Reviewerly/exHarmony huggingface dataset:
Processing and interpretation of geochemical data for mineral exploration

This chapter starts with describing different geochemical sampling media, be they rock chip, till, soil, or stream sediment samples. In the next section this chapter describes the analytical techniques applied to geochemical samples. This is followed by defining the state-of-the-art methods used for the processing and interpretation of geochemical data. The compositional nature of geochemical data will be covered in this chapter, together with the introduction of methods well suited for addressing the effects of compositional data. The emphasis will be placed on strategies for delineating geochemical anomalies linked to buried, covered, and blind deposits.
Decentralized SGX-Based Cloud Key Management

The adoption of cloud computing is expanding at an astonishing pace for personal and professional usage. The high computational power and storage capacity have created a preference for traditional on-premise service provisioning. However, security remains the primary issue when dealing with the cloud paradigm. Encryption schemes have been considered a straightforward approach to protecting sensitive data. Yet, several key management schemes are vulnerable to various threats that weaken the long-term efficiency of the encryption scheme. This paper proposes MultiSGX-KMS, an efficient and secure cloud key management scheme. The suggested scheme aims to protect exchanged user keys while ensuring fault tolerance using secret sharing and trusted execution environments. In addition, the encryption keys are deployed in the SGX instance in the cloud while being fully controlled by the end user. The scheme is robust against proactive attacks and mitigates against a computationally bounded attacker without assuming a possible hardware attack on an SGX enclave. The proposed scheme presents a limited overhead of 0.1% for a file size of 50 MB compared to the traditional approach. The overhead further decreases proportionally as file sizes increase.
Modular Polynomial Multiplication Using RSA/ECC Coprocessor

Modular polynomial multiplication is a core and costly operation of ideal lattice-based schemes. In the context of embedded devices, previous works transform the polynomial multiplication to an integer one using Kronecker substitution. Thanks to this transformation, existing coprocessors which handle large-integer operations can be re-purposed to speed-up lattice-based cryptography. In a nutshell, the Kronecker substitution transforms by evaluation the polynomials to integers, multiplies it with an integer multiplication and gets back to a polynomial result using a radix conversion. The previous work focused on optimization of the integer multiplication using coprocessors. In this work, we pursue the seminal research by optimizing the evaluation, radix conversion and the modular reductions modulo q with today’s RSA/ECC coprocessor. In particular we show that with a coprocessor handling addition/subtraction, (modular) multiplication, shift and logical AND on large integers, the whole modular polynomial multiplication can be computed. The efficiency of our modular polynomial multiplication depends on the component specification and on the cryptosystem parameters set. Hence, we assess our algorithm on a chip for several lattice-based schemes, which are finalists of the NIST standardization. Moreover, we compare our modular polynomial multiplication with other polynomial multiplication techniques.
Analysis of Elementary Math Word Problems Based on AI Deep Learning

Natural language processing (NLP) has greatly advanced in machine learning, but math education software lacks AI integration for solving math word problems in English.We propose using the BertGen pre-trained Transformer model, along with the MAWPS dataset augmented by our dataset augmenter.The Transformer model, with its multi-head attention mechanisms, excels at capturing longrange dependencies and referential relationships, crucial for math word problems at the primary school level.Our accuracy tests and performance on different datasets validate the effectiveness and generalizability of our approach.Moreover, our augmented dataset outperforms smaller unaugmented datasets, while maintaining diversity.The math word problem augmenter can be adapted for other math problem sets, supporting future research in the field.
The Practice and Thinking of Junior High School English Situational Reading Teaching Oriented to Deep Learning

It is necessary and urgent for teachers to carry out contextualized English reading teaching to deepen students' learning, which can not only effectively improve students' reading ability, but also promote their thinking ability.In English reading class at junior middle school, teachers should create situations conducive to students' meaningful learning based on contemporary students' personality, characteristics and interests, and focus on the thematic meaning of each lesson, so that students can participate in learning activities actively under meaningful circumstances, thus construct meaningful knowledge in the process of cooperation with others, and eventually realize deep learning in situational classes.Based on a specific case, this paper expounds how to carry out contextualized English reading teaching activities that aim at deep learning in English classroom teaching to promote junior students' deep learning.
Construction of Enterprise Information Security Guarantee System in the Digitalization Background

With the continuous improvement of enterprise informatization in the digital background, the problem of enterprise information security has become more and more prominent.The construction of information security assurance system has become an inevitable trend of enterprise informatization development.In this paper, we discuss the thinking points of constructing the enterprise information security system under the digital background from the aspects of security strategy, security policy, security monitoring and emergency response, and puts forward corresponding suggestions.
High-Resolution Organic Geochemistry Reveals Unusual Biomarkers in the Pimenteiras Formation, ParnaíBa Basin - Brazil

Preview this conference paper: High-Resolution Organic Geochemistry Reveals Unusual Biomarkers in the Pimenteiras Formation, ParnaíBa Basin - Brazil, Page 1 of 1 < Previous page | Next page > /docserver/preview/fulltext/2214-4609/2023/imog-2023/153-1.gif
Forest fire prediction based on multi-sensor technology

Forest fires have a great impact on the ecology.In order to reduce the loss, our team uses multi-sensor technology and DHT11 sensor to monitor the ambient temperature and humidity, which is transmitted to the mobile phone through Bluetooth device for real-time supervision.At the same time, the risk prediction model is built by mathematical modeling.Through research, the main cause of forest fires lies in the combustion of dead combustibles.Our team also conducted many experiments to obtain the relationship between the water content of dead combustibles and air humidity and temperature.Through mathematical modeling and multi-logistics algorithm, the fire probability is calculated to realize risk supervision and early warning.
Integrating Information Technology and Teaching Modes to Innovate College English Teaching

The integration of information technology and teaching is a hot research topic in college English education.The purpose of this study is to explore how to use information technology to improve the effectiveness and quality of college English teaching.Firstly, through literature review, the researchers found that information technology has a wide range of applications in the field of education.It can provide students with more resources and interaction opportunities, stimulate students' learning interest and initiative.Secondly, the researchers concluded from teaching practice that information technology helps to increase teacher-student interaction and promote cooperative learning.Most students thought that this kind of teaching improved their learning results.At the same time, the teaching observation also shows that the use of information technology and the integration of teaching methods can improve the teaching effects.Finally, the researchers summarized several key points of the integration of information technology and teaching in innovative teaching, emphasizing that the integration of information technology and teaching in innovative teaching, as a potential teaching method, can improve the effectiveness and quality of college English teaching, but it also needs teachers to overcome the relevant challenges in teaching through curriculum design.
Research on training mode of innovative talents in universities under the background of digital economy

Digital economy has become a key factor in the current global resource restructuring, economic structure reshaping and competition pattern changing.If China wants to seize the opportunity of digital economy and seize the commanding heights of economic development, talent and innovation are the key.The lack of innovative talents has also become an important hindrance to the development of China's digital economy.Colleges and universities are the key talent cultivation bases in our country, and the cultivation of innovative talents is a new task entrusted to colleges and universities in the new era.Based on the current situation of talent demand under the digital economy, this paper conducts an in-depth study on the digital economy and innovative talent training from several aspects: changing the concept of innovative talent training, optimizing the discipline layout of innovative talent, innovating innovative talent training methods, strengthening the innovative talent training team, and deepening the integration of industry-university-research.It aims to promote universities to train more talents for society who can lead the development of digital economy.
Analysis of teaching function concepts in high school based on contextual cognitive learning theory

Contextual cognitive learning theory is an important branch of the cognitive psychology school that places emphasis on developing students' problem-solving skills in different contexts and on the integration of students' knowledge and action.Under the guidance of contextual cognitive learning theory, teachers can analyse students' nearest developmental zone to understand their knowledge readiness and set multiple teaching objectives, so as to select appropriate contexts and cognitive strategies for instructional design.The introduction of contextual cognitive learning theory into the conceptual teaching design of high school functions can facilitate the transfer of knowledge to everyday life contexts, provide a reference for other researchers' research on contextual cognitive learning theory, and help high school teachers to use the theory in teaching in a complete and systematic way.
The Pathway to Precise Employment of College Students in the Context of the New Era

Following the post-pandemic era, China has witnessed new changes in its industrial and employment landscapes.Against the backdrop of the new era, achieving precise employment of college students has become particularly crucial.Precise employment not only maximizes students' professional abilities, enhances industry competitiveness, and optimizes industrial structures but also reduces students' employment costs and fulfills their employment needs.By actively utilizing emerging data technologies, designing relevant activities, providing personalized and comprehensive services to students, and implementing employment assistance, it is possible to effectively reduce students' employment costs and assist them in achieving precise employment.
A Comprehensive Quality Evaluation System for University Students Based on Teacher Performance Coefficients

The comprehensive quality assessment of university students is an important method to promote the comprehensive development of the moral, intellectual and physical qualities of university students.There are many shortcomings in the current comprehensive quality evaluation of college students in China.In this paper, after the introduction of teacher performance coefficient in the comprehensive quality evaluation system of students, the theoretical framework of the system is more complete, and students' performance in school is more positive, which has achieved better results.
The Initiative for Open Abstracts: Celebrating our first anniversary

Authors: Ludo Waltman, Bianca Kramer, David Shotton &lt;em&gt;In this blog post, Ludo Waltman, Bianca Kramer, and David Shotton, co-founders with colleagues of the Initiative for Open Abstracts, celebrate the first anniversary of the initiative.&lt;/em&gt; On September 24 last year, the Initiative for Open Abstracts (I4OA) was launched. We started the initiative as a group of colleagues working in the publishing industry, for scholarly infrastructure organizations, at university libraries, and at science studies research centers.
Analysis of Data’s Privacy and Anonymity Aspects of Contact Tracing Apps via Smartphones – A Use Case of COVID-19

Privacy and anonymity aspects are playing a vital role in accessing smartphone apps. This is more evident in unexpected epidemic situations like COVID-19 while working with contact tracing apps. A human connectivity model is essential to analyse the widespread cases of viruses and vaccination patterns during the timeframe of March 2020 to May 2021. Smartphone apps that are supported by technologies like IoT and blockchain have already proven effective in tracing the Ebola epidemic. Thus, this technology, coupled with privacy-preserving features, would help to discover clusters with infectious contacts and alert the respective authorities. Besides, this can also allow us to understand the human connectivity model and the effectiveness of vaccines, which can aid in developing a plan of action for future epidemics. Hence, this article focuses on the analysis of data collected from contact tracing apps and a number of affected cases. It includes a study on early solutions with existing technologies, an overview and analysis of existing COVID-19 apps with vulnerabilities, proposed solutions, and data analysis on privacy and anonymity aspects of smartphone apps using the ARIMA model. It is evaluated by correlating it with the usage of contact tracing apps. The results assured a positive correlation between the number of downloads and the number of cases. This infers that even though the Indian government released these contact tracing apps, it all depends on the citizens to utilise them to their fullest. As a policy suggestion, it is stated that regardless of the prevalence of contact tracing apps, people must follow the rules and regulations suggested by the local health authorities and maintain social distancing in public places.
FSD: Fully-Specialized Detector via Neural Architecture Search

Most generic object detectors are mainly built for “standard” object detection tasks such as COCO [1] and PAS-CAL VOC [2]. They might not work well and/or efficiently on tasks of other domains consisting of images that are visually different from standard datasets. To this end, many advances have been focused on adapting a general-purposed object detector with limited domain-specific designs. However, designing a successful task-specific detector requires extraneous manual experiments and parameter tuning through trial and error. In this paper, we first propose and examine a fully-automatic pipeline to design a fully-specialized detector (FSD) which mainly incorporates a neural-architectural-searched model by exploring ideal network structures over the backbone and task-specific head. On the DeepLesion dataset, extensive results show that FSD can achieve 3.1 mAP gain while using approximately 40% fewer parameters on binary lesion detection task and improved the mAP by around 10% on multi-type lesion detection task via our region-aware graph modeling compared with existing general-purposed medical lesion detection networks.
A Compact Switch by Tuning of Effective Thermo-optic Coefficient of Waveguide Supermodes

We have demonstrated a compact thermo-optic switch based on the differential effective thermo-optic coefficient of different waveguide supermodes. The extinction ratio exceeds 18 dB across a 40 nm wavelength range.
On the construction of near-MDS matrices

The optimal branch number of MDS matrices makes them a preferred choice for designing diffusion layers in many block ciphers and hash functions. However, in lightweight cryptography, Near-MDS (NMDS) matrices with sub-optimal branch numbers offer a better balance between security and efficiency as a diffusion layer, compared to MDS matrices. In this paper, we study NMDS matrices, exploring their construction in both recursive and nonrecursive settings. We provide several theoretical results and explore the hardware efficiency of the construction of NMDS matrices. Additionally, we make comparisons between the results of NMDS and MDS matrices whenever possible. For the recursive approach, we study the DLS matrices and provide some theoretical results on their use. Some of the results are used to restrict the search space of the DLS matrices. We also show that over a field of characteristic 2, any sparse matrix of order $$n\ge 4$$ with fixed XOR value of 1 cannot be an NMDS when raised to a power of $$k\le n$$ . Following that, we use the generalized DLS (GDLS) matrices to provide some lightweight recursive NMDS matrices of several orders that perform better than the existing matrices in terms of hardware cost or the number of iterations. For the nonrecursive construction of NMDS matrices, we study various structures, such as circulant and left-circulant matrices, and their generalizations: Toeplitz and Hankel matrices. In addition, we prove that Toeplitz matrices of order $$n>4$$ cannot be simultaneously NMDS and involutory over a field of characteristic 2. Finally, we use GDLS matrices to provide some lightweight NMDS matrices that can be computed in one clock cycle. The proposed nonrecursive NMDS matrices of orders 4, 5, 6, 7, and 8 can be implemented with 24, 50, 65, 96, and 108 XORs over $$\mathbb {F}_{2^4}$$ , respectively.
Features of Psychological Readiness of Teachers to use Information and Communication Technologies in Pedagogical Activity

The objectives of the research: The article considers the problem of preparing teachers for activities in the information space, which covers both the virtual space, where electronic communications are provided via various Internet networks, and certain technical devices.Considerable attention is paid to the study of the peculiarities of the psychological readiness of teachers to use information and communication technologies.The complex of research methods is used in the work: Psychological features and problems of the influence of information and communication technologies on the implementation of effective interaction between teachers and students are identified.Theoretical results: Based on the analysis, the essence and structure of psychological readiness of teachers to interact with students in the digital educational environment are revealed, which contains cognitive, motivational and operational components.Empirical results: A set of methods for studying the levels of psychological readiness of teachers to use information technology and interaction with students in the educational environment is grounded and the results of their application in a representative sample of educators are presented.It is noted that the introduction of ICT in the educational environment contributes to improving its quality, as well as improving the organization of educational institutions and their management.
Optimization of partially monotonic functions subject to bipolar fuzzy relation equations

A method to solve a latticed optimization problem constrained by a bipolar fuzzy relation equation is presented in this paper, under the hypothesis of a partially monotonic objective function. The solving strategy consists of transforming the problem into optimizing an order-preserving function in all arguments subject to another bipolar fuzzy relation equation. As a result, all the solutions of the original optimization problem can be deduced from the extremal elements of the feasible domain of the transformed problem. The presented approach embraces the particular case of linear optimization constrained by bipolar fuzzy relation equations.
Towards Children-Centred Trustworthy Conversational Agents

Conversational agents (CAs) have been increasingly used in various domains, including education, health and entertainment. One of the growing areas of research is the use of CAs with children. However, the development and deployment of CAs for children come with many specific challenges and ethical and social responsibility concerns. This chapter aims to review the related work on CAs and children, point out the most popular topics and identify opportunities and risks. We also present our proposal for ethical guidelines on the development of trustworthy artificial intelligence (AI), which provide a framework for the ethical design and deployment of CAs with children. The chapter highlights, among other principles, the importance of transparency and inclusivity to safeguard user rights in AI technologies. Additionally, we present the adaptation of previous AI ethical guidelines to the specific case of CAs and children, highlighting the importance of data protection and human agency. Finally, the application of ethical guidelines to the design of a conversational agent is presented, serving as an example of how these guidelines can be integrated into the development process of these systems. Ethical principles should guide the research and development of CAs for children to enhance their learning and social development.
Controllable GAN Synthesis Using Non-Rigid Structure-from-Motion

In this paper, we present an approach for combining non-rigid structure-from-motion (NRSfM) with deep generative models, and propose an efficient framework for discovering trajectories in the latent space of 2D GANs corresponding to changes in 3D geometry. Our approach uses recent advances in NRSfM and enables editing of the camera and non-rigid shape information associated with the latent codes without needing to retrain the generator. This formulation provides an implicit dense 3D reconstruction as it enables the image synthesis of novel shapes from arbitrary view angles and non-rigid structure. The method is built upon a sparse backbone, where a neural regressor is first trained to regress parameters describing the cameras and sparse non-rigid structure directly from the latent codes. The latent trajectories associated with changes in the camera and structure parameters are then identified by estimating the local inverse of the regressor in the neighborhood of a given latent code. The experiments show that our approach provides a versatile, systematic way to model, analyze, and edit the geometry and non-rigid structures of faces.
LSFSL: Leveraging Shape Information in Few-shot Learning

Few-shot learning (FSL) techniques seek to learn the underlying patterns in data using fewer samples, analogous to how humans learn from limited experience. In this limiteddata scenario, the challenges associated with deep neural networks, such as shortcut learning and texture bias behaviors, are further exacerbated. Moreover, the significance of addressing shortcut learning is not yet fully explored in the few-shot setup. To address these issues, we propose LSFSL, which enforces the model to learn more generalizable features utilizing the implicit prior information present in the data. Through comprehensive analyses, we demonstrate that LSFSL-trained models are less vulnerable to alteration in color schemes, statistical correlations, and adversarial perturbations leveraging the global semantics in the data. Our findings highlight the potential of incorporating relevant priors in few-shot approaches to increase robustness and generalization.