Reviewerly/exHarmony huggingface dataset:
Floods Relevancy and Identification of Location from Twitter Posts using NLP Techniques

This paper presents our solutions for the MediaEval 2022 task on DisasterMM. The task is composed of two subtasks, namely (i) Relevance Classification of Twitter Posts (RCTP), and (ii) Location Extraction from Twitter Texts (LETT). The RCTP subtask aims at differentiating flood-related and non-relevant social posts while LETT is a Named Entity Recognition (NER) task and aims at the extraction of location information from the text. For RCTP, we proposed four different solutions based on BERT, RoBERTa, Distil BERT, and ALBERT obtaining an F1-score of 0.7934, 0.7970, 0.7613, and 0.7924, respectively. For LETT, we used three models namely BERT, RoBERTa, and Distil BERTA obtaining an F1-score of 0.6256, 0.6744, and 0.6723, respectively.
Generalized PTR: User-Friendly Recipes for Data-Adaptive Algorithms with Differential Privacy

The ''Propose-Test-Release'' (PTR) framework is a classic recipe for designing differentially private (DP) algorithms that are data-adaptive, i.e. those that add less noise when the input dataset is nice. We extend PTR to a more general setting by privately testing data-dependent privacy losses rather than local sensitivity, hence making it applicable beyond the standard noise-adding mechanisms, e.g. to queries with unbounded or undefined sensitivity. We demonstrate the versatility of generalized PTR using private linear regression as a case study. Additionally, we apply our algorithm to solve an open problem from ''Private Aggregation of Teacher Ensembles (PATE)'' -- privately releasing the entire model with a delicate data-dependent analysis.
On Causality Problem in Natural Language Processing Field

Natural language processing (NLP) field has been developing rapidly recently. This article consists mainly of literature review of the basic understanding and solving the causality problem in natural language processing field. Existing models may benefit from the concept of causality because conventional language models are brittle and spurious [10]. Incorporating the principle of causality could assist in resolving this issue. Since this issue affects seriously on the accuracy value of NLP methods and algorithms, it is worth paying attention to. Content of the article includes the authors who have been covered this topic and have made researches respecting mentioned problem, the results that have been achieved, the methods and approached that have been used and the data that was used in researches.
Integrating Semantic Information into Sketchy Reading Module of Retro-Reader for Vietnamese Machine Reading Comprehension

Machine Reading Comprehension has become one of the most advanced and popular research topics in the fields of Natural Language Processing in recent years. The classification of answerability questions is a relatively significant sub-task in machine reading comprehension; however, there haven't been many studies. Retro-Reader is one of the studies that has solved this problem effectively. However, the encoders of most traditional machine reading comprehension models in general and Retro-Reader, in particular, have not been able to exploit the contextual semantic information of the context completely. Inspired by SemBERT, we use semantic role labels from the SRL task to add semantics to pre-trained language models such as mBERT, XLM-R, PhoBERT. This experiment was conducted to compare the influence of semantics on the classification of answerability for the Vietnamese machine reading comprehension. Additionally, we hope this experiment will enhance the encoder for the Retro-Reader model's Sketchy Reading Module. The improved Retro-Reader model's encoder with semantics was first applied to the Vietnamese Machine Reading Comprehension task and obtained positive results.
Trojaning semi-supervised learning model via poisoning wild images on the web

Wild images on the web are vulnerable to backdoor (also called trojan) poisoning, causing machine learning models learned on these images to be injected with backdoors. Most previous attacks assumed that the wild images are labeled. In reality, however, most images on the web are unlabeled. Specifically, we study the effects of unlabeled backdoor images under semi-supervised learning (SSL) on widely studied deep neural networks. To be realistic, we assume that the adversary is zero-knowledge and that the semi-supervised learning model is trained from scratch. Firstly, we find the fact that backdoor poisoning always fails when poisoned unlabeled images come from different classes, which is different from poisoning the labeled images. The reason is that the SSL algorithms always strive to correct them during training. Therefore, for unlabeled images, we implement backdoor poisoning on images from the target class. Then, we propose a gradient matching strategy to craft poisoned images such that their gradients match the gradients of target images on the SSL model, which can fit poisoned images to the target class and realize backdoor injection. To the best of our knowledge, this may be the first approach to backdoor poisoning on unlabeled images of trained-from-scratch SSL models. Experiments show that our poisoning achieves state-of-the-art attack success rates on most SSL algorithms while bypassing modern backdoor defenses.
Navigating Alignment for Non-identical Client Class Sets: A Label Name-Anchored Federated Learning Framework

Traditional federated classification methods, even those designed for non-IID clients, assume that each client annotates its local data with respect to the same universal class set. In this paper, we focus on a more general yet practical setting, non-identical client class sets, where clients focus on their own (different or even non-overlapping) class sets and seek a global model that works for the union of these classes. If one views classification as finding the best match between representations produced by data/label encoder, such heterogeneity in client class sets poses a new significant challenge -- local encoders at different clients may operate in different and even independent latent spaces, making it hard to aggregate at the server. We propose a novel framework, FedAlign, to align the latent spaces across clients from both label and data perspectives. From a label perspective, we leverage the expressive natural language class names as a common ground for label encoders to anchor class representations and guide the data encoder learning across clients. From a data perspective, during local training, we regard the global class representations as anchors and leverage the data points that are close/far enough to the anchors of locally-unaware classes to align the data encoders across clients. Our theoretical analysis of the generalization performance and extensive experiments on four real-world datasets of different tasks confirm that FedAlign outperforms various state-of-the-art (non-IID) federated classification methods.
Adaptive Fine-tuning for Multiclass Classification over Software Requirement Data

The analysis of software requirement specifications (SRS) using Natural Language Processing (NLP) methods has been an important study area in the software engineering field in recent years. Especially thanks to the advances brought by deep learning and transfer learning approaches in NLP, SRS data can be utilized for various learning tasks more easily. In this study, we employ a three-stage domain-adaptive fine-tuning approach for three prediction tasks regarding software requirements, which improve the model robustness on a real distribution shift. The multi-class classification tasks involve predicting the type, priority and severity of the requirement texts specified by the users. We compare our results with strong classification baselines such as word embedding pooling and Sentence BERT, and show that the adaptive fine-tuning leads to performance improvements across the tasks. We find that an adaptively fine-tuned model can be specialized to particular data distribution, which is able to generate accurate results and learns from abundantly available textual data in software engineering task management systems.
EmoGator: A New Open Source Vocal Burst Dataset with Baseline Machine Learning Classification Methodologies

Vocal Bursts -- short, non-speech vocalizations that convey emotions, such as laughter, cries, sighs, moans, and groans -- are an often-overlooked aspect of speech emotion recognition, but an important aspect of human vocal communication. One barrier to study of these interesting vocalizations is a lack of large datasets. I am pleased to introduce the EmoGator dataset, which consists of 32,130 samples from 357 speakers, 16.9654 hours of audio; each sample classified into one of 30 distinct emotion categories by the speaker. Several different approaches to construct classifiers to identify emotion categories will be discussed, and directions for future research will be suggested. Data set is available for download from https://github.com/fredbuhl/EmoGator.
Lightweight Image Inpainting by Stripe Window Transformer with Joint Attention to CNN

Image inpainting is an important task in computer vision. As admirable methods are presented, the inpainted image is getting closer to reality. However, the result is still not good enough in the reconstructed texture and structure based on human vision. Although recent advances in computer hardware have enabled the development of larger and more complex models, there is still a need for lightweight models that can be used by individuals and small-sized institutions. Therefore, we propose a lightweight model that combines a specialized transformer with a traditional convolutional neural network (CNN). Furthermore, we have noticed most researchers only consider three primary colors (RGB) in inpainted images, but we think this is not enough. So we propose a new loss function to intensify color details. Extensive experiments on commonly seen datasets (Places2 and CelebA) validate the efficacy of our proposed model compared with other state-of-the-art methods. Index Terms: HSV color space, image inpainting, joint attention, stripe window, transformer
Analysing Discrete Self Supervised Speech Representation for Spoken Language Modeling

This work profoundly analyzes discrete self-supervised speech representations (units) through the eyes of Generative Spoken Language Modeling (GSLM). Following the findings of such an analysis, we propose practical improvements to the discrete unit for the GSLM. First, we start comprehending these units by analyzing them in three axes: interpretation, visualization, and resynthesis. Our analysis finds a high correlation between the speech units to phonemes and phoneme families, while their correlation with speaker or gender is weaker. Additionally, we found redundancies in the extracted units and claim that one reason may be the units' context. Following this analysis, we propose a new, unsupervised metric to measure unit redundancies. Finally, we use this metric to develop new methods that improve the robustness of units' clustering and show significant improvement considering zero-resource speech metrics such as ABX. Code and analysis tools are available under the following link: https://github.com/slp-rl/SLM-Discrete-Representations
Dynamically Modular and Sparse General Continual Learning

Real-world applications often require learning continuously from a stream of data under ever-changing conditions. When trying to learn from such non-stationary data, deep neural networks (DNNs) undergo catastrophic forgetting of previously learned information. Among the common approaches to avoid catastrophic forgetting, rehearsal-based methods have proven effective. However, they are still prone to forgetting due to task-interference as all parameters respond to all tasks. To counter this, we take inspiration from sparse coding in the brain and introduce dynamic modularity and sparsity (Dynamos) for rehearsal-based general continual learning. In this setup, the DNN learns to respond to stimuli by activating relevant subsets of neurons. We demonstrate the effectiveness of Dynamos on multiple datasets under challenging continual learning evaluation protocols. Finally, we show that our method learns representations that are modular and specialized, while maintaining reusability by activating subsets of neurons with overlaps corresponding to the similarity of stimuli.
Deep Recurrent Learning Through Long Short Term Memory and TOPSIS

Enterprise resource planning (ERP) software brings resources, data together to keep software-flow within business processes in a company. However, cloud computing's cheap, easy and quick management promise pushes business-owners for a transition from monolithic to a data-center/cloud based ERP. Since cloud-ERP development involves a cyclic process, namely planning, implementing, testing and upgrading, its adoption is realized as a deep recurrent neural network problem. Eventually, a classification algorithm based on long short term memory (LSTM) and TOPSIS is proposed to identify and rank, respectively, adoption features. Our theoretical model is validated over a reference model by articulating key players, services, architecture, functionalities. Qualitative survey is conducted among users by considering technology, innovation and resistance issues, to formulate hypotheses on key adoption factors.
IRT2: Inductive Linking and Ranking in Knowledge Graphs of Varying Scale

We address the challenge of building domain-specific knowledge models for industrial use cases, where labelled data and taxonomic information is initially scarce. Our focus is on inductive link prediction models as a basis for practical tools that support knowledge engineers with exploring text collections and discovering and linking new (so-called open-world) entities to the knowledge graph. We argue that - though neural approaches to text mining have yielded impressive results in the past years - current benchmarks do not reflect the typical challenges encountered in the industrial wild properly. Therefore, our first contribution is an open benchmark coined IRT2 (inductive reasoning with text) that (1) covers knowledge graphs of varying sizes (including very small ones), (2) comes with incidental, low-quality text mentions, and (3) includes not only triple completion but also ranking, which is relevant for supporting experts with discovery tasks. We investigate two neural models for inductive link prediction, one based on end-to-end learning and one that learns from the knowledge graph and text data in separate steps. These models compete with a strong bag-of-words baseline. The results show a significant advance in performance for the neural approaches as soon as the available graph data decreases for linking. For ranking, the results are promising, and the neural approaches outperform the sparse retriever by a wide margin.
Robust Inference for Federated Meta-Learning

Synthesizing information from multiple data sources is critical to ensure knowledge generalizability. Integrative analysis of multi-source data is challenging due to the heterogeneity across sources and data-sharing constraints due to privacy concerns. In this paper, we consider a general robust inference framework for federated meta-learning of data from multiple sites, enabling statistical inference for the prevailing model, defined as the one matching the majority of the sites. Statistical inference for the prevailing model is challenging since it requires a data-adaptive mechanism to select eligible sites and subsequently account for the selection uncertainty. We propose a novel sampling method to address the additional variation arising from the selection. Our devised CI construction does not require sites to share individual-level data and is shown to be valid without requiring the selection of eligible sites to be error-free. The proposed robust inference for federated meta-learning (RIFL) methodology is broadly applicable and illustrated with three inference problems: aggregation of parametric models, high-dimensional prediction models, and inference for average treatment effects. We use RIFL to perform federated learning of mortality risk for patients hospitalized with COVID-19 using real-world EHR data from 16 healthcare centers representing 275 hospitals across four countries.
Polynomial representation of additive cyclic codes and new quantum codes

We give a polynomial representation for additive cyclic codes over $\mathbb{F}_{p^2}$. This representation will be applied to uniquely present each additive cyclic code by at most two generator polynomials. We determine the generator polynomials of all different additive cyclic codes. A minimum distance lower bound for additive cyclic codes will also be provided using linear cyclic codes over $\mathbb{F}_p$. We classify all the symplectic self-dual, self-orthogonal, and nearly self-orthogonal additive cyclic codes over $\mathbb{F}_{p^2}$. Finally, we present ten record-breaking binary quantum codes after applying a quantum construction to self-orthogonal and nearly self-orthogonal additive cyclic codes over $\mathbb{F}_{4}$.
The Undesirable Dependence on Frequency of Gender Bias Metrics Based on Word Embeddings

Numerous works use word embedding-based metrics to quantify societal biases and stereotypes in texts. Recent studies have found that word embeddings can capture semantic similarity but may be affected by word frequency. In this work we study the effect of frequency when measuring female vs. male gender bias with word embedding-based bias quantification methods. We find that Skip-gram with negative sampling and GloVe tend to detect male bias in high frequency words, while GloVe tends to return female bias in low frequency words. We show these behaviors still exist when words are randomly shuffled. This proves that the frequency-based effect observed in unshuffled corpora stems from properties of the metric rather than from word associations. The effect is spurious and problematic since bias metrics should depend exclusively on word co-occurrences and not individual word frequencies. Finally, we compare these results with the ones obtained with an alternative metric based on Pointwise Mutual Information. We find that this metric does not show a clear dependence on frequency, even though it is slightly skewed towards male bias across all frequencies.
Parameterized Lower Bounds for Problems in P via Fine-Grained Cross-Compositions

We provide a general framework to exclude parameterized running times of the form $O(\ell^\beta+ n^\gamma)$ for problems that have polynomial running time lower bounds under hypotheses from fine-grained complexity. Our framework is based on cross-compositions from parameterized complexity. We (conditionally) exclude running times of the form $O(\ell^{{\gamma}/{(\gamma-1)} - \epsilon} + n^\gamma)$ for any $1<\gamma<2$ and $\epsilon>0$ for the following problems: - Longest Common Subsequence: Given two length-$n$ strings and $\ell\in\mathbb{N}$, is there a common subsequence of length $\ell$? - Discrete Fr\'echet Distance: Given two lists of $n$ points each and $k\in \mathbb{N}$, is the Fr\'echet distance of the lists at most $k$? Here $\ell$ is the maximum number of points which one list is ahead of the other list in an optimum traversal. Moreover, we exclude running times $O(\ell^{{2\gamma}/{(\gamma -1)}-\epsilon} + n^\gamma)$ for any $1<\gamma<3$ and $\epsilon>0$ for: - Negative Triangle: Given an edge-weighted graph with $n$ vertices, is there a triangle whose sum of edge-weights is negative? Here $\ell$ is the order of a maximum connected component. - Triangle Collection: Given a vertex-colored graph with $n$ vertices, is there for each triple of colors a triangle whose vertices have these three colors? Here $\ell$ is the order of a maximum connected component. - 2nd Shortest Path: Given an $n$-vertex edge-weighted directed graph, two vertices $s$ and $t$, and $k \in \mathbb{N}$, has the second longest $s$-$t$-path length at most $k$? Here $\ell$ is the directed feedback vertex set. Except for 2nd Shortest Path all these running time bounds are tight, that is, algorithms with running time $O(\ell^{{\gamma}/{(\gamma-1)}} + n^\gamma )$ for any $1 < \gamma < 2$ and $O(\ell^{{2\gamma}/{(\gamma -1)}} + n^\gamma)$ for any $1 < \gamma < 3$, respectively, are known.
CHILDREN IN IQBAL’S POETRY

Children are the future of any nation, so parents, teachers, and writers are responsible for their better education, proper care, and proper care, so they must be familiar with the psychology of children Literature helps to nurture the new generation. The unique place that children's literature has in the world today is only the result of the efforts of writers and poets.&#x0D; The importance of children's etiquette cannot be denied. Literature not only instills in children feelings of joy and happiness but also enlightens them with the light of life and high values ​​and gives children high ambitions to face the difficulties that may arise in life to come. Allama Iqbal has a special feature in the creation of children's literature. He was not only a great poet but he was also a good prose writer. He has also written excellent poems for children. Iqbal has certainly given some moral lessons through these poems but he does not teach ethics directly. There is a poetic motive in the poems. They do not allow their poems to be simple and simple as to make them feel uninteresting. This article tries to find out what Iqbal wrote for children in our social life. What are the effects?
Influence of the Parental Family on the Trajectories of Professional Mobility of Postgraduate Students

The article examines the phenomenon of the parental family as a factor influencing the social (professional) mobility of postgraduate students. The influence of the parental family is analyzed in two directions / spheres: 1) the period of study (from the moment of admission to the defense of the dissertation) and 2) the formation of the views and perceptions of postgraduate students about career prospects and opportunities. The empirical base of the study is represented by data from qualitative in-depth interviews with postgraduate students of Moscow universities (N=30). There is proposed differentiation of students in postgraduate school into two groups (“goal-oriented” and “coincidental”), and the following conclusions are drawn: 1) at the initial stage of education, the role and importance of the parental family in the view of postgraduate students is minimal; 2) by the time of completing postgraduate studies, goal-oriented postgraduate students change their minds, recognizing the importance of the family as a supporting factor of social and professional mobility; 3) in contrast to the period of studying in graduate school the parental family becomes a significant factor of mobility in the views and perceptions of postgraduate students about professional prospects and the formation of a career trajectory.
Construction of Image Datasets for Chemical Experiments and Numerical Assessment of Object Detection Methods

In recent years, the remarkable advances in artificial intelligence technology have led to digital transformation (DX) in various fields. The automated construction of laboratory notebook through filming experiments is a promising application of image recognition for chemistry． In this study, we created an image dataset of chemical experiment, which contains 2376 images and consists of 7 classes of objects. Object detection methods and a multiple object tracking method were implemented and assessed using the dataset toward to develop automated laboratory notebook system.
Real-time Fault Diagnosis and Method of Photovoltaic Module Based on Intelligent UAV Cruise

Abstract The authors have requested that this preprint be removed from Research Square.