Reviewerly/exHarmony huggingface dataset:
Implementing a chatbot on Facebook to reach and collect data from thousands of health care providers: PharmindBot as a case

<h2>Abstract</h2><h3>Background</h3> The world is moving fast towards digital transformation as we live in the artificial intelligence (AI) era. The COVID-19 pandemic accelerates this movement. Chatbots were used successfully to help researchers collect data for research purposes. <h3>Objective</h3> To implement a chatbot on the Facebook® platform to establish connections with healthcare professionals who had subscribed to the chatbot, provide medical and pharmaceutical educational content, and collect data for online pharmacy research projects. Facebook® was chosen because it has billions of daily active users which offers a massive potential audience for research projects. <h3>Practice Description</h3> The chatbot was successfully implemented on the Facebook® platform following three consecutive steps. Firstly, the ChatPion script was installed on the Pharmind website to establish the chatbot system. Secondly, the PharmindBot application was developed on Facebook®. Finally, the PharmindBot app was integrated with the chatbot system. <h3>Practice Innovation</h3> The chatbot responds automatically to public comments and sends subscribers private responses using artificial intelligence. The chatbot collected quantitative and qualitative data with minimal costs. <h3>Evaluation Methods</h3> The chatbot's auto-reply function was tested using a post published on a specific page on Facebook®. Testers were asked to leave pre-defined keywords to test its functionality. The chatbot's ability to collect and save data was tested by asking testers to fill out an online survey within Facebook Messenger® for quantitative data and answer pre-defined questions for qualitative data. <h3>Results</h3> The chatbot was tested on 1000 subscribers who interacted with it. Almost all testers (n = 990, 99%) obtained a successful private reply from the chatbot after sending a pre-defined keyword. Also, the chatbot replied privately to almost all public comments (n = 985, 98.5%) which helped to increase the organic reach and to establish a connection with the chatbot subscribers. No missing data was found when the chatbot was used to collect quantitative and qualitative data. <h3>Conclusions</h3> The chatbot reached thousands of healthcare professionals and provided them with automated responses. At a low cost, the chatbot was able to gather both qualitative and quantitative data without relying on Facebook® ads to reach the intended audience. The data collection was efficient and effective. Using chatbots by pharmacy and medical researchers will help do more feasible online studies using AI to advance healthcare research.
SAR Ship Detection Algorithm Based on Deep Dense Sim Attention Mechanism Network

Ship detection is of great significance in the interpretation of synthetic aperture radar (SAR) images. However, SAR generates inherent speckle noise when producing images, which poses many challenges for ship detection tasks. One major issue during detection is low accuracy caused by noise interference near the ship. To address this issue, this study aims to design a deep dense attention detection network for improving the accuracy of ship target detection in SAR. The proposed algorithm primarily uses a multilayer deep dense network to preliminarily extract ship image features and subsequently introduces an attention network to further enhance these features. Finally, an anchor point mechanism is utilized to perform ship positioning regression estimation. Experimental results on public SAR ship datasets, including SAR ship detection dataset (SSDD) and SAR-Ship-Dataset, demonstrate that the proposed algorithm performs well in terms of speed and accuracy and has better robustness and real-time performance compared to similar detection algorithms.
Super Sparse 3D Object Detection

As the perception range of LiDAR expands, LiDAR-based 3D object detection contributes ever-increasingly to the long-range perception in autonomous driving. Mainstream 3D object detectors often build dense feature maps, where the cost is quadratic to the perception range, making them hardly scale up to the long-range settings. To enable efficient long-range detection, we first propose a fully sparse object detector termed FSD. FSD is built upon the general sparse voxel encoder and a novel sparse instance recognition (SIR) module. SIR groups the points into instances and applies highly-efficient instance-wise feature extraction. The instance-wise grouping sidesteps the issue of the center feature missing, which hinders the design of the fully sparse architecture. To further enjoy the benefit of fully sparse characteristic, we leverage temporal information to remove data redundancy and propose a super sparse detector named FSD++. FSD++ first generates residual points, which indicate the point changes between consecutive frames. The residual points, along with a few previous foreground points, form the super sparse input data, greatly reducing data redundancy and computational overhead. We comprehensively analyze our method on the large-scale Waymo Open Dataset, and state-of-the-art performance is reported. To showcase the superiority of our method in long-range detection, we also conduct experiments on Argoverse 2 Dataset, where the perception range ([Formula: see text] m) is much larger than Waymo Open Dataset ([Formula: see text] m).
Intelligent Attributes of Voice Assistants and User’s Love for AI: A SEM-Based Study

Adoption of Voice Assistants (VA) has been a hot topic of research currently.However, the existing studies are based on traditional models like the Technology Acceptance Model that is less suitable for investigating adoption of AI-based technologies like VAs.This is because the conventional models do not consider the anthropomorphic nature of AI-based technologies that has the potential to form emotional bonds between man and machines.Therefore, in this work we propose artificial autonomy as an intelligent AIbased attribute founded on three task primitives of sensing, thinking, and action autonomy.Further, we use the concept of interpersonal love and apply it in the context of human-AI relationship based on Sternberg's Triangular Theory of love.Data is collected from 607 participants across two Asian countries.Partial Least Squares (PLS) based Structural Equation Modelling (SEM) has been used for the purpose of model building and testing.The results highlight the importance of artificial autonomy on all the three aspects of love, indicating the presence of consummate love.''Love for AI'' is different from ''brand love'' since in the later case some of the love aspects of passion, intimacy, and commitment are missing.Theoretically, this study improves our understanding of artificial autonomy as an intelligent AI-attribute and the perceptions of love in Information Systems research.The findings can help technology companies develop novel strategies for improving the human-likeliness of these VAs.
High-precision dynamic measurement of roll angle based on digital holography

Among the six degrees of freedom for a moving body, measuring roll angle is the most difficult. In this study, a noncontact, no-imaging dynamic roll angle measurement system based on holography is proposed. Two beams of light are used to irradiate the measured object symmetrically, then two reflected light and the reference light interfere with each other to produce a hologram. By extracting the phase information of the two reflected light from two illumination beams using single hologram, this method achieves dynamic measurement of in-plane deformation, and then the roll angle is obtained through subsequent data processing. The effectiveness of the proposed method in-plane roll angle measurement is validated based on theoretical and experimental results. Experimental results show that the proposed method measures roll angle with high accuracy at both fixed and varied distances.
MLAAPDE: A Machine Learning Dataset for Determining Global Earthquake Source Parameters

Abstract The Machine Learning Asset Aggregation of the Preliminary Determination of Epicenters (MLAAPDE) dataset is a labeled waveform archive designed to enable rapid development of machine learning (ML) models used in seismic monitoring operations. MLAAPDE consists of more than 5.1 million recordings of 120 s long three-component broadband waveform data (raw counts) for P, Pn, Pg, S, Sn, and Sg arrivals. The labeled catalog is collected from the U.S. Geological Survey National Earthquake Information Center’s (NEIC) Preliminary Determination of Epicenters bulletin, which includes local to teleseismic observations for earthquakes ∼M 2.5 and larger. Each arrival in the labeled dataset has been manually reviewed by NEIC staff. An accompanying Python module enables users to develop customized training datasets, which includes different time-series lengths, distance ranges, sampling rates, and/or phase lists. MLAAPDE is distinct from other publicly available datasets in containing local (14%), regional (36%), and teleseismic (50%) observations, in which local, regional, and teleseismic distance are 0°–3°, 3°–30°, and 30°+, respectively. A recent version of the dataset is publicly available (see Data and Resources), and user-specific versions can be generated locally with the accompanying software. MLAAPDE is an NEIC supported, curated, and periodically updated dataset that can contribute to seismological ML research and development.
Lightweight integration of 3D features to improve 2D image segmentation

Scene understanding has made tremendous progress over the past few years, as data acquisition systems are now providing an increasing amount of data of various modalities (point cloud, depth, RGB...). However, this improvement comes at a large cost on computation resources and data annotation requirements. To analyze geometric information and images jointly, many approaches rely on both a 2D loss and 3D loss, requiring not only 2D per pixel-labels but also 3D per-point labels. However, obtaining a 3D groundtruth is challenging, time-consuming and error-prone. In this paper, we show that image segmentation can benefit from 3D geometric information without requiring a 3D groundtruth, by training the geometric feature extraction and the 2D segmentation network jointly, in an end-to-end fashion, using only the 2D segmentation loss. Our method starts by extracting a map of 3D features directly from a provided point cloud by using a lightweight 3D neural network. The 3D feature map, merged with the RGB image, is then used as an input to a classical image segmentation network. Our method can be applied to many 2D segmentation networks, improving significantly their performance with only a marginal network weight increase and light input dataset requirements, since no 3D groundtruth is required.
Quantum Web Services: Development and Deployment

Quantum computing has gained attention from the scientific community and industry, resulting in the development of increasingly powerful quantum computers and supporting technology. Major computer companies have created functional quantum computers, programming languages, and simulators that can be used by developers. This infrastructure is available through the cloud, similar to Infrastructure as a Service. However, utilizing these computers requires a deep understanding of quantum programming and hardware, which is different from traditional cloud computing. To enable a coexistence between quantum and classical computing, we believe that a transition period is necessary. One solution for coexistence is through web services. This tutorial will provide an overview of how quantum algorithms can be converted into web services, and how they can be deployed using the Amazon Braket platform for quantum computing and invoked through classical web service endpoints. Finally, we will propose a process for creating and deploying quantum services using an extension of OpenAPI and GitHub Actions. This extension allows developers to use the same methodology that they are used to for classical services.
Spacecraft depth completion based on the gray image and the sparse depth map

Perceiving the three-dimensional (3D) structure of the spacecraft is a prerequisite for successfully executing many on-orbit space missions, and it can provide critical input for many downstream vision algorithms. In this paper, we propose to sense the 3D structure of spacecraft using light detection and ranging sensor (LIDAR) and a monocular camera. To this end, Spacecraft Depth Completion Network (SDCNet) is proposed to recover the dense depth map based on gray image and sparse depth map. Specifically, SDCNet decomposes the object-level spacecraft depth completion task into foreground segmentation subtask and foreground depth completion subtask, which segments the spacecraft region first and then performs depth completion on the segmented foreground area. In this way, the background interference to foreground spacecraft depth completion is effectively avoided. Moreover, an attention-based feature fusion module is also proposed to aggregate the complementary information between different inputs, which deduces the correlation between different features along the channel and the spatial dimension sequentially. Besides, four metrics are also proposed to evaluate object-level depth completion performance, which can more intuitively reflect the quality of spacecraft depth completion results. Finally, a large-scale satellite depth completion dataset is constructed for training and testing spacecraft depth completion algorithms. Empirical experiments on the dataset demonstrate the effectiveness of the proposed SDCNet, which achieves 0.225m mean absolute error of interest and 0.778m mean absolute truncation error, surpassing state-of-the-art methods by a large margin. The spacecraft pose estimation experiment is also conducted based on the depth completion results, and the experimental results indicate that the predicted dense depth map could meet the needs of downstream vision tasks.
Sign Language Translation: A Survey of Approaches and Techniques

Sign language is the main communication way for deaf and hard-of-hearing (i.e., DHH) people, which is unfamiliar to most non-deaf and hard-of-hearing (non-DHH) people. To break down the communication barriers between DHH and non-DHH people and to better promote communication among DHH individuals, we have summarized the research progress on sign language translation. We provide the necessary background on sign language translation and introduce its four subtasks (i.e., sign2gloss2text, sign2text, sign2(gloss+text), and gloss2text). We distill the basic mode of sign language translation (SLT) and introduce the transformer-based framework of SLT. We analyze the main challenges of SLT and propose possible directions for its development.
Modelling the sustainability of a primary school digital education curricular reform and professional development program

Sustaining changes in teachers' practices is a challenge that determines the success of curricular reforms, from which Digital Education (DE) is not exempt. As the literature on sustainability is considered "scarce" and "scattered", long-term studies modelling the factors impacting teachers' sustained uptake of DE pedagogical content are lacking. Thus, we investigate whether and how 287 in-service teachers sustained a primary school DE curricular reform over a year after they completed their two-year DE professional development program. We model the sustainability of the reform through Structural Equation Modelling, and identify critical sustainability-factors. The validated Sustainable Adoption of Digital Education (SADE) model confirms that sustainability in the fourth year of the reform depends on perceived usefulness of teaching the new content, ease of implementation, and access to sufficient support in schools. Such factors should thus be evaluated, accounted for in the implementation phase of the reform, and sustained over time. The findings confirm that the DE curricular reform model contributes to positive self-efficacy to teach DE, provides sufficient in-school support, and promotes increasing adoption over time. However, as teachers' practices have not yet stabilised, and teachers may still adopt more to cover the breadth of DE-concepts, it is important to remain attentive to remaining sustainability barriers: lack of time, effort required to teach DE with teachers preferring to delegate, and lack of student-learning evidence, the latter being a significant challenge to address in the literature. These barriers must therefore be jointly addressed by researchers and practitioners in the field in order to promote the sustainability of the reform.
Interpretability-Aware Industrial Anomaly Detection Using Autoencoders

The past decade has witnessed wide applications of deep neural networks in anomaly detection. However, the dearth of interpretability in neural networks often hinders their reliability, especially for industrial applications where practical users heavily rely on interpretable methods to provide explanations for their decision-making. In this paper, we propose a reconstruction-based approach to unsupervised detection of anomalies in industrial defect data. Our algorithm employs an interpretability score during both the training and test phases. Specifically, we train an autoencoder with a loss function that incorporates an interpretability-aware error term. After training, the autoencoder processes a specific feature from the difference between the test image and the average of training images and produces an attention map that is used for detecting the anomalies. Our method not only achieves competitive performance compared with non-interpretability-aware methods but also produces attention maps that facilitate a direct explanation of detection results, which can potentially be useful for industrial practitioners.
Efficient and secure image encryption using key substitution process with discrete wavelet transform

Over the past few years, there has been a rise in the utilization of chaotic encryption algorithms for securing images. The majority of chaos-based encryption algorithms adhere to the conventional model of confusion and diffusion, which typically involves either implementing multiple encryption rounds or employing a single round of intricate encryption to guarantee robust security. However, such kind of approaches reduces the computational efficiency of the encryption process but compromises security. There is a trade-off between security and computational efficiency. Prioritizing security may require high computational processes. To overcome this issue, a key substitution encryption process with discrete wavelet transform (KSP-DWT) is developed in the proposed image encryption technique (IET). Based on KSP-DWT and IET, the abbreviation of the proposed work is used in this paper as KSP-DWT-IET. The proposed KSP-DWT algorithm employs a key scheming technique to update the initial keys and uses a novel substitution method to encrypt digital images of different sizes. Additionally, the integration of DWT can result in the compression of frequency sub-bands of the source image, leading to lower computational overheads without compromising the security of the encryption. The KSP-DWT-IET performs a single encryption round and is highly secure and efficient. The simulation results and security analysis conducted on KSP-DWT-IET confirm its effectiveness in ensuring high-security image encryption while minimizing computational overhead. The proposed encryption technique undergoes various security analyses, including entropy, contrast, correlation, energy, NPCR (Number of Pixel Changes Rate), UACI (Unified Average Change Intensity) and computational complexity. The statistical values obtained for such parameters are 7.9991, 10.9889, 0.0001, 0.0152, 33.6767, and 33.6899, respectively, which indicate that the encryption technique performs very well in terms of security and computational efficiency. The proposed encryption scheme is also analyzed for its computational time in addition to its security. The analysis shows that the scheme can efficiently encrypt images of varying sizes with a high level of security in a short amount of time (i.e., 2 ms). Therefore, it is feasible to use this encryption scheme in real-time applications without causing any significant delays. Moreover, the key space of the proposed encryption scheme is large enough (i.e. Keyspace > 2100) to resist the brute force attack.
Development of Large-Scale Farming Based on Explainable Machine Learning for a Sustainable Rural Economy: The Case of Cyber Risk Analysis to Prevent Costly Data Breaches

Risk management is essential to every organization’s management plan. It is the strategy by which organizations handle the risks involved with their actions to profit or avoid making decisions that will cost them financially in each activity. Identifying and mitigating potential digital threats and developing and implementing procedures to significantly reduce the likelihood of an organization being targeted by cyberattacks are at the core of effective risk management. This is especially true regarding the risks associated with an organization’s digital footprint. A particularly significant threat to reputation, but also at the same time indirect and direct costs, is the data breach that occurs when a security incident occurs about the data for which the organization is responsible, which results in a violation of confidentiality, availability, or the integrity of the data it manages. On the other hand, the rapid development of technology has transformed the agricultural industry, allowing for large-scale farming based on machine learning and other advanced tools. However, this transformation also exposes farms to cyber risks that can lead to costly data breaches. In this study, we propose a framework for incorporating explainable machine learning (exML) techniques into large-scale farming to enhance cyber risk analysis, mitigate cyber threats, and foster a sustainable rural economy. Specifically, given that an organization needs to implement the appropriate technical and organizational measures to avoid possible data breaches, this work presents an analytical stochastic modeling of risk with a multi-criteria objective function of low complexity, a factor in an incentive system, to model investment value and cost balancing. The motivation behind the proposed method is to improve cybersecurity, protect data and reputation, foster a sustainable rural economy, leverage explainable machine learning, and adapt to the changing technological landscape. By addressing these motivations, the method aims to provide a comprehensive and effective approach to cyber risk analysis in large-scale farming.
Astrea: Accurate Quantum Error-Decoding via Practical Minimum-Weight Perfect-Matching

Quantum devices suffer from high error rates, which makes them ineffective for running practical applications. Quantum computers can be made fault tolerant using Quantum Error Correction (QEC), which protects quantum information by encoding logical qubits using data qubits and parity qubits. The data qubits collectively store the quantum information and the parity qubits are measured periodically to produce a syndrome, which is decoded by a classical decoder to identify the location and type of errors. To prevent errors from accumulating and causing a logical error, decoders must accurately identify errors in real-time, necessitating the use of hardware solutions because software decoders are slow. Ideally, a real-time decoder must match the performance of the Minimum-Weight Perfect Matching (MWPM) decoder. However, due to the complexity of the underlying Blossom algorithm, state-of-the-art real-time decoders either use lookup tables, which are not scalable, or use approximate decoding, which significantly increases logical error rates.