Reviewerly/exHarmony huggingface dataset:
Meaning creation in novel noun-noun compounds: humans and language models

ABSTRACTThe interpretation of novel noun-noun compounds (NNCs, e.g. “devil salary”) requires the combination of nouns in the absence of syntactic cues, an interesting facet of complex meaning creation. Here we examine unconstrained interpretations of a large set of novel NNCs, to investigate how NNC constituents are combined into novel complex meanings. The data show that words’ lexical-semantic features (e.g. material, agentivity, imageability, semantic similarity) differentially contribute to the grammatical relations and the semantics of NNC interpretations. Further, we demonstrate that passive interpretations incur higher processing cost (longer interpretation times and more eye-movements) than active interpretations. Finally, we show that large language models (GPT-2, BERT, RoBERTa) can predict whether a NNC is interpretable by human participants and estimate differences in processing cost, but do not exhibit sensitivity to more subtle grammatical differences. The experiments illuminate how humans can use lexical-semantic features to interpret NNCs in the absence of explicit syntactic information.KEYWORDS: Noun-noun compoundslexical-semantic featuresverb diathesiseye-trackinglanguage models Disclosure statementNo potential conflict of interest was reported by the author(s).Data availability statementStimuli and data are available at osf.io/ady6s/.Additional informationFundingThis work was supported by the Leon Levy Foundation (AZ) and the Ernst Strüngmann Foundation (DP).
Educational Game Guess the Picture of Balanced Nutrition in Groups of Elementary School Children: Health Education

The group of elementary school age children is a stage of life where children experience rapid growth and development. Imbalance between nutritional intake and daily needs can cause nutritional health problems. This community service activity was carried out as an effort to promote health in groups of elementary school age children. Balanced nutrition education using picture guessing game media. Participants in this community service were 70 students from grades 1-6 in one of the elementary schools in Sungai Bawang Village, Kutai Kartanegara Regency. The final results of this activity show an increase in the mean value of knowledge from 5.96 to 6.23 and the p value (0.000). Health education using games can increase children's interest in learning about efforts to maintain health
Raising the Buddha’s Hand

Deity yoga is a practice found in Tibetan Buddhism involving visualizations that have the normative goal of “becoming one” with a supernatural being. During the practice, practitioners report experiencing that their own body transforms into the body of the deity. This paper offers a potential cognitive explanation of how such an experience is possible. Applying findings from cognitive science on the phenomenon of illusory ownership, we argue that the practice of deity yoga has the necessary means to cause an experience analogous to the famous “rubber hand illusion” in which one misattributes their ownership to a fake hand. In this paper, we 1) introduce deity yoga practice and its key aspects; 2) discuss illusory ownership and its explanation embedded in a predictive processing framework; 3) argue that visualization in deity yoga may induce the experience of illusory ownership; and 4) conclude with a short discussion of the hypothesis’ limitations and of ways to test our hypothesis. Overall, the paper suggests how the practice of visualization in deity yoga may lead to an experience of a transfer of identity onto an imagined supernatural agent.
An Analysis of Politeness Strategies Used by Teacher and Students in English Class at SMPN 1 Mojo Kediri

Politeness is a vital component in communication as it assists in preventing misunderstandings, reducing conflict, and building harmony between interlocutors, such as communication between the educator and learners in the educational setting. This descriptive qualitative study attempts to explore the types and preferred politeness techniques employed by both educators and learners in an English class. An English teacher and 40 students of class 7 G SMPN 1 Mojo Kediri were the participants of this research. The research's primary instrument was an observation by video recording and documentation, which was afterward examined by the researchers through data reduction, display of data, conclusion drawing, and triangular validation. The data was evaluated employing Leech's politeness theory, involving the following maxims: tact maxim, generosity maxim, approbation maxim, modesty maxim, agreement maxim, and sympathy maxim. The findings revealed that the instructor employed all six maxims, namely tact, generosity, approbation, modesty, agreement, and sympathy maxim. While the students only employed four maxims, they displayed tact, modesty, agreement, and sympathy maxim. There are 132 utterances between the instructor and students, and it was discovered that the agreement maxim was the most frequently employed politeness approach by the teacher and students, with 41 frequencies used by the teacher and 37 frequencies used by the students.
An Exploratory Research Analysis of Four Cyber Security Trends in Mobile Technology Using the ISO 270K Framework

This chapter conducts an exploratory analysis of four major cyber security trends within mobile technology, utilizing the ISO 27001 framework as a guiding principle. First, this research investigates mobile devices' growing prevalence and associated security implications, highlighting the necessity of implementing robust security protocols. The text scrutinizes the developing hazard environment and underscores the increasing complexity of cyber assaults aimed at mobile devices. Second, the study highlights the significance of user education and awareness in augmenting mobile security, acknowledging the role of human factors as significant contributors to security breaches. Finally, the research investigates the dynamic nature of regulatory frameworks and the imperative for enterprises to synchronize their security protocols with the stipulated compliance standards.
Aligning Risk Perspectives Between the Chief Risk Officer and the Chief Information Security Officer

The Chief Risk Officer (CRO) and the Chief Information Security Officer (CISO) are critical in managing risk for organizational success. Although these two leaders have distinct focus areas, they must often work together toward safeguarding the organization against various risks, including financial, reputational, and cyber threats. Differing perspectives often lead to less effective risk management. The study explores the significance of advancing the CRO and CISO relationship and provides several steps organizations can take to foster increased understanding.
Smart Weapon Detection System Using Thermal Imaging Through Machine Learning Algorithms

According to numerous statistics, it can be inferred that the rate of violence using firearms and dangerous weapons is rising annually, making it difficult for law enforcement organizations to address this problem promptly. There are several locations where there are high rates of crime with firearms or knives, particularly in areas with lax gun restrictions. For the security of citizens, the early identification of violent crime is crucial. We see a lot of crimes and attacks on public transportation these days. Different ways have been developed to hide the weapons to launch savage attacks on the unwary population. These criminals' primary goals are to manipulate people and harm public property. All these things only occur as a result of weapons and other dangerous items being readily transported into buses and trains. To protect the public and its assets, it is important to create and implement highly effective technologies across the nation. By spotting the existence of deadly weapons like knives and firearms, these situations can be avoided.
Leveraging Quantum Computing for the Ising Model to Simulate Two Real Systems: Magnetic Materials and Biological Neural Networks (BNNs)

Quantum computing is a field with increasing relevance as quantum hardware improves and more applications of quantum computing are discovered. In this paper, we demonstrate the feasibility of modeling Ising Model Hamiltonians on the IBM quantum computer. We developed quantum circuits to simulate these systems more efficiently for both closed and open boundary Ising models, with and without perturbations. We tested these various geometries of systems in both 1-D and 2-D space to mimic two real systems: magnetic materials and biological neural networks (BNNs). Our quantum model is more efficient than classical computers, which can struggle to simulate large, complex systems of particles.
Composite Sorting

We propose a new sorting framework: composite sorting. Composite sorting comprises of (1) distinct worker types assigned to the same occupation, and (2) a given worker type simultaneously being part of both positive and negative sorting. Composite sorting arises when fixed investments mitigate variable costs of mismatch. We completely characterize optimal sorting and additionally show it is more positive when mismatch costs are less concave. We then characterize equilibrium wages. Wages have a regional hierarchical structure − relative wages depend solely on sorting within skill groups. Quantitatively, composite sorting can generate a sizable portion of within-occupations wage dispersion in the US.Institutional subscribers to the NBER working paper series, and residents of developing countries may download this paper without additional charge at www.nber.org.
Conclusion

This chapter revisits narrative styles, language styles, and research styles in conjunction with the varied application of these topics, as observed in the primary texts. Narrative and language features are evidenced as changeable across True Crime. Similarly, the distribution methods for research materials are a varied area. Though the necessity for research to have been conducted was not shown as flexible and instead appears as an essential characteristic of True Crime. Consideration is also given to the social responsibility of True Crime, and the ways in which deconstructing the genre into representative subgenres allows greater awareness among consumers insofar as the dissemination of accurate information relating to criminal cases. The need for True Crime to be better considered within literary studies is also outlined.
A study of cybercrime and cybersecurity

At present, millions of people use mobile phones and computers in their daily lives. They are using these devices for online shopping, banking transactions, online gaming, share market, etc. Sometimes people have no knowledge of cybercrime and are easy victims of cyber-related fraud. This paper discusses different types of cybercrime, such as identity theft, cyberstalking, cyberbullying, financial fraud, and the importance of cybersecurity techniques including risk management, internal audit, and the benefits of a cybersecurity audit. The conclusion emphasizes the importance of expanding cybersecurity measures to protect against evolving cyber threats.
An exploratory cybercrime analysis and its impact on india

In the modern era, the rapid use of the internet has led to reports of cybercrimes. The number of cybercrimes is constantly increasing. The purpose of this paper is to identify the root causes of cybercrime and find insights that can be used by the administration to take fruitful decisions to overcome cybercrime offenses. It has collected data related to the registered cybercrime cases in various Indian states on different parameters for the years 2016 to 2019. The preprocessing was applied to individual datasets, and a common data set was prepared. Data analysis using Power BI (business intelligence) and Microsoft (MS) Excel tools was performed for the Indian states, and its impact was analyzed through year-wise analysis. The outcome of this paper can be utilized by the Indian administration, cyber experts, working professionals, and users to take care of the affecting factors and limit cybercrimes.
A study and comparison of the various cryptography technique standards within fifteen factors

This paper focuses on analyzing the performance of various encryption algorithms, namely RSA, ECC, and DSA, in terms of encrypting and decrypting sensitive data. Encryption is crucial for ensuring the security of data, and there are two types of encryption algorithms: Symmetric key and Asymmetric-key encryption. RSA is highly secure but has high computation time, whereas ECC provides equivalent security with lower computing power and battery usage. The study compares the implementation and analysis of DES, 3DES, AES, RSA, ECC, and DSA, highlighting their strengths and weaknesses. The paper aims to provide a comprehensive comparison of these cryptographic techniques, including their performances, to assist in selecting the most appropriate algorithm for specific applications.
Introduction to Policy Issues Related to the Development and Use of Digital Resources in Mathematics Education

In this chapter, we introduce the section “Politics of Digital Resources,” which focuses on the relationship between politics and digital resources in teaching and learning mathematics. We explore the politics surrounding digital resources in mathematics education, including the rules and regulations imposed by the political and bureaucratic system and those developed from within the practice. The authors examine digital development’s impact on equity, mathematical literacy’s role in civic education, as well as on evaluation and assessment. The politics of digital resources are viewed from three perspectives: implementation, regulation of tools, and democratic obligations in a digital era. Implementation is crucial for realizing educational decisions, but rapid technological changes can create ongoing implementation and change challenges. The role of digital tools in education has been a point of contention in many countries, with conflicting tendencies regarding their use. Assessment of mathematical skills and learning using digital tools present additional challenges. Finally, the teaching of mathematics has a role in civic education. The increased digitalization of society and the educational system means that digital resources and their influences on mathematical reasoning and life, in general, should be an object of mathematics instruction.
Assessing Deep Learning: A Work Program for the Humanities in the Age of Artificial Intelligence

Following the success of deep learning (DL) in research, we are now witnessing the fast and widespread adoption of artificial intelligence (AI) in daily life, influencing the way we act, think, and organize our lives. However, much still remains a mystery when it comes to how these systems achieve such high performance and why they reach the outputs they do. This presents us with an unusual combination: of technical mastery on the one hand, and a striking degree of mystery on the other. This conjunction is not only fascinating, but it also poses considerable risks, which urgently require our attention. Awareness of the need to analyze ethical implications, such as fairness, equality, and sustainability, is growing. However, other dimensions of inquiry receive less attention, including the subtle but pervasive ways in which our dealings with AI shape our way of living and thinking, transforming our culture and human self-understanding. If we want to deploy AI positively in the long term, a broader and more holistic assessment of the technology is vital, involving not only scientific and technical perspectives but also those from the humanities. To this end, we present outlines of a work program for the humanities that aim to contribute to assessing and guiding the potential, opportunities, and risks of further deploying DL systems. This paper is structured in four modular parts: a general introduction (section 1), an introduction to the workings of DL for uninitiated non-technical readers (section 2), a more mathematical introduction to DL (appendix A), and a main part, containing the outlines of a work program for the humanities (section 3). Readers familiar with mathematical notions might want to skip 2 and instead read A. Readers familiar with DL in general might want to ignore sections 2 and A altogether and instead directly read 3 after 1.
The Influence of University Support and Online Learning Tools on Learning Outcomes to Increase Student Satisfaction with Online Learning

This research aims to explore how university support, online learning tools, and learning outcomes influence e-learning satisfaction. This study explores e-learning satisfaction through quantitative investigations using 19 constructs with 5 variables that make up the proposed research model. Each indicator is used as an online survey instrument using a likert scale with 1–6 point types from strongly disagree to strongly agree. This study obtained the results of 570 respondents who were declared valid and analyzed using SMARTPLS 3.0 software. The results of the study show that student satisfaction in online learning using e-learning media is influenced by University Support, Online Learning Tools, and Perceived Learning Outcomes. The contribution of these findings shows that the role of universities in providing support in preparing for the development of online learning by optimizing online learning tools can increase satisfaction.
Machine Translation Use in Language Learning

In recent years, there has been an increasing development in promoting newer ways to integrate artificial intelligence (AI) into various digital tools, applications, and platforms. On the other hand, machine translation (MT), which is processed through AI, has already been in use for translation purposes for about three decades. Additionally, it has been a software frequently resorted for language learning purposes through free online platforms. The association between MT use and language learning tends to bring about several related issues such as ethical concerns. Diving into depths of these issues, this chapter presents a literature review on the key concepts related to MT use in language learning, which is supported by research findings and implications.
Corpus-Assisted Multidimensional AI Applications in Language Teaching and an Intelligent Virtual Speaker

The authors propose an evidence-based virtual AI application geared to using a successful implementation of AI for language learners and language teaching professionals and a virtual speaking tool that will serve as a foundation for further research and experimentation. The study promotes interdisciplinary collaboration among corpus linguists, language educators, and computer engineers for fostering the skills and the competences and the exchange of ideas and a shared vision for the future of AI-assisted language learning. The main objective of this action plan is to propose an application in which language teaching professionals and language learners will engage in English-speaking practice through a multidimensional (MD) environment incorporating cubic images and bilateral sound perception (hearing and speaking). The AI application will assign tasks to multidimensional characters using realistic sound effects to create a highly immersive and interactive environment for language teaching professionals.
Geometric Magnification-based Attention Graph Convolutional Network for Skeleton-based Micro-Gesture Recognition

Micro-Gesture (MG) recognition is an emerging and challenging task due to the short duration and small amplitude of joints. MGs indicate subtle movements of the body in response to stress, which are more difficult to recognize than regular gestures. To solve the above problems, for the modeling of micro-gesture skeleton data, we propose a Geometric Magnification-Based Attention Graph Convolutional Network (MA-GCN) to magnify and select features. The network mainly consists of two modules: the geometric magnification module (GM module) controls the magnification of different joints, and the spatial temporal attention graph convolution module (STA module) selects valid information by weighting different joints and frames to focus on subtle movements. Extensive experiments on two MG datasets prove that our method achieves remarkable performance.
Random Segmentation: New Traffic Obfuscation against Packet-Size-Based Side-Channel Attacks

Despite encryption, the packet size is still visible, enabling observers to infer private information in the Internet of Things (IoT) environment (e.g., IoT device identification). Packet padding obfuscates packet-length characteristics with a high data overhead because it relies on adding noise to the data. This paper proposes a more data-efficient approach that randomizes packet sizes without adding noise. We achieve this by splitting large TCP segments into random-sized chunks; hence, the packet length distribution is obfuscated without adding noise data. Our client–server implementation using TCP sockets demonstrates the feasibility of our approach at the application level. We realize our packet size control by adjusting two local socket-programming parameters. First, we enable the TCP_NODELAY option to send out each packet with our specified length. Second, we downsize the sending buffer to prevent the sender from pushing out more data than can be received, which could disable our control of the packet sizes. We simulate our defense on a network trace of four IoT devices and show a reduction in device classification accuracy from 98% to 63%, close to random guessing. Meanwhile, the real-world data transmission experiments show that the added latency is reasonable, less than 21%, while the added packet header overhead is only about 5%.
Enhancing Next Active Object-Based Egocentric Action Anticipation with Guided Attention

Short-term action anticipation (STA) in first-person videos is a challenging task that involves understanding the next active object interactions and predicting future actions. Existing action anticipation methods have primarily focused on utilizing features extracted from video clips, but often overlooked the importance of objects and their interactions. To this end, we propose a novel approach that applies a guided attention mechanism between the objects, and the spatiotemporal features extracted from video clips, enhancing the motion and contextual information, and further decoding the object-centric and motion-centric information to address the problem of STA in egocentric videos. Our method, GANO (Guided Attention for Next active Objects) is a multi-modal, end-to-end, single transformer-based network. The experimental results performed on the largest egocentric dataset demonstrate that GANO outperforms the existing state-of-the-art methods for the prediction of the next active object label, its bounding box location, the corresponding future action, and the time to contact the object. The ablation study shows the positive contribution of the guided attention mechanism compared to other fusion methods. Moreover, it is possible to improve the next active object location and class label prediction results of GANO by just appending the learnable object tokens with the region of interest embeddings. Related implementations are available at: sanketsans.github.io/guided-attention-egocentric.html