later in the book.
Measuring similarity in co-occurrence data using ego-networks
Venue: arXiv cs.IR 2020
Year: 2020

The co-occurrence association is widely observed in many empirical data. Mining the information in co-occurrence data is essential for advancing our understanding of systems such as social networks, ecosystem, and brain network. Measuring similarity of entities is one of the important tasks, which can usually be achieved using a network-based approach. Here we show that traditional methods based on the aggregated network can bring unwanted in-directed relationship. To cope with this issue, we propose a similarity measure based on the ego network of each entity, which effectively considers the change of an entity's centrality from one ego network to another. The index proposed is easy to calculate and has a clear physical meaning. Using two different data sets, we compare the new index with other existing ones. We find that the new index outperforms the traditional network-based similarity measures, and it can sometimes surpass the embedding method. In the meanwhile, the measure by the new index is weakly correlated with those by other methods, hence providing a different dimension to quantify similarities in co-occurrence data. Altogether, our work makes an extension in the network-based similarity measure and can be potentially applied in several related tasks.
Stabilizing Bi-Level Hyperparameter Optimization using Moreau-Yosida Regularization
Venue: arXiv cs.LG 2020
Year: 2020

This research proposes to use the Moreau-Yosida envelope to stabilize the convergence behavior of bi-level Hyperparameter optimization solvers, and introduces the new algorithm called Moreau-Yosida regularized Hyperparameter Optimization (MY-HPO) algorithm. Theoretical analysis on the correctness of the MY-HPO solution and initial convergence analysis is also provided. Our empirical results show significant improvement in loss values for a fixed computation budget, compared to the state-of-art bi-level HPO solvers.
Feature visualization of Raman spectrum analysis with deep convolutional neural network
Venue: arXiv cs.CV 2020
Year: 2020

We demonstrate a recognition and feature visualization method that uses a deep convolutional neural network for Raman spectrum analysis. The visualization is achieved by calculating important regions in the spectra from weights in pooling and fully-connected layers. The method is first examined for simple Lorentzian spectra, then applied to the spectra of pharmaceutical compounds and numerically mixed amino acids. We investigate the effects of the size and number of convolution filters on the extracted regions for Raman-peak signals using the Lorentzian spectra. It is confirmed that the Raman peak contributes to the recognition by visualizing the extracted features. A near-zero weight value is obtained at the background level region, which appears to be used for baseline correction. Common component extraction is confirmed by an evaluation of numerically mixed amino acid spectra. High weight values at the common peaks and negative values at the distinctive peaks appear, even though the model is given one-hot vectors as the training labels (without a mix ratio). This proposed method is potentially suitable for applications such as the validation of trained models, ensuring the reliability of common component extraction from compound samples for spectral analysis.
Bernoulli Meets PBFT: Modeling BFT Protocols in the Presence of Dynamic Failures
Venue: arXiv cs.DC 2020
Year: 2020

The publication of the pivotal state machine replication protocol PBFT laid the foundation for a large body of BFT protocols. While many successors to PBFT have been developed, there is no general technique to compare these protocols under realistic network conditions such as unreliable links. In this paper, we introduce a probabilistic model for evaluating BFT protocols in the presence of dynamic link and crash failures. Based on modeling techniques from communication theory, the network state of replicas is captured and used to derive the success probability of the protocol execution. To this end, we examine the influence of link and crash failure rates as well as the number of replicas. The model is derived from the communication pattern, making it implementation-independent and facilitating an adaptation to other BFT protocols. The model is validated with a simulation of PBFT and BFT-SMaRt. Further, a comparison in protocol behavior of PBFT, Zyzzyva and SBFT is performed and critical failure thresholds are identified.e thresholds are identified.
Towards an ontology of HTTP interactions
Venue: arXiv cs.AI 2020
Year: 2020

Enterprise information systems have adopted Web-based foundations for exchanges between heterogeneous programmes. These programs provide and consume via Web APIs some resources identified by URIs, whose representations are transmitted via HTTP. Furthermore HTTP remains at the heart of all Web developments (Semantic Web, linked data, IoT...). Thus, situations where a program must be able to reason about HTTP interactions (request-response) are multiplying. This requires an explicit formal specification of a shared conceptualization of those interactions. A proposal for an RDF vocabulary exists, developed with a view to carrying out web application conformity tests and record the tests outputs. This vocabulary has already been reused. In this paper we propose to adapt and extend it for making it more reusable.
Ari: The Automated R Instructor
Venue: arXiv cs.MM 2020
Year: 2020

We present the ari package for automatically generating technology-focused educational videos. The goal of the package is to create reproducible videos, with the ability to change and update video content seamlessly. We present several examples of generating videos including using R Markdown slide decks, PowerPoint slides, or simple images as source material. We also discuss how ari can help instructors reach new audiences through programmatically translating materials into other languages.
Deep Multi-Task Learning for Cooperative NOMA: System Design and Principles
Venue: arXiv cs.IT 2020
Year: 2020

Envisioned as a promising component of the future wireless Internet-of-Things (IoT) networks, the non-orthogonal multiple access (NOMA) technique can support massive connectivity with a significantly increased spectral efficiency. Cooperative NOMA is able to further improve the communication reliability of users under poor channel conditions. However, the conventional system design suffers from several inherent limitations and is not optimized from the bit error rate (BER) perspective. In this paper, we develop a novel deep cooperative NOMA scheme, drawing upon the recent advances in deep learning (DL). We develop a novel hybrid-cascaded deep neural network (DNN) architecture such that the entire system can be optimized in a holistic manner. On this basis, we construct multiple loss functions to quantify the BER performance and propose a novel multi-task oriented two-stage training method to solve the end-to-end training problem in a self-supervised manner. The learning mechanism of each DNN module is then analyzed based on information theory, offering insights into the proposed DNN architecture and its corresponding training method. We also adapt the proposed scheme to handle the power allocation (PA) mismatch between training and inference and incorporate it with channel coding to combat signal deterioration. Simulation results verify its advantages over orthogonal multiple access (OMA) and the conventional cooperative NOMA scheme in various scenarios.
A Novel adaptive optimization of Dual-Tree Complex Wavelet Transform for Medical Image Fusion
Venue: arXiv cs.CV 2020
Year: 2020

In recent years, many research achievements are made in the medical image fusion field. Fusion is basically extraction of best of inputs and conveying it to the output. Medical Image fusion means that several of various modality image information is comprehended together to form one image to express its information. The aim of image fusion is to integrate complementary and redundant information. In this paper, a multimodal image fusion algorithm based on the dual-tree complex wavelet transform (DT-CWT) and adaptive particle swarm optimization (APSO) is proposed. Fusion is achieved through the formation of a fused pyramid using the DTCWT coefficients from the decomposed pyramids of the source images. The coefficients are fused by the weighted average method based on pixels, and the weights are estimated by the APSO to gain optimal fused images. The fused image is obtained through conventional inverse dual-tree complex wavelet transform reconstruction process. Experiment results show that the proposed method based on adaptive particle swarm optimization algorithm is remarkably better than the method based on particle swarm optimization. The resulting fused images are compared visually and through benchmarks such as Entropy (E), Peak Signal to Noise Ratio, (PSNR), Root Mean Square Error (RMSE), Standard deviation (SD) and Structure Similarity Index Metric (SSIM) computations.
VFL: A Verifiable Federated Learning with Privacy-Preserving for Big Data in Industrial IoT
Venue: arXiv cs.CR 2020
Year: 2020

Due to the strong analytical ability of big data, deep learning has been widely applied to train the collected data in industrial IoT. However, for privacy issues, traditional data-gathering centralized learning is not applicable to industrial scenarios sensitive to training sets. Recently, federated learning has received widespread attention, since it trains a model by only relying on gradient aggregation without accessing training sets. But existing researches reveal that the shared gradient still retains the sensitive information of the training set. Even worse, a malicious aggregation server may return forged aggregated gradients. In this paper, we propose the VFL, verifiable federated learning with privacy-preserving for big data in industrial IoT. Specifically, we use Lagrange interpolation to elaborately set interpolation points for verifying the correctness of the aggregated gradients. Compared with existing schemes, the verification overhead of VFL remains constant regardless of the number of participants. Moreover, we employ the blinding technology to protect the privacy of the gradients submitted by the participants. If no more than n-2 of n participants collude with the aggregation server, VFL could guarantee the encrypted gradients of other participants not being inverted. Experimental evaluations corroborate the practical performance of the presented VFL framework with high accuracy and efficiency.
Towards Accuracy-Fairness Paradox: Adversarial Example-based Data Augmentation for Visual Debiasing
Venue: arXiv cs.CV 2020
Year: 2020

Machine learning fairness concerns about the biases towards certain protected or sensitive group of people when addressing the target tasks. This paper studies the debiasing problem in the context of image classification tasks. Our data analysis on facial attribute recognition demonstrates (1) the attribution of model bias from imbalanced training data distribution and (2) the potential of adversarial examples in balancing data distribution. We are thus motivated to employ adversarial example to augment the training data for visual debiasing. Specifically, to ensure the adversarial generalization as well as cross-task transferability, we propose to couple the operations of target task classifier training, bias task classifier training, and adversarial example generation. The generated adversarial examples supplement the target task training dataset via balancing the distribution over bias variables in an online fashion. Results on simulated and real-world debiasing experiments demonstrate the effectiveness of the proposed solution in simultaneously improving model accuracy and fairness. Preliminary experiment on few-shot learning further shows the potential of adversarial attack-based pseudo sample generation as alternative solution to make up for the training data lackage.
Black-Box Face Recovery from Identity Features
Venue: arXiv cs.CV 2020
Year: 2020

In this work, we present a novel algorithm based on an it-erative sampling of random Gaussian blobs for black-box face recovery, given only an output feature vector of deep face recognition systems. We attack the state-of-the-art face recognition system (ArcFace) to test our algorithm. Another network with different architecture (FaceNet) is used as an independent critic showing that the target person can be identified with the reconstructed image even with no access to the attacked model. Furthermore, our algorithm requires a significantly less number of queries compared to the state-of-the-art solution.