Reviewerly/exHarmony huggingface dataset:
Performance comparison of Gilbert's algorithm and machine learning in classifying Bell-diagonal two-qutrit entanglement

Abstract While classifying states as entangled or separable is one of the fundamental tasks in quantum information theory, it is also extremely challenging. This task is highly nontrivial even for relatively simple cases, such as two-qutrit Bell-diagonal states, i.e., mixtures of nine mutually orthogonal maximally entangled states. In this article we apply Gilbert's algorithm to revise previously obtained results for this class. In particular we use ''entanglement cartography'' to argue that most states left in [Hiesmayr, B. C. {\em Scientific Reports} {\bf 11}, 19739 (2021)] as unknown to be entangled or separable are most likely indeed separable, or very weakly entangled, beyond any practical relevance. The presented technique can find endless applications in more general cases.
Dampak Pandemi Covid-19 Terhadap Pola Prediksi Return Saham Berdasarkan Harga Minyak Dunia

The Covid-19 pandemic has had a very serious impact, not only on the health sector, but also on the economic sector. Investors cannot predict the effect of a decline or increase in world oil prices during the Covid-19 pandemic on their investment choice decisions. Through this research, an analysis will be carried out on whether Covid-19 will have an influence on the effect of the relationship between world oil prices and stock return predictions using daily Jakarta Composite Index (JCI) data from 03/01/2011 to 14/03/2022. The data to be analyzed will be divided into 3 types of data panels to facilitate the analysis in finding a comparison of the effect of Covid-19 based on a certain period of time. Panel A presents full sample data, Panel B contains pre Covid-19 sample data and Panel C presents data for samples during the time of Covid-19. Based on the results of the study, it can be inffered that the effect of the Covid-19 pandemic can decrase the effectiveness of economic activities and make the capital market unstable, so that the effect of oil prices on stock returns is inaccurate
Access Structures Determined by Uniform Polymatroids

An access structure is said to be multipartite, if the set of participants is divided into several parts and all participants in the same part play an equivalent role. The search for ideal secret sharing schemes for some special interesting families of multipartite access structures, has been carried out by many authors. In this paper a new concept of study of ideal access structures is proposed. We do not consider special classes of access structures defined by imposing certain prescribed assumptions, but we investigate all access structures obtained from uniform polymatroids using the method developed by Farr\`as, Mart\'i-Farr\'e and Padr\'o. They satisfy necessary condition to be ideal, i.e., they are matroid ports. Moreover some objects in this family can be useful for the applications of secret sharing. The choice of uniform polymatroids is motivated by the fact that each such polymatroid defines ideal access structures. The method presented in this article is universal and can be continued with other classes of polymatroids in further similar studies. Here we are especially interested in hierarchy of participants determined by the access structure and we distinguish two main classes: they are compartmented and hierarchical access structures. The vast majority of papers discussing hierarchical access structures consider access structures which are compartment or totally hierarchical. The main results are summarized in Section 4, which presents situations where partial hierarchy properties may arise. In particular, hierarchical orders of obtained structures are described. It is surprising, that the hierarchical orders of access structures obtained from uniform polymatroids are flat, i.e., every chain has at most 2 elements. The ideality of some families of hierarchical access structures is proved in Section 5.
Deep Ontology Alignment using Natural Language Processing Approach for Automatic M2M Translation in IIoT

The technical capabilities of modern Industry 4.0 and Industry 5.0 are rather vast and growing exponentially daily. The present-day Industrial Internet of Things (IIoT) combines manifold underlying technologies that require real-time interconnections and communications among heterogeneous devices. Smart cities are established with sophisticated designs and control for seamless Machine-to-Machine (M2M) communication to optimize resources, costs, performances, and energy distribution. All the sensory devices within a building interact to maintain a sustainable climate for residents and intuitively optimize the energy distribution to optimize energy production. However, it encompasses quite a few challenges for devices that lack compatible and interoperable designs. Conventional solutions are restricted to limited domains or rely on engineers to design and deploy translators for each pair of ontologies. This is a costly process in terms of engineering efforts and computational resources. The issue persists that a new device with a different ontology must be integrated into an existing IoT network. We propose a self-learning model that can determine the taxonomy of devices given their ontology meta-data and structural information. The model finds matches between two distinct ontologies using the Natural Language Processing (NLP) approach for learning linguistic contexts. Then, by visualizing the ontology network as a knowledge graph, it is possible to learn the structure of the meta-data and understand the device's message formulation. Finally, it can align entities of both ontology graphs similar in context and structure. Furthermore, the model performs dynamic M2M translation without requiring extra engineering or hardware efforts.
Single-Photon Superradiance and Subradiance as Collective Emission from Symmetric and Anti-Symmetric States

Recent works have shown that collective single-photon spontaneous emission from an ensemble of N resonant two-level atoms is a rich field of study. Superradiance describes the emission from a completely symmetric state of N atoms, with a single excited atom prepared with a given phase, for instance, imprinted by an external laser. Instead, subradiance is associated with the emission from the remaining N−1 asymmetric states, with a collective decay rate less than the single-atom value. Here, we discuss the properties of the orthonormal basis of symmetric and asymmetric states and the entanglement properties of superradiant and subradiant states. On the one hand, by separating the symmetric superradiant state from the subradiant ones, we are able to determine the subradiant fraction induced in the system by the laser. On the other hand, we show that, as the external laser is switched off and the atomic excitation decays, entanglement in the atomic ensemble appears when the superradiant fraction falls below the threshold 1/N.
The Mixed Blessings of an Iterative Design Strategy

In design research, it is widely accepted that an ‘incremental build’ or iterative style of development will allow users to offer feedback at key points in the design process, therefore, creating a more user-oriented type of design. This paper presents a case that complicates and challenges this view. Based on a 12-month ethnographic field study of a design team, we argue that certain aspects of the iterative organization of design work may in fact impede the objective of incorporating user-feedback into the design process. The article explains this surprising finding by tracing how a variety of material and rhetorical representations of users were handled and incorporated by a design team. It explores how different prevailing agendas facilitated the adoption and rejection of user representations, and it reflects on the role played by the design team’s efforts to speed up the development process by using so-called out-of-the-box features – pieces of ready-made third-party software.
STRATEGI DAN TANTANGAN PELESTARIAN MANUSKRIP DI PERPUSTAKAAN RUMOH MANUSKRIP ACEH

Preservation is a process or way of protection and damage. This research was conducted at the Aceh Manuscript Rumoh Library (PRMA). This study aims to determine the strategies and challenges of preserving manuscripts in the Aceh manuscript house library. The research method used is descriptive qualitative research. Data collection techniques used by observation, interviews, and documentation. Researchers conducted interviews with one librarian who manages PRMA. The results of the study found that the preservation was carried out by PRMA by carrying out physical preservation and content preservation. Preservation of manuscripts is carried out by carrying out maintenance, conservation and restoration. Damage to manuscripts is caused by damage factors such as biological factors, physical factors, chemical factors, humans, and natural disasters. The strategy undertaken by PRMA is to continue to improve room cleanliness, provide equipment, facilities, and also attend seminars or training for library managers. The challenge for PRMA in carrying out conservation is the constraints on facilities, tools, and materials. Not all librarians are experts at preserving, so libraries need to recruit employees who are capable of preserving it so that the treasures can be enjoyed and used for a long time. Digitalization or media transfer is also a challenge for libraries. By transferring media, the information contained in the manuscript can be used and the value of the information is maintained.
KOMPARASI MODEL COLLABORATIVE COLLECTION DEVELOPMENT DAN JUST-IN-TIME COLLECTION DEVELOPMENT PADA PERPUSTAKAAN PENDIDIKAN

This study aims to answer the question of how the application of collaborative and just-in-time model collection development and how the comparison of the two models that have been applied to educational libraries. The method used in this research is a critical review method with a literature study approach. The writer made comparation and analyze between two articles which adopted in different model of collection development. The research findings are as follows: 1) The collaborative collection development model utilizes library networks to save budgets and accelerate collection availability, while the just-in-time collection development model applies a responsive access-based collection development approach. This policy is to provide the right and fast collection and save funds. 2) The difference between these two models is that the collaborative model requires a network so as to facilitate the process of both procurement together in order to create budget savings, while the just-in-time collection development is a single model that does not really require a network so that it has absolute autonomy.
Investigation of mineralogical, textural and geochemical properties in Amir Abad copper prospect area, Northwest of Neyshabur

Investigation of mineralogical, textural and geochemical properties in Amir Abad copper prospect area, Northwest of Neyshabur
Conclusions and Perspectives

In this study, a nanowire-assisted electroporation disinfection system based on the internal filtration treatment mode using porous electrodes was constructed.
Transitioning a Project-Based Course between Onsite and Online. An Experience Report

We present an investigation regarding the challenges faced by student teams across four consecutive iterations of a team-focused, project-based course in software engineering. The studied period includes the switch to fully online activities in the spring of 2020, and covers the return to face-to-face teaching two years later. We cover the feedback provided by over 1,500 students, collected in a free-text form on the basis of a survey. A qualitative research method was utilized to discern and examine the challenges and perceived benefits of a course that was conducted entirely online. We show that technical challenges remain a constant in project-based courses, with time management being the most affected by the move to online. Students reported that the effective use of collaborative tools eased team organization and communication while online. We conclude by providing a number of action points regarding the integration of online activities in face-to-face course unfolding related to project management, communication tools, the importance of teamwork, and of active mentor participation.
Establishing trust in automated reasoning

Since its beginnings in the 1940s, automated reasoning by computers has become a tool of ever growing importance in scientific research. So far, the rules underlying automated reasoning have mainly been formulated by humans, in the form of program source code. Rules derived from large amounts of data, via machine learning techniques, are a complementary approach currently under intense development. The question of why we should trust these systems, and the results obtained with their help, has been discussed by philosophers of science but has so far received little attention by practitioners. The present work focuses on independent reviewing, an important source of trust in science, and identifies the characteristics of automated reasoning systems that affect their reviewability. It also discusses possible steps towards increasing reviewability and trustworthiness via a combination of technical and social measures.
SIAK-NG User Interface Design with Design Thinking Method to Support System Integration

This research aims to improve the user interface of SIAK-NG (Next Generation Academic Information System), the academic portal website of the University of Indonesia through a design thinking approach. Despite being in existence for several decades and undergoing multiple improvements, there has been no specific effort to evaluate the quality of the user interface. In line with the revamping of SIAK-NG according to the University of Indonesia 2019-2024 master plan, this study aims to provide the necessary insights. The research focuses on redesigning the website interface of SIAK-NG to address user complaints and difficulties, particularly related to the unsatisfactory interface design. The Design Thinking approach is employed to generate solutions that meet the needs of active University of Indonesia students who are the primary users. Through methods such as storyboarding, empathy mapping, usability testing, and others, the author will design recommendations that align with user requirements.
Examining the Influence of Varied Levels of Domain Knowledge Base Inclusion in GPT-based Intelligent Tutors

Recent advancements in large language models (LLMs) have facilitated the development of chatbots with sophisticated conversational capabilities. However, LLMs exhibit frequent inaccurate responses to queries, hindering applications in educational settings. In this paper, we investigate the effectiveness of integrating a knowledge base (KB) with LLM intelligent tutors to increase response reliability. To achieve this, we design a scaleable KB that affords educational supervisors seamless integration of lesson curricula, which is automatically processed by the intelligent tutoring system. We then detail an evaluation, where student participants were presented with questions about the artificial intelligence curriculum to respond to. GPT-4 intelligent tutors with varying hierarchies of KB access and human domain experts then assessed these responses. Lastly, students cross-examined the intelligent tutors' responses to the domain experts' and ranked their various pedagogical abilities. Results suggest that, although these intelligent tutors still demonstrate a lower accuracy compared to domain experts, the accuracy of the intelligent tutors increases when access to a KB is granted. We also observe that the intelligent tutors with KB access exhibit better pedagogical abilities to speak like a teacher and understand students than those of domain experts, while their ability to help students remains lagging behind domain experts.
Two-qubit quantum gates with minimal pulse sequences

Working with trapped atoms at close distance to each other, we show that one can implement entangling gates based on non-independent qubits using a single pulse per qubit, or a single structured pulse. The optimal parameters depend on approximate solutions of Diophantine equations, causing the fidelity to never be exactly perfect, even under ideal conditions, although the errors can be made arbitrarily smaller at the cost of stronger fields. We fully characterize the mechanism by which the gates operate, and show that the main source of error in realistic implementations comes from fluctuations in the peak intensity, which especially damages the fidelity of the gates that use stronger fields. Working with two-pulse sequences, instead of one, enables the use of a plethora of mechanisms and a broad range of optimal parameters to choose from, to achieve high-fidelity gates.
Folding Rays: a Bimanual Occluded Target Interaction Technique

As Virtual Reality becomes commonplace in the world, it is important for developers to focus on user interaction with the virtual world. Currently, there are limitations to some selection and navigation techniques that have not yet been completely overcome. Focusing specifically on enhancing ray-casting, we present the advanced technique of folding rays which allows for the selection of occluded targets without any unnecessary physical navigation around a virtual environment. By improving upon current approaches, our technique allows for the selection of these targets without any manipulation of the virtual environment itself using rays that can bend at user-determined points. With their potential to be used in conjunction with teleportation as a virtual navigation technique, folding rays can be used in a variety of scenarios to enhance a user's interactive experience in virtual environments.
A Theory of Multimodal Learning

Human perception of the empirical world involves recognizing the diverse appearances, or 'modalities', of underlying objects. Despite the longstanding consideration of this perspective in philosophy and cognitive science, the study of multimodality remains relatively under-explored within the field of machine learning. Nevertheless, current studies of multimodal machine learning are limited to empirical practices, lacking theoretical foundations beyond heuristic arguments. An intriguing finding from the practice of multimodal learning is that a model trained on multiple modalities can outperform a finely-tuned unimodal model, even on unimodal tasks. This paper provides a theoretical framework that explains this phenomenon, by studying generalization properties of multimodal learning algorithms. We demonstrate that multimodal learning allows for a superior generalization bound compared to unimodal learning, up to a factor of $O(\sqrt{n})$, where $n$ represents the sample size. Such advantage occurs when both connection and heterogeneity exist between the modalities.
A Toolchain for Privacy-Preserving Distributed Aggregation on Edge-Devices

Valuable insights, such as frequently visited environments in the wake of the COVID-19 pandemic, can oftentimes only be gained by analyzing sensitive data spread across edge-devices like smartphones. To facilitate such an analysis, we present a toolchain for a distributed, privacy-preserving aggregation of local data by taking the limited resources of edge-devices into account. The distributed aggregation is based on secure summation and simultaneously satisfies the notion of differential privacy. In this way, other parties can neither learn the sensitive data of single clients nor a single client's influence on the final result. We perform an evaluation of the power consumption, the running time and the bandwidth overhead on real as well as simulated devices and demonstrate the flexibility of our toolchain by presenting an extension of the summation of histograms to distributed clustering.
Bayesian improved cross entropy method with categorical mixture models

We employ the Bayesian improved cross entropy (BiCE) method for rare event estimation in static networks and choose the categorical mixture as the parametric family to capture the dependence among network components. At each iteration of the BiCE method, the mixture parameters are updated through the weighted maximum a posteriori (MAP) estimate, which mitigates the overfitting issue of the standard improved cross entropy (iCE) method through a novel balanced prior, and we propose a generalized version of the expectation-maximization (EM) algorithm to approximate this weighted MAP estimate. The resulting importance sampling distribution is proved to be unbiased. For choosing a proper number of components $K$ in the mixture, we compute the Bayesian information criterion (BIC) of each candidate $K$ as a by-product of the generalized EM algorithm. The performance of the proposed method is investigated through a simple illustration, a benchmark study, and a practical application. In all these numerical examples, the BiCE method results in an efficient and accurate estimator that significantly outperforms the standard iCE method and the BiCE method with the independent categorical distribution.
CodePlan: Repository-level Coding using LLMs and Planning

Software engineering activities such as package migration, fixing errors reports from static analysis or testing, and adding type annotations or other specifications to a codebase, involve pervasively editing the entire repository of code. We formulate these activities as repository-level coding tasks. Recent tools like GitHub Copilot, which are powered by Large Language Models (LLMs), have succeeded in offering high-quality solutions to localized coding problems. Repository-level coding tasks are more involved and cannot be solved directly using LLMs, since code within a repository is inter-dependent and the entire repository may be too large to fit into the prompt. We frame repository-level coding as a planning problem and present a task-agnostic framework, called CodePlan to solve it. CodePlan synthesizes a multi-step chain of edits (plan), where each step results in a call to an LLM on a code location with context derived from the entire repository, previous code changes and task-specific instructions. CodePlan is based on a novel combination of an incremental dependency analysis, a change may-impact analysis and an adaptive planning algorithm. We evaluate the effectiveness of CodePlan on two repository-level tasks: package migration (C#) and temporal code edits (Python). Each task is evaluated on multiple code repositories, each of which requires inter-dependent changes to many files (between 2-97 files). Coding tasks of this level of complexity have not been automated using LLMs before. Our results show that CodePlan has better match with the ground truth compared to baselines. CodePlan is able to get 5/6 repositories to pass the validity checks (e.g., to build without errors and make correct code edits) whereas the baselines (without planning but with the same type of contextual information as CodePlan) cannot get any of the repositories to pass them.