Reviewerly/exHarmony huggingface dataset:
Exploration of Dynamic Stratification and Smart Information Pushing Functions of General English Information Platform of Vocational Colleges in The Internet Era

With the rapid development of information technology, the Internet's role in education has become increasingly prominent. "Teaching students according to their aptitude" is an eternal principle in education. With the support of the Internet, the concept of "teaching students according to their aptitude" in public English information teaching in higher vocational colleges will be more realized and optimized. Based on the analysis of the single learning test method, the lack of personalized customization function, and the inability to carry out individualized teaching on the public English information teaching platform of higher vocational colleges in the current Internet era, this paper puts forward a new idea of creating hierarchical learning test function in combination with information teaching practice, so as to optimize the hierarchical learning test function of the English information teaching platform of higher vocational colleges. To provide students with a higher quality teaching experience.
Human Rights Due Diligence Law for Gender Equality in Multinational Enterprises

Women's equal employment rights are one of the key research topics. The researchers found that only relying on human rights norms could not effectively regulate women's unequal employment rights problems. In addition, Multinational Enterprises (MNEs) and their host and home countries are responsible for women's unequal employment. Mandatory human rights due diligence (HRDD) laws can effectively regulate this gender inequality. However, the law on mandatory HRDD is still in its infancy and needs to be improved. The present study employs a literature review and empirical research methods to examine the factors hindering the guarantee of women's equal employment. Additionally, this paper scrutinizes the justifications for introducing mandatory HRDD laws. Meanwhile, the study analyses the advantages of mandatory HRDD laws and evaluates the current effectiveness, scope of application, and implementation. It is argued that the present situation needs to be revised to safeguard women's equal employment rights better. Finally, the paper offers corresponding suggestions to enhance the mandatory HRDD laws, and the research purpose of this paper is to promote women's employment equality and human rights protection.
Evaluating Accelerators for a High-Throughput Hash-Based Security Protocol

Security threats are rising due to widely available computational power and near-future quantum computers. New cryptographic protocols have been developed to address these challenges, but very few protocols take advantage of parallel computing. In this paper, we propose optimizations to the cryptography protocol Response-Based Cryptography (RBC). Since the protocol is general-purpose, it can be incorporated into post-quantum cryptography systems to authenticate users in resource-constrained environments, like Internet of Thing (IoT) devices. The optimizations proposed in this paper allow for clients to be authenticated faster. Additionally, this paper makes a cross-platform comparison of the performance of the optimized RBC protocol on the Graphics Processing Unit (GPU), the Central Processing Unit (CPU), and the Associative Processing Unit (APU). We find that the GPU and APU yield similar performance but the APU can be much more energy efficient. Furthermore, we evaluate the multi-GPU scalability of the algorithm and achieve a minimum speedup of 2.66 × on 3 × A100 GPUs.
A low carbon management model for regional energy economies based on blockchain technology

As the issue of sustainable energy development becomes more and more important in national economic construction, the potential dangers of climate change are gradually attracting widespread attention from countries around the world. In order to better carry out the low-carbon management of the regional energy economy, based on the analysis of the characteristics of blockchain technology, the present study utilized this technology to achieve intelligent and digital management of carbon emissions, and established a carbon emission prediction system. The cuckoo algorithm is used to improve the long-term memory network, and the improved algorithm is used in carbon emission prediction and management. The experimental results show that the improved Long Short Term Memory networks are close to the target precision in 240 iterations, and the convergence speed is fast. In the short-term regional carbon emission prediction, the average absolute error of the method is only 2%, which is highly consistent with the actual carbon emission. In the long-term carbon emission prediction, the average prediction accuracy of the upgraded long-term short-term memory networks can reach 97.26%, and the running time is only 19.46s. With high precision and running efficiency, the upgraded Long Short Term Memory networks can efficiently monitor regional carbon emission and provide a technical reference for the low-carbon management of the regional power industry.
Information structure in the speech of individuals with schizophrenia

This paper focuses on information structure in the speech of individuals with schizophrenia, according to the theoretical framework found in the Language into Act Theory. A specific textual structure, called stanza, and its informational complexity, have been compared using two corpora: the C-ORAL-ESQ for pathological speech, and the C-ORAL-BRASIL for non-pathological speech. Firstly, we briefly explain the theory and the concept of stanza. Then, we explain in depth the methodology adopted for comparing pathological and non-pathological speech, using the two corpora. Additionally, we discuss the results found, part of which have statistical significance. Finally, we propose a first explanation of the results, focusing on cognitive and prosodic aspects that could provide insights for further research.
Discrimination of Insurance Fraud Based on Machine Learning

In the insurance industry, Insurance fraud is a common phenomenon. However, according to statistics, among all types of insurance, automobile insurance fraud is the high incidence time of Insurance fraud. Based on 39 characteristic variables in the insurance claims database, this paper completes data preprocessing through normalization and coding of Categorical variable; Then it analyzes the correlation between data characteristics and Insurance fraud; Then, the principal component analysis method is used to reduce dimensionality and extract features from multi-dimensional features; Finally, the SVM classifier is trained to effectively identify Insurance fraud. The research results show that the model is effective in identifying Insurance fraud, and can achieve 79% accurate discrimination.
Monocular free-head gaze tracking method for driving electric sickbed

Abstract Building a free-head gaze tracking model with high accuracy, simple equipment, and not limited to wearing glasses is a challenge. In this paper, a monocular free-head gaze-tracking method based on machine learning are proposed. Two lightweight, high-precision and real-time gaze-tracking models are constructed, which can measure the 2D gaze point and 3D gaze direction respectively. In addition, we combined our gaze-tracking technology with electric sickbed to create an eye-gaze control based electric sickbed system that allows the patient to control the sickbed with their eyes. The experimental results show that the measurement errors of the two models on the MPIIGaze dataset are 4.84 cm and 4.8 ∘ respectively. After commissioning, user feedback has shown that this eye-gaze controlled electric sickbed system can enhance the lives of patients.
Support of Sparse Tensor Computing for MLIR HLS

Nowadays, sparse tensor computations are widely used in machine learning. Without the multiplications in zero values, sparse tensor computation can significantly reduce the latency and power consumption. Famous frameworks like TensorFlow, PyTorch, Pandas, etc., already have the support of sparse tensor computing. MLIR also integrated this idea and implemented the compilation flow. Integrating sparse tensor computing and MLIR into High-level Synthesis (HLS) can generate more powerful RTL and further implement specified hardware. However, MLIR flow isn’t well done now while translating into LLVM IR and does not fully support HLS tools, which are not supporting MLIR. In this paper, we propose a flow in MLIR to lower sparse tensor computations into HLS-readable LLVM IR, which can then be synthesized into RTL. To demonstrate the effectiveness of our proposed flow, we devise experiments by implementing matrix multiplication operations in the convolution layers, and the data is pruned to maximize the sparsity. Our proposed flow speeds up the latency by about 3.6 times, as demonstrated in our experiment with Xilinx Vitis HLS.
Pectoral muscle removal in mammogram images: A novel approach for improved accuracy and efficiency

Accurate pectoral muscle removal is critical in mammographic breast density estimation and many other computer-aided algorithms. We propose a novel approach to remove pectoral muscles form mediolateral oblique (MLO) view mammograms and compare accuracy and computational efficiency with existing method (Libra).A pectoral muscle identification pipeline was developed. The image is first binarized to enhance contrast and then the Canny algorithm was applied for edge detection. Robust interpolation is used to smooth out the pectoral muscle region. Accuracy and computational speed of pectoral muscle identification was assessed using 951 women (1,902 MLO mammograms) from the Joanne Knight Breast Health Cohort at Washington University School of Medicine.Our proposed algorithm exhibits lower mean error of 12.22% in comparison to Libra's estimated error of 20.44%. This 40% gain in accuracy was statistically significant (p < 0.001). The computational time for the proposed algorithm is 5.4 times faster when compared to Libra (5.1 s for proposed vs. 27.7 s for Libra per mammogram).We present a novel approach for pectoral muscle removal in mammogram images that demonstrates significant improvement in accuracy and efficiency compared to existing method. Our findings have important implications for the development of computer-aided systems and other automated tools in this field.
Who really wrote Martin v. Hunter's Lessee?

This article applies multiple machine learning methods to strongly suggest that Chief Justice John Marshall behaved unethically in an important 19th Century case of American constitutional law: Martin v. Hunter's Lessee. He wrote large swathes of the opinion even though he had a large financial stake in the outcome of the case and even though he had formally recused himself. We establish this through multiple machine learning methods, including a character-sequence-to-image method known as the frequency chaos game representation (FCGR), sentence embeddings using BERT, and the reknowned Delta method involving bags of words. All these methods of stylometry reach the same conclusions.
On Normative Arrows and Comparing Tax Automation Systems

Automation of legal norms can only exist because of compromises made in translating laws in natural languages to code (executables) in formal languages. Tax and benefits law is canonically considered one of the least compromising domains of law in terms of this nuance. In this paper, I compare two domain-specific languages used in the automation of tax and benefits law: Catala, which is proposed for use by the French Tax Authority, and Regelspraak, which the Dutch Tax Authority uses. The comparison is based on a top-down modeling approach, which does not try to go all the way down to the technical differences but aims to identify the points at which the two approaches diverge in the way the norm is shaped. It becomes evident that a lot of decisions that affect normativity are made inside the technical components of these systems. Safeguarding legal protection at the creation of automation of norms will likely require cross-domain efforts.
Study of Differences in Educational Qualifications of Elementary and Secondary School Teachers

By observing the academic qualifications of primary and secondary school teachers in recent years, we can find that more and more graduate students have joined the primary and secondary school teachers, and now some school recruitment requirements have changed to graduate students, and high school, middle school, and elementary school teachers all follow the same trend of educational upgrading. With the entry of highly educated teachers into primary and secondary schools, primary and secondary school teachers also face many challenges that require continuous improvement to truly improve the professional level of primary and secondary school teachers, thus promoting the quality of primary and secondary school education.
The Feasibility Study of “Manipulation” and “Rewriting” theory on Guiding English Translation of Petroleum Science and Technology

In the existing literature, there are many translation theories to guide the translation practice of petroleum science and technology English, such as communicative translation theory, text type theory, interpretive theory and so on. But a few people regard André Lefevere’s “manipulation” and “rewriting” theory as the guiding theory of English translation of petroleum science and technology. Therefore, this paper aims to introduce the content of this theory and compare the advantages and disadvantages of the theory with other translation theories in petroleum text to analyze the feasibility of the theory as a guiding theory in science and technology translation.
Towards Wait-and-Catch Routine of a Dynamic Swinging Object Using a Prototype Robotic Arm Manipulator

A robotic ball-catcher test demonstration built from a unique prototype manipulator is presented. Other than in previous work, the gripper catches a dynamic object moving within the workspace of the robot. The challenge is to be able to use a slow vision system yielding deficient measurements. We present theory for deriving the explicit (analytical) solution of the forward and inverse kinematics for the 5 degrees-of-freedom robotic manipulator. A dynamical model of the swinging object is used to predict its future trajectory, including the intercept point, algorithm tested in simulation and partly on the real experimental setup.
Deconstructing Design Ethnography through Critical Thinking: A Comprehensive Analysis

Design ethnography possesses a unique ability to uncover both overt and covert user needs and requirements. Its prowess in identifying latent problems and cultivating a deeper understanding of these challenges makes it an essential tool for informing initial design decisions and subsequent choices. The primary objective of this article is to delve into the core principles of design innovation, illuminate the intrinsic value of design ethnography, dissect its key methodologies, evaluate its merits and demerits, and address the ethical and practical complexities it entails.
The relationship between AI stimuli and customer stickiness, and the roles of social presence and customer traits

Purpose This study investigates the influence of artificial intelligence (AI) stimuli on customer stickiness (CS), the mediation effects of social presence (SP) and the moderating impacts of customer traits in this influencing process. Design/methodology/approach Drawing on the arousal theory and social response theory, a conceptual model was established and tested by a data set of 268 customers in the catering industry. Findings The results indicate that AI stimuli, such as perceived personalization and perceived interactivity, positively affect CS. SP partially mediates the influence of AI stimuli on CS. Customer traits such as customers' need for interaction (NFI) and novelty seeking (NS) actively moderate the mediating effects of SP. Originality/value This study advances the interactive marketing literature from three aspects. Firstly, instead of focusing on the functional aspects of AI stimuli, it extends our understanding of AI-enabled interactive marketing by examining the effects of social and emotional aspects of AI stimuli on customer response. Secondly, it extends our understanding of social response by illuminating the mediating effects of SP between AI stimuli and CS. Finally, it provides new insights and empirical evidence for the research focus on customer traits in AI-enabled interactive marketing.
EQUALS

Legal Question Answering (LQA) is a promising artificial intelligence application with high practical value. A professional and effective legal question answering (QA) agent can assist in reducing the workload of lawyers and judges, and help to achieve judicial accessibility. However, the NLP community lacks a large-scale LQA dataset with high quality, making it difficult to develop practical data-driven LQA agents. To tackle this bottleneck, this work presents EQUALS, a well-annotated real-world dataset for lEgal QUestion Answering via reading Chinese LawS. EQUALS contains 6,914 {question, article, answer} triplets as well as a pool of articles of laws that covers 10 different collections of Chinese Laws. Questions and the corresponding answers in EQUALS are collected from a professional law consultation forum. More importantly, the exact spans of law articles are annotated by senior law students as the answers. In this way, we could assure the quality and professionalism of EQUALS. Furthermore, this work proposes a QA framework that encompasses a law article retrieval module and a machine reading comprehension module for extracting accurate answers from the law article. We conduct thorough experiments with representative baselines on EQUALS, and the results indicate that EQUALS is a challenging question answering task. To the best of our knowledge, EQUALS is the largest real-world LQA dataset which shall significantly promote the research of LQA tasks. The work has been open-sourced and is available at: https://github.com/andongBlue/EQUALS.
Detecting Water in Visual Image Streams from UAV with Flight Constraints

Unmanned Ariel Vehicles (UAVs) require identifying water surfaces during flight maneuvers, mainly for safety in execution and its applications. We introduce two novel techniques to identify water surfaces from front-facing and downward-facing cameras mounted on a UAV. The first method — UNet-RAU, a unique architecture based on UNet and Reflection Attention Units, segments water pixels from front-facing camera views, utilizing the reflection property of water surfaces. On the On-Road and Off-Road datasets of Puddle-1000, UNet-RAU improved its performance by 2% over the state-of-the-art FCN-RAU. Additionally, the UNet-RAU generated an F1-score of 80.97% on our Drone-Water-Front dataset. The second method — Dense Optical Flow based Water Detection (DOF-WD), detects water surfaces in videos of downward-facing cameras. This method utilizes downwash-generated ripples and natural texture features on a water surface to identify water in low and high altitudes, respectively. We empirically validated the performance of the DOF-WD method using our Drone-Water-Down dataset.
Research on Intelligent Safety Supervision System of Chemical Industry Park Based on Neural Network

With the continuous development of society, video surveillance technology is widely used in many fields such as urban security, transportation, education safety training, and intelligent furniture. However, the traditional and backward monitoring methods can no longer meet the needs of modern urban development. In order to improve the monitoring ability of video and increase the speed of face recognition, it is particularly important to establish a smart security monitoring system based on artificial intelligence in this paper. This article uses the YOLO algorithm as the basis for neural network model building, training and prediction. A smart security monitoring system based on the chemical industry park has been designed. Through intelligent monitoring, it can effectively identify faces and analyze people's unsafe factors and behaviors to ensure the safety of people's lives and property.
The Library's Changing Role in Supporting Music Research: Part of the special collection on MusicLab Copenhagen.

The widespread use of digital tools to access scientific literature and other relevant information combined with a shift towards Open Research is changing how research is conducted and academic libraries are adapting their research support services to respond to this change. In this paper, we present the case of MusicLab, and especially MusicLab Copenhagen – Absorption with The Danish String Quartet, which served as a mechanism for the University of Oslo Library to test how it can support researchers in developing Open Research practices. MusicLab is a collaboration between the University of Oslo Library and the University of Oslo's Centre for Interdisciplinary Studies in Rhythm, Time and Motion. Through this case, we outline the role of the research library in supporting Open Research including the different legal and data management issues occurring when conducting open research on intellectual works and people. The insights from this paper will be useful for other academic libraries looking to expand their research support services and for researchers looking to further develop their Open Research practices.
Towards DAOs of Difference

With this article, I explore the connections between blockchain technology, coloniality, and decolonial practices. Drawing on Sylvia Wynter’s thought on the interdependent systems of colonialism, capitalism, and knowledge, as well as more recent work on the coloniality of digital technologies, I argue that blockchain-based systems reproduce certain dynamics at work in historical colonialism. Additionally, Wynter’s decolonial propositions provide a generative framework to understand countercultural practices with. Inspired by Wynter, Patricia de Vries explores the notion of “plot work as artistic praxis” to ask how artistic work, implicated as it is in capitalist logics, can create space for relating differently in the context of the exploitations of those dominant logics. I apply this notion to examine how Decentralised Autonomous Organisations (DAOs) in the countercultural blockchain space might contribute to this praxis.
A General Object Size Attention Mechanism

In recent years, a variety of very effective attention mechanisms have emerged in object detection technology, greatly improving the detection performance of network models. This paper proposes a new attention mechanism, which is applicable to various network models and datasets and can improve the detection accuracy of neural network models in general. Experimental observations show that neural networks are generally able to accurately identify objects of large size, but it is difficult to identify small objects accurately.This paper proposes a series of function models. By changing the proportion of different size objects in the loss function, the neural network model can focus more on smaller size objects. The attention mechanism proposed in this paper makes the network more sensitive to small objects in the image. In this paper, different backbone network models are selected, trained and tested on the open data set, and a large number of comparative experiments are done. The experimental results show that the object size attention mechanism generally improves the average detection accuracy of network model detection and recognition.