Reviewerly/exHarmony huggingface dataset:
Content-Aware GAN Compression

Generative adversarial networks (GANs), e.g., StyleGAN2, play a vital role in various image generation and synthesis tasks, yet their notoriously high computational cost hinders their efficient deployment on edge devices. Directly applying generic compression approaches yields poor results on GANs, which motivates a number of recent GAN compression works. While prior works mainly accelerate conditional GANs, e.g., pix2pix and CycleGAN, compressing state-of-the-art unconditional GANs has rarely been explored and is more challenging. In this paper, we propose novel approaches for unconditional GAN compression. We first introduce effective channel pruning and knowledge distillation schemes specialized for unconditional GANs. We then propose a novel content-aware method to guide the processes of both pruning and distillation. With content-awareness, we can effectively prune channels that are unimportant to the contents of interest, e.g., human faces, and focus our distillation on these regions, which significantly enhances the distillation quality. On StyleGAN2 and SN-GAN, we achieve a substantial improvement over the state-of-the-art compression method. Notably, we reduce the FLOPs of StyleGAN2 by 11x with visually negligible image quality loss compared to the full-size model. More interestingly, when applied to various image manipulation tasks, our compressed model forms a smoother and better disentangled latent manifold, making it more effective for image editing.
Unsupervised Paraphrasing with Pretrained Language Models

Paraphrase generation has benefited extensively from recent progress in the designing of training objectives and model architectures. However, previous explorations have largely focused on supervised methods, which require a large amount of labeled data that is costly to collect. To address this drawback, we adopt a transfer learning approach and propose a training pipeline that enables pre-trained language models to generate high-quality paraphrases in an unsupervised setting. Our recipe consists of task-adaptation, self-supervision, and a novel decoding algorithm named Dynamic Blocking (DB). To enforce a surface form dissimilar from the input, whenever the language model emits a token contained in the source sequence, DB prevents the model from outputting the subsequent source token for the next generation step. We show with automatic and human evaluations that our approach achieves state-of-the-art performance on both the Quora Question Pair (QQP) and the ParaNMT datasets and is robust to domain shift between the two datasets of distinct distributions. We also demonstrate that our model transfers to paraphrasing in other languages without any additional finetuning.
A Comprehensive Survey of Neural Architecture Search: Challenges and  Solutions

Deep learning has made breakthroughs and substantial in many fields due to its powerful automatic representation capabilities. It has been proven that neural architecture design is crucial to the feature representation of data and the final performance. However, the design of the neural architecture heavily relies on the researchers' prior knowledge and experience. And due to the limitations of human' inherent knowledge, it is difficult for people to jump out of their original thinking paradigm and design an optimal model. Therefore, an intuitive idea would be to reduce human intervention as much as possible and let the algorithm automatically design the neural architecture. Neural Architecture Search (NAS) is just such a revolutionary algorithm, and the related research work is complicated and rich. Therefore, a comprehensive and systematic survey on the NAS is essential. Previously related surveys have begun to classify existing work mainly based on the key components of NAS: search space, search strategy, and evaluation strategy. While this classification method is more intuitive, it is difficult for readers to grasp the challenges and the landmark work involved. Therefore, in this survey, we provide a new perspective: beginning with an overview of the characteristics of the earliest NAS algorithms, summarizing the problems in these early NAS algorithms, and then providing solutions for subsequent related research work. Besides, we conduct a detailed and comprehensive analysis, comparison, and summary of these works. Finally, we provide some possible future research directions.
Dynamic Path Planning Analysis of Warehouse Handling Robot

In order to further improve the operational efficiency and the flexibility of automated warehouse, a path planning algorithm for a warehouse handling robot was evaluated. Under the background of determining the operation scene and environment of multihandling robot, a path planning algorithm with time window is proposed for multihandling robot, and a two-stage path planning method is also analyzed. The results show that the route planning using the improved Dijkstra algorithm saves 7.16 meters compared with the traditional algorithm, and the task is completed 3.17 seconds earlier. Comprehensively considering the heat of the various paths to turn to the required time, using the improved Dijkstra algorithm path, handling the robot’s total running distance than the traditional algorithm saves 14.45 meters, the total run time saves 8.45 seconds, and whether in time or in the distance, crane robot handling efficiency compared with the traditional algorithm has an obvious increase, closer to reality.
UC3M-PUCPR at SemEval-2022 Task 11: An Ensemble Method of Transformer-based Models for Complex Named Entity Recognition

This study introduces the system submitted to the SemEval 2022 Task 11: MultiCoNER (Multilingual Complex Named Entity Recognition) by the UC3M-PUCPR team.We proposed an ensemble of transformer-based models for entity recognition in cross-domain texts.Our deep learning method benefits from the transformer architecture, which adopts the attention mechanism to handle the long-range dependencies of the input text.Also, the ensemble approach for named entity recognition (NER) improved the results over baselines based on individual models on two of the three tracks we participated in.The ensemble model for the codemixed task achieves an overall performance of 76.36% F1-score, a 2.85 percentage point increase upon our individually best model for this task, XLM-RoBERTa-large (73.51%), outperforming the baseline provided for the shared task by 18.26 points.Our preliminary results suggest that contextualized language models ensembles can, even if modestly, improve the results in extracting information from unstructured data.
Hand Anatomy and Neural Network Based Recognition for Sign Language

The primary source of communication used by hard-of-hearing people is sign language. The task of automatically recognising sign language motions is difficult. For the recognition of Indian sign language, a model based on hand anatomy and neural networks is proposed in this research. Features are extracted through FAST and SIFT techniques, and hand anatomy is utilised for the selection of hand landmarks out of these detected points. Finally, training and testing of the model have been done by the neural network. Experimentation was performed on challenging and publicly available datasets for isolated single-letter gestures (alphabets and numbers) and word gestures (various real-life gestures) with different backgrounds and lighting conditions. Experimental results prove that fast and highly accurate results are achieved by the proposed model when compared with other available models. An accuracy of 98.90% has been achieved for isolated gestures and 97.58% for word gestures.
Augmenting Training Data for Massive Semantic Matching Models in Low-Traffic E-commerce Stores

Extreme multi-label classification (XMC) systems have been successfully applied in ecommerce (Shen et al., 2020;Dahiya et al., 2021) for retrieving products based on customer behavior.Such systems require large amounts of customer behavior data (e.g.queries, clicks, purchases) for training.However, behavioral data is limited in low-traffic e-commerce stores, impacting performance of these systems.In this paper, we present a technique that augments behavioral training data via query reformulation.We use the Aggregated Label eXtreme Multi-label Classification (AL-XMC) system (Shen et al., 2020) as an example semantic matching model and show via crowd-sourced human judgments that, when the training data is augmented through query reformulations, the quality of AL-XMC improves over a baseline that does not use query reformulation.We also show in online A/B tests that our method significantly improves business metrics for the AL-XMC model.
STBPU: A Reasonably Secure Branch Prediction Unit

Modern processors have suffered a deluge of threats exploiting branch instruction collisions inside the branch prediction unit (BPU), from eavesdropping on secret-related branch operations to triggering malicious speculative executions. Protecting branch predictors tends to be challenging from both security and performance perspectives. For example, partitioning or flushing BPU can stop certain collision-based exploits but only to a limited extent. Meanwhile, such mitigations negatively affect branch prediction accuracy and further CPU performance. This paper proposes Secret Token Branch Prediction Unit (STBPU), a secure BPU design to defend against collision-based transient execution attacks and BPU side channels while incurring minimal performance overhead. STBPU resolves the challenges above by customizing data representation inside BPU for each software entity requiring isolation. In addition, to prevent an attacker from using brute force techniques to trigger malicious branch instruction collisions, STBPU actively monitors the prediction-related events and preemptively changes BPU data representation.
Exploiting Numerical-Contextual Knowledge to Improve Numerical Reasoning in Question Answering

Numerical reasoning over text is a challenging subtask in question answering (QA) that requires both the understanding of texts and numbers.However, existing language models in these numerical reasoning QA models tend to overly rely on the pre-existing parametric knowledge at inference time, which commonly causes hallucination in interpreting numbers.Our work proposes a novel attention masked reasoning model, the NC-BERT, that learns to leverage the number-related contextual knowledge to alleviate the over-reliance on parametric knowledge and enhance the numerical reasoning capabilities of the QA model.The empirical results suggest that understanding of numbers in their context by reducing the parametric knowledge influence, and refining numerical information in the number embeddings lead to improved numerical reasoning accuracy and performance in DROP, a numerical QA dataset.* Equal contribution.
longhorns at DADC 2022: How many linguists does it take to fool a Question Answering model? A systematic approach to adversarial attacks.

Developing methods to adversarially challenge NLP systems is a promising avenue for improving both model performance and interpretability.Here, we describe the approach of the team "longhorns" on Task 1 of the The First Workshop on Dynamic Adversarial Data Collection (DADC), which asked teams to manually fool a model on an Extractive Question Answering task.Our team finished first, with a model error rate of 62%. 1 We advocate for a systematic, linguistically informed approach to formulating adversarial questions, and we describe the results of our pilot experiments, as well as our official submission.
BFCAI at SemEval-2022 Task 6: Multi-Layer Perceptron for Sarcasm Detection in Arabic Texts

This paper describes the systems submitted to iSarcasm shared task.The aim of iSarcasm is to identify the sarcastic contents in Arabic and English text.Our team participated in iSarcasm for the Arabic language.A multi-Layer machine learning based model has been submitted for Arabic sarcasm detection.In this model, a vector space TF-IDF has been used as for feature representation.The submitted system is simple and does not need any external resources.The test results show encouraging results.
MACER: Attack-free and Scalable Robust Training via Maximizing Certified  Radius

Adversarial training is one of the most popular ways to learn robust models but is usually attack-dependent and time costly. In this paper, we propose the MACER algorithm, which learns robust models without using adversarial training but performs better than all existing provable l2-defenses. Recent work shows that randomized smoothing can be used to provide a certified l2 radius to smoothed classifiers, and our algorithm trains provably robust smoothed classifiers via MAximizing the CErtified Radius (MACER). The attack-free characteristic makes MACER faster to train and easier to optimize. In our experiments, we show that our method can be applied to modern deep neural networks on a wide range of datasets, including Cifar-10, ImageNet, MNIST, and SVHN. For all tasks, MACER spends less training time than state-of-the-art adversarial training algorithms, and the learned models achieve larger average certified radius.
Genetic algorithm for qubits initialisation in noisy intermediate-scale quantum machines

Discrete-variable gate-model quantum machines are promising quantum systems considering their wide applicability. Being in their noisy-intermediate-scale era, they allow executing only circuits of limited complexity and fitting the machines' features. Thus, such systems implement a key and unavoidable tailoring process to produce the most possible compact and device-compliant circuit. The qubits' initialisation is a primary and complex step that can ease/jeopardise the tailoring process and restrict/extend the machine's computational capacities. Ultimately, this bottleneck can be responsible of making quantum leaps like quantum supremacy. As a step towards the former, this work investigates how evolutionary algorithms can enhance the qubits' initialisation by tackling it as a single-objective problem using a genetic algorithm. The experiments used instances representing 19 real IBM quantum machines with 7 to 65 qubits and 9 different qubit topologies. Also, 76 GHZ circuits of sizes 7-65 qubits and 25%-100% of entanglement were created and studied. Extensive standard and statistical comparisons have been made against the IBM qubit initialiser that is currently used in real quantum machines. Results showed that the proposal outperforms IBM in 64 instances and is similar to it in 10 ones, with an average circuit-compression gain up to 46%.
Interpolants and Explicit Definitions in Extensions of the Description Logic EL

We show that the vast majority of extensions of the description logic EL do not enjoy the Craig interpolation nor the projective Beth definability property. This is the case, for example, for EL with nominals, EL with the universal role, EL with role hierarchies and transitive roles, and for ELI. It follows in particular that the existence of an explicit definition of a concept or individual name cannot be reduced to subsumption checking via implicit definability. We show that nevertheless the existence of interpolants and explicit definitions can be decided in polynomial time for standard tractable extensions of EL (such as EL++) and in ExpTime for ELI and various extensions. It follows that these existence problems are not harder than subsumption which is in sharp contrast to the situation for expressive DLs. We also obtain tight bounds for the size of interpolants and explicit definitions and the complexity of computing them: single exponential for tractable standard extensions of EL and double exponential for ELI and extensions. We close with a discussion of Horn-DLs such as Horn-ALCI.
Performance Comparison of Deep Residual Networks-Based Super Resolution Algorithms Using Thermal Images: Case Study of Crowd Counting

Abstract Humans are able to perceive objects only in the visible spectrum range which limits the perception abilities in poor weather or low illumination conditions. The limitations are usually handled through technological advancements in thermographic imaging. However, thermal cameras have poor spatial resolutions compared to RGB cameras. Super-resolution (SR) techniques are commonly used to improve the overall quality of low-resolution images. There has been a major shift of research among the Computer Vision researchers towards SR techniques particularly aimed for thermal images. This paper analyzes the performance of three deep learning-based state-of-the-art SR algorithms namely Enhanced Deep Super Resolution (EDSR), Residual Channel Attention Network (RCAN) and Residual Dense Network (RDN) on thermal images. The algorithms were trained from scratch for different upscaling factors of ×2 and ×4. The dataset was generated from two different thermal imaging sequences of BU-TIV benchmark. The sequences contain both sparse and highly dense type of crowds with a far field camera view. The trained models were then used to super-resolve unseen test images. The quantitative analysis of the test images was performed using common image quality metrics such as PSNR, SSIM and LPIPS, while qualitative analysis was provided by evaluating effectiveness of the algorithms for crowd counting application. After only 54 and 51 epochs of RCAN and RDN respectively, both approaches were able to output average scores of 37.878, 0.986, 0.0098 and 30.175, 0.945, 0.0636 for PSNR, SSIM and LPIPS respectively. The EDSR algorithm took the least computation time during both training and testing because of its simple architecture. This research proves that a reasonable accuracy can be achieved with fewer training epochs when an application-specific dataset is carefully selected.
Analysis of Multifeatured Threshold Filtered-Based Real-Time Video Segmentation and Tracking in Video Surveillance

Moving object segmentation and detection have become an important topic in computer perspective. As such, it is widely used in video surveillance such as driving assistance program, robots, traffic monitoring, and crime pattern identification. In addition, video object tracking is an important function in video surveillance systems because it provides temporary interactive information about moving objects. An important function of video object segmentation is to find and separate important elements in the video frame behind the domain. The purpose of video tracking is to combine targeted objects into consecutive video frames. First of all, enhanced threshold filtered video object detection and tracking (TFVODT) is designed to classify objects according to their size, color, and to get better accuracy of video object detection. Initially, the TFVODT framework distinguishes a video object by its characteristics such as size and color. The TFVODT framework performs the function of distinguishing an object through the median filter-based enhanced Laplacian thresholding process. Along with the support of the split object, the TFVODT framework does well to track the video object. Second, threshold filtered video object detection and tracking (ITFVODT) is developed to distinguish video’s elements based on their features such as texture, durability, and performance of video object detection. All video frames found in the ITFVODT framework contain the similar features as quality and contrast.
Self-supervised global context graph neural network for session-based recommendation

Session-based recommendation (SBR) aims to recommend the next items based on anonymous behavior sequences over a short period of time. Compared with other recommendation paradigms, the information available in SBR is very limited. Therefore, capturing the item relations across sessions is crucial for SBR. Recently, many methods have been proposed to learn article transformation relationships over all sessions. Despite their success, these methods may enlarge the impact of noisy interactions and ignore the complex high-order relationship between non-adjacent items. In this study, we propose a self-supervised global context graph neural network (SGC-GNN) to model high-order transition relations between items over all sessions by using virtual context vectors, each of which connects to all items in a given session and enables to collect and propagation information beyond adjacent items. Moreover, to improve the robustness of the proposed model, we devise a contrastive self-supervised learning (SSL) module as an auxiliary task to jointly learn more robust representations of the items in sessions and train the model to fulfill the SBR task. Experimental results on three benchmark datasets demonstrate the superiority of our model over the state-of-the-art (SOTA) methods and validate the effectiveness of context vectors and the self-supervised module.
Simulation and Analysis of Hand Gesture Recognition for Indian Sign Language using CNN

Sign Language Recognition is a device or program to help deaf and mute people. However, communication has always been difficult for a person with verbal and physical disabilities. Sign language recognition communication between the average person and the disabled using this device easily communicates with people who cannot communicate with the average person, this program reduces the communication gap between people. In total, the world has a population of about 15 -20% of the deaf and mute population which is a clear indication of the need for a Sign Language Awareness Program. Different methods are used to identify sign language but they are not effective due to the economic and commercial situation so we use this cheap and affordable method for people. Therefore, sign language recognition systems based on image processing and sensory networks are preferred over gadget programs as they are more accurate and easier to implement. This paper aims to create an easy-to-use and accurate sign language recognition system trained in the neural network thus producing text and speech input.
Analyzing a Blockchain Application for the Educational Domain from the Perspective of a Software Ecosystem

Many educational institutions have the challenge of dealing with different data types. Some of this data is related to the issuance of student certificates. Software Ecosystem (SECO) refers to a collection of software products with some degree of symbiotic relationship.Blockchain can be used in different domains and its features of transparency and immutability are key concerns that make it an attractive technology. This article presents a discussion on an educational domain application from a SECO viewpoint that uses blockchain resources to treat the students' certificates from higher education institutions. Our findings were: a model of an educational application that uses blockchain and research opportunities related to SECO and blockchain for educational domain.
Serverless Searching with XSLT and JavaScript

Increasing awareness of the burdens of technical debt and the risks and costs associated with complex server infrastructures have prompted digital humanists to consider moving to purely static HTML/CSS/JS websites for projects such as online digital editions. Building static sites is not difficult, but digital editions also require sophisticated search capabilities, incorporating text search with stemming and wildcards as well as a rich array of filters tuned to the contents of the edition itself. Responding to this need, we created staticSearch, a system which builds search indexes offline with XSLT and queries them statically using JavaScript, providing a straightforward approach to search for purely static websites. This presentation will describe how staticSearch works, and discuss some of the interesting and challenging problems that such a system has to overcome.