Reviewerly/exHarmony huggingface dataset:
Phase Transitions for One-Vs-One and One-Vs-All Linear Separability in Multiclass Gaussian Mixtures

We study a fundamental statistical question in multiclass classification: When are data linearly separable? Unlike binary classification, linear separability in multiclass settings can be defined in different ways. Here, we focus on the so called one-vs-one (OvO) and one-vs-all (OvA) linear separability. We consider data generated from a Gaussian mixture model (GMM) in a linear asymptotic high-dimensional regime. In this setting, we prove that both the OvO and OvA separability undergo a sharp phase-transition as a function of the overparameterization ratio. We present precise formulae characterizing the phase transitions as a function of the data geometry and the number of classes. Existing results on binary classification follow as special cases of our new formulae. Numerical simulations verify the validity of the asymptotic predictions in finite dimensions.
Video Anomaly Detection by Estimating Likelihood of Representations

Video anomaly detection is a challenging task not only because it involves solving many sub-tasks such as motion representation, object localization and action recognition, but also because it is commonly considered as an unsupervised learning problem that involves detecting outliers. Traditionally, solutions to this task have focused on the mapping between video frames and their low-dimensional features, while ignoring the spatial connections of those features. Recent solutions focus on analyzing these spatial connections by using hard clustering techniques, such as K-Means, or applying neural networks to map latent features to a general understanding, such as action attributes. In order to solve video anomaly in the latent feature space, we propose a deep probabilistic model to transfer this task into a density estimation problem where latent manifolds are generated by a deep denoising autoencoder and clustered by expectation maximization. Evaluations on several benchmarks datasets show the strengths of our model, achieving outstanding performance on challenging datasets.
Improving On-line Scientific Resource Profiling by Exploiting Resource Citation Information in the Literature

We study the task of on-line scientific resource profiling, which aims at better understanding and summarizing on-line scientific resources to promote resource search and recommendation systems. To this end we propose to exploit the resource citation information in scientific literature by extracting the fine-grained relations between the cited on-line resources and other resource-related scientific terms. In this paper we create a dataset (SciResTR) and develop a framework (SciResTR-IE) which jointly extracts all the related scientific terms and the resource-term relations. Extensive experiments demonstrate that our framework outperforms other baselines significantly, by around 5% in scientific information extraction tasks absolutely. We further show that our proposed system can automatically construct several on-line-resource-centered networks from a large corpus of scientific articles, which is a first step towards utilizing resource citation information in the literature to improve on-line scientific resource profiling.
An orthogonal classifier for improving the adversarial robustness of neural networks

Neural networks are susceptible to artificially designed adversarial perturbations. Recent efforts have shown that imposing certain modifications on classification layer can improve the robustness of the neural networks. In this paper, we explicitly construct a dense orthogonal weight matrix whose entries have the same magnitude, thereby leading to a novel robust classifier. The proposed classifier avoids the undesired structural redundancy issue in previous work. Applying this classifier in standard training on clean data is sufficient to ensure the high accuracy and good robustness of the model. Moreover, when extra adversarial samples are used, better robustness can be further obtained with the help of a special worst-case loss. Experimental results show that our method is efficient and competitive to many state-of-the-art defensive approaches. Our code is available at https://github.com/MTandHJ/roboc.
Independent Sign Language Recognition with 3d Body, Hands, and Face Reconstruction

Independent Sign Language Recognition is a complex visual recognition problem that combines several challenging tasks of Computer Vision due to the necessity to exploit and fuse information from hand gestures, body features and facial expressions. While many state-of-the-art works have managed to deeply elaborate on these features independently, to the best of our knowledge, no work has adequately combined all three information channels to efficiently recognize Sign Language. In this work, we employ SMPL-X, a contemporary parametric model that enables joint extraction of 3D body shape, face and hands information from a single image. We use this holistic 3D reconstruction for SLR, demonstrating that it leads to higher accuracy than recognition from raw RGB images and their optical flow fed into the state-of-the-art I3D-type network for 3D action recognition and from 2D Openpose skeletons fed into a Recurrent Neural Network. Finally, a set of experiments on the body, face and hand features showed that neglecting any of these, significantly reduces the classification accuracy, proving the importance of jointly modeling body shape, facial expression and hand pose for Sign Language Recognition.
Design and Implementation of Fuzzy Comprehensive Evaluation System for Teaching Effect of Physical Education in Colleges and Universities

In recent years, with the development of modern education in China, higher requirements are put forward for physical education. At present, the evaluation of education effect mainly depends on the teaching evaluation system, but the evaluation of the system in physical education teaching is not reasonable. Therefore, this paper puts forward the design and implementation of the fuzzy comprehensive evaluation system of college physical education teaching effect. In this paper, the fuzzy comprehensive evaluation and the application of this method in the field of physical education teaching evaluation are deeply studied. Combined with the main problems existing in the field of physical education teaching evaluation, a new evaluation system of physical education teaching effect is established through fuzzy comprehensive evaluation. In order to make the fuzzy comprehensive evaluation method more suitable for the application in the field of physical education teaching, this paper optimizes and improves the calculation method of fuzzy comprehensive evaluation method. By simplifying the calculation method and optimizing the formula structure, the calculation accuracy of the model is effectively improved. According to the actual situation of PE teaching evaluation, this paper establishes the corresponding evaluation index, and introduces the weight calculation of the index. In order to further verify the effectiveness of this method, the corresponding test experiments are established. In this experiment, the score of teaching emphasis is the highest, all above 8, while the lowest level of teaching input is 2-3. The analysis shows that the model in this paper can better analyze the existing problems in physical education teaching, and provide reference for the optimization and improvement of college physical education.
A Homomorphic Digit Fragmentation Encryption Scheme Based on the Polynomial Reconstruction Problem

In the fast moving field of computer cryptography, researchers are actively working on addressing various emerging challenges to keep tomorrow’s systems secure. Many efforts are focused on addressing the computation speed and the massive data size requirements of today’s systems. Significant research efforts are targeting the computational complexity by focusing on the number of cryptographic operations or the scope of application. In this paper, we present an asymmetric homomorphic encryption scheme that relies on converting base numbers to a given base in such a way that Leveled Fully Homomorphic Encryption (LFHE) is performed. This lightweight technique is easy to implement and applicable in many fields, especially those that require high speed and safety.
Blind Deblurring Based on Sigmoid Function

Blind image deblurring, also known as blind image deconvolution, is a long-standing challenge in the field of image processing and low-level vision. To restore a clear version of a severely degraded image, this paper proposes a blind deblurring algorithm based on the sigmoid function, which constructs novel blind deblurring estimators for both the original image and the degradation process by exploring the excellent property of sigmoid function and considering image derivative constraints. Owing to these symmetric and non-linear estimators of low computation complexity, high-quality images can be obtained by the algorithm. The algorithm is also extended to image sequences. The sigmoid function enables the proposed algorithm to achieve state-of-the-art performance in various scenarios, including natural, text, face, and low-illumination images. Furthermore, the method can be extended naturally to non-uniform deblurring. Quantitative and qualitative experimental evaluations indicate that the algorithm can remove the blur effect and improve the image quality of actual and simulated images. Finally, the use of sigmoid function provides a new approach to algorithm performance optimization in the field of image restoration.
Video Summarization with a Dual Attention Capsule Network

In this paper, we address the problem of video summarization, which aims at selecting a subset of video frames as a summary to represent the original video contents compactly and completely. We propose a simple but effective supervised approach with a dual attention capsule network towards this end. Unlike existing LSTM based methods, it pays attention to short- and long-term dependencies among video frames through an elaborate dual self-attention architecture, which can handle longer-term dependencies and admit parallel computing. To reconcile the outputs of dual self-attention, we rely on a two-stream capsule network to learn the underlying frame selection criteria. Experiments on real-world datasets show the advantages of the proposed approach compared with state-of-the-art methods.
A Joint Learning Framework for the CCKS-2020 Financial Event Extraction Task

This paper presents a winning solution for the CCKS-2020 financial event extraction task, where the goal is to identify event types, triggers and arguments in sentences across multiple event types. In this task, we focus on resolving two challenging problems (i.e., low resources and element overlapping) by proposing a joint learning framework, named SaltyFishes. We first formulate the event extraction task as a joint probability model. By sharing parameters in the model across different types, we can learn to adapt to low-resource events based on high-resource events. We further address the element overlapping problems by a mechanism of Conditional Layer Normalization, achieving even better extraction accuracy. The overall approach achieves an F1-score of 87.8% which ranks the first place in the competition.
A Blockchain to Bridge Business Information Systems and Industrial Automation Environments in Supply Chains

The design of information systems using distributed ledger technologies needs to balance the potential of disruptive technologies with community needs. The NutriSafe project implements a blockchain-based infrastructure for food supply chains using the Hyperledger Fabric framework. The paper presents results from engineering a blockchain-based solution for food production and logistics with process models, architecture, and concepts and two focus areas: the bridges between business information systems to blockchain and from Internet of Things technology to blockchain. The article discusses the concepts of integrating the blockchain infrastructure across all layers of the automation pyramid, with the implications of integrating operational technology and storing semantics between products and machinery on a distributed ledger. Throughout, ideas and designs take the needs of small and medium-sized organizations into account and aim for an open, distributed approach inspired by the edge computing philosophy.
Adversarial Attacks on Coarse-to-Fine Classifiers

Adversarial attacks have exposed the vulnerability of one-stage classifiers to carefully crafted perturbations which were shown to drastically alter their predictions while remaining imperceptible. In this paper, we examine the susceptibility of coarse-to-fine hierarchical classifiers to such types of attacks. We formulate convex programs to generate perturbations attacking these models and propose a generic solution based on the Alternating Direction Method of Multipliers (ADMM). We evaluate the performance of the proposed models using the degradation in classification accuracy and imperceptibility measures in comparison to perturbations generated to fool one-stage classifiers.
A Survey on the Application of Fault Analysis on Lightweight Cryptography

With a growing number of connected devices the demand for secure communication increases steadily. Therefore, the National Institute of Standards and Technology started the Lightweight Cryptography competition for authenticated encryption with associated data and hash applications. Its goal is to identify small, power efficient and secure cryptographic primitives. The 32 second round candidates of the Lightweight Cryptography competition are classified into three categories according to their architecture, i.e. block, sponge, or stream based ciphers. Fault Analysis is an approach to perform implementation level attacks and is composed of a broad spectrum of attack strategies. These strategies are classified into analytical and statistical approaches. In the following we provide an overview of state-of-the-art fault attacks and their application. We will then categorize Fault Analysis strategies by their application on block, sponge, or stream based ciphers. This gives an idea of the applicability of different kinds of Fault Analysis on the second round candidates of the Lightweight Cryptography competition.
Generating Human Readable Transcript for Automatic Speech Recognition with Pre-Trained Language Model

Modern Automatic Speech Recognition (ASR) systems can achieve high performance in terms of recognition accuracy. However, a perfectly accurate transcript still can be challenging to read due to disfluency, filter words, and other errata common in spoken communication. Many downstream tasks and human readers rely on the output of the ASR system; therefore, errors introduced by the speaker and ASR system alike will be propagated to the next task in the pipeline. In this work, we propose an ASR post-processing model that aims to transform the incorrect and noisy ASR output into a readable text for humans and downstream tasks. We leverage the Metadata Extraction (MDE) corpus to construct a task-specific dataset for our study. Since the dataset is small, we propose a novel data augmentation method and use a two-stage training strategy to fine-tune the RoBERTa pre-trained model. On the constructed test set, our model outperforms a production two-step pipeline-based post-processing method by a large margin of 13.26 on readability-aware WER (RA-WER) and 17.53 on BLEU metrics. Human evaluation also demonstrates that our method can generate more human-readable transcripts than the baseline method.
Blockchain Privacy Through Merge Avoidance and Mixing Services

Cryptocurrencies typically aim at preserving the privacy of their users. Different cryptocurrencies preserve privacy at various levels, some of them requiring users to rely on strategies to raise the privacy level to their needs. Among those strategies, we focus on two of them: merge avoidance and mixing services. Such strategies may be adopted on top of virtually any blockchain-based cryptocurrency. In this paper, we show that whereas optimal merge avoidance leads to an NP-hard optimization problem, incentive-compatible mixing services are subject to a certain class of impossibility results. Together, our results contribute to the body of work on fundamental limits of privacy mechanisms in blockchainbased cryptocurrencies.
An Augmented Reality Online Assistance Platform for Repair Tasks

Our augmented reality online assistance platform enables an expert to specify 6DoF movements of a component and apply the geometrical and physical constraints in real-time. We track the real components on the expert’s side to monitor the operations of an expert. We leverage a remote rendering technique that we proposed previously to relieve the rendering burden of the augmented reality end devices. By conducting a user study, we show that the proposed method outperforms conventional instructional videos and sketches. The answers to the questionnaires show that the proposed method receives higher recommendation than sketching, and, compared to conventional instructional videos, is outstanding in terms of instruction clarity, preference, recommendation, and confidence of task completion. Moreover, as to the overall user experience, the proposed method has an advantage over the video method.
Virtual Equipment System: Face Mask and Voodoo Doll for User Privacy and Self-Expression Options in Virtual Reality

Current trends in immersive technologies suggest an increase in capturing user's data to drive interactions and avatar representations. With growing numbers of data types being collected, users need an easy way to view and control their privacy settings. In this demo, we present a method for users to adjust options related to privacy settings, user data collection, and self-expression through the use of 3D user interface metaphors such as a mask and a voodoo doll.
A Low-Power Elliptic Curve Pairing Crypto-Processor for Secure Embedded Blockchain and Functional Encryption

Pairing-based cryptography (PBC), a variant of elliptic curve cryptography (ECC), uses bilinear maps between elliptic curves and finite fields to enable several novel applications beyond traditional key exchange and signatures [1]. Fig. 1 shows two such applications - (a) signature aggregation, where arbitrarily large number of signatures are aggregated into one to resolve communication bottleneck in mesh networks such as blockchain [9], and (b) functional encryption, which allows computation on encrypted data with a function embedded in the decryption key [7]. In particular, pairing-based function-hiding inner product functional encryption [10] can be used for privacy-preserving data classification, thus enabling a new paradigm in the field of secure computation.
A MapReduce Improved ID3 Decision Tree for Classifying Twitter Data

In this contribution, we introduce an innovative classification approach for opinion mining. We have used the feature extractor Fast Text to detect and capture the given tweets' relevant data efficiently. Then, we have applied the feature selector Information Gain to reduce the dimensionality of the high feature. Finally, we have employed the obtained features to carry out the classification task using our improved ID3 decision tree classifier, which aims to calculate the weighted information gain instead of information gain used in traditional ID3. In other words, to measure the weighted information gain for the current conditioned feature, we follow two steps: First, we compute the weighted correlation function of the current conditioned feature. Second, we multiply the obtained weighted correlation function by the information gain of this current conditioned feature. This work is implemented in a distributed environment using the Hadoop framework, with its programming framework MapReduce and its distributed file system HDFS. Its primary goal is to enhance the performance of a well-known ID3 classifier in terms of accuracy, execution time, and ability to handle massive datasets. We have performed several experiments that aim to evaluate our suggested classifier's effectiveness compared to some other contributions chosen from the literature.
Towards Immediate Backchannel Generation Using Attention-Based Early Prediction Model

Backchannel feedback from the spoken dialogue system makes the human-machine interaction more sophisticated. To predict suitable timing and forms, backchannel prediction technology has been studied. Most studies have combined acoustic and lexical features into the model for better prediction. However, extracting lexical features leads to a delay caused by the automatic speech recognition (ASR) process. To make accurate predictions on the basis of delayed ASR outputs, we propose early prediction for backchannel opportunity and backchannel category based on attention-based LSTM mechanisms. The loss is calculated with a weighting value that gradually increases when a sequence is closer to a suitable response timing. The proposed backchannel prediction uses a two-step approach that first detects a backchannel opportunity and then predicts a backchannel category. Evaluation results show that the early prediction model can predict a backchannel opportunity and category better than the current state-of-the-art algorithm even under a 2.0-second ASR delay condition.
ResNet-like Architecture with Low Hardware Requirements

One of the most computationally intensive parts in modern recognition systems is an inference of deep neural networks that are used for image classification, segmentation, enhancement, and recognition. The growing popularity of edge computing makes us look for ways to reduce its time for mobile and embedded devices. One way to decrease the neural network inference time is to modify a neuron model to make it more efficient for computations on a specific device. The example of such a model is a bipolar morphological neuron model. The bipolar morphological neuron is based on the idea of replacing multiplication with addition and maximum operations. This model has been demonstrated for simple image classification with LeNet-like architectures [1]. In the paper, we introduce a bipolar morphological ResNet (BM-ResNet) model obtained from a much more complex ResNet architecture by converting its layers to bipolar morphological ones. We apply BM-ResNet to image classification on MNIST and CIFAR-10 datasets with only a moderate accuracy decrease from 99.3% to 99.1 % and from 85.3% to 85.1 %. We also estimate the computational complexity of the resulting model. We show that for the majority of ResNet layers, the considered model requires 2.1-2.9 times fewer logic gates for implementation and 15-30 % lower latency.
Testing Framework for Black-box AI Models

With widespread adoption of AI models for important decision making, ensuring reliability of such models remains an important challenge. In this paper, we present an end-to-end generic framework for testing AI Models which performs automated test generation for different modalities such as text, tabular, and time-series data and across various properties such as accuracy, fairness, and robustness. Our tool has been used for testing industrial AI models and was very effective to uncover issues present in those models. Demo video link-https://youtu.be/984UCU17YZI