Reviewerly/exHarmony huggingface dataset:
Adaptive museum lighting using CNN-based image segmentation

Modern smart lighting systems have the potential to improve lighting conditions by employing adaptive dimming. However, the light intensity pattern of the individual luminaires is often not optimally matching with the region(s) of interest. By using adaptive lighting fixtures with a tunable radiation pattern, much more targeted illumination can be enabled. The already demonstrated adaptive lighting fixtures are typically quite bulky, and require user input to optimize the emitted light. In this paper, a compact lighting fixture is proposed that consists of a high-power LED, a sequence of lenses, an adjustable mirror and tunable diffuser, to achieve an adaptive lighting functionality. This adaptive luminaire is integrated with an embedded system and camera, to realize autonomous beam adjustment based on computer vision in a museum setting. The embedded system performs semantic segmentation to detect painting masks, and adapts the light beam direction and spot size to the detected painting frame via a feedback loop. The system can be considered as a first demonstration of the integration between state-of-the-art computer vision and adaptive light fixtures for indoor lighting.
Designing and building OSCEBot ® for virtual OSCE – Performance evaluation

With AI's advancing technology and chatbots becoming more intertwined in our daily lives, pedagogical challenges are occurring. While chatbots can be used in various disciplines, they play a particularly significant role in medical education. We present the development process of OSCEBot ®, a chatbot to train medical students in the clinical interview approach. The SentenceTransformers, or SBERT, framework was used to develop this chatbot. To enable semantic search for various phrases, SBERT uses siamese and triplet networks to build sentence embeddings for each sentence that can then be compared using a cosine-similarity. Three clinical cases were developed using symptoms that followed the SOCRATES approach. The optimal cutoffs were determined, and each case's performance metrics were calculated. Each question was divided into different categories based on their content. Regarding the performance between cases, case 3 presented higher average confidence values, explained by the continuous improvement of the cases following the feedback acquired after the sessions with the students. When evaluating performance between categories, it was found that the mean confidence values were highest for previous medical history. It is anticipated that the results can be improved upon since this study was conducted early in the chatbot deployment process. More clinical scenarios must be created to broaden the options available to students.
On the Effect of Emotion Identification from Limited Translated Text Samples Using Computational Intelligence

Abstract Emotion identification from text data has recently gained focus of the research community. This has multiple utilities in an assortment of domains. Many times, the original text is written in a different language and the end-user translates it to her native language using online utilities. Therefore, this paper presents a framework to detect emotions on translated text data in four different languages. The source language is English, whereas the four target languages include Chinese, French, German, and Spanish. Computational intelligence (CI) techniques are applied to extract features, dimensionality reduction, and classification of data into five basic classes of emotions. Results show that when English text is translated to French, classification accuracy is higher than others, i.e., 99.04%. Whereas, when the same is translated to Chinese language, its detection rate is lowest among target languages. It is concluded that emotions remain preserved after translation to some extent. Framework consists of TFIDF features. PCA and Discriminant Analysis perform good to detect emotions from translated data.
Practical Quantum Anonymous Private Information Retrieval Based on Quantum Key Distribution

Quantum Anonymous Private Information Retrieval (QAPIR) allows a user to retrieve an item from a database anonymously thus achieving better user privacy. Recently, Khan <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">et al</i> . proposed the first QAPIR protocol [IEEE Trans. Commun. 70(6), 4026-4037 (2022)] which is the only known QAPIR protocol. However, a symbol coincidence error occurs when the main ingredients are combined to form this QAPIR protocol. Furthermore, it requires perfect ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</i> +1)-partite <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">d</i> -dimensional Greenberger-Horne-Zeilinger (GHZ) states to be pre-shared among the nodes where <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">D</i> ≥ <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">N</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">N</i> is the size of the database. However, the difficulties in implementing the preparation of high-dimensional GHZ states and the quantum-computing-based queries under current technologies lead to the impracticability of this QAPIR protocol due to the extremely high dimensionalities of GHZ states and oracle operations when large databases are involved. To address these issues, we first point out the symbol coincidence error in Khan <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">et al</i> .’s QAPIR protocol and then give an improvement on it. Finally, we propose a practical QAPIR protocol based on quantum key distribution. We analyze its performance under certain types of noise. We also prove the security of our QAPIR protocol in an active adversary scenario where active dishonest users and database owner are taken into account. Compared with Khan <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">et al</i> .’s protocol, our protocol is easier to implement under current technologies, and can work over the noisy quantum channel.1It provides a practical way for the design of QAPIR protocols.
Generalized Voice Spoofing Detection via Integral Knowledge Amalgamation

Most of the voice spoofing detection methods are designed for specific kinds of spoofing attacks, synthetic or replay. In practice, however, there is no prior information about these two kinds of spoofing attacks. To this end, this paper proposes a generalized voice spoofing detection method based on integral knowledge amalgamation to detect jointly synthetic attacks and replay attacks. Two amalgamation mechanisms, feature amalgamation and structure amalgamation, are designed from different perspectives, so that the model can generalize better and run fast. Specifically, the feature amalgamation transfers the high-level sematic knowledge from two teacher models to the compact model. The structure amalgamation employs the adversarial learning to ensure the global structure consistency of two teacher models and a student model. In addition, the feature matching loss is introduced to capture the distinctive features of synthetic attacks and replay attacks. We conduct extensive experiments on logical access (LA) scenario and physical access (PA) scenario of ASVspoof 2019 dataset to verify the validity of the proposed method. The experimental results show that compared with the most advanced generalized voice spoofing detection methods, the proposed method achieves a comparable or even better performance. In particular, our method gains the state-of-the-art detection capability on LA scenario. Moreover, our method achieves similar or even outstanding detectability when compared with specialized anti-spoofing methods.
Realizing all-to-all couplings among detachable quantum modules using a microwave quantum state router

Abstract One of the primary challenges in realizing large-scale quantum processors is the realization of qubit couplings that balance interaction strength, connectivity, and mode confinement. Moreover, it is very desirable for the device elements to be detachable, allowing components to be built, tested, and replaced independently. In this work, we present a microwave quantum state router, centered on parametrically driven, Josephson-junction based three-wave mixing, that realizes all-to-all couplings among four detachable quantum modules. We demonstrate coherent exchange among all four communication modes, with an average full- i SWAP time of 764 ns and average inferred inter-module exchange fidelity of 0.969, limited by mode coherence. We also demonstrate photon transfer and pairwise entanglement between module qubits, and parallel operation of simultaneous i SWAP exchange across the router. Our router-module architecture serves as a prototype of modular quantum computer that has great potential for enabling flexible, demountable, large-scale quantum networks of superconducting qubits and cavities.
Deep learning‐based panoptic segmentation: Recent advances and perspectives

Abstract In recent years, panoptic segmentation has drawn increasing amounts of attention, leading to the rapid emergence of numerous related algorithms. A variety of deep neural networks have been used more frequently for panoptic segmentation, which is motivated by the significant success of deep learning methods in other tasks. This article presents a comprehensive exploration of panoptic segmentation, focusing on the analysis and understanding of RGB image data. Initially, the authors introduce the background of panoptic segmentation, including deep learning models and image segmentation. Then, the authors thoroughly cover a variety of panoptic segmentation‐related topics, such as datasets connected to the field, evaluation metrics, panoptic segmentation models, and derived subfields based on panoptic segmentation. Finally, the authors examine the difficulties and possibilities in this area and identify its future paths.
Partial multi-label learning via three-way decision-based tri-training

In real-world application scenarios, multi-label learning (MLL) datasets often contain some irrelevant noisy labels, which degrades the performance of traditional multi-label learning models. In order to deal with this problem, partial multi-label learning (PML) is proposed, in which each instance is associated with a candidate label set, which includes multiple relevant ground-truth labels and some irrelevant noisy labels. The common strategy to deal with this problem is disambiguating the candidate label set, but the co-occurrence of noisy labels and ground-truth labels makes the disambiguation technique susceptible to error. In this paper, a novel disambiguation-free PML approach named PML-TT is proposed. Specifically, by adapting the tri-training framework, mutual cooperation and iteration between classifiers are used to correct noisy labels and improve the performance of the learning model. Moreover, the three-way decision is adapted to solve the conflict problem of the base classifier and obtain more useful training samples. In addition, the precise supervisory information of the non-candidate labels is exploited to make the predictions of the base classifier more accurate. Finally, experimental results on both synthetic and real-world PML datasets show that the proposed PML-TT approach can effectively reduce the negative influence of noisy labels and learn a robust model.
Cycle-attention-derain: unsupervised rain removal with CycleGAN

Single image deraining is a fundamental task in computer vision, which can greatly improve the performance of subsequent high-level tasks under rainy conditions. Existing data-driven rain removal methods rely heavily on paired training data, which is expensive to collect. In this paper, we propose a new unsupervised method, called Cycle-Attention-Derain, which removes rain from single images in the absence of paired data. Our method is based on the CycleGAN framework with two major novelties. First, since rain removal is highly correlated with analyzing the texture features of an input image, we propose a novel attention fusion module (AFM) with complementary channel attention and spatial attention, which can effectively learn more discriminative features for rain removal. Second, to further improve the generalization ability of our model, we propose a global–local attention discriminator architecture with an attention mechanism to guide the network training, so that the rain removal results are realistic, both globally and locally. Our proposed model is able to remove rain streaks and raindrops of varying degrees without paired training images. Extensive experiments on synthetic and real datasets demonstrate that the proposed method outperforms most of the state-of-the-art unsupervised rain removal methods in terms of both PSNR and SSIM on Rain800 datasets and achieves slightly close results to other popular supervised learning methods.
GOSS: towards generalized open-set semantic segmentation

Abstract In this paper, we extend Open-set Semantic Segmentation (OSS) into a new image segmentation task called Generalized Open-set Semantic Segmentation (GOSS). Previously, with well-known OSS, the intelligent agents only detect unknown regions without further processing, limiting their perception capacity of the environment. It stands to reason that further analysis of the detected unknown pixels would be beneficial for agents’ decision-making. Therefore, we propose GOSS, which holistically unifies the abilities of two well-defined segmentation tasks, i.e. OSS and generic segmentation. Specifically, GOSS classifies pixels as belonging to known classes, and clusters (or groups) of pixels of unknown class are labelled as such. We propose a metric that balances the pixel classification and clustering aspects to evaluate this newly expanded task. Moreover, we build benchmark tests on existing datasets and propose neural architectures as baselines. Our experiments on multiple benchmarks demonstrate the effectiveness of our baselines. Code is made available at https://github.com/JHome1/GOSS_Segmentor .
A Privacy-Preserving Framework Using Homomorphic Encryption for Smart Metering Systems

Smart metering systems (SMSs) have been widely used by industrial users and residential customers for purposes such as real-time tracking, outage notification, quality monitoring, load forecasting, etc. However, the consumption data it generates can violate customers' privacy through absence detection or behavior recognition. Homomorphic encryption (HE) has emerged as one of the most promising methods to protect data privacy based on its security guarantees and computability over encrypted data. However, SMSs have various application scenarios in practice. Consequently, we used the concept of trust boundaries to help design HE solutions for privacy protection under these different scenarios of SMSs. This paper proposes a privacy-preserving framework as a systematic privacy protection solution for SMSs by implementing HE with trust boundaries for various SMS scenarios. To show the feasibility of the proposed HE framework, we evaluated its performance on two computation metrics, summation and variance, which are often used for billing, usage predictions, and other related tasks. The security parameter set was chosen to provide a security level of 128 bits. In terms of performance, the aforementioned metrics could be computed in 58,235 ms for summation and 127,423 ms for variance, given a sample size of 100 households. These results indicate that the proposed HE framework can protect customer privacy under varying trust boundary scenarios in SMS. The computational overhead is acceptable from a cost-benefit perspective while ensuring data privacy.
A novel chaotic chimp sine cosine algorithm Part-I: For solving optimization problem

In the realm of solving complex optimization problems, it becomes crucial to fulfill multiple objectives to achieve optimal system performance while adhering to various conditions and limitations. Traditional optimization methods often face challenges such as sluggish convergence speed and confinement to local optima, making it difficult to find satisfactory solution. To tackle this challenge, the development of multi-objective programming is directed towards satisfying physical constraints. In line of the above problems and limitations, this work aims to contribute by proposing a hybrid optimization technique that can effectively address such optimization issues. The proposed algorithm, called the chaotic chimp sine cosine (C-CHOA-SC) algorithm, combines the chimp optimization algorithm (CHOA) and the sine cosine algorithm (SCA) to overcome problems of slow convergence and getting stuck in local optima. It does this by using the SCA after the CHOA and incorporating chaos to explore and exploit the search space effectively. This hybrid approach aims to improve the algorithm's performance (offers enhanced exploration capabilities, efficient exploitation of the search space, and improved convergence speed) and find better solutions to optimization problems. These features make the C-CHOA-SC algorithm well-suited for solving complex optimization problems with multiple objectives, providing a contemporary and effective solution. To evaluate the performance of the proposed C-CHOA-SC algorithm, three phases of analysis are conducted. In the first phase, the exploration, exploitation, and avoidance of local optima are tested using the CEC 2019 benchmark problems. The second phase involves tracking and validating the performance of C-CHOA-SC using multiple performance metrics on two-dimensional test functions. Finally, non-parametric alternative test i.e., the Wilcoxon rank sum test and Friedman test are performed. The results demonstrate that the proposed technique exhibits significantly better performance compared to other methods, with a 95 % significance level. As concerned to Friedman test, the C-CHOA-SC algorithm obtains a lower rank, it implies that it outperforms the other competing algorithms in terms of its overall performance.
Efficient Feedback and Partial Credit Grading for Proof Blocks Problems

Proof Blocks is a software tool that allows students to practice writing mathematical proofs by dragging and dropping lines instead of writing proofs from scratch. Proof Blocks offers the capability of assigning partial credit and providing solution quality feedback to students. This is done by computing the edit distance from a student’s submission to some predefined set of solutions. In this work, we propose an algorithm for the edit distance problem that significantly outperforms the baseline procedure of exhaustively enumerating over the entire search space. Our algorithm relies on a reduction to the minimum vertex cover problem. We benchmark our algorithm on thousands of student submissions from multiple courses, showing that the baseline algorithm is intractable, and that our proposed algorithm is critical to enable classroom deployment. Our new algorithm has also been used for problems in many other domains where the solution space can be modeled as a DAG, including but not limited to Parsons Problems for writing code, helping students understand packet ordering in networking protocols, and helping students sketch solution steps for physics problems. Integrated into multiple learning management systems, the algorithm serves thousands of students each year.
A Secure and Cost-Efficient Blockchain Facilitated IoT Software Update Framework

As resource-constrained Internet-of-Things (IoT) devices become popular targets of various malicious attacks, frequent updates to keep their software up to date are essential to their security. However, state-of-the-art software delivery and payment systems incorporate multiple services in a client-server structure requiring multiple transits of information between client and server, while also creating a wide attack surface. We propose a blockchain-based end-to-end secure software update delivery framework for Internet of Things (IoT) devices, which aims to ensure confidentiality, integrity, availability, efficiency, and audit-ability for verified software delivery, while offloading the cryptographic computation from resource-constrained IoT devices to a decentralized blockchain system. In particular, we leverage Ciphertext-Policy Attribute-Based Encryption (CP-ABE) and design a customized authorization policy to not only ensure that software updates can only be decrypted and installed on authorized IoT devices but also significantly reduce the computational overhead for key generation and key delivery on the manufacturer side. Furthermore, secure and atomic software delivery and payments between IoT devices and the manufacturer are assured through smart contracts. The authenticity of the delivered software is guaranteed by offloading the computation-based signature validation to smart contracts. Compliance audits are satisfied through immutable records on the blockchain’s public ledger, and the smart contracts efficiently guarantee the delivery of software updates in exchange for payment. Security analysis and experiments are performed to compare the proposed framework with state-of-the-art studies and validate its effectiveness.
A Light Gradient-Boosting Machine algorithm with Tree-Structured Parzen Estimator for breast cancer diagnosis

Breast cancer is a common and potentially life-threatening disease. Early and accurate diagnosis of breast cancer is crucial for effective treatment and improved patient outcomes. This study proposed using the Light Gradient-Boosting Machine (LightGBM) algorithm, Borderline- Synthetic Minority Oversampling Technique (SMOTE), and the Tree-Structured Parzen Estimator (TPE) for hyperparameter tuning to enhance the effectiveness of the Machine Learning (ML) model for diagnosing breast cancer. A 10-fold cross-validated TPE optimized Borderline-SMOTE LightGBM classifier was modelled on the Wisconsin Diagnostic Breast Cancer (WDBC) Dataset and evaluated for its performance compared to a baseline LightGBM model. The TPE-optimized Borderline-SMOTE LightGBM model exhibited a significant improvement in performance over the baseline model, achieving an average accuracy of 99.12%, specificity of 100%, precision of 100%, recall of 97.62%, F1-score of 98.80%, and a Mathews Correlation Coefficient of 98.12%. Compared to previous studies, the TPE-optimized Borderline-SMOTE LightGBM model performed exceptionally well. The study demonstrates the effectiveness of using data augmentation and hyperparameter optimization techniques to improve the performance of ML models for breast cancer diagnosis, which has significant implications for the medical field where the accurate and efficient diagnosis of breast cancer is critical.
Improving molecular machine learning through adaptive subsampling with active learning

Active machine learning can be used to sample training data in an autonomous manner to improve machine learning performance. This approach is competitive with state-of-the-art data sampling approaches, especially on erroneous data.