Reviewerly/exHarmony huggingface dataset:
Linguistik Struktural: Analisis Proses Morfofonemik dalam Karya Tulis Ilmiah Siswa Kelas IX SMP Al-Zahra Indonesia

This research discusses the object of language research, namely word formation through morphophonemic processes. This study aims to determine the process of morphophonemic formation in the scientific papers of grade IX students of SMP Al-Zahra Indonesia. The method used is a qualitative descriptive method and the results of the analysis indicate the presence of morphophonemic processes in the work. Factors that cause this process include the bilinguality of speakers, insufficient vocabulary and unfaithfulness in using Indonesian.
Legal aspects of electronic learning in the Distance Learning Code of Conduct: Evidence of the United Arab Emirates legislation

This paper addresses the issue of to which extent law keeps pace with the tremendous development in the education field. The main aim of this paper is to analyze the Distance Learning Code of Conduct in Public Education Institutions in the United Arab Emirates (UAE) No. 262 of 2020 (hereinafter, the Code of Conduct) texts to find out the extent of its success in facing the legal problems caused by distance learning. That requires using an analytical approach fundamentally. The importance of this research is manifested in the fact that it is the first to examine the texts of the Code of Conduct and analyze it legally. Legal references in this field are scarce. We will mention one of them (Al-Khasawneh, 2020) in particular. That works to reduce cyberbullying and reviews the adequacy of electronic legislation to reduce cyberbullying. Among the crucial results of our paper is that the rules of the Code of Conduct are considered the guide for any educational institution establishing its own rules for regulating distance learning. Another crucial result is to call on the UAE legislator to expand the statement of legal provisions for electronic learning (e-learning) and not to limit to regulating the students’ behavior.
Fortran... et puis quoi encore ?

Modern Fortran is a standardized language that includes object-oriented and parallel programming paradigms. The Fortran-lang community, created at the end of 2019, is actively working to modernize its ecosystem. New compilers are under development. And the fourth Fortran standard of the 21st century is due to be published in autumn 2023.
Advancements in Blockchain Based Architecture for E-Voting System

Blockchain based E-voting system can be revolution- ary in terms of security and tamper proof, transparent elections and hence empower the democracy. Blockchain E-voting is already implemented at organisational level. But at state/national level it faces challenges such as privacy, stability, latency etc. To counter them we require advanced implementation techniques from multiple disciplines. These techniques are 2-layer-solution, sharding for scalability issue, optimizing consensus algorithm and off-chain transaction for reducing latency, zero knowledge proof, using multiple protocols for user privacy etc. General architecture of blockchain is explained in details which combines election creation, voter registration, voter transaction, tallying the results and vote verification. We have discussed how integrating E-voting with blockchain document verification system can be benificial. It is complicated to implement at state, national level and need additional research and implementation to make blockchain E- voting acceptable in elections across the world. Through research and innovation we can make blockchain voting mainstream.
A simple example of blocking the Craig trick

William Craig observed that, under quite general conditions, a theory with a recursively enumerable axiomatization is also recursively axiomatizable. The paper discusses these conditions and builds a simple example of a theory for which the conditions of the Craig’s theorem are not satisfied; in particular, the constructed theory has a recursively enumerable axiomatization, but does not have a recursive axiomatization with the same set of inference rules. At the same time, extending the set of inference rules with some rules adm
Microsoft's Acquisition of Activision Blizzard: SWOT and Prospect Analysis

In this era of raging pandemic, a growing number of people pay attention to games. A stunning piece of news has just been released, causing a shockwave in the gaming community. The acquisition of Activision Blizzard by Microsoft is expected to be completed in fiscal year 2023, as announced by Microsoft. As a console game player, it made a decision to conduct a thorough SWOT analysis of the acquisition's effects from eight different perspectives, with a particular focus on its impact on the development of the Metaverse. This report discusses Microsoft's proposed accession of Activision Blizzard, including some implicit impacts on being product requests, labour requests and on product requests that don't presently live but may develop in the future. For this acquisition, the industry generally believes that both Microsoft and Activision Blizzard are poised to benefit mutually, resulting in a win-win situation, and this report show the potential for the internet industry and VR technology to replace many aspects of people's lives, and how Microsoft's acquisition of Activision Blizzard could help to create new social trends while facing challenges such as aligning production models, managing IP exclusivity, and maintaining their competitive advantage and corporate culture. Therefore, it is advisable to give greater attention to this acquisition and consider making prudent investments of free time.
The Electronic Resources Troubleshooting Guide <b>The Electronic Resources Troubleshooting Guide</b> , by Holly Talbott and Ashley Zmau, ALA Editions American Library Association, 2020, 173 pp., $59.95, ISBN 9780838947647

"The Electronic Resources Troubleshooting Guide." Journal of Electronic Resources in Medical Libraries, ahead-of-print(ahead-of-print), pp. 1–2
Ship Displacement Form Factor Prediction Through the Application of the Robust Least Squares Method

A ship model test is considered one of the most effective methods of determining the size of a ship's drag, where the ship's shape factor determines the ship's drag at full scale. The use of the Prohaska method to determine the value of the form factor can be carried out experimentally by drawing a ship model in the towing tank basin with a Fr of 0.1-0.2. This research is a continuation of the research of Widodo et al. by utilizing the main ship data such as LWL, B, CB, CP, CM, WSA, T, and ∆. The S-estimation RLS method is used. In this method, the error value obtained is 0.1-3%, which is a bias value between the actual and the predicted values. This very small bias value can be used as a reference for using the regression equation in order to obtain form factor values as an alternative to the Prohaska method. Subsequent research is the process of validating the form factor from the s-estimation RLS and the Prohaska method through the displacement ship model resistance test data.
A Semantic-Aware Attention and Visual Shielding Network for Cloth-Changing Person Re-Identification

Cloth-changing person re-identification (ReID) is a newly emerging research topic that aims to retrieve pedestrians whose clothes are changed. Since the human appearance with different clothes exhibits large variations, it is very difficult for existing approaches to extract discriminative and robust feature representations. Current works mainly focus on body shape or contour sketches, but the human semantic information and the potential consistency of pedestrian features before and after changing clothes are not fully explored or are ignored. To solve these issues, in this work, a novel semantic-aware attention and visual shielding network for cloth-changing person ReID (abbreviated as SAVS) is proposed where the key idea is to shield clues related to the appearance of clothes and only focus on visual semantic information that is not sensitive to view/posture changes. Specifically, a visual semantic encoder is first employed to locate the human body and clothing regions based on human semantic segmentation information. Then, a human semantic attention (HSA) module is proposed to highlight the human semantic information and reweight the visual feature map. In addition, a visual clothes shielding (VCS) module is also designed to extract a more robust feature representation for the cloth-changing task by covering the clothing regions and focusing the model on the visual semantic information unrelated to the clothes. Most importantly, these two modules are jointly explored in an end-to-end unified framework. Extensive experiments demonstrate that the proposed method can significantly outperform state-of-the-art methods, and more robust features can be extracted for cloth-changing persons. Compared with multibiometric unified network (MBUNet) (published in TIP2023), this method can achieve improvements of 17.5% (30.9%) and 8.5% (10.4%) on the LTCC and Celeb-reID datasets in terms of mean average precision (mAP) (rank-1), respectively. When compared with the Swin Transformer (Swin-T), the improvements can reach 28.6% (17.3%), 22.5% (10.0%), 19.5% (10.2%), and 8.6% (10.1%) on the PRCC, LTCC, Celeb, and NKUP datasets in terms of rank-1 (mAP), respectively.
Successor Feature-Based Transfer Reinforcement Learning for Video Rate Adaptation with Heterogeneous QoE Preferences

In adaptive video streaming, the design of an adaptive bitrate (ABR) strategy is critical for the quality-of-experience (QoE) perceived by users. Though current learning-based ABR algorithms achieve state-of-the-art performance for users with a given QoE metric setting for training, they may unfortunately suffer the poor generalization issue for other users with different QoE preferences. Besides, how to quantitatively characterize the distinct QoE preference for a user has also not been extensively studied yet. In this paper, we propose STEER, a successor feature-based transfer reinforcement learning framework for fast learning the ABR strategies on heterogeneous QoE preferences. Specifically, we first develop a QoE preference analysis scheme to infer the personal QoE preference of a single user based on the user's actual viewing history. We then formulate the personalized QoE maximization problem as a reinforcement learning (RL) task, which optimizes the ABR strategy to maximize the overall QoE perceived by the user. Further, we model the QoE maximization problem for multiple users with heterogeneous QoE preferences as a multi-task RL problem, with each task distinguished by the user-distinct QoE preference. To efficiently address this problem, the proposed STEER solves for each RL-based ABR task by learning its optimal successor feature (SF) function, which can be exploited as shared knowledge across tasks to facilitate the transfer between tasks. With SF functions, STEER can quickly evaluate the optimal policies of previously learned tasks on a new task, and further use the generalized policy improvement operation to obtain a jumpstart policy. Both theoretically and empirically, we show that this jumpstart policy is a good initial policy with a performance guarantee for better generalization in the new task, and can also lead to a faster convergence to the optimal policy of the new task.
KepSalinst: Using Peripheral Points to Delineate Salient Instances

Salient instance segmentation (SIS) is an emerging field that evolves from salient object detection (SOD), aiming at identifying individual salient instances using segmentation maps. Inspired by the success of dynamic convolutions in segmentation tasks, this article introduces a keypoints-based SIS network (KepSalinst). It employs multiple keypoints, that is, the center and several peripheral points of an instance, as effective geometrical guidance for dynamic convolutions. The features at peripheral points can help roughly delineate the spatial extent of the instance and complement the information inside the central features. To fully exploit the complementary components within these features, we design a differentiated patterns fusion (DPF) module. This ensures that the resulting dynamic convolutional filters formed by these features are sufficiently comprehensive for precise segmentation. Furthermore, we introduce a high-level semantic guided saliency (HSGS) module. This module enhances the perception of saliency by predicting a map for the input image to estimate a saliency score for each segmented instance. On four SIS datasets (ILSO, SOC, SIS10K, and COME15K), our KepSalinst outperforms all previous models qualitatively and quantitatively.
Deep Counterfactual Representation Learning for Visual Recognition against Weather Corruptions

Deep learning has been widely studied for processing and understanding multimedia data, and it does help improve performance. Recent research has shown that deep models are vulnerable to images containing adverse weather corruptions, leading to a safety risk for numerous safety-critical systems ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e.g.,</i> autonomous driving systems). There are two problems with the current situation. First, collecting data under different weather scenarios is highly difficult in practice. Second, the performance degrades significantly when the training and test data are from different distributions, as exemplified by the weather corrupted test data. As a result, it is challenging to train a model without access to the images containing variations of various weather conditions, and it is difficult to make trained model generalized to unknown data under different weather conditions. In this paper, we introduce a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Counterfactual Representation Learning</i> (CRL) method to address these problems. Without access to training data including weather condition variations, our CRL makes the model resistant to unseen test data that has been corrupted by weather condition variations. Our basic idea is inspired by the perspective of counterfactual regularization. We build a causal model that introduces a counterfactual variable to eliminate the unobserved characteristics brought about by weather conditions. In particular, such a counterfactual variable is approximated by randomly shuffled features, echoing the previous empirical observation that the shuffling technique can perturb the shape details while preserving the local textures. We use information theoretic representation learning to encourage the neural networks to learn more powerful and robust features, which consist of two components. We conduct experiments on five benchmark datasets, namely, CIFAR-100-C, ImageNet-C, KITTI-C, BDD100k, and CityScapes-C, all of which contain weather corruption. The results of our experiments show that our proposed method can not only be a plug-and-play technique but also work nicely for both object recognition and detection.
OSCAR: OOD State-Conservative Offline Reinforcement Learning for Sequential Decision Making

Offline reinforcement learning (RL) is a data-driven learning paradigm for sequential decision making. Mitigating the overestimation of values originating from out-of-distribution (OOD) states induced by the distribution shift between the learning policy and the previously-collected offline dataset lies at the core of offline RL. To tackle this problem, some methods underestimate the values of states given by learned dynamics models or state-action pairs with actions sampled from policies different from the behavior policy. However, since these generated states or state-action pairs are not guaranteed to be OOD, staying conservative on them may adversely affect the in-distribution ones. In this paper, we propose an OOD state-conservative offline RL method (OSCAR), which aims to address the limitation by explicitly generating reliable OOD states that are located near the manifold of the offline dataset, and then design a conservative policy evaluation approach that combines the vanilla Bellman error with a regularization term that only underestimates the values of these generated OOD states. In this way, we can prevent the value errors of OOD states from propagating to in-distribution states through value bootstrapping and policy improvement. We also theoretically prove that the proposed conservative policy evaluation approach guarantees to underestimate the values of OOD states. OSCAR along with several strong baselines is evaluated on the offline decision-making benchmarks D4RL and autonomous driving benchmark SMARTS. Experimental results show that OSCAR outperforms the baselines on a large portion of the benchmarks and attains the highest average return, substantially outperforming existing offline RL methods.
A Multi-scale Multi-modal Multi-dimension Joint Transformer for Two-Stream Action Classification

Multi-modal, multi-scale, and multi-dimensional (spatiotemporal) video representation learning have each been studied adequately in its own form, respectively, but rather in an isolated way from each other, not yet jointly. It is well known in statistical machine learning that joint data distributions provide new information that cannot be achieved by its individual components. Therefore we propose M $$^3$$ T : a Multi-scale Multi-modal Multi-dimension (M $$^3$$ ) joint Transformer model for two-stream video representation learning, which is built upon a two-stream multi-scale vision transformer backbone. M $$^3$$ T is densely augmented with three attention modules, which are mutually orthogonal against each other, at each down-sampling layer of the backbone. Experiments conducted on the Kinetics 400 data set demonstrate the effectiveness of the proposed method. The qualitative performance also demonstrates that our model can learn more informative complementary representation.
Graph-Guided Latent Variable Target Inference for Mitigating Concept Drift in Time Series Forecasting

With the proliferation of the Internet of Things (IoT), there is an abundance of data available to humans. However, the monitoring environments for data collection are becoming increasingly diverse, leading to the occurrence of concept drift in the collected data. Concept drift refers to the phenomenon where the distribution of data changes over time, making it challenging for prediction models trained on historical data to adapt to the changing distribution. Previous research has primarily focused on predicting or compensating for distributions with fixed durations in Euclidean space to mitigate non-stationarity. However, we have observed that concept drift often occurs at different time scales, and detecting them using fixed scales has inherent limitations. Based on this observation, we propose a Graph-Guided Latent Variable Target Inference network that maps current data and variable duration query targets onto a graph neural network in latent space. We apply self-attention transformations to the representations and correlations on the graph in the dimensions of time, features, and query targets. The model updates its parameters based on these non-Euclidean correlation patterns, enabling the graph to evolve towards the direction of the query targets and obtain an evolved latent distribution. Finally, the decoder generates a prediction data stream regarding the query targets based on the evolved latent distribution. The experiments were conducted on five datasets, where our proposed method was compared against the five most advanced baselines. The findings demonstrated a substantial advantage in prediction performance provided by our approach.
AutoShape: Automatic Design of Click-Through Rate Prediction Models Using Shapley Value

As a main part of automated machine learning, Neural Architecture Search (NAS) has been employed in exploring effective Click-Through Rate (CTR) prediction models of recommender systems in recent studies. However, these studies update the architecture parameters by different strategies, such as gradient-based methods or evolutionary algorithms, and may suffer from either interference problems or high time-consuming in the search stage. Besides, the blocks in the search space were usually homogeneous, which means they have the same candidate operations to choose. Such design will result in redundancy in the search space, because many structures are inherently invalid and just increase the complexity of searching. To address the above issues, we implement the three-level automatic design of CTR prediction models using NAS via Shapley value, named as AutoShape. For the search space, we divide it into three parts according to the characteristics of the CTR model. Each part comprises distinctive candidate operations and forms a cell as an individual processing level, which improves the stability of the searched models’ effect and reduces the computational complexity. For the search strategy, we leverage Shapley value, a metric derived from cooperative game theory, to estimate the contribution of the operations in each block and the connections between the blocks, which can find effective models and reduce the time cost. Furthermore, experiments on different benchmark datasets show that the structure obtained from the search performs better than several hand-crafted architectures and is more stable than another NAS-based algorithm.
KSRE-CNER: A Knowledge and Semantic Relation Enhancement Framework for Chinese NER

Chinese named entity recognition (CNER) constitutes a pivotal undertaking entailing the identification and classification of named entities present within Chinese text. Traditional approaches based on CNN and BiLSTM have been effective for sequence labeling tasks. Additionally, graph neural networks (GNNs) have shown promising results in improving Chinese NER performance by incorporating lexical knowledge. However, these methods may still face challenges in handling ambiguity and inaccurate boundary recognition in Chinese NER. To tackle these challenges, we propose a knowledge and semantic relation enhancement framework. This framework integrates N-gram information and lexical knowledge into a gated graph neural network (GGNN) to capture Chinese lexical information and reduce ambiguity. Moreover, we leverage the Transformer model to update the weight information of each node, aiming to eliminate the influence of incorrect matching lexicons and augment the model’s capability to recognize entity boundaries. Comprehensive experiments conducted on diverse datasets, including Resume, CCKS2017, MSRA, and a self-constructed History dataset, substantiate that our proposed model attains comparable results.
CSS: Contrastive Span Selector for Multi-span Question Answering

This study investigates the task of Multi-span Question Answering (MSQA). Currently, the MSQA task is primarily modeled as a sequence tagging problem, predicting whether each word is a part of an answer. However, this approach independently predicts words without fully utilizing a comprehensive understanding of the complexities in MSQA. In this paper, we propose a novel model, Contrastive Span Selector. Our model utilizes a multi-head biaffine attention mechanism to generate the span representations and employs a CNN block for span-wise interaction. Additionally, we incorporate the question and a global token into the encoding process, projecting all vectors into a shared representation space. To train our model, we employ contrastive learning with a dynamic threshold to control the similarity boundary between answer spans and non-answer spans. Our model outperforms the tagger model by 6.32 in F1 score for exact match on the MultiSpanQA multi-span setting and 5.69 on the expand setting, establishing it as the state-of-the-art model for MSQA. The code is available at: https://github.com/phzh24/Contrastive-Span-Selector .