Reviewerly/exHarmony huggingface dataset:
RUNNER: Responsible UNfair NEuron Repair for Enhancing Deep Neural Network Fairness

Authors:
raw_author_name: Yue Cao
raw_affiliation_string: Nanyang Technological University, Singapore, Singapore
raw_author_name: Yihao Huang
raw_affiliation_string: Nanyang Technological University, Singapore, Singapore
raw_author_name: Aishan Liu
raw_affiliation_string: Beihang University, Beijing, China
raw_author_name: Qing Guo
raw_affiliation_string: ASTAR, Singapore, China
raw_author_name: Yang Liu
raw_affiliation_string: Nanyang Technological University, Singapore, Singapore

Abstract: Deep Neural Networks (DNNs), an emerging software technology, have achieved impressive results in a variety of fields. However, the discriminatory behaviors towards certain groups (a.k.a. unfairness) of DNN models increasingly become a social concern, especially in high-stake applications such as loan approval and criminal risk assessment. Although there has been a number of works to improve model fairness, most of them adopt an adversary to either expand the model architecture or augment training data, which introduces excessive computational overhead. Recent work diagnoses responsible unfair neurons first and fixes them with selective retraining. Unfortunately, existing diagnosis process is time-consuming due to multi-step training sample analysis, and selective retraining may cause a performance bottleneck due to indirectly adjusting unfair neurons on biased samples. In this paper, we propose Responsible UNfair NEuron Repair (RUNNER) that improves existing works in three key aspects: (1) efficiency: we design the Importance-based Neuron Diagnosis that identifies responsible unfair neurons in one step with a novel importance criterion of neurons; (2) effectiveness: we design the Neuron Stabilizing Retraining by adding a loss term that measures the activation distance of responsible unfair neurons from different subgroups in all sources; (3) generalization: we investigate the effectiveness on both structured tabular data and large-scale unstructured image data, which is often ignored in prior studies. Our extensive experiments across 5 datasets show that RUUNER can effectively and efficiently diagnose and repair the DNNs regarding unfairness. On average, our approach significantly reduces computing overhead from 341.7s to 29.65s, and achieves improved fairness up to 79.3%. Besides, RUNNER also keeps state-of-the-art results on the unstructured dataset.
FAIR: Flow Type-Aware Pre-Training of Compiler Intermediate Representations

Authors:
raw_author_name: Changan Niu
raw_affiliation_string: State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, Jiangsu, China
raw_author_name: Chuanyi Li
raw_affiliation_string: State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, Jiangsu, China
raw_author_name: Vincent Ng
raw_affiliation_string: Human Language Technology Research Institute, University of Texas at Dallas, Richardson, TX 75083-0688, Dallas, Texas, United States of America
raw_author_name: David Lo
raw_affiliation_string: School of Computing and Information Systems, Singapore Management University, Singapore, Singapore

Abstract: While the majority of existing pre-trained models from code learn source code features such as code tokens and abstract syntax trees, there are some other works that focus on learning from compiler intermediate representations (IRs). Existing IR-based models typically utilize IR features such as instructions, control and data flow graphs (CDFGs), call graphs, etc. However, these methods confuse variable nodes and instruction nodes in a CDFG and fail to distinguish different types of flows, and the neural networks they use fail to capture long-distance dependencies and have over-smoothing and over-squashing problems. To address these weaknesses, we propose FAIR, a Flow type-Aware pre-trained model for IR that involves employing (1) a novel input representation of IR programs; (2) Graph Transformer to address over-smoothing, over-squashing and long-dependencies problems; and (3) five pre-training tasks that we specifically propose to enable FAIR to learn the semantics of IR tokens, flow type information, and the overall representation of IR. Experimental results show that FAIR can achieve state-of-the-art results on four code-related downstream tasks.
Mitigating Algorithm Aversion in Recruiting: A Study on Explainable AI for Conversational Agents

Authors:
raw_author_name: Stefan Thalmann
raw_affiliation_string: University of Graz, Graz, Austria

Abstract: The use of conversational agents (CAs) based on artificial intelligence (AI) is becoming more common in the field of recruiting. Organizations are now adopting AI-based CAs for applicant (pre-)selection, but negative news coverage, especially the black-box character of AI, has hindered adoption. So far, little is known about the contextual factors influencing users' perception of AI-based CAs in general and the effect of provided explanations by explainable AI (XAI) in particular. While research on algorithm aversion provides some initial explanations, information regarding the effects of different XAI approaches on different types of decisions on the attitudes of (potential) applicants is scarce. Therefore, in this study, we use a quantitative, quota-representative study (n = 490) to assess the acceptance of CAs in recruiting. By applying an experimental within-subject design, we provide a more nuanced perspective on why and when providing explanations increases user acceptance. We also show that contextual factors such as the type of assessed skills are major determinants of this effect, and we conclude that XAI is not a "one-size-fits-all approach." Based on the insight that contextual factors of the decision problem are more important than the type of XAI approach itself, we argue that the use and the effects of explainability in recruiting need a more nuanced perspective, focusing on the fit of explanations with the user's characteristics and preferences.
Enhancing online health consultations through fuzzy logic-integrated attribute-based encryption system

Authors:
raw_author_name: Haewon Byeon
raw_affiliation_string: Department of Digital Anti-Aging Healthcare, Inje University, Gimhae, Republic of Korea
raw_author_name: Manoj Ram Tammina
raw_affiliation_string: Sr Software Developer, Innovation, Bread Financial, Columbus, Ohio, USA
raw_author_name: Mukesh Soni
raw_affiliation_string: Department of CSE, University Centre for Research & Development, Chandigarh University, Mohali, Punjab, India; Dr. D.Y. Patil Vidyapeeth, Pune, Dr. D.Y. Patil School of Science & Technology, Tathawade, Pune, India
raw_author_name: Ismail Keshta
raw_affiliation_string: Computer Science and Information Systems Department, College of Applied Sciences, AlMaarefa University, Riyadh, Saudi Arabia
raw_author_name: Mrunalini Harish Kulkarni
raw_affiliation_string: School of Pharmacy, Vishwakarma University, India

Abstract: Online health consultations are becoming more popular as a result of technological improvements. Patients routinely look for information about medical disorders online, which could jeopardize the privacy of medical records and increase the workload of healthcare professionals. Nonetheless, academics continue to be extremely concerned about issues related to the quality characteristics that relate to the current architectural models, such as energy consumption, latency, resource utilization, scalability, and packet loss. This method, however, also results in a significant strain being placed on medical experts who must sort through vast amounts of medical records to extract certain information. This paper presents a novel ciphertext policy attribute-based encryption method coupled with fuzzy logic to overcome these issues. This solution uses a hybrid structure of IPFS and blockchain to store data and enables complex bidirectional access control. Before being added to IPFS, medical records are encrypted. To ensure data integrity, related IPFS hash indexes are then added to the blockchain. Utilizing attribute-based technology, users’ data is encrypted to give them fine-grained bidirectional access control. A thorough security analysis proves the system’s resilience, especially when faced with chosen plaintext assaults inside the random oracle model. Tests for this study were conducted using 10–50 attribute sets. This paper’s technique solely makes use of a hash operation. All things considered; the study demonstrates that the proposed design is more efficient than earlier schemes. Thus, from the comparison study above, it can be concluded that the system presented in this work is more efficient. Results from simulations provide additional support for the suggested methodology by highlighting the improved computing efficiency of users as compared to baseline conventional systems. This study demonstrates how technological advancement and healthcare requirements can coexist harmoniously, paving the way for secure and effective online medical consultations that are powered by fuzzy logic.
Modeling the enablers of blockchain technology implementation for information management in healthcare supply chains

Authors:
raw_author_name: Matthew Quayson
raw_affiliation_string: Ho Technical University, Ho, Ghana

Abstract: Purpose Blockchain technology enhances information management in healthcare supply chains by securing healthcare information and providing medical resource traceability. However, there is no decision framework to support blockchain implementation for managing information, especially in emerging economies’ healthcare supply chains. This paper develops a hierarchical decision model for implementing blockchain technology for information management in emerging economies’ healthcare supply chains. Design/methodology/approach This study uses 20 health supply chain experts in Ghana to rank 17 decision criteria for implementing blockchain for healthcare information management using the best-worst method (BWM) multi-criteria decision technique. Findings The results show that “security” and “privacy,” “infrastructural facility” and “presence of training facilities” are the top three critical factors impacting blockchain adoption in the health supply chain for healthcare information management. Other sub-factors are prioritized. Practical implications To implement blockchain effectively to enhance information management in the healthcare supply chain, health institutions, blockchain technology providers and state authorities should concentrate on the highly critical factors extracted from the study. Originality/value This is the first study that develops a hierarchical decision model for implementing blockchain technology in emerging economies' health supply chains.
A Lightweight Image Super-Resolution Reconstruction Algorithm Based on the Residual Feature Distillation Mechanism

Authors:
raw_author_name: Zihan Yu
raw_affiliation_string: School of Electronic Information and Electrical Engineering, Yangtze University, Jingzhou 434023, China
raw_author_name: Kai Xie
raw_affiliation_string: School of Electronic Information and Electrical Engineering, Yangtze University, Jingzhou 434023, China
raw_author_name: Chang Wen
raw_affiliation_string: School of Computer Science, Yangtze University, Jingzhou 434023, China
raw_author_name: Jianbiao He
raw_affiliation_string: School of Computer Science, Central South University, Changsha 410083, China
raw_author_name: Wei Zhang
raw_affiliation_string: School of Electronic Information, Central South University, Changsha 410083, China

Abstract: In recent years, the development of image super-resolution (SR) has explored the capabilities of convolutional neural networks (CNNs). The current research tends to use deeper CNNs to improve performance. However, blindly increasing the depth of the network does not effectively enhance its performance. Moreover, as the network depth increases, more issues arise during the training process, requiring additional training techniques. In this paper, we propose a lightweight image super-resolution reconstruction algorithm (SISR-RFDM) based on the residual feature distillation mechanism (RFDM). Building upon residual blocks, we introduce spatial attention (SA) modules to provide more informative cues for recovering high-frequency details such as image edges and textures. Additionally, the output of each residual block is utilized as hierarchical features for global feature fusion (GFF), enhancing inter-layer information flow and feature reuse. Finally, all these features are fed into the reconstruction module to restore high-quality images. Experimental results demonstrate that our proposed algorithm outperforms other comparative algorithms in terms of both subjective visual effects and objective evaluation quality. The peak signal-to-noise ratio (PSNR) is improved by 0.23 dB, and the structural similarity index (SSIM) reaches 0.9607.
Understanding poetry using natural language processing tools: a survey

Authors:
raw_author_name: Mirella De Sisto
raw_affiliation_string: Tilburg University , The Netherlands
raw_author_name: Laura Hernández-Lorenzo
raw_affiliation_string: National Distance Education University , Spain
raw_author_name: Javier De la Rosa
raw_affiliation_string: National Library of Norway , Norway
raw_author_name: Salvador Ros
raw_affiliation_string: National Distance Education University , Spain
raw_author_name: Elena González-Blanco
raw_affiliation_string: IE University , Spain

Abstract: Abstract Analyzing poetry with automatic tools has great potential for improving verse-related research. Over the last few decades, this field has expanded notably and a large number of tools aiming at analyzing various aspects of poetry have been developed. However, the concrete connection between these tools and traditional scholars investigating poetry and metrics is often missing. The purpose of this article is to bridge this gap by providing a comprehensive survey of the automatic poetry analysis tools available for European languages. The tools are described and classified according to the language for which they are primarily developed, and to their functionalities and purpose. Particular attention is given to those that have open-source code or provide an online version with the same functionality. Combining more traditional research with these tools has clear advantages: it provides the opportunity to address theoretical questions with the support of large amounts of data; also, it allows for the development of new and diversified approaches.
A metastable RNG using interleaved NAND- and NOR-based TEROs

Authors:
raw_author_name: Chua-Chin Wang
raw_affiliation_string: Department of Electrical Engineering, National Sun Yat-Sen University, Kaohsiung, Taiwan
raw_author_name: Lean Karlo S. Tolentino
raw_affiliation_string: Department of Electrical Engineering, National Sun Yat-Sen University, Kaohsiung, Taiwan
raw_author_name: Shao-Wei Lu
raw_affiliation_string: Department of Electrical Engineering, National Sun Yat-Sen University, Kaohsiung, Taiwan
raw_author_name: Ralph Gerard B. Sangalang
raw_affiliation_string: Department of Electrical Engineering, National Sun Yat-Sen University, Kaohsiung, Taiwan; Department of Electronics Engineering, Batangas State University - The National Engineering University, Batangas, Philippines

Abstract: This study introduces a novel approach to address the limitations observed in existing random number generator (RNG) designs, focusing on the resolution for insufficiently covered problem statements and study purposes. In response to these challenges, we propose a high-throughput metastable-based RNG tailored for cryptography applications. Our design is featured with a two-layer transient effect ring oscillator (TERO) architecture, meticulously crafted to mitigate the locking phenomenon and enhance RNG randomness. Leveraging interleaved two-layer TEROs, realised through NAND and NOR SR latches, our RNG achieves unpredictable irregular sampling, crucial for cryptographic applications. To further enhance irregular sampling, the first layer of the RNG employs a multiplexer that selects inputs randomly. This multiplexer is governed by selection signals generated by the second layer of the RNG, ensuring superior randomness. Implemented using TSMC 40-nm CMOS process, our RNG passed the FIPS 140–1 randomness test at a clock frequency of 100 MHz, boasting a remarkable throughput of 50 Mbps. This study represents a significant advancement in RNG technology, addressing previous shortcomings and paving the way for high-speed communication systems' secure cryptographic applications.
DCLGM: Fusion Recommendation Model Based on LightGBM and Deep Learning

Authors:
raw_author_name: Bin Zhao
raw_affiliation_string: School of Information Science and Engineering, Linyi University, Shandong, 276000, China
raw_author_name: Jiqun Zhang
raw_affiliation_string: School of Information Science and Engineering, Linyi University, Shandong, 276000, China
raw_author_name: Wei Cao
raw_affiliation_string: School of Information Science and Engineering, Linyi University, Shandong, 276000, China
raw_author_name: Yilong Gao
raw_affiliation_string: School of Information Science and Engineering, Linyi University, Shandong, 276000, China

Abstract: Abstract The recommendation system can mine valuable information according to user preferences, so it is widely used in various industries. However, the performance of recommendation systems is generally affected by the problem of data sparsity, and LightGBM can alleviate the impact caused by data sparsity to a certain extent. To this end, this paper proposes a fusion recommendation model based on the LightGBM and deep learning—CLGM model. The model is composed of LighGBM, cross network and deep neural network. First, the features in the dataset are fused and extracted through LightGBM, and the feature with the highest classification accuracy is selected as the input of the neural network layer; Then, using the cross network and the deep neural network, the linear cross combination feature relationship and nonlinear correlation relationship between high-order features are respectively obtained; finally, the results obtained by the pre-order network are linearly weighted and combined to obtain the final recommendation result. In this paper, AUC and Logloss are used as evaluation indicators to verify the model on the public dataset Criteo and dataset Avazu. The simulation experiment results show that, compared with the four typical recommendation models, the recommendation effect of this model is better.
Evaluating the Effectiveness of Bayesian Knowledge Tracing Model-Based Explainable Recommender

Authors:
raw_author_name: Kyosuke Takami
raw_affiliation_string: Education Data Science Center, National Institute for Educational Policy Research, Tokyo, Japan
raw_author_name: Brendan Flanagan
raw_affiliation_string: Center for Innovative Research and Education in Data Science, Kyoto University, Kyoto, Japan
raw_author_name: Yiling Dai
raw_affiliation_string: Academic Center for Computing and Media Studies, Kyoto University, Kyoto, Japan
raw_author_name: Hiroaki Ogata
raw_affiliation_string: Academic Center for Computing and Media Studies, Kyoto University, Kyoto, Japan

Abstract: Explainable recommendation, which provides an explanation about why a quiz is recommended, helps to improve transparency, persuasiveness, and trustworthiness. However, little research examined the effectiveness of the explainable recommender, especially on academic performance. To survey its effectiveness, the authors evaluate the math academic performance among middle school students (n=115) by giving pre- and post-test questions based evaluation techniques. During the pre- and post-test periods, students were encouraged to use the Bayesian Knowledge Tracing model based explainable recommendation system. To evaluate how well the students were able to do what they could not do, the authors defined growth rate and found recommended quiz clicked counts had a positive effect on the total number of solved quizzes (R=0.343, P=0.005) and growth rate (R=0.297, P=0.017) despite no correlation between the total number of solved quizzes and growth rate. The results suggest that the use of an explainable recommendation system that learns efficiently will enable students to do what they could not do before.
Density estimation based soft actor-critic: deep reinforcement learning for static output feedback control with measurement noise

Authors:
raw_author_name: Kenji Kashima
raw_affiliation_string: Graduate School of Informatics, Kyoto University, Kyoto, Japan

Abstract: The state-of-the-art deep reinforcement learning (DRL) methods, including Deep Deterministic Policy Gradient (DDPG), Twin Delayed DDPG (TD3), Proximal Policy Optimization (PPO), Soft Actor-Critic (SAC), among others, demonstrate significant capability in solving the optimal static state feedback control (SSFC) problem. This problem can be modeled as a fully observed Markov decision process (MDP). However, the optimal static output feedback control (SOFC) problem with measurement noise is a typical partially observable MDP (POMDP), which is difficult to solve, especially for the continuous state-action-observation space with high dimensions. This paper proposes a two-stage framework to address this challenge. In the laboratory stage, both the states and the noisy outputs are observable; the SOFC policy is converted to a constrained stochastic SSFC policy, of which the probability density function is generally not analytical. To this end, a density estimation based SAC algorithm is proposed to explore the optimal SOFC policy by learning the optimal constrained stochastic SSFC. Consequently, in the real-world stage, only the noisy outputs and the learned SOFC policy are required to solve the optimal SOFC problem. Numerical simulations and the corresponding experiments with robotic arms are provided to illustrate the effectiveness of our method. The code is available at https://github.com/RanKyoto/DE-SAC.