Reviewerly/exHarmony huggingface dataset:
A Conceptual Paper: Study on the Effect of Peer Relationships on the Academic Achievement among College Students

Authors:
raw_author_name: Guo Yaxin

Abstract: The aim of this paper is to examine the relationship between peer relation influences on the college students’ academic achievement. The study assessed peer relation influences in terms of the level of academic achievement impact they have on learners. This study will introduce peer relationships, learning engagement, and positive academic emotion as variables to analyse their effects on the academic achievement of university students. This study will adopt quantitative research methods?The respondents were selected using simple random sampling technique. Questionnaires were used to collect data, which were analyzed by multiple regressions. It is hoped that the findings of this study will be useful to teachers, principals and parents to gain more insight into the psychosocial factors that affect college students’ academic achievement and therefore help improve their academic achievement. Providing theoretical support and practical basis for improving undergraduate education.
Computer Vision and Image Segmentation

Authors:
raw_author_name: Dhyan Patel

Abstract: Abstract: Image segmentation is a critical step in image processing, computer vision, and pattern recognition, which involves dividing an image into different regions or segments. Image segmentation plays an essential role in many applications, such as object recognition, medical image analysis, autonomous driving, and robotics. This paper aims to provide an overview of image segmentation techniques, including traditional and deep learning-based approaches. The paper also discusses the challenges associated with image segmentation, such as noise, illumination variations, and occlusions. Finally, the paper provides a brief discussion on the evaluation metrics used to assess the performance of image segmentation algorithms.
Enhancing Transparency and Interpretability in Deep Learning Models: A Comprehensive Study on Explainable AI Techniques

Authors:
raw_author_name: Dr.Shashank Singh
raw_author_name: Dr. Dhirendra Pratap Singh

Abstract: Abstract: Deep learning models have demonstrated remarkable capabilities across various domains, but their inherent complexity often leads to challenges in understanding and interpreting their decisions. The demand for transparent and interpretable artificial intelligence (AI) systems is particularly crucial in fields such as healthcare, finance, and autonomous systems. This research paper presents a comprehensive study on the application of Explainable AI (XAI) techniques to enhance transparency and interpretability in deep learning models. Keywords: Explainable AI (XAI), artificial intelligence (AI).
Read and Play Digital Music (MIDI) in R using the *fluidsynth* package

Authors:
raw_author_name: Jeroen Ooms

Abstract: A few weeks ago, prof Matt Crump wrote a blog post in which he explores tools to handle MIDI data in R, in preparation for a cognition experiment that involves creating musical stimuli. In the article he ends up using a mix of external command line tools ffmpeg and fluidsynth and a python module.
Healthcare System Using Blockchain

Authors:
raw_author_name: IJSREM Journal

Abstract: The global Electronic Health Record (EHR) market is growing dramatically and is expected to reach $39.7 billion by 2022. To safeguard the security and privacy of EHR, access control is an essential mechanism for managing EHR data. This paper proposes a hybrid architecture to facilitate access control of EHR data by using both blockchain and edge nodes. Within the architecture, a blockchain-based controller manages identity and access control policies and serves as a tamper-proof log of access events. In addition, off-chain edge nodes store the EHR data and apply policies specified in Abbreviated Language For Authorization (ALFA) to enforce attribute-based access control on EHR data in collaboration with the blockchain-based access control logs. We evaluate the proposed hybrid architecture by utilizing the Hyperledger Composer Fabric blockchain to measure the performance of executing smart contracts and ACL policies in terms of transaction processing time and response time against unauthorized data retrieval. Keywords:Attribute-based Access Control, Access Control List, Blockchain, Edge Computing, Hyperledger, Smart Contract
Photovoltaic power generation prediction and optimization configuration model based on GPR and improved PSO algorithm

Authors:
raw_author_name: Lingwei Zhang
raw_affiliation_string: State Grid Gansu Electric Power Company

Abstract: As the growing demand for energy as well as the strengthening of environmental awareness, photovoltaic power generation, as a clean and renewable energy source, has gradually attracted people's attention and attention. To facilitate the dispatching and planning of power system, this study uses historical data and meteorological data to build a photovoltaic power generation prediction and configuration optimization model on the ground of Gaussian process regression and improved particle swarm optimization algorithm. The simulation results show that the regression prediction curve of the Gaussian process regression prediction model is the closest to the real curve, and the prediction curve is stable and not easily disturbed by noise data. The Root-mean-square deviation and the average absolute proportional error of the model are small, and the disparity in the predicted value and the true value of the model is small; The integration of multi factor data has improved the accuracy of prediction data, and the regression prediction effect is good. The improved Particle swarm optimization algorithm could continuously enhance in the search for the optimal solution, and the Rate of convergence is fast. The Pareto solution can provide different solutions suitable for photovoltaic power generation optimization. Reasonable optimization configuration can effectively reduce active power line loss and voltage deviation, with the maximum reduction values reaching 132kW and 0.028, respectively. The research and design of predictive models and optimized configuration models can promote the formation of smart grids.
Native Market Factors for Pricing Cryptocurrencies

Authors:
raw_author_name: Helder Sebastião
raw_affiliation_string: University of Coimbra

Abstract: The cryptocurrency market has been growing frantically in number of cryptocurrencies, online exchanges, and market capitalization, which has amplified the need for comprehensive and robust pricing models. Using a database of all eligible cryptocurrencies listed on the CoinMarketCap website, we study the relationship between returns and several potential pricing factors, such as size (market capitalization), momentum, liquidity, and maturity. The analysis was conducted from December 27, 2013, to December 29, 2020, using weekly data for 3'667 cryptocurrencies. Results point out that portfolios of cryptocurrencies with smaller market capitalization, higher reversal, lower liquidity, and lower maturity tend to offer higher returns. The 5-factor model that additionally includes illiquidity and maturity performs better than the 3-factor model previously proposed in the literature, meaning that illiquidity and maturity significantly help capture the cross-sectional cryptocurrency risk premia. The 5-factor model presented seems robust to different procedures to construct portfolios and factors.
Dual unitaries as maximizers of the distance to local product gates

Authors:
raw_author_name: Shrigyan Brahmachari
raw_affiliation_string: Department of Mechanical Engineering, Indian Institute of Technology Madras, Chennai 600036, India
raw_author_name: Rohan Narayan Rajmohan
raw_affiliation_string: Department of Physics, Indian Institute of Technology Madras, Chennai 600036, India
raw_author_name: Suhail Ahmad Rather
raw_affiliation_string: Department of Physics, Indian Institute of Technology Madras, Chennai 600036, India
raw_author_name: Arul Lakshminarayan
raw_affiliation_string: Department of Physics, Indian Institute of Technology Madras, Chennai 600036, India

Abstract: The problem of finding the resource-free, closest local unitary, to any bipartite unitary gate $U$ is addressed. Previously discussed as a measure of nonlocality, the distance ${K}_{D}(U)$ to the nearest product unitary has implications for circuit complexity and related quantities. Dual unitaries, currently of great interest in models of complex quantum many-body systems, are shown to have a preferred role as these are maximally and equally away from the set of local unitaries. This is proved here for the case of qubits, and we present strong numerical and analytical evidence that it is true in general. An analytical evaluation of ${K}_{D}(U)$ is presented for general two-qubit gates. For arbitrary local dimensions, that ${K}_{D}(U)$ is largest for dual unitaries is substantiated by its analytical evaluations for an important family of dual-unitary and for certain nondual gates. A closely allied result concerns for any bipartite unitary the existence of a pair of maximally entangled states that it connects. We give efficient numerical algorithms to find such states and to find ${K}_{D}(U)$ in general.
Path-Planning Strategy: Adaptive Ant Colony Optimization Combined with an Enhanced Dynamic Window Approach

Authors:
raw_author_name: Dongri Shan
raw_affiliation_string: School of Electronics and Information, Aerospace Information Technology University, Jinan 250200, China; School of Mechanical Engineering, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250300, China
raw_author_name: Shuaishuai Zhang
raw_affiliation_string: School of Information and Automation Engineering, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250300, China
raw_author_name: Xiaofang Wang
raw_affiliation_string: School of Information and Automation Engineering, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250300, China
raw_author_name: Peng Zhang
raw_affiliation_string: School of Information and Automation Engineering, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250300, China

Abstract: Aiming to resolve the problems of slow convergence speed and inability to plan in real time when ant colony optimization (ACO) performs global path planning, we propose a path-planning method that improves adaptive ant colony optimization (IAACO) with the dynamic window approach (DWA). Firstly, the heuristic information function is modified, and the adaptive adjustment factor is added to speed up the algorithm’s convergence rate; secondly, elite ants and max–min ants systems are implemented to enhance the global pheromone updating process, and an adaptive pheromone volatilization factor is aimed at preventing the algorithm from enhancing its global search capabilities; then, the path optimization and withdrawal mechanism is utilized to enable smoother functioning and to avoid the deadlocks; finally, a new distance function is introduced in the evaluation function of DWA to the enhance real-time obstacle-avoidance ability. The simulation experiment results reveal that the path length of the IAACO can be shortened by 10.1% and 13.7% in contrast to the ACO. The iteration count can be decreased by 63.3% and 63.0%, respectively, leading to an enhanced optimization performance in global path planning and achieving dynamic real-time obstacle avoidance for local path planning.
ParlayANN: Scalable and Deterministic Parallel Graph-Based Approximate Nearest Neighbor Search Algorithms

Authors:
raw_author_name: Magdalen Dobson Manohar
raw_affiliation_string: Carnegie Mellon University, Pittsburgh, USA
raw_author_name: Guy Blelloch
raw_affiliation_string: Carnegie Mellon University, Pittsburgh, USA
raw_author_name: Laxman Dhulipala
raw_affiliation_string: University of Maryland, College Park, College Park, United States
raw_author_name: Yan Gu
raw_affiliation_string: University of California, Riverside, Riverside, United States of America
raw_author_name: Harsha Vardhan Simhadri
raw_affiliation_string: Microsoft Research Lab India, Bangalore, India
raw_author_name: Yihan Sun
raw_affiliation_string: University of California, Riverside, Riverside, United States of America

Abstract: Approximate nearest-neighbor search (ANNS) algorithms are a key part of the modern deep learning stack due to enabling efficient similarity search over high-dimensional vector space representations (i.e., embeddings) of data. Among various ANNS algorithms, graph-based algorithms are known to achieve the best throughput-recall tradeoffs. Despite the large scale of modern ANNS datasets, existing parallel graph-based implementations suffer from significant challenges to scale to large datasets due to heavy use of locks and other sequential bottlenecks, which 1) prevents them from efficiently scaling to a large number of processors, and 2) results in non-determinism that is undesirable in certain applications.
Evaluation of the effectiveness of search and rescue ships in rescuing the overboard personnel based on cloud model

Authors:
raw_author_name: Yufeng Yang
raw_affiliation_string: China Coast Guard Academy (China)
raw_author_name: Zhiqiang Wang
raw_affiliation_string: China Coast Guard Academy (China)

Abstract: Conducting effectiveness evaluation and analysis of the key elements of rescuing the overboard personnel by search and rescue ships, an evaluation index system for rescuing the overboard personnel is constructed. Considering the randomness and fuzziness of various evaluation indicators for rescuing the overboard personnel, a qualitative concept and quantitative calculation are transformed based on cloud models to establish an effectiveness evaluation model for rescuing the overboard personnel. Analytic Hierarchy Process is used to calculate the weights of each evaluation indicator and combined with the evaluation indicators, upper level indicators parameter of cloud model are calculated. The effectiveness evaluation conclusion of the overall goal of rescuing the overboard personnel is obtained by comparing it with the evaluation standard cloud model.
Quantum Theory in Finite Dimension Cannot Explain Every General Process with Finite Memory

Authors:
raw_author_name: Marco Fanizza
raw_affiliation_string: Física Teòrica: Informació i Fenòmens Quàntics, Departament de Física, Universitat Autònoma de Barcelona, 08193, Bellaterra (Barcelona), Spain
raw_author_name: Andreas Winter
raw_affiliation_string: Física Teòrica: Informació i Fenòmens Quàntics, Departament de Física, Universitat Autònoma de Barcelona, 08193, Bellaterra (Barcelona), Spain

Abstract: Abstract Arguably, the largest class of stochastic processes generated by means of a finite memory consists of those that are sequences of observations produced by sequential measurements in a suitable generalized probabilistic theory (GPT). These are constructed from a finite-dimensional memory evolving under a set of possible linear maps, and with probabilities of outcomes determined by linear functions of the memory state. Examples of such models are given by classical hidden Markov processes, where the memory state is a probability distribution, and at each step it evolves according to a non-negative matrix, and hidden quantum Markov processes, where the memory is a finite-dimensional quantum system, and at each step it evolves according to a completely positive map. Here we show that the set of processes admitting a finite-dimensional explanation do not need to be explainable in terms of either classical probability or quantum mechanics. To wit, we exhibit families of processes that have a finite-dimensional explanation, defined manifestly by the dynamics of an explicitly given GPT, but that do not admit a quantum, and therefore not even classical, explanation in finite dimension. Furthermore, we present a family of quantum processes on qubits and qutrits that do not admit a classical finite-dimensional realization, which includes examples introduced earlier by Fox, Rubin, Dharmadikari and Nadkarni as functions of infinite-dimensional Markov chains, and lower bound the size of the memory of a classical model realizing a noisy version of the qubit processes.
On the Analysis of Mobile Apps. Why is most of our Research on Android?

Authors:
raw_author_name: Alessandra Gorla
raw_affiliation_string: IMDEA Software Institute, Spain

Abstract: Over the past decade, my research has been dedicated to the analysis of mobile applications, with a primary focus on the Android platform. As we delve into the intricacies of mobile app quality and trustworthiness, it becomes imperative to question the prevailing bias in our research toward the Android platform. This keynote talk aims to shed light on the reasons behind this predominant focus and provide insights into the shift of my research paradigm towards the iOS platform.
Modeling of Sports Training Simulation Based on Energy Harvesting in Wireless Sensor Networks

Authors:
raw_author_name: Mei Gong
raw_affiliation_string: Changsha Normal University, China

Abstract: For patients with limb motor dysfunction, the effect of physical exercise is directly related to their future quality of life. This article combines the physical training plan of rehabilitation therapists with the training of rehabilitation robots, which can effectively improve the training performance of existing lower limb rehabilitation robots. Therefore, a teaching and training method and a wireless data acquisition system based on energy acquisition wireless network sensor are proposed. Based on wireless wearable technology, wireless network sensors, PCs and electronic devices are used to monitor the activity information of human walking and standing in real time, and the physical fitness is tested by means of mean, variance, and standard deviation. Through the analysis of rehabilitation health, this article consists of two parts: power module and physical exercise. Finally, experiments show that the accuracy of wireless network sensors based on SVM algorithm is the most accurate under physical training. It provides a good means for wireless body area network technology.
APRE: Annotation-Aware Prompt-Tuning for Relation Extraction

Authors:
raw_author_name: Chao Wei
raw_author_name: Yanping Chen
raw_author_name: Kai Wang
raw_author_name: Yongbin Qin
raw_author_name: Ruizhang Huang
raw_author_name: Qinghua Zheng

Abstract: Abstract Prompt-tuning has been successfully applied to support classification tasks in natural language processing and has achieved promising performance. The main characteristic of prompt-tuning based classification is to verbalize class labels and predict masked tokens like a cloze-like task. It has the advantage to make use of knowledge in pre-trained language models (PLMs). Because prompt templates are manually designed, they are more prone to overfitting. Furthermore, traditional prompt templates are appended in the tail of an original sentence. They are far from some semantic units in a sentence. It is weak to decode semantic information of an input relevant to PLMs. To aggregate more semantic information from PLMs for masked token prediction, we propose an annotation-aware prompt-tuning model for relation extraction. In our method, entity type representations are used as entity annotations. They are implanted near the site of entities in a sentence for decoding semantic information of PLMs. It is effective to make full use of knowledge in PLMs for relation extraction. In the experiment section, our method is validated on the Chinese literature text and SemEval 2010 Task datasets and achieves 89.3% and 90.6% in terms of F1-score, respectively. It achieves the state-of-the-art performance on two public datasets. The result further demonstrates the effectiveness of our model to decode semantic information in PLMs.
On Optimizing Ensemble Models using Column Generation

Authors:
raw_author_name: Ouyang Wu
raw_author_name: Eligius M. T. Hendrix
raw_author_name: Jan Kronqvist

Abstract: Abstract In recent years, an interest appeared in integrating various optimization algorithms in machine learning. We study the potential of ensemble learning in classification tasks and how to efficiently decompose the underlying optimization problem. Ensemble learning has become popular for machine learning applications and it is particularly interesting from an optimization perspective due to its resemblance to column generation. The challenge for learning is not only to obtain a good fit for the training data set, but also good generalization, such that the classifier is generally applicable. Deep networks have the drawback that they require a lot of computational effort to get to an accurate classification. Ensemble learning can combine various weak learners, which individually require less computational time. We consider binary classification problems studying a three-phase algorithm. After initializing a set of base learners refined by a bootstrapping approach, base learners are generated using the solution of an linear programming (LP) master problem and then solving a machine learning sub-problem regarding a reduced data set, which can be viewed as a so-called pricing problem. We theoretically show that the algorithm computes an optimal ensemble model in the convex hull of a given model space. The implementation of the algorithm is part of an ensemble learning framework called decolearn . Numerical experiments with CIFAR-10 data set show that the base learners are diverse and that both the training and generalization error are reduced after several iterations.
Application of the QDST algorithm for the Schrödinger particle simulation in the infinite potential well

Authors:
raw_author_name: Marcin Ostrowski
raw_affiliation_string: Institute of Information Technology FTIMS, Technical University of Łódź, Wólczańska 215, Łódź, 90-924, Poland

Abstract: Abstract This paper examines whether a quantum computer can efficiently simulate the time evolution of the Schrödinger particle in a one-dimensional infinite potential well. In order to solve the Schrödinger equation in the quantum register, an algorithm based on the Quantum Discrete Sine Transform (QDST) is applied. The paper compares the results obtained in this way with the results given by the previous method (based on the QFT algorithm).
Genealogical Data-Driven Visits of Historical Cemeteries

Authors:
raw_author_name: Angelica Lo Duca
raw_affiliation_string: Institute of Informatics and Telematics, National Research Council, 56124 Pisa, Italy
raw_author_name: Matteo Abrate
raw_affiliation_string: Institute of Informatics and Telematics, National Research Council, 56124 Pisa, Italy
raw_author_name: Andrea Marchetti
raw_affiliation_string: Institute of Informatics and Telematics, National Research Council, 56124 Pisa, Italy

Abstract: This paper describes the Integration of Archives and Cultural Places (IaCuP) project, which aims to integrate information about a historical cemetery, including its map and grave inventory, with genealogical and documentary knowledge extracted from relevant historical archives. The integrated data are accessible to cemetery visitors through an interactive mobile application, enabling them to navigate a graphical representation of the cemetery while exploring comprehensive visualizations of genealogical data. The basic idea stems from the desire to provide people with access to the rich context of cultural sites, which have often lost their original references over the centuries, making it challenging for individuals today to interpret the meanings embedded within them. The proposed approach leverages large language models (LLMs) to extract information from relevant documents and Web technologies to represent such information as interactive visualizations. As a practical case study, this paper focuses on the Jewish Cemetery in Pisa and the Historical Archives of the Jewish Community in Pisa, working on the genealogical tree of one of the most representative families resting in the cemetery.