Miracle-dz/UniFAR-FTUs huggingface dataset:
Construction and application of intelligent mental health management system for prisoners

As a special group, prisoners' mental health problems endanger the safety of supervision and social security. The traditional psychological assessment scale for prisoners has a large number of topics and a complicated assessment process, which makes it difficult to quickly obtain and quantify psychological risks, and also lacks follow-up health promotion intervention. It is a practical and efficient method to carry out early risk assessment and early warning management on the psychological symptoms of prisoners by means of information technology. The intelligent health management system constructed in this study conforms to the development direction of intelligent medical treat-ment, and can make the mental health management of prisoners more objective, scientific and accurate. The system mainly uses the prediction model of integrated learning algorithm to evaluate the mental state of prisoners, and constructs a relevant knowledge base for personalized health management guidance. The system evaluation results show that the accuracy of the risk assessment model has reached 0.974, and the personalized health promotion function is also feasible. Therefore, the intelligent health management system can effectively assist the existing psychological assessment and correction work, find out the potential psychological abnormalities of criminals in time, intervene and correct in advance, so as to maintain the normal supervision and reform work, and ensure the safety of supervision and social stability.
On the integrability problem for systems of partial differential equations in one unknown function, I

We discuss the integration problem for systems of partial differential equations in one unknown function and special attention is given to the first order systems. The Grassmannian contact structures are the basic setting for our discussion and the major part of our considerations inquires on the nature of the Cauchy characteristics in view of obtaining the necessary criteria that assure the existence of solutions. In all the practical applications of partial differential equations, what is mostly needed and what in fact is hardest to obtains are the solutions of the system or, occasionally, some specific solutions. This work is based on four most enlightening Mémoires written by Élie Cartan in the beginning of the last century.
Teachers’ use of augmented reality in the classroom: reasons, practices, and needs

: Research on augmented reality (AR) in education shows various advantages and challenges for learners while research on teachers is scarce. In practice, teachers drive the implementation of AR in classes, and it is important to examine current practices and requirements to be able to effectively support their efforts. N = 16 teachers who had used AR in their classes took part in a survey with open-ended questions focusing on reasons for, current practice of, and needs when using AR in education. The answers were classified into different (sub-)categories. As reasons for usage, different instructional and non-instructional purposes are mentioned. In the current practices various technologies, design-platforms and educational materials are described, and needs include content availability, pedagogical integration, and technical infrastructure. The results are discussed with a focus on unexpected outcomes and the potentials of collaboration between researchers, practitioners, and other stakeholders to support teachers AR classroom integration.
Cultural and Religious Rifts of Kazakhs and Kirgiz's in the Era of Global Peace: Comparative Analysis Aiman Gappassova

Central Asia's developing nations' cultural and religious space faces many obstacles in reacting to global challenges. The society's reaction in Central Asia to adopting global liberal values is not unambiguous. The study examines the evolution of Kazakh and Kyrgyz religious, cultural, and civilizational values in the era of globalization. In addition, this study examines the impact of cultural and religious divides on global challenges in a period of global peace. This research utilizes primary data for data analysis and uses the partial least square (PLS) approach. In a period of global peace, the study concluded that cultural and religious differences substantially impacted global challenges. The research assesses the operations of government agencies utilizing modern technology to combat the takeover of a person's spiritual essence by extremist ideological groupings. Theoretically, this research is noteworthy because prior studies do not address the literature gap. The future directions of this research are essential for advancing the body of knowledge.
Chaos, Solitons and Fractals

In Thermodynamics and Statistical Physics, a system’s property is extensive when it grows with the system size. When it happens, the system can be decomposed into separate components, which has been done in many systems with weakly interacting components, such as for various gas models. Similarly, Ruelle conjectured 40 years ago that the Lyapunov exponents (LEs) of some sufficiently large chaotic systems are extensive, which led to study the extensivity properties of chaotic systems with strong interactions. Because of the complexities in these systems, most results achieved so far are restricted to numerical simulations. Here, we derive closed-form expressions for the LEs and entropy rate of coupled maps in finite-and infinite-sized regular graphs, according to the coupling strength, map’s chaoticity, and graph’s spectral properties. We show that this type of system has either 4 or 5 cases for the LEs, depending on the graph’s extreme Laplacian eigenvalues. These cases represent qualitatively different collective behaviours emerging in parameter space, including chaotic synchronisation ( 𝑁 −1 negative LEs) and incoherent chaos ( 𝑁 positive LEs). From the entropy rate, we show that the ring and complete graphs (nearest-neighbour and all-to-all couplings, respectively) are extensive in all parameter regions outside the chaotic synchronisation region. Although our derivations are restricted to one-dimensional maps with constant positive derivative (i.e., chaotic), our approach can be used to find LE and entropy rates for other regular graphs (such as for cyclic graphs) or be the basis for tackling small world graphs via perturbative methods.
Engineering Degree Project Streamline searches in a database

The objective of this thesis is to explore technologies and solutions and see if it is possible to make a logistical flow more efficient. The logistical flow consists of a database containing materiel for purchase or reparation. As of now, searches may either result in too many results, of which several are irrelevant, or no results at all. The search needs to be very specific to retrieve the exact item, which requires extensive knowledge about the database and its contents. Areas that will be explored include Natural Language Processing and Machine Learning techniques. To solve this, a literature study will be conducted to gain insights into existing work and possible solutions. Exploratory Data Analysis will be used to understand the patterns and limitations of the data.
Structured Prediction with Stronger Consistency Guarantees

We present an extensive study of surrogate losses for structured prediction supported by H -consistency bounds . These are recently introduced guarantees that are more relevant to learning than Bayes-consistency, since they are not asymptotic and since they take into account the hypothesis set H used. We ﬁrst show that no non-trivial H -consistency bound can be derived for widely used surrogate structured prediction losses. We then deﬁne several new families of surrogate losses, including structured comp-sum losses and structured constrained losses , for which we prove H -consistency bounds and thus Bayes-consistency. These loss functions readily lead to new structured prediction algorithms with stronger theoretical guarantees, based on their minimization. We describe efﬁcient algorithms for minimizing several of these surrogate losses, including a new structured logistic loss .
Free Trade Integration Effect on International Relations through Cross Border Entrepreneurship: A Study from Indonesia and Malaysia International Business Perspective

In the modern age, the trade free integration has become an integral part which is playing a significant part for the international relations. For enhancing cross border cost-effective accomplishments, the free trade integration role is an important which could help to enhance the international relations. Therefore, the research aim is to investigate the free trade integration and cross border entrepreneurship role in development of international relations. To get this objective, the relationship among free trade integration (FRTI), financial integration (FII), cross border entrepreneurship (CRBE), bilateral trade (BIT), and international relations (INR) was being inspected. The data was collected through self-administered questionnaire from the 200 from Indonesia and 130 from Malaysia by using purposive sampling technique. The research design was quantitative, and research approach was quantitative, and applied partial Least Square (PLS)- Structural Equation Modeling (SEM). The key outcomes shown that FRTI has positive and significant effect on FII, BII, CRBE and IR. In the same vein, the key findings also shown that BIT and FII also significantly and positively effect to CRBE. On the other hand, it was also further found that BIT, FII significantly and positively mediated between the relationship of FRTI and CRBE. The further findings shown that CRBE also significantly mediated between the relationship of FRTI and INR. All these accepted hypotheses added a body of literature in the extant literature that could add a body of literature in the extant literature that could become a new research area in future. This is a reason the current research has a valuable contribution in the literature through the relationship among FII, BIT, CRBE, FRTI and INR.
K NOWLEDGE - BASED C YBER P HYSICAL S ECURITY AT S MART H OME : A R EVIEW

Smart home systems represent the future of modern building infrastructure, integrating numerous devices and applications to elevate the overall quality of life. These systems establish connectivity among smart devices, leveraging network technologies and algorithmic control to monitor and manage the physical environment. However, ensuring robust security in smart homes, alongside securing the smart devices themselves, presents a formidable challenge. A substantial part of security solutions for smart homes rely on data-driven approaches (e
Multinational EFL Teachers Perspective of Using L1 in the L2 Classroom 1

While some researchers claim that the use of the first language (L1) in second language (L2) classrooms should be discouraged since they say that success in learning an L2 is heavily determined by the maximum exposure to it, but others argue that the use of L1 is beneficial particularly in EFL classrooms. This study was conducted to investigate the beliefs of EFL language teachers in using L1 in the English classroom with the hope of providing new insight and evidence for the abovementioned theories on L1 use in L2 classrooms. A modified 5-Likert scale questionnaire and individual follow-up interview were used to collect the data. The participants were 47 foreign teachers teaching in a Thai EFL context. The data obtained using mixed-method study design revealed that the majority of teacher-participants felt that judicious use of L1 in the English classroom is useful for learners. The reported benefits of using L1 in L2 classrooms included improving the classroom environment, improving the outcome of vocabulary and grammar knowledge, a deeper understanding of the lesson, and bringing the positive relationship between teachers and students. However, a few participants argued that the use of L1 in L2 classrooms should be discouraged since it lessens the students’ opportunities to listen and speak the target language.
This paper is included in the Proceedings of the 32nd USENIX Security Symposium.

IP preﬁx hijacks allow adversaries to redirect and intercept trafﬁc, posing a threat to the stability and security of the Internet. To prevent preﬁx hijacks, networks should deploy RPKI and ﬁlter bogus BGP announcements with invalid routes. In this work we evaluate the impact of RPKI deployments on the security and resilience of the Internet. We aim to understand which networks ﬁlter invalid routes and how effective that ﬁltering is in blocking preﬁx hijacks. We extend previous data acquisition and analysis methodologies to obtain more accurate identiﬁcation of networks that ﬁlter invalid routes with RPKI. We ﬁnd that more than 27% of networks enforce RPKI ﬁltering and show for the ﬁrst time that deployments follow the business incentives of inter-domain routing: providers have an increased motivation to ﬁlter in order to avoid losing customers’ trafﬁc. Analyzing the effectiveness of RPKI, we ﬁnd that the current trend to deploy RPKI on routeservers of Internet Exchange Points (IXPs) only provides a localized protection against hijacks but has negligible impact on preventing their spread globally. In contrast, we show that RPKI ﬁltering in Tier-1 providers greatly beneﬁts the security of the Internet as it limits the spread of hijacks to a localized scope. Based on our observations, we provide recommendations on the future roadmap of RPKI deployment. We make our datasets 1 available for public use.
Standard-based approach to programmable hybrid networks

Programmable hybrid networks are considered in this article as a challenge to deploy flexible solutions for next-generation networks. A modular programmable router architecture, which takes advantage of emerging standards, is proposed to support dynamic management and configuration capabilities of network resources and services. The new node design concepts allow vendor-independent development of hybrid router functionalities and possible investigation of new standard interfaces. Validation of the proposed approach is proven in an open source software router context, emulating a hybrid circuit/packet service based on optical switching technology. The software emulator obtained allows programmable hybrid router functionalities to be tested at different levels of abstraction and with different modeling granularities, and is able to represent both logical and physical aspects.
Avoidant-restrictive food intake disorder and autism: epidemiology, etiology, complications, treatment, and outcome

Purpose of review There is considerable overlap between the features of avoidant-restrictive food intake disorder (ARFID) and autism. The purpose of this scoping review is to provide an overview of studies published on ARFID and autism in 2022 and the first half of 2023. Recent findings ARFID and autism are highly heritable conditions that often co-occur. In a large autism cohort, 21% of participants and 17% of their parents presented with avoidant-restrictive features. Of children diagnosed with ARFID, 8.2–54.8% are autistic. More than half of individuals with ARFID also have other neurodevelopmental, psychiatric, or somatic diagnoses. Anxiety, depression, sleep disorders, and learning difficulties are particularly common co-occurring issues. Various strategies have been developed to support autistic children with feeding difficulties. It appears that their feeding difficulties, particularly sensory sensitivities, food preferences, and mealtime rituals and routines frequently persist into adolescence and adulthood, but research on optimal support for adults and adolescents is still scarce. Untreated ARFID in autistic individuals may lead to serious complications. Summary Individuals seeking specialist care for autism, eating disorders, or gender dysphoria should be screened for ARFID. More research is needed on how to support autistic adolescents and adults with features of ARFID.
This paper is included in the Proceedings of the 32nd USENIX Security Symposium.

Fuzz drivers are essential for fuzzing library APIs. However, manually composing fuzz drivers is difficult and time-consuming. Therefore, several works have been proposed to generate fuzz drivers automatically. Although these works can learn correct API usage from the consumer programs of the target library, three challenges still hinder the quality of the generated fuzz drivers: 1) How to learn and utilize the control dependencies in API usage; 2) How to handle the noises of the learned API usage, especially for complex real-world consumer programs; 3) How to organize independent sets of API usage inside the fuzz driver to better coordinate with fuzzers. To solve these challenges,we propose R UBICK ,an automata-guided control-flow-sensitive fuzz driver generation technique. R UBICK has three key features: 1) it models the API usage (including API data and control dependencies) as a deterministic finite automaton; 2) it leverages active automata learning algo-rithm to distill the learned API usage; 3) it synthesizes a single automata-guided fuzz driver, which provides scheduling interface for the fuzzer to test independent sets of API usage during fuzzing. During the experiments, the fuzz drivers generated by R UBICK showed a significant performance advantage over the baselines by covering an average of 50.42% more edges than fuzz drivers generated by F UZZ G EN and 44.58% more edges than manually written fuzz drivers from OSS-Fuzz or human experts. By learning from large-scale open source projects, R UBICK has generated fuzz drivers for 11 popular
Advancing TTP Analysis: Harnessing the Power of Encoder-Only and Decoder-Only Language Models with Retrieval Augmented Generation

Tactics, Techniques, and Procedures (TTPs) outline the methods attackers use to exploit vulnerabilities. The interpretation of TTPs in the MITRE ATT&CK framework can be challenging for cybersecurity practitioners due to presumed expertise, complex dependencies, and inherent ambiguity. Meanwhile, advancements with Large Language Models (LLMs) have led to recent surge in studies exploring its uses in cybersecurity operations. This leads us to question how well encoder-only (e.g., RoBERTa) and decoder-only (e.g., GPT-3.5) LLMs can comprehend and summarize TTPs to inform analysts of the intended purposes (i.e., tactics) of a cyberattack procedure. The state-of-the-art LLMs have shown to be prone to hallucination by providing inaccurate information, which is problematic in critical domains like cybersecurity. Therefore, we propose the use of Retrieval Augmented Generation (RAG) techniques to extract relevant contexts for each cyberattack procedure for decoder-only LLMs (without fine-tuning). We further contrast such approach against supervised fine-tuning (SFT) of encoder-only LLMs. Our results reveal that both the direct-use of decoder-only LLMs (i.e., its pre-trained knowledge) and the SFT of encoder-only LLMs offer inaccurate interpretation of cyberattack procedures. Significant improvements are shown when RAG is used for decoder-only LLMs, particularly when directly relevant context is found. This study further sheds insights on the limitations and capabilities of using RAG for LLMs in interpreting TTPs.
HQ-VAE: Hierarchical Discrete Representation Learning with Variational Bayes

Vector quantization (VQ) is a technique to deterministically learn features with discrete codebook representations. It is commonly performed with a variational autoencoding model, VQ-VAE, which can be further extended to hierarchical structures for making high-fidelity reconstructions. However, such hierarchical extensions of VQ-VAE often suffer from the codebook/layer collapse issue, where the codebook is not efficiently used to express the data, and hence degrades reconstruction accuracy. To mitigate this problem, we propose a novel unified framework to stochastically learn hierarchical discrete representation on the basis of the variational Bayes framework, called hierarchically quantized variational autoencoder (HQ-VAE). HQ-VAE naturally generalizes the hierarchical variants of VQ-VAE, such as VQ-VAE-2 and residual-quantized VAE (RQ-VAE), and provides them with a Bayesian training scheme. Our comprehensive experiments on image datasets show that HQ-VAE enhances codebook usage and improves reconstruction performance. We also validated HQ-VAE in terms of its applicability to a different modality with an audio dataset.
Bidirectional Trained Tree-Structured Decoder for Handwritten Mathematical Expression Recognition

The Handwritten Mathematical Expression Recognition (HMER) task is a critical branch in the field of OCR. Recent studies have demonstrated that incorporating bidirectional context information significantly improves the performance of HMER models. However, existing methods fail to effectively utilize bidirectional context information during the inference stage. Furthermore, current bidirectional training methods are primarily designed for string decoders and cannot adequately generalize to tree decoders, which offer superior generalization capabilities and structural analysis capacity. In order to overcome these limitations, we propose the Mirror-Flipped Symbol Layout Tree (MF-SLT) and Bidirectional Asynchronous Training (BAT) structure. Our method extends the bidirectional training strategy to the tree decoder, allowing for more effective training by leveraging bidirectional information. Additionally, we analyze the impact of the visual and linguistic perception of the HMER model separately and introduce the Shared Language Modeling (SLM) mechanism. Through the SLM, we enhance the model's robustness and generalization when dealing with visual ambiguity, particularly in scenarios with abundant training data. Our approach has been validated through extensive experiments, demonstrating its ability to achieve new state-of-the-art results on the CROHME 2014, 2016, and 2019 datasets, as well as the HME100K dataset. The code used in our experiments will be publicly available.
CMB lensing from early-formed dark matter halos

Some theoretical models for the early universe predict a spike-type enhancement in the primordial power spectrum on a small scale, which would result in forming early-formed dark matter halos (EFHs). In this work, we study the CMB lensing effect, considering the existence of EFHs, and investigate the potential to probe the EFHs and the primordial perturbations on scales smaller than $1\mathrm{Mpc}$. We numerically calculate the angular power spectrum of the lensing potential and the lensed CMB anisotropy of temperature, E-mode, and B-mode polarization, including the nonlinear effects of EFHs. We find the possibility that the lensed CMB temperature anisotropy is significantly enhanced on small scales, $\ell>1000$, and could be tested by component decomposition of observed signals through multi-frequency observations. Through the calculation with different models of the spiky-type power spectrum, we demonstrate that the accurate measurements of the CMB lensing effect would provide insight into the abundance of EFHs within the limited mass range around $10^{12}M_\odot$ and the primordial power spectrum on the limited scales around $k\sim 1\mathrm{Mpc}^{-1}$. In particular, we find that the existence of such EFHs can amplify the lensed anisotropy of CMB B-mode polarization even on large scales, $\ell<100$, as the overall enhancement by $\sim 10 \%$ level compared to the standard structure formation model without EFHs. Therefore, future CMB measurements such as the LiteBIRD satellite can probe the existence of the EFHs and the spike-type primordial power spectrum through the precise measurement of the large-scale CMB B-mode polarization.