Reviewerly/exHarmony huggingface dataset:
<i>LMFingerprints</i>: Visual Explanations of Language Model Embedding Spaces through Layerwise Contextualization Scores

Abstract Language models, such as BERT, construct multiple, contextualized embeddings for each word occurrence in a corpus. Understanding how the contextualization propagates through the model's layers is crucial for deciding which layers to use for a specific analysis task. Currently, most embedding spaces are explained by probing classifiers; however, some findings remain inconclusive. In this paper, we present LMFingerprints, a novel scoring‐based technique for the explanation of contextualized word embeddings. We introduce two categories of scoring functions, which measure (1) the degree of contextualization, i.e., the layerwise changes in the embedding vectors, and (2) the type of contextualization, i.e., the captured context information. We integrate these scores into an interactive explanation workspace. By combining visual and verbal elements, we provide an overview of contextualization in six popular transformer‐based language models. We evaluate hypotheses from the domain of computational linguistics, and our results not only confirm findings from related work but also reveal new aspects about the information captured in the embedding spaces. For instance, we show that while numbers are poorly contextualized, stopwords have an unexpected high contextualization in the models' upper layers, where their neighborhoods shift from similar functionality tokens to tokens that contribute to the meaning of the surrounding sentences.
Detection of Bangla Hate Comments and Cyberbullying in Social Media Using NLP and Transformer Models

Hate speech and cyberbullying detection on social media is one of the most trending natural language processing tasks in the current scenario. However, due to the lack of resources, a few work has been done in detection of cyberbullying in social media for the Bangla language. In this paper, we have applied different transformer models to detect cyberbullying in social media. We have used a Bangla text dataset with 44,001 Bangla comments which are collected from Facebook posts. This dataset is labeled into five categories: sexual, threat, religious, troll, and not-bully. We have applied three transformer models: Bangla BERT, Bengali DistilBERT, and XLM-RoBERTa. Using transformer models, we achieved a very satisfactory score and the best performance we got was using the XML-RoBERTa model which achieved the highest accuracy of 85% and F1-score of 86%.
HGA: Hierarchical Feature Extraction With Graph and Attention Mechanism for Linguistic Steganalysis

Linguistic steganalysis is an important topic in the field of information security and signal processing. In recent years, linguistic steganalysis have mainly utilized deep learning techniques and make great success. But suffer from the following major disadvantages. From the perspective of model structure, current methods only extract coarse features of the text, without focusing on the fine-grained representations. In terms of application, most of the studies only focus on single hidden scene and ignore the more realistic mixed hidden scenes which are more complex and realistic. These weaknesses limit the performance and the application of linguistic steganalysis in reality. In this paper, we propose a novel linguistic steganalysis method to overcome these weaknesses. This proposed method can extract distinguished text representation which fuses hierarchical features and perform excellently in sophisticated conditions. Firstly, we adapt gated graph neural networks as the coarse graph updater to update node representations on the graph level. Then we design a fine graph updater composed of the graph attention mechanism to focus on the highlighted nodes on the node-level. Moreover, we extract the most notable feature on the dimension-level of node by the graph channel attention module. Finally, the readout function is designed to fuse the hierarchical features and make the classification. The experimental results show that our method achieves the best results compared with the previous methods in both single hidden scene and mixed hidden scenes, which prove the effectiveness of the proposed method.
Improving MapReduce heterogeneous performance using KNN fair share scheduling

MapReduce is one of the essential programming models for parallel processing and distributed storage of enormous data sets. The default Hadoop implementation assumes that the executing nodes are homogeneous. Data Locality is an important feature that Hadoop introduced to improve the performance of the traditional MapReduce model. The key idea is to move the map task closer to the node where the actual data resides rather than transferring the vast data set near the computation. Data Locality helps in lowering the network congestion and improving performance. However, this practice fails when processing the data in a heterogeneous Hadoop cluster. In a heterogeneous setup, nodes with different computational capabilities pose a crucial challenge. Nodes with a faster processing capacity finish the job compared to the nodes with slower processing ability. This paper proposes a KNN based scheduler that focuses on speculative prefetching and clustering of the data. The process starts with speculative prefetching and then performing the KNN clustering on the intermediate map output before directing it to the reducer for final processing. The performance evaluation of scheduler performance is analysed by executing different workloads like WordCount, RandomText, RandomNum, and Sort. The results show that the proposed idea improves the performance of job execution
To Empower or Provoke? Exploring approaches for participatory design at schools for neurodiverse individuals in India

As we push the boundaries of participatory design (PD) to empower children across the world, barriers to participation for those currently underserved require further attention. This includes neurodiverse individuals at special schools in India, whose day-to-day experiences are heavily influenced by the larger socio-cultural context of the schools comprising diverse stakeholders with diverse agendas and motivations. In this paper, we consider how-to PD at special schools in India. Employing the lenses of genuine participation and cultural translation, we critically examine two design projects with neurodiverse individuals: a gesture-based application to promote joint attention and using a mobile app to promote composting and entrepreneurial skills. We identified several challenges for adapting PD to the context of special schools in India. Reflecting on our experiences and coupled with previous literature on PD with similar contexts, we suggest potential solutions for these challenges. This includes how-to negotiate roles and responsibilities among stakeholders, handle conflicts among stakeholders’ socio-technical aspirations, balance power differentials and censorships, and identify possibilities for long-term real-world impact. Our work paves the way to adapt PD, from a provocation or privilege to a possibility, to empower neurodiverse individuals in all corners of the world.
Auto-encoder Based Co-training Multi-view Representation Learning

Multi-view learning is a learning problem that utilizes the various representations of an object to mine valuable knowledge and improve the performance of learning algorithm, and one of the significant directions of multi-view learning is sub-space learning. As we known, auto-encoder is a method of deep learning, which can learn the latent feature of raw data by reconstructing the input, and based on this, we propose a novel algorithm called Auto-encoder based Co-training Multi-View Learning (ACMVL), which utilizes both complementarity and consistency and finds a joint latent feature representation of multiple views. The algorithm has two stages, the first is to train auto-encoder of each view, and the second stage is to train a supervised network. Interestingly, the two stages share the weights partly and assist each other by co-training process. According to the experimental result, we can learn a well performed latent feature representation, and auto-encoder of each view has more powerful reconstruction ability than traditional auto-encoder.
BFF R-CNN: Balanced Feature Fusion for Object Detection

In the neck part of a two-stage object detection network, feature fusion is generally carried out in either a top-down or bottom-up manner. However, two types of imbalance may exist: feature imbalance in the neck of the model and gradient imbalance in the region of interest extraction layer due to the scale changes of objects. The deeper the network is, the more abstract the learned features are, that is to say, more semantic information can be extracted. However, the extracted image background, spatial location, and other resolution information are less. In contrast, the shallow part can learn little semantic information, but a lot of spatial location information. We propose the Both Ends to Centre to Multiple Layers (BEtM) feature fusion method to solve the feature imbalance problem in the neck and a Multi-level Region of Interest Feature Extraction (MRoIE) layer to solve the gradient imbalance problem. In combination with the Region-based Convolutional Neural Network (R-CNN) framework, our Balanced Feature Fusion (BFF) method offers significantly improved network performance compared with the Faster R-CNN architecture. On the MS COCO 2017 dataset, it achieves an average precision (AP) that is 1.9 points and 3.2 points higher than those of the Feature Pyramid Network (FPN) Faster R-CNN framework and the Generic Region of Interest Extractor (GRoIE) framework, respectively.
Evonne: Interactive Proof Visualization for Description Logics (System Description)

Abstract Explanations for description logic (DL) entailments provide important support for the maintenance of large ontologies. The “justifications” usually employed for this purpose in ontology editors pinpoint the parts of the ontology responsible for a given entailment. Proofs for entailments make the intermediate reasoning steps explicit, and thus explain how a consequence can actually be derived. We present an interactive system for exploring description logic proofs, called Evonne , which visualizes proofs of consequences for ontologies written in expressive DLs. We describe the methods used for computing those proofs, together with a feature called signature-based proof condensation . Moreover, we evaluate the quality of generated proofs using real ontologies.
Chinese Named Entity Recognition of Geological News Based on BERT Model

With the ongoing progress of geological survey work and the continuous accumulation of geological data, extracting accurate information from massive geological data has become increasingly difficult. To fully mine and utilize geological data, this study proposes a geological news named entity recognition (GNNER) method based on the bidirectional encoder representations from transformers (BERT) pre-trained language model. This solves the problems of traditional word vectors that are difficult to represent context semantics and the single extraction effect and can also help construct the knowledge graphs of geological news. First, the method uses the BERT pre-training model to embed words in the geological news text, and the dynamically obtained word vector is used as the model’s input. Second, the word vector is sent to a bidirectional long short-term memory model for further training to obtain contextual features. Finally, the corresponding six entity types are extracted using conditional random field sequence decoding. Through experiments on the constructed Chinese geological news dataset, the average F1 score identified by the model is 0.839. The experimental results show that the model can better identify news entities in geological news.
Time Delay Estimation in Radar System using Fuzzy Based Iterative Unscented Kalman Filter

RSs (Radar Systems) identify and trace targets and are commonly employed in applications like air traffic control and remote sensing.They are necessary for monitoring precise target trajectories.Estimations of RSs are nonlinear as the parameters TDEs (time delay Estimations) and Doppler shifts are computed on receipt of echoes where EKFs (Extended Kalman Filters) and UKFs (Unscented Kalman Filters) have not been examined for computations.RSs, certain times result in poor accuracies and SNRs (low signal to noise ratios) especially, while encountering complicated environments.This work proposes IUKFs (Iterated UKFs) to track online filter performances while using optimization techniques to enhance outcomes.The use of cost functions can assist state corrections while lowering costs.A new parameter is optimized using MCEHOs (Mutation Chaotic Elephant Herding Optimizations) by linearly approximating system non-linearity where OIUKFs (Optimized Iterative UKFs) predict a target's unknown parameters.To obtain optimal solutions theoretically, OIUKFs take less iteration, resulting in shorter execution times.The proposed OIUKFs provide numerical approximations which are derivative-free implementations.Simulation evaluation results with estimators show better performances in terms of reduced NMSEs (Normalized Mean Square Errors), RMSEs (Root Mean Squared Errors), SNRs, variances, and better accuracies than current approaches.
Hybrid Approach for Privacy Enhancement in Data Mining Using Arbitrariness and Perturbation

Imagine numerous clients, each with personal data; individual inputs are severely corrupt, and a server only concerns the collective, statistically essential facets of this data.In several data mining methods, privacy has become highly critical.As a result, various privacy-preserving data analysis technologies have emerged.Hence, we use the randomization process to reconstruct composite data attributes accurately.Also, we use privacy measures to estimate how much deception is required to guarantee privacy.There are several viable privacy protections; however, determining which one is the best is still a work in progress.This paper discusses the difficulty of measuring privacy while also offering numerous random sampling procedures and statistical and categorized data results.Furthermore, this paper investigates the use of arbitrary nature with perturbations in privacy preservation.According to the research, arbitrary objects (most notably random matrices) have "predicted" frequency patterns.It shows how to recover crucial information from a sample damaged by a random number using an arbitrary lattice spectral selection strategy.This filtration system's conceptual framework posits, and extensive practical findings indicate that sparse data distortions preserve relatively modest privacy protection in various situations.As a result, the research framework is efficient and effective in maintaining data privacy and security.
Blended Teaching Strategies of College English Translation under the Background of Internet

Blended learning has taken center stage in higher education because it involves overseeing of “Online learning” and the ongoing growth of teaching reform in colleges and universities. Internet promotes the reconfiguration and integration of educational resources. The Internet has greatly magnified the role and value of high-quality educational resources. Blended teaching combines the advantages of traditional teaching and network teaching to complement each other; it not only gives full play to the flexibility and autonomy of network teaching, but also retains the connection of teacher-student emotional communication in traditional teaching. Translation teaching is an important part of college English teaching. This article mainly introduces the research of college English translation (CET) blended teaching strategies under the Internet background. This paper proposes a research plan for CET blended teaching strategies under the Internet background, including literature research method, questionnaire survey method, analytic hierarchy process, expert interview method, teaching evaluation sentiment classification algorithm based on feature-weighted Stacking algorithm, and analytic hierarchy process of the teaching effect evaluation algorithm. The experimental results of this article show that the average value of the questionnaire Cronbach’s α coefficient is 0.915, demonstrating that the study's information is both extremely plausible and reasonably real.
The Structural Role of Smart Contracts and Exchanges in the Centralisation of Ethereum-Based Cryptoassets

In this paper, we use the methods of networks science to analyse the transaction networks of tokens running on the Ethereum blockchain. We start with a deep dive on four of them: Ampleforth (AMP), Basic Attention Token (BAT), Dai (DAI) and Uniswap (UNI). We study two types of blockchain addresses, smart contracts (SC), which run code, and externally owned accounts (EOA), run by human users, or off-chain code, with the corresponding private keys. We use preferential attachment and network dismantling strategies to evaluate their importance for the network structure. Subsequently, we expand our view to all ERC-20 tokens issued on the Ethereum network. We first study multilayered networks composed of Ether (ETH) and individual tokens using a dismantling approach to assess how the deconstruction starting from one network affects the other. Finally, we analyse the Ether network and Ethereum-based token networks to find similarities between sets of high-degree nodes. For this purpose, we use both the traditional Jaccard Index and a new metric that we introduce, the Ordered Jaccard Index (OJI), which considers the order of the elements in the two sets that are compared. Our findings suggest that smart contracts and exchange-related addresses play a structural role in transaction networks both in DeFi and Ethereum. The presence in the network of nodes associated to addresses of smart contracts and exchanges is positively correlated with the success of the token network measured in terms of network size and market capitalisation. These nodes play a fundamental role in the centralisation of the supposedly decentralised finance (DeFi) ecosystem: without them, their networks would quickly collapse.
Adaptive segmented webpage text based malicious website detection

Malicious websites pose a great threat to the physical and mental health and property security of netizens, and malicious website detection is a key research topic in network security . Many researchers first extract features from webpage texts, and then detect malicious websites through deep learning methods . However, due to the limited sequence length that the deep learning model can process, researchers generally directly discard the webpage text that exceeds the maximum sequence length (MSL), which will affect the website detection accuracy to a great extent. Therefore, a malicious website detection model based on adaptive segmented webpage text is proposed. By ensuring the integrity of webpage text, the malicious website detection results are significantly improved, which greatly contribute to the accurate handling of malicious websites in cyberspace. Firstly, according to the MSL set by deep learning model and the length of webpage text, the number of text segments of each webpage text is determined, and rich features are extracted from multiple spaces of each segmented text through Multi-Head Self-Attention (MHSA) mechanism. Secondly, in order to solve the problem of missing context continuity caused by segmentation, the hidden vector obtained by Bi-directional Long Short-Term Memory (BILSTM) in the previous segment is combined with the embedding vector of the latter segment, and then input into the MHSA network . Thirdly, combining the results of multiple segments into multi-channel vector, classification is carried out by multi-channel text convolution (MCTC) network. In order to avoid misclassification of website type caused by webpage text alone, the digital certificate and Uniform Resource Locator (URL) of website are used to assist in detecting malicious websites. Multiple comparison experiment results on the malicious and benign website datasets demonstrate that the proposed model outperforms the state-of-the-art comparison models, and experiment results on public text classification datasets demonstrate that the proposed model has strong generalization ability .
Domain adversarial tangent subspace alignment for explainable domain adaptation

Deep learning is reaching state of the art in many applications. However, the generalization capabilities of the learned networks are limited to the training or source domain. The predictive power decreases when these models are evaluated in a target domain different from the source domain. Joint adversarial domain adaptation networks are currently the preferred models for source-to-target domain adaptation due to their good empirical performance. These models simultaneously learn a classifier, an invariant representation through an adversarial min–max game, and adapt local structures between domains. For the latter, it is common practice to incorporate pseudo labels that can be, however, unreliable due to false predictions on challenging tasks. This work proposes the Domain Adversarial Tangent Subspace Alignment (DATSA) network, which models data as affine subspaces and adversarially aligns local approximations of manifolds across domains. DATSA addresses the drawbacks of the joint adversarial domain adaptation networks by not requiring pseudo labels for local alignment because it relies on self-supervised learning for subspace alignment. Additionally, DATSA adaptations are explainable to some extent and the results show that they are competitive to other models in terms of accuracy.
Unsupervised face frontalization using disentangled representation-learning CycleGAN

Face frontalization aims to normalize profile face images to frontal ones for pose-invariant face-related recognition tasks. Current works have achieved outstanding results in face frontalization by using deep learning techniques. However, paired training data is usually required to train a deep face frontalization model and it is always difficult and time-consuming to acquire this kind of data. To solve this problem, we propose an unsupervised face frontalization framework, named Disentangled Representation-learning Cycle Generative Adversarial Network (DRCycleGAN). The model can be trained with unpaired data through embedding face images onto two spaces, identity feature space and pose feature space, and jointly inputting the identity feature and the pose feature to the generators to implement a paired forward and backward mapping (i.e., face frontalization and its inverse process). To adapt to the face frontalization task, a semantic-level cycle consistency loss is proposed, which implements consistency constraint supervision by measuring high-level semantic feature differences. Extensive experiment results demonstrate that the proposed method can achieve promising face frontalization performance and improve the pose-invariant face recognition performance.