Reviewerly/exHarmony huggingface dataset:
Flexible entangled state generation in linear optics

Fault-tolerant quantum computation can be achieved by creating constant-sized, entangled resource states and performing entangling measurements on subsets of their qubits. Linear optical quantum computers can be designed based on this approach, even though entangling operations at the qubit level are non-deterministic in this platform. Probabilistic generation and measurement of entangled states must be pushed beyond the required threshold by some combination of scheme optimisation, introduction of redundancy and auxiliary state assistance. We report progress in each of these areas. We explore multi-qubit fusion measurements on dual-rail photonic qubits and their role in measurement-based resource state generation, showing that it is possible to boost the success probability of photonic GHZ state analysers with single photon auxiliary states. By incorporating generators of basic entangled "seed" states, we provide a method that simplifies the process of designing and optimising generators of complex, encoded resource states by establishing links to ZX diagrams.
The Geometry of Truth: Emergent Linear Structure in Large Language Model Representations of True/False Datasets

Large Language Models (LLMs) have impressive capabilities, but are also prone to outputting falsehoods. Recent work has developed techniques for inferring whether a LLM is telling the truth by training probes on the LLM's internal activations. However, this line of work is controversial, with some authors pointing out failures of these probes to generalize in basic ways, among other conceptual issues. In this work, we curate high-quality datasets of true/false statements and use them to study in detail the structure of LLM representations of truth, drawing on three lines of evidence: 1. Visualizations of LLM true/false statement representations, which reveal clear linear structure. 2. Transfer experiments in which probes trained on one dataset generalize to different datasets. 3. Causal evidence obtained by surgically intervening in a LLM's forward pass, causing it to treat false statements as true and vice versa. Overall, we present evidence that language models linearly represent the truth or falsehood of factual statements. We also introduce a novel technique, mass-mean probing, which generalizes better and is more causally implicated in model outputs than other probing techniques.
Generating and Evaluating Tests for K-12 Students with Language Model Simulations: A Case Study on Sentence Reading Efficiency

Developing an educational test can be expensive and time-consuming, as each item must be written by experts and then evaluated by collecting hundreds of student responses. Moreover, many tests require multiple distinct sets of questions administered throughout the school year to closely monitor students' progress, known as parallel tests. In this study, we focus on tests of silent sentence reading efficiency, used to assess students' reading ability over time. To generate high-quality parallel tests, we propose to fine-tune large language models (LLMs) to simulate how previous students would have responded to unseen items. With these simulated responses, we can estimate each item's difficulty and ambiguity. We first use GPT-4 to generate new test items following a list of expert-developed rules and then apply a fine-tuned LLM to filter the items based on criteria from psychological measurements. We also propose an optimal-transport-inspired technique for generating parallel tests and show the generated tests closely correspond to the original test's difficulty and reliability based on crowdworker responses. Our evaluation of a generated test with 234 students from grades 2 to 8 produces test scores highly correlated (r=0.93) to those of a standard test form written by human experts and evaluated across thousands of K-12 students.
Scalable Semantic Non-Markovian Simulation Proxy for Reinforcement Learning

Recent advances in reinforcement learning (RL) have shown much promise across a variety of applications. However, issues such as scalability, explainability, and Markovian assumptions limit its applicability in certain domains. We observe that many of these shortcomings emanate from the simulator as opposed to the RL training algorithms themselves. As such, we propose a semantic proxy for simulation based on a temporal extension to annotated logic. In comparison with two high-fidelity simulators, we show up to three orders of magnitude speed-up while preserving the quality of policy learned. In addition, we show the ability to model and leverage non-Markovian dynamics and instantaneous actions while providing an explainable trace describing the outcomes of the agent actions.
ResNetMF: Enhancing Recommendation Systems with Residual Network Matrix Factorization

In this paper, we introduce ResNetMF, a groundbreaking approach that harnesses the power of residual network matrix factorization to revolutionize recommendation systems. ResNetMF integrates residual networks, renowned for their ability to capture intricate patterns and features, with matrix factorization techniques that excel in modelling user-item interactions. This fusion presents a novel solution that surpasses the limitations of traditional recommendation systems. Through comprehensive experimentation and evaluation of diverse datasets, ResNetMF demonstrates remarkable enhancements in recommendation accuracy and efficiency. By effectively capturing both linear and nonlinear relationships in user-item interactions, ResNetMF provides superior recommendation quality. The outcomes from experiments unequivocally highlight the superiority of ResNetMF over existing state-of-the-art recommendation approaches, thereby validating its innovative nature and underscoring its potential to shape the future of recommendation systems. Through the integration of the deep residual network, ResNetMF approach facilitates the training of neural networks, enabling them to explore the underlying data layers more comprehensively. Extensive experimentation and evaluation across various datasets provide compelling evidence for the superiority of ResNetMF. Moreover, the proposed method utilized natural language processing (NLP) techniques for targeted information dissemination in recommendation systems, emphasizing the importance of personalized and relevant recommendations for user satisfaction and engagement.
Collision in double-image encryption scheme based on spatial encoding and phase-truncation Fourier transforms

In this paper, the security strength of a double-image cryptosystem using spatial encoding and phase-truncation Fourier transforms (PTFTs) is evaluated. Unlike the conventional PTFT-based cryptosystem, where two random phase masks (RPMs) are used as public keys to provide enough phase constrains in the estimation, in the improved cryptosystem, the RPM generated by a random amplitude mask (RAM) is treated as an unknown parameter. Due to this fixed RAM, the number of constraints in the estimation decreases to achieve high robustness against potential iterative attacks. Moreover, instead of two phase-only masks (POMs), here the two POMs and the RAM are utilized as the private keys in the improved cryptosystem; thus, the key space of the double-image cryptosystem has been enlarged. However, we noticed that the RAM used to encode plaintexts spatially and to generate the phase encryption key is independent of the plaintexts. This could be recovered by a known pair of plaintexts and the ciphertext. Once the information of the RAM is retrieved, the phase key RPM can also be produced making the cryptosystem vulnerable. Based on this finding, new hybrid algorithms, including a known-plaintext attack and a known key attack are proposed to crack the enhanced PTFT-based cryptosystem. The information of the plaintexts can be retrieved from one POM using the proposed algorithms without any knowledge of another POM and the corresponding ciphertext. Numerical simulations have been carried out to validate the information disclosure problem still exists in the double-image cryptosystem based on spatial encoding and PTFTs.
Study of the electronic properties of pure nanostructured hexagonal Zinc Oxide by DFT method

(ZnO) Zinc oxide is considered a semiconducting material from the family of transparent conductive oxide materials. Our study aims to study the electronic properties of pure Nano-sized hexagonal zinc oxide using the DFT method using the (GGA-PBE) approximation and the (HSE03) approximation, as we demonstrated through our study on the unit cell In the large cell (3×3×3) and the small cell (1×1×1), zinc oxide has a direct energy gap in the (GGA-PBE) approximation, and its value is (e V) 1.74) and in the (HSE03) approximation, the value of the energy gap is (e V2.79). The difference in the two approximations is very clear, and it is evidence that when using the (GGA-PBE) approximation for the DFT method, it reduces the value of the energy gap for zinc oxide, and when using the (HSE03) approximation, the energy gap increases because it is more mathematically accurate, even though it takes more time to calculate.
Step-by-Step Learning

&lt;p&gt;The natural or generational learning process consists of building models based on available experiences. Each generation learns from the models obtained by its predecessors and gets a new model for its own batch of experiences. In this note, we discuss this step-by-step learning procedure for supervised classification and regression problems on large datasets. We show that the stepwise learning procedure performs competitively with respect to the approach that uses a single model for the entire dataset.&lt;/p&gt;
Software Design Flaw and Security Assurance Gap in Component-based Application Security &amp; Privacy

&lt;p&gt;Using existing functional business logic in social e banking was always encouraged by the early methods of component reuse in banking. Although this concept is intriguing, it results in a functional logic design error in the program, which is why even the strongest security defenses can be readily circumvented by a few hacking attempts. As a result, the traditional approach does not meet contemporary business criteria for e-commerce and e-business interfaces in modern banking. The research team has evaluated and targeted banking domain to serve as an example of the issue reuse of business logic and has then proposed a method of secure reuse of business function and component reuse through existing banking application. In terms of social interaction logic in order to meet modern banking requirements in the context of a modern economy is based on a comprehensive security assurance process, which this paper has attempted to cover. &lt;/p&gt;
Overview of Optimization Techniques in Geometric Path Planning for Mobile Robots with a new smooth PSO-IPF design

&lt;p&gt;In this paper, we explore the practical implications of our research, offering significant advantages to the autonomous and robotics sector. Our focus revolves around enhancing geometric path planning for mobile robots, a pivotal aspect of automation. Notably, we not only delve into how to formulate optimal navigation problems while considering practical constraints and applications, but we also introduce a novel Smoothed PSO-IPF algorithm. This algorithm serves as an illustrative example of an innovative and context-specific approach to addressing navigation challenges. It furnishes engineers and practitioners in the field with a comprehensive framework for designing navigational solutions. By presenting the PSO-IPF method as a hybrid approach, we effectively bridge the gap between classical and reactive methods. Consequently, it leads to enhanced navigation efficiency, reduced collisions, and heightened mobile robot reliability. This innovation not only optimizes navigation issues but also underscores its potential for diverse applications across various industries.&lt;/p&gt;
TINGKAT LITERASI DIGITAL GURU BAHASA INDONESIA DALAM MENYAJIKAN PEMBELAJARAN TINGKAT SMA DI KABUPATEN GOWA

This study aims to reveal the level of ability and forms of digital literacy of Indonesian language teachers at the high school level in Gowa Regency. This research is a quantitative research. The data in this study is the level of digital literacy of Indonesian language teachers in presenting learning at school. The data source of this research is Indonesian language teachers. Data collection was done by questionnaire, observation and documentation. Data analysis was done by quantitative and descriptive analysis. The results showed that (the level of digital literacy of Indonesian language teachers in the technological aspect was in the low category, the cognitive aspect was in the low category and the ethical aspect was in the medium category. The forms of digital literacy used by Indonesian language teachers in presenting learning are maximising the use of Gadgets to assist in assignments to students, using the internet to find trusted learning resources, searching for information about learning using google applications and similar applications, maximising the availability of school facilities to assist the learning process, and creating online quizzes to check student understanding.
MAPPING OF STUDENTS' FIELD RESEARCH BY MEANS OF WEB TECHNOLOGIES

Modern educational technologies offer great opportunities for teachers of general education institutions to develop key competencies in students in the geography and ecology classes. Curriculum guidelines in geography pay great attention to improving the skills of unsupervised activities, including using the resources of geographic information systems. The outcomes of mastering the main educational program should reflect the knowledge of geoinformation technologies as a tool for collecting and processing research materials and as a source of geographic information. The aim of geoinformation technologies in the educational process is to form the information competency of the participants in the educational process, and to ensure the evolution of the cognitive activity of schoolchildren. In practice, the teacher faces various problems. Poor technical equipment of the school, lack of motivation and knowledge, lack of proper competencies are significant �gaps� in the teacher�s information competence. Field observations and their office analysis are an integral process of studying geography and ecology both in a general educational institution and in a university. Currently, the study and research of the natural environment is quite closely related to the use of geographic information systems and various types of geoinformation resources: electronic digital maps, sets and databases of spatial data, GIS services and many others, students should master their capabilities and actively apply them in their research. Geoinformation software systems enable to process a huge amount of data obtained during various studies as well as to organize and regulate information that has a spatial reference.
Results of a Three-Year Survey on the Implementation of Research Data Management and the Electronic Laboratory Notebook (ELN) Chemotion in an Advanced Inorganic Lab Course

As ongoing digitalization accelerates the execution of experiments and the documentation and storage of the corresponding data substantially, appropriate research data management (RDM) is a necessity to enable sustainable research at all. Consequently, a rethinking is currently taking place in academia. This process becomes visible by the utilization of electronic laboratory notebooks (ELNs) for documentation, the publication of research data in repositories, or the publishers’ requirement for authors to provide a data availability statement. Despite the growing awareness of RDM in academia, the integration of the topic into curricula of chemistry studies is, if at all, in its infancy. As an initial example of incorporating RDM into curricula, an ELN was implemented in an advanced inorganic lab course for upper-division undergraduate students three years ago, supported by learning materials on RDM in general. A survey among the students helped, first, tracking the implementation of the ELN and the integration of RDM and, second, improving teaching materials and concepts. The three-year follow-up shows a growing awareness of RDM and higher acceptance toward ELNs among the students. The survey results highlight the need for and progress in teaching RDM in an early stage of chemistry studies already. Moreover, the possibility to integrate the topic here as a hands-on experience is a facile method for fast integration into teaching without changing the complete curricula.
Musical-performing interpretation as a means of forming artistic and analytical skills of future Musical Art teachers

The article actualizes the problem of forming artistic and analytical skills of future Musical Art teachers by means of musical-performing interpretation and proves that the process of training a competent specialist in the field of music education is impossible without developing students’ artistic and analytical skills. On the other hand, the article substantiates the opinion about the significance of artistic and analytical skills as an indispensable factor in the processes of modeling musical-performing interpretation. On the basis of the theoretical analysis of scientific sources, it has been revealed that objectively there are contradictions of scientific and pedagogical nature – between the development of theoretical foundations of artistic interpretation in the field of musicology and musical performance and the lack of scientific research revealing the pedagogical aspects of this problem in the context of artistic and analytical skills formation. The specificity of musical-performing interpretation has been revealed, which is determined by its integrative nature, which is expressed by the unity of artistic and analytical skills of the performer. The authors have proposed their own definition of musical-performing interpretation as a creative and analytical activity of a musician to create the author’s performing interpretation of cultural meanings contained in a musical work. The content and component structure of future Musical Art teachers’ artistic and analytical skills are disclosed. It is proved that the process of formation of artistic and analytical skills during the musical-performing interpretation of the future Musical Art teacher has a systematic complex construction, where at the first stage artistic and analytical skills related to the theoretical-semantic component are formed, at the second stage – the artistic and analytical skills of the methodological-pedagogical component are involved, and at the final stage – the artistic-interpretive component was formed. The study has established that all components of artistic and analytical skills include the integrated formation of intellectual and emotional support, and all components of the intelligence and emotional sphere of the future Musical Art teacher become components of musical-performing interpretation.
Boundary Discretization and Reliable Classification Network for Temporal Action Detection

Temporal action detection aims to recognize the action category and determine the starting and ending time of each action instance in untrimmed videos. The mixed methods have achieved remarkable performance by simply merging anchor-based and anchor-free approaches. However, there are still two crucial issues in the mixed framework: (1) Brute-force merging and handcrafted anchors design affect the performance and practical application of the mixed methods. (2) A large number of false positives in action category predictions further impact the detection performance. In this paper, we propose a novel Boundary Discretization and Reliable Classification Network (BDRC-Net) that addresses the above issues by introducing boundary discretization and reliable classification modules. Specifically, the boundary discretization module (BDM) elegantly merges anchor-based and anchor-free approaches in the form of boundary discretization, avoiding the handcrafted anchors design required by traditional mixed methods. Furthermore, the reliable classification module (RCM) predicts reliable action categories to reduce false positives in action category predictions. Extensive experiments conducted on different benchmarks demonstrate that our proposed method achieves favorable performance compared with the state-of-the-art. For example, BDRC-Net hits an average mAP of 68.6% on THUMOS'14, outperforming the previous best by 1.5%. The code will be released at https://github.com/zhenyingfang/BDRC-Net.
Streaming Anchor Loss: Augmenting Supervision with Temporal Significance

Streaming neural network models for fast frame-wise responses to various speech and sensory signals are widely adopted on resource-constrained platforms. Hence, increasing the learning capacity of such streaming models (i.e., by adding more parameters) to improve the predictive power may not be viable for real-world tasks. In this work, we propose a new loss, Streaming Anchor Loss (SAL), to better utilize the given learning capacity by encouraging the model to learn more from essential frames. More specifically, our SAL and its focal variations dynamically modulate the frame-wise cross entropy loss based on the importance of the corresponding frames so that a higher loss penalty is assigned for frames within the temporal proximity of semantically critical events. Therefore, our loss ensures that the model training focuses on predicting the relatively rare but task-relevant frames. Experimental results with standard lightweight convolutional and recurrent streaming networks on three different speech based detection tasks demonstrate that SAL enables the model to learn the overall task more effectively with improved accuracy and latency, without any additional data, model parameters, or architectural changes.
RESEARCH ON AUGMENTED REALITY TECHNOLOGY

With the coming of the 21st century, man is entering into a brand-new multimedia age. Digital Video processing, as the most important, expressive and complex one in multimedia, has progressed quite a lot. An augmented reality ( AR) system generates a composite view for the user. It is a combination of the real scene viewed by the user and a virtual reality (VR) scene generated by the computer that augments the scene with additional information. It is a new technology which can combine the real world information with the virtual world information seamless. In this paper, a simple registration method using natural features based on the projective reconstruction technique is proposed. This method consists of two steps: embedding and rendering. Embedding involves specifying four points to build the world coordinate system on which a virtual object will be superimposed. In rendering, the Kanade-Lucas-Tomasi (KLT) feature tracker is used to track the natural feature correspondences in the live video. The natural features that have been tracked are used to estimate the corresponding projective matrix in the image sequence.