Reviewerly/exHarmony huggingface dataset:
A Comparative Analysis of Automatic Speech Recognition Errors in Small Group Classroom Discourse

In collaborative learning environments, effective intelligent learning systems need to accurately analyze and understand the collaborative discourse between learners (i.e., group modeling) to provide adaptive support. We investigate how automatic speech recognition (ASR) errors influence discourse models of small group collaboration in noisy real-world classrooms. Our dataset consisted of 30 students recorded by consumer off-the-shelf microphones (Yeti Blue) while engaging in dyadic- and triadic- collaborative learning in a multi-day STEM curriculum unit. We found that two state-of-the-art ASR systems (Google Speech and OpenAI Whisper) yielded very high word error rates (0.822, 0.847) but very different profiles of error with Google being more conservative, rejecting 38% of utterances instead of 12% for Whisper. Next, we examined how these ASR errors influenced down-stream small group modeling based on pre-trained large language models for three tasks: Abstract Meaning Representation parsing (AMRParsing), on-task/off-task detection (OnTask), and Accountable Productive Talk prediction (TalkMove). As expected, models trained on clean human transcripts yielded degraded performance on all three tasks, measured by the transfer ratio (TR). However, the TR of the specific sentence-level AMRParsing task (.39 - .62) was much lower than that of the abstract discourse-level OnTask (.63- .94) and TalkMove tasks (.64-.72). Furthermore, different training strategies that incorporated ASR transcripts alone or as augmentations of human transcripts increased accuracy for the discourse-level tasks (OnTask and TalkMove) but not AMRParsing. Simulation experiments suggested that the models were tolerant of missing utterances in the dialog context, and that jointly improving ASR accuracy on important word classes (e.g., verbs and nouns) can improve performance across all tasks. Overall, our results provide insights into how different types of NLP-based tasks might be tolerant of ASR errors under extremely noisy conditions and provide suggestions for how to improve accuracy in small group modeling settings for a more equitable, engaging, and adaptive collaborative learning environment.
Blockchain adoption in container shipping: An empirical study on barriers, approaches, and recommendations

Blockchain is an emerging technology in logistics and supply chain management. However, its adoption is still in a doldrum with limited success. This study explores container shipping service providers' (CSSPs) current adoption status and perceived barriers in integrating blockchain applications before providing adoption recommendations. A qualitative analysis was conducted on thirty-two interviews with experts holding managing positions from container terminal operators, shipping companies, and freight forwarders. Thematic analysis was employed to extract and aggregate information from the qualitative database. This study revealed the difference between CSSPs' perceptions of blockchain adoption barriers and, accordingly, their adopting approaches. A challenging implementing process for the current blockchain applications in container shipping was depicted with ten barriers and twenty-nine connections. Industry experts provided six recommendations for successful blockchain adoption to address the complicated situation. This paper deepens the knowledge about blockchain in supply chains, especially maritime logistics operations. It sheds light on the complexity of perceived adoption barriers, their effect on CSSPs' approaches to blockchain applications, and potential digitalization policies to be taken.
Edge-Based Segmentation for Accurate Detection of Malaria Parasites in Microscopic Blood Smear Images: A Novel Approach using FCM and MPP Algorithms

Malaria is a significant global health communicable disease found in maximum epidemics areas, causing millions of cases of deaths including infant mortality each year. In this article, a segmentation technique is proposed to detect parasite cells of malaria in thin blood smear images using edge-based segmentation. Used gamma equalization to adjust lighting and the FCM soft clustering method to extract infected erythrocytes. The MPP algorithm was then used to improve edge-based segmentation. The proposed method achieved high accuracy, with SE, SP, PVP, and PVN values of 97.8%, 92.2%, 98.53%, and 91.38%, and MSER and F-measure values of 0.03 and 0.9, respectively. On the basis of the results, it can be inferred that the edge-based segmentation technique proposed in this study is capable of accurately segmenting red blood cells in blood smear images. This, in turn, can aid in the detection and segmentation of malaria parasites and facilitate the classification process.
Offensive Language Detection for Low Resource Language Using Deep Sequence Model

Social media platforms are heavily used by people to express their views in their native languages. Besides positive views, people often use abusive or offensive language to express their anger or frustration. Resource-rich languages have offensive language detection systems which automatically monitor and block offensive content, however, they are very rare for low-resourced languages. This is because of the nonavailability of datasets for local languages. This article proposes a model which automatically detects offensive language for a very low-resource language, i.e., Pashto. The Roman Pashto dataset is created by picking 60 thousand comments from different social media and labeling them manually. The proposed model is trained and tested using three different feature extraction approaches, i.e., bag-of-words (BoW), term frequency-inverse document frequency (TF-IDF), and sequence integer encoding. Four traditional classifiers and a deep sequence model are used to train on this task. Experimental result shows that the random forest classifier works best and give 94.07 % testing accuracy on a combination of unigrams, bigrams, and trigrams. The same classifier gives maximum accuracy of 93.90 % with TF-IDF. However, the overall highest testing accuracy of 97.21% is achieved by using bidirectional long short-term memory (BLSTM). The corpus created in this work is made available for the researcher working in this domain.
U–Pb Dating of Mineral Deposits: From Age Constraints to Ore-Forming Processes

Abstract The timing and duration of ore-forming processes are amongst the key parameters required in the study of mineral systems. After more than a century of technical developments, innovations and investigation, the U–Pb system arguably is the most mature radioisotopic system in our possession to conduct absolute dating of a wide range of minerals across geological environments and metallogenic processes. Here, we review the basics of U–Pb geochronology, the key historic developments of the method, and the most commonly used analytical techniques (including data reduction, Pb-correction, uncertainty propagation and data presentation) and minerals while pointing out their respective advantages, weaknesses and potential pitfalls. We also highlight critical aspects that need to be considered when interpreting a date into the age of a geological process (including field and petrographic constraints, open-system behavior, handling and interpretation of uncertainties). While U–Pb geochronology is strongly biased toward zircon dating, we strive to highlight the great diversity of minerals amenable to U–Pb dating (more than 16 mineral species) in the context of mineral systems, and the variety of geological events they can potentially date (magmatism, hydrothermal activity, ore-formation, cooling, etc.). Finally, through two case studies we show (1) how multi-mineral geochronological studies have been used to bracket and decipher the age of multiple geological events associated with the world-class Witwatersrand gold province, and (2) how rather than the absolute age, the duration and rate of the mineralizing event at porphyry copper deposits opens new avenues to understand ore-forming processes and the main controls on the size of such deposits. The improving precision, accuracy and spatial resolution of analyses in tandem with high-quality field and petrographic observations, numerical modelling and geochemical data, will continue to challenge paradigms of ore-forming processes and contribute significant breakthroughs in ore deposit research and potentially to the development of new exploration tools.
Blockchain-Aided Privacy-Preserving Medical Data Sharing Scheme for E-Healthcare System

Due to the massive applications of Internet of Things (IoT) and the prevalence of wearable devices, e-healthcare systems are widely deployed in medical institutions. As a significant carrier of medical data, electronic medical record (EMR) is convenient to be stored and retrieved, which greatly simplifies the experience of medical treatment and cuts down the trivial work of paramedics. However, EMRs usually include much sensitive information, such as patients’ identification numbers or home addresses that may be easily captured by unauthorized doctors and cloud servers. Based on this concern, e-healthcare systems can make use of attribute-based encryption (ABE) to protect private information while achieving fine-grained access control of encrypted EMRs. Whereas, most ABE schemes do not support both policy hiding and keyword search. To address the above issues, we propose an inner product searchable encryption scheme with multikeyword search (MK-IPSE) based on blockchain to provide full privacy preservation and efficient ciphertext retrieval for EMRs. Inner product encryption (IPE) can not only specify access permissions such that only users with matched attributes can get the target files but also support access policy hiding. Besides, the proposed scheme combines searchable encryption (SE) and federated blockchain (FB) to implement efficient and stable multikeyword search. Compared with the existing schemes, MK-IPSE shows better performance on computation and storage. Additionally, security analysis demonstrates that our scheme can resist IND-CKA and collusion attacks.
Cross-segment validation of customer support for AI-based service robots at luxury, fine-dining, casual, and quick-service restaurants

Purpose With the trend of adopting and studying artificial intelligence (AI) service robots at restaurants, the authors’ understanding of how customers perceive robots differently across restaurant segments remains limited. Therefore, building upon expectancy theory, this study aims to propose a trust-based mechanism to explain customers’ support for AI-based service robots. Design/methodology/approach For cross-segment validation, data were collected from online survey participants under the scenarios of experiencing AI service robots in luxury ( n = 428), fine-dining ( n = 420), casual ( n = 409) and quick-service ( n = 410) restaurant scenarios. Findings In all four segments, trust in technology increased willingness to accept AI service robots, which was then positively related to customers’ support for AI-based service robots. Meanwhile, customers’ AI performance expectancy mediated the relationship between trust in technology and willingness to accept AI service robots. On the other hand, at luxury, fine-dining and casual restaurants, males perceived a stronger positive relationship between trust in technology and AI performance expectancy. No generational differences were found in the four restaurant segments between trust in technology and AI performance expectancy. Originality/value To the best of the authors’ knowledge, this study is one of the first attempts in hospitality research to examine cross-segment validation of customers’ responses to AI-based service robots in the luxury, fine-dining, casual and quick-service restaurant segments.
A self-organizing modular neural network based on empirical mode decomposition with sliding window for time series prediction

Time series is mostly with a chaotic nature and non-stationary characteristic in real-word, which makes it difficult to be modeled and predicted accurately. To solve this problem, we introduce a novel self-organizing modular neural network based on the empirical mode decomposition with the sliding window mechanism (SWEMD-MNN) for time series prediction. In SWEMD-MNN, the improved empirical mode decomposition with sliding window (SWEMD) is developed to decompose time series online, which can effectively alleviate the limitation that the traditional EMD-based models cannot handle the long term or online problem and end effect. Thus, SWEMD-MNN can decompose time series based on time characteristic effectively and dynamically, and improve the prediction accuracy of the classical modular neural networks dividing time series based on sample space. Then time subseries are dynamically assigned to the subnetworks with a single layer feedforward neural network using the sample entropy and Euclidean distance for learning. Experimental investigations using benchmark chaotic and real-world time series show that SWEMD-MNN can decompose time series effectively and dynamically, and provides a better prediction accuracy than the fully coupled networks and other MNN models for time series prediction.
A duplication-free quantum neural network for universal approximation

The universality of a quantum neural network refers to its ability to approximate arbitrary functions and is a theoretical guarantee for its effectiveness. A non-universal neural network could fail in completing the machine learning task. One proposal for universality is to encode the quantum data into identical copies of a tensor product, but this will substantially increase the system size and the circuit complexity. To address this problem, we propose a simple design of a duplication-free quantum neural network whose universality can be rigorously proved. Compared with other established proposals, our model requires significantly fewer qubits and a shallower circuit, substantially lowering the resource overhead for implementation. It is also more robust against noise and easier to implement on a near-term device. Simulations show that our model can solve a broad range of classical and quantum learning problems, demonstrating its broad application potential.
Comparison between Machine Learning and Deep Learning Approaches for the Detection of Toxic Comments on Social Networks

The way we communicate has been revolutionised by the widespread use of social networks. Any kind of online message can reach anyone in the world almost instantly. The speed with which information spreads is undoubtedly the strength of social networks, but at the same time, any user of these platforms can see how toxic messages spread in parallel with likes, comments and ratings about any person or entity. In such cases, the victim feels even more helpless and defenceless as a result of the rapid spread. For this reason, we have implemented an automatic detector of toxic messages on social media. This allows us to stop toxicity in its tracks and protect victims. In particular, the aim of the survey is to demonstrate how traditional Machine Learning methods of Natural Language Processing (NLP) work on equal terms with Deep Learning methods represented by a Transformer architecture and characterised by a higher computational cost. In particular, the paper describes the results obtained by testing different supervised Machine Learning classifiers (Logistic Regression, Random Forest and Support Vector Machine) combined with two topic-modelling techniques of NLP, (Latent Semantic Analysis and Latent Dirichlet Allocation). A pre-trained Transformer named BERTweet was also tested. All models performed well in this task, so much so that values close to or above 90% were achieved in terms of the F1 score evaluation metric. The best result achieved by Transformer BERTweet, 91.40%, was therefore not impressive in this context, as the performance gains are too small compared to the computational overhead.
Assigning AI: Seven Approaches for Students, with Prompts

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Towards Modern Development of Cloud Applications

When writing a distributed application, conventional wisdom says to split your application into separate services that can be rolled out independently. This approach is well-intentioned, but a microservices-based architecture like this often backfires, introducing challenges that counteract the benefits the architecture tries to achieve. Fundamentally, this is because microservices conflate logical boundaries (how code is written) with physical boundaries (how code is deployed). In this paper, we propose a different programming methodology that decouples the two in order to solve these challenges. With our approach, developers write their applications as logical monoliths, offload the decisions of how to distribute and run applications to an automated runtime, and deploy applications atomically. Our prototype implementation reduces application latency by up to 15× and reduces cost by up to 9× compared to the status quo.
Deep learning techniques for solar tracking systems: A systematic literature review, research challenges, and open research directions

Solar tracking systems have gained attention in recent years due to their potential to increase the efficiency of various solar energy applications. Both traditional machine learning (ML) and deep learning (DL) techniques have been employed in various solar tracking systems. However, traditional ML models have limitations in processing large datasets and require extensive knowledge of data representation. In contrast, DL models have shown promise in addressing different problems such as temperature forecasting and solar irradiance prediction. Researchers are continuously working to improve the performance of DL models by designing new network architectures and optimization techniques. However, research advancements on DL techniques for solar tracking systems are scattered across several studies, making it difficult to select feasible approaches. Thus, this systematic literature review aims to provide an overview of the state-of-the-art of DL techniques for solar tracking systems. It examines dataset usage, preprocessing methods, feature engineering methods, DL algorithms, and performance metrics used in the identified studies. The review covers articles published from 2012 to 2022, with 37 academic papers meeting the inclusion criteria. The results indicate that deep hybrid learning models were the most popular among researchers. The study also identified research challenges and open research directions related to DL-based solar tracking systems. These include data availability, image data, data normalization, data decomposition, and feature engineering methods. This review is expected to be useful to current and future researchers in addressing trends and challenges related to the application of DL in solar trackers.
CF-YOLO: Cross Fusion YOLO for Object Detection in Adverse Weather With a High-Quality Real Snow Dataset

Snow is one of the toughest adverse weather conditions for object detection (OD). Currently, not only there is a lack of snowy OD datasets to train cutting-edge detectors, but also these detectors have difficulties of learning latent information beneficial for detection in snow. To alleviate the two above problems, we first establish a real-world snowy OD dataset, named RSOD. Besides, we develop an unsupervised training strategy with a distinctive activation function, called <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$Peak Act$ </tex-math></inline-formula> , to quantitatively evaluate the effect of snow on each object. Peak Act helps grade the images in RSOD into four-difficulty levels. To our knowledge, RSOD is the first quantitatively evaluated and graded real-world snowy OD dataset. Then, we propose a novel Cross Fusion (CF) block to construct a lightweight OD network based on YOLOv5s (called CF-YOLO). CF is a plug-and-play feature aggregation module, which integrates the advantages of Feature Pyramid Network and Path Aggregation Network in a simpler yet more flexible form. Both RSOD and CF lead our CF-YOLO to possess an optimization ability for OD in real-world snow. That is, CF-YOLO can handle unfavorable detection problems of vagueness, distortion and covering of snow. Experiments show that our CF-YOLO achieves better detection results on RSOD, compared to SOTAs. The code and dataset are available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/qqding77/CF-YOLO-and-RSOD</uri> .
Noise-Resilient Phase Estimation with Randomized Compiling

We develop an error mitigation method for the control-free phase estimation. We prove a theorem that under the first-order correction, the phases of a unitary operator are immune to the noise channels with only Hermitian Kraus operators, and therefore, certain benign types of noise for phase estimation are identified. By further incorporating the randomized compiling protocol, we can convert the generic noise in the phase estimation circuits into stochastic Pauli noise, which satisfies the condition of our theorem. Thus, we achieve a noise-resilient phase estimation without any quantum resource overhead. The simulated experiments show that our method can significantly reduce the estimation error of the phases by up to 2 orders of magnitude. Our method paves the way for the utilization of quantum phase estimation before the advent of fault-tolerant quantum computers.
Round-trip multi-band quantum access network

The quantum network makes use of quantum states to transmit data, which will revolutionize classical communication and allow for some breakthrough applications. Quantum key distribution (QKD) is one prominent application of quantum networks, and can protect data transmission through quantum mechanics. In this work, we propose an expandable and cost-effective quantum access network, in which the round-trip structure makes quantum states travel in a circle to carry information, and the multi-band technique is proposed to support multi-user access. Based on the round-trip multi-band quantum access network, we realize multi-user secure key sharing through the continuous-variable QKD (CV-QKD) protocol. Due to the encoding characteristics of CV-QKD, the quadrature components in different frequency bands can be used to transmit key information for different users. The feasibility of this scheme is confirmed by comprehensive noise analysis, and is verified by a proof-of-principle experiment. The results show that each user can achieve excess noise suppression and 600 bit/s level secure key generation under 30 km standard fiber transmission. Such networks have the ability of multi-user access theoretically and could be expanded by plugging in simple modules. Therefore, it paves the way for near-term large-scale quantum secure networks.
Exploring Artificial Intelligence using Automated Writing Evaluation for Writing Skills

This study aims to investigate the effectiveness of AI software which is Automated Writing Evaluation (AWE) in detecting grammatical errors in improving writing skills among Malaysian public university students and examine their perceptions of AI software in improving writing skills. The findings of this study show the positive perceptions of students towards the implementation of AWE. The study provided implications for the effective use of AI software (AWE) in ESL writing classrooms. Finally, future researchers are expected to conduct research on a similar topic to enhance the impact and significance of this study