Reviewerly/exHarmony huggingface dataset:
Hybrid Particle Swarm Optimization for High-Dimensional Latin Hypercube Design Problem

Latin Hypercube Design (LHD) is widely used in computer simulation to solve large-scale, complex, nonlinear problems. The high-dimensional LHD (HLHD) problem is one of the crucial issues and has been a large concern in the long run. This paper proposes an improved Hybrid Particle Swarm Optimization (IHPSO) algorithm to find the near-optimal HLHD by increasing the particle evolution speed and strengthening the local search. In the proposed algorithm, firstly, the diversity of the population is ensured through comprehensive learning. Secondly, the Minimum Point Distance (MPD) method is adopted to solve the oscillation problem of the PSO algorithm. Thirdly, the Ranked Ordered Value (ROV) rule is used to realize the discretization of the PSO algorithm. Finally, local and global searches are executed to find the near-optimal HLHD. The comparisons show the superiority of the proposed method compared with the existing algorithms in obtaining the near-optimal HLHD.
The robotic-surgery propositional bank

Abstract Robot-assisted minimally invasive surgery is the gold standard for the surgical treatment of many pathological conditions since it guarantees to the patient shorter hospital stay and quicker recovery. Several manuals and academic papers describe how to perform these interventions and thus contain important domain-specific knowledge. This information, if automatically extracted and processed, can be used to extract or summarize surgical practices or develop decision making systems that can help the surgeon or nurses to optimize the patient’s management before, during, and after the surgery by providing theoretical-based suggestions. However, general English natural language understanding algorithms have lower efficacy and coverage issues when applied to domain others than those they are typically trained on, and a domain specific textual annotated corpus is missing. To overcome this problem, we annotated the first robotic-surgery procedural corpus, with PropBank-style semantic labels. Starting from the original PropBank framebank, we enriched it by adding new lemmas, frames and semantic arguments required to cover missing information in general English but needed in procedural surgical language, releasing the Robotic-Surgery Procedural Framebank (RSPF). We then collected from robotic-surgery textbooks as-is sentences for a total of 32,448 tokens, and we annotated them with RSPF labels. We so obtained and publicly released the first annotated corpus of the robotic-surgical domain that can be used to foster further research on language understanding and procedural entities and relations extraction from clinical and surgical scientific literature.
Design and Implementation of a Dual-Axis Solar Tracking System STS

Solar energy is converted into electrical energy using photovoltaic panels. The production of electricity from the solar panel is increased by the increase in the collection of solar radiation by the solar panel. To track the sun in vertical and horizontal directions, a dual-axis tracking prototype has been developed to capture the maximum sun rays by tracking the movement of the sun in four different directions. One axis is horizontal, which allows the solar panel to move left and right. The other axis is vertical and allows the panel to turn up and down. The result of this new development provides the solar panels with extensive freedom of movement. This process makes use of the Light Depending Resistor (LDR), which is important for detecting the sun's light by following the source of the light location. Proteus software is being used to design the circuit for the Arduino mega microcontrollers and H-Bridge IC chip. This implemented system can save more energy and probably offer a greater reduction in cost. The project discusses the process of hardware development and the control process of tracking the sun, as well as the circuit design.
Quantum computation with cat qubits

These are the lecture notes from the 2019 Les Houches Summer School on “Quantum Information Machines”. After a brief introduction to quantum error correction and bosonic codes, we focus on the case of cat qubits stabilized by a nonlinear multi-photon driven dissipation process. We argue that such a system can be seen as a self-correcting qubit where bit-flip errors are robustly and exponentially suppressed. Next, we provide some experimental directions to engineer such a multi-photon driven dissipation process with superconducting circuits. Finally, we analyze various logical gates that can be implemented without re-introducing bit-flip errors. This set of bias-preserving gates pave the way towards a hardware-efficient and fault-tolerant quantum processor.
An Explainable Vision Transformer Model Based White Blood Cells Classification and Localization

Blood cell analysis is a crucial diagnostic process in medical practice. In particular, detecting white blood cells (WBCs) is essential for diagnosing of many diseases. The manual screening of blood films is a time-consuming and subjective process, which can lead to inconsistencies and errors. Therefore, automated detection of blood cells can improve the accuracy and efficiency of the screening process. In this study, an explainable Vision Transformer (ViT) model was proposed for the automatic detection of WBCs from blood films. The proposed model utilizes the self-attention mechanism to extract relevant features from the input images and leverages transfer learning by incorporating pre-trained model weights to improve its performance. The proposed model achieved a classification accuracy of 99.40% for five distinct types of WBCs and exhibited potential in reducing the time required for manual screening of blood films by pathologists. Upon examination of the misclassified test samples, it was observed that incorrect predictions were correlated with the presence or absence of granules in the cell samples. To validate this observation, the dataset was divided into two classes, namely Granulocytes and Agranulocytes, and a secondary training process was conducted. The resulting ViT model trained for binary classification achieved an accuracy of 99.70%, recall of 99.54%, precision of 99.32%, and F-1 score of 99.43% during the test phase. To ensure the reliability of the ViT model&amp;#039;s multi-class classification of WBCs, the pixel areas that the model focuses on in its predictions are visualized through the Score-CAM algorithm.
Perancangan sistem informasi peminjaman ruangan pada PT. Multi Aneka Pangan Nusantara

PT. Multi Aneka Pangan Nusantara is one of the producers in Indonesia engaged in the chocolate processing industry and has presented a variety of premium quality products. The company office has many rooms that can be borrowed for meeting purposes. The company has an IT section whose job is to control all parts related to media and communications. PT. Multi Aneka Pangan Nusantara, whose center is in Surabaya, has a logistics unit whose one of its tasks is to manage meeting room loans and manage equipment at the PT. Multi Aneka Pangan Nusanatara. This web-based room loan application plan aims to make it easier for consumers to borrow rooms in a structured manner so that companies are able to create new innovations and can be used in the future. The design that will be carried out is the creation of a room lending application as access to meeting rooms and other activities where this application uses an online PHP and MySQL-based website that is more efficient and scheduled in detail. Room lending information system application at PT. Multi Aneka Pangan Nusantara has features that are suitable to answer existing problems, namely user data, room data, providing information on the number of borrowers, viewing and searching for user data, rooms and transactions. The existence of this information system can answer problems by providing loan management solutions that can be more quickly and efficiently.
Analysis of Supervised and Unsupervised Deep Learning Approaches for Identifying and Localizing Image Forgeries

The field of image forensics has become important in recent years as the use of digital images continues to grow. With the rise of sophisticated image editing software, it has become increasingly difficult to detect whether an image has been tampered with or not. Moreover, social media platforms have made the distribution of forged images to the general public a simple task. It is hence very important to develop automated methods that can detect such forgeries. In this study, we detect and localize splicing and copy-move image forgeries in images by using two different deep-learning techniques - Convolutional Neural Networks (CNN), which is a supervised approach and Self-Consistency Learn- ing, an unsupervised approach. By comparing and contrasting the perfor- mance of these approaches, the research aims to gain a better understanding of how to effectively detect and locate image forgeries using deep learning. Ulti- mately, this research will contribute to the development of more reliable and accurate image forensic techniques, which will be of great benefit in various fields such as criminal investigations, digital media, and photojournalism.
Efficient Quantum Imaginary Time Evolution by Drifting Real-Time Evolution: An Approach with Low Gate and Measurement Complexity

Quantum imaginary time evolution (QITE) is one of the promising candidates for finding the eigenvalues and eigenstates of a Hamiltonian on a quantum computer. However, the original proposal suffers from large circuit depth and measurements due to the size of the Pauli operator pool and Trotterization. To alleviate the requirement for deep circuits, we propose a time-dependent drifting scheme inspired by the qDRIFT algorithm [Campbell, E. Phys. Rev. Lett. 2019, 123, 070503]. We show that this drifting scheme removes the depth dependency on the size of the operator pool and converges inversely with respect to the number of steps. We further propose a deterministic algorithm that selects the dominant Pauli term to reduce the fluctuation for the ground state preparation. We also introduce an efficient measurement reduction scheme across Trotter steps that removes its cost dependence on the number of iterations. We analyze the main source of error for our scheme both theoretically and numerically. We numerically test the validity of depth reduction, convergence performance of our algorithms, and the faithfulness of the approximation for our measurement reduction scheme on several benchmark molecules. In particular, the results on the LiH molecule give circuit depths comparable to that of the advanced adaptive variational quantum eigensolver (VQE) methods while requiring much fewer measurements.
Object Detection Based on Lightweight YOLOX for Autonomous Driving

Abstract Object detection algorithm is one of the key elements for implementing autonomous driving technology, and the high accuracy and fast inference speed requirements of the algorithm are crucial for safe autonomous driving. In the process of autonomous driving, low accuracy or slow inference speed can both lead to fatal accidents. Therefore, a high-precision and high-efficiency detection algorithm is needed for autonomous driving applications. In this paper, a lightweight feature extraction model, ShuffDet, is proposed to replace the CSPDark53 model used by YOLOX, by improving the YOLOX algorithm. At the same time, an attention mechanism is introduced into the feature pyramid network (FPN) to make the network focus more on important information in the network, thereby improving the accuracy of the model. This model, which combines two methods, is called ShuffYOLOX, and it can improve the accuracy of the model while keeping it lightweight. The performance of the ShuffYOLOX model on the KITTI dataset is tested in this paper, and the experimental results show that compared to the original network, the mean average precision (mAP) of the ShuffYOLOX model on the KITTI dataset reaches 92.20%. In addition, the number of parameters of the ShuffYOLOX model is reduced by 34.57%, the Gflops is reduced by 42.19%, and the FPS is increased by 65%. Therefore, the ShuffYOLOX model is very suitable for autonomous driving applications.
Transforming collections of Pauli operators into equivalent collections of Pauli operators over minimal registers

Transformations which convert between Fermionic modes and qubit operations have become a ubiquitous tool in quantum algorithms for simulating systems. Similarly, collections of Pauli operators might be obtained from solutions of non-local games and satisfiability problems. Drawing on ideas from entanglement-assisted quantum error-correcting codes and quantum convolutional codes, we prove the obtainable lower-bound for the number of qubits needed to represent such Pauli operations which are equivalent and provide a procedure for determining such a set of minimal register Pauli operations.
Pengabdian Masyarakat Penyaringan Air Menggunakan Alat Sederhana untuk Meningkatkan Kejernihan Air di Desa Kedungpeluk Sidoarjo

Clean water is the most important thing in life. Every day, all living things need water to survive. Through clean water, life is naturally healthier. However, it is different from the water in Kedungpeluk Village in Candi Sidoarjo District. the water in the village of Kedungpeluk is cloudy. Kedungpeluk Village, which is surrounded by rice fields and ponds and has a land area of 1,128,665 ha, is due to water conditions that do not work optimally for bathing, cooking, and washing in general. This activity aims to educate the people of Kedungpeluk Village about overcoming water problems and being able to make water filtration. Community service is carried out in stages through a water filtration method that removes suspended substances from water through a porous membrane using a simple device. The community service partner is Kedungpeluk Village in Sidoarjo. The results of the Community Service obtained were that a simple filtering method for well water in the village of Kedungluk was successfully cleaned.
Looped Pipelines Enabling Effective 3D Qubit Lattices in a Strictly 2D Device

Many quantum computing platforms are based on a two-dimensional physical layout. Here we explore a concept called looped pipelines which permits one to obtain many of the advantages of a 3D lattice while operating a strictly 2D device. The concept leverages qubit shuttling, a well-established feature in platforms like semiconductor spin qubits and trapped-ion qubits. The looped pipeline architecture has similar hardware requirements to other shuttling approaches, but can process a stack of qubit arrays instead of just one. Even a stack of limited height is enabling for diverse schemes ranging from NISQ-era error mitigation through to fault-tolerant codes. For the former, protocols involving multiple states can be implemented with a space-time resource cost comparable to preparing one noisy copy. For the latter, one can realise a far broader variety of code structures; as an example we consider layered 2D codes within which transversal CNOTs are available. Under reasonable assumptions this approach can reduce the space-time cost of magic state distillation by two orders of magnitude. Numerical modelling using experimentally-motivated noise models verifies that the architecture provides this benefit without significant reduction to the code's threshold.
Deep learning for opinion mining and topic classification of course reviews

Abstract Student opinions for a course are important to educators and administrators, regardless of the type of the course or the institution. Reading and manually analyzing open-ended feedback becomes infeasible for massive volumes of comments at institution level or online forums. In this paper, we collected and pre-processed a large number of course reviews publicly available online. We applied machine learning techniques with the goal to gain insight into student sentiments and topics. Specifically, we utilized current Natural Language Processing (NLP) techniques, such as word embeddings and deep neural networks, and state-of-the-art BERT (Bidirectional Encoder Representations from Transformers), RoBERTa (Robustly optimized BERT approach) and XLNet (Generalized Auto-regression Pre-training). We performed extensive experimentation to compare these techniques versus traditional approaches. This comparative study demonstrates how to apply modern machine learning approaches for sentiment polarity extraction and topic-based classification utilizing course feedback. For sentiment polarity, the top model was RoBERTa with 95.5% accuracy and 84.7% F1-macro, while for topic classification, an SVM (Support Vector Machine) was the top classifier with 79.8% accuracy and 80.6% F1-macro. We also provided an in-depth exploration of the effect of certain hyperparameters on the model performance and discussed our observations. These findings can be used by institutions and course providers as a guide for analyzing their own course feedback using NLP models towards self-evaluation and improvement.
Content Moderation in the Metaverse Could Be a New Frontier to Attack Freedom of Expression

Abstract This commentary examines the challenges faced by metaverse platforms in cross-border content moderation, focusing on the implications for freedom of expression and nondiscrimination. It highlights the difficulties in determining what to remove for which users as well as how to do so, which has serious implications for freedom of expression and our shared sense of reality. Proto-metaverse platforms such as Roblox and Minecraft face similar questions, but have not yet encountered major cross-jurisdictional issues because, as looking at traditional social media platforms reveals, content moderation is not merely a question of law and policy, but also of geopolitics and government priorities. To avoid a “lowest common denominator effect” where freedom of expression is infringed upon worldwide and discrimination is entrenched, this commentary argues that metaverse platforms must clarify their moderation policies, assess their entry into specific markets based on local laws and their own values, and be prepared to exit overly restrictive markets.
Variational quantum non-orthogonal optimization

Current universal quantum computers have a limited number of noisy qubits. Because of this, it is difficult to use them to solve large-scale complex optimization problems. In this paper we tackle this issue by proposing a quantum optimization scheme where discrete classical variables are encoded in non-orthogonal states of the quantum system. We develop the case of non-orthogonal qubit states, with individual qubits on the quantum computer handling more than one bit classical variable. Combining this idea with Variational Quantum Eigensolvers (VQE) and quantum state tomography, we show that it is possible to significantly reduce the number of qubits required by quantum hardware to solve complex optimization problems. We benchmark our algorithm by successfully optimizing a polynomial of degree 8 and 15 variables using only 15 qubits. Our proposal opens the path towards solving real-life useful optimization problems in today's limited quantum hardware.
NATURAL SCIENCE AND TECHNOLOGY EDUCATION: BalticSTE2023

Time passes very quickly. It seems that the first BalticSTE symposium took place quite recently. However, eight years have already passed since the first symposium. During this time, when there is a rapid change in all areas of life, a lot has undoubtedly changed. The issues that were discussed during the first symposium have changed significantly. As an example, we can mention the possibilities of (AI), in this case, in the field of education. For example, last year saw the launch of ChatGPT, a chatbot programme that responds to text messages. It is a large language model developed by OpenAI that is trained to analyse and generate text in various contexts. This is just one example showing a rapid change in science and technology. It is gratifying that great attention is paid to these issues and to scientific articles published in this publication.
Convolutional Neural Network for Colorization of Black and White Photos

People today are very fond of capturing moments by taking pictures. Various photo functions are used to document all forms of information that you want to store. In photos with digital images that have black and white, the information obtained is less than optimal, so an image processing process is needed to get color photos. Based on this, the author wants to change photos from black and white to color photos. The method used in this research is Convolutional Neural Network (CNN). This study uses Atlas 200 DK hardware and Ascend 310 processor. The data used in this study are 32 black and white photos in .jpg format as training data and perform 6 experimental scenarios with different numbers of black and white photos in each experiment. The total black and white photos used to experiment were 81 photos. The results obtained are models that successfully perform processing in the form of color photos with the appropriate color results in predicting the possible color of the object in each pixel in the photo. Based on this research, the trend of artificial intelligence can be implemented in changing the color of photos according to color predictions.
EXPLORING THE ROLE OF TEXT-TO-IMAGE AI IN CONCEPT GENERATION

Abstract Artificial intelligence (AI) capable of generating images from a text prompt are becoming increasingly prevalent in society and design. The general public can use their computers and mobile devices to ask a complex text-to-image AI to create an image which is in some cases indistinguishable from that which a human could create using a computer graphics package. These images are shared on social media and have been used in the creation of art projects, documents and publications. This exploratory study aimed to identify if modern text-to-image AI (Midjourney, DALL-E 2, and Disco Diffusion) could be used to replace the designer in the concept generation stage of the design process. Teams of design students were asked to evaluate AI generated concepts from 15 to a final concept. The outcomes of this research are a first of its kind for the field of engineering design, in the identification of barriers in the use of current text-to-image AI for the purpose of engineering design. The discussion suggests how this can be overcome in the short term and what knowledge the research community needs to build to overcome these barriers in the long term.
EFFECTS OF IMMERSIVE VIRTUAL REALITY IN ENHANCING CREATIVITY

Abstract In recent years, the use of virtual reality (VR) has been a promising opportunity to improve the immersive experience in virtual environments. In this study, we explore the effectiveness of immersive VR experiences on an individual's creativity. To do this, we first identified the characteristics of VR that are closely related to creative performance. Considering these factors, we designed and implemented the interaction method and the three outdoor and indoor virtual environments (Wilderness, City Park, and Office). We evaluated the effectiveness of the virtual environments with 12 participants. The user study results show that outdoor and indoor immersive virtual experiences improved their creativity skills. We concluded by analysing how factors such as simulator sickness and perceived workload influence creativity levels. Moreover, our study showed the potentiality of using immersive virtual environments to enhance individuals’ creative performance.