Reviewerly/exHarmony huggingface dataset:
Opportunities for the development of soft skills in the professional training of social educators

In a rapidly changing world, the requirements for specialists are changing, and in this regard, the problem of developing soft skills becomes relevant. The authors analyzed the state of knowledge of soft skills, hard skills in domestic and foreign scientific, pedagogical, psychological literature. In order to determine the importance of Soft skills for social educators, the tasks and functions imposed on specialists of socio-pedagogical activity are differentiated. Within the framework of the article, soft skills were considered as a factor ensuring success in professional activity. According to the research of foreign scientists, the features, types and structures of soft skills have been identified. The foreign experience is analyzed and the ways of soft skills development in the training of specialists are determined. The article examines the views and attitudes of students of the specialty “Social Pedagogy and self-knowledge” to soft skills. During the research and theoretical analysis of soft skills, we found that they are difficult to track and measure because of their humanitarian aspects that do not meet real mathematical criteria. Analyzing the results of theoretical analysis and research, the authors identified the advantages of soft skills among future social educators, which have a positive impact on professional activity through the development of these skills.
Natural Language to Code Generation in Interactive Data Science Notebooks

Computational notebooks, such as Jupyter notebooks, are interactive computing environments that are ubiquitous among data scientists to perform data wrangling and analytic tasks.To measure the performance of AI pair programmers that automatically synthesize programs for those tasks given natural language (NL) intents from users, we build ARCADE, a benchmark of 1,078 code generation problems using the pandas data analysis framework in data science notebooks.AR-CADE features multiple rounds of NL-to-code problems from the same notebook.It requires a model to understand rich multi-modal contexts, such as existing notebook cells and their execution states as well as previous turns of interaction.To establish a strong baseline on this challenging task, we develop PACH-INCO, a 62B code language model (LM) for Python computational notebooks, which significantly outperforms public code LMs.Finally, we explore few-shot prompting strategies to elicit better code with step-by-step decomposition and NL explanations, showing the potential to improve the diversity and explainability of model predictions.ARCADE is publicly available at https:
SafeConv: Explaining and Correcting Conversational Unsafe Behavior

One of the main challenges open-domain endto-end dialogue systems, or chatbots, face is the prevalence of unsafe behavior, such as toxic languages and harmful suggestions.However, existing dialogue datasets do not provide enough annotation to explain and correct such unsafe behavior.In this work, we construct a new dataset called SAFECONV for the research of conversational safety: (1) Besides the utterancelevel safety labels, SAFECONV also provides unsafe spans in an utterance, information able to indicate which words contribute to the detected unsafe behavior; (2) SAFECONV provides safe alternative responses to continue the conversation when unsafe behavior detected, guiding the conversation to a gentle trajectory.By virtue of the comprehensive annotation of SAFECONV, we benchmark three powerful models for the mitigation of conversational unsafe behavior, including a checker to detect unsafe utterances, a tagger to extract unsafe spans, and a rewriter to convert an unsafe response to a safe version.Moreover, we explore the huge benefits brought by combining the models for explaining the emergence of unsafe behavior and detoxifying chatbots.Experiments show that the detected unsafe behavior could be well explained with unsafe spans and popular chatbots could be detoxified by a huge extent.The dataset is available at https://github.com/mianzhang/SafeConv.
Probing Structural Knowledge from Pre-trained Language Model for Argumentation Relation Classification

Extracting fine-grained structural information between argumentation component (AC) pairs is essential for argumentation relation classification (ARC).However, most previous studies attempt to model the relationship between AC pairs using AC level similarity or semantically relevant features.They ignore the complex interaction between AC pairs and cannot effectively reason the argumentation relation deeply.Therefore, in this paper, we propose a novel dual prior graph neural network (DPGNN) to jointly explore the probing knowledge derived from pre-trained language models (PLMs) and the syntactical information for comprehensively modeling the relationship between AC pairs.Specifically, we construct a probing graph by using probing knowledge derived from PLMs to recognize and align the relational information within and across the argumentation components.In addition, we propose a mutual dependency graph for the AC pair to reason the fine-grained syntactic structural information, in which the syntactical correlation between words is set by the dependency information within AC and the mutual attention mechanism across ACs.The knowledge learned from the probing graph and the dependency graph are combined to comprehensively capture the aligned relationships of AC pairs for improving the results of ARC.Experimental results on three public datasets show that DPGNN outperforms the state-of-the-art baselines by a noticeable margin.
Preemptive Detection of Fake Accounts on Social Networks via Multi-Class Preferential Attachment Classifiers

In this paper, we describe a new algorithm called Preferential Attac hment k-class Classifier (PreAttacK) for detecting fake accounts in a social network. Recently, several algorithms have obtained high accuracy on this problem. However, they have done so by relying on information about fake accounts' friendships or the content they share with others-the very things we seek to prevent.
RGAT at SemEval-2023 Task 2: Named Entity Recognition Using Graph Attention Network

In this paper, we (team RGAT) describe our approach for the SemEval 2023 Task 2: Multilingual Complex Named Entity Recognition (MultiCoNER II). The goal of this task is to locate and classify named entities in unstructured short complex texts in 12 different languages and one multilingual setup. We use the dependency tree of the input query as additional feature in a Graph Attention Network along with the token and part-of-speech features. We also experiment with additional layers like BiLSTM and Transformer in addition to the CRF layer. However, we have not included any external Knowledge base like Wikipedia to enrich our inputs. We evaluated our proposed approach on the English NER dataset that resulted in a clean-subset F1 of 61.29\% and overall F1 of 56.91\%. However, other approaches that used external knowledge base performed significantly better.
Double-Branch Multi-Attention based Graph Neural Network for Knowledge Graph Completion

Graph neural networks (GNNs), which effectively use topological structures in the knowledge graphs (KG) to embed entities and relations in low-dimensional spaces, have shown great power in knowledge graph completion (KGC).KG has abundant global and local structural information, however, many GNN-based KGC models cannot capture these two types of information about the graph structure by designing complex aggregation schemes and are not designed well to learn representations of seen entities with sparse neighborhoods in isolated subgraphs.In this paper, we find that a simple attention-based method can outperform a general GNN-based approach for KGC.We then propose a double-branch multi-attentionbased graph neural network (MA-GNN) to learn more expressive entity representations that contain rich global-local structural information.Specifically, we first explore the graph attention network-based local aggregator to learn entity representations.Furthermore, we propose a snowball local attention mechanism by leveraging the semantic similarity between two-hop neighbors to enrich the entity embedding.Finally, we use Transformer-based selfattention to learn long-range dependence between entities to obtain richer representations with the global graph structure and entity features.Experimental results on five benchmark datasets show that MA-GNN achieves significant improvements over strong baselines for inductive KGC.
NLP DI at NADI Shared Task Subtask-1: Sub-word Level Convolutional Neural Models and Pre-trained Binary Classifiers for Dialect Identification

In this paper, we describe our systems submitted to the NADI Subtask 1: country-wise dialect classifications.We designed two types of solutions.The first type is convolutional neural network CNN) classifiers trained on subword segments of optimized lengths.The second type is fine-tuned classifiers with BERT-based language specific pre-trained models.To deal with the missing dialects in one of the test sets, we experimented with binary classifiers, analyzing the predicted probability distribution patterns and comparing them with the development set patterns.The better performing approach on the development set was fine-tuning language specific pre-trained model (best Fscore 26.59%).On the test set, on the other hand, we obtained the best performance with the CNN model trained on subword tokens obtained with a Unigram model (the best F-score 26.12%).Re-training models on samples of training data simulating missing dialects gave the maximum performance on the test set version with a number of dialects lesser than the training set (F-score 16.44%)
A Generative Model for End-to-End Argument Mining with Reconstructed Positional Encoding and Constrained Pointer Mechanism

Argument mining (AM) is a challenging task as it requires recognizing the complex argumentation structures involving multiple subtasks.To handle all subtasks of AM in an end-to-end fashion, previous works generally transform AM into a dependency parsing task.However, such methods largely require complex pre-and post-processing to realize the task transformation.In this paper, we investigate the endto-end AM task from a novel perspective by proposing a generative framework, in which the expected outputs of AM are framed as a simple target sequence.Then, we employ a pretrained sequence-to-sequence language model with a constrained pointer mechanism (CPM) to model the clues for all the subtasks of AM in the light of the target sequence.Furthermore, we devise a reconstructed positional encoding (RPE) to alleviate the order biases induced by the autoregressive generation paradigm.Experimental results show that our proposed framework achieves new state-of-the-art performance on two AM benchmarks.
Describing Sets of Images with Textual-PCA

We seek to semantically describe a set of images, capturing both the attributes of single images and the variations within the set.Our procedure is analogous to Principle Component Analysis, in which the role of projection vectors is replaced with generated phrases.First, a centroid phrase that has the largest average semantic similarity to the images in the set is generated, where both the computation of the similarity and the generation are based on pretrained vision-language models.Then, the phrase that generates the highest variation among the similarity scores is generated, using the same models.The next phrase maximizes the variance subject to being orthogonal, in the latent space, to the highest-variance phrase, and the process continues.Our experiments show that our method is able to convincingly capture the essence of image sets and describe the individual elements in a semantically meaningful way within the context of the entire set.Our code is available at:
RobustLR: A Diagnostic Benchmark for Evaluating Logical Robustness of Deductive Reasoners

Transformers have been shown to be able to perform deductive reasoning on inputs containing rules and statements written in English natural language.However, it is unclear if these models indeed follow rigorous logical reasoning to arrive at the prediction, or rely on spurious correlation patterns in making decision.A strong deductive reasoning model should consistently understand the semantics of different logical operators.To this end, we present ROBUSTLR, a deductive reasoning-based diagnostic benchmark that evaluates the robustness of language models to minimal logical edits in the inputs and different logical equivalence conditions.In our experiments with RoBERTa, T5, and GPT3, we show that the models trained on deductive reasoning datasets with various logical operations do not perform consistently on the RO-BUSTLR test set, thus showing that the models are not robust to our proposed logical perturbations.Further, we observe that the models find it especially hard to learn logical negation operator.Our results demonstrate the shortcomings of current language models in logical reasoning, and call for the development of better inductive biases to teach the logical semantics to language models.All the datasets and code base have been made publicly available. 1f1: Charlie is tall.
Subword Evenness (SuE) as a Predictor of Cross-lingual Transfer to Low-resource Languages

Pre-trained multilingual models, such as mBERT, XLM-R and mT5, are used to improve the performance on various tasks in lowresource languages via cross-lingual transfer.In this framework, English is usually seen as the most natural choice for a transfer language (for fine-tuning or continued training of a multilingual pre-trained model), but it has been revealed recently that this is often not the best choice.The success of cross-lingual transfer seems to depend on some properties of languages, which are currently hard to explain.Successful transfer often happens between unrelated languages and it often cannot be explained by data-dependent factors.In this study, we show that languages written in non-Latin and non-alphabetic scripts (mostly Asian languages) are the best choices for improving performance on the task of Masked Language Modelling (MLM) in a diverse set of 30 low-resource languages and that the success of the transfer is well predicted by our novel measure of Subword Evenness (SuE).Transferring language models over the languages that score low on our measure results in the lowest average perplexity over target low-resource languages.Our correlation coefficients obtained with three different pre-trained multilingual models are consistently higher than all the other predictors, including text-based measures (type-token ratio, entropy) and linguistically motivated choice (genealogical and typological proximity).
Product Image Recommendation with Transformer Model Using Deep Reinforcement Learning

A product image recommendation algorithm with transformer model using deep reinforcement learning is proposed. First, the product image recommendation architecture is designed to collect users’ historical product image clicking behaviors through the log information layer. The recommendation strategy layer uses collaborative filtering algorithm to calculate users’ long-term shopping interest and gated recurrent unit to calculate users’ short-term shopping interest, and predicts users’ long-term and short-term interest output based on users’ positive and negative feedback sequences. Second, the prediction results are fed into the transformer model for content planning to make the data format more suitable for subsequent content recommendation. Finally, the planning results of the transformer model are input to Deep Q-Leaning Network to obtain product image recommendation sequences under the learning of this network, and the results are transmitted to the data result layer, and finally presented to users through the presentation layer. The results show that the recommendation results of the proposed algorithm are consistent with the user’s browsing records. The average accuracy of product image recommendation is 97.1%, the maximum recommended time is 1.0[Formula: see text]s, the coverage and satisfaction are high, and the practical application effect is good. It can recommend more suitable products for users and promote the further development of e-commerce.
Near-Storage Accelerator for Bulk Graph Ingestion

Due to the ever-increasing interest in using large-scale graph analytics on important applications including security, bioinformatics, and social network analysis, achieving high performance with graph databases and analytics platforms on such large datasets is also becoming an increasingly crucial issue. One important component of graph database performance is graph ingestion, because the rate of data collection is often vastly higher than what graph databases can support while managing the inherently complex data structures necessary to maintain a graph. In this work, we present a system for fast bulk ingestion of large graphs, while only using two relatively general-purpose near-storage accelerator modules: A JSON parser and a sorting accelerator. The sorting accelerator efficiently transforms random insertions into the index and sparse graph data structures into a sequential stream for efficient storage update. Using a prototype implementation on a Samsung SmartSSD, we demonstrate that our accelerator can reduce the host server utilization of processor and memory to almost zero, without performance impact. The fully in-storage operations also remove the memory capacity limitation, meaning it can also ingest much larger graphs than existing columnar and graph databases such as MonetDB and Neo4J.
A Multi-label Filter Feature Selection Method Based on Approximate Pareto Dominance

The Pareto dominance has been applied to resolve the issue of choosing significant features from a multi-label dataset.High-dimensional labels will directly result in the difficulty of forming Pareto dominance.This work proposes a multi-label feature selection approach based on the approximate Pareto dominance (MAPD) to address this issue.It maps the multi-label feature selection to the problem of solving the approximate Pareto dominant solution set.By introducing an approximate parameter, it is possible to efficiently cut down on the amount of features in the chosen feature subset while also raising its quality.To verify the performance of MAPD, this research compares the MAPD algorithm with alternative approaches in terms of Hamming loss, accuracy, and chosen feature size using nine publicly available multi-label datasets.The findings indicate that the MAPD method performs better in terms of classification accuracy, Hamming loss, and the amount of features that may be chosen.
Dual wavelet frame packets with non-square iterative matrices

Traditional wavelet frame packets are based on square iterative matrices and frame scaling functions. Since the Fourier transform of frame scaling functions are discontinuous, the resulting wavelet frame packets do not possess time-frequency localization. In this study, we will develop the new construction of wavelet frame packets by using non-square iterative matrices: one is to construct wavelet frame packets directly from known non-GMRA wavelet frames to obtain finer time-frequency structure, the other is based on GMRA wavelet frames constructed from smooth generalization scaling functions, the corresponding quasi-projection formula and fast algorithm are also given.
Analysis of the model of integrated utilities network provision and infrastructure in Denpasar City

The utility network serves as a crucial support system for fulfilling the various needs of a city, encompassing cable and pipe networks for electricity, clean water, waste management, telecommunications, and other essential services. However, the installation of utility networks has often been lacking in coordination, resulting in frequent disassembly and reinstallation. Given these circumstances, the objective of this research is to provide valuable insights and ideas to the government regarding the model of Integrated Utility Network Facilities, along with effective implementation methods and accurate construction cost estimation. This will be done while considering the presence of existing facilities and infrastructure. The research employed a quantitative descriptive method, utilizing a case study approach focusing on several roads in the city center as a pilot project. The objective of implementing integrated utility networks and infrastructure (ducting utilities) in Denpasar City is to establish coordination among agencies responsible for underground network utilities. The goals include maintaining a neat, orderly, and clean city environment, facilitating easier and more cost-effective network maintenance, avoiding damage or interference caused by third-party works, enabling information sharing in the event of network damages, and promoting environmental friendliness.
Improved post detection integration

In many signal processing applications, conducting a coherent integration over the whole observation duration is not possible due to uncontrolled phase evolutions. In such a case one may refer to Post Detection Integration that consists in wisely combine shorter time integration outputs. In this paper, we propose a new detector that improves the state of art ones. Compared with the GLRT from which it is derived, it consists in a closed-form expression. Moreover, it is based on a simple linear phase assumption and can be used, by the way, in a wide area of applications.
Correction to Supporting Information for Zhang et al., To save sturgeons, we need river channels around hydropower dams

Errors in clinical decision-making are disturbingly common. Here, we show that structured information–sharing networks among clinicians significantly reduce diagnostic errors, and improve treatment recommendations, as compared to groups of ...Errors in clinical decision-making are disturbingly common. Recent studies have found that 10 to 15% of all clinical decisions regarding diagnoses and treatment are inaccurate. Here, we experimentally study the ability of structured information–sharing ...
Facial Animation at Scale

This talk covers the complete pipeline and process used to generate facial animation data for all the narrative content in the latest installment of the Horizon game series, created by Guerrilla. We developed a proprietary technique for embedding per-frame timecode and source take information into all our narrative animation data as the basis of this pipeline. Then we added a fully automated facial animation solving pipeline where animators could push animation quality by improving generic FaceWare models. Any update to these models would automatically cause the re-processing of all dependent facial animation data. To manage all this data a review pipeline was set up in Atlassian Jira that provided both a high-level overview as well as granular control over the more than 15.000 narrative facial animations in the game.
Design and Build a Virtual Reality Based Cycling Simulator Game

At the present time, there is a lot of digitalization being carried out. The current busy work also often makes people need to carry out an activity without leaving the place. One of them is exercising, in this case is cycling.Therefore, an idea emerged to design a VR-based cycling simulator game. The purpose of this study is to find out how the interaction between cycling and VR is, and to find out the interesting points of this game and its use as a new alternative to cycling. The method used in this study was to test the "Simulator Sepeda VR" game on several users and collect data through interviews. The results of this study indicate that the "Simulator Sepeda VR" game is able to provide an interesting experience and can also be an alternative for cycling at home.