Reviewerly/exHarmony huggingface dataset:
Development of Building Construction Blended Curriculum for Korean Universities

The COVID-19 pandemic and the Fourth Industrial Revolution have brought about significant changes in education at Korean universities. Two typical examples are the expansion of online learning and the spread of innovative teaching methods. Online education has advantages in that it allows learners to repeat learning and learn freely, regardless of time and place. It
Phatic Communion: How It Works in Face-To-Face and Online Communications

This is a paper that demonstrates how phatic communion works in both face-to-face and online communications. Communication is the act of transferring information from the speaker(s) to the hearer(s). Phatic communion is a type of communication that occurs in social interactions. In many communicative situations, speakers intend to convey information, but in some situations, it
SISTEM PENDUKUNG KEPUTUSAN PENERIMA PKH MENGGUNAKAN METODE SAW

The Family Hope Program is a conditional social assistance program for poor and vulnerable families and/or individuals. The current problem is that the process of determining PKH beneficiaries by PKH administrators and assistants in Bendolo Village is still done manually and has not been supported by an optimal system. Decision Support System In determining beneficiaries, there are 6 criteria used. Problems often occur in the selection of beneficiaries, one of which is the long process of determining beneficiaries and the emergence of community prejudice regarding beneficiaries of the program. The result of this study is the formation of a decision support system to determine eligible and ineligible PKH beneficiary candidates using the SAW method.
Revisiting the Efficiency-Accuracy Tradeoff in Adapting Transformer Models via Adversarial Fine-Tuning

Adversarial fine-tuning (i.e., training on adversarial perturbed inputs) has demonstrated promising results in improving the accuracy of natural language understanding tasks. However, the improved accuracy does not come for free but is accompanied by a significantly prolonged training time, limiting their applicability to larger and more complex models. This work revisits the efficiency-accuracy trade-off in adversarial fine-tuning by systematically analyzing if adversarial fine-tuning methods, in conjunction with several efficiency optimizations, are suitable for adapting pre-trained Transformer models for natural language understanding tasks. Our results show that multiple design choices are crucial in determining the efficiency-accuracy trade-off, and we introduce a method, ScaLA, that achieves better accuracy-vs-speed trade-off than prior methods. We show in experiments that our proposed method attains up to 14.7× adaptation speedups on BERT, RoBERTa, and T5, while achieving comparable accuracy to existing methods.
Unsupervised Graph Structure-Assisted Personalized Federated Learning

Non-IID data presents a significant challenge for federated learning(FL), and personalized FL is a natural solution to address this challenge. Recently, Graph Neural Network (GNN) has recently emerged to model the complex client relationship using a client graph to refine personalized models. However, this approach depends on an existing client relation graph on the server, making it impractical unless this prerequisite is satisfied. Furthermore, noisy and missing connections in the original graph structures can degrade personalization performance. In this work, we propose an unsupervised structure learning approach to improve personalized FL, where the server learns a dynamic client graph through self-supervision and generates structure-based client representations. These representations are then broadcasted to users, regulating local training using the learned knowledge as an inductive bias. Empirical studies on benchmark datasets demonstrate the significant effectiveness of our approach and the high quality of the client graphs. The code is available at https://github.com/lazyJane/FedSKA.
Evolving Dictionary Representation for Few-Shot Class-Incremental Learning

New objects are continuously emerging in the dynamically changing world and a real-world artificial intelligence system should be capable of continual and effectual adaptation to new emerging classes without forgetting old ones. In view of this, in this paper we tackle a challenging and practical continual learning scenario named few-shot class-incremental learning (FSCIL), in which labeled data are given for classes in a base session but very limited labeled instances are available for new incremental classes. To address this problem, we propose a novel and succinct approach by introducing deep dictionary learning which is a hybrid learning architecture that combines dictionary learning and visual representation learning to provide a better space for characterizing different classes. We simultaneously optimize the dictionary and the feature extraction backbone in the base session, but only finetune the dictionary in the incremental session for adaptation to novel classes, which can alleviate the forgetting problem on base classes compared to finetuning the entire model. To further facilitate future adaptation, we also incorporate multiple pseudo classes into the base session training so that certain space projected by dictionary can be reserved for future new concepts. Moreover, we adopt a novel optimization process by implicitly incorporating the dictionary reconstruction loss into the learning process without adding extra loss terms. The extensive experimental results on CIFAR100, miniImageNet and CUB200 validate the effectiveness of our approach compared to other SOTA methods.
No Agreement Without Loss: Learning and Social Choice in Peer Review

In peer review systems, reviewers are often asked to evaluate various features of submissions, such as technical quality or novelty. A score is given to each of the predefined features and based on these the reviewer has to provide an overall quantitative recommendation. It may be assumed that each reviewer has her own mapping from the set of features to a recommendation, and that different reviewers have different mappings in mind. This introduces an element of arbitrariness known as commensuration bias. In this paper we discuss a framework, introduced by Noothigattu, Shah and Procaccia, and then applied by the organizers of the AAAI 2022 conference. Noothigattu, Shah and Procaccia proposed to aggregate reviewer’s mapping by minimizing certain loss functions, and studied axiomatic properties of this approach, in the sense of social choice theory. We challenge several of the results and assumptions used in their work and report a number of negative results. On the one hand, we study a trade-off between some of the axioms proposed and the ability of the method to properly capture agreements of the majority of reviewers. On the other hand, we show that dropping a certain unrealistic assumption has dramatic effects, including causing the method to be discontinuous.
Pareto Rank-Preserving Supernetwork for Hardware-Aware Neural Architecture Search

In neural architecture search (NAS), training every sampled architecture is very time-consuming and should be avoided. Weight-sharing is a promising solution to speed up the evaluation process. However, training the supernetwork incurs many discrepancies between the actual ranking and the predicted one. Additionally, efficient deep-learning engineering processes require incorporating realistic hardware-performance metrics into the NAS evaluation process, also known as hardware-aware NAS (HW-NAS). In HW-NAS, estimating task-specific performance and hardware efficiency are both required. This paper proposes a supernetwork training methodology that preserves the Pareto ranking between its different subnetworks resulting in more efficient and accurate neural networks for a variety of hardware platforms. The results show a 97% near Pareto front approximation in less than 2 GPU days of search, which provides 2x speed up compared to state-of-the-art methods. We validate our methodology on NAS-Bench-201, DARTS, and ImageNet. Our optimal model achieves 77.2% accuracy (+1.7% compared to baseline) with an inference time of 3.68ms on Edge GPU for ImageNet, which yields a 2.3x speedup. Training implementation can be found: https://github.com/IHIaadj/PRP-NAS.
Improving Visual Reinforcement Learning with Discrete Information Bottleneck Approach

Contrastive learning has been used to learn useful low-dimensional state representations in visual reinforcement learning (RL). Such state representations substantially improve the sample efficiency of visual RL. Nevertheless, existing contrastive learning-based RL methods have the problem of unstable training. Such instability comes from the fact that contrastive learning requires an extremely large batch size (e.g., 4096 or larger), while current contrastive learning-based RL methods typically set a small batch size (e.g., 512). In this paper, we propose an approach of discrete information bottleneck (DIB) to address this problem. DIB applies the technique of discretization and information bottleneck to contrastive learning in representing the state with concise discrete representation. Using this discrete representation for policy learning results in more stable algorithm training and higher sample efficiency with a small batch size. We demonstrate the advantage of discrete state representation of DIB on several continuous control tasks in the DeepMind Control suite. In the experiments, DIB outperforms prior visual RL methods, both model-based and model-free, in terms of performance and sample efficiency.
Rethinking Temporal Information in Session-Based Recommendation: A Position-Agnostic Approach

Session-based Recommendation (SBR) aims to predict the next item for a session, which consists of several clicked items in a transaction. Most SBR approaches follow an underlying assumption that all sequential information should be strictly utilized. Thus, they model temporal information for items using implicit, explicit, or ensemble methods. In fact, users may recall previously clicked items but might not remember the exact order in which they were clicked. Therefore, focusing on representing item temporal information in various ways could make learning session intents challenging. In this paper, we rethink the necessity of temporal information for items in SBR. We propose Aggregating the Contextual intents of the session with Attentive networks, namely ACARec. Specifically, we avoid explicitly modeling positional embeddings and learn contextual intents through aggregation methods (convolutions or poolings). We also demonstrate that even an entirely position-agnostic aggregation approach can yield promising results. Extensive experiments on real-world datasets validate our arguments. We hope our study can provide insights into SBR and inspire future research in the community.
Towards Legal Judgment Summarization: A Structure-Enhanced Approach

Judgment summaries are beneficial for legal practitioners to comprehend and retrieve case law efficiently. Unlike summaries in general domains, e.g., news, judgment summaries often require a clear structure. Such a structure helps readers grasp the information contained in the summary and reduces information loss. To the best of our knowledge, none of the existing text summarizers can generate summaries aligned with the summary structure in the legal domain. Inspired by this observation, this paper introduces a Summary Structure-Enhanced (SSE) method to synthesize structured summaries for legal documents. SSE can easily be incorporated into the Encoder-Decoder framework, which is commonly adopted in state-of-the-art text summarizers. Experiments on the datasets of New Zealand and Chinese judgments show that the proposed method consistently improves the performance of state-of-the-art summarizers in terms of Rouge scores.
LFAA: Crafting Transferable Targeted Adversarial Examples with Low-Frequency Perturbations

Deep neural networks are susceptible to adversarial attacks, which pose a significant threat to their security and reliability in real-world applications. The most notable adversarial attacks are transfer-based attacks, where an adversary crafts an adversarial example to fool one model, which can also fool other models. While previous research has made progress in improving the transferability of untargeted adversarial examples, the generation of targeted adversarial examples that can transfer between models remains a challenging task. In this work, we present a novel approach to generate transferable targeted adversarial examples by exploiting the vulnerability of deep neural networks to perturbations on high-frequency components of images. We observe that replacing the high-frequency component of an image with that of another image can mislead deep models, motivating us to craft perturbations containing high-frequency information to achieve targeted attacks. To this end, we propose a method called Low-Frequency Adversarial Attack (LFAA), which trains a conditional generator to generate targeted adversarial perturbations that are then added to the low-frequency component of the image. Extensive experiments on ImageNet demonstrate that our proposed approach significantly outperforms state-of-the-art methods, improving targeted attack success rates by a margin from 3.2% to 15.5%.
Region-Aware Dynamic Filtering Network for 3D Hand Reconstruction

3D hand reconstruction from RGB image has attracted a lot of attention due to its crucial role in human-computer interaction. Nevertheless, it is still challenging to perform 3D hand reconstruction under conditions of hand-object interaction due to severe mutual occlusion. Previous methods usually adopt fixed convolution kernel to extract features. We argue that simply sharing the static filter for all regions is impertinent, given that the occlusion degree varies across different regions, resulting in inconsistent visual representations. To address this issue, we proposed Region-aware Dynamic Filtering Network (RDFNet), which dynamically generates convolution kernels based on the features of different regions, thereby adaptively extracting region-related information. Furthermore, we introduce a dynamic receptive field selection mechanism to determine the most appropriate scale for the convolution kernel. For the severely occluded regions, larger receptive field is needed to capture semantic-related features, while the visible regions are mainly concerned with their own local pattern to accumulate spatial-related features and avoid the interference of irrelevant information. Our proposed RDFNet outperforms state-of-the-art methods by a large margin on several challenging hand-object interaction datasets.
Story Ending Generation Using Commonsense Casual Reasoning and Graph Convolutional Networks

Story Ending Generation is a task of generating a coherent and sensible ending for a given story. The key challenges of this task are i) how to obtain a good understanding of context, ii) how to capture hidden information between lines, and iii) how to obtain causal progression. However, recent machine learning models can only partially address these challenges due to the lack of causal entailment and consistency. The key novelty in our proposed approach is to capture the hidden story by generating transitional commonsense sentences between each adjacent context sentence, which substantially enriches causal and consistent story flow. Specifically, we adopt a soft causal relation using people’s everyday commonsense knowledge to mimic the cognitive understanding process of readers. We then enrich the story with causal reasoning and utilize dependency parsing to capture long range text relations. Finally, we apply multi-level Graph Convolutional Networks to deliver enriched contextual information across different layers. Both automatic and human evaluation results show that our proposed model can significantly improve the quality of generated story endings.
Stackelberg Attacks on Auctions and Blockchain Transaction Fee Mechanisms

We study a multi-unit single-demand auction in a setting where agents can arbitrarily commit to strategies that may depend on the commitments of other agents. Such commitments non-trivially change the equilibria of the auction by inducing a metagame, in which agents commit to strategies. We demonstrate a strategy an attacker may commit to that ensures they receive one such item for free, while forcing the remaining agents to enter a lottery for the remaining items. The attack is detrimental to the auctioneer, who loses most of their revenue. We show that the strategy works as long as the agents have valuations that are somewhat concentrated. The attack is robust to a large fraction of the agents being either oblivious to the attack or having exceptionally high valuations. The attacker may coerce these agents into cooperating by promising them a free item. We show that the conditions for the attack to work hold with high probability when (1) the auction is not too congested, and (2) the valuations are sampled i.i.d. from either a uniform distribution or a Pareto distribution. The attack works for first-price auctions, second-price auctions, and the transaction fee mechanism EIP-1559 used by Ethereum.
Expediting Self-Play Learning in AlphaZero-Style Game-Playing Agents

One of the main appeals of AlphaZero-style game-playing agents, which combine deep learning and Monte Carlo Tree Search, is that they can be trained autonomously without external expert-level domain knowledge. However, training such agents is generally computationally expensive, with the most computationally time-consuming step being generating training data via self-play. Here we propose an improved strategy for generating self-play training data, resulting in higher-quality samples, especially in earlier training phases. The new strategy initially emphasizes the latter game phases and gradually extends those phases to entire games as the training progresses. In our test domains, the games Connect4 and Breakthrough, we show that game-playing agents using the improved training approach learn significantly faster than counterpart agents using a standard approach. Furthermore, we empirically show that the proposed strategy is (in our test domains) superior to several recently proposed strategies for expediting self-play learning in game playing.
Power Grid Maintenance Cost Prediction Based on Machine Learning Method

This paper designs an easy-to-use intelligent substation monitoring and fault detection platform. The software realizes the integration of the 3D model of the substation and the 3D perspective of the panoramic monitoring data. Make the working status and monitoring data of the substation clearer. The maintenance items of the substation are classified and applied to the analysis of horizontal and vertical data, and the method based on machine learning is used to estimate the maintenance cost. Through the interactive inspection of the original data, the original training mode can be accurately corrected. This method adopts the genetic optimization method to correct and improve the initial value and the threshold point of the machine learning. Therefore, an improved algorithm for repair and maintenance costs based on BP neural network is proposed. Taking a substation in a city as an example, the operation and maintenance costs of the power grid are estimated. The comparison shows that this method can effectively improve the prediction accuracy of the power system, and it has certain reference significance for the power grid to allocate maintenance costs in the power distribution system.
Design of an international flight information contact service platform based on HTML hybrid development technology

This paper introduces an international flight information contact service platform based on HTML hybrid development technology---The Flight Manager App, which gathers demanders (askers) and providers (bringers) of the same international flight passengers through a cell phone app, and provides a platform for both parties to contact each other for matching supply and demand. The platform provides a platform for both parties to match supply and demand. The guide can provide services such as customs clearance translation, form filling, transferring, accompanying and guiding for the enquirers, thus solving various problems encountered by self-service travelers abroad in customs Q&A, transferring, form filling, etc., and accompanying and watching the elderly and children and teenagers who travel alone during the flight. This paper describes in detail the design idea, development technology and operation mode of this platform, and analyzes its market prospect and social value.
Research on Image Tampering Detection and Localization Based on Iterative GAN

Image tampering detection and localization are important tasks in digital image forensics. Its purpose is to identify and locate the modified or forged regions in the image. With the development of image processing technology, the possibilities of image manipulation are becoming more and more diversified and hidden, which poses challenges for conventional methods based on manual feature extraction. This paper proposes research on image tampering detection and localization based on an Iterative generative adversarial network (Iterative GAN). First, a fake-to-true transform generator GT is designed to generate fake images consistent with the actual image style according to image style transformation. Secondly, a positioning generator GM based on a multi-decoder single-task strategy is proposed to eliminate the artifacts caused by the generator GT, which is used to predict the mask and confidence of tampered regions. Thirdly, according to the characteristics of the iterative generative adversarial network, a loss function that comprehensively considers the feedback of the generator and discriminator is constructed to accurately describe the problem of image tampering detection and localization. Finally, an image tampering detection and localization system based on the iterative generative adversarial network is constructed, and the effectiveness of the proposed method is proved by simulation and experimental verification.
Count Me Too: Sentiment Analysis of Roman Sindhi Script

Social media has given voice to people around the globe. However, all voices are not counted due to the scarcity of lexical computational resources. Such resources could harness the torrent of social media text data. Computational resources for rich languages such as English are available. More are being developed, meanwhile strengthening and enhancing the current ones. However, Roman Sindhi, a resource-poor writing style, is a phonetically rich language lacking computational resources, creating a working space for researchers. This work attempts to develop lexical sentiment resources that will help calculate the public opinion expressed in Roman Sindhi and bring their point of view into the limelight. This work is one of the initial efforts to develop lexical Roman Sindhi sentiment dictionary resources to help detect sentiment orientation in a text. Furthermore, it also developed two interfaces to leverage the lexical resources—a Roman Sindhi to English translator (RoSET) that translates a Roman Sindhi feature into an equivalent English word and a Roman Sindhi rule-based sentiment scorer ( RBRS 3 ) that assigns sentiment score to a Roman Sindhi script features. The results obtained from the developed system accommodated the bilingual dataset (Roman Sindhi + English) more adequately. An increase of 20.8% was recorded for positive sentence detection, and a 16% increase was obtained for negative sentences, whereas neutral sentences were marginalized to a lower number (59.31% decrease). The resultant system makes those public voices expressed in the Roman Sindhi script get counted, which otherwise are in vain.