Reviewerly/exHarmony huggingface dataset:
Krotov Type Optimization of Coherent and Incoherent Controls for Open Two-Qubit Systems

This work considers two-qubit open quantum systems driven by coherent and incoherent controls. Incoherent control induces time-dependent decoherence rates via time-dependent spectral density of the environment which is used as a resource for controlling the system. The system evolves according to the Gorini–Kossakowski– Sudarshan–Lindblad master equation with time-dependent coefficients. For two types of interaction with coherent control, three types of objectives are considered: 1) maximizing the Hilbert–Schmidt overlap between the final and target density matrices; 2) minimizing the Hilbert–Schmidt distance between these matrices; 3) steering the overlap to a given value. For the first problem, we develop the Krotov type methods directly in terms of density matrices with or without regularization for piecewise continuous controls with constaints and find the cases where the methods produce (either exactly or with some precision) zero controls which satisfy the Pontryagin maximum principle and produce the overlap’s values close to their upper bounds. For the problems 2) and 3), we find cases when the dual annealing method steers the objectives close to zero and produces a non-zero control.
Robust Ellipsoid Fitting Using Combination of Axial and Sampson Distances

In random sample consensus (RANSAC), the problem of ellipsoid fitting can be formulated as a problem of minimization of point-to-model distance, which is realized by maximizing model score. Hence, the performance of ellipsoid fitting is affected by distance metric. In this paper, we proposed a novel distance metric called the axial distance, which is converted from the algebraic distance by introducing a scaling factor to solve nongeometric problems of the algebraic distance. There is complementarity between the axial distance and Sampson distance because their combination is a stricter metric when calculating the model score of sample consensus and the weight of the weighted least squares (WLS) fitting. Subsequently, a novel sample-consensus-based ellipsoid fitting method is proposed by using the combination between the axial distance and Sampson distance (CAS). We compare the proposed method with several representative fitting methods through experiments on synthetic and real datasets. The results show that the proposed method has a higher robustness against outliers, consistently high accuracy, and a speed close to that of the method based on sample consensus.
The Effect of System Quality and User Quality of Information Technology on Internal Audit Effectiveness in Jordan, And the Moderating Effect of Management Support

The goal of this study is to ascertain the moderating role that management support has in internal audit effectiveness in Jordan, as well as the impact of system quality and user quality of information technology. There were 172 responders in all, and they were split across Jordanian auditors. In the data analysis process, the quantitative analysis test— which consists of the validity test, reliability test, test of conventional assumptions, and hypothesis test—is applied. Information technology system and user quality are independent variables in this study. The dependent variable in this study is internal audit effectiveness, and the moderating variable is management support. The results of this study show that the effectiveness of internal audits is significantly impacted by the system quality and user quality of information technology. Additionally, with Management support acting as a moderating factor, the link between System quality and Audit effectiveness improves. The findings also indicate that when moderating variables are present, the connection between User quality and Audit effectiveness changes from positive to negative. Future research might look at risk management.
Implementasi Algoritma Convolutional Neural Network Pada Kendaraan Tanpa Awak Skala Kecil

Autonomous Vehicle is a vehicle capable of navigating the car independently without requiring input from the driver. This research aims to design and manufacture a prototype of an unmanned vehicle that can maneuver across a simple artificial road. This study also aims to analyze the performance of the NVIDIA Jetson Nano in processing deep learning models and driving actuators according to the predictions given by the model. The research stages include designing a prototype, creating an artificial path, taking image data, conducting training, and then implementing the training model on the car prototype. After testing the prototype, the training model made the correct steering angle prediction using epoch 50 with RMSE train and validation, 0.1792 and 0.1896, respectively. NVIDIA Jetson Nano also performs well in computing steering angle predictions with live input from the camera.
Understanding neural network through neuron level visualization

Neurons are the fundamental units of neural networks. In this paper, we propose a method for explaining neural networks by visualizing the learning process of neurons. For a trained neural network, the proposed method obtains the features learned by each neuron and displays the features in a human-understandable form. The features learned by different neurons are combined to analyze the working mechanism of different neural network models. The method is applicable to neural networks without requiring any changes to the architectures of the models. In this study, we apply the proposed method to both Fully Connected Networks (FCNs) and Convolutional Neural Networks (CNNs) trained using the backpropagation learning algorithm. We conduct experiments on models for image classification tasks to demonstrate the effectiveness of the method. Through these experiments, we gain insights into the working mechanisms of various neural network architectures and evaluate neural network interpretability from diverse perspectives.
Hamiltonian Cycle Reconfiguration with Answer Set Programming

The Hamiltonian cycle reconfiguration problem is defined as determining, for a given Hamiltonian cycle problem and two among its feasible solutions, whether one is reachable from another via a sequence of feasible solutions subject to certain transition constraints. We develop an approach to solving the Hamiltonian cycle reconfiguration problem based on Answer Set Programming (ASP). Our approach relies on a high-level ASP encoding and delegates both the grounding and solving tasks to an ASP-based solver. To show the effectiveness of our approach, we conduct experiments on the benchmark set of Flinders Hamiltonian Cycle Project.
Generalized linear models

Machine learning (ML) algorithms use statistical models to find patterns or structures in data. These models can formulate predictions for new observations on the basis of these patterns, which the ML algorithm can translate into decisions.
Use cases for science blogs: grant-funded projects

The Rogue Scholar science blog archive is open to science blogs that want to be enhanced by adding long-term archiving, DOI registration, and full-text search. The currently 56 participating blogs represent a broad spectrum of topics, people, and communities. Today I want to go into more detail into one particular Rogue Scholar use case: science blogs for grant-funded projects.
Preliminary Structured Literature Review Results Using ChatGPT: Towards a Pragmatic Framework for Product Managers at Software Startups

As a crucial factor in the success of software startups, the role of the Product Manager (PM) is often overlooked, despite the high failure rate (63%). To bridge this gap, a Structured Literature Review (SLR) is getting conducted. Following a rigorous study selection strategy, 134 studies have been selected, of which 79 directly linked to the Requirements Engineering (RE) domain. By focusing on the RE studies, and applying ChatGPT, 343 unique PM tasks, spanning 98 activities have been identified. Out of these studies, only 7 (8,86%) consider explicitly both a startup context and the role of the PM. This study aims to contribute to the field by providing valuable input to develop a novel, startup-specific framework that helps to refocus the PM to those activities that are expected will capture most of the expected value. The future framework aims to enhance early-stage product decision-making for founders, increasing their probability of success. It not only has practical implications for software startups, but could also spur further collaborative academic RE research. Furthermore, the study could serve as a valuable resource for researchers and practitioners in the domain of RE and startups.
Omnivariant Generalized Least Squares Regression: Theory, Geochronological Applications, and Making the Case for Reconciled Δ47 calibrations

Least-squares regression methods are mathematically powerful, conceptually and computationally simple, and widely used in many fields. However, none of the commonly-used flavors of least-squares regression, such as York regression or Generalized Least Squares (GLS), take into account the full set of covariances between all observed (x,y) values. Here we describe the Omnivariant Generalized Least Squares (OGLS) method to fit a model of the form y=f(x), accounting for the full error correlation structure of the (x,y) data, based on a first-order linear propagation of the uncertainties in all variables into errors in y residuals, followed by minimizing the vector of y residuals with respect to the Mahalanobis norm defined by its covariance matrix. This approach may be described as a generalization of both York regression and GLS. It is mathematically exact for straight-line fits, and is also suitable for many non-linear models. Here we describe the principles of OGLS regression and discuss its properties, caveats, and practical use, and provide two consistent open-source implementations in Python and R. To illustrate how various fields of geochronology and stable-isotope geochemistry may benefit from this new method, we discuss how OGLS may specifically apply to Ar/Ar dating and how it provides robust mathematical evidence that Δ carbonate calibrations in the recently defined I-CDES metrological scale are statistically indistinguishable, effectively solving long-standing methodological discrepancies.
Synergy Between AI and Robotics: A Comprehensive Integration

The emergence of artificial intelligence is mostly linked to software-driven robotic systems, including mobile robots, unmanned aerial aircraft, and, to a growing extent, semi-autonomous automobiles. Nevertheless, the significant disparity between the algorithmic realm and the physical realm hinders current systems from achieving the desired outcome of creating intelligent and user-friendly robots that can effectively engage with and manipulate our human-centric environment. The nascent field of machine intelligence (MI), which combines robotics and artificial intelligence, strives to develop reliable and embodiment-aware artificial intelligence systems. These systems possess self-awareness and an understanding of their environment, enabling them to adapt to the interacting body they are operating. The incorporation of artificial intelligence (AI) and robotics into control, perception, and machine-learning systems is necessary for the realization of fully autonomous intelligent systems in our everyday existence. This review provides an overview of the historical development of machine intelligence, tracing its origins to the twelfth century. It then proceeds to examine the present state of robotics and artificial intelligence (AI), discussing significant systems and contemporary research directions. Additionally, the article outlines the remaining challenges in these fields and speculates on the potential future of human-machine interactions that has yet to be realized.
Arabic Sentiment Analysis for Student Evaluation using Machine Learning and the AraBERT Transformer

Recently, Sentiment Analysis (SA) has become a crucial area of research as it enables us to gauge people's opinions from various sources such as student evaluations, social media posts, product reviews, etc. This paper aims to create an Arabic dataset derived from student satisfaction surveys conducted at the University of Jeddah regarding their subjects and instructors. In addition, this study presents an evaluation of classical machine learning models such as Naive Bayes, Support Vector Machine, Logistic Regression, Decision Tree, and Random Forest classifier for Arabic SA, whereas the results are compared using various metrics. Furthermore, AraBERT was used for the pre-trained transformer to improve the performance, achieving an accuracy of 78%. The paper fills the lack of SA research in the education domain in the Arabic language.
WaterLo: Protect Images from Deepfakes Using Localized Semi-Fragile Watermark

Most existing contributions in the field of Deepfake detection focus on passive detection methods, where the detector only analyzes the doctored image. However, this approach often lacks the ability to generalize to unseen data and struggles to detect Deepfakes generated using new deepfake models. To address this limitation, our paper proposes an active detection approach, where we have access to the image before the Deepfake is generated. Our solution involves applying a watermark that disappears in modified regions, allowing our detector to identify image modifications and localize them accurately. Additionally, we incorporate a compression module into our training pipeline to enhance the watermark’s robustness against JPEG compression. Experimental results demonstrate the effectiveness of our proposed solution, achieving a remarkable detection accuracy of 97.83% while maintaining significantly higher image quality compared to previous works. Furthermore, by incorporating the compression module in the training pipeline, we improve the detection accuracy on compressed samples, albeit with a slight decrease in accuracy for non-compressed samples. This contribution also provides a valuable tool for video owners to verify if their videos have been tampered with and safeguard them against unauthorized use. The code of the proposed framework is available at https://github.com/beuve/waterlo.
Graph-|<i>Q</i>⟩⟨<i>C</i>|: A Quantum Algorithm with Reduced Quantum Circuit Depth for Electronic Structure

The accurate determination of chemical properties is known to have a critical impact on multiple fundamental chemical problems but is deeply hindered by the steep algebraic scaling of electron correlation calculations and the exponential scaling of quantum nuclear dynamics. With the advent of new quantum computing hardware and associated developments in creating new paradigms for quantum software, this avenue has been recognized as perhaps one way to address exponentially complex challenges in quantum chemistry and molecular dynamics. In this paper, we discuss a new approach to drastically reduce the quantum circuit depth (by several orders of magnitude) and help improve the accuracy in the quantum computation of electron correlation energies for large molecular systems. The method is derived from a graph-theoretic approach to molecular fragmentation and enables us to create a family of projection operators that decompose quantum circuits into separate unitary processes. Some of these processes can be treated on quantum hardware and others on classical hardware in a completely asynchronous and parallel fashion. Numerical benchmarks are provided through the computation of unitary coupled-cluster singles and doubles (UCCSD) energies for medium-sized protonated and neutral water clusters using the new quantum algorithms presented here.
UHD Aerial Photograph Categorization by Leveraging Deep Multiattribute Matrix Factorization

There are thousands of observation satellites orbiting the earth, each of which captures massive-scale photographs covering millions of square kilometers everyday. In practice, these aerial photos are with ultra-high-definitions (UHD) and may contain tens to hundreds of ground objects ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e.g</i> ., vehicles and rooftops). Understanding the multiple categories of a rich variety of UHD aerial photos is an indispensable technique for many applications, such as intelligent transportation, natural disaster prediction, and smart agriculture. In this work, we propose a novel multi-label UHD aerial photo categorization pipeline, wherein the key is to topologically represent the spatial layouts of the ground objects and further deeply encode them using a deep multi-clue matrix factorization (DMCMF) that robustly handles noisy labels at image-level. More specifically, for each UHD aerial photo, we extract visually/semantically salient object patches inside it. To explicitly encode their spatial layout, we construct a graphlet by linking the spatially adjacent object patches into a small graph. Subsequently, a binary MF is designed to intelligently exploit the semantics of these graphlets, wherein four clues: i) binary hash codes learning, ii) noisy labels refinement, iii) deep image-level semantics, and iv) adaptive data graph updating are incorporated. Such DMCMF can be solved iteratively and each graphlet is then converted into the discrete hash codes. Finally, the hash codes corresponding to graphlets within each UHD aerial photo are quantized into a feature vector by a kernel machine for multi-label categorization. Toward a comprehensive comparative study, we complied a million-scale UHD aerial photo set collected from 100 top-ranking cities worldwide. Experiments have shown that 1) our method is highly competitive in learning categorization model from imperfect labels at image-level, and 2) the four clues are elaborately designed and seamlessly combined to learn hash codes for representing UHD aerial photos.
Workspace decomposition based path planning for fruit-picking robot in complex greenhouse environment

The working environment of fruit-picking robots is very complicated. Generally, there are a large number of obstacles such as branches, immature crops, etc. Besides, the agricultural scene is not as static. For sampling-based methods such as Probabilistic RoadMap (PRM) and Rapidly-exploring Random Trees (RRT), they suffer from inferior path quality in obstacle condensed environments. This paper proposes a local search path planning method in the feasible region of fruit-picking robots based on discrete workspace guidance and configuration space exploration correction. It is used to plan the motion of fruit-picking robots for avoiding obstacles and grasping in dynamic and complex agricultural environments. First, the workspace is discretized, which makes the location information of obstacles computable. Then, a connected region path is found to guide the robot to search in the local configuration space. This greatly improves the continuity of the generated paths in the configuration space. The local search is further used to correct the weights of discrete regions of the workspace to find better connected paths with higher planning speed. The experimental results show that the quality of paths planned by proposed method is generally better than that of RRT and RRT-CONNECT, and the planning speed is faster than that of Task-Space RRT (TSRRT). Proposed algorithm enables real-world picking at 10.5 piece/s and a recovery rate of 80.0 %. This method significantly improves the path quality in dynamic and complex agricultural scenarios.
SE-Lightweight YOLO: Higher Accuracy in YOLO Detection for Vehicle Inspection

Against the backdrop of ongoing urbanization, issues such as traffic congestion and accidents are assuming heightened prominence, necessitating urgent and practical interventions to enhance the efficiency and safety of transportation systems. A paramount challenge lies in realizing real-time vehicle monitoring, flow management, and traffic safety control within the transportation infrastructure to mitigate congestion, optimize road utilization, and curb traffic accidents. In response to this challenge, the present study leverages advanced computer vision technology for vehicle detection and tracking, employing deep learning algorithms. The resultant recognition outcomes provide the traffic management domain with actionable insights for optimizing traffic flow management and signal light control through real-time data analysis. The study demonstrates the applicability of the SE-Lightweight YOLO algorithm, as presented herein, showcasing a noteworthy 95.7% accuracy in vehicle recognition. As a prospective trajectory, this research stands poised to serve as a pivotal reference for urban traffic management, laying the groundwork for a more efficient, secure, and streamlined transportation system in the future. To solve the existing vehicle detection problems in vehicle type recognition, recognition and detection accuracy need to be improved, alongside resolving the issues of slow detection speed, and others. In this paper, we made innovative changes based on the YOLOv7 framework: we added the SE attention transfer mechanism in the backbone module, and the model achieved better results, with a 1.2% improvement compared with the original YOLOv7. Meanwhile, we replaced the SPPCSPC module with the SPPFCSPC module, which enhanced the trait extraction of the model. After that, we applied the SE-Lightweight YOLO to the field of traffic monitoring. This can assist transportation-related personnel in traffic monitoring and aid in creating big data on transportation. Therefore, this research has a good application prospect.
GEMINI: Controlling The Sentence-Level Summary Style in Abstractive Text Summarization

Human experts write summaries using different techniques, including extracting a sentence from the document and rewriting it, or fusing various information from the document to abstract it. These techniques are flexible and thus difficult to be imitated by any single method. To address this issue, we propose an adaptive model, GEMINI, that integrates a rewriter and a generator to mimic the sentence rewriting and abstracting techniques, respectively. GEMINI adaptively chooses to rewrite a specific document sentence or generate a summary sentence from scratch. Experiments demonstrate that our adaptive approach outperforms the pure abstractive and rewriting baselines on three benchmark datasets, achieving the best results on WikiHow. Interestingly, empirical results show that the human summary styles of summary sentences are consistently predictable given their context. We release our code and model at https://github.com/baoguangsheng/gemini.
Directions of Development of Educational Organizations in Conditions of Digital Transformation of Science Field and Higher Education

In modern conditions of digital transformation, the problem of the development of educational organizations becomes relevant. The trend determined by state policy for the science field and higher education, aimed at creating a training system that ensures the formation of digital competencies of citizens and personalization of education, determines the strategic directions for the development of educational organizations. The purpose of the research was to consider the most important areas of development of educational organizations, one of which is the development of digital services. Further use of digital services will increase the efficiency and speed of acquiring digital competencies not only by students, but also by scientists, academics, administrators and managers of an educational institution. The analysis identified conditions required for the transition to the management of educational organizations based on data. The study showed that in the long term educational institutions will be able to move to management according to the model of a digital university and further — a digital twin of the university. Thus, predictive and prescriptive analytics should become the basis for effective management decisions that will increase the competitiveness of educational organizations in the educational services market and create conditions for the development of the country's economy.