Reviewerly/exHarmony huggingface dataset:
PMC-LLaMA: Towards Building Open-source Language Models for Medicine

Recently, Large Language Models (LLMs) have showcased remarkable capabilities in natural language understanding. While demonstrating proficiency in everyday conversations and question-answering situations, these models frequently struggle in domains that require precision, such as medical applications, due to their lack of domain-specific knowledge. In this paper, we describe the procedure for building a powerful, open-source language model specifically designed for medicine applications, termed as PMC-LLaMA. Our contributions are threefold: (i) we systematically investigate the process of adapting a general-purpose foundation language model towards medical domain, this involves data-centric knowledge injection through the integration of 4.8M biomedical academic papers and 30K medical textbooks, as well as comprehensive fine-tuning for alignment with domain-specific instructions; (ii) we contribute a large-scale, comprehensive dataset for instruction tuning. This dataset encompasses medical question-answering (QA), rationale for reasoning, and conversational dialogues, comprising a total of 202M tokens; (iii) we conduct thorough ablation studies to demonstrate the effectiveness of each proposed component. While evaluating on various public medical question-answering benchmarks, our lightweight PMCLLaMA, which consists of only 13 billion parameters, exhibits superior performance, even surpassing ChatGPT. All models, codes, datasets can be found in https://github.com/chaoyi-wu/PMC-LLaMA.
AI and Machine Learning Applications to Enhance Customer Support

The aim of this study is to investigate the applications of machine learning (ML) and artificial intelligence (AI) techniques in customer support and to make recommendations for future research directions. Based on that, this study analyzed the articles linked to both AI and ML in customer service published on various scientific platforms using a systematic literature review methodology. The findings suggested that different types of AI and ML approaches are helpful for organizations in providing improved customer support and service for different sub-issues in different dimensions (integrated product service offerings, word of mouth, service excellence, and self-service technology) of customer support. The current study also provides businesses with helpful knowledge about how AI and ML technologies may be used to enhance customer service. Practitioners might also get advice from the current study on the need for further crucial measures and improvements.
ViP-NeRF: Visibility Prior for Sparse Input Neural Radiance Fields

Neural radiance fields (NeRF) have achieved impressive performances in view synthesis by encoding neural representations of a scene. However, NeRFs require hundreds of images per scene to synthesize photo-realistic novel views. Training them on sparse input views leads to overfitting and incorrect scene depth estimation resulting in artifacts in the rendered novel views. Sparse input NeRFs were recently regularized by providing dense depth estimated from pre-trained networks as supervision, to achieve improved performance over sparse depth constraints. However, we find that such depth priors may be inaccurate due to generalization issues. Instead, we hypothesize that the visibility of pixels in different input views can be more reliably estimated to provide dense supervision. In this regard, we compute a visibility prior through the use of plane sweep volumes, which does not require any pre-training. By regularizing the NeRF training with the visibility prior, we successfully train the NeRF with few input views. We reformulate the NeRF to also directly output the visibility of a 3D point from a given viewpoint to reduce the training time with the visibility constraint. On multiple datasets, our model outperforms the competing sparse input NeRF models including those that use learned priors. The source code for our model can be found on our project page: https://nagabhushansn95.github.io/publications/2023/ViP-NeRF.html.
Entanglement-interference complementarity and experimental demonstration in a superconducting circuit

Abstract Quantum entanglement between an interfering particle and a detector for acquiring the which-path information plays a central role for enforcing Bohr’s complementarity principle. However, the quantitative relation between this entanglement and the fringe visibility remains untouched upon for an initial mixed state. Here we find an equality for quantifying this relation. Our equality characterizes how well the interference pattern can be preserved when an interfering particle, initially carrying a definite amount of coherence, is entangled, to a certain degree, with a which-path detector. This equality provides a connection between entanglement and interference in the unified framework of coherence, revealing the quantitative entanglement-interference complementarity. We experimentally demonstrate this relation with a superconducting circuit, where a resonator serves as a which-path detector for an interfering qubit. The measured fringe visibility of the qubit’s Ramsey signal and the qubit-resonator entanglement exhibit a complementary relation, in well agreement with the theoretical prediction.
Experimental Realization of Entangled Coherent States in Two-dimensional Harmonic Oscillators of a Trapped Ion

Entangled coherent states play pivotal roles in various fields such as quantum computation, quantum communication, and quantum sensing. We experimentally demonstrate the generation of entangled coherent states with the two-dimensional motion of a trapped ion system. Using Raman transitions with appropriate detunings, we simultaneously drive the red and blue sidebands of the two transverse axes of a single trapped ion and observe multi-periodic entanglement and disentanglement of its spin and two-dimensional motion. Then, by measuring the spin state, we herald entangled coherent states of the transverse motions of the trapped ion and observe the corresponding modulation in the parity of the phonon distribution of one of the harmonic oscillators. Lastly, we trap two ions in a linear chain and realize Molmer-Sorensen gate using two-dimensional motion.
The digital silver lining of the pandemic: The impact on preservice teachers’ technological knowledge and beliefs

Abstract COVID-19 drastically disrupted teaching and learning worldwide and across all educational levels. Technology took on a central role in redefining education under these exceptional circumstances and frequently revealed challenges related to both infrastructure and to teachers’ and learners’ technological skills and readiness. This study aimed to investigate whether the experience of emergency remote education significantly impacted preservice teachers’ knowledge and beliefs for their future teaching with technology. We investigated three cohorts of preservice teachers (pre-lockdown, n = 179; during lockdown, n = 48; post-lockdown, n = 228) and explored differences in their self-reported technological pedagogical content knowledge (TPACK) and their technological beliefs. Findings showed positive effects in the post-lockdown cohort, reflected in higher levels of technological knowledge (TK) and technological pedagogical content knowledge (TPCK) compared to the pre-lockdown cohort. In addition, unique positive effects on content knowledge (CK) and pedagogical content knowledge (PCK) were found in the post-lockdown cohort among preservice teachers with prior teaching experiences. No effects of either cohort or experience emerged for preservice teachers’ technological beliefs. These findings indicate that, despite the challenges related to COVID-19 lockdowns, preservice teachers not only appear to have maintained positive beliefs towards technology but may have even been able to draw benefits from the experience of lockdown. These findings and the positive effects associated with teaching experience are discussed with regard to their implication for teacher education.
Analisis Sentimen Ibukota Negara Baru Menggunakan Metode Naïve Bayes Classifier

Twitter is one of the most popular social media websites on the internet today. The site covers a wide range of topics, including law, politics, culture, economics and more. On Twitter, many people talked about the idea of moving the capital of Indonesia. But there is disagreement between people regarding the discourse, because each side has different opinions and views. In order to understand the process of data automatically extracted from unstructured texts, then sentiment analysts were developed. This process is used to determine the overall opinion of a population about a subject in public discourse, as can be derived from diverse opinions on Twitter. In the application of sentiment analysis, the study used the naïve bayes method to be applied to tweets of new capital topics for the purpose of class classification of sentiment on social media tweeters. Technical classification is carried out by classifying into 2 classes, namely positive and negative. Based on the results of testing conducted on the new capital's sentiment tweets from social media twitter as many as 1,065 tweets tested sentiment about the new capital from social media twitter 289 stated positive and 776 stated negative Naïve Bayes method obtained accuracy = 94.18%,)
Development of Computer Vision-enabled Augmented Reality Games to Increase Motivation for Sports

This research paper presents the development of computer vision-enabled augmented reality games based on action detection to increase motivation for sports.With the increasing popularity of digital games, physical activity and sports participation have been declining, especially among the younger generation.To address this issue, we developed a series of augmented reality games that require players to perform physical actions to progress and succeed in the game.These games were developed using computer vision technology to detect the players' movements and provide real-time feedback, enhancing the gaming experience and promoting physical activity.The results of our user study showed that participants who played the augmented reality games reported higher levels of motivation to engage in physical activity and sports.The findings suggest that computer vision-enabled augmented reality games can be an effective tool to promote physical activity and sports participation, especially among younger generations.
Detects Damage Car Body using YOLO Deep Learning Algorithm

This journal presents a technique for detecting scratches, cracks and other damage to car bodies using machine learning methods. This method is used to improve process efficiency and checking accuracy and can also reduce the cost and time required for manual inspection. The method includes collecting image datasets of cars in good and damaged condition, followed by preprocessing and segmentation to separate damaged or damaged car parts. not broken. Then, it is followed by a deep learning algorithm, namely You Only Look Once, or Faster Region-based Convolutional Neural Networks, which is used to build a detection model. The model is trained and tuned using the collected data, then evaluated using the test data to measure the accuracy and precision of the detection results. The experimental results show that the proposed method achieves high accuracy and efficiency in detecting scratches, cracks, and other defects on the car body, with precision of an average of more than 70%. This method provides a promising approach to improving the car body inspection process which can be used by taxi companies to help inspect and maintain vehicles more quickly and accurately, to help with insurance, avoid accidents and so on.
Research on Construction and Application of Aircraft Fault Knowledge Graphs

When an aircraft malfunctions, quickly and accurately identifying the faulty unit is essential for ensuring normal operation. Unfortunately, maintenance engineers often struggle to acquire the necessary fault-related knowledge due to poor management and utilization of aircraft fault documents. To address this issue, we introduce knowledge graph technology into the field of aircraft fault diagnosis, exploring its construction and application for effective knowledge management. Our work starts by analyzing the critical knowledge elements required for aircraft fault diagnosis and designing a schema layer for fault knowledge graphs. We then we then combine deep learning and heuristic rules to extract fault knowledge from both structured and unstructured data, enabling the construction of aircraft fault knowledge graphs. Finally, we develop a fault question-answering system based on fault knowledge graphs that can accurately give solutions to questions posed by maintenance engineers. Our practice demonstrates that knowledge graphs provide an effective means of managing aircraft fault knowledge, assisting engineers in locating fault reasons accurately and quickly.
CLUSTSEG: Clustering for Universal Segmentation

We present CLUSTSEG, a general, transformer-based framework that tackles different image segmentation tasks (i.e., superpixel, semantic, instance, and panoptic) through a unified neural clustering scheme. Regarding queries as cluster centers, CLUSTSEG is innovative in two aspects:1) cluster centers are initialized in heterogeneous ways so as to pointedly address task-specific demands (e.g., instance- or category-level distinctiveness), yet without modifying the architecture; and 2) pixel-cluster assignment, formalized in a cross-attention fashion, is alternated with cluster center update, yet without learning additional parameters. These innovations closely link CLUSTSEG to EM clustering and make it a transparent and powerful framework that yields superior results across the above segmentation tasks.
Indian Sign Language Generation from Live Audio or Text for Tamil

Communication is the key behind the whole human evolution, and there would be nothing possible if the stream for communicating our thoughts to one another were cut off. Language is a significant factor when it comes to conveying one's ideas, thoughts, and information to other people. Sign language is used to communicate hard-of-hearing people's opinions. Every country has different sign languages. In this paper, we have used ISL (Indian Sign Language). To ease communication between ordinary people and deaf/dumb people, we propose the design and implementation of a model that translates a live voice, audio recordings, or text of a native Indian regional language (Tamil) to text and then further matches it to sign language animations from the video animation dataset. The speech is converted to text using two deep learning models LSTM(Long Short Term Memory), Bi-LSTM, and Google API. Then the text is transformed into a sign using ISL (Indian Sign Language) dataset. The proposed models achieved 45%, 65%, and 95% accuracy for LSTM, Bi-LSTM, and Google API, respectively.
Assessing the effectiveness of a chatbot workshop as experiential teaching and learning tool to engage undergraduate students

In this paper, we empirically examine and assess the effectiveness of a chatbot workshop as experiential teaching and learning tool to engage undergraduate students enrolled in an elective course “Doing Business with A.I.” in the Lee Kong Chian School of Business (LKCSB) at Singapore Management University. The chatbot workshop provides non-STEM students with an opportunity to acquire basic skills to build a chatbot prototype using the ‘Dialogflow’ program. The workshop and the experiential learning activity are designed to impart conversation and user-centric design know how and know why to students. A key didactical aspect which informs the design and flow of the chatbot workshop is that novice learners with no or very little knowledge about A.I. recognize and create the important linkage between knowledge inputs and outputs of conversational agents powered by natural language processing (NLP) so that user queries can be effectively addressed. According to the study results, 90.7% of all surveyed students (n = 43) were satisfied with the experiential learning chatbot workshop; 81.4% of the respondents felt engaged while 81.3% of the participants reported moderate to high levels of competencies (81.3%) as result of the hands-on workshop. Almost all students surveyed (97.7%) felt that the experiential chatbot workshop had met the expected learning outcomes. Besides presenting empirical data that underscore the pedagogical usefulness of conducting an experiential Chatbot workshop in basic A.I. classes with special reference to NLP, we are trying to corroborate a conceptual model developed from learning theories and technology mediated learning (TML) models aimed at measuring the effects of a chatbot practicum on students’ engagement and motivation as potential drivers of successfully acquiring basic NLP skills and learner satisfaction. The paper provides useful practical information for instructors interested in deploying a practical chatbot workshop as effective TML tool in a tertiary educational context aimed at making learners ‘future-ready’.
A benchmark dataset for deep learning-based airplane detection: HRPlanes

Airplane detection from satellite imagery is a challenging task due to the complex backgrounds in the images and differences in data acquisition conditions caused by the sensor geometry and atmospheric effects. Deep learning methods provide reliable and accurate solutions for automatic detection of airplanes; however, huge amount of training data is required to obtain promising results. In this study, we create a novel airplane detection dataset called High Resolution Planes (HRPlanes) by using images from Google Earth (GE) and labeling the bounding box of each plane on the images. HRPlanes include GE images of several different airports across the world to represent a variety of landscape, seasonal and satellite geometry conditions obtained from different satellites. We evaluated our dataset with two widely used object detection methods namely YOLOv4 and Faster R-CNN. Our preliminary results show that the proposed dataset can be a valuable data source and benchmark data set for future applications. Moreover, proposed architectures and results of this study could be used for transfer learning of different datasets and models for airplane detection.
Evaluation and Comparison of Lattice-based Cryptosystems for a Secure Quantum Computing Era

The rapid development of quantum computing devices promises powerful machines with capabilities that solve a wide range of problems that traditional computers cannot. Therefore, quantum computers generate new threats at unprecedented speed and scale and specifically pose an enormous threat to encryption. Lattice-based cryptography is considered to be the rival to a quantum computer attack and the future of post-quantum cryptography. So, cryptographic protocols based on lattices have a variety of benefits, like security, efficiency, lower energy consumption, and speed. In this work, we study the most well-known lattice-based cryptosystems while a systematic evaluation and comparison is presented also, and focuses on their strengths and weaknesses.
Unsupervised Keyword Extraction for Hashtag Recommendation in Social Media

Hashtag recommendation aims to suggest hashtags to users to annotate and describe the key information in the text, or categorize their posts. In recent years, several hashtag recommendation methods are proposed and developed to speed up processing of the texts and quickly find out the critical phrases. The methods use different approaches and techniques to obtain critical information from a large amount of data. This paper investigates the efficiency of unsupervised keyword extraction methods for hashtag recommendation. To do so, well-known unsupervised keyword extraction methods are applied to three real-world datasets including a new dataset containing texts of user-generated posts on a social learning platform. Experimental evaluations demonstrate that statistical methods performs newer methods including graph-based and embedding-based approaches in generating hashtags for long text, whereas the embedding-based approaches works better on generating hashtags for short texts. As a consequence, it can be concluded that unsupervised keyword extraction models can be adapted for hashtag recommendation when the social platform is new or there is no existing data to develop dedicated supervised learning models.
A Bayesian-network-based quantum procedure for failure risk analysis

Abstract Studying the propagation of failure probabilities in interconnected systems such as electrical distribution networks is traditionally performed by means of Monte Carlo simulations. In this paper, we propose a procedure for creating a model of the system on a quantum computer using a restricted representation of Bayesian networks. We present examples of this implementation on sample models using Qiskit and test them using both quantum simulators and IBM Quantum hardware. The results show a correlation in the precision of the results when considering the number of Monte Carlo iterations alongside the sum of shots in a single quantum circuit execution.
QR Shares based Secret Sharing Scheme using DWT, Random Permutation and Arithmetic Modulo Operation for QR Secret

The traditional way of sharing secret images through Secret Image Sharing (SIS) has become more susceptible to malicious users due to its noise-like shared images. Therefore, it is important to investigate new techniques that can provide a secure way of distributing secret images as meaningful shares. To this end, researchers have been exploring different encryption methods, secure communication protocols, and complex patterns for sharing secrets as meaningful shares, which can make it harder to be identified by malicious users. This proposed approach introduces a QR shares-based secret sharing scheme using Discrete Wavelet Transform (DWT), Random Permutation (RP), and Arithmetic Modulo (AM) operation. The proposed (n, n) perfect secret sharing scheme requires exactly n shares to reconstruct the secret, and its lightweight operations make it computationally efficient. Although it may not be the most robust approach, it provides a high level of protection as malicious attacks cannot reverse or restore the data. The experimental results demonstrate that the proposed method produces QR shares of better quality and a restored QR secret with a lossy nature with better visual quality. The performance of the scheme is evaluated using parameters such as correlation, RMSE, PSNR, NPCR, and UACI, showing that it gives better results for QR secrets.
Unraveling-paired dynamical maps recover the input of quantum channels

Abstract We explore algebraic and dynamical consequences of unraveling general time-local master equations. We show that the ‘influence martingale’, the paramount ingredient of a recently discovered unraveling framework, pairs any time-local master equation with a one parameter family of Lindblad–Gorini–Kossakowski–Sudarshan master equations. At any instant of time, the variance of the influence martingale provides an upper bound on the Hilbert–Schmidt distance between solutions of paired master equations. Finding the lowest upper bound on the variance of the influence martingale yields an explicit criterion of ‘optimal pairing’. The criterion independently retrieves the measure of isotropic noise necessary for the structural physical approximation of the flow the time-local master equation with a completely positive flow. The optimal pairing also allows us to invoke a general result on linear maps on operators (the ‘commutant representation’) to embed the flow of a general master equation in the off-diagonal corner of a completely positive semi-group which in turn solves a time-local master equation that we explicitly determine. We use the embedding to reverse a completely positive evolution, a quantum channel, to its initial condition thereby providing a protocol to preserve quantum memory against decoherence. We thus arrive at a model of continuous-time error correction by a quantum channel.