Reviewerly/exHarmony huggingface dataset:
An Improved BP Neural Network-based Quality Evaluation Model for Chinese International Education Teaching Courses

For learners of Chinese as a second language, it is difficult to learn Chinese in the target language environment. With the large-scale applications of intelligent technology in Chinese international education, Chinese teaching is moving towards digitalization, networking and informatization, and computer technology is promoting the development of Chinese international education in a more convenient way. In order to effectively improve the teaching quality of Chinese international education, a scientific and reasonable evaluation system of course teaching quality needs to be formulated. This paper addresses the shortcomings of the traditional course teaching quality evaluation methods, introduces BP neural network to establish the teaching evaluation base model, and sets the corresponding course evaluation indicatores as the input quantity. Considering the problems of local optimum and low convergence efficiency of BP neural network, an improved BP neural network is proposed to optimize the initial weights and thresholds of BP neural network using the beetle antennae search algorithm, so as to obtain the improved teaching quality evaluation model of Chinese international education courses. Then, the performance test of the model is conducted in this paper. The test results show that the proposed teaching quality evaluation model can accurately evaluate the teaching quality of the curriculum and help improve the teaching courses of Chinese international education, which is an innovative attempt to combine computer technology with Chinese international education. Finally, this paper puts forward corresponding suggestions for the combination of Chinese international education and computer technology, in order to promote the further development of Chinese international education.
FastRE: Towards Fast Relation Extraction with Convolutional Encoder and Improved Cascade Binary Tagging Framework

Recent work for extracting relations from texts has achieved excellent performance. However, most existing methods pay less attention to the efficiency, making it still challenging to quickly extract relations from massive or streaming text data in realistic scenarios. The main efficiency bottleneck is that these methods use a Transformer-based pre-trained language model for encoding, which heavily affects the training speed and inference speed. To address this issue, we propose a fast relation extraction model (FastRE) based on convolutional encoder and improved cascade binary tagging framework. Compared to previous work, FastRE employs several innovations to improve efficiency while also keeping promising performance. Concretely, FastRE adopts a novel convolutional encoder architecture combined with dilated convolution, gated unit and residual connection, which significantly reduces the computation cost of training and inference, while maintaining the satisfactory performance. Moreover, to improve the cascade binary tagging framework, FastRE first introduces a type-relation mapping mechanism to accelerate tagging efficiency and alleviate relation redundancy, and then utilizes a position-dependent adaptive thresholding strategy to obtain higher tagging accuracy and better model generalization. Experimental results demonstrate that FastRE is well balanced between efficiency and performance, and achieves 3-10$\times$ training speed, 7-15$\times$ inference speed faster, and 1/100 parameters compared to the state-of-the-art models, while the performance is still competitive. Our code is available at \url{https://github.com/seukgcode/FastRE}.
Phrase-level Textual Adversarial Attack with Label Preservation

Generating high-quality textual adversarial examples is critical for investigating the pitfalls of natural language processing (NLP) models and further promoting their robustness. Existing attacks are usually realized through word-level or sentence-level perturbations, which either limit the perturbation space or sacrifice fluency and textual quality, both affecting the attack effectiveness. In this paper, we propose Phrase-Level Textual Adversarial ATtack (PLAT) that generates adversarial samples through phrase-level perturbations. PLAT first extracts the vulnerable phrases as attack targets by a syntactic parser, and then perturbs them by a pre-trained blank-infilling model. Such flexible perturbation design substantially expands the search space for more effective attacks without introducing too many modifications, and meanwhile maintaining the textual fluency and grammaticality via contextualized generation using surrounding texts. Moreover, we develop a label preservation filter leveraging the likelihoods of language models fine-tuned on each class, rather than textual similarity, to rule out those perturbations that potentially alter the original class label for humans. Extensive experiments and human evaluation demonstrate that PLAT has a superior attack effectiveness as well as a better label consistency than strong baselines.
A High-Performance Hardware Architecture for ECC Point Multiplication over Curve25519

As one of the most secure ECC curves, Curve25519 is employed by some secure protocols, such as TLS 1.3, IRTF’s RFC7748, Diffie-Hellman Private Set Intersection (DH-PSI) protocol, etc. High performance implementation of ECC is required, especially for the DH-PSI protocol. Point multiplication, the chief cryptographic primitive in ECC, is computationally expensive. To improve the performance of DH-PSI protocol, we propose a novel and high-performance hardware architecture for point multiplication over Curve25519. The proposed architecture features a pipelined Finite-field Arithmetic Unit (FAU) and a simple and highly efficient instruction set architecture (ISA). Compared to the best existing work on Xilinx Zynq 7000 series FPGA, our implementation with one Processing Element (PE) can achieve 3.14x speedup on the same device. To the best of our knowledge, our implementation appears to be the fastest among the state-of-the-art works. We also have implemented our proposed architecture consisting of 4 Compute Groups (CGs), each with 16 PEs, on an Intel Agilex AGF027 FPGA. The experimental results show the peak performance of 4.52 Mops/s (million point multiplication operations per seconds) can be achieved. Moreover, the measured performance of 4.48 Mops/s is achieved, with the PE utilization of 99% and at the cost of 86 Watts power, which is the record-setting performance for point multiplication over Curve25519 on FPGAs.
Modularized Transfer Learning with Multiple Knowledge Graphs for Zero-shot Commonsense Reasoning

Commonsense reasoning systems should be able to generalize to diverse reasoning cases.However, most state-of-the-art approaches depend on expensive data annotations and overfit to a specific benchmark without learning how to perform general semantic reasoning.To overcome these drawbacks, zero-shot QA systems have shown promise as a robust learning scheme by transforming a commonsense knowledge graph (KG) into synthetic QAform samples for model training.Considering the increasing type of different commonsense KGs, this paper aims to extend the zero-shot transfer learning scenario into multiple-source settings, where different KGs can be utilized synergetically.Towards this goal, we propose to mitigate the loss of knowledge from the interference among the different knowledge sources, by developing a modular variant of the knowledge aggregation as a new zero-shot commonsense reasoning framework.Results on five commonsense reasoning benchmarks demonstrate the efficacy of our framework, improving the performance with multiple KGs.
Task-Customized Self-Supervised Pre-training with Scalable Dynamic Routing

Self-supervised learning (SSL), especially contrastive methods, has raised attraction recently as it learns effective transferable representations without semantic annotations. A common practice for self-supervised pre-training is to use as much data as possible. For a specific downstream task, however, involving irrelevant data in pre-training may degenerate the downstream performance, observed from our extensive experiments. On the other hand, for existing SSL methods, it is burdensome and infeasible to use different downstream-task-customized datasets in pre-training for different tasks. To address this issue, we propose a novel SSL paradigm called Scalable Dynamic Routing (SDR), which can be trained once and deployed efficiently to different downstream tasks with task-customized pre-trained models. Specifically, we construct the SDRnet with various sub-nets and train each sub-net with only one subset of the data by data-aware progressive training. When a downstream task arrives, we route among all the pre-trained sub-nets to get the best along with its corresponding weights. Experiment results show that our SDR can train 256 sub-nets on ImageNet simultaneously, which provides better transfer performance than a unified model trained on the full ImageNet, achieving state-of-the-art (SOTA) averaged accuracy over 11 downstream classification tasks and AP on PASCAL VOC detection task.
Compressed video ensemble based pseudo-labeling for semi-supervised action recognition

Some recent studies have focused on deep learning based semi-supervised learning for action recognition. However, it is difficult to scale up their training because their input is RGB frames, the obtainment of which incurs computational and storage costs. In this paper, we propose a semi-supervised action recognition method that makes it easy to scale up the training by using features stored in compressed videos. Our method directly extracts multiple types of input features from compressed videos without any decoding and generates artificial labels of unlabeled videos through the ensembling of the predictions from these features. In addition to the standard supervised training on labeled videos, our models are trained to predict the artificial labels from strongly augmented features in unlabeled compressed videos. We show that our method is more efficient and achieves a better classification performance on some widely used datasets than conventional semi-supervised learning methods applying RGB frames.
Metrics for evaluating adversarial attack patterns

The machine learning community has seen an explosion in the sophistication of adversarial attacks against deep neural network-based computer vision models. In particular, researchers have successfully used adversarial patterns to trigger false positive or false negative results in both research and real-world settings. However, researchers have not yet codified performance metrics for evaluating the efficacy of attack techniques. This evaluation is needed to adequately assess performance improvements of novel adversarial attack methods. This study aims to contribute the following: adversarial pattern performance metrics, demonstration of each metric’s strengths and contributions on a case study, and an initial standardized performance evaluation strategy for novel adversarial pattern attacks. We train state-of-the-art deep neural network-based object detection models on an open-source dataset. We then use these trained models to evaluate trained adversarial patterns for both false positive and false negative attacks and evaluate their performance using our suite of metrics in order to establish and codify a workflow to be used when evaluating future adversarial pattern algorithms.
Implicit Interaction Approach for Car-related Tasks On Smartphone Applications - A Demo

Implicit interaction is a possible approach to improve the user experience of smartphone apps in car-related environments. Indeed, it can enhance safety and avoids unnecessary and repetitive interactions on the user’s part.
Top-down effect of body representation on pain perception

Many studies on body representation intend to change the perceived size, material, and structure of the body. However, whether the perception of a stimulus can be modified by manipulating body representation remains largely unexplored. Thus, the current study investigated the relationship between transparency of body representation and pain perception. Using augmented reality technology, we made the participants' limbs transparent and analyzed changes in body representation. Using a questionnaire, we confirmed that the participants perceived their limb as transparent. Simultaneously, their sense of ownership of the limb decreased, because they felt that it no longer belonged to their body. The participants were given an electrical stimulus to assess their subjective perception of pain intensity. An increase in limb opacity decreased the perception of pain, which, in turn, increased the feeling of transparency. These results suggested that the feeling of transparency in their limb favored the decrease in perceived pain. This effect was modified by body ownership, where high levels reinforced the analgesic effect. However, body ownership displayed a positive relationship with perceived pain. The study suggests that body transparency may constitute a strategy for decreasing refractory pain given that body ownership is retained at a high level.
A Simple Meta-learning Paradigm for Zero-shot Intent Classification with Mixture Attention Mechanism

Zero-shot intent classification is a vital and challenging task in dialogue systems, which aims to deal with numerous fast-emerging unacquainted intents without annotated training data. To obtain more satisfactory performance, the crucial points lie in two aspects: extracting better utterance features and strengthening the model generalization ability. In this paper, we propose a simple yet effective meta-learning paradigm for zero-shot intent classification. To learn better semantic representations for utterances, we introduce a new mixture attention mechanism, which encodes the pertinent word occurrence patterns by leveraging the distributional signature attention and multi-layer perceptron attention simultaneously. To strengthen the transfer ability of the model from seen classes to unseen classes, we reformulate zero-shot intent classification with a meta-learning strategy, which trains the model by simulating multiple zero-shot classification tasks on seen categories, and promotes the model generalization ability with a meta-adapting procedure on mimic unseen categories. Extensive experiments on two real-world dialogue datasets in different languages show that our model outperforms other strong baselines on both standard and generalized zero-shot intent classification tasks.
Analysis of Risk Factors in Global Software Development: A Cross-Continental Study Using Modified Firefly Algorithm

In today’s competitive world, software organizations are moving towards global software development (GSD). This became even more significant in times such as COVID-19 pandemic, where team members residing in different geographical locations and from different cultures had to work from home to carry on their tasks and responsibilities as travelling was restricted. These teams are distributed in nature and work on the same set of goals and objectives. Some of the key challenges which software practitioners face in GSD environment are cultural differences, communication issues, use of different software models, temporal and spatial distance, and risk factors. Risks can be considered as a biggest challenge of other challenges, but not many researchers have addressed risks related to time, cost, and resources. In this research paper, a comprehensive analysis of software project risk factors in GSD environment has been performed. Based on the literature review, 54 risk factors were identified in the context of software development. These were further classified by practitioners into three dimensions, i.e., time, cost, and resource. A Pareto analysis has been performed to discover the most important risk factors, which could have bad impact on software projects. Furthermore, a modified firefly algorithm has been designed and implemented to evaluate and prioritize the pertinent risk factors obtained after the Pareto analysis. All important risks have been prioritized according to the fitness values of individual risks. The top three risks are “failure to provide resources,” “cultural differences of participants,” and “inadequately trained development team members.”
Toward a secure global contact tracing app for Covid-19

The outbreak of the covid-19 pandemic has devastated many sectors of each country and led to the development of contact tracing applications for controlling its spread. Contact tracing apps have been promoted to track infected contacts. However, contact tracing has gained significant debate due to its security and privacy concerns. The goal of this study is to examine the most popular contact tracing apps, their impact on pandemic control, as well security and privacy concerns. The multivocal literature review (MLR) brings the results from the state-of-the-art literature. We extracted 23 studies from both formal and grey literature to achieve the research objectives and found several security and privacy threats in the existing contact tracing applications. Additionally, the best practices to address these threats were also identified. We further proposed a preliminary structure of a secure global contact tracing app using blockchain technology
Emotion-Controllable Generalized Talking Face Generation

Despite the significant progress in recent years, very few of the AI-based talking face generation methods attempt to render natural emotions. Moreover, the scope of the methods is majorly limited to the characteristics of the training dataset, hence they fail to generalize to arbitrary unseen faces. In this paper, we propose a one-shot facial geometry-aware emotional talking face generation method that can generalize to arbitrary faces. We propose a graph convolutional neural network that uses speech content feature, along with an independent emotion input to generate emotion and speech-induced motion on facial geometry-aware landmark representation. This representation is further used in our optical flow-guided texture generation network for producing the texture. We propose a two-branch texture generation network, with motion and texture branches designed to consider the motion and texture content independently. Compared to the previous emotion talking face methods, our method can adapt to arbitrary faces captured in-the-wild by fine-tuning with only a single image of the target identity in neutral emotion.
Family heads’ preparedness facing landslides: simulation game method and focus group discussion

Natural disasters can occur suddenly or over an extended period. Earthquakes are notoriously difficult to anticipate in terms of their timing, location and magnitude. Simulated catastrophes in Celaket, Pacet and Mojokerto sparked this investigation of the preparedness efficacy of family leaders via the use of simulation game (SIG) and focus group discussion (FGD). The study designed was quasi-experimental with a pre/post test. The sample size was 150 responders. MANOVA test indicates a p value of 0.05, indicating that Ha is accepted and Ho is rejected. SIG is more successful than FGD in assessing family heads’ preparedness to deal with landslides in Celaket, Pacet and Mojokerto. SIG is also more effective than FGD on family heads’ preparedness in the mitigation, emergency and recovery of landslides. Educational institutions, BPBD, village officials and related parties need to be more intensive in providing disaster training to communities in disaster risk areas.
Can Eye Movement Synchronicity Predict Test Performance With Unreliably-Sampled Data in an Online Learning Context?

Webcam-based eye-tracking promises easy and quick data collection without the need for specific or additional eye-tracking hardware. This makes it especially attractive for educational research, in particular for modern formats, such as MOOCs. However, in order to fulfill its promises, webcam-based eye tracking has to overcome several challenges, most importantly, varying spatial and temporal resolutions. Another challenge that the educational domain faces especially, is that typically individual students are of interest in contrast to average values. In this paper, we explore whether an attention measure that is based on eye movement synchronicity of a group of students can be applied with unreliably-sampled data. Doing so we aim to reproduce earlier work that showed that, on average, eye movement synchronicity can predict performance in a comprehension quiz. We were not able to reproduce the findings with unreliably-sampled data, which highlights the challenges that lie ahead of webcam-based eye tracking in practice.
Interactive Visualization and Computation of 2D and 3D Probability Distributions

Mathematical modeling, probability estimation, and statistical inference represent core elements of modern artificial intelligence (AI) approaches for data-driven prediction, forecasting, classification, risk estimation, and prognosis. Currently, there are many tools that help calculate and visualize univariate probability distributions. However, very few resources venture beyond into multivariate distributions, which are commonly used in advanced statistical inference and AI decision-making. This article presents a new web-calculator that enables some calculation and visualization of bivariate and trivariate probability distributions. Several methods are explored to compute the joint bivariate and trivariate probability densities, including the optimal multivariate modeling using Gaussian copula. We developed an interactive webapp to visually illustrate the parallels between the mathematical formulation, computational implementation, and graphical depiction of multivariate probability density and cumulative distribution functions. To ensure the interface and functionality are hardware platform independent, scalable, and functional, the app and its component widgets are implemented using HTML5 and JavaScript. We validated the webapp by testing the multivariate copula models under different experimental conditions and inspecting the performance in terms of accuracy and reliability of the estimated multivariate probability densities and distribution function values. This article demonstrates the construction, implementation, and utilization of multivariate probability calculators. The proposed webapp implementation is freely available online ( https://socr.umich.edu/HTML5/BivariateNormal/BVN2/ ) and can be used to assist with education and research of a diverse array of data scientists, STEM instructors, and AI learners.
Knowledge management and ethical vulnerability in AI

We have covered the current and future prospects of the combined use of AI and KM with respect to the Indian economy and its future automation prospects. The most likely fields to see automation in the coming years that would help the Indian approach to this field to be more efficient have been covered by us. Certain fields such as healthcare, farming, finances, and the IT sector are the primary concern around which the future prospects of India evolve. All in all, we have discussed the ethical vulnerability and ways to cope up and fill the gap between the goal and the issues that coincide.