Reviewerly/exHarmony huggingface dataset:
Geospatial patterns and geochemical compositional characteristics of molybdenum in different mediums of an urban environment

Urban areas are characterized by the presence of certain potentially toxic elements including molybdenum (Mo). Therefore, compositional data analysis combined with geospatial mapping was applied in this study to reveal the spatial distribution characteristics of Mo in courtyard surface dust (dust), soils, and river sediments (sediments), to identify potential sources of Mo, and to reveal Mo geochemical associations in different urban environmental mediums. The mean contents of Mo decreased in the following order: dust (11.9 mg/kg) – soil (5.84 mg/kg) – sediment (4.87 mg/kg). The highest maximum Mo content among the studied mediums (61.8 mg/kg) was detected in dust. It was the only investigated medium where a very high level of Mo enrichment was observed (4.4% of samples). Moreover, a significant level of enrichment predominated in dust (47% of samples) whereas in soil, moderate enrichment prevails (68.5%). A significant correlation of Mo contents was observed with Zn contents in all the studied mediums, and with Ca contents in soil and sediments. A significant negative correlation was observed only between Mo contents in dust and sampling site altitudes suggesting that high-rise buildings might play the role of geochemical barriers. Principal component analysis, k-means and hierarchical clustering showed that in the geogenic elements soil group Mo showed an affinity to be bound by Fe/Mn oxide/hydroxides whereas the Mo coprecipitation, complexation and absorption by carbonates predominated in the Mo-related soil group (geochemical compositional association of Mo, Zn, Cu, Pb, and Ca) under anthropogenic influence. For dust, the geochemical compositional association was the same, but in the geogenic-related group, Cu was the most closely associated element instead of Zn. The spatial location of the Mo-related group of samples identified by k-means clustering indicates that Mo concentrate processing plant may be a potential source of Mo introduction into the urban environment.
PANSATZ: pulse-based ansatz for variational quantum algorithms

Quantum computers promise a great computational advantage over classical computers, which might help solve various computational challenges such as the simulation of complicated quantum systems, finding optimum in large optimization problems, and solving large-scale linear algebra problems. Current available quantum devices have only a limited amount of qubits and a high level of noise, limiting the size of problems that can be solved accurately with those devices. Variational quantum algorithms (VQAs) have emerged as a leading strategy to address these limitations by optimizing cost function based on measurement results of shallow depth circuits. Recently, various pulse engineering methods were suggested in order to improve VQA results, including optimizing pulse parameters instead of gate angles as part of the VQA optimization process. In this paper, we suggest a novel pulse-based ansatz, which is parameterized mainly by pulses’ duration of pre-defined pulse structures. This ansatz structure provides relatively low amounts of optimization parameters while maintaining high expressibility, allowing fast convergence. In addition, the ansatz has structured adaptivity to the entanglement level required by the problem, allowing low noise and accurate results. We tested this ansatz against quantum chemistry problems. Specifically, finding the ground-state energy associated with the electron configuration problem, using the variational quantum eigensolver (VQE) algorithm for several different molecules. We manage to achieve chemical accuracy both in simulation for several molecules and on one of IBM’s NISQ devices for the H 2 molecule in the STO-3G basis, without the need for extensive error mitigation. Our results are compared to a common gate-based ansatz and show better accuracy and significant latency reduction—up to 7× shorter ansatz schedules.
A Rapid Parallel Mosaicking Algorithm for Massive Remote Sensing Images Utilizing Read Filtering

Mosaicking is a crucial step in the application of remote sensing images. The amount of remote sensing image data has grown rapidly, along with the expansion of observed areas and increased image resolution. As a result, traditional serial mosaicking techniques are facing significant challenges. In recent times, various studies have utilized high-performance computing to hasten image mosaicking and attain favorable outcomes. Nevertheless, the current research only accelerates mosaicking through external technology, without optimizing from the perspective of algorithm flow, which introduces unnecessary data I/O and slows down the mosaicking. This paper introduces a rapid parallel remote sensing image mosaicking algorithm utilizing read filtering. To begin with, the target images are divided into blocks and stored in a distributed file system. Subsequently, the image blocks are read and filtered based on a designated input format. Finally, the overlapping and non-overlapping areas are read and processed asynchronously, reducing the data I/O and computing overhead, thereby improving the efficiency of parallel computing. The experiments indicate that the mosaicking algorithm introduced in this paper enhances throughput and speedup by an average of 1.38 MB/S and 0.87 relative to the current techniques, respectively, concerning various datasets and cores. This study provides a theoretical foundation and novel ideas for processing remote sensing images on cluster platforms.
PSNet: A Deep Learning Model-Based Single-Shot Digital Phase-Shifting Algorithm

In contrast to traditional phase-shifting (PS) algorithms, which rely on capturing multiple fringe patterns with different phase shifts, digital PS algorithms provide a competitive alternative to relative phase retrieval, which achieves improved efficiency since only one pattern is required for multiple PS pattern generation. Recent deep learning-based algorithms further enhance the retrieved phase quality of complex surfaces with discontinuity, achieving state-of-the-art performance. However, since much attention has been paid to understanding image intensity mapping, such as supervision via fringe intensity loss, global temporal dependency between patterns is often ignored, which leaves room for further improvement. In this paper, we propose a deep learning model-based digital PS algorithm, termed PSNet. A loss combining both local and global temporal information among the generated fringe patterns has been constructed, which forces the model to learn inter-frame dependency between adjacent patterns, and hence leads to the improved accuracy of PS pattern generation and the associated phase retrieval. Both simulation and real-world experimental results have demonstrated the efficacy and improvement of the proposed algorithm against the state of the art.
WES-BTM: A Short Text-Based Topic Clustering Model

User comments often contain their most practical requirements. Using topic modeling of user comments, it is possible to classify and downscale text data, mine the information in user comments, and understand users’ requirements and preferences. However, user comment texts are usually short and lack rich word frequency and contextual information with sparsity. The traditional topic model cannot model and analyze these short texts well. The biterm topic model (BTM), while solving the sparsity problem, suffers from accuracy and noise problems. In order to eliminate information barriers and further ensure information symmetry, a new topic clustering model, termed the word-embedding similarity-based BTM (WES-BTM), is proposed in this paper. The WES-BTM builds on the BTM by converting word pairs into word vectors and calculating their similarity to perform word pair filtering, which in turn improves clustering accuracy. Based on the experimental results using actual data, the WES-BTM outperforms the BTM, LDA, and NMF models in terms of topic coherence, perplexity, and Jensen–Shannon divergence. It is verified that the WES-BTM can effectively reduce noise and improve the quality of topic clustering. In this way, the information in user comments can be better mined.
CapGAN: Text-to-Image Synthesis Using Capsule GANs

Text-to-image synthesis is one of the most critical and challenging problems of generative modeling. It is of substantial importance in the area of automatic learning, especially for image creation, modification, analysis and optimization. A number of works have been proposed in the past to achieve this goal; however, current methods still lack scene understanding, especially when it comes to synthesizing coherent structures in complex scenes. In this work, we propose a model called CapGAN, to synthesize images from a given single text statement to resolve the problem of global coherent structures in complex scenes. For this purpose, skip-thought vectors are used to encode the given text into vector representation. This encoded vector is used as an input for image synthesis using an adversarial process, in which two models are trained simultaneously, namely: generator (G) and discriminator (D). The model G generates fake images, while the model D tries to predict what the sample is from training data rather than generated by G. The conceptual novelty of this work lies in the integrating capsules at the discriminator level to make the model understand the orientational and relative spatial relationship between different entities of an object in an image. The inception score (IS) along with the Fréchet inception distance (FID) are used as quantitative evaluation metrics for CapGAN. IS recorded for images generated using CapGAN is 4.05 ± 0.050, which is around 34% higher than images synthesized using traditional GANs, whereas the FID score calculated for synthesized images using CapGAN is 44.38, which is ab almost 9% improvement from the previous state-of-the-art models. The experimental results clearly demonstrate the effectiveness of the proposed CapGAN model, which is exceptionally proficient in generating images with complex scenes.
Facebook for digital agricultural extension services: The case of rooftop gardeners in Bangladesh

The use of social media platforms has revolutionized the way rural farmers receive information and support from agricultural experts and extension services. However, the role of social media in meeting the information and technological needs of urban rooftop gardeners remains unclear. The study utilizes the Theory of Virtual Community Practice (VCoP) to address this research gap and employs a qualitative, inductive research approach. We investigate the potential of Facebook groups in bridging the extension service gap and addressing the information deficit faced by urban rooftop gardeners. Our study involves extracting data from two Facebook-based VCoPs using CrowdTangle and analysis using Atlas-ti software. The findings of our study highlight six communication behaviors: supporting outreach, crowdsourcing, knowledge sharing and learning, engaging groups or communities, cooperating, and popularity and promotion. These behaviors provide insights into various aspects of community engagement, interaction, and outcomes. With the help of social media platforms, rooftop gardeners can connect, share experiences, seek advice, and access valuable information on rooftop gardening. This study is the first to explore the potential of Facebook groups in bridging the extension service gap and addressing the information deficit faced by urban rooftop gardeners.
Evaluating the Usability of a Gaze-Adaptive Approach for Identifying and Comparing Raster Values between Multilayers

Raster maps provide intuitive visualizations of remote sensing data representing various phenomena on the Earth’s surface. Reading raster maps with intricate information requires a high cognitive workload, especially when it is necessary to identify and compare values between multiple layers. In traditional methods, users need to repeatedly move their mouse and switch their visual focus between the map content and legend to interpret various grid value meanings. Such methods are ineffective and may lead to the loss of visual context for users. In this research, we aim to explore the potential benefits and drawbacks of gaze-adaptive interactions when interpreting raster maps. We focus on the usability of the use of low-cost eye trackers on gaze-based interactions. We designed two gaze-adaptive methods, gaze fixed and gaze dynamic adaptations, for identifying and comparing raster values between multilayers. In both methods, the grid content of different layers is adaptively adjusted depending on the user’s visual focus. We then conducted a user experiment by comparing such adaptation methods with a mouse dynamic adaptation method and a traditional method. Thirty-one participants (n = 31) were asked to complete a series of single-layer identification and multilayer comparison tasks. The results indicated that although gaze interaction with adaptive legends confused participants in single-layer identification, it improved multilayer comparison efficiency and effectiveness. The gaze-adaptive approach was well received by the participants overall, but was also perceived to be distracting and insensitive. By analyzing the participants’ eye movement data, we found that different methods exhibited significant differences in visual behaviors. The results are helpful for gaze-driven adaptation research in (geo)visualization in the future.
A Micro-Topography Measurement and Compensation Method for the Key Component Surface Based on White-Light Interferometry

The grinding grooves of material removal machining and the residues of a machining tool on the key component surface cause surface stress concentration. Thus, it is critical to carry out precise measurements on the key component surface to evaluate the stress concentration. Based on white-light interferometry (WLI), we studied the measurement distortion caused by the reflected light from the steep side of the grinding groove being unable to return to the optical system for imaging. A threshold value was set to eliminate the distorted measurement points, and the cubic spline algorithm was used to interpolate the eliminated points for compensation. The compensation result agrees well with the atomic force microscope (AFM) measurement result. However, for residues on the surface, a practical method was established to obtain a microscopic 3D micro-topography point cloud and a super-depth-of-field fusion image simultaneously. Afterward, the semantic segmentation network U-net was adopted to identify the residues in the super-depth-of-field fusion image and achieved a recognition accuracy of 91.06% for residual identification. Residual feature information, including height, position, and size, was obtained by integrating the information from point clouds and super-depth-of-field fusion images. This work can provide foundational data to study surface stress concentration.
A Hierarchical Blockchain-Based Trust Measurement Method for Drone Cluster Nodes

In response to the challenge of low accuracy in node trust evaluation due to the high dynamics of entry and exit of drone cluster nodes, we propose a hierarchical blockchain-based trust measurement method for drone cluster nodes. This method overcomes the difficulties related to trust inheritance for dynamic nodes, trust re-evaluation of dynamic clusters, and integrated trust calculation for drone nodes. By utilizing a multi-layer unmanned cluster blockchain for trusted historical data storage and verification, we achieve scalability in measuring intermittent trust across time intervals, ultimately improving the accuracy of trust measurement for drone cluster nodes. We design a resource-constrained multi-layer unmanned cluster blockchain architecture, optimize the computing power balance within the cluster, and establish a collaborative blockchain mechanism. Additionally, we construct a dynamic evaluation method for trust in drone nodes based on task perception, integrating and calculating the comprehensive trust of drone nodes. This approach addresses trusted sharing and circulation of task data and resolves the non-inheritability of historical data. Experimental simulations conducted using NS3 and MATLAB demonstrate the superior performance of our trust value measurement method for unmanned aerial vehicle cluster nodes in terms of accurate malicious node detection, resilience to trust value fluctuations, and low resource delay retention.
Toward Unified and Quantitative Cinematic Shot Attribute Analysis

Cinematic Shot Attribute Analysis aims to analyze the intrinsic attributes of movie shots, such as movement and scale. In previous methods, specialized architectures were designed for each specific task and relied on the use of optical flow maps. In this paper, we consider shot attribute analysis as a unified task of motion–static weight allocation, and propose a motion–static dual-path architecture for recognizing various shot attributes. In this architecture, we design a new action cue generation module for adapting the end-to-end training process instead of a pre-trained optical flow network; and, to address the issue of limited samples in movie shot datasets, we design a fixed-size adjustment strategy to enable the network to directly utilize pre-trained vision transformer models while adapting to shot data inputs at arbitrary sample rates. In addition, we quantitatively analyze the sensitivity of different shot attributes to motion and static features for the first time. Subsequent experimental results on two datasets, MovieShots and AVE, demonstrate that our proposed method outperforms all previous approaches without increasing computational cost.
Orthognathic surgical planning using graph CNN with dual embedding module: External validations with multi-hospital datasets

Despite recent development of AI, prediction of the surgical movement in the maxilla and mandible by OGS might be more difficult than that of tooth movement by orthodontic treatment. To evaluate the prediction accuracy of the surgical movement using pairs of pre-(T0) and post-surgical (T1) lateral cephalograms (lat-ceph) of orthognathic surgery (OGS) patients and dual embedding module-graph convolution neural network (DEM-GCNN) model.599 pairs from 3 institutions were used as training, internal validation, and internal test sets and 201 pairs from other 6 institutions were used as external test set. DEM-GCNN model (IEM, learning the lat-ceph images; LTEM, learning the landmarks) was developed to predict the amount and direction of surgical movement of ANS and PNS in the maxilla and B-point and Md1crown in the mandible. The distance between T1 landmark coordinates actually moved by OGS (ground truth) and predicted by DEM-GCNN model and pre-existed CNN-based Model-C (learning the lat-ceph images) was compared.In both internal and external tests, DEM-GCNN did not exhibit significant difference from ground truth in all landmarks (ANS, PNS, B-point, Md1crown, all P > 0.05). When the accumulated successful detection rate for each landmark was compared, DEM-GCNN showed higher values than Model-C in both the internal and external tests. In violin plots exhibiting the error distribution of the prediction results, both internal and external tests showed that DEM-GCNN had significant performance improvement in PNS, ANS, B-point, Md1crown than Model-C. DEM-GCNN showed significantly lower prediction error values than Model-C (one-jaw surgery, B-point, Md1crown, all P < 0.005; two-jaw surgery, PNS, ANS, all P < 0.05; B point, Md1crown, all P < 0.005).We developed a robust OGS planning model with maximized generalizability despite diverse qualities of lat-cephs from 9 institutions.
Internet of Things (IoT) with AI

The web of things and man-made brainpower are a couple of innovations that together structure, alluded to as Industry 4.0. These support points incorporate the modern web of things, information, and investigation, whereas the last option can incorporate synthetic intelligence and its techniques. Artificial intelligence is the cycle of creating clever machines and performing assignments of human knowledge, for example, direction, discourse acknowledgment, etc. This cycle has to think and be capable of self-remedy. Learning is the securing of data and rules for employing the information; thinking is rules to arrive at rough or distinct resolutions. AI is utilized in different areas of trend-setting innovations, for example, blockchain thinking, decentralized AI, the intellectual prowess of things, machines, etc. Coordination of AI and IoT gives the best approach to gathering the data, examining it, and observing the proper realization of utilized for applications like medical care, home, shrewd cultivating, and astute vehicles. This chapter explains the architecture, applications, use cases, challenges and open issues of the integration.
Examining risk and risk perception on LSD and MDMA in online marketplaces

The anonymity provided in underground forums has led to the growth of illicit substance drug markets. Online markets seem to be safer for drug transactions, with reduced risks of being arrested. However, criminals seem to be conscious of the risks of keeping a dark web market user friendly. This study investigates both surface and dark web forums, focusing on online underground markets. This study aims to: a) investigate the ways risk is perceived among members and vendors of underground forums throughout stages of transactions and b) identify risk avoidance practices utilised within these online underground markets. We hypothesise that the discourse in these forums focuses on the risks of detection, both offline and in cyberspace, as well as relevant techniques for reducing such risks. Our findings indicate discourse on techniques and strategies for reducing the risk of detection and arrest. In addition, our findings highlight that decision-making with drug use is highly individualised and should be based on some research of the effects and benefits of each drug. These findings provide useful insights and can inform practices in policing, leading to the development of intervention approaches aiming to interrupt online underground markets.