Reviewerly/exHarmony huggingface dataset:
Multi-step network traffic prediction using echo state network with a selective error compensation strategy

Communication networks grow exponentially in this globalization era; thus, the network traffic modelling and prediction plays a crucial role in network management and security warning. Solely, the multi-step network traffic prediction may involve greater errors hence worsening prediction performance. To overcome this problem, an optimized echo state network model with selective error compensation is proposed. In the optimized echo state network-based multi-step prediction model, an improved fruit–fly optimization algorithm based on cloud model (named LVCMFOA) is used to select optimum values of four key parameters of the model. The proposed LVCMFOA algorithm uses the levy-flight function to redefine the generation of the fruit–fly population, which can randomly change the search radius and help getting out of a possible local optimal solution and prevent local optimum. To reduce the calculation time but improve the prediction accuracy simultaneously, a sophisticated selective error compensation strategy employing the variable sliding window technology is proposed so as to avoid the error accumulation problem in the multi-step prediction. The effectiveness of the proposed method is verified by applying it to Henon mapping chaotic series, Mackey–Glass chaotic series and two public network traffic data sets all known in the literature.
Universal and Uniform Action Reversibility

The problem of action reversibility studies whether effects of a given action can be reversed (or undone) by a sequence of (other) actions. For example, actions whose effects can be reversed cannot lead to dead-ends. In the usual settings, the problem of action reversibility is PSPACE-complete, that is, as hard as deciding plan existence. In this paper, we focus on subclasses of the action reversibility problem, universal and uniform action reversibility, where the former considers all states in which the action in question is applicable, while the latter requires a single reverting action sequence, independent of the considered states. Specifically, we study the relations between projection abstractions and the subclasses of the action reversibility problem and we show that universal uniform reversibility of a given action can be decided on projection consisting of only the variables present in the schema of the action in question.
RR-Net: Relation Reasoning for End-to-End Human-Object Interaction Detection

The task of Human-Object Interaction (HOI) detection devotes to learn how humans interact with surrounding objects via inferring fine-grained triplets of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\langle $ </tex-math></inline-formula> human, verb, object <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\rangle $ </tex-math></inline-formula> . Most HOI feature learning techniques are dependent on pre-detected instance regions or human body-part regions, which are computationally expensive and hardly applicable to end-to-end detectors in real applications. In this paper, based on an end-to-end HOI detector, we make a first try to explore region-independent relation reasoning for HOI detection. We first present a Relation-aware Frame, which brings a progressive structure for interaction inference. Upon the Relation-aware Frame, an Interaction Intensifier Module and a Correlation Parsing Module are carefully designed, where: a) interactive semantics from humans can be exploited and passed to objects to intensify interactions, b) interactive correlations among humans, objects and interactions are integrated to promote predictions. Based on modules above, we construct a fully differentiable and end-to-end trainable network named Relation Reasoning Network (abbr. RR-Net). Extensive experiments show that our proposed RR-Net leads to competitive results compared with the state-of-the-art methods on both V-COCO and HICO-DET benchmarks and improves the baseline about 7.6% and 11.1% relatively, validating that this first effort in exploring region-independent relation reasoning has brought obvious improvement for end-to-end HOI detection.
A Glimpse into the Lived Experiences and Challenges Faced of PWD Students towards Online Learning in the Philippines Amidst COVID-19 Pandemic

Amidstthe global pandemic, everyone is allowed to be vulnerable and heightened byburnout. But the most unheard and most vulnerable voices linger among personswith disabilities. Persons with disabilities (PWDs) are one of the concerns insociety. But, social exclusions and biases put PWDs in a disadvantageousposition. There has always been an issue in employment, social roles, andaccessibility to education. These issues are eventually turning into self-doubtand feelings of inferiority, which are reasonable evidence suggesting thatpersons with disabilities received negative attitudes from non-PWD individuals.This study’s primary goal is to explore the lived experiences of PWD Studentstowards online learning during the pandemic, specifically, describe theirexperiences, challenges faced, and coping mechanisms. Employing theInterpretative Phenomenological Analysis (IPA), the findings of the study were:(1)PWD students experienced social exclusions primarily in the rise of onlinelearning during the pandemic. (2) Most of the PWD students were challenged bythe limitations of assistive technologies and internet connectivity. (3) PWDstudents remained resilient because of having a healthy support system behindthem.
Faculty e-Learning Adoption During the COVID-19 Pandemic: A Case Study of Shaqra University

Learning can generally be applied by employing learning management system (LMS) platforms designed to support an instructor to develop, manage, and provide online courses to learners.During the COVID-19 pandemic, several LMS platforms were adopted in Saudi Arabian institutions, such as Moodle and Blackboard.However, in order to adopt e-learning and operate LMS platforms, there is a need to investigate factors that influence the capability of faculty to utilize e-learning and its perceived benefits on students.This paper examines how training support and LMS readiness factors influence the capability of faculty to adopt e-learning and student perceived benefits.A quantitative research method was conducted using an online questionnaire survey method.Research data was collected from 274 faculty members, who used Moodle as a main LMS platform, at Shaqra University in the Kingdom of Saudi Arabia (KSA).The results reveal that training support and LMS readiness have a positive influence on the faculty's capability to adopt elearning, which leads to enhancing students' perceived benefits.By identifying the factors that influence e-learning adoption, universities can provide enhanced e-learning services to students and support faculty through providing adequate training and powerful e-learning platform.
Cross-Dataset Collaborative Learning for Semantic Segmentation in Autonomous Driving

Semantic segmentation is an important task for scene understanding in self-driving cars and robotics, which aims to assign dense labels for all pixels in the image. Existing work typically improves semantic segmentation performance by exploring different network architectures on a target dataset. Little attention has been paid to build a unified system by simultaneously learning from multiple datasets due to the inherent distribution shift across different datasets. In this paper, we propose a simple, flexible, and general method for semantic segmentation, termed Cross-Dataset Collaborative Learning (CDCL). Our goal is to train a unified model for improving the performance in each dataset by leveraging information from all the datasets. Specifically, we first introduce a family of Dataset-Aware Blocks (DAB) as the fundamental computing units of the network, which help capture homogeneous convolutional representations and heterogeneous statistics across different datasets. Second, we present a Dataset Alternation Training (DAT) mechanism to facilitate the collaborative optimization procedure. We conduct extensive evaluations on diverse semantic segmentation datasets for autonomous driving. Experiments demonstrate that our method consistently achieves notable improvements over prior single-dataset and cross-dataset training methods without introducing extra FLOPs. Particularly, with the same architecture of PSPNet (ResNet-18), our method outperforms the single-dataset baseline by 5.65\%, 6.57\%, and 5.79\% mIoU on the validation sets of Cityscapes, BDD100K, CamVid, respectively. We also apply CDCL for point cloud 3D semantic segmentation and achieve improved performance, which further validates the superiority and generality of our method. Code and models will be released.
An Improved Image Filtering Algorithm for Mixed Noise

In recent years, image filtering has been a hot research direction in the field of image processing. Experts and scholars have proposed many methods for noise removal in images, and these methods have achieved quite good denoising results. However, most methods are performed on single noise, such as Gaussian noise, salt and pepper noise, multiplicative noise, and so on. For mixed noise removal, such as salt and pepper noise + Gaussian noise, although some methods are currently available, the denoising effect is not ideal, and there are still many places worthy of improvement and promotion. To solve this problem, this paper proposes a filtering algorithm for mixed noise with salt and pepper + Gaussian noise that combines an improved median filtering algorithm, an improved wavelet threshold denoising algorithm and an improved Non-local Means (NLM) algorithm. The algorithm makes full use of the advantages of the median filter in removing salt and pepper noise and demonstrates the good performance of the wavelet threshold denoising algorithm and NLM algorithm in filtering Gaussian noise. At first, we made improvements to the three algorithms individually, and then combined them according to a certain process to obtain a new method for removing mixed noise. Specifically, we adjusted the size of window of the median filtering algorithm and improved the method of detecting noise points. We improved the threshold function of the wavelet threshold algorithm, analyzed its relevant mathematical characteristics, and finally gave an adaptive threshold. For the NLM algorithm, we improved its Euclidean distance function and the corresponding distance weight function. In order to test the denoising effect of this method, salt and pepper + Gaussian noise with different noise levels were added to the test images, and several state-of-the-art denoising algorithms were selected to compare with our algorithm, including K-Singular Value Decomposition (KSVD), Non-locally Centralized Sparse Representation (NCSR), Structured Overcomplete Sparsifying Transform Model with Block Cosparsity (OCTOBOS), Trilateral Weighted Sparse Coding (TWSC), Block Matching and 3D Filtering (BM3D), and Weighted Nuclear Norm Minimization (WNNM). Experimental results show that our proposed algorithm is about 2–7 dB higher than the above algorithms in Peak Signal-Noise Ratio (PSNR), and also has better performance in Root Mean Square Error (RMSE), Structural Similarity (SSIM), and Feature Similarity (FSIM). In general, our algorithm has better denoising performance, better restoration of image details and edge information, and stronger robustness than the above-mentioned algorithms.
Application of Deep Learning in Financial Management Evaluation

The competition among enterprises is becoming increasingly fierce. The research on the financial management evaluation model is helpful for enterprises to find possible risks as soon as possible. This paper constructs the financial management evaluation model based on the deep belief network and applies the analytic hierarchy process to determine the weight of financial management evaluation indicators, which is compared with other classical deep learning evaluation methods, such as KNN, SVM-RBF, and SVM linear. It has achieved an accuracy of more than 81%, showing a satisfactory prediction effect, which is of great significance to formulate corresponding countermeasures, strengthen financial management, improve the capital market system, and promote high-quality economic development. In addition, aiming at the problem of abnormal financial data, this paper uses the new sample dataset obtained by principal component analysis for convolution neural network model learning, which enhances the prediction accuracy of the model and fully shows that deep learning is feasible in the research of financial management prediction, and there is still a lot of space to explore.
Frame Semantic-Enhanced Sentence Modeling for Sentence-level Extractive Text Summarization

Sentence-level extractive text summarization aims to select important sentences from a given document. However, it is very challenging to model the importance of sentences. In this paper, we propose a novel Frame Semantic-Enhanced Sentence Modeling for Extractive Summarization, which leverages Frame semantics to model sentences from both intra-sentence level and inter-sentence level, facilitating the text summarization task. In particular, intra-sentence level semantics leverage Frames and Frame Elements to model internal semantic structure within a sentence, while inter-sentence level semantics leverage Frame-to-Frame relations to model relationships among sentences. Extensive experiments on two benchmark corpus CNN/DM and NYT demonstrate that our model outperforms six state-of-the-art methods significantly.
Developing a Clinical Language Model for Swedish: Continued Pretraining of Generic BERT with In-Domain Data

The use of pretrained language models, finetuned to perform a specific downstream task, has become widespread in NLP.Using a generic language model in specialized domains may, however, be sub-optimal due to differences in language use and vocabulary.In this paper, it is investigated whether an existing, generic language model for Swedish can be improved for the clinical domain through continued pretraining with clinical text.The generic and domain-specific language models are fine-tuned and evaluated on three representative clinical NLP tasks: (i) identifying protected health information, (ii) assigning ICD-10 diagnosis codes to discharge summaries, and (iii) sentence-level uncertainty prediction.The results show that continued pretraining on in-domain data leads to improved performance on all three downstream tasks, indicating that there is a potential added value of domain-specific language models for clinical NLP.
A Pre-trained Transformer and CNN model with Joint Language ID and Part-of-Speech Tagging for Code-Mixed Social-Media Text

Code-mixing (CM) is a frequently observed phenomenon that uses multiple languages in an utterance or sentence.There are no strict grammatical constraints observed in codemixing, and it consists of non-standard variations of spelling.The linguistic complexity resulting from the above factors made the computational analysis of the code-mixed language a challenging task.Language identification (LI) and part of speech (POS) tagging are the fundamental steps that help analyze the structure of the code-mixed text.Often, the LI and POS tagging tasks are interdependent in the code-mixing scenario.We project the problem of dealing with multilingualism and grammatical structure while analyzing the code-mixed sentence as a joint learning task.In this paper, we jointly train and optimize language detection and part of speech tagging models in the code-mixed scenario.We used a Transformer with convolutional neural network architecture.We train a joint learning method by combining POS tagging and LI models on code-mixed social media text obtained from the ICON shared task.
ADAS Vision System with Video Super Resolution

The concern about road security and the protection of lives is increasing gradually due to intensified mobility, unfocused driving, and control over the vehicle. This interest led to development of advanced driver-assistance systems, which are intended to offer requisite information, caution, and instinctive intrusion to lower the risk/severity of an accident. The positional awareness of danger is the outcome of the automotive vision system. The input to the vision system is video streams collected from front, rear, and side cameras fitted on the vehicle. The quality of input received from the cameras is an important issue to be solved, but it remains a mostly unfocused area. Other challenges associated with the system are increased data rates, availability of memory, requirement of high-speed data processing and communication, and low-quality video streams. The low resolution of video streams is responsible for ineffective decisions made for the services provided by the system such as recognition of traffic light signaling, lane tracking, collision avoidance, and status of the driver.. The developments in the vision system and even machine learning-based automotive systems are mainly focused on services and not quality of input—i.e. video resolution. This study is a systematic look at the basics, recent developments, challenges, and scope of the vision system, with video resolution as the key parameter. New researchers in the area of image/video processing will benefit from the information provided and be motivated to contribute in this growing area.
Advancing social justice through linguistic justice: Strategies for building equity fluent NLP technology

Language and social reality are mutually reinforcing; as a result, natural language processing (NLP) presents a unique opportunity to shift social reality at scale, advancing social justice by promoting linguistic justice. We provide an overview of how language and bias are intertwined and implications for building NLP tools that actively advance equity and inclusion. Then, we present a framework for centering inclusion and social justice in NLP design at four overlapping layers of linguistic structure. The goal is to provide a foundation for adopting equity-centered principles in the creation of NLP tools that don’t simply mitigate social biases, but actively advance inclusion and social justice through language. This work aims to be practical and builds from a partnership between researchers at the Center for Equity, Gender, and Leadership at the UC Berkeley Haas School of Business and leaders and practitioners at a large Silicon Valley tech firm. This framework can foster equity-centered thinking to lead to greater “equity fluent” NLP tools that have the potential to advance justice more broadly.
BERT Embeddings for Automatic Readability Assessment

Automatic readability assessment (ARA) is the task of evaluating the level of ease or difficulty of text documents for a target audience.For researchers, one of the many open problems in the field is to make such models trained for the task show efficacy even for low-resource languages.In this study, we propose an alternative way of utilizing the information-rich embeddings of BERT models with handcrafted linguistic features through a combined method for readability assessment.Results show that the proposed method outperforms classical approaches in readability assessment using English and Filipino datasets-obtaining as high as 12.4% increase in F1 performance.We also show that the general information encoded in BERT embeddings can be used as a substitute feature set for low-resource languages like Filipino with limited semantic and syntactic NLP tools to explicitly extract feature values for the task.
SPACE: Structured Compression and Sharing of Representational Space for Continual Learning

Humans learn incrementally from sequential experiences throughout their lives, which has proven hard to emulate in artificial neural networks. Incrementally learning tasks causes neural networks to overwrite relevant information learned about older tasks, resulting in ‘Catastrophic Forgetting’. Efforts to overcome this phenomenon often utilize resources poorly, for instance, by growing the network architecture or needing to save parametric importance scores, or violate data privacy between tasks. To tackle this, we propose SPACE, an algorithm that enables a network to learn continually and efficiently by partitioning the learnt space into a Core space, that serves as the condensed knowledge base over previously learned tasks, and a Residual space, which is akin to a scratch space for learning the current task. After learning each task, the Residual is analyzed for redundancy, both within itself and with the learnt Core space. A minimal number of extra dimensions required to explain the current task are added to the Core space and the remaining Residual is freed up for learning the next task. We evaluate our algorithm on P-MNIST, CIFAR and a sequence of 8 different datasets, and achieve comparable accuracy to the state-of-the-art methods while overcoming catastrophic forgetting. Additionally, our algorithm is well suited for practical use. The partitioning algorithm analyzes all layers in one shot, ensuring scalability to deeper networks. Moreover, the analysis of dimensions translates to filter-level sparsity, and the structured nature of the resulting architecture gives us up to 5x improvement in energy efficiency during task inference over the current state-of-the-art.
TWT: Table with Written Text for Controlled Data-to-Text Generation

Large pre-trained neural models have recently shown remarkable progress in text generation.In this paper, we propose to generate text conditioned on the structured data (table) and a prefix (the written text) by leveraging the pretrained models.We present a new data-to-text dataset, Table with Written Text (TWT), by repurposing two existing datasets: ToTTo and TabFact.TWT contains both factual and logical statements that are faithful to the structured data, aiming to serve as a useful benchmark for controlled text generation.Compared with existing data-to-text task settings, TWT is more intuitive, the prefix (usually provided by the user) controls the topic of the generated text.Existing methods usually output hallucinated text that is not faithful on TWT.Therefore, we design a novel approach with table-aware attention visibility and copy mechanism over the table.Experimental results show that our approach outperforms state-of-the-art methods under both automatic and human evaluation metrics.