Reviewerly/exHarmony huggingface dataset:
Stereo Plane R-CNN: Accurate Scene Geometry Reconstruction Using Planar Segments and Camera-Agnostic Representation

The letter introduces a novel method for planar segments detection and description from a stereo pair of images. The existing systems for planes detection utilize single RGB images and have accuracy- and scale-related problems regarding 3D reconstruction with the obtained planar segments. The proposed approach draws inspiration from deep-learning-based systems for plane detection and depth reconstruction. Firstly, we improve the planes detection in the image. Secondly, we enhance geometry reconstruction accuracy using a stereo setup. To achieve the 3D model of the observed planes, we introduce a novel neural network architecture and training strategy that jointly optimizes the prediction of disparity, normal vectors, and plane parameters. Moreover, the proposed approach utilizes an efficient camera-agnostic representation of the problem. Finally, we show that our system outperforms existing approaches to planar segments detection and parameters estimation and improves the reconstruction accuracy of indoor environments.
Zonotope-Based Asynchronous Fault Detection for Markov Jump Systems Subject to Deception Attacks via Dynamic Event-Triggered Communication

By Markov jump systems (MJSs) modeling, this article investigates event-triggered asynchronous fault detection (FD) for quarter-car suspension system (QCSS) via zonotopic residual evaluation (RE).To save communication resources, a stochastic dynamic event-triggered scheme (DETS) is developed to transmit necessary sampled signals while considering deception attacks (DAs) during signal transmission.By means of the hidden Markov model (HMM) with partially accessible mode information, the asynchronization phenomenon between the original system and the FD filter (FDF) as well as the event generator is fully characterized.In such a framework, a mixed l 1 /H ∞ asynchronous FDF design criterion is derived to guarantee the resultant residual system is sensitive to system fault while being robust to amplitude-bounded exogenous disturbances and measurement noise.Compared with existing results, the proposed approach avoids the mode synchronization limitation and the requirement of ideally known probability information in the transformed probability matrix and conditional probability matrix, and thus can be better applied to practical engineering while covering some previous works as special cases.Moreover, for the first time, a novel zonotope-based dynamic threshold evaluation approach is developed for FD in MJSs instead of the traditional constant threshold scheme for RE.Such an algorithm is designed to effectively avoid inappropriate human empirical threshold selection, which can both shorten the FD time and avoid false alarms hence ensuring satisfactory FD performance.Finally, a quarter-car suspension system is utilized to demonstrate the effectiveness of the proposed design strategy.
Robust Lottery Tickets for Pre-trained Language Models

Recent works on Lottery Ticket Hypothesis have shown that pre-trained language models (PLMs) contain smaller matching subnetworks(winning tickets) which are capable of reaching accuracy comparable to the original models. However, these tickets are proved to be notrobust to adversarial examples, and even worse than their PLM counterparts. To address this problem, we propose a novel method based on learning binary weight masks to identify robust tickets hidden in the original PLMs. Since the loss is not differentiable for the binary mask, we assign the hard concrete distribution to the masks and encourage their sparsity using a smoothing approximation of L0 regularization.Furthermore, we design an adversarial loss objective to guide the search for robust tickets and ensure that the tickets perform well bothin accuracy and robustness. Experimental results show the significant improvement of the proposed method over previous work on adversarial robustness evaluation.
Understanding Data Toward Going to Data Science

Data in terms of term have filled the daily activities of human life. This term becomes important, when humans are increasingly dependent on technology called computers or computing machines in terms of calculating from speed side. Data is a picture of the world of life itself. Mastery over it becomes important and so important. Therefore, various scientific fields, mainly physics, are present, but focus on certain things from the data, which results in overlapping and inefficiency in the study. Data science emerged to reveal in an integrated manner about data so that the world need understands the data as a whole.
Scaling Language Model Size in Cross-Device Federated Learning

Jae Ro, Theresa Breiner, Lara McConnaughey, Mingqing Chen, Ananda Suresh, Shankar Kumar, Rajiv Mathews. Proceedings of the First Workshop on Federated Learning for Natural Language Processing (FL4NLP 2022). 2022.
A Genetic Algorithm-Based Approach to Support Forming Multiple Scrum Project Teams

Forming effective teams is an essential but challenging task, especially for organizations that carry out multiple projects simultaneously, a problem known as the Multiple Team Formation (MTF) problem. The literature presents several solutions for the MTF problem, mostly modeling it as a search problem. However, the existing solutions are not suitable for Scrum projects. We addressed this gap by developing an approach composed of two main steps. First, we designed a Structured Task Model to support creating developers&#x2019; profiles given their performance on past Scrum projects. Then, given a set of target projects&#x2019; technology requirements and the available developers&#x2019; profiles, we developed a Genetic Algorithm to form the teams for a set of target projects. We evaluated the proposed approach by comparing the teams formed by our approach with the ones formed by project managers from one organization. Our approach achieved 85% of precision when compared with the teams provided by the project managers who worked on the same target projects. We also recorded an acceptance rate of up to 75%. The significant value of precision achieved suggests that our approach can provide teams close to the project managers&#x2019; expectations. In addition, our Structured Task Model offers a promising way to build technical profiles semi-automatically for Scrum developers.
Supervised classification approach for cervical cancer detection using Pap smear images

Cervical cancer is found in women and is the global life threatening problem. Papanicolaou test is the well-known technique used for diagnosing the cancer at the early stage. However, the pathological screening is manual, tedious and time consuming process. Therefore, the proposed method employs adaptive fuzzy k means clustering to segment the cell containing nucleus and cytoplasm from the unwanted background from the pathological Pap smear image. Thereafter, the 40 features are extracted from the segmented images based on the shape, size, intensity, orientation, colour, energy and entropy of nucleus and cytoplasm individually. Finally, the performance of supervised classification approach utilising multilayer perceptron with three kernels and support vector machine with five different kernels as the classifiers to predict the cancerous cells is at par with the existing techniques. The classifier is trained and tested on benchmark database with 280 Pap smear images. The performance of these two classifiers are evaluated and it is found that the MLP classifier with hyperbolic tangent activation function outperforms SVM classifier in all the performance criteria, with classification accuracy of 97.14%, sensitivity of 98%, specificity of 95% and positive predictive value of 98%.
What is wrong with you?: Leveraging User Sentiment for Automatic Dialog Evaluation

Accurate automatic evaluation metrics for open-domain dialogs are in high demand.Existing model-based metrics for system response evaluation are trained on human annotated data, which is cumbersome to collect.In this work, we propose to use information that can be automatically extracted from the next user utterance, such as its sentiment or whether the user explicitly ends the conversation, as a proxy to measure the quality of the previous system response.This allows us to train on a massive set of dialogs with weak supervision, without requiring manual system turn quality annotations.Experiments show that our model is comparable to models trained on human annotated data.Furthermore, our model generalizes across both spoken and written opendomain dialog corpora collected from real and paid users.
Improving Controllable Text Generation with Position-Aware Weighted Decoding

Weighted decoding methods composed of the pretrained language model (LM) and the controller have achieved promising results for controllable text generation.However, these models often suffer from a control strength/fluency trade-off problem as higher control strength is more likely to generate incoherent and repetitive text.In this paper, we illustrate this tradeoff is arisen by the controller imposing the target attribute on the LM at improper positions.And we propose a novel framework based on existing weighted decoding methods called CAT-PAW 1 , which introduces a lightweight regulator to adjust bias signals from the controller at different decoding positions.Experiments on positive sentiment control, topic control, and language detoxification show the effectiveness of our CAT-PAW upon 4 SOTA models 2 .
A rotation invariant template matching algorithm based on Sub-NCC

This paper proposes an anti-rotation template matching method based on a portion of the whole pixels. To solve the problem that the speed of the original template matching method based on NCC (Normalized cross correlation) is too slow for the rotated image, a template matching method based on Sub-NCC is proposed, which improves the anti-jamming ability of the algorithm. At the same time, in order to improve the matching speed, the rotation invariant edge points are selected from the rotation invariant pixels, and the selected points are used for rough matching to quickly screen out the unmatched areas. The theoretical analysis and experimental results show that the accuracy of this method is more than 95%. For the search map at any angle with the resolution at the level of 300,000 pixel, after selecting the appropriate pyramid series and threshold, the matching time can be controlled to within 0.1 s.
Detection of Web Cross-Site Scripting (XSS) Attacks

Most applications looking for XSS vulnerabilities have a variety of weaknesses related to the nature of constructing internet applications. Existing XSS vulnerability packages solely scan public net resources, which negatively influences the safety of internet resources. Threats may be in non-public sections of internet resources that can only be accessed by approved users. The aim of this work is to improve available internet functions for preventing XSS assaults by creating a programme that detects XSS vulnerabilities by completely mapping internet applications. The innovation of this work lies in its use of environment-friendly algorithms for locating extraordinary XSS vulnerabilities in addition to encompassing pre-approved XSS vulnerability scanning in examined internet functions to generate a complete internet resource map. Using the developed programme to discover XSS vulnerabilities increases the effectiveness of internet utility protection. This programme also simplifies the use of internet applications. Even customers unfamiliar with the fundamentals of internet security can use this programme due to its capability to generate a document with suggestions for rectifying detected XSS vulnerabilities.
Improved YOLOv4 for Pedestrian Detection and Counting in UAV Images

UAV (unmanned aerial vehicle) captured images have small pedestrian targets and loss of key information after multiple down sampling, which are difficult to overcome by existing methods. We propose an improved YOLOv4 model for pedestrian detection and counting in UAV images, named YOLO-CC. We used the lightweight YOLOv4 for pedestrian detection, which replaces the backbone with CSPDarknet-34, and two feature layers are fused by FPN (Feature Pyramid Networks). We expanded the perception field using multiscale convolution based on the high-level feature map and generated the population density map by feature dimension reduction. By embedding the density map generation method into the network for end-to-end training, our model can effectively improve the accuracy of detection and counting and make feature extraction more focused on small targets. Our experiments demonstrate that YOLO-CC achieves 21.76 points AP50 higher than that of the original YOLOv4 on the VisDrone2021-counting data set while running faster than the original YOLOv4.
Test-time Fourier Style Calibration for Domain Generalization

The topic of generalizing machine learning models learned on a collection of source domains to unknown target domains is challenging. While many domain generalization (DG) methods have achieved promising results, they primarily rely on the source domains at train-time without manipulating the target domains at test-time. Thus, it is still possible that those methods can overfit to source domains and perform poorly on target domains. Driven by the observation that domains are strongly related to styles, we argue that reducing the gap between source and target styles can boost models’ generalizability. To solve the dilemma of having no access to the target domain during training, we introduce Test-time Fourier Style Calibration (TF-Cal) for calibrating the target domain style on the fly during testing. To access styles, we utilize Fourier transformation to decompose features into amplitude (style) features and phase (semantic) features. Furthermore, we present an effective technique to Augment Amplitude Features (AAF) to complement TF-Cal. Extensive experiments on several popular DG benchmarks and a segmentation dataset for medical images demonstrate that our method outperforms state-of-the-art methods.
KUNet: Imaging Knowledge-Inspired Single HDR Image Reconstruction

Recently, with the rise of high dynamic range (HDR) display devices, there is a great demand to transfer traditional low dynamic range (LDR) images into HDR versions. The key to success is how to solve the many-to-many mapping problem. However, the existing approaches either do not consider constraining solution space or just simply imitate the inverse camera imaging pipeline in stages, without directly formulating the HDR image generation process. In this work, we address this problem by integrating LDR-to-HDR imaging knowledge into an UNet architecture, dubbed as Knowledge-inspired UNet (KUNet). The conversion from LDR-to-HDR image is mathematically formulated, and can be conceptually divided into recovering missing details, adjusting imaging parameters and reducing imaging noise. Accordingly, we develop a basic knowledge-inspired block (KIB) including three subnetworks corresponding to the three procedures in this HDR imaging process. The KIB blocks are cascaded in the similar way to the UNet to construct HDR image with rich global information. In addition, we also propose a knowledge inspired jump-connect structure to fit a dynamic range gap between HDR and LDR images. Experimental results demonstrate that the proposed KUNet achieves superior performance compared with the state-of-the-art methods. The code, dataset and appendix materials are available at https://github.com/wanghu178/KUNet.git.
Perceptual Learned Video Compression with Recurrent Conditional GAN

This paper proposes a Perceptual Learned Video Compression (PLVC) approach with recurrent conditional GAN. We employ the recurrent auto-encoder-based compression network as the generator, and most importantly, we propose a recurrent conditional discriminator, which judges raw vs. compressed video conditioned on both spatial and temporal features, including the latent representation, temporal motion and hidden states in recurrent cells. This way, the adversarial training pushes the generated video to be not only spatially photo-realistic but also temporally consistent with the groundtruth and coherent among video frames. The experimental results show that the learned PLVC model compresses video with good perceptual quality at low bit-rate, and that it outperforms the official HEVC test model (HM 16.20) and the existing learned video compression approaches for several perceptual quality metrics and user studies. The project page is available at https://github.com/RenYang-home/PLVC.
Recommendation System with Biclustering

The massive growth of online commercial data has raised the request for an automatic recommender system to benefit both users and merchants.One of the most frequently used recommendation methods is collaborative filtering, but its accuracy is limited by the sparsity of the rating dataset.Most existing collaborative filtering methods consider all features when calculating user/item similarity and ignore much local information.In collaborative filtering, selecting neighbors and determining users' similarities are the most important parts.For the selection of better neighbors, this study proposes a novel biclustering method based on modified fuzzy adaptive resonance theory.To reflect the similarity between users, a new measure that considers the effect of the number of users' common items is proposed.Specifically, the proposed novel biclustering method is first adopted to obtain local similarity and local prediction.Second, item-based collaborative filtering is used to generate global predictions.Finally, the two resultant predictions are fused to obtain a final one.Experiment results demonstrate that the proposed method outperforms state-of-the-art models in terms of several aspects on three benchmark datasets.
Accelerating Attention Mechanism on FPGAs based on Efficient Reconfigurable Systolic Array

Transformer model architectures have recently received great interest in natural language, machine translation, and computer vision, where attention mechanisms are their building blocks. However, the attention mechanism is expensive because of its intensive matrix computations and complicated data flow. The existing hardware architecture has some disadvantages for the computing structure of attention, such as inflexibility and low efficiency. Most of the existing papers accelerate attention by reducing the amount of computation through various pruning algorithms, which will affect the results to a certain extent with different sparsity. This paper proposes the hardware accelerator for the multi-head attention (MHA) on field-programmable gate arrays (FPGAs) with reconfigurable architecture, efficient systolic array, and hardware-friendly radix-2 softmax. We propose a novel method called Four inputs Processing Element (FPE) to double the computation rate of the data-aware systolic array (SA) and make it efficient and load balance. Especially, the computation framework is well designed to ensure the utilization of SA efficiently. Our design is evaluated on a Xilinx Alveo U250 card, and the proposed architecture achieves 51.3×, 17.3× improvement in latency, and 54.4×, 17.9× energy savings compared to CPU and GPU.
TFermion: A non-Clifford gate cost assessment library of quantum phase estimation algorithms for quantum chemistry

Quantum Phase Estimation is one of the most useful quantum computing algorithms for quantum chemistry and as such, significant effort has been devoted to designing efficient implementations. In this article, we introduce TFermion, a library designed to estimate the T-gate cost of such algorithms, for an arbitrary molecule. As examples of usage, we estimate the T-gate cost of a few simple molecules and compare the same Taylorization algorithms using Gaussian and plane-wave basis.
Using virtual environments to facilitate refugee integration in third countries

Virtual experiences (VEs) have significant potential to enrich emotional interactions, to encourage socialisation and improve communication. In education, VEs offer new approaches for delivering content. In this paper we consider the application of VEs for assisting refugees in Senegal to learn how to navigate the complexities of the UK health system; a substantial stumbling block for their integration into society and for their own health. Participants ( N=122 ), refugees awaiting to be repatriated, were exposed to material presented via three different media text, 360° videos and virtual reality (VR) across a total of seven different modalities. The experiment investigated specific attributes of the media that would facilitate refugees' integration, such as knowledge received and retained, experience, usability and presence. The results show that interactive media, in particular VR, was significantly better across all tested attributes.
FedTune: Automatic Tuning of Federated Learning Hyper-Parameters from System Perspective

Federated learning (FL) hyper-parameters significantly affect the training overheads in terms of computation time, transmission time, computation load, and transmission load. However, the current practice of manually selecting FL hyper-parameters puts a high burden on FL practitioners since various applications prefer different training preferences. In this paper, we propose FedTune, an automatic FL hyper-parameter tuning algorithm tailored to applications' diverse system requirements of FL training. FedTune is lightweight and flexible, achieving 8.48%-26.75% improvement for different datasets compared to fixed FL hyper-parameters.
Multi-Agent Interaction to Assist Visually-Impaired and Elderly People

A voice-controlled smart home system based on conversational agents can address the specific needs of older people, proactively providing support, compensating for cognitive decline, and coping with solitude, among other features. In particular, Multi-Agent Systems (MAS) platforms provide considerable support for complex adaptive systems that are naturally distributed and situated in dynamic environments, such as Ambient intelligence (AmI) applications. Such autonomous intelligent agents are capable of independent reasoning and joint analysis of complex situations to support high-level interaction with humans, besides providing typical characteristics of MAS, such as cooperation and coordinated action. In this context, we developed an approach using a MAS previously evaluated for visually impaired users, where most of the system’s functionalities are also helpful for the elderly. Our methodology is based on the four steps of the interactive design process. As a result, we determined that our approach has elements that allow for natural interaction with users, and we identified and discussed improvements and new features for future work. We believe that our findings can point to directions for building AmI systems that are capable of more natural interaction with users.
Beyond Characters: Subword-level Morpheme Segmentation

This paper presents DeepSPIN's submissions to the SIGMORPHON 2022 Shared Task on Morpheme Segmentation.We make three submissions, all to the word-level subtask.First, we show that entmax-based sparse sequence-tosequence models deliver large improvements over conventional softmax-based models, echoing results from other tasks.Then, we challenge the assumption that models for morphological tasks should be trained at the character level by building a transformer that generates morphemes as sequences of unigram language model-induced subwords.This subword transformer outperforms all of our character-level models and wins the word-level subtask.Although we do not submit an official submission to the sentence-level subtask, we show that this subword-based approach is highly effective there as well.