Reviewerly/exHarmony huggingface dataset:
Embedding by Unembedding

Embedding is a language development technique that implements the object language as a library in a host language. There are many advantages of the approach, including being lightweight and the ability to inherit features of the host language. A notable example is the technique of HOAS, which makes crucial use of higher-order functions to represent abstract syntax trees with binders. Despite its popularity, HOAS has its limitations. We observe that HOAS struggles with semantic domains that cannot be naturally expressed as functions, particularly when open expressions are involved. Prominent examples of this include incremental computation and reversible/bidirectional languages. In this paper, we pin-point the challenge faced by HOAS as a mismatch between the semantic domain of host and object language functions, and propose a solution. The solution is based on the technique of unembedding , which converts from the finally-tagless representation to de Bruijn-indexed terms with strong correctness guarantees. We show that this approach is able to extend the applicability of HOAS while preserving its elegance. We provide a generic strategy for Embedding by Unembedding, and then demonstrate its effectiveness with two substantial case studies in the domains of incremental computation and bidirectional transformations. The resulting embedded implementations are comparable in features to the state-of-the-art language implementations in the respective areas.
Dependent Session Protocols in Separation Logic from First Principles (Functional Pearl)

We develop an account of dependent session protocols in concurrent separation logic for a functional language with message-passing. Inspired by minimalistic session calculi, we present a layered design: starting from mutable references, we build one-shot channels, session channels, and imperative channels. Whereas previous work on dependent session protocols in concurrent separation logic required advanced mechanisms such as recursive domain equations and higher-order ghost state, we only require the most basic mechanisms to verify that our one-shot channels satisfy one-shot protocols, and subsequently treat their specification as a black box on top of which we define dependent session protocols. This has a number of advantages in terms of simplicity, elegance, and flexibility: support for subprotocols and guarded recursion automatically transfers from the one-shot protocols to the dependent session protocols, and we easily obtain various forms of channel closing. Because the meta theory of our results is so simple, we are able to give all definitions as part of this paper, and mechanize all our results using the Iris framework in less than 1000 lines of Coq.
Handling Multi-Class Problem by Intuitionistic Fuzzy Twin Support Vector Machines Based on Relative Density Information

The intuitionistic fuzzy twin support vector machine (IFTSVM) merges the idea of the intuitionistic fuzzy set (IFS) with the twin support vector machine (TSVM), which can reduce the negative impact of noise and outliers. However, this technique is not suitable for multi-class and high-dimensional feature space problems. Furthermore, the computational complexity of IFTSVM is high because it uses the membership and non-membership functions to build a score function. We propose a new version of IFTSVM by using relative density information. This idea approximates the probability density distribution in multi-dimensional continuous space by computing the K-nearest-neighbor distance of each training sample. Then, we evaluate all the training points by a one-versus-one-versus-rest strategy to construct the k-class classification hyperplanes. A coordinate descent system is utilized to reduce the computational complexity of the training. The bootstrap technique with a 95 <inline-formula><tex-math notation="LaTeX">$\%$</tex-math></inline-formula> confidence interval and Friedman test are conducted to quantify the significance of the performance improvements observed in numerical evaluations. Experiments on 24 benchmark datasets demonstrate the proposed method produces promising results as compared with other support vector machine models reported in the literature.
A variationist corpus analysis of the definite article with personal names across three varieties of Spanish

It has been shown that the presence of a definite article (DA) prior to a personal name (PN) varies not only across languages, but also across varieties of the same language. Spanish is an example of a language that demonstrates DA+PN variation across varieties. However, few studies have quantitatively analyzed this DA+PN variation. The current study examined which linguistic and social factors govern the variation of DA+PN in the speech (sociolinguistic interviews) of 54 speakers from Santiago (Chile), Mexico City, and Granada (Spain) from the online PRESEEA Corpus. Statistical analyses found that the DA+PN realizations are most favored by speakers from Santiago across all educational levels. Speakers from Santiago also favor DA+PN realizations more with female names than male names, demonstrating a gendering of language. Speakers from Mexico City and Granada demonstrated few overall DA+PN realizations, with the least realizations among those with more educational attainment, possibly indicating the effect of a standard language ideology for these communities with regard to DA+PN. Thus, not only do these varieties differ in overall frequency in which DA+PN appears to be the norm in Santiago and much less common in Granada and Mexico City, they also differ in terms of which social factors govern DA+PN realizations in each variety.
Amenable Sparse Network Investigator

We present “Amenable Sparse Network Investigator" (ASNI) algorithm that utilizes a novel pruning strategy based on a sigmoid function that induces sparsity level globally over the course of one single round of training. The ASNI algorithm fulfills both tasks that current state-of-the-art strategies can only do one of them. The ASNI algorithm has two subalgorithms: 1) ASNI-I, 2) ASNI-II. ASNI-I learns an accurate sparse off-the-shelf network only in one single round of training. ASNI-II learns a sparse network and an initialization that is quantized, compressed, and from which the sparse network is trainable. The learned initialization is quantized since only two numbers are learned for initialization of nonzero parameters in each layer L. Thus, quantization levels for the initialization of the entire network is 2L. Also, the learned initialization is compressed because it is a set consisting of 2L numbers. The special sparse network that can be trained from such a quantized and compressed initialization is called amenable. For example, in order to initialize more than 25 million parameters of an amenable ResNet-50, only 2 $$\,\times \,$$ 54 numbers are needed. To the best of our knowledge, there is no other algorithm that can learn a quantized and compressed initialization from which the network is still trainable and is able to solve both pruning tasks. Our numerical experiments show that there is a quantized and compressed initialization from which the learned sparse network can be trained and reach to an accuracy on a par with the dense version. This is one step ahead towards learning an ideal network that is sparse and quantized in a very few levels of quantization. We experimentally show that these 2L levels of quantization are concentration points of parameters in each layer of the learned sparse network by ASNI-I. In other words, we show experimentally that for each layer of a deep neural network (DNN) there are two distinct normal-like distributions whose means can be used for initialization of an amenable network. To corroborate the above, we have performed a series of experiments utilizing networks such as ResNets, VGG-style, small convolutional, and fully connected ones on ImageNet, CIFAR10, and MNIST datasets.
SCL(FOL) Can Simulate Non-Redundant Superposition Clause Learning

Abstract We show that SCL(FOL) can simulate the derivation of non-redundant clauses by superposition for first-order logic without equality. Superposition-based reasoning is performed with respect to a fixed reduction ordering. The completeness proof of superposition relies on the grounding of the clause set. It builds a ground partial model according to the fixed ordering, where minimal false ground instances of clauses then trigger non-redundant superposition inferences. We define a respective strategy for the SCL calculus such that clauses learned by SCL and superposition inferences coincide. From this perspective the SCL calculus can be viewed as a generalization of the superposition calculus.
Sustainable Serverless Computing with Cold-start Optimization and Automatic Workflow Resource Scheduling

In recent years, serverless computing has garnered significant attention owing to its high scalability, pay-as-you-go billing model, and efficient resource management provided by cloud service providers. Optimal resource scheduling of serverless computing has become imperative to reduce energy consumption and enable sustainable computing. However, existing serverless platforms encounter two significant challenges: the cold-start problem of containers and the absence of an effective resource allocation strategy for serverless workflows. Existing pre-warm strategies are associated with high computational overhead, while current resource scheduling techniques inadequately account for the intricate structure of serverless workflows. To address these challenges, we present SSC, a pre-warming and automatic resource allocation framework designed explicitly for serverless workflows. We introduce an innovative gradient-based algorithm for pre-warming containers, significantly reducing cold start hit rates. Moreover, leveraging a critical path and priority queue-based algorithm, SSC enables efficient allocation of resources for serverless workflows. In our experimental evaluation, SSC reduces the cold start hit rate by nearly <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$50\%$</tex-math></inline-formula> and achieves substantial cost savings of approximately <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$30\%$</tex-math></inline-formula> .
Efficient Search for Superspecial Hyperelliptic Curves of Genus Four with Automorphism Group Containing $${\textbf{C}}_6$$

Abstract In arithmetic and algebraic geometry, superspecial (s.sp. for short) curves are one of the most important objects to be studied, with applications to cryptography and coding theory. If $$g \ge 4$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>g</mml:mi> <mml:mo>≥</mml:mo> <mml:mn>4</mml:mn> </mml:mrow> </mml:math> , it is not even known whether there exists such a curve of genus g in general characteristic $$p &gt; 0$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>p</mml:mi> <mml:mo>&gt;</mml:mo> <mml:mn>0</mml:mn> </mml:mrow> </mml:math> , and in the case of $$g=4$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>g</mml:mi> <mml:mo>=</mml:mo> <mml:mn>4</mml:mn> </mml:mrow> </mml:math> , several computational approaches to search for those curves have been proposed. In the genus-4 hyperelliptic case, Kudo-Harashita proposed a generic algorithm to enumerate all s.sp. curves, and recently Ohashi-Kudo-Harashita presented an algorithm specific to the case where automorphism group contains the Klein 4-group as a subgroup. In this paper, we propose an algorithm with complexity $${\tilde{O}}(p^4)$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mover> <mml:mi>O</mml:mi> <mml:mo>~</mml:mo> </mml:mover> <mml:mrow> <mml:mo>(</mml:mo> <mml:msup> <mml:mi>p</mml:mi> <mml:mn>4</mml:mn> </mml:msup> <mml:mo>)</mml:mo> </mml:mrow> </mml:mrow> </mml:math> in theory but $${\tilde{O}}(p^3)$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mover> <mml:mi>O</mml:mi> <mml:mo>~</mml:mo> </mml:mover> <mml:mrow> <mml:mo>(</mml:mo> <mml:msup> <mml:mi>p</mml:mi> <mml:mn>3</mml:mn> </mml:msup> <mml:mo>)</mml:mo> </mml:mrow> </mml:mrow> </mml:math> in practice to enumerate s.sp. hyperelliptic curves of genus 4 with automorphism group containing the cyclic group of order 6. By executing the algorithm over Magma, we enumerate those curves for p up to 1000. We also succeeded in finding a s.sp. hyperelliptic curve of genus 4 in every p with $$p \equiv 2 \pmod {3}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>p</mml:mi> <mml:mo>≡</mml:mo> <mml:mn>2</mml:mn> <mml:mspace /> <mml:mo>(</mml:mo> <mml:mo>mod</mml:mo> <mml:mspace /> <mml:mn>3</mml:mn> <mml:mo>)</mml:mo> </mml:mrow> </mml:math> .
Multicultural Competence as a Teacher’s Metacognition to Achieve a Positive School Climate

Our modern times are marked by socio-economic and cultural processes whose effects are rapidly crossing national borders and creating a global community for which multiculturalism is no longer just a concept but a social reality. As a key tool for its harmonization, intercultural education is determined, which provides conditions for interaction between cultures and prepares multicultural individuals. There is a need for the teacher to acquire the appropriate intercultural readiness and competence to enable her to posit the climate in the classroom. At the same time, there is a lack of research on the psychological climate in culturally mixed classrooms, characteristic of most schools today. The aim of the study is to investigate the importance of teachers’ “intercultural readiness” factors for achieving a positive psychological climate in a multicultural educational environment. The research instrument synthesizes questionnaires on the classroom, on the organization of the educational environment and on the psychological climate and adapts them to the level of primary school education. The sample consisted of 95 teachers from Attica in Greece. The data were analyzed with the statistical package SPSS and one-way ANOVA was applied. The research assumption is confirmed that universities preparing pedagogical specialists are responsible for developing master’s degree programs in intercultural education or courses for additional professional qualification with a focus on the formation of intercultural competence as a teacher’s metacognition.
Machine Learning based Cryptocurrency Price Prediction using Historical Data and Social Media Sentiment

The purpose of this research is to investigate the impact of social media sentiments on predicting the Bitcoin price using machine learning models, with a focus on integrating onchain data and employing a Multi Modal Fusion Model. For conducting the experiments, the crypto market data, on-chain data, and corresponding social media data (Twitter) has been collected from 2014 to 2022 containing over 2000 samples. We trained various models over historical data including K-Nearest Neighbors, Logistic Regression, Gaussian Naive Bayes, Support Vector Machine, Extreme Gradient Boosting and a Multi Modal Fusion. Next, we added Twitter sentiment data to the models, using the Twitter-roBERTa and VADAR models to analyse the sentiments expressed in social media about Bitcoin. We then compared the performance of these models with and without the Twitter sentiment data and found that the inclusion of sentiment feature resulted in consistently better performance, with TwitterRoBERTa-based sentiment giving an average F1 scores of 0.79. The best performing model was an optimised Multi Modal Fusion classifier using Twitter-RoBERTa based sentiment, producing an F1 score of 0.85. This study represents a significant contribution to the field of financial forecasting by demonstrating the potential of social media sentiment analysis, onchain data integration, and the application of a Multi Modal Fusion model to improve the accuracy and robustness of machine learning models for predicting market trends, providing a valuable tool for investors, brokers, and traders seeking to make informed decisions
FACILITY MANAGER’S ACCEPTANCE OF CMMS IN MALAYSIA: AN EXPLORATORY STUDY USING PLS-SEM APPROACH

Computerised Maintenance Management System (CMMS) is a widely recognised software in Facilities Management (FM) that provides numerous advantages to facility managers by enhancing productivity at work. The objective of this research is to provide insight and assess the factors influencing the acceptance of CMMS among facility managers in Malaysia. As a result, an improved comprehension of CMMS technology acceptance can be formed, as well as a better understanding of other FM technology adoption. Multiple factors influence CMMS acceptance, and this study used a quantitative technique to verify the hypothesis, using the Technology Acceptance Model (TAM) in conjunction with a Partial Least Square-Structural Equation Model (PLS-SEM). In addition, 125 facility managers completed an online survey that was utilised to collect primary data. The quantitative and structural models were analysed using PLS-SEM. Based on the study's findings, perceived usefulness (PU) and perceived ease of use (PEOU) have a favourable impact on behavioural intention (BI), which influences actual usage (AU) of CMMS. The research aims to improve awareness of the degree of acceptance of CMMS among facility managers and to contribute to FM organisations' better understanding of future technology adoptions.
Characterizing Polarization Switching in Gain-Switched Vertical-Cavity Surface-Emitting Lasers for Quantum Random-Number Generation

Semiconductor lasers are normally used as single-photon sources in most commercial and research quantum key distribution (QKD) systems [1]. These lasers are also used for quantum random number generation (QRNG) that is a necessary security requirement for QKD. In device-dependent QRNGs unpredictability is guaranteed by comparing experimental measurements with simulation data obtained from a model of the physical entropy source [2]. The characterization of phase fluctuations in a gain-switched edge-emitting semiconductor laser for QRNG has been recently performed [2]. Very recently a QRNG based on the random excitation of the linearly polarized modes of a gain-switched vertical-cavity surface-emitting laser (VCSEL) has also been proposed [3]. In this work we characterize the polarization fluctuations found in this system by comparing experimental measurements with the results obtained from the simulation of a VCSEL's model that incorporates the device intrinsic parameters found using state-of-the-art experimental techniques [4].
“CNN Can Kiss My As$”

News consumption in the United States is polarized and fragmented, with an abundance of partisan news publications appealing to political identities on both the left and the right. Yet, there is an abundance of hyperpartisan news on the right, the content of which has been shown to be harmful to democracy. This study captures one of the most comprehensive pictures to date of the U.S. Americans who consume far-right media and makes connections between such media use and the state of American democracy. After a lengthy data-cleaning process, we analyzed open-ended survey responses from a nationally representative sample of more than 10,000 U.S. American adults, finding that nearly ten percent self-report at least one far-right news outlet as a primary news source. Furthermore, significantly more U.S. Americans report consuming far-right and moderate-right news outlets than counterparts on the left. We then examined the characteristics of this small but significant group, given the current political climate in the United States, finding that far-right news consumers are overwhelmingly white, male, Republican, Christian, and without a college degree. This study reinforces previous findings that patterns in hyperpartisan media usage demonstrate growing extremism in the U.S., and that it is deeply rooted in identity.
Experimental demonstration of phase-sensitive multimode continuous variable quantum key distribution with improved secure key rate

We develop and experimentally demonstrate a phase-sensitive continuous variable quantum key distribution system with improved secure key rate. This is achieved using multimode coherent states with phase-conjugated subcarrier modulation and phase-sensitive detection. The local oscillator for phase-sensitive detection is regenerated from a polarization-multiplexed carrier wave via optical injection locking. The proposed scheme has a higher classical information capacity at a given number of received photons and exhibits a higher secure key rate when applying the security analysis of the GG02 protocol. Experimental results confirm the higher secret key rate and better excess noise tolerance of the new scheme compared to the typical implementation of GG02.
Video action recognition collaborative learning with dynamics via PSO-ConvNet Transformer

Recognizing human actions in video sequences, known as Human Action Recognition (HAR), is a challenging task in pattern recognition. While Convolutional Neural Networks (ConvNets) have shown remarkable success in image recognition, they are not always directly applicable to HAR, as temporal features are critical for accurate classification. In this paper, we propose a novel dynamic PSO-ConvNet model for learning actions in videos, building on our recent work in image recognition. Our approach leverages a framework where the weight vector of each neural network represents the position of a particle in phase space, and particles share their current weight vectors and gradient estimates of the Loss function. To extend our approach to video, we integrate ConvNets with state-of-the-art temporal methods such as Transformer and Recurrent Neural Networks. Our experimental results on the UCF-101 dataset demonstrate substantial improvements of up to 9% in accuracy, which confirms the effectiveness of our proposed method. In addition, we conducted experiments on larger and more variety of datasets including Kinetics-400 and HMDB-51 and obtained preference for Collaborative Learning in comparison with Non-Collaborative Learning (Individual Learning). Overall, our dynamic PSO-ConvNet model provides a promising direction for improving HAR by better capturing the spatio-temporal dynamics of human actions in videos. The code is available at https://github.com/leonlha/Video-Action-Recognition-Collaborative-Learning-with-Dynamics-via-PSO-ConvNet-Transformer .
A bytecode‐based integrated detection and repair method for reentrancy vulnerabilities in smart contracts

Abstract The reentrancy vulnerability in smart contracts has caused significant losses in the digital currency economy. Existing solutions for detecting and repairing this vulnerability are limited in scope and lack a comprehensive framework. Additionally, there is currently a lack of guidance methods for effectively pinpointing the location of vulnerabilities. The proposed bytecode‐level method addresses these challenges by incorporating a detection module, an auxiliary localization module, and a repair module. An opcode classification method is introduced using vulnerability features and a BiLSTM‐Attention‐based sequence model to enhance detection accuracy. To overcome difficulties in vulnerability localization, an auxiliary localization method based on data flow and control flow analysis is proposed, enabling developers to better locate vulnerabilities. Current reentrancy vulnerability repair methods are analyzed and strategies for three reachable patterns are proposed. The bytecode rewriting strategy utilizes Trampoline technology for repair, while a fuel optimization method reduces bytecode generation length to optimize gas costs. Through extensive experimental validation, the effectiveness and superiority of the proposed methods are confirmed, further validating the feasibility of the entire framework. Experimental results demonstrate that the framework offers enhanced protection against reentrancy vulnerability attacks in smart contracts.