Reviewerly/exHarmony huggingface dataset:
A risk-oriented ontological model for supporting management decision-making in a cargo port based on multi-level goal setting

Authors:
raw_author_name: Anastasia Olegovna Belova
raw_affiliation_string: Astrakhan State Technical  University

Abstract: In today's unstable market environment, transport logistics enterprises face the task of predicting possible risk situations in order to avoid them, as well as developing tools that allow them to form management decisions, evaluate the effectiveness of each of them and choose the most appropriate one, taking into account specific tasks and various influencing factors. Situations of failure to achieve the goals of a cargo port, as well as cases of failure by indicators to assess these goals of normative values, are considered as risks. The goals and indicators are outlined and described in the previously built Balanced Scorecard (MTSP). A set of links between goals and indicators of their assessment with risks at various levels of management is presented in the form of a developed multi-level hybrid logical-probabilistic (LV) risk model of a cargo port, as well as in detailed scenario models of this LV model. The integrated use of SSP and LV modeling technologies allowed us to build a general concept of multi-level goal setting, the main advantage of which lies in the detailed elaboration of the company's goals subordinate to the main strategic one, which allows, if necessary, to influence operational events and obtain a positive result in the longer term, in tactical and strategic plans. The ontological model contains all the information about the interaction of elements that affect the occurrence of risky events, and allows you to select options for getting out of a risky situation in accordance with various specified conditions. The results of queries to the risk-oriented ontological model are variants of management decisions aimed at reducing risks in the cargo port.
Blockchain Enabled Smart Contracts for Digital Assets

Authors:
raw_author_name: None Sahana V
raw_affiliation_string: Global Academy of Technology, Bangalore, India

Abstract: The diverse applications and benefits of blockchain technology and smart contracts across various sectors are explored in this overview. Decentralized Finance (DeFi) projects, supply chain management, tokenization of assets, NFT platforms, and identity verification stand out as prominent examples. Smart contracts are lauded for their cost efficiency and security, underpinned by blockchain's cryptographic features that fortify system integrity. Blockchain's global accessibility facilitates cross-border transactions, while decentralized exchanges (DEX) mitigate censorship and counterparty risks, empowering users with asset control. Moreover, blockchain expands investment opportunities through asset tokenization, encompassing diverse classes such as real estate and intellectual property, bolstering security and transparency through immutable ownership records. Oracles play a vital role in integrating real-time data into DeFi ecosystems, informing decision-making by linking off-chain data with blockchain networks.Diverse token standards like ERC-20 and ERC-721 are pivotal, catering to fungible and non-fungible token ecosystems respectively. Lastly, the advantages of proof of stake (PoS) over proof of work (PoW) are highlighted, emphasizing PoS's energy efficiency, transaction throughput, and scalability
Comparing Remote and Proximal Sensing of Agrometeorological Parameters across Different Agricultural Regions in Croatia: A Case Study Using ERA5-Land, Agri4Cast, and In Situ Stations during the Period 2019–2021

Authors:
raw_author_name: Ivana Podnar Žarko
raw_affiliation_string: Internet of Things Laboratory, Faculty of Electrical Engineering and Computing, University of Zagreb, HR-10000 Zagreb, Croatia

Abstract: The paper evaluates the usability of remote satellite-based and proximal ground-based agrometeorological data sources for precision agriculture and crop production in Croatia. The compared agrometeorological datasets stem from the open-access data sources Copernicus CDS and the Agri4Cast portal, and commercial in situ agrometeorological stations (PinovaMeteo) which monitor environmental parameters relevant to the physiological state of crops. The study compares relevant parameters for 10 different locations in Croatia for three consecutive years (2019, 2020, and 2021) to investigate whether model-based data from ERA5-Land and Agri4Cast are well-correlated with ground measurements from independent in situ stations (PinovaMeteo) for specific agrometeorological parameters (air and soil temperature, and precipitation). Our results indicate the following: both the ERA5-Land and Agri4Cast datasets show mostly strong positive correlations with ground observations for air temperature, modest correlations for soil temperature, but modest or even low correlations for precipitation. Analysis of the residuals indicates higher overall residual values, especially in areas with complex topography and near large bodies of water or the sea, and deviations of residuals that may limit the usability of satellite- and model-based data for decision-making in agriculture.
Applicability of Neural Combinatorial Optimization: A Critical View

Authors:
raw_author_name: Andoni I. Garmendia
raw_affiliation_string: University of the Basque Country UPV/EHU, Donostia-San Sebastian, Spain
raw_author_name: Josu Ceberio
raw_affiliation_string: University of the Basque Country UPV/EHU, Donostia-San Sebastian, Spain
raw_author_name: Alexander Mendiburu
raw_affiliation_string: University of the Basque Country UPV/EHU, Donostia-San Sebastian, Spain

Abstract: Neural Combinatorial Optimization has emerged as a new paradigm in the optimization area. It attempts to solve optimization problems by means of neural networks and reinforcement learning. In the last few years, due to their novelty and presumably good performance, many research papers have been published introducing new neural architectures for a variety of combinatorial problems. However, the incorporation of such models in the conventional optimization portfolio raises many questions related to their performance compared to other existing methods, such as exact algorithms, heuristics or metaheuristics. This paper aims to present a critical view of these new proposals, discussing their benefits and drawbacks with respect to the tools and algorithms already present in the optimization field. For this purpose, a comprehensive study is carried out to analyse the fundamental aspects of such methods, including performance, computational cost, transferability and reusability of the trained model. Moreover, this discussion is accompanied by the design and validation of a new neural combinatorial optimization algorithm on two well-known combinatorial problems, the Linear Ordering Problem and the Permutation Flowshop Scheduling Problem. Finally, new directions for future work in the area of Neural Combinatorial Optimization algorithms are suggested.
A Security Information Transmission Method Based on DHR for Seafloor Observation Network

Authors:
raw_author_name: Fei Ying
raw_affiliation_string: College of Electronic and Information Engineering, Tongji University, Shanghai 201804, China; Key Laboratory of Embedded System and Service Computing, Ministry of Education, Shanghai 201804, China
raw_author_name: Shengjie Zhao
raw_affiliation_string: College of Electronic and Information Engineering, Tongji University, Shanghai 201804, China; Engineering Research Center of Key Software Technologies for Smart City Perception and Planning, Ministry of Education, Shanghai 200003, China; Key Laboratory of Embedded System and Service Computing, Ministry of Education, Shanghai 201804, China; School of Software Engineering, Tongji University, Shanghai 201804, China
raw_author_name: Jia Wang
raw_affiliation_string: School of Advanced Technology, Xi’an Jiaotong-Liverpool University, Suzhou 215123, China

Abstract: A seafloor observation network (SON) consists of a large number of heterogeneous devices that monitor the deep sea and communicate with onshore data centers. Due to the long-distance information transmission and the risk of malicious attacks, ensuring the integrity of data in transit is essential. A cryptographically secure frame check sequence (FCS) has shown great advantages in protecting data integrity. However, the commonly used FCS has a collision possibility, which poses a security risk; furthermore, reducing the encryption calculation cost is a challenge. In this paper, we propose a secure, lightweight encryption scheme for transmitted data inspired by mimic defense from dynamic heterogeneous redundancy theory. Specifically, we use dynamic keys to encrypt a data block and generate multiple encrypted heterogeneous blocks for transmission. These continuously changing encrypted data blocks increase the confusion regarding the original encoded data, making it challenging for attackers to interpret and modify the data blocks. Additionally, the redundant information from the multiple blocks can identify and recover tampered data. Our proposed scheme is suitable for resource-constrained environments where lightweight encryption is crucial. Through experimental demonstrations and analysis methods, we determine the effectiveness of our encryption scheme in reducing computational costs and improving security performance to protect data integrity.
Empowering Product Teams to Advance Inclusive Language and Mitigate Hateful Speech on Social Media Products

Authors:
raw_author_name: Genevieve Smith
raw_affiliation_string: University of California Berkeley, USA
raw_author_name: Ishita Rustagi
raw_affiliation_string: University of California Berkeley, USA

Abstract: As social media use proliferates, so does hateful language on such platforms among users. How might product teams be empowered to tackle hateful speech and promote inclusive language proactively among users? We developed an intervention that targets the organizational culture of tech companies and their product teams as a driver for change. More specifically, the intervention aimed to advance inclusive language practices within product teams, while introducing product development frameworks for advancing inclusive language and mitigating hateful language among social media product users. For this study, we partnered with a large U.S.-headquartered tech firm that owns several social media products to develop and pilot the intervention. The intervention was implemented with 238 participants—representing employees of the firm across several global locations, with particular representation from the United States—in seven workshops. Forty participants were tracked semilongitudinally across three surveys—prior to the intervention, immediately after, and 4 months after. Two participants participated in in-depth interviews 6 months after the intervention. Survey data were submitted to ordinal regression models that examined difference in workshop attendees’ confidence levels across the three time periods. Findings reveal positive impacts over time among product teams in regard to enhanced agency in advancing inclusive language and mitigating hateful speech among users. More specifically, confidence levels to guide one’s team in preventing or mitigating harmful language among product users significantly increased by 13.6% ( p &lt; .0001) from before the intervention to both postworkshop surveys. Confidence levels to guide one’s team in enhancing inclusive language among users significantly increased by 18.1% ( p &lt; .0001) pre- to postworkshop. Positive correlations are present between the measures. Lastly, qualitative responses in the surveys and interviews express appreciation of learning gained in the workshops, but also the need to continue such interventions as an effort to maintain critical understandings of inclusive language practices.
Quantum Measurement Classification using Statistical Learning

Authors:
raw_author_name: Zachery Utt
raw_affiliation_string: University of Florida, Gainesville, USA
raw_author_name: Daniel Volya
raw_affiliation_string: University of Florida, Gainesville, USA
raw_author_name: Prabhat Mishra
raw_affiliation_string: University of Florida, Gainesville, USA

Abstract: Interpreting the results of a quantum computer can pose a significant challenge due to inherent noise in these mesoscopic quantum systems. Quantum measurement, a critical component of quantum computing, involves determining the probabilities linked with qubit states post-multiple circuit computations based on quantum readout values provided by hardware. While there are promising classification-based solutions, they can either misclassify or necessitate excessive measurements, thereby proving to be costly. This paper puts forth an efficient method to discern the quantum state by analyzing the probability distributions of data post-measurement. Specifically, we employ cumulative distribution functions to juxtapose the measured distribution of a sample against the distributions of basis states. The efficacy of our approach is demonstrated through experimental results on a superconducting transmon qubit architecture, which show a substantial decrease (88%) in single qubit readout error compared to state of the art measurement techniques. Moreover, we report additional error reduction (12%) compared to state-of-the-art measurement techniques when our technique is applied to enhance existing multi-qubit classification techniques. We also demonstrate the applicability of our proposed method for higher dimensional quantum systems, including classification of single qutrits as well as multiple qutrits.
The Influence of Affect in Help-Seeking Behaviors and Performance in a Math Intelligent Tutoring System

Authors:
raw_author_name: Matthew L. Bernacki

Abstract: This study investigates the association between help-seeking behaviors (hints, hints per step, hints with steps requests, and hint to error), affect (boredom, confusion, frustration, happiness, and engagement), and performance in seventh and eighth-grade students using the Cognitive Tutor Bridge to Algebra as a self-regulated learning environment. Analyses focused on correlations between students’ help-seeking behaviors and their affect in units 4 and 14. Affect was also used to predict help-seeking behaviors in the next units (e.g., 5 and 15). Moreover, we examined how associations between help-seeking behaviors and performance differed as a function of affect. The results showed that a pattern emerged in which students showed more executive help-seeking behaviors rather than instrumental ones. Students feeling bored, confused, and frustrated tended to use more hints, and they were less likely to switch to external help-seeking sources. Also, those feeling happy or engaged were less likely to use hints.
<scp>TLPGNN</scp> : A Lightweight Two-Level Parallelism Paradigm for Graph Neural Network Computation on Single and Multiple GPUs

Authors:
raw_author_name: Qiang Fu
raw_affiliation_string: Advanced Micro Devices, Austin, USA
raw_author_name: Yuede Ji
raw_affiliation_string: University of North Texas, Denton, USA
raw_author_name: H. Howie Huang
raw_affiliation_string: George Washington University, Washington, USA

Abstract: Graph Neural Networks (GNNs) are an emerging class of deep learning models specifically designed for graph-structured data. They have been effectively employed in a variety of real-world applications, including recommendation systems, drug development, and analysis of social networks. The GNN computation includes regular neural network operations and general graph convolution operations, which take most of the total computation time. Though several recent works have been proposed to accelerate the computation for GNNs, they face the limitations of heavy pre-processing, low efficiency atomic operations, and unnecessary kernel launches. In this paper, we design TLPGNN , a lightweight two-level parallelism paradigm for GNN computation. First, we conduct a systematic analysis of the hardware resource usage of GNN workloads to understand the characteristics of GNN workloads deeply. With the insightful observations, we then divide the GNN computation into two levels, i.e., vertex parallelism for the first level and feature parallelism for the second. Next, we employ a novel hybrid dynamic workload assignment to address the imbalanced workload distribution. Furthermore, we fuse the kernels to reduce the number of kernel launches and cache the frequently accessed data into registers to avoid unnecessary memory traffic. To scale TLPGNN to multi-GPU environments, we propose an edge-aware row-wise 1-D partition method to ensure a balanced workload distribution across different GPU devices. Experimental results on various benchmark datasets demonstrate the superiority of our approach, achieving substantial performance improvement over state-of-the-art GNN computation systems, including Deep Graph Library (DGL), GNNAdvisor, and FeatGraph, with speedups of 6.1 ×, 7.7 ×, and 3.0 ×, respectively, on average. Evaluations of multiple-GPU TLPGNN also demonstrate that our solution achieves both linear scalability and a well-balanced workload distribution.
Named Entity Recognition in Government Audit Texts Based on ChineseBERT and Character-Word Fusion

Authors:
raw_author_name: Baohua Huang
raw_affiliation_string: School of Computer, Electronics and Information, Guangxi University, Nanning 530004, China
raw_author_name: Yunjie Lin
raw_affiliation_string: School of Computer, Electronics and Information, Guangxi University, Nanning 530004, China
raw_author_name: Si Pang
raw_affiliation_string: School of Computer, Electronics and Information, Guangxi University, Nanning 530004, China
raw_author_name: Long Fu
raw_affiliation_string: School of Computer, Electronics and Information, Guangxi University, Nanning 530004, China

Abstract: Named entity recognition of government audit text is a key task of intelligent auditing. Aiming at the problems of scarcity of corpus in the field of governmental auditing, insufficient utilization of traditional character vector word-level information features, and insufficient capturing of auditing entity features, this study builds its own dataset in the field of auditing and proposes the model CW-CBGC for recognizing named entities in governmental auditing text based on ChineseBERT and character-word fusion. First, the ChineseBERT pre-training model is used to extract the character vector that integrates the features of glyph and pinyin, combining with word vectors dynamically constructed by the BERT pre-training model; then, the sequences of character-word fusion vectors are input into the bi-directional gated recurrent neural network (BiGRU) to learn the textual features. Finally, the global optimal sequence label is generated by Conditional Random Field (CRF), and the GHM classification loss function is used in the model training to solve the problem of error evaluation under the conditions of noisy entities and unbalanced number of entities. The F1 value of this study’s model on the audit dataset is 97.23%, which is 3.64% higher than the baseline model’s F1 value; the F1 value of the model on the public dataset Resume is 96.26%, which is 0.73–2.78% higher than the mainstream model. The experimental results show that the model proposed in this paper can effectively recognize the entities in government audit texts and has certain generalization ability.
Improving Breast Tumor Multi-Classification from High-Resolution Histological Images with the Integration of Feature Space Data Augmentation

Authors:
raw_author_name: Nadia Brancati
raw_affiliation_string: Institute for High Performance Computing and Networking, National Research Council of Italy (ICAR-CNR), Via P. Castellino 111, 80131 Naples, Italy
raw_author_name: Maria Frucci
raw_affiliation_string: Institute for High Performance Computing and Networking, National Research Council of Italy (ICAR-CNR), Via P. Castellino 111, 80131 Naples, Italy

Abstract: To support pathologists in breast tumor diagnosis, deep learning plays a crucial role in the development of histological whole slide image (WSI) classification methods. However, automatic classification is challenging due to the high-resolution data and the scarcity of representative training data. To tackle these limitations, we propose a deep learning-based breast tumor gigapixel histological image multi-classifier integrated with a high-resolution data augmentation model to process the entire slide by exploring its local and global information and generating its different synthetic versions. The key idea is to perform the classification and augmentation in feature latent space, reducing the computational cost while preserving the class label of the input. We adopt a deep learning-based multi-classification method and evaluate the contribution given by a conditional generative adversarial network-based data augmentation model on the classifier’s performance for three tumor classes in the BRIGHT Challenge dataset. The proposed method has allowed us to achieve an average F1 equal to 69.5, considering only the WSI dataset of the Challenge. The results are comparable to those obtained by the Challenge winning method (71.6), also trained on the annotated tumor region dataset of the Challenge.
Portrait Semantic Segmentation Method Based on Dual Modal Information Complementarity

Authors:
raw_author_name: Guang Feng
raw_affiliation_string: College of Automation, Guangdong University of Technology, Guangzhou 511400, China
raw_author_name: Chong Tang
raw_affiliation_string: College of Computer, Guangdong University of Technology, Guangzhou 511400, China

Abstract: Semantic segmentation of human images is a research hotspot in the field of computer vision. At present, the semantic segmentation models based on U-net generally lack the ability to capture the spatial information of images. At the same time, semantic incompatibility exists because the feature maps of encoder and decoder are directly connected in the skip connection stage. In addition, in low light scenes such as at night, it is easy for false segmentation and segmentation accuracy to appear. To solve the above problems, a portrait semantic segmentation method based on dual-modal information complementarity is proposed. The encoder adopts a double branch structure, and uses a SK-ASSP module that can adaptively adjust the convolution weights of different receptor fields to extract features in RGB and gray image modes respectively, and carries out cross-modal information complementarity and feature fusion. A hybrid attention mechanism is used in the jump connection phase to capture both the channel and coordinate context information of the image. Experiments on human matting dataset show that the PA and MIoU coefficients of this algorithm model reach 96.58% and 94.48% respectively, which is better than U-net benchmark model and other mainstream semantic segmentation models.