Reviewerly/exHarmony huggingface dataset:
Verifying Global Neural Network Specifications using Hyperproperties

Current approaches to neural network verification focus on specifications that target small regions around known input data points, such as local robustness. Thus, using these approaches, we can not obtain guarantees for inputs that are not close to known inputs. Yet, it is highly likely that a neural network will encounter such truly unseen inputs during its application. We study global specifications that — when satisfied — provide guarantees for all potential inputs. We introduce a hyperproperty formalism that allows for expressing global specifications such as monotonicity, Lipschitz continuity, global robustness, and dependency fairness. Our formalism enables verifying global specifications using existing neural network verification approaches by leveraging capabilities for verifying general computational graphs. Thereby, we extend the scope of guarantees that can be provided using existing methods. Recent success in verifying specific global specifications shows that attaining strong guarantees for all potential data points is feasible.
ANTONIO: Towards a Systematic Method of Generating NLP Benchmarks for Verification

Verification of machine learning models used in Natural Language Processing (NLP) is known to be a hard problem. In particular, many known neural network verification methods that work for computer vision and other numeric datasets do not work for NLP. Here, we study technical reasons that underlie this problem. Based on this analysis, we propose practical methods and heuristics for preparing NLP datasets and models in a way that renders them amenable to state-of-the-art verification methods. We implement these methods as a Python library called ANTONIO that links to the neural network verifiers ERAN and Marabou. We perform evaluation of the tool using an NLP dataset R-U-A-Robot suggested as a benchmark for verifying legally critical NLP applications. We hope that, thanks to its general applicability, this work will open novel possibilities for including NLP verification problems into neural network verification competitions, and will popularise NLP problems within this community.
Supporting Standardization of Neural Networks Verification with VNNLIB and CoCoNet

The interest in the verification of neural networks has been growing steadily in recent years and there have been several advancements in theory, algorithms and tools for the verification of neural networks. Also propelled by VNNCOMP — the annual competition of tools for the verification of neural networks — the community is making steady progress to close the gap with practical applications. In this scenario, we believe that researchers and practitioners should rely on some commonly accepted standard to describe (trained) networks and their properties, as well as a toolset to visualize and to convert from common formats to such standard. The purpose of VNN-LIB and CoCoNet is precisely to provide such standard and toolset, respectively. In this paper we briefly describe the principles and design choices behind the current version of VNN-LIB standard, and we give an overview of the current and planned capabilities of CoCoNet.
Service Automation Implementation for Delivering CaaS at the Ministry of Finance of Indonesia

Evaluation is an essential aspect of service improvement. Within the Ministry of Finance, there is an organization responsible for providing IT services to various units and employees. One of the services offered by this organization is cloud computing, which supports the development of information systems. However, there are several challenges related to service fulfillment. For instance, the time required to fulfill a service is relatively long, taking two days, in contrast to public cloud providers, which can deliver their services in minutes. Additionally, there is a potential for human errors in the manual process carried out by the Request Fulfillment Team (RFT) during service delivery. This study aims to explore the design and implementation of automated container service fulfillment, transforming them into self-service products. The author employs the Finite-State Automata (FSA) model to test the input and output of the automation system using seven states and inputs. The results indicate that the container service cycle, when compiled and tested with FSA using predetermined inputs, can generate containers according to user-selected specifications. As a result, the implementation of an automated and self-service model is proposed to reduce delivery time and mitigate potential errors in the container-as-a-service (CaaS) offering.
An Investigation on the Use of Heterogeneous Ensemble Machine Learning in Predicting Two Measures of Software Maintainability

The software maintainability is considered as an indispensable factor to acclaim the quality of a particular software. This attribute indicates the amount of work required to correct or modify software. The prediction of the maintainability of software systems makes it possible to give an answer in terms of effort for software developers. To date, many works in the literature have used several machine learning techniques to predict the maintainability. The maintainability is often measured via the change metric which calculates the number of lines changed per class in an object oriented system. In this paper, we focus our investigation on the use of heterogeneous ensemble learning techniques (Stacking and Voting) for the prediction of two maintainability metrics: the change and the maintainability index. We propose a model that combines the predictions of three individual models that we have chosen in order to improve the accuracy calculated via Mean Absolute Error (MAE), and Mean Magnitude of Relative Error (MMRE). Our study has shown that Stacking is the best model to use to predict maintainability.
Unsupervised Person and Vehicle Re-identification via Relative Hard Samples in Industrial Surveillance System

Person and Vehicle Re-Identification (Re-ID) is a critical task in the realm of intelligent industrial surveillance systems. It aims to identify the same person or vehicle across different camera views or scenes, facilitating individual tracking across multiple cameras within industrial environments. Re-Id in an industrial environment can be more challenging than in a general environment due to its unique setting and limited annotated dataset. Hence, this paper focuses on addressing the fully unsupervised re-ID problem, aiming to develop a re-ID solution that can learn without the need for any human-annotated labeled data. Furthermore, recent studies have demonstrated the effectiveness of self-supervised Momentum Contrastive learning (MoCo) as an unsupervised object re-ID method. However, MoCo neglects the hard sample learning. Here, we introduced Relative Hard Samples (RHS) learning to ensure selection in an adaptive and stable way by considering the characteristics of each sample. Experimental results confirm the effectiveness of our proposed hard sample learning strategy RHS selection and RHS learning. Comprehensive experiments have been conducted on one vehicle re-ID dataset and two person re-ID datasets.
Distributed Dual Coordinate Ascent With Imbalanced Data on a General Tree Network

In this paper, we investigate the impact of imbalanced data on the convergence of distributed dual coordinate ascent in a tree network for solving an empirical loss minimization problem in distributed machine learning. To address this issue, we propose a method called delayed generalized distributed dual coordinate ascent that takes into account the information of the imbalanced data, and provide the analysis of the proposed algorithm. Numerical experiments confirm the effectiveness of our proposed method in improving the convergence speed of distributed dual coordinate ascent in a tree network.
Handwritten Text Recognition using Deep Learning Methods

Offline handwritten text recognition has been widely utilized in various fields including historical document analysis. Deep learning techniques have demonstrated their effectiveness in digitizing handwritten text as each technique is precisely designed to tackle a specific task or solve a particular problem. In this article, we use convolutional neural network for extracting distinct character features and a recurrent neural network for handling character combinations within sequential data. By combining these models, we create a hybrid deep neural network consisting of three CNN layers followed by a bidirectional LSTM layer. This architecture effectively encodes input images and generates character probability matrices with which the connectionist temporal classification operation computes the loss function. Extensive experimentation with various parameter values allowed us to optimize our model, which we evaluated on the IAM dataset, yielding a reasonably low error rate.
Pilot-Free Semantic Communication Systems for Frequency-Selective Fading Channels

With the paradigm shift from traditional communication systems to machine learning-driven communication systems, deep learning-based semantic communication is considered as a potential approach to improve transmission efficiency. However, the time-varying and multipath effects of wireless channels significantly influence the performance of existing semantic communication systems. The conventional solutions to overcome channel impairments, i.e., pilot-driven channel estimation and channel equalization approaches, will complicate the system and introduce extra overhead. Therefore, this paper proposes a deep learning-driven pilot-free semantic communication framework for frequency-selective fading channels. The convolutional neural network-based channel feature extraction and data recovery modules are introduced in the receiver to implicitly extract channel information directly from the received signal and recover the semantic information. The simulation demonstrates that the proposed network can be applied to various frequency-selective fading channels and outperforms existing schemes in terms of computational complexity and semantic transmission accuracy.
Spectralisms in the Music of Rozalie Hirs and Their Points of Departure in Research and Innovation

Abstract The term spectral music is often understood to refer strictly to use of spectral analysis as the basis for additive instrumental synthesis. This definition is limiting, however, and overlooks the aesthetic impulse at the heart of this practice, namely the quest for new musical materials grounded in empirical research into acoustic and psychoacoustic phenomena. This chapter attempts to expand the definition of spectral compositional practice by proposing a variety of compositional techniques that share the same aesthetic impulse, with applied examples drawn from the authors’ own catalogue: Sacro monte (1997), Book of mirrors (2001), In LA (2003), article 4 (2004), Pulsars (2007), Roseherte (2008), and Venus (2010), among others.
Assessing the digital literacy competence of pre-service English teacher in Indonesia and Thailand

The notion of digital literacy has become the trend in any field, including this research. This research aimed to find the level of digital literacy and adequate and inadequate domains in each pre-service teachers of English education digital literacy group. Quantitative research with digital competence framework or DCF then adjusted and validated through the expert judgment by Aiken V as the main questionnaire occurred to measure the level of digital literacy. Indonesia and Thailand pre-service teacher in various universities participated in this research. The research found that: i) The most level of digital literacy whether in Indonesia and Thailand, the pre-service teacher has an intermediate level followed by basic and advanced and ii) The researcher claims ‘information’ as an inadequate domain and ‘implement tech to learning’ as the adequate domain in each level of digital literacy. Further research is needed to examine effectiveness of project-based learning, problem-based learning, and case study in enhancing digital literacy through qualitative and quantitative research.
Personalized Spatial Audio Tools for Immersive Audio Production and Rendering

This paper introduces a comprehensive set of personalized spatial audio production tools tailored for immersive applications. The tools presented include a 3D Panner, Visualizer, and Virtual Studio Renderer, each incorporating personalized head-related transfer functions (HRTFs). We delve into the signal processing techniques, design principles, and implementation details of these tools, providing a thorough understanding of their functionality. We delve into the technical aspects of these tools, discussing the underlying signal processing algorithms and the design considerations involved. By providing a comprehensive overview of the implementation details, we offer practitioners and researchers a solid foundation for incorporating these tools into their own workflows. Moreover, we briefly touch upon camera-based Head Tracking tools, which leverage visual tracking techniques to enhance the spatial audio experience, aligning the auditory and visual cues for a more cohesive and realistic perception of sound.
Relativity of Mindfulness: Team Collaboration in Digital and Physical Educational Escape Rooms

This study focuses on collaboration among team members in educational escape rooms in higher education. The objective of this study was to understand how collective mindfulness and less mindful behavior unfold in physical and digital game-based learning. The video data were collected from three different courses comprising 107 students on 28 teams, totaling more than 16 hours of material. The qualitative analysis revealed both collectively mindful and less mindful behaviors in team interactions. This paper contributes to collective mindfulness literature in understanding team collaboration by observing that mindfulness may be relative depending on the observation perspective. It also presents factors that affect member equality in both digital and physical escape rooms. Lastly, a nuanced description of how team collaboration occurred in a short-term problem-solving situation is developed.
Fire Disaster Mitigation in Dense Settlements in Batu Merah Country

Fire is a disaster that cannot be predicted when and where it will occur. Many fire disasters occur in dense settlements, so dense settlements become vulnerable to the danger of fire disasters. Negeri Batu Merah is one of the densely populated settlements located near the central area of Ambon City. This dense residential condition has resulted in settlements in Batu Merah Country becoming areas that are potentially vulnerable to fire disasters. This research aims to determine the distribution of fire disaster vulnerability levels in dense settlements in Batu Merah State. Apart from that, this research also aims to provide recommendations for reducing the level of fire vulnerability in the study area. The analytical methods used in this research are spatial analysis, appreciation analysis and descriptive analysis. Based on the results of the vulnerability analysis in the study area, the level of fire vulnerability is divided into three, namely low, high and very high levels of vulnerability. Interventions to reduce the level of fire vulnerability can be carried out by mitigating efforts both structurally and non-structurally by adding fire extinguishing facilities, widening residential roads, and widening roads in the Batu Merah settlements.
The Past, Present and Future of the Prior Internet Resources

In 2016, researchers from six different Danish universities gathered together to begin a research project called The Primacy of Tense - A. N. Prior Now and Then. The goals of the project were as diverse as its participants, ranging from themes in philosophy and logic to explorations in information science and history. This diversity was held together by a key idea: that it is important to study the links between Prior’s canonical published work and the extensive collection of unpublished material he left behind after his death. Thus a key practical goal of the project was to further explore and transcribe Prior’s Nachlass and make more of it available on the Prior Internet Resources (PIR). In this article we describe how the PIR came about, discuss how it has changed, and suggest several directions for its further development, so it can continue fostering research into Arthur Prior’s work in the future.
New Look at Soft Skills Development Through Debate Technology

The article highlights theoretical and practical aspects of the relevant contemporary issue of development of soft skills of pre-service primary school teachers by means of intensification of debate in the classroom. Modern society requires from specialists not only deep knowledge in their fields, but also the ability to effectively communicate and interact with others. Soft skills development through debate technology is a contemporary and innovative approach to enhancing essential interpersonal and communication skills. Debate technologies contribute to the development of argumentation skills, the ability to listen, respond to the arguments of opponents and express one's opinions clearly and convincingly. The approach combines the traditional practice of debate with modern technology to provide a more engaging and effective learning experience. The author analyses the problem from two perspectives: development of students’ soft skills, on the one hand, and pedagogical potential of debate in this context, on the other. The definition of the concepts “soft skills” vs “hard skills” is generalized, the factors of efficient application of debate technologies are outlined. The researcher proved that ability to arrange engaging debate in different academic courses at the university is a characteristic feature of professional competence of an innovative educator as it helps develop students’ key skills. The formation of the latter takes place both in individual subjects teaching and in the process of subjects’ integration.
Synergy of Reflection and Critical Thinking: a Catalyst for Students’ Intellectual Growth

The article is dedicated to exploring the interaction between reflection and critical thinking in the context of developing students' intellectual abilities. Special attention is given to studying the impact of reflective practices on stimulating critical thinking in younger students and their ability to analyze and perceive information critically and objectively.&#x0D; The article analyzes the fundamental aspects of reflective activities, including its role in developing students' consciousness, self-awareness, and internal processing of acquired information. The authors investigate various methods and techniques of reflection that educators can use to activate students' critical thinking. Specifically, the article offers practical case studies of implementing reflective approaches in the educational process.&#x0D; The primary aim of the article is to raise educators' awareness of the significance of the interaction between reflection and critical thinking as key elements in students' intellectual development. The information presented in the article will contribute to the expansion of methodological foundations for promoting reflective processes in teaching and supporting the development of critical thinking among younger students. The research findings can be practically applied in educational institutions and contribute to improving the quality of education.
A Study on the Impact of Higher Education Expansion on Human Capital Accumulation and Economic Development - An Empirical Analysis Based on Structural Equation Model

Abstract Based on structural equation modeling, this paper investigates the impact of expanding higher education levels on human capital accumulation and economic development. The relationship between latent and latent variables is used to estimate the maximum likelihood estimates of scale expansion by calculating unstabilized and stabilized weights. A weighted linear combination of weights in the weights pool is used to construct the SL estimator. Based on the structural equation model, the prediction model of the impact of higher education scale expansion on human capital and economic development is constructed, and four impact hypotheses are proposed. The results show that the rate of return to higher education for expanding the higher education scale is 4.36%. The rate of human capital accumulation is 102.36%, while the level of economic development is 63.89%. It indicates that the positive effect of expanding higher education levels on human capital accumulation and economic development is significant.
Implementation of Strengthening Governance of Cultural and Citizenship Literacy in Gunungpati State Kindergarten, Semarang City

This research is motivated by the importance of cultural and citizenship literacy (LBK) in PAUD institutions in Ulya's research (2023) especially in the dimensions of strengthening governance. This study aims to determine the implementation of LBK in PAUD Institutions in Gunungpati District and to determine the implementation of strengthening governance in Gunungpati State Kindergarten (TK N) Semarang City. This research uses a qualitative approach with secondary data study methods and case studies. The subjects in this research were educators at TK Negeri Gunungpati. Data collection techniques use observation, interviews, documents and triangulation. The data analysis technique uses analysis before, during using the Miles and Huberman model, and after in the field. The results showed that PAUD institutions in Gunungpati District had implemented LBK well with a percentage of 77% and TK Negeri Gunungpati had also implemented LBK from the dimension of time allocation or schedule; budget allocation; forming a school literacy team; school policy making; and strengthening the role of the school committee. Even though supervisors have not been involved in the school literacy team and the school committee has not collaborated with outside parties to support activities related to LBK. The limitations of this research are that this research is only limited to the implementation of the dimensions of strengthening cultural and citizenship literacy governance which is only carried out in Gunungpati N Kindergarten using participating educators. It is better if further research can look at the implementation of LBK governance from the school principal, supervisors, school committees, as well as students and within the scope of other schools, families and communities. Keywords: Implementation; Strengthening Governance; Cultural and Citizenship Literacy; Kindergarten
Research on Expanding Grassroots Employment Space for College Students in the Context of Rural Revitalization + Data Platform

Abstract In this paper, in the context of rural revitalization + data platform, a grassroots employment platform for college students is designed according to the data architecture, and the data is processed hierarchically to achieve the organization and knowledge accumulation of cross-domain employment data, which makes multiple employment data standardized. Using data analysis parallelization algorithm and dimensionality reduction operation method, the training data are read and loaded into the distributed memory of the employment data platform to select the best employment features. According to the design platform of this paper, the employment rate for those employed in the eastern region and 80.16% in the central region has reached 91.15%. This paper's design model can improve employee efficiency and expand the grassroots employment space of college students, as indicated.