Reviewerly/exHarmony huggingface dataset:
Video-Annotated Augmented Reality Assembly Tutorials

We present a system for generating and visualizing interactive 3D Augmented Reality tutorials based on 2D video input, which allows viewpoint control at runtime. Inspired by assembly planning, we analyze the input video using a 3D CAD model of the object to determine an assembly graph that encodes blocking relationships between parts. Using an assembly graph enables us to detect assembly steps that are otherwise difficult to extract from the video, and generally improves object detection and tracking by providing prior knowledge about movable parts. To avoid information loss, we combine the 3D animation with relevant parts of the 2D video so that we can show detailed manipulations and tool usage that cannot be easily extracted from the video. To further support user orientation, we visually align the 3D animation with the real-world object by using texture information from the input video. We developed a presentation system that uses commonly available hardware to make our results accessible for home use and demonstrate the effectiveness of our approach by comparing it to traditional video tutorials.
An Image Encryption Scheme Using a 1D Chaotic Double Section Skew Tent Map

Because of their complex behaviour, simple mathematical and digital hardware representation, one-dimensional (1D) chaotic functions were the earliest choices of cryptologists for applying chaos theory to cryptography. It was discovered later that most of these functions suffer from orbits collapsing into a specific period, especially under finite precision realization and weakness because of their limited number of control parameter(s). This in turn exposed many security issues and was proven to be vulnerable to various types of attacks. This paper addresses the issue of limited number of control parameters by introducing a 1D chaotic function with five control parameters (in addition to the initial condition). Analysis of the function implies its chaotic properties in addition to its ability to generate a cryptographically secured random stream of numbers. To further elaborate on its robustness, a new image encryption algorithm incorporating the function as its random number generator is presented. Various analyses of the new scheme confirm it to be secure with good confusion-diffusion properties.
New insights into the existing image encryption algorithms based on DNA coding

Because a DNA nucleotide sequence has the characteristics of large storage capacity, high parallelism, and low energy consumption, DNA cryptography is favored by information security researchers. Image encryption algorithms based on DNA coding have become a research hotspot in the field of image encryption and security. In this study, based on a comprehensive review of the existing studies and their results, we present new insights into the existing image encryption algorithms based on DNA coding. First, the existing algorithms were summarized and classified into five types, depending on the type of DNA coding: DNA fixed coding, DNA dynamic coding, different types of base complement operation, different DNA sequence algebraic operations, and combinations of multiple DNA operations. Second, we analyzed and studied each classification algorithm using simulation and obtained their advantages and disadvantages. Third, the DNA coding mechanisms, DNA algebraic operations, and DNA algebraic combination operations were compared and discussed. Then, a new scheme was proposed by combining the optimal coding mechanism with the optimal DNA coding operation. Finally, we revealed the shortcomings of the existing studies and the future direction for improving image encryption methods based on DNA coding.
Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks

Large pre-trained language models have been shown to store factual knowledge in their parameters, and achieve state-of-the-art results when fine-tuned on downstream NLP tasks. However, their ability to access and precisely manipulate knowledge is still limited, and hence on knowledge-intensive tasks, their performance lags behind task-specific architectures. Additionally, providing provenance for their decisions and updating their world knowledge remain open research problems. Pre-trained models with a differentiable access mechanism to explicit non-parametric memory can overcome this issue, but have so far been only investigated for extractive downstream tasks. We explore a general-purpose fine-tuning recipe for retrieval-augmented generation (RAG) -- models which combine pre-trained parametric and non-parametric memory for language generation. We introduce RAG models where the parametric memory is a pre-trained seq2seq model and the non-parametric memory is a dense vector index of Wikipedia, accessed with a pre-trained neural retriever. We compare two RAG formulations, one which conditions on the same retrieved passages across the whole generated sequence, the other can use different passages per token. We fine-tune and evaluate our models on a wide range of knowledge-intensive NLP tasks and set the state-of-the-art on three open domain QA tasks, outperforming parametric seq2seq models and task-specific retrieve-and-extract architectures. For language generation tasks, we find that RAG models generate more specific, diverse and factual language than a state-of-the-art parametric-only seq2seq baseline.
DUET: Boosting Deep Neural Network Efficiency on Dual-Module Architecture

Deep Neural Networks (DNNs) have been driving the mainstream of Machine Learning applications. However, deploying DNNs on modern hardware with stringent latency requirements and energy constraints is challenging because of the compute-intensive and memory-intensive execution patterns of various DNN models. We propose an algorithm-architecture co-design to boost DNN execution efficiency. Leveraging the noise resilience of nonlinear activation functions in DNNs, we propose dual-module processing that uses approximate modules learned from original DNN layers to compute insensitive activations. Therefore, we can save expensive computations and data accesses of unnecessary sensitive activations. We then design an Executor-Speculator dual-module architecture with support for balance execution and memory access reduction. With acceptable model inference quality degradation, our accelerator design can achieve 2.24x speedup and 1.97x energy efficiency improvement for compute-bound Convolutional Neural Networks (CNNs) and memory-bound Recurrent Neural Networks (RNNs).
Superpixel Segmentation Using Dynamic and Iterative Spanning Forest

As constituent parts of image objects, superpixels can improve several higher-level operations. However, image segmentation methods might have their accuracy seriously compromised for reduced numbers of superpixels. We have investigated a solution based on the Iterative Spanning Forest (ISF) framework. In this work, we present Dynamic ISF (DISF) -- a method based on the following steps. (a) It starts from an image graph and a seed set with considerably more pixels than the desired number of superpixels. (b) The seeds compete among themselves, and each seed conquers its most closely connected pixels, resulting in an image partition (spanning forest) with connected superpixels. In step (c), DISF assigns relevance values to seeds based on superpixel analysis and removes the most irrelevant ones. Steps (b) and (c) are repeated until the desired number of superpixels is reached. DISF has the chance to reconstruct relevant edges after each iteration, when compared to region merging algorithms. As compared to other seed-based superpixel methods, DISF is more likely to find relevant seeds. It also introduces dynamic arc-weight estimation in the ISF framework for more effective superpixel delineation, and we demonstrate all results on three datasets with distinct object properties.
Introducing a New Dataset for Event Detection in Cybersecurity Texts

Detecting cybersecurity events is necessary to keep us informed about the fast growing number of such events reported in text.In this work, we focus on the task of event detection (ED) to identify event trigger words for the cybersecurity domain.In particular, to facilitate the future research, we introduce a new dataset for this problem, characterizing the manual annotation for 30 important cybersecurity event types and a large dataset size to develop deep learning models.Comparing to the prior datasets for this task, our dataset involves more event types and supports the modeling of document-level information to improve the performance.We perform extensive evaluation with the current state-of-the-art methods for ED on the proposed dataset.Our experiments reveal the challenges of cybersecurity ED and present many research opportunities in this area for the future work.
A Technical Question Answering System with Transfer Learning

Wenhao Yu, Lingfei Wu, Yu Deng, Ruchi Mahindru, Qingkai Zeng, Sinem Guven, Meng Jiang. Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations. 2020.
Counterfactually-Augmented SNLI Training Data Does Not Yield Better Generalization Than Unaugmented Data

A growing body of work shows that models exploit annotation artifacts to achieve state-ofthe-art performance on standard crowdsourced benchmarks-datasets collected from crowdworkers to create an evaluation task-while still failing on out-of-domain examples for the same task.Recent work has explored the use of counterfactually-augmented data-data built by minimally editing a set of seed examples to yield counterfactual labels-to augment training data associated with these benchmarks and build more robust classifiers that generalize better.However, Khashabi et al. (2020) find that this type of augmentation yields little benefit on reading comprehension tasks when controlling for dataset size and cost of collection.We build upon this work by using English natural language inference data to test model generalization and robustness and find that models trained on a counterfactuallyaugmented SNLI dataset do not generalize better than unaugmented datasets of similar size and that counterfactual augmentation can hurt performance, yielding models that are less robust to challenge examples.Counterfactual augmentation of natural language understanding data through standard crowdsourcing techniques does not appear to be an effective way of collecting training data and further innovation is required to make this general line of work viable.
Deep ensemble neural networks for recognizing isolated Arabic handwritten characters

In recent years, handwritten character recognition has become an active research field. In particular, digitalization has triggered the interest of researchers from various computing disciplines to address several handwriting related challenges. Despite these efforts, there are still opportunities for the development and improvement of the recognition of the handwritten Arabic letters. In this paper, we designed and developed a deep ensemble architecture in which ResNet-18 architecture is exploited to model and classify character images. Specifically, we adapted ResNet-18 by adding a dropout layer after all convolutional layer and integrated it in multiple ensemble models to automatically recognize isolated handwritten Arabic characters. A standard Arabic Handwritten Character Dataset (AHCD) was used in the experiments to train and assess all the proposed models. Satisfactory results were obtained using all models. The best-attained accuracy was 98.30% using a typical ResNet-18 model. Similarly, 98.00% and 98.03% accuracies were obtained using an ensemble model with one fully connected layer (1 FC) and an ensemble with two fully connected layers (2 FC) coupled with a dropout layer, respectively.
Abusive Language Detection in Online Conversations by Combining Content- and Graph-Based Features

In recent years, online social networks have allowed world-wide users to meet and discuss. As guarantors of these communities, the administrators of these platforms must prevent users from adopting inappropriate behaviors. This verification task, mainly done by humans, is more and more difficult due to the ever growing amount of messages to check. Methods have been proposed to automatize this moderation process, mainly by providing approaches based on the textual content of the exchanged messages. Recent work has also shown that characteristics derived from the structure of conversations, in the form of conversational graphs, can help detecting these abusive messages. In this paper, we propose to take advantage of both sources of information by proposing fusion methods integrating content- and graph-based features. Our experiments on raw chat logs show that the content of the messages, but also of their dynamics within a conversation contain partially complementary information, allowing performance improvements on an abusive message classification task with a final F-measure of 93.26%.
Salience-based object prioritization during active viewing of naturalistic scenes in young and older adults

Abstract Whether fixation selection in real-world scenes is guided by image salience or by objects has been a matter of scientific debate. To contrast the two views, we compared effects of location-based and object-based visual salience in young and older (65 + years) adults. Generalized linear mixed models were used to assess the unique contribution of salience to fixation selection in scenes. When analysing fixation guidance without recurrence to objects, visual salience predicted whether image patches were fixated or not. This effect was reduced for the elderly, replicating an earlier finding. When using objects as the unit of analysis, we found that highly salient objects were more frequently selected for fixation than objects with low visual salience. Interestingly, this effect was larger for older adults. We also analysed where viewers fixate within objects, once they are selected. A preferred viewing location close to the centre of the object was found for both age groups. The results support the view that objects are important units of saccadic selection. Reconciling the salience view with the object view, we suggest that visual salience contributes to prioritization among objects. Moreover, the data point towards an increasing relevance of object-bound information with increasing age.
Duplication of the bodily self: a perceptual illusion of dual full-body ownership and dual self-location

Previous research has shown that it is possible to use multisensory stimulation to induce the perceptual illusion of owning supernumerary limbs, such as two right arms. However, it remains unclear whether the coherent feeling of owning a full-body may be duplicated in the same manner and whether such a dual full-body illusion could be used to split the unitary sense of self-location into two. Here, we examined whether healthy human participants can experience simultaneous ownership of two full-bodies, located either close in parallel or in two separate spatial locations. A previously described full-body illusion, based on visuo-tactile stimulation of an artificial body viewed from the first-person perspective (1PP) via head-mounted displays, was adapted to a dual-body setting and quantified in five experiments using questionnaires, a behavioural self-location task and threat-evoked skin conductance responses. The results of experiments 1–3 showed that synchronous visuo-tactile stimulation of two bodies viewed from the 1PP lying in parallel next to each other induced a significant illusion of dual full-body ownership. In experiment 4, we failed to find support for our working hypothesis that splitting the visual scene into two, so that each of the two illusory bodies was placed in distinct spatial environments, would lead to dual self-location. In a final exploratory experiment (no. 5), we found preliminary support for an illusion of dual self-location and dual body ownership by using dynamic changes between the 1PPs of two artificial bodies and/or a common third-person perspective in the ceiling of the testing room. These findings suggest that healthy people, under certain conditions of multisensory perceptual ambiguity, may experience dual body ownership and dual self-location. These findings suggest that the coherent sense of the bodily self located at a single place in space is the result of an active and dynamic perceptual integration process.
NLPDove at SemEval-2020 Task 12: Improving Offensive Language Detection with Cross-lingual Transfer

This paper describes our approach to the task of identifying offensive languages in a multilingual setting.We investigate two data augmentation strategies: using additional semi-supervised labels with different thresholds and cross-lingual transfer with data selection.Leveraging the semisupervised dataset resulted in performance improvements compared to the baseline trained solely with the manually-annotated dataset.We propose a new metric, Translation Embedding Distance, to measure the transferability of instances for cross-lingual data selection.We also introduce various preprocessing steps tailored for social media text along with methods to fine-tune the pre-trained multilingual BERT (mBERT) for offensive language identification.Our multilingual systems achieved competitive results in Greek, Danish, and Turkish at OffensEval 2020.
Future English Teachers’ Perspective towards the Implementation of E-Learning in Covid-19 Pandemic Era

&lt;p&gt;&lt;em&gt;This research was conducted to investigate the future English teachers’ perspective towards the implementation of e-learning in Covid-19 pandemic era. There were 60 respondents involved in this research. A survey method was carried out in this research. The data of this research was collected by distributing a questionnaire. The questionnaire was formulated based on the theories about the pros and cons of the e-learning implementation. Descriptive analysis was done to analyzed this research data. The data of this research was transformed into percentage. The result of this research showed that the future English teachers find e-learning is beneficial for them, but still have some lacks to overcome. Besides, the subjects of this research are aware of things they need to prepare before they conduct e-learning, and they also suggest the stakeholders in Indonesia to improve the technology development to support the e-learning implementation.&lt;/em&gt;&lt;/p&gt;
High-speed and High-accurate SAR Ship Detection Based on a Depthwise Separable Convolution Neural Network

With the development of artificial intelligence, Synthetic-Aperture Radar (SAR) ship detection using deep learning technology can effectively avoid traditionally complex feature design and thereby greatly improve detection accuracy. However, most existing detection models often improve detection accuracy at the expense of detection speed that limits some real-time applications of SAR such as emergency military deployment, rapid maritime rescue, and real-time marine environmental monitoring. To solve this problem, a high-speed and high-accuracy SAR ship detection method called SARShipNet-20 based on a Depthwise Separable Convolution Neural Network (DS-CNN) has been proposed in this paper, that replaces the Traditional Convolution Neural Network (T-CNN) and combines Channel Attention (CA) and Spatial Attention (SA). As a result, high-speed and high-accuracy SAR ship detection can be simultaneously achieved. This method has certain practical significance in the field of real-time SAR application, and its lightweight model is helpful for future FPGA or DSP hardware transplantation.
SESCF: A Secure and Efficient Supply Chain Framework via Blockchain-Based Smart Contracts

The demands for the fairness, security, and efficiency of the supply chain have grown significantly due to the rise of globalization. However, some problems of the information flow, logistics, and capital flow in the supply chain remain a challenge, such as the information asymmetry between upstream and downstream, substandard quality of goods, difficulty in traceability, and default of payment. Therefore, this paper proposes a blockchain-based supply chain framework (SESCF), which solves the supply chain problems securely and efficiently. First, the use of blockchain and smart contracts ensures the information symmetry in the supply chain system. Second, the radio frequency identification (RFID) provides a unique identity of goods, which helps in real-time quality monitoring. Additionally, the immutability and distributed storage of the blockchain play an important role in tracking the origin of goods. Third, the efficient payment channel is used to solve the problem of payment defaults. Furthermore, simulations of smart contracts along with the security analyses are presented in this paper. We also implement a blockchain-based supply chain system (SescfDapp), which is built upon a Consortium blockchain. Large-scale experiments and detailed analysis prove the feasibility and efficiency of our proposed system.
: Hybrid Associations Models for Sequential Recommendation

Sequential recommendation aims to identify and recommend the next few items for a user that the user is most likely to purchase/review, given the user's purchase/rating trajectories. It becomes an effective tool to help users select favorite items from a variety of options. In this manuscript, we developed hybrid associations models (HAM) to generate sequential recommendations. using three factors: 1) users' long-term preferences, 2) sequential, high-order and low-order association patterns in the users' most recent purchases/ratings, and 3) synergies among those items. HAM uses simplistic pooling to represent a set of items in the associations, and element-wise product to represent item synergies of arbitrary orders. We compared HAM models with the most recent, state-of-the-art methods on six public benchmark datasets in three different experimental settings. Our experimental results demonstrate that HAM models significantly outperform the state of the art in all the experimental settings. with an improvement as much as 46.6%. In addition, our run-time performance comparison in testing demonstrates that HAM models are much more efficient than the state-of-the-art methods. and are able to achieve significant speedup as much as 139.7 folds.