Reviewerly/exHarmony huggingface dataset:
Open quantum system in the indefinite environment

In this paper, we investigate the interference engineering of the open quantum system, where the environment is made indefinite either through the use of an interferometer or the introduction of auxiliary qubits. The environments are modeled by fully connected qubit baths with exact analytical dynamics. As the system passes through the interferometer or is controlled by auxiliary qubits, it is propagated along different paths or their superpositions, leading to distinct interactions with the environment in each path. This results in the superposition of the environments, which can be detected through specific measurements that retain certain coherent information about the paths. Our results demonstrate that the indefiniteness of the environment can significantly enhance the quantum correlations. However, only the statistical mixture of the influences from the environments preserves provided that the path coherence is destructed. We also examine the serviceability of the indefiniteness as a resource for teleportation and quantum parameter estimation. Additionally, we discuss how to quantify the indefiniteness and the ways in which it affects the system's dynamics from the perspective of wave-particle-entanglement-ignorance complementarity. Overall, our study highlights the potential benefits of an indefinite environment in quantum information processing and sheds light on the fundamental principles underlying its effects.
A note on the incompleteness of Afshari &amp; Leigh's system Clo

The system $\mathsf{Clo}$ is a cyclic, cut-free proof system for the modal $\mu$-calculus. It was introduced by Afshari & Leigh as an intermediate system in their intent to show the completeness of Kozen's axiomatisation for the modal $\mu$-calculus. We prove that $\mathsf{Clo}$ is incomplete by giving a valid sequent that is not provable in $\mathsf{Clo}$.
SecureFalcon: The Next Cyber Reasoning System for Cyber Security

Software vulnerabilities leading to various detriments such as crashes, data loss, and security breaches, significantly hinder the quality, affecting the market adoption of software applications and systems. Although traditional methods such as automated software testing, fault localization, and repair have been intensively studied, static analysis tools are most commonly used and have an inherent false positives rate, posing a solid challenge to developer productivity. Large Language Models (LLMs) offer a promising solution to these persistent issues. Among these, FalconLLM has shown substantial potential in identifying intricate patterns and complex vulnerabilities, hence crucial in software vulnerability detection. In this paper, for the first time, FalconLLM is being fine-tuned for cybersecurity applications, thus introducing SecureFalcon, an innovative model architecture built upon FalconLLM. SecureFalcon is trained to differentiate between vulnerable and non-vulnerable C code samples. We build a new training dataset, FormAI, constructed thanks to Generative Artificial Intelligence (AI) and formal verification to evaluate its performance. SecureFalcon achieved an impressive 94% accuracy rate in detecting software vulnerabilities, emphasizing its significant potential to redefine software vulnerability detection methods in cybersecurity.
Intent-calibrated Self-training for Answer Selection in Open-domain Dialogues

Answer selection in open-domain dialogues aims to select an accurate answer from candidates. Recent success of answer selection models hinges on training with large amounts of labeled data. However, collecting large-scale labeled data is labor-intensive and time-consuming. In this paper, we introduce the predicted intent labels to calibrate answer labels in a self-training paradigm. Specifically, we propose the intent-calibrated self-training (ICAST) to improve the quality of pseudo answer labels through the intent-calibrated answer selection paradigm, in which we employ pseudo intent labels to help improve pseudo answer labels. We carry out extensive experiments on two benchmark datasets with open-domain dialogues. The experimental results show that ICAST outperforms baselines consistently with 1%, 5% and 10% labeled data. Specifically, it improves 2.06% and 1.00% of F1 score on the two datasets, compared with the strongest baseline with only 5% labeled data.
The Frobenius transform of a symmetric function

We define an abelian group homomorphism $\mathscr{F}$, which we call the Frobenius transform, from the ring of symmetric functions to the ring of the symmetric power series. The matrix entries of $\mathscr{F}$ in the Schur basis are the restriction coefficients $r_\lambda^\mu = \dim \operatorname{Hom}_{\mathfrak{S}_n}(V_\mu, \mathbb{S}^\lambda \mathbb{C}^n)$, which are known to be nonnegative integers but have no known combinatorial interpretation. The Frobenius transform satisfies the identity $\mathscr{F}\{fg\} = \mathscr{F}\{f\} \ast \mathscr{F}\{g\}$, where $\ast$ is the Kronecker product. We prove for all symmetric functions $f$ that $\mathscr{F}\{f\} = \mathscr{F}_{\mathrm{Sur}}\{f\} \cdot (1 + h_1 + h_2 + \cdots)$, where $\mathscr{F}_{\mathrm{Sur}}\{f\}$ is a symmetric function with the same degree and leading term as $f$. Then, we compute the matrix entries of $\mathscr{F}_{\mathrm{Sur}}\{f\}$ in the complete homogeneous, elementary, and power sum bases and of $\mathscr{F}^{-1}_{\mathrm{Sur}}\{f\}$ in the complete homogeneous and elementary bases, giving combinatorial interpretations of the coefficients where possible. In particular, the matrix entries of $\mathscr{F}^{-1}_{\mathrm{Sur}}\{f\}$ in the elementary basis count words with a constraint on their Lyndon factorization. As an example application of our main results, we prove that $r_\lambda^\mu = 0$ if $|\lambda \cap \mu'| < 2|\mu'| - |\lambda|$, where $\mu'$ is the partition formed by removing the first part of $\mu$. We also prove that $r_\lambda^\mu = 0$ if the Young diagram of $\mu$ contains a square of side length greater than $2^{\lambda_1 - 1}$, and this inequality is tight.
Prompts Should not be Seen as Secrets: Systematically Measuring Prompt Extraction Attack Success

The text generated by large language models is commonly controlled by prompting, where a prompt prepended to a user's query guides the model's output. The prompts used by companies to guide their models are often treated as secrets, to be hidden from the user making the query. They have even been treated as commodities to be bought and sold. However, anecdotal reports have shown adversarial users employing prompt extraction attacks to recover these prompts. In this paper, we present a framework for systematically measuring the effectiveness of these attacks. In experiments with 3 different sources of prompts and 11 underlying large language models, we find that simple text-based attacks can in fact reveal prompts with high probability. Our framework determines with high precision whether an extracted prompt is the actual secret prompt, rather than a model hallucination. Prompt extraction experiments on real systems such as Bing Chat and ChatGPT suggest that system prompts can be revealed by an adversary despite existing defenses in place.
Towards a resolution of the spin alignment problem

Consider minimizing the entropy of a mixture of states by choosing each state subject to constraints. If the spectrum of each state is fixed, we expect that in order to reduce the entropy of the mixture, we should make the states less distinguishable in some sense. Here, we study a class of optimization problems that are inspired by this situation and shed light on the relevant notions of distinguishability. The motivation for our study is the recently introduced spin alignment conjecture. In the original version of the underlying problem, each state in the mixture is constrained to be a freely chosen state on a subset of $n$ qubits tensored with a fixed state $Q$ on each of the qubits in the complement. According to the conjecture, the entropy of the mixture is minimized by choosing the freely chosen state in each term to be a tensor product of projectors onto a fixed maximal eigenvector of $Q$, which maximally "aligns" the terms in the mixture. We generalize this problem in several ways. First, instead of minimizing entropy, we consider maximizing arbitrary unitarily invariant convex functions such as Fan norms and Schatten norms. To formalize and generalize the conjectured required alignment, we define alignment as a preorder on tuples of self-adjoint operators that is induced by majorization. We prove the generalized conjecture for Schatten norms of integer order, for the case where the freely chosen states are constrained to be classical, and for the case where only two states contribute to the mixture and $Q$ is proportional to a projector. The last case fits into a more general situation where we give explicit conditions for maximal alignment. The spin alignment problem has a natural "dual" formulation, versions of which have further generalizations that we introduce.
Min-Max Optimization under Delays

Delays and asynchrony are inevitable in large-scale machine-learning problems where communication plays a key role. As such, several works have extensively analyzed stochastic optimization with delayed gradients. However, as far as we are aware, no analogous theory is available for min-max optimization, a topic that has gained recent popularity due to applications in adversarial robustness, game theory, and reinforcement learning. Motivated by this gap, we examine the performance of standard min-max optimization algorithms with delayed gradient updates. First, we show (empirically) that even small delays can cause prominent algorithms like Extra-gradient (\texttt{EG}) to diverge on simple instances for which \texttt{EG} guarantees convergence in the absence of delays. Our empirical study thus suggests the need for a careful analysis of delayed versions of min-max optimization algorithms. Accordingly, under suitable technical assumptions, we prove that Gradient Descent-Ascent (\texttt{GDA}) and \texttt{EG} with delayed updates continue to guarantee convergence to saddle points for convex-concave and strongly convex-strongly concave settings. Our complexity bounds reveal, in a transparent manner, the slow-down in convergence caused by delays.
InternVid: A Large-scale Video-Text Dataset for Multimodal Understanding and Generation

This paper introduces InternVid, a large-scale video-centric multimodal dataset that enables learning powerful and transferable video-text representations for multimodal understanding and generation. The InternVid dataset contains over 7 million videos lasting nearly 760K hours, yielding 234M video clips accompanied by detailed descriptions of total 4.1B words. Our core contribution is to develop a scalable approach to autonomously build a high-quality video-text dataset with large language models (LLM), thereby showcasing its efficacy in learning video-language representation at scale. Specifically, we utilize a multi-scale approach to generate video-related descriptions. Furthermore, we introduce ViCLIP, a video-text representation learning model based on ViT-L. Learned on InternVid via contrastive learning, this model demonstrates leading zero-shot action recognition and competitive video retrieval performance. Beyond basic video understanding tasks like recognition and retrieval, our dataset and model have broad applications. They are particularly beneficial for generating interleaved video-text data for learning a video-centric dialogue system, advancing video-to-text and text-to-video generation research. These proposed resources provide a tool for researchers and practitioners interested in multimodal video understanding and generation.
Business Educators’ Perception of Government (Tetfund) Human Capital Development as a Strategy for Moving Nigeria Towards Knowledge Based Economy in Delta State

This study focused on business educators’ perception of government (TETFund) human capital development as a strategy for moving Nigeria towards knowledge based economy in Delta State. Three research questions guided the study and two null hypotheses which was tested at 0.05 level of significance. A descriptive survey research design was adopted. The population for the study consisted of 157 business educators drawn from tertiary institutions that offer business education programme in Delta State. There was no sample since the population was of manageable size. A structured questionnaire of 30-item on four-point Likert scale was used for data collection. The instrument was validated by two experts in business education. The reliability of the instrument was determined by using Cronbach Alpha reliability method and the reliability coefficient was 0,72 The data collected was analyzed using mean statistics and standard deviation to answer the research questions while the t-test statistics was used to test the null hypotheses at 0.05 level of significance. The findings revealed that continued Tetfund intervention for business educators in training, research and development, infrastructure and learning aids can lead to knowledge based economy in Nigeria. It was recommended that Heads and management of tertiary institutions in Nigeria should set up a committee to supervise TETFund projects to ensure judicious use of the fund released by TETFund for provision or maintenance of infrastructure. Business educators who benefited from TETFund intervention in training and development locally or international, should endeavour to return to their school of Primary assignment after graduation to use the new knowledge and skill, among others.
Exploring Sentiment Analysis: Applications, and Challenges —A Comprehensive Survey

Sentiment analysis, also known as opinion mining, has emerged as a crucial field of research due to the exponential growth of user-generated content on various online platforms. This paper presents a comprehensive survey of sentiment analysis applications and challenges. It provides an overview of the different techniques employed for sentiment analysis, ranging from traditional machine learning approaches to more recent deep learning models. Furthermore, the survey examines the diverse applications of sentiment analysis across domains such as social media, e-commerce, customer reviews, and political analysis. Additionally, the paper highlights the major challenges and open research questions in sentiment analysis, including handling sarcasm, irony, and ambiguity, addressing data sparsity and imbalance issues, and ensuring cross-lingual and cross-domain generalization. By analyzing the existing literature, this survey aims to offer insights into the current state-of-the-art in sentiment analysis and provide directions for future research in this dynamic field. Key Words: Sentimental Analysis, languages, text, multimode, opinion
A Study of Performance Measures and Throughput of Raft Consensus Algorithm

Abstract: Due to its decentralised structure, blockchain technology has acquired great traction in fields such as banking, business, healthcare, and e-voting due to its decentralised nature. Because of the difficulty of lesser throughput, many blockchain systems have sought efficiency enhancements. Mathematical models such as Markov processes, queuing theory, and game theory are employed to analyze various aspects of blockchain systems, including mining processes, consensus mechanisms, and overall performance. This paper focuses on using M/M/1 queuing theory to analyze blockchain mining processes, utilizing the Raft algorithm for consensus. A simulation done by using python is also provided
Changing Scenario of Crime with Special Reference to Cyber Crime

Abstract: Crime may be defined as those actions committed by human beings that cause harm to others and violate societal norms. But with the advancement in science and technology that has reshaped every aspect of human life the scenario of crime has also changed from that of traditional to technological because in ancient times the concept of crime was prevalent in the society but the nature of crime was not so sophisticated as we see in the technological era. The invention of computers and smart phones has facilitated different types of cyber crimes in the society. Almost everyone is possessing a smart phone and the usage of it depends upon the people who will use it in a positive way. The concept of digital education has emerged as an important tool in the hands of learners and educators to propagate the education. Learners of lower age group are also using these technological equipments so parents should keep a strict vigil and supervision over their children in order to avoid such types of activities that cause moral harm to the norms and values of society. The main aim of this paper is to analyse how the nature of crime has changed and the different types of crime particularly in consideration with Cyber crime
IMPLEMENTASI FUNGSI-FUNGSI MANAJEMEN DALAM MENGELOLA PERPUSTAKAAN DI SMK NEGERI 2 SANGATTA UTARA

This article focuses on implementing management functions in the Library of SMK Negeri 2 North Sangatta. This study aims to: (1) describe the implementation of management functions in library management at SMK Negeri 2 North Sangatta, (2) describe the constraints in implementing management functions at the library at SMK Negeri 2 North Sangatta. Based on the results of observations and direct interviews with librarians, it can be summarized as follows. First, the application management function, especially in processing library materials in the library at SMK Negeri 2 North Sangatta has not run optimally, starting from inventorying, classifying, and processing library materials. Second, there were obstacles faced by human resources (HR) or librarian personnel who are experts in the library, as well as limited books on majors such as the Department of heavy equipment engineering which were not yet available.
Peranan Media Gambar dalam Meningkatan Keterampilan Membaca pada Siswa Kelas I

This study aims to improve students' reading skills by using media images in class I SD Negeri Karawaci 6. This type of research is classroom action research. The research subjects were grade I students. This classroom action research was conducted in two cycles with four components in each cycle, namely planning, action implementation, observation, and reflection. Data collection techniques were carried out using observation, interviews, documentation and field notes. The data analysis technique used is qualitative data analysis. Criteria for success seen from the process and product. The success of the process is measured based on improving the learning process in a better direction. The success of the product is measured based on the assessment of the final written test questions. The results of this study indicate that the use of flash card media can improve the reading skills of grade I students at SD Negeri Karawaci 6. Improvements can be seen in processes and products. Improving the quality of the process seen in student activities in learning. Improved quality of the process can be seen from the condition of students who are more confident in reading, more enthusiastic, and more independent. Process quality improvement has a positive impact on product quality improvement.
Eğitimde Web 3.0 Kullanımı: Potansiyel ve Zorlukları

Development of information and communication technologies, particularly the advancement of the internet and web technologies, has facilitated students' access to information and enriched their learning experiences. However, as the need for accessing accurate information increases within the vast sea of knowledge, web technologies are also evolving to serve this purpose. Web 3.0 technology, also known as the semantic web, makes information meaningful, enabling users to access information more effectively. The use of semantic web in education has the potential to make learning experiences more flexible, personalized, and effective. However, there are also challenges associated with the use of semantic web in education. Security concerns, costs, standardization, and pedagogical issues may limit the widespread adoption of semantic web. Keywords: Semantic web, web 3.0, educational semantic web
A semantics-driven framework for scalable demand flexibility control applications

This paper summarizes the doctoral research of the first author, discusses the contributions to knowledge, and outlines the initial results.The research proposes a semanticsdriven framework that leverages semantic models to support the scalability of demand flexibility control applications through improved interoperability and portability.The framework is evaluated in a standardized building testing platform, particularly focusing on the portability of a generic load shedding application across two buildings.This research is an important step towards the needed scale of demand flexibility applications, streamlining their development and deployment using minimal manual effort.
Enabling scalable deployment of data-driven applications across building portfolios

Despite the promising benefits of data-driven applications to improve building performance, they are still developed on an ad-hoc basis, mainly due to the burden of discovering and reusing building data across deployment sites.Recent efforts in semantic data modelling aim to overcome these barriers, though application development remains building-specific, leading to bespoke configurations that cannot scale.This research seeks to establish a new regime of data-driven applications that are developed once and run across multiple buildings -similarly to the applications we download and use in our mobile phones.This research contributes to realising this vision in two ways: introducing (a) a method for automatic building metadata model generation through the lifecycle, and (b) a portable application development paradigm that offsets the burden of configuration in the authoring process.An overview of this vision and contributions are illustrated in Figure 1.These advancements are expected to overcome expertise barriers, reduce time for applications' configuration and deployment, and thus, accelerate their adoption at scale.
Development of Arabic E-Book Media Based on Flip PDF Professional Application for Students

The learning process in which the atmosphere brings boredom and laziness and the lack of use of learning media is one of the factors in the lack of effective learning and the lack of enthusiasm of students in learning. For example, when learning takes place in schools, educators still use package books as materials in the learning process, so that educators cannot create a learning atmosphere. Therefore, educators need to increase their creativity by developing skills in making teaching materials in the form of utilizing technology-based learning media such as developing Arabic e-books using the Flip PDF Professional application. The purpose of this study was to develop Arabic e-books with the professional flip pdf application for junior high school students. In developing this e-book, researchers used the Research and Development (R&amp;D) method with the Borg &amp; Gall technique, the subjects in this study were 125 junior high school students. Based on large-scale testing, the results of the questionnaire of students' learning independence in the e-book developed reached an average percentage of 82%, namely the "Good" assessment and also included the "High" category. The results of small-scale testing in the questionnaire assessment of students' interest in learning in the e-book developed reached an average percentage of 85%, namely the "Good" assessment and included the "High" category. Based on small-scale testing, the results of the questionnaire of students' interest in learning in the e-book developed reached an average percentage of 85%, namely the assessment of "Good" and included the "High" category. From the results of this study, the researcher concluded that the Flip PDF Proffessional-based E-book for students developed was able to increase students' interest in learning and learning independence.