Reviewerly/exHarmony huggingface dataset:
Multifaceted irradiance prediction by exploiting hybrid decomposition-entropy-Spatiotemporal attention based Sequence2Sequence models

Accurate prediction models enable the efficacious utilization and integration of solar energy into the power system. Therefore, this study aims to develop novel hybrid prediction models by employing correlation analysis (CA), decomposition techniques, sample entropy (SE), and spatio-temporal attention (STA) based sequence2sequence (S2S) algorithm for accurate prediction of global horizontal irradiance (GHI). The decomposition techniques include variational mode decomposition (VMD), improved complete ensemble empirical mode decomposition with additive noise (ICEEMDAN), and maximum overlap discrete wavelet transform (MODWT). The VMD-STA-S2S hybrid model surmounts the associated hybrid and standalone prediction models by revealing the highest prediction efficiency and the lowest error. Compared to the SARIMAX, SVR, ANN, XGB, GRU, LSTM, and S2S models, the VMD-STA-S2S model reduced RMSE during testing by 80.927 W/m2, 75.426 W/m2, 73.487 W/m2, 62.394 W/m2, 57.811 W/m2, 52.007 W/m2, and 41.836 W/m2, respectively. Similarly, the reductions in RMSE by VMD-STA-S2S model compared to SA-S2S, TA-S2S, STA-S2S, MODWT-STA-S2S, ICEEMDAN-STA-S2S, ICEEMDAN-SE-STA-S2S, and VMD-SE-STA-S2S models are 24.054 W/m2, 20.951 W/m2, 12.702 W/m2, 15.396 W/m2, 9.921 W/m2, 6.103 W/m2 and 0.484 W/m2, respectively, during testing. Furthermore, considering NSE during testing, the VMD-STA-S2S model is 8.66%, 7.72%, 7.41%, 5.71%, 5.07%, 4.31%, 3.11%, 1.43%, 1.19%, 0.64%, 0.81%, 0.47%, 0.35% and 0.07%, more efficient than the SARIMAX, SVR, ANN, XGB, GRU, LSTM, S2S, SA-S2S, TA-S2S, STA-S2S, MODWT-STA-S2S, ICEEMDAN-STA-S2S, ICEEMDAN-SE-STA-S2S, and VMD-SE-STA-S2S models, respectively. The superior performance of VMD-STA-S2S over its counterparts corroborates the integration of the VMD technique and STA-based S2S algorithm for GHI prediction. The multivariate meteorological data of this study is decomposed by VMD into subcomponents more effectively than the ICEEMDAN and MODWT techniques. VMD decomposed subcomponents are further fed to the STA-S2S to efficiently extract and learn the spatial and temporal features, resulting in the enhanced and superior prediction outcomes of the VMD-STA-S2S model compared to all the counterpart models. Besides GHI prediction, the proposed model is also appropriate for other time-series data, including renewable energy, electrical load, and environment monitoring.
ILDAE: Instance-Level Difficulty Analysis of Evaluation Data

Knowledge of difficulty level of questions helps a teacher in several ways, such as estimating students' potential quickly by asking carefully selected questions and improving quality of examination by modifying trivial and hard questions.Can we extract such benefits of instance difficulty in Natural Language Processing?To this end, we conduct Instance-Level Difficulty Analysis of Evaluation data (ILDAE) in a largescale setup of 23 datasets and demonstrate its five novel applications: 1) conducting efficientyet-accurate evaluations with fewer instances saving computational cost and time, 2) improving quality of existing evaluation datasets by repairing erroneous and trivial instances, 3) selecting the best model based on application requirements, 4) analyzing dataset characteristics for guiding future data creation, 5) estimating Out-of-Domain performance reliably.Comprehensive experiments for these applications lead to several interesting results, such as evaluation using just 5% instances (selected via ILDAE) achieves as high as 0.93 Kendall correlation with evaluation using complete dataset and computing weighted accuracy using difficulty scores leads to 5.2% higher correlation with Out-of-Domain performance.We release the difficulty scores 1 and hope our work will encourage research in this important yet understudied field of leveraging instance difficulty in evaluations.
Gurmukhi Numerals Recognition Using ANN

Computer recognition of hand written characters has been a basic requirement nowadays and has been a subject of intensive research for the last few decades. Use of Artificial Intelligence (AI) techniques for recognizing the hand written characters play an important role in bringing human and machine closer. The Gurmukhi, a religion-specific language originated from India, is one of the most popular writing script languages of the entire world. The proposed approach is based on the application of a neural network model to recognize handwritten Gurmukhi numerals. The data set consists of 1500 numerals that have been tested on the neural network. For classification, 144 binary features have been extracted from each digit and a recognition accuracy of 93.66% is reported.
Predicting Sentence Deletions for Text Simplification Using a Functional Discourse Structure

Document-level text simplification often deletes some sentences besides performing lexical, grammatical or structural simplification to reduce text complexity.In this work, we focus on sentence deletions for text simplification and use a news genre-specific functional discourse structure, which categorizes sentences based on their contents and their function roles in telling a news story, for predicting sentence deletion.We incorporate sentence categories into a neural net model in two ways for predicting sentence deletions, either as additional features or by jointly predicting sentence deletions and sentence categories.Experimental results using human-annotated data show that incorporating the functional structure improves the recall of sentence deletion prediction by 6.5% and 10.7% respectively using the two methods, and improves the overall F1-score by 3.6% and 4.3% respectively.
DiS-ReX: A Multilingual Dataset for Distantly Supervised Relation Extraction

Our goal is to study the novel task of distant supervision for multilingual relation extraction (Multi DS-RE).Research in Multi DS-RE has remained limited due to the absence of a reliable benchmarking dataset.The only available dataset for this task, RELX-Distant (Köksal and Özgür, 2020), displays several unrealistic characteristics, leading to a systematic overestimation of model performance.To alleviate these concerns, we release a new benchmark dataset for the task, named DiS-ReX.We also modify the widely-used bag attention models using an mBERT encoder and provide the first baseline results on the proposed task.We show that DiS-ReX serves as a more challenging dataset than RELX-Distant, leaving ample room for future research in this domain.
<i>PuzzleMesh</i>: A Puzzle Model to Build Mesh of Agnostic Services for Edge-Fog-Cloud

This paper presents the design, development, and evaluation of PuzzleMesh, an agnostic service mesh composition model to process large volumes of data in edge-fog-cloud environments. This model is based on a puzzle metaphor where pieces, puzzles, and metapuzzles represent self-contained autonomous and reusable software artifacts encapsulated into containers and published as microservices. A <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">piece</i> represents the integration of apps with I/O interfaces ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">loops</i> / <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">sockets</i> ), parallel processing, and management software. A <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">puzzle</i> represents a processing structure (e.g., workflows) built coupling pieces through loops and sockets. Puzzles integrate structures with a microservice architecture, implicit continuous dataflows, and transparent data exchange management software. A <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">metapuzzle</i> represents a recursive assemble of puzzles. A mesh represents a pool of pieces, puzzles, and metapuzzles available for designers to choose artifacts to build services. A prototype developed using PuzzleMesh model was evaluated through case studies about the automatic construction of processing services for the acquisition, pre-processing, manufacturing, preserving, and visualizing of satellite imagery. A qualitative comparison revealed that PuzzleMesh provides a flexible way to build reusable and portable services and to improve the usability of the services. The case study also revealed that PuzzleMesh yielded better performance results than other state-of-the-art tools.
Centralized and Distributed Robust State Estimation Over Sensor Networks Using Elliptical Distribution

We consider the robust state estimation over sensor networks with non-Gaussian noise, which is often encountered in many applications. Motivated by the fact that the elliptical distribution is the natural extension of the Gaussian distribution and includes a variety of non-Gaussian distributions with heavy-tailed characteristics, we here adopt the elliptical distribution to model the heavy-tailed process and measurement noise. The general state evolution model is used to replace the process equation and the elliptical distribution is denoted as a Gaussian mixture form. Based on that, the posterior estimation of the system state together with the parameters of the process and measurement noises can be inferred by the variational Bayes method. Moreover, the corresponding distributed estimation algorithm is then provided, which enables distributed implementation. The target tracking over sensor networks is used to show the estimation accuracy of the proposed algorithms.
Data Denoising Based on Hadamard Matrix Transformation and Rayleigh Quotient Maximization: Application to GNSS Signal Classification

Global navigation satellite system (GNSS) signal type classification based on machine learning is an effective way to improve urban positioning performance. However, GNSS signal type features extracted are unrelated and the number of features is limited, referred to as non-local-feature and few-feature issues, which limits the classification performance. This paper presents a new data denoising theory to boost the classification performance based on concepts of Hadamard matrix transformation and Rayleigh quotient maximization. Hadamard matrix transformation increases the distance between different classes, i.e., inter-class distance, by projecting the data into a new space, thereby increasing the classification performance. To improve the signal-to-noise ratio (SNR) of features, we maximize the Rayleigh quotient of the inter-class distance. The proposed denoising approach is, in particular, effective for non-local-feature and few-feature signals. We applied the proposed data denoising theory to the GNSS signal type classification problem. Results indicate that GNSS signal type classification performance (micro-averaging Recall, i.e., Recall<sub>&#x03BC;</sub>) can be improved by about 5&#x007E;10% in static test. For dynamic test, about 1.5&#x007E;3.5% improvement is achieved.
Synthetic Minority Oversampling Technique Based on Adaptive Local Mean Vectors and Improved Differential Evolution

SMOTE is a classical oversampling method and aims to improve imbalanced classification by creating synthetic minority class samples.Overgeneralization is a great challenge in SMOTE and its improvements.Multiple variations of SMOTE are proposed against imbalances between classes and overgeneralization.However, they still have the following issues: a) most methods depend on too many parameters; b) most methods fail to detect suspicious noise effectively and modify them; c) interpolation of almost all methods is susceptible to abnormal samples.To overcome the above issues, a new synthetic minority oversampling technique based on adaptive local mean vectors and improved differential evolution (SMOTE-LMVDE) is proposed.First, a new noise detection technique based on the defined adaptive local mean vectors (NDALMV) is proposed to find suspicious noise.Second, an improved differential evolution is proposed to modify and improve detected suspicious noise.Finally, a new interpolation based on the defined adaptive local mean vectors is proposed to create synthetic minority class samples.Experiments prove that the proposed method superior to 7 popular oversampling approaches on extensive data sets in the training nearest neighbor classifier and the decision tree classifier.
Tree-KGQA: An Unsupervised Approach for Question Answering Over Knowledge Graphs

Most Knowledge Graph-based Question Answering (KGQA) systems rely on training data to reach their optimal performance. However, acquiring training data for supervised systems is both time-consuming and resource-intensive. To address this, in this paper, we propose <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Tree-KGQA</b> , an unsupervised KGQA system leveraging pre-trained language models and tree-based algorithms. Entity and relation linking are essential components of any KGQA system. We employ several pre-trained language models in the entity linking task to recognize the entities mentioned in the question and obtain the contextual representation for indexing. Furthermore, for relation linking we incorporate a pre-trained language model previously trained for language inference task. Finally, we introduce a novel algorithm for extracting the answer entities from a KG, where we construct a forest of interpretations and introduce tree-walking and tree disambiguation techniques. Our algorithm uses the linked relation and predicts the tree branches that eventually lead to the potential answer entities. The proposed method achieves 4.5% and 7.1% gains in F1 score in entity linking tasks on LC-QuAD 2.0 and LC-QuAD 2.0 (KBpearl) datasets, respectively, and a 5.4% increase in the relation linking task on LC-QuAD 2.0 (KBpearl). The comprehensive evaluations demonstrate that our unsupervised KGQA approach outperforms other supervised state-of-the-art methods on the WebQSP-WD test set (1.4% increase in F1 score) - without training on the target dataset.
UMUTeam@LT-EDI-ACL2022: Detecting homophobic and transphobic comments in Tamil

This working-notes are about the participation of the UMUTeam in a LT-EDI shared task concerning the identification of homophobic and transphobic comments in YouTube.These comments are written in English, which has high availability to machine-learning resources; Tamil, which has fewer resources; and a transliteration from Tamil to Roman script combined with English sentences.To carry out this shared task, we train a neural network that combines several feature sets applying a knowledge integration strategy.These features are linguistic features extracted from a tool developed by our research group and contextual and noncontextual sentence embeddings.We ranked 7th for English subtask (macro f1-score of 45%), 3rd for Tamil subtask (macro f1-score of 82%), and 2nd for Tamil-English subtask (macro f1-score of 58%).
Secure Register Allocation for Trusted Code Generation

In this letter, we investigate the inherent vulnerability of register allocation (RA) in which the variables of a source program are mapped to the registers in hardware. This letter makes three important contributions. Specifically, we show that RA is secure if there is no spilling. Next, we show that RA with spilling does not preserve the security properties of the source program. Our experimental evaluation using a wide variety of benchmarks demonstrates that RA in LLVM is not secure. Finally, we propose a secure RA in LLVM that will not introduce additional information leaks in the generated code due to spilling.
Associations Between Ancillary Body Movements and Acoustic Parameters of Pitch, Dynamics and Timbre in Clarinet Playing

When playing an instrument, there are two main categories of body movements: instrumental movements, which are necessary for the sound production, and ancillary movements, which are associated with individual musical intentions and expressions. In this study, the particular purpose of ancillary movements of clarinet player was investigated especially in respect to how these movements were related to the musical structure of the piece and to specific audio parameters. 3D motion capture data of 19 clarinet players performing the same piece were analyzed regarding common motion patterns during the performance and in accordance with acoustic features related to pitch, dynamics (RMS energy) and timbre (spectral centroid and flux). A focus of the body movements was on the arms and the knees. The results showed that there were certain motion patterns performed by the players depending on specific musical structures. When playing a melodic part, the players often did so by bending their knees. At musical transitions, however, the knees were mainly stretched. Similarly, arm movements were more pronounced during playing melodious parts. At transitions, the arms were put closer to the torso. Considering the connection with the acoustics, a larger range of knee motions was correlated with a larger variation of the timbre. Moreover, at specific moments during the performance, when some players strongly bent their knees or lifted the arms, the RMS energy of the signal was significantly higher. The correlations of the body movements and the acoustic features showed that some players synchronized their movements with particular audio parameters more than others did. In summary, the ancillary movements of the clarinetists pursued both musical expressive intentions and physiologically necessary movements and tended to be performed with individual differences in terms of visual and auditory expression.
Fine-Tuning Graph Neural Networks via Graph Topology Induced Optimal Transport

Recently, the pretrain-finetuning paradigm has attracted tons of attention in graph learning community due to its power of alleviating the lack of labels problem in many real-world applications. Current studies use existing techniques, such as weight constraint, representation constraint, which are derived from images or text data, to transfer the invariant knowledge from the pre-train stage to fine-tuning stage. However, these methods failed to preserve invariances from graph structure and Graph Neural Network (GNN) style models. In this paper, we present a novel optimal transport-based fine-tuning framework called GTOT-Tuning, namely, Graph Topology induced Optimal Transport fine-Tuning, for GNN style backbones. GTOT-Tuning is required to utilize the property of graph data to enhance the preservation of representation produced by fine-tuned networks. Toward this goal, we formulate graph local knowledge transfer as an Optimal Transport (OT) problem with a structural prior and construct the GTOT regularizer to constrain the fine-tuned model behaviors. By using the adjacency relationship amongst nodes, the GTOT regularizer achieves node-level optimal transport procedures and reduces redundant transport procedures, resulting in efficient knowledge transfer from the pre-trained models. We evaluate GTOT-Tuning on eight downstream tasks with various GNN backbones and demonstrate that it achieves state-of-the-art fine-tuning performance for GNNs.
DictBERT: Dictionary Description Knowledge Enhanced Language Model Pre-training via Contrastive Learning

Although pre-trained language models (PLMs) have achieved state-of-the-art performance on various natural language processing (NLP) tasks, they are shown to be lacking in knowledge when dealing with knowledge driven tasks. Despite the many efforts made for injecting knowledge into PLMs, this problem remains open. To address the challenge, we propose DictBERT, a novel approach that enhances PLMs with dictionary knowledge which is easier to acquire than knowledge graph (KG). During pre-training, we present two novel pre-training tasks to inject dictionary knowledge into PLMs via contrastive learning: dictionary entry prediction and entry description discrimination. In fine-tuning, we use the pre-trained DictBERT as a plugin knowledge base (KB) to retrieve implicit knowledge for identified entries in an input sequence, and infuse the retrieved knowledge into the input to enhance its representation via a novel extra-hop attention mechanism. We evaluate our approach on a variety of knowledge driven and language understanding tasks, including NER, relation extraction, CommonsenseQA, OpenBookQA and GLUE. Experimental results demonstrate that our model can significantly improve typical PLMs: it gains a substantial improvement of 0.5%, 2.9%, 9.0%, 7.1% and 3.3% on BERT-large respectively, and is also effective on RoBERTa-large.
Initializing Then Refining: A Simple Graph Attribute Imputation Network

Representation learning on the attribute-missing graphs, whose connection information is complete while the attribute information of some nodes is missing, is an important yet challenging task. To impute the missing attributes, existing methods isolate the learning processes of attribute and structure information embeddings, and force both resultant representations to align with a common in-discriminative normal distribution, leading to inaccurate imputation. To tackle these issues, we propose a novel graph-oriented imputation framework called initializing then refining (ITR), where we first employ the structure information for initial imputation, and then leverage observed attribute and structure information to adaptively refine the imputed latent variables. Specifically, we first adopt the structure embeddings of attribute-missing samples as the embedding initialization, and then refine these initial values by aggregating the reliable and informative embeddings of attribute-observed samples according to the affinity structure. Specially, in our refining process, the affinity structure is adaptively updated through iterations by calculating the sample-wise correlations upon the recomposed embeddings. Extensive experiments on four benchmark datasets verify the superiority of ITR against state-of-the-art methods.
Meta-Learning Based Knowledge Extrapolation for Knowledge Graphs in the Federated Setting

We study the knowledge extrapolation problem to embed new components (i.e., entities and relations) that come with emerging knowledge graphs (KGs) in the federated setting. In this problem, a model trained on an existing KG needs to embed an emerging KG with unseen entities and relations. To solve this problem, we introduce the meta-learning setting, where a set of tasks are sampled on the existing KG to mimic the link prediction task on the emerging KG. Based on sampled tasks, we meta-train a graph neural network framework that can construct features for unseen components based on structural information and output embeddings for them. Experimental results show that our proposed method can effectively embed unseen components and outperforms models that consider inductive settings for KGs and baselines that directly use conventional KG embedding methods.