Reviewerly/exHarmony huggingface dataset:
Automated Text Analysis of Organic Chemistry Students’ Written Hypotheses

Generating a testable hypothesis is a necessary skill for engaging in science, requiring both general reasoning skills and specific content knowledge of the phenomenon being investigated. While many students have the reasoning skills necessary for developing testable hypotheses in a general science context, it can be challenging for students to apply these skills to content areas where the phenomena being studied are unobservable, such as problems in organic chemistry. Generating hypotheses for experiments is a skill that students are expected to apply in chemistry teaching laboratories. Students can benefit from receiving real-time feedback to support developing this ability. However, providing feedback on writing can be challenging for instructors, especially in large-enrollment courses. For this study, we explored and compared the performance of several machine learning algorithms to classify organic chemistry students' written hypotheses for a science-general scenario and an organic chemistry-specific scenario. These models were trained to analyze students' written hypotheses for the presence of five features: (1) a testable prediction in a given situation; (2) a predicted change in the independent variable that is related to the dependent variable; (3) scientific content addressing the research question provided in the prompt; (4) a correct and completely defined independent variable; and (5) a correct and completely defined dependent variable. This work has implications for guiding future research on the feasibility of providing instructors with information about students' hypothesis-generating abilities that can support the delivery of real-time feedback.
The Effect of Family Education Program on Paternity Role and Children's Play Skills

The objective of the study was to evaluate the effects of fathers' participation in the Father Support Education Programme on their perception of their role as fathers and the development of play skills in their children. The study sample comprised 40 fathers whose children, aged 5 and 6, were enrolled in independent pre-schools located in the city centre of Bartın. The experimental design of pre-test and post-test control group was adopted to facilitate the study. All technical terms were defined when first used. The study comprised 20 fathers, who were divided into control and study groups. Fathers in the study group received the Father Support Education Program over a period of 10 weeks, while no education program was provided to the control group. Information was collected using the "Personal Information Form," "Father Role Perception Scale (FRPS)," and "Game Skills Assessment Scale (PSAS)." The research's quantitative data was analysed using the SPSS (Statistical Package for Social Sciences) programme for Windows 22.0. To compare continuous data between two independent groups, the t-test was employed. The analysis established that following the educational programme, fathers in the study group exhibited a significant difference between their pre-test and post-test mean scores for their roles in their children's play skills. It was found that the Father Support Education Programme had a positive impact on fathers' roles and the development of children's play skills.
Exact Non-Markovian Quantum Dynamics on the NISQ Device Using Kraus Operators

The theory of open quantum systems has many applications ranging from simulating quantum dynamics in condensed phases to better understanding quantum-enabled technologies. At the center of theoretical chemistry are the developments of methodologies and computational tools for simulating charge and excitation energy transfer in solutions, biomolecules, and molecular aggregates. As a variety of these processes display non-Markovian behavior, classical computer simulation can be challenging due to exponential scaling with existing methods. With quantum computers holding the promise of efficient quantum simulations, in this paper, we present a new quantum algorithm based on Kraus operators that capture the exact non-Markovian effect at a finite temperature. The implementation of the Kraus operators on the quantum machine uses a combination of singular value decomposition (SVD) and optimal Walsh operators that result in shallow circuits. We demonstrate the feasibility of the algorithm by simulating the spin-boson dynamics and the exciton transfer in the Fenna–Matthews–Olson (FMO) complex. The NISQ results show very good agreement with the exact ones.
Graph neural network recommendation algorithm based on improved dual tower model

In this era of information explosion, recommendation systems play a key role in helping users to uncover content of interest among massive amounts of information. Pursuing a breadth of recall while maintaining accuracy is a core challenge for current recommendation systems. In this paper, we propose a new recommendation algorithm model, the interactive higher-order dual tower (IHDT), which improves current models by adding interactivity and higher-order feature learning between the dual tower neural networks. A heterogeneous graph is constructed containing different types of nodes, such as users, items, and attributes, extracting richer feature representations through meta-paths. To achieve feature interaction, an interactive learning mechanism is introduced to inject relevant features between the user and project towers. Additionally, this method utilizes graph convolutional networks for higher-order feature learning, pooling the node embeddings of the twin towers to obtain enhanced end-user and item representations. IHDT was evaluated on the MovieLens dataset and outperformed multiple baseline methods. Ablation experiments verified the contribution of interactive learning and high-order GCN components.
Milestones in speaker recognition

Abstract This article reviews significant research in the domain of speaker recognition, i.e., the task of determining the speaker’s identity from its speech. Unlike conventional review articles, this document strives to be concise and selective, provide a historical context, and reach a wider audience. In this endeavour, a summary of selected key works of every decade is provided which highlights the theme(s) of research of that period. At first, an overview of the humble beginnings of the 1960s and 70s is provided, followed by the key developments in the 80s and 90s. The prime focus of the research community in the 2000s is then discussed, leading to various non-conventional features, modelling techniques, and hybrid or fusion systems. The developments of the last decade (the 2010s), such as the i-vector-based systems, are then discussed. Modern speaker recognition based on Artificial Intelligence (AI), such as the x-vector system, and refinements of the i-vector-based systems using deep neural networks, are then discussed. The article concludes with a concise discussion of the evolving recent trends and allied research in speaker recognition.
Uncertainty-aware human-like driving policy learning with deep Bayesian inverse reinforcement learning

The application of deep reinforcement learning in driving policy learning for automated vehicles is limited by the difficulty of designing reward functions. Most existing inverse reinforcement learning (IRL) methods make a structure assumption on the reward function and omit the uncertainty of neural networks. In view of this, this paper proposes a novel deep Bayesian IRL method that addresses both the reward function designing and the uncertainty-measuring issues by learning an approximate posterior distribution over the reward function. Furthermore, we propose to train uncertainty-aware human-like driving policies by maximising the predicted reward and penalising its uncertainty. Finally, the proposed methods were validated in simulated highway driving scenarios. The results show that AVRL models uncertainty and learns reward functions significantly outperforming the existing IRL method applied in automated driving. It was also found that penalising the uncertainty of the reward function during policy training improves the success rate and human likeness of the learned policy.
Minimal time required to charge a quantum system

We introduce a quantum charging distance as the minimal time that it takes to reach one state (charged state) from another state (depleted state) via a unitary evolution, assuming limits on the resources invested into the driving Hamiltonian. For pure states it is equal to the Bures angle, while for mixed states its computation leads to an optimization problem. Thus, we also derive easily computable bounds on this quantity. The charging distance tightens the known bound on the mean charging power of a quantum battery, it quantifies the quantum charging advantage, and it leads to an always achievable quantum speed limit. In contrast with other similar quantities, the charging distance does not depend on the eigenvalues of the density matrix, it depends only on the corresponding eigenspaces. This research formalizes and interprets quantum charging in a geometric way, and provides a measurable quantity that one can optimize to maximize the speed of charging of future quantum batteries.
Frequency and Spatial Domain-Based Approaches for Recognition of Indian Sign Language Gestures

Objectives: The objective of this paper is to introduce and demonstrate an innovative approach for the recognition of Indian sign language gestures, with a focus on bridging communication gap between the deaf and hearing communities. The goal is to contribute to the development of effective tools and technologies that facilitate seamless communication between individuals using sign language and the people with no knowledge about sign language. Methods: The methodology consists of three key steps. First, data pre-processing involves resizing and contours extraction. Next, feature extraction employs Fourier descriptors for frequency domain analysis and gray-level-co-occurrence matrix for spatial domain analysis. Finally, various machine learning models including SVM, Random Forest, Logistic Regression, K-Nearest Neighbor and Naive Bayes are trained on a standard dataset. Findings: In our controlled experimental setup, we applied a diverse set of machine learning classifiers to evaluate the proposed approach for gesture recognition. Among the classifiers tested, K-Nearest Neighbors demonstrated the highest accuracy, achieving 99.82%. To validate the robustness of our approach, we employed k-fold cross-validation with 5 folds. Novelty: This study presents an innovative method for sign language recognition by employing a dual-domain fusion strategy that prominently emphasizes the frequency domain. Through the integration of Fourier descriptors, the research conducts a detailed frequency domain analysis to characterize the contour shapes of sign language gestures. The synergy with gray-level co-occurrence matrix texture features in the spatial domain analysis, contributes to the creation of a comprehensive feature vector. The proposed approach ensures a thorough exploration of gesture features, there by advancing the precision and efficacy of sign language recognition. Keywords: Indian Sign Language (ISL), Sign Language Recognition (SLR), Frequency domain, Spatial domain, Fourier descriptors, Gray level co­occurrence matrix (GLCM), K­ Fold
Increasing the motivation of madrasah Aliyah students to learn cross-interest English subject by applying the game “relay race”

Cross-interest subjects are programs and policies from the government that aim to provide opportunities for students to choose and study subjects that are not in the specialization program so that they will add insight, experience, and knowledge that they have later. In theory, students should have a high motivation or enthusiasm for learning to study the cross-interest subjects they have chosen. However, the facts in the classroom show the opposite. This study aims to increase student learning motivation in English Cross Interest subjects in class XI MIPA Madrasah Aliyah Negeri Kota Batu through the application of a "relay race" or relay game. This research is a class action research with the research subject of XI MIPA 2 class with 36 students. The research was conducted with two cycles on simple past-tense material. Data collection techniques were carried out by observation through daily notes filled by both the teacher and students, and questionnaires filled by students to determine the level of student learning motivation. The results showed increased student learning motivation in cross-interest English subjects, with an average percentage in the first cycle of 81% and increased by 1% to 82% in the second cycle. Thus, it can be concluded that applying the "relay race" game can increase students' learning motivation while still paying attention to the teacher’s pedagogical skills during the learning process.
A Crypto Yield Model for Staking Return

We introduce a model that derives a metric to answer the question: what is the expected gain of a staker? We calculate the rewards as the staking return in a Proof-of-Stake (PoS) consensus context. For each period of block validation and by a forward approach, we prove that the interest is given by the ratio of the average staking gain to the total staked coins. Some additional PoS features are considered in the model, such as slash rate and Maximal Extractable Value (MEV), which marks the originality of this approach. In particular, we prove that slashing diminishes the rewards, reflecting the fact that the blockchain can consider stakers to potentially validate incorrectly. Regarding MEV, the approach we have sheds light on the relation between transaction fees and the average staking gain. We illustrate the developed model with Ethereum 2.0 and apply a similar process in a Proof-of-Work consensus context.
The influence of leadership and work flexibility on employee performance

This research aims to determine the magnitude of the influence of leadership and work flexibility on performance and employee performance at PT. First, Logistics Service, Tanjung Priok Branch. The research method used in this research uses quantitative methods. This research took a sample of 82 respondents. Data analysis uses multiple regression analysis. The research results show that leadership and work flexibility have a positive and significant effect, both partially and simultaneously, on employee performance.
Evaluation and Critical Reflection on The Listening and Speaking Activities Taught in an English for Specific Purpose Course

The following paper is a combination of two assignments for the Master’s program “The Teaching of English as a Foreign/ International Language”. It concerns the evaluation and critical reflection on the listening and speaking activities offered to an adult learners’ course during their “English Terminology for Paramedics” class, along with a redesigned lesson plan. The paper analyses the students’ profiles, describes the listening activities used in a particular unit, evaluates them, and suggests improvements to their teaching. An evaluation of the speaking activities based on Johnson’s principles and Nation’s features, a redesigned lesson plan based on the Communicative Language Approach, and a critical evaluation on it follow.
Coherent control of two Jaynes–Cummings cavities

We uncover new features on the study of a two-level atom interacting with one of two cavities in a coherent superposition. The James-Cummings model is used to describe the atom-field interaction and to study the effects of quantum indefiniteness on such an interaction. We show that coherent control of the two cavities in an undefined manner allows novel possibilities to manipulate the atomic dynamics on demand which are not achievable in the conventional way. In addition, it is shown that the coherent control of the atom creates highly entangled states of the cavity fields taking a Bell-like or Schrödinger-cat-like state form. Our results are a step forward to understand and harness quantum systems in a coherent control, and open a new research avenue in the study of atom-field interaction exploiting quantum indefiniteness.
Some generalisations and extensions of a remarkable geometry puzzle

There is a very interesting mathematical puzzle involving the geometrical configuration in the book Mathematical Curiosities [1, 2] by Alfred Posamentier and Ingmar Lehmann. It is shown in Figure 1.
Rancang Bangun Aplikasi Pengolahan Data Barang Inventaris Berbasis Mobile di Bidang Pemuda dan Olahraga

Inventory processing is an item data management activity carried out through a shopping budget or donations. This application is designed to provide easy access and speed in recording, managing and monitoring inventory in real-time. The aim of this research is to produce an Inventory Application at the Sergai Youth and Sports Office which can be used to process inventory data to make it more effective and accurate. This research uses data collection methods, namely interviews and literature studies, and for application development methods using the waterfall method. The results of this research obtained a system that is useful for processing inventory item data that will be used at the Youth and Sports Sector Office of DISPORAPARBUD SERGAI.
Exploring students’ immersive VR experiences as resources for collaborative meaning making and learning

Abstract By adopting a situated and interactional approach, we explore students’ immersive VR experiences as resources for collaborative meaning making and learning. We draw on data from a project in which teachers and researchers co-developed a learning design for upper secondary students involving immersive VR technology. In this learning design, students viewed a cinematic VR film where they encountered different people telling personal stories about exclusion and discrimination, followed by reflective group dialogues with their teacher about their experiences in this environment. Through a detailed interaction analysis of these dialogues, we identify four dimensions that characterize students’ meaning making: (1) the feeling of taking part in conversations, (2) attending to bodily expressions of others, (3) students’ own bodily responses, (4) teacher guidance. We discuss how the findings from our analysis contribute to the field of CSCL, and which also have implications for instructional work that includes the use of immersive VR environments.
Beyond images: an integrative multi-modal approach to chest x-ray report generation

Image-to-text radiology report generation aims to automatically produce radiology reports that describe the findings in medical images. Most existing methods focus solely on the image data, disregarding the other patient information accessible to radiologists. In this paper, we present a novel multi-modal deep neural network framework for generating chest x-rays reports by integrating structured patient data, such as vital signs and symptoms, alongside unstructured clinical notes. We introduce a conditioned cross-multi-head attention module to fuse these heterogeneous data modalities, bridging the semantic gap between visual and textual data. Experiments demonstrate substantial improvements from using additional modalities compared to relying on images alone. Notably, our model achieves the highest reported performance on the ROUGE-L metric compared to relevant state-of-the-art models in the literature. Furthermore, we employed both human evaluation and clinical semantic similarity measurement alongside word-overlap metrics to improve the depth of quantitative analysis. A human evaluation, conducted by a board-certified radiologist, confirms the model’s accuracy in identifying high-level findings, however, it also highlights that more improvement is needed to capture nuanced details and clinical context.
Dual-Mode Encryption for UC-Secure String OT from Learning with Errors

Universal composability (UC) is a primary security flavor for designing oblivious transfer (OT) due to its advantage of arbitrary composition. However, the study of UC-secure OT over lattices is still far behind compared with constructions over prequantum assumptions. Relying on the learning with errors (LWE) assumption, Quach proposes a dual-mode encryption scheme (SCN’20) for deriving a two-round OT whose security is provably UC-secure in the common reference string (CRS) model. Due to its use of a randomized rounding function proposed by Benhamouda et al. (PKC’18), this OT can only be limited to transmitting single-bit messages. Therefore, conducting trivial repetitions of Quach’s OT when transmitting multibit strings would be very costly. In this work, we put forward a modified dual-mode encryption cryptosystem under the decisional LWE assumption, from which we can derive a UC-secure string OT with both full-fledged dual-mode security and better efficiency on transmitting strings. The key technique we adopt is a key reconciliation scheme proposed by Jiang et al. (PKC’20), which is utilized to extend the single-bit symmetric encryption key (produced by the aforementioned rounding function) to a multibit case. Through a comprehensive performance analysis, we demonstrate that our proposal can indeed strike a balance between security and efficiency.
Effects of detuning on entropic uncertainty bound and quantum correlations in dissipative environments

Abstract One of the fundamental arguments in quantum information theory is the uncertainty principle. According to this principle, two incompatible observables cannot be measured with high precision at the same time. In this work, we will use the entropic uncertainty relation in the presence of quantum memory. Considering a dissipative environment, the effects of the detuning between the transition frequency of a quantum memory and the center frequency of a cavity on entropic uncertainty bound and the quantum correlation between quantum memory and measured particle will be studied. It is shown that by increasing the detuning, quantum correlation is maintained. As a result, due to the inverse relationship between the uncertainty bound and quantum correlations, the measurement results are guessed more accurately.
Campus Security

An overview of the key topics faced by disability services providers with citations to noteworthy cases, statutes, regulations, and additional sources.
Quantum circuit distillation and compression

Abstract Quantum coherence in a qubit is vulnerable to environmental noise. When long quantum calculation is run on a quantum processor without error correction, the noise causes fatal errors and messes up the calculation. Here, we propose quantum-circuit distillation to generate quantum circuits that are short but have enough functions to produce an output similar to that of the original circuits. The distilled circuits are less sensitive to the noise and can complete calculation before the quantum coherence is broken. We created a quantum-circuit distillator by building a reinforcement learning model, and applied it to the inverse quantum Fourier transform (IQFT) and Shor’s quantum prime factorization. The obtained distilled circuit allows correct calculation on IBM-Quantum processors. By working with the distillator, we also found a general rule to generate quantum circuits approximating the general $n$-qubit IQFTs. The quantum-circuit distillator offers a new approach to improve performance of noisy quantum processors.