Reviewerly/exHarmony huggingface dataset:
GIS based JEPX spot prices forecasting system using solar power generation focusing on lowest prices

In recent years, the proportion of solar power generation in Japan's power generation facilities has been increasing. The amount of solar power generation affects the JEPX (Japan Electric Power Exchange) spot prices. When the amount of solar power generation is excessive during the low demand period, the lowest prices sometimes appear in the spot market. On the other hand the existing forecasting method cannot forecast the lowest prices; hence the forecasting error of the existing method is large. In this study, the authors focused on the appearance of the lowest prices and analyzed the conditions under which the lowest prices are likely to appear. As a result, the authors identified the conditions where the lowest price is likely to appear by comparing with the season and past forecasted solar power generation. Then, the JEPX spot prices forecasting system was developed considering the appearance of the lowest prices. The system combined these conditions, and the authors developed the JEPX spot prices forecasting system. This system can accurately forecast the appearance of the lowest prices and spot prices.
The implications of generative artificial intelligence in academic research and higher education in tourism and hospitality

The purpose of this study is to critically review the effect of generative artificial intelligence (GAI) tools on higher education and research in the tourism and hospitality (TH) field. This manuscript identifies capabilities and implications of these GAI applications through a theoretical lens. GAI adoption in TH education can facilitate personalized learning experiences, enhance the technological competence of students, and foster a more diverse and inclusive learning environment. For academic research, GAI-enabled technologies may revolutionize data collection, analysis, and writing in a myriad of ways. However, there are multiple ethical and legal concerns associated with adoption that must be considered. At the end of this paper, we propose ten discussion questions aimed at stimulating conversation about adoption of GAI tools in TH education and research.
SEEC and CHASE: An emotion-cause pair-oriented approach and conversational dataset with heterogeneous emotions for empathetic response generation

Empathetic Response Generation (ERG) in dialog agents has gained tremendous attention in the recent past. Among various methods used to impart empathy in the generated response, the contemporary one is using the emotion cause. Previous works of ERG have 2 major flaws: (1) usage of the emotion detected using the entire conversation to generate the empathetic response is heavily misleading as the speaker might be going through a completely different emotion in his last utterance. This erroneous emotion detection, in turn, leads to incorrect detection of the cause of that emotion. (2) Usage of entire utterance has proven to be inefficient in the cause extraction task. Consequently, existing works fail to capture multiple emotion clauses and their corresponding emotion causes for efficient ERG. In our work, we introduce a new dataset,1 CHASE, which is a compilation of conversations extracted from various plays to highlight the above-mentioned change in emotion and how one can show empathy in such a case. In this dataset, we use Dr. Brene Brown’s 2 notion of empathy on how to administer this change in emotion in the response and still sound empathetic by generating golden responses for each conversation. To address the aforementioned flaws, we also propose a model, SEEC, that utilizes the Emotion-Cause Pair Extraction task on the conversation to find various {emotion clause, cause clause} pairs and use these to impart empathy appropriately to the responses. Our qualitative and quantitative results prove the efficiency in generating enhanced empathetic responses of both SEEC and CHASE.
Aim point management system

The main objective of an aim point management system is the safe operation of solar tower power plants at higher efficiencies than with conventional control techniques.To achieve this aim, aim point optimization is indispensable, even though it suffers from highly fluctuating environmental conditions such as cloud courses.Modern plants, equipped with measurement systems, can predict the cloud movements and measure flux density distributions on the receiver's surface.In this work, the aim point management system is coupled with these measurement systems.Moreover, it comprises a novel closed-loop aim point control technique that includes aim point optimization.Based on the variability of the clouds, the system selects a safe operation mode.Furthermore, the system's modelling error is reduced by including the measurements.Finally, the feedback of the flux density distribution allows the controller to react on disturbances.In this work, the aim point management system is successfully tested at the solar tower in Jülich.There, the controller reacts on sudden changes in the allowable flux density within two to three control steps, each requiring ten seconds.
High-dimensional multi-fidelity Bayesian optimization for quantum control

Abstract We present the first multi-fidelity Bayesian optimization (BO) approach for solving inverse problems in the quantum control of prototypical quantum systems. Our approach automatically constructs time-dependent control fields that enable transitions between initial and desired final quantum states. Most importantly, our BO approach gives impressive performance in constructing time-dependent control fields, even for cases that are difficult to converge with existing gradient-based approaches. We provide detailed descriptions of our machine learning methods as well as performance metrics for a variety of machine learning algorithms. Taken together, our results demonstrate that BO is a promising approach to efficiently and autonomously design control fields in general quantum dynamical systems.
Security Risks, User Privacy Risks, and a Trust Framework for the Metaverse Space

The Metaverse is an incredible 3D virtual reality experience that anyone worldwide can access. It is a digital space not limited by geography, and it is made up of interconnected systems with unique rules and capabilities. The best thing about the Metaverse is that it allows people to interact with others in an environment that they might not have been able to visit otherwise. It is a perfect way to immerse oneself in other domains, such as entertainment and education, and it is also a great way to collaborate on projects with people worldwide. However, it is essential to note that the trustworthiness of the data collected from the disparate systems for the Metaverse needs to be studied in some areas. Trust is a significant issue in the Metaverse because users will interact with each other and virtual objects in a fully immersive environment. Security risks are also to consider, such as the attack surface that contributes to integrating various cutting-edge technology in the Metaverse. Additionally, the immersive workplace metaverse is subject to privacy risks to the users. Therefore, exploring the Metaverse’s security, privacy, and trust components is crucial to assist in research in these emerging areas. This research paper defines the trust framework and goals for Metaverse space, which are the key ingredients that ensure users use the products or services to buy, renew, and continue to trust their data. It is essential to address the trust issues to ensure the Metaverse is a safe, secure, and trustworthy experience for all users.
An Energy-Efficient Neural Network Accelerator with Improved Protections Against Fault-Attacks

Embedded neural network (NN) implementations are susceptible to misclassification under fault attacks. Injecting strong electromagnetic (EM) pulses is a non-invasive yet detrimental attack that affects the NN operations by (i) causing faults in the NN model/inputs while being read by the NN computation unit, and (ii) corrupting NN computations results to cause misclassification eventually. This paper presents the first ASIC demonstration of an energy-efficient NN accelerator with inbuilt fault attack detection. We incorporated lightweight cryptography-aided checks using the Craft cipher for on-chip verification to detect model/input errors and also as a fault detection sensor. Our developed ASIC has demonstrated excellent error detection capabilities (100% detection for 100k error attempts) with a minimal area overhead of 5.9% and negligible NN accuracy degradation.
Efficient Mediated Quantum Secret Sharing Protocol in a Restricted Quantum Environment

Abstract The multiparty‐mediated quantum secret sharing (MQSS) protocol proposed by Tsai et al. [ Quantum Inf . Process ., 2022 , 21 , 63] allows n restricted users with limited quantum capabilities to share secret information using a dishonest third party with full quantum capabilities. Although the MQSS protocol allows restricted users to achieve secret sharing with lightweight quantum capabilities, the qubit efficiency of this protocol can be further improved. Therefore, this study proposes a measurement property of the graph state to design an efficient mediated quantum secret‐sharing protocol in the same quantum environment as that of Tsai et al.’s protocol. The proposed MQSS protocol not only inherits the lightweight property of Tsai et al.’s protocol but also improves the qubit efficiency of Tsai et al.’s protocol by times. Security analysis is performed to show that the proposed MQSS protocol can avoid collective, collusion, and Trojan horse attacks. Furthermore, this study uses quantum network simulation software to implement Tsai et al.’s protocol and the proposed protocol to prove the feasibility of the proposed MQSS protocol and show that it is more efficient than Tsai et al.’s protocol.
Uncertainty Estimation in Liver Tumor Segmentation Using the Posterior Bootstrap

Deep learning-based medical image segmentation is widely used and has achieved the state-of-the-art segmentation performance, in which nnU-Net is a particularly successful pipeline due to its pre-processing and auto-configuration features. However, the output predicted probabilities from neural networks are generally not properly calibrated and don't necessarily indicate segmentation errors, which are problematic for clinical use. Bayesian deep learning is a promising way to address these problems by improving the probability calibration and error localisation ability. In this paper, we proposed a novel Bayesian approach based on posterior bootstrap theory to sample the neural network parameters from a posterior distribution. Based on nnU-Net, we implemented our method and other Bayesian approaches, and evaluated their uncertainty estimation quality. The results show that the proposed posterior bootstrap method provides improvement on uncertainty estimation with equivalent segmentation performance. The proposed method is easy to implement, compatible with any deep learning-based image segmentation pipeline, and doesn't require additional hyper-parameter tuning, enabling it to totally preserve nnU-Net's auto-configuration feature.
Pitfalls of Conformal Predictions for Medical Image Classification

Reliable uncertainty estimation is one of the major challenges for medical classification tasks. While many approaches have been proposed, recently the statistical framework of conformal predictions has gained a lot of attention, due to its ability to provide provable calibration guarantees. Nonetheless, the application of conformal predictions in safety-critical areas such as medicine comes with pitfalls, limitations and assumptions that practitioners need to be aware of. We demonstrate through examples from dermatology and histopathology that conformal predictions are unreliable under distributional shifts in input and label variables. Additionally, conformal predictions should not be used for selecting predictions to improve accuracy and are not reliable for subsets of the data, such as individual classes or patient attributes. Moreover, in classification settings with a small number of classes, which are common in medical image classification tasks, conformal predictions have limited practical value.
Quantifying environment nonclassicality in dissipative open quantum dynamics

Open quantum systems are inherently coupled to their environments, which in turn also obey quantum dynamical rules. Restricting ourselves to dissipative dynamics, here we propose a measure that quantifies how far the action of an environment on a system departs from the influence of classical noise fluctuations. It is based on the lack of commutativity between the initial reservoir state and the total system-environment Hamiltonian. Independently of the nature of the dissipative system evolution, Markovian or non-Markovian, the measure can be written in terms of the dual propagator that defines the evolution of system operators. The physical meaning and properties of the proposed definition are discussed in detail and also characterized through different paradigmatic dissipative Markovian and non-Markovian open quantum dynamics.
A Quantitative Approach to Coordinated Scaling of Resources in Complex Cloud Computing Workflows

Resource scaling is widely employed in cloud computing to adapt system operation to internal (i.e., application) and external (i.e., environment) changes. We present a quantitative approach for coordinated vertical scaling of resources in cloud computing workflows, aimed at satisfying an agreed Service Level Objective (SLO) by improving the workflow end-to-end (e2e) response time distribution. Workflows consist of IaaS services running on dedicated clusters, statically reserved before execution. Services are composed through sequence, choice/merge, and balanced split/join blocks, and have generally distributed (i.e., non-Markovian) durations possibly over bounded supports, facilitating fitting of analytical distributions from observed data. Resource allocation is performed through an efficient heuristics guided by the mean makespans of sub-workflows. The heuristics performs a top-down visit of the hierarchy of services, and it exploits an efficient compositional method to derive the response time distribution and the mean makespan of each sub-workflow. Experimental results on a workflow with high concurrency degree appear promising for feasibility and effectiveness of the approach.
Generative adversarial networks with multi-scale and attention mechanisms for underwater image enhancement

The images captured underwater are usually degraded due to the effects of light absorption and scattering. Degraded underwater images exhibit color distortion, low contrast, and blurred details, which in turn reduce the accuracy of marine biological monitoring and underwater object detection. To address this issue, a generative adversarial network with multi-scale and an attention mechanism is proposed to improve the quality of underwater images. To extract more effective features within the generative network, several modules are introduced: a multi-scale dilated convolution module, a novel attention module, and a residual module. These modules are utilized to design a generative network with a U-shaped structure. The multi-scale dilated convolution module is designed to extract features at multiple scales and expand the receptive field to capture more global information. The attention module directs the network’s focus towards important features, thereby reducing the interference from redundant feature information. To improve the discriminative power of the adversarial network, a multi-scale discriminator is designed. It has two output feature maps with different scales. Additionally, an improved loss function for the generative adversarial network is proposed. This improvement involves incorporating the total variation loss into the traditional loss function. The performance of different methods for enhancing underwater images is evaluated using the EUVP dataset and UIEB dataset. The experimental results demonstrate that the enhanced underwater images exhibit better quality and visual effects compared to other methods.
Unsupervised Liver Tumor Segmentation with Pseudo Anomaly Synthesis

Liver lesion segmentation is a challenging task. Liver lesions often appear as regional heterogeneity in various shapes and intensities, while collecting a comprehensive dataset for supervised learning is costly. To address this issue, this study formulates unsupervised liver tumor segmentation as an anomaly segmentation problem and presents a pseudo-supervised anomaly segmentation solution with synthetic anomalies. In this regard, we investigate two fundamental, yet under-explored questions: (1) how to generate anomalies? and (2) how to address a covariant shift between synthesis data and real tumor samples in model training? To the first question, instead of fabricating anomalies approximating the known abnormal patterns, we propose to generate anomalies spreading over a broader spectrum to encourage a model to learn the cluster boundary of normal samples. Our rationale toward the second question suggests light training on synthesis data for model generalizability. Based on these insights, this study incorporates a random-shaped anomaly synthesis module and two-stage training strategy into the DRAEM architecture for unsupervised liver tumor segmentation. Experiments on the public benchmark show that the proposed method trained on various synthetic anomalies has good generalizability on real tumor and achieves a comparable performance to prior arts. Our code is available at: https://github.com/nono-zz/LiTs-Segmentation .
A New Social Media Analytics Method for Identifying Factors Contributing to COVID-19 Discussion Topics

Since the onset of the COVID-19 crisis, scholarly investigations and policy formulation have harnessed the potent capabilities of artificial intelligence (AI)-driven social media analytics. Evidence-driven policymaking has been facilitated through the proficient application of AI and natural language processing (NLP) methodologies to analyse the vast landscape of social media discussions. However, recent research works have failed to demonstrate a methodology to discern the underlying factors influencing COVID-19-related discussion topics. In this scholarly endeavour, an innovative AI- and NLP-based framework is deployed, incorporating translation, sentiment analysis, topic analysis, logistic regression, and clustering techniques to meticulously identify and elucidate the factors that are relevant to any discussion topics within the social media corpus. This pioneering methodology is rigorously tested and evaluated using a dataset comprising 152,070 COVID-19-related tweets, collected between 15th July 2021 and 20th April 2023, encompassing discourse in 58 distinct languages. The AI-driven regression analysis revealed 37 distinct observations, with 20 of them demonstrating a higher level of significance. In parallel, clustering analysis identified 15 observations, including nine of substantial relevance. These 52 AI-facilitated observations collectively unveil and delineate the factors that are intricately linked to five core discussion topics that are prevalent in the realm of COVID-19 discourse on Twitter. To the best of our knowledge, this research constitutes the inaugural effort in autonomously identifying factors associated with COVID-19 discussion topics, marking a pioneering application of AI algorithms in this domain. The implementation of this method holds the potential to significantly enhance the practice of evidence-based policymaking pertaining to matters concerning COVID-19.
A Federated Learning Model Based on Hardware Acceleration for the Early Detection of Alzheimer’s Disease

Alzheimer's disease (AD) is a progressive illness with a slow start that lasts many years; the disease's consequences are devastating to the patient and the patient's family. If detected early, the disease's impact and prognosis can be altered significantly. Blood biosamples are often employed in simple medical testing since they are cost-effective and easy to collect and analyze. This research provides a diagnostic model for Alzheimer's disease based on federated learning (FL) and hardware acceleration using blood biosamples. We used blood biosample datasets provided by the ADNI website to compare and evaluate the performance of our models. FL has been used to train a shared model without sharing local devices' raw data with a central server to preserve privacy. We developed a hardware acceleration approach for building our FL model so that we could speed up the training and testing procedures. The VHDL hardware description language and an Altera 10 GX FPGA are utilized to construct the hardware-accelerator approach. The results of the simulations reveal that the proposed methods achieve accuracy and sensitivity for early detection of 89% and 87%, respectively, while simultaneously requiring less time to train than other algorithms considered to be state-of-the-art. The proposed algorithms have a power consumption ranging from 35 to 39 mW, which qualifies them for use in limited devices. Furthermore, the result shows that the proposed method has a lower inference latency (61 ms) than the existing methods with fewer resources.
The Application of Computer-Aided Under-Resourced Language Translation for Malay into Kadazandusun

A computer-aided language translation using a Machine translation (MT) is an application performed by computers (machines) that translates one natural language to another. There are many online language translation tools, but thus far none offers a sequence of text translations for the under-resourced Kadazandusun language. Although there are web-based and mobile applications of Kadazandusun dictionaries available, the systems do not translate more than one word. Hence, this paper aims to present the discussion of the preliminary translation of Malay to Kadazandusun. The basic word-to-word with dictionary alignment translation based on Direct Machine Translation (DMT) is selected to begin the exploration of the translation domain where DMT is one of the earliest translation methods which relies on the word-to-word approach (sequence-to-sequence model). This paper aims to investigate the under-resourced language and the task of translating from the Malay language to the Kadazandusun language or vice versa. This paper presents the application and the process as well as the results of the system according to the basic Kadazandusun word arrangement (Verb-Subject-Object) and its translation quality using the Bilingual Evaluation Understudy (BLEU) score. Several phases are involved during the process, including data collection (word pair translation), preprocessing, text selection, translation procedures, and performance evaluation. The preliminary language translation approach is proven to be capable of producing up to 0.5 BLEU scores which indicate that the translation is readable, however, requires post-editing for better comprehension. The findings are significant for the quality of the under-resourced language translation and as a starting point for other machine translation methodologies such as statistical or deep learning-based translation.