Reviewerly/exHarmony huggingface dataset:
Chimera: A Lossless Decoding Method for Accelerating Large Language  Models Inference by Fusing all Tokens

Authors:
raw_author_name: Zeng, Ziqian
raw_author_name: Yu, Jiahong
raw_author_name: Wang, Zihao
raw_author_name: Zhuang, Huiping
raw_author_name: Chen, Cen

Abstract: Large language models (LLMs) have demonstrated remarkable capabilities across various tasks. However, their widespread application is hindered by the resource-intensive decoding process. To address this challenge, current approaches have incorporated additional decoding heads to enable parallel prediction of multiple subsequent tokens, thereby achieving inference acceleration. Nevertheless, the accuracy of these decoding heads falls short of the auto-regressive decoding approach. In light of these limitations, we propose Chimera, a novel framework specifically designed for speculative sampling. Within this framework, we introduce a lightweight draft model that effectively utilizes previously generated tokens to predict subsequent words. To ensure both accuracy and efficiency, we present two strategies within the lightweight draft model. Firstly, we focus on capturing short-range dependencies at the bottom layer. Secondly, we leverage the readily available representations from the original LLM.Through empirical evaluation on the Vicuna and LlaMA-2 series, Chimera demonstrates impressive results, achieving an average latency speedup ratio of 2.7x compared to the vanilla auto-regressive decoding approach. This highlights the potential of our proposed framework in significantly improving the efficiency of large language models during the decoding process.
SportQA: A Benchmark for Sports Understanding in Large Language Models

Authors:
raw_author_name: Wang, Yuqing
raw_author_name: Zhao, Yun
raw_author_name: Wang, Yuan-fang
raw_author_name: Shen, Weining

Abstract: A deep understanding of sports, a field rich in strategic and dynamic content, is crucial for advancing Natural Language Processing (NLP). This holds particular significance in the context of evaluating and advancing Large Language Models (LLMs), given the existing gap in specialized benchmarks. To bridge this gap, we introduce SportQA, a novel benchmark specifically designed for evaluating LLMs in the context of sports understanding. SportQA encompasses over 70,000 multiple-choice questions across three distinct difficulty levels, each targeting different aspects of sports knowledge from basic historical facts to intricate, scenario-based reasoning tasks. We conducted a thorough evaluation of prevalent LLMs, mainly utilizing few-shot learning paradigms supplemented by chain-of-thought (CoT) prompting. Our results reveal that while LLMs exhibit competent performance in basic sports knowledge, they struggle with more complex, scenario-based sports reasoning, lagging behind human expertise. The introduction of SportQA marks a significant step forward in NLP, offering a tool for assessing and enhancing sports understanding in LLMs.
MATHWELL: Generating Educational Math Word Problems at Scale

Authors:
raw_author_name: Hartvigsen, Thomas

Abstract: Math word problems are critical K-8 educational tools, but writing them is time-consuming and requires domain expertise. We suggest that language models can support K-8 math education by automatically generating problems at scale. To be educational, generated problems must be 1) solvable, 2) accurate, and 3) appropriate. Existing datasets are unlabeled for these criteria, making them ill-suited for training problem generators. We introduce MATHWELL, a Llama-2 (70B) model iteratively finetuned to generate K-8 math word problems using data from expert annotation. Using MATHWELL, we generate the largest English word problem dataset to date, containing 20,490 problems. 3,484 are scored by domain experts who find MATHWELL has a 40% higher share of problems that have executable solutions and meet all criteria than alternatives, with 74% of its problems with executable solutions being solvable, accurate, and appropriate.
GraphWiz: An Instruction-Following Language Model for Graph Problems

Authors:
raw_author_name: Chen, Nuo
raw_author_name: Li, Yuhan
raw_author_name: Tang, Jianheng
raw_author_name: Li, Jia

Abstract: Large language models (LLMs) have achieved impressive success across several fields, but their proficiency in understanding and resolving complex graph problems is less explored. To bridge this gap, we introduce GraphInstruct, a novel and comprehensive instruction-tuning dataset designed to equip language models with the ability to tackle a broad spectrum of graph problems using explicit reasoning paths. Utilizing GraphInstruct, we build GraphWiz, an open-source language model capable of resolving various graph problem types while generating clear reasoning processes. To enhance the model's capability and reliability, we incorporate the Direct Preference Optimization (DPO) framework into the graph problem-solving context. The enhanced model, GraphWiz-DPO, achieves an average accuracy of 65% across nine tasks with different complexity levels, surpassing GPT-4 which has an average accuracy of 43.8%. Moreover, our research delves into the delicate balance between training data volume and model performance, highlighting the potential for overfitting with increased data. We also explore the transferability of the model's reasoning ability across different graph tasks, indicating the model's adaptability and practical application potential. Our investigation offers a new blueprint and valuable insights for developing LLMs specialized in graph reasoning and problem-solving.
Towards Accurate Post-training Quantization for Reparameterized Models

Authors:
raw_author_name: Zhang, Luoming
raw_author_name: He, Yefei
raw_author_name: Fei, Wen
raw_author_name: Lou, Zhenyu
raw_author_name: Wu, Weijia
raw_author_name: Zhou, Hong

Abstract: Model reparameterization is a widely accepted technique for improving inference speed without compromising performance. However, current Post-training Quantization (PTQ) methods often lead to significant accuracy degradation when applied to reparameterized models. This is primarily caused by channel-specific and sample-specific outliers, which appear only at specific samples and channels and impact on the selection of quantization parameters. To address this issue, we propose RepAPQ, a novel framework that preserves the accuracy of quantized reparameterization models. Different from previous frameworks using Mean Squared Error (MSE) as a measurement, we utilize Mean Absolute Error (MAE) to mitigate the influence of outliers on quantization parameters. Our framework comprises two main components: Quantization Protecting Reparameterization and Across-block Calibration. For effective calibration, Quantization Protecting Reparameterization combines multiple branches into a single convolution with an affine layer. During training, the affine layer accelerates convergence and amplifies the output of the convolution to better accommodate samples with outliers. Additionally, Across-block Calibration leverages the measurement of stage output as supervision to address the gradient problem introduced by MAE and enhance the interlayer correlation with quantization parameters. Comprehensive experiments demonstrate the effectiveness of RepAPQ across various models and tasks. Our framework outperforms previous methods by approximately 1\% for 8-bit PTQ and 2\% for 6-bit PTQ, showcasing its superior performance. The code is available at \url{https://github.com/ilur98/DLMC-QUANT}.
Synergizing edge computing and blockchain for <scp>cyber‐physical</scp> systems

Authors:
raw_author_name: Payal Thakur
raw_affiliation_string: Department of Computer Science &amp; Engineering and Information Technology Jaypee University of Information Technology Solan India
raw_author_name: Vivek Kumar Sehgal
raw_affiliation_string: Department of Computer Science &amp; Engineering and Information Technology Jaypee University of Information Technology Solan India

Abstract: Summary As a foundational technology for managing decentralized systems like smart grids and healthcare systems, blockchain is attracting a lot of interest. However, owing to excessive resource requirements and low scalability with frequent‐intensive transactions, its use in resource‐constrained mobile devices is restricted. To let mobile devices offload processing tasks to cloud resources, edge computing can be used. For scalability and secure transactions, the integration with edge computing guarantees consistent access, distributed computing, and non‐compromised storage. To accomplish effective integration, critical challenges such as reliability, adaptability, and resource planning must be resolved. Despite these efforts, further study is still needed to address issues with confidentiality, flexibility, and reliability to develop a workable, secure decentralized data storage system. This study utilizes edge computing to build an Internet of Things (IoT) architecture that satisfies the safety and scalability necessary criteria for integration along with the integration of peer‐to‐peer and blockchain technologies. Already‐existing blockchain and related technologies have also been investigated to suggest solutions that address issues like secrecy, reliability, and scalability in order to successfully incorporate blockchain in IoT systems. Our experimental findings indicate that achieving low rates of stale blocks and promoting decentralization requires a meticulous selection of block sizes and generation intervals. It is essential to maintain block creation intervals as short as practically possible, and utilizing blocks with a size below 1 MB is advisable.
PENGARUH KECERDASAN EMOSI TERHADAP KECEMASAN MENGHADAPI ULANGAN AKHIR SEMESTER PADA SISWA KELAS X SMK NEGERI 3 YOGYAKARTA [CORRELATION BETWEEN EMOTIONAL INTELLIGENCE WITH ANXIETY ON PREPARING FINAL SEMESTER EXAM FOR TENTH GRADE STUDENTS OF VOCATIONAL HIGH SCHOOL 3 YOGYAKARTA]

Authors:
raw_author_name: Caraka Putra Bhakti
raw_affiliation_string: Program Studi Bimbingan dan Konseling, Fakultas Keguruan dan Ilmu Pendidikan, Universitas Ahmad Dahlan, Yogyakarta, Indonesia

Abstract: This research aims to examine the influence of emotional intelligence on anxiety facing the Final Semester Examination in Class X students at SMK Negeri 3 Yogyakarta. This research is correlational research using a quantitative approach. The research sampling technique used random sampling technique. Data collection instruments used an emotional intelligence level scale and an anxiety scale for facing end-of-semester exams. This research concludes that there is a significant relationship between the level of emotional intelligence and the level of anxiety facing the final semester exams in class X students at SMK Negeri 3 Yogyakarta. The correlation coefficient value is -0.447 and the determination coefficient is 0.200.
Graph-CNN Hybrid Advance Model for Accurate Anomaly Detection in Multivariate Time Series IoT Streams

Authors:
raw_author_name: Mansoor Farooq
raw_affiliation_string: University of Kashmir

Abstract: Abstract The Internet of Things (IoT) has revolutionized various industries by enabling the collection of vast amounts of data from interconnected devices. However, the sheer volume and complexity of IoT data pose significant challenges for anomaly detection, especially in multivariate time series data. Traditional methods struggle to effectively capture the intricate relationships and temporal dependencies within such data. In response to these challenges, this paper introduces a pioneering approach that harnesses the combined power of Graph Neural Networks (GNNs) and Convolutional Neural Networks (CNNs) for anomaly detection in multivariate time series IoT data. The novelty of our approach lies in its holistic integration of GNNs and CNNs, enabling the comprehensive analysis of both global dependencies and local temporal features within the IoT data. Through rigorous experimentation on real-world datasets, showcases the efficacy and superiority of our method over existing state-of-the-art techniques. Our approach consistently achieves superior performance metrics, compared to state-of-the-art methods. Underscoring its effectiveness in accurately detecting anomalies amidst the complexities of IoT data.
Graph Convolutional Network with elastic topology

Authors:
raw_author_name: Zhihao Wu
raw_affiliation_string: College of Computer and Data Science, Fuzhou University, Fuzhou, 350108, China; Key Laboratory of Intelligent Metro, Fuzhou University, Fuzhou, 350108, China
raw_author_name: Zhaoliang Chen
raw_affiliation_string: College of Computer and Data Science, Fuzhou University, Fuzhou, 350108, China; Key Laboratory of Intelligent Metro, Fuzhou University, Fuzhou, 350108, China
raw_author_name: Shide Du
raw_affiliation_string: College of Computer and Data Science, Fuzhou University, Fuzhou, 350108, China; Key Laboratory of Intelligent Metro, Fuzhou University, Fuzhou, 350108, China
raw_author_name: Shiping Wang
raw_affiliation_string: College of Computer and Data Science, Fuzhou University, Fuzhou, 350108, China; Key Laboratory of Intelligent Metro, Fuzhou University, Fuzhou, 350108, China

Abstract: Graph Convolutional Network (GCN) has drawn widespread attention in data mining on graphs due to its outstanding performance and rigor theoretical guarantee. However, some recent studies have revealed that GCN-based methods may mine latent information insufficiently owing to the underutilization of the feature space. Besides, the unlearnable topology also significantly imperils the performance of GCN-based methods. In this paper, we conduct experiments to investigate these issues, finding that GCN does not fully consider the potential structure in the feature space, and a fixed topology deteriorates the robustness of GCN. Thus, it is desired to distill node features and establish a learnable graph. Motivated by this goal, we propose a framework dubbed Graph Convolutional Network with elastic topology (GCNet1). With the analysis of the optimization for the proposed flexible Laplacian embedding, GCNet is naturally constructed by alternative graph convolutional layers and adaptive topology learning layers. GCNet aims to deeply explore the feature space and employ the mined information to construct a learnable topology, which leads to a more robust graph representation. In addition, a set-level orthogonal loss is utilized to meet the orthogonal constraint required by the flexible Laplacian embedding and promote better class separability. Moreover, comprehensive experiments indicate that GCNet achieves remarkable performance and generalization on several real-world datasets.
Boosting constrained teaching learning based optimization algorithm through group discussion

Authors:
raw_author_name: Muhammad Sagheer
raw_affiliation_string: Institute of Numerical Sciences, Kohat University of Science & Technology, Kohat, 26000, Khyber Pakhtunkhwa, Pakistan
raw_author_name: Muhammad Asif Jan
raw_affiliation_string: Institute of Numerical Sciences, Kohat University of Science & Technology, Kohat, 26000, Khyber Pakhtunkhwa, Pakistan
raw_author_name: Wali Khan Mashwani
raw_affiliation_string: Institute of Numerical Sciences, Kohat University of Science & Technology, Kohat, 26000, Khyber Pakhtunkhwa, Pakistan
raw_author_name: Zahir Shah
raw_affiliation_string: Department of Mathematical Sciences, University of Lakki Marwat, Lakki Marwat, 28420, Khyber Pakhtunkhwa, Pakistan
raw_author_name: Rashida Adeeb Khanum
raw_affiliation_string: Jinnah College for Women, University of Peshawar, Peshawar, 25000, Khyber Pakhtunkhwa, Pakistan

Abstract: Teaching learning based optimization algorithm (TLBO) is an efficient population-based nature-inspired meta-heuristic designed initially to solve unconstrained optimization problems. TLBO being a swam intelligence based algorithm is renowned for fast convergence. This characteristic, however, occasionally causes it to converge too soon to a non-globally optimal solution. Group discussion techniques are used in educational institutions all over the world to help participants to exchange ideas and information and to improve their skills. This work imitates the outlined strategy, therefore innovating TLBO. Here, a group of virtual students is selected corresponding to each student in the learner phase of TLBO to produce diversity in the population and to balance the exploration and exploitation capabilities of it, hence reducing the likelihood of early convergence. GTLBO stands for the new algorithm that incorporates group discussion. It is essential to incorporate some constraint handling techniques (CHTs) into the TLBO framework in order to handle constrained optimization problems (COPs). The most recent, effective, and enhanced version of the widely used CHT stochastic ranking (SR) is called hybrid stochastic ranking (HSR). HSR is added to the GTLBO structure as a selection operator for managing COPs; the resulting setup is referred to as HSR-GTLBO. Additionally, the suggested algorithm’s group size for discussion is sensitive, resulting in a number of constrained variants. The efficacy of these variations is assessed with the suit CEC 2017’s constrained problems. In conformity with the parameters settlement, comparison, and ranking criteria of CEC 2017, the simulation results of the proposed versions are compared and ranked using the most advanced state-of-the-art algorithms. As a result, in the competition, the newly developed HSR-GTLBO variants placed first, second, and third.
Investigating the Robustness of Vision Transformers against Label Noise  in Medical Image Classification

Authors:
raw_author_name: Khanal, Bidur
raw_author_name: Khanal, Bishesh
raw_author_name: Bhattarai, Binod
raw_author_name: Linte, Cristian A.

Abstract: Label noise in medical image classification datasets significantly hampers the training of supervised deep learning methods, undermining their generalizability. The test performance of a model tends to decrease as the label noise rate increases. Over recent years, several methods have been proposed to mitigate the impact of label noise in medical image classification and enhance the robustness of the model. Predominantly, these works have employed CNN-based architectures as the backbone of their classifiers for feature extraction. However, in recent years, Vision Transformer (ViT)-based backbones have replaced CNNs, demonstrating improved performance and a greater ability to learn more generalizable features, especially when the dataset is large. Nevertheless, no prior work has rigorously investigated how transformer-based backbones handle the impact of label noise in medical image classification. In this paper, we investigate the architectural robustness of ViT against label noise and compare it to that of CNNs. We use two medical image classification datasets -- COVID-DU-Ex, and NCT-CRC-HE-100K -- both corrupted by injecting label noise at various rates. Additionally, we show that pretraining is crucial for ensuring ViT's improved robustness against label noise in supervised training.
Probing anyonic statistics via Mach-Zehnder interferometry in quantum  computers

Authors:
raw_author_name: Zhou, Shiyu
raw_author_name: Teng, Yi
raw_author_name: Chamon, Claudio
raw_author_name: Rahmani, Armin

Abstract: We introduce a synthetic Mach-Zehnder interferometer for digitized quantum computing devices to probe fractional exchange statistics of anyonic excitations that appear in quantum spin liquids. Employing an IonQ quantum computer, we apply this scheme to the toric ladder, a quasi-one-dimensional reduction of the toric code. We observe interference patterns resulting from the movement of `electric' excitations in the presence and absence of `magnetic' ones. We model the noise in IonQ via depolarizing Lindbladian dynamics, and find quantitative agreement with the measurements obtained from the quantum device. The synthetic Mach-Zehnder interferometer can thus also serve as an effective means to probe the coherence length and time scales of multi-qubit noisy quantum devices.
On the probability of a Pareto record

Authors:
raw_author_name: Fill, James Allen
raw_author_name: Sun, Ao

Abstract: Given a sequence of independent random vectors taking values in ${\mathbb R}^d$ and having common continuous distribution $F$, say that the $n^{\rm \scriptsize th}$ observation sets a (Pareto) record if it is not dominated (in every coordinate) by any preceding observation. Let $p_n(F) \equiv p_{n, d}(F)$ denote the probability that the $n^{\rm \scriptsize th}$ observation sets a record. There are many interesting questions to address concerning $p_n$ and multivariate records more generally, but this short paper focuses on how $p_n$ varies with $F$, particularly if, under $F$, the coordinates exhibit negative dependence or positive dependence (rather than independence, a more-studied case). We introduce new notions of negative and positive dependence ideally suited for such a study, called negative record-setting probability dependence (NRSPD) and positive record-setting probability dependence (PRSPD), relate these notions to existing notions of dependence, and for fixed $d \geq 2$ and $n \geq 1$ prove that the image of the mapping $p_n$ on the domain of NRSPD (respectively, PRSPD) distributions is $[p^*_n, 1]$ (resp., $[n^{-1}, p^*_n]$), where $p^*_n$ is the record-setting probability for any $F$ governing independent coordinates.
A Cardiac Audio Classification Method Based on Multidimensional Feature Expression

Authors:
raw_author_name: Jing Hu
raw_affiliation_string: National Key Laboratory of Science and Technology on Multi-Spectral Information Processing
raw_author_name: Siqi Lv
raw_affiliation_string: Huazhong Univ. of Science and Technology
raw_author_name: Ren Jie
raw_affiliation_string: Huazhong Univ. of Science and Technology
raw_author_name: Yan Ouyang
raw_affiliation_string: Air Force Early Warning Academy
raw_author_name: Jia He
raw_affiliation_string: Chinese People’s Liberation Army

Abstract: Abstract Heart sound auscultation plays a crucial role in the early diagnosis of cardiovascular diseases. In recent years, great achievements have been made in automatic heart sound classification; however, most methods are based on segmentation features and traditional classifiers, and do not fully use existing deep networks. This paper proposes a cardiac audio classification method based on image expression of multidimensional features (CACIEMDF). First, a 102-dimensional feature vector is designed by combining the characteristics of heart sound data in the time, frequency and statistical domains. Based on the feature vector, a two-dimensional feature projection space is constructed by PCA dimensionality reduction and the convex hull algorithm, and 102 pairs of coordinate representations of the feature vector in the two-dimensional plane are calculated. Each one-dimensional component of the feature vector corresponds to a pair of 2-d coordinate representations. Finally, the one-dimensional feature component value and its divergence from the two categories are used to fill the three channels of a color image, and a Gaussian model is used to dye the image to enrich its content. The color image is subsequently sent to a deep network such as ResNet50 for classification tasks. In this paper, three public heart sound datasets are fused, and experiments are conducted using the above methods. The results show that the proposed method can achieve a classification accuracy of 95.72% for the two-classification task of heart sounds when combined with the current deep network.