Reviewerly/exHarmony huggingface dataset:
The Trend of Online Child Sexual Abuse and Exploitations: A Profile of Online Sexual Offenders and Criminal Justice Response

During the COVID-19 pandemic, the number of technology-facilitated crimes against children worldwide has increased substantially and become one of the most serious crime problems. Due to these considerations, there is a lack of large-scale systematic reviews investigating Cybercrime in and of itself could be challenging to investigate in comparison to traditional ones due to the elusiveness of the cyber realm. Specifically, investigating internet crimes against children comes with specific challenges. These offenses target vulnerable children who are less likely to realize their victimization, lowering the probability of reporting to the proper authorities. With these obstacles in mind, this research study utilizes data information regarding the characteristics of online CSAM users and their practices to inform law enforcement, parents, and the public for preventative and strategic purposes. Furthermore, this study diagnoses the significant challenges of investigating technology-facilitated crimes against children by examining how the current criminal justice system responds to these incidents. The policy recommendations discussed offer a holistic lens for highlighting this critical issue and implementing practical and proactive training solutions for law enforcement and the public.
You Are How You Use Apps: User Profiling Based on Spatiotemporal App Usage Behavior

Mobile apps have become an indispensable part of people’s daily lives. Users determine what apps to use and when and where to use them based on their tastes, interests, and personal demands, depending on their personality traits. This article aims to infer user profiles from their spatiotemporal mobile app usage behavior. Specifically, we first transform mobile app usage records into a heterogeneous graph. On the graph, nodes represent users, apps, locations, and time slots. Edges describe the co-occurrence of entities in usage records. We then develop a multi-relational heterogeneous graph attention network (MRel-HGAN), an end-to-end system for user profiling. MRel-HGAN first adopts a neighbor sampling strategy based on bootstrapping to sample heavily connected neighbors of a fixed size for each node. Next, we design a relational graph convolutional operation and a multi-relational attention operation. Through such modules, MRel-HGAN can generate node embedding by sufficiently leveraging the rich semantic information of the multi-relational structure in the mobile app usage graph. Experimental results on real-world mobile app usage datasets show the effectiveness and superiority of our MRel-HGAN in the user profiling task for attributes of gender and age.
Assessing and predicting the quality of peer reviews: a text mining approach

Purpose This paper aims to quantify the quality of peer reviews, evaluate them from different perspectives and develop a model to predict the review quality. In addition, this paper investigates effective features to distinguish the reviews' quality. Design/methodology/approach First, a fine-grained data set including peer review data, citations and review conformity scores was constructed. Second, metrics were proposed to evaluate the quality of peer reviews from three aspects. Third, five categories of features were proposed in terms of reviews, submissions and responses using natural language processing (NLP) techniques. Finally, different machine learning models were applied to predict the review quality, and feature analysis was performed to understand effective features. Findings The analysis results revealed that reviewers become more conservative and the review quality becomes worse over time in terms of these indicators. Among the three models, random forest model achieves the best performance on all three tasks. Sentiment polarity, review length, response length and readability are important factors that distinguish peer reviews’ quality, which can help meta-reviewers value more worthy reviews when making final decisions. Originality/value This study provides a new perspective for assessing review quality. Another originality of the research lies in the proposal of a novelty task that predict review quality. To address this task, a novel model was proposed which incorporated various of feature sets, thereby deepening the understanding of peer reviews.
Distance-based resource quantification for sets of quantum measurements

The advantage that quantum systems provide for certain quantum information processing tasks over their classical counterparts can be quantified within the general framework of resource theories. Certain distance functions between quantum states have successfully been used to quantify resources like entanglement and coherence. Perhaps surprisingly, such a distance-based approach has not been adopted to study resources of quantum measurements, where other geometric quantifiers are used instead. Here, we define distance functions between sets of quantum measurements and show that they naturally induce resource monotones for convex resource theories of measurements. By focusing on a distance based on the diamond norm, we establish a hierarchy of measurement resources and derive analytical bounds on the incompatibility of any set of measurements. We show that these bounds are tight for certain projective measurements based on mutually unbiased bases and identify scenarios where different measurement resources attain the same value when quantified by our resource monotone. Our results provide a general framework to compare distance-based resources for sets of measurements and allow us to obtain limitations on Bell-type experiments.
Predicting new mineral occurrences and planetary analog environments via mineral association analysis

The locations of minerals and mineral-forming environments, despite being of great scientific importance and economic interest, are often difficult to predict due to the complex nature of natural systems. In this work, we embrace the complexity and inherent "messiness" of our planet's intertwined geological, chemical, and biological systems by employing machine learning to characterize patterns embedded in the multidimensionality of mineral occurrence and associations. These patterns are a product of, and therefore offer insight into, the Earth's dynamic evolutionary history. Mineral association analysis quantifies high-dimensional multicorrelations in mineral localities across the globe, enabling the identification of previously unknown mineral occurrences, as well as mineral assemblages and their associated paragenetic modes. In this study, we have predicted (i) the previously unknown mineral inventory of the Mars analogue site, Tecopa Basin, (ii) new locations of uranium minerals, particularly those important to understanding the oxidation-hydration history of uraninite, (iii) new deposits of critical minerals, specifically rare earth element (REE)- and Li-bearing phases, and (iv) changes in mineralization and mineral associations through deep time, including a discussion of possible biases in mineralogical data and sampling; furthermore, we have (v) tested and confirmed several of these mineral occurrence predictions in nature, thereby providing ground truth of the predictive method. Mineral association analysis is a predictive method that will enhance our understanding of mineralization and mineralizing environments on Earth, across our solar system, and through deep time.
Efficient Secure Computation from SM Series Cryptography

The wireless network suffers from many security problems, and computation in a wireless network environment may fail to preserve privacy as well as correctness when the adversaries conduct attacks through backdoors, steganography, kleptography, etc. Secure computation ensures the execution security in such an environment, and compared with computation on the plaintext, the performance of secure computation is bounded by the underlying cryptographic algorithms and the network environment between the involved parties. Besides, the Chinese cryptography laws require the cryptographic algorithms that appeared in the commercial market to be authorized. In this work, we show how to implement oblivious transfer (OT), an important primitive in secure multiparty computation (MPC), using the Chinese government-approved SM2 and SM3 algorithms. The SM2 algorithm is based on the elliptic curve cryptography and is much faster than the discrete logarithm-based solutions. Moreover, by adopting the standard OT extension technique, we can extend the number of OTs efficiently with one more round of communication and invocations to the SM3 and SM4 algorithms. The OT primitive can be used in the Beaver multiplication triple generation and other MPC protocols, e.g., private set intersection. Therefore, we can utilize the SM series cryptography, specifically, the SM2, SM3, and SM4 algorithms, to build highly efficient secure computation frameworks which are suitable for the wireless network environment and for commercial applications in China. The experimental evaluation results show that our protocols have comparable performance to existing protocols; specifically, our protocols are quite suitable for bad network environments.
Only Classical Parameterised States have Optimal Measurements under Least Squares Loss

Measurements of quantum states form a key component in quantum-information processing. It is therefore an important task to compare measurements and furthermore decide if a measurement strategy is optimal. Entropic quantities, such as the quantum Fisher information, capture asymptotic optimality but not optimality with finite resources. We introduce a framework that allows one to conclusively establish if a measurement is optimal in the non-asymptotic regime. Our method relies on the fundamental property of expected errors of estimators, known as risk, and it does not involve optimisation over entropic quantities. The framework applies to finite sample sizes and lack of prior knowledge, as well as to the asymptotic and Bayesian settings. We prove a no-go theorem that shows that only classical states admit optimal measurements under the most common choice of error measurement: least squares. We further consider the less restrictive notion of an approximately optimal measurement and give sufficient conditions for such measurements to exist. Finally, we generalise the notion of when an estimator is inadmissible (i.e. strictly worse than an alternative), and provide two sufficient conditions for a measurement to be inadmissible.
Driver Head Pose Detection From Naturalistic Driving Data

Driver behavior analysis plays an important role in driver assistance systems. A driver’s face and head pose hold the key towards understanding whether the driver’s attention and concentration are on the road while driving. Naturalistic driving studies (NDS) allow observing drivers in real-time under naturalistic traffic conditions. Yet, data collected in NDS often comprise low-resolution videos usually with more challenging camera positions compared to controlled studies. For instance, when the camera is not directly facing the driver, classifying head pose becomes more challenging, since the variation between different classes becomes much smaller. In this paper, we propose three different approaches to classify a driver’s head pose from naturalistic videos, which were captured by a camera providing a side view, instead of directly facing the driver. These approaches employ a sequence of five key points on the driver’s face. We compare these three proposed approaches with each other as well as with three different baselines by using leave-one-driver-out cross-validation on nine different drivers. Results show that our proposed method employing a Bidirectional Gated Recurrent Unit (BiGRU) outperforms the best performing baseline by 11% in terms of overall accuracy.
Pose-Aided Video-Based Person Re-Identification via Recurrent Graph Convolutional Network

Existing methods for video-based person re-identification (ReID) mainly learn the appearance feature of a given pedestrian via a feature extractor and a feature aggregator. However, the appearance models would fail to learn a large inter-class variance when different pedestrians have similar appearances. Considering that different pedestrians have different walking postures and body proportions, we propose to learn the discriminative pose feature beyond the appearance feature for video retrieval. Specifically, we implement a two-branch architecture to separately learn the appearance feature and pose feature, and then concatenate them together for inference. To learn the pose feature, we first detect the pedestrian pose in each frame through an off-the-shelf pose detector, and construct a temporal graph using the pose sequence. We then exploit a recurrent graph convolutional network (RGCN) to learn the node embeddings of the temporal pose graph, which devises a global information propagation mechanism to simultaneously achieve the neighborhood aggregation of intra-frame nodes and message passing among inter-frame graphs. Finally, we propose a dual-attention method (DAM) consisting of node-attention and time-attention to obtain the temporal graph representation from the node embeddings, where the self-attention mechanism is employed to learn the importance of each node and each frame. We verify the proposed method on three video-based ReID datasets, i.e., Mars, DukeMTMC and iLIDS-VID, whose experimental results demonstrate that the learned pose feature can effectively improve the performance of existing appearance models.
Deep Learning Model for Recognition of Handwritten Devanagari Numerals With Low Computational Complexity and Space Requirements

The broad application area and accompanying challenges make machine learning-based recognition of handwritten scripts a demanding field.Individuals' writing practices and inherent variations in the size, shape, and tilt of characters may increase the difficulty level.Deep convolutional neural network (DCNN) models have been successful in solving pattern recognition problems, but at the expense of a considerable number of trainable parameters and heavy computational loads.The proposed work addresses these problems by using the shifted window (SWIN) transformer method to recognize handwritten Devanagari numerals for the first time.In the presented model, the SWIN transformer is finely tuned to withstand popular DCNN models, such as VGG-16Net, ResNet-50, and DenseNet-121, in terms of recognition accuracy, space requirement, and computational complexity.The model successfully attained a recognition accuracy of 99.20% with only 0.218 million trainable parameters and 0.0912 giga floating-point operations per second (FLOPs).This indicates the validity and soundness of the proposed model for recognizing handwritten Devanagari numerals.
Like being there: How the soft VR technology could change consumers’ initial decision-making

With the sweeping wave of digital transformation, Virtual Reality (VR) technology has become one of the key technologies for building the metaverse with its characteristic of lifting the physical interaction restrictions between people and things, which has attracted the extensive attention of scholars and practitioners. However, the VR industry is gradually evolving from traditional device-based hard VR to soft VR, where the market is larger but less researched. Additionally, although most businesses have high expectations for the potential of VR technology, little is known about whether a huge investment in VR will deliver the expected returns. The objective of this study is to explore whether and how soft VR technology can play a significant positive role in promoting consumers' initial decision-making. This study collected and empirically analyzed the data of 51,495 online second-hand condominiums on an online platform for housing transactions and services. The results show that the soft VR technology can significantly increase consumers' attention to the product and prompt their initial decision-making. Moreover, it is interesting that the enhancement effect of soft VR technology will show an equilibrating trend due to the different features of the search products. This study contributes to scholarship and practice by initiating the study of the emerging phenomenon of soft VR and empirically testing its effect on consumers' initial decision-making.
Exaggeration of emotional responses in online communication

Two studies tested differences in reported (Study 1, N = 197) and perceived (Study 2, N = 875) level of exaggerated emotional responses published on four online platforms: Facebook, WhatsApp, Instagram, and email. We found differences between platforms that may reflect divergent communication norms. Participants judged the level of exaggeration by comparing a given message to a message that they would have published as well as to the norms that govern the communication platform. Overall, participants reported that they exaggerated less than other users. Content format (text, picture, or video) and perceived privacy level moderated the impact of the platform on judgment of exaggeration. We suggest that since online media filter out communication cues, users tend to amplify their emotional responses. This amplification generates an atmosphere in which exaggerating is the norm of communication.
Multi-scale convolution based breast cancer image segmentation with attention mechanism in conjunction with war search optimization

Numerous studies have explored different techniques for segmenting breast cancer images, in particular deep learning-based Computer-Aided Diagnosis (CAD) has recently netted attention. However, due to their down-and-out pursuance, the existing approaches like FCN (Fully Convolutional Network), PSPNet (Pyramid Scene Parsing Network), U-Net, and SegNet still required improvement for offering better semantic segmentation while identifying breast cancer. In this paper, the newly proposed breast cancer tumor segmentation method consists of four steps pre-processing, augmentation, segmenting image using multi-scale convolution and multi- attention mechanisms respectively. The proposed method utilizes the ResNet (Residual Network) backbone network with multi-scale convolution for feature map prediction. Also, the effectiveness of the multi-channel attention module with a pyramid dilated nodule is employed for semantic segmentation. Gated axial, position, and channel attention are combined to create a multi-channel attention mechanism. Additionally, War Search Optimization (WSO) algorithm is being utilized to enhance the accuracy of the segmented images. Experimentations are conducted on two datasets, viz., Breast Cancer Cell Segmentation Database and Breast Cancer Semantic Segmentation (BCSS) Database, with different existing networks. The effectiveness of the network is evaluated based on various criteria in terms of precision, accuracy, recall, mIoU (mean Intersection of Union), IoU (Intersection of Union), etc.
The promise and pitfalls of the metaverse for science

The metaverse can improve the accessibility of scientific laboratories and meetings, aid in reproducibility efforts and provide new opportunities for experimental design. But researchers and research institutions must plan ahead and be ready to mitigate potential harms.
Artificial Intelligence, Machine Learning, and IoT Architecture to Support Smart Governance

The usage of artificial intelligence (AI) technologies that depend on massive volumes of data, which are frequently made available through IoT, is thus directly related to the creation of smart governments. In order to increase the effectiveness of governance and the standard of living for citizens, Internet - of - things enabled AI technologies can be used in several important areas of smart government. In order to provide fulfilled government functions, AI and its subdomain innovations have the opportunity to address a number of current organizational inadequacies. In this chapter the foundations, benefits, and challenges implementing these technologies in the public sector or government are discussed. Following that, the various AI, Ml technologies and IoT frameworks for smart governance are explored. Then the focus is on the applications and use cases of IoT, AI and machine learning for smart governance.
Object Grasp Control of a 3D Robot Arm by Combining EOG Gaze Estimation and Camera-Based Object Recognition

The purpose of this paper is to quickly and stably achieve grasping objects with a 3D robot arm controlled by electrooculography (EOG) signals. A EOG signal is a biological signal generated when the eyeballs move, leading to gaze estimation. In conventional research, gaze estimation has been used to control a 3D robot arm for welfare purposes. However, it is known that the EOG signal loses some of the eye movement information when it travels through the skin, resulting in errors in EOG gaze estimation. Thus, EOG gaze estimation is difficult to point out the object accurately, and the object may not be appropriately grasped. Therefore, developing a methodology to compensate, for the lost information and increase spatial accuracy is important. This paper aims to realize highly accurate object grasping with a robot arm by combining EMG gaze estimation and the object recognition of camera image processing. The system consists of a robot arm, top and side cameras, a display showing the camera images, and an EOG measurement analyzer. The user manipulates the robot arm through the camera images, which can be switched, and the EOG gaze estimation can specify the object. In the beginning, the user gazes at the screen's center position and then moves their eyes to gaze at the object to be grasped. After that, the proposed system recognizes the object in the camera image via image processing and grasps it using the object centroid. The object selection is based on the object centroid closest to the estimated gaze position within a certain distance (threshold), thus enabling highly accurate object grasping. The observed size of the object on the screen can differ depending on the camera installation and the screen display state. Therefore, it is crucial to set the distance threshold from the object centroid for object selection. The first experiment is conducted to clarify the distance error of the EOG gaze estimation in the proposed system configuration. As a result, it is confirmed that the range of the distance error is 1.8-3.0 cm. The second experiment is conducted to evaluate the performance of the object grasping by setting two thresholds from the first experimental results: the medium distance error value of 2 cm and the maximum distance error value of 3 cm. As a result, it is found that the grasping speed of the 3 cm threshold is 27% faster than that of the 2 cm threshold due to more stable object selection.
Scrutinizing joint remote state preparation under decoherence

This research examines the effect of an open system containing the squeezed generalized amplitude damping channel on the joint remote preparation quantum communication protocol using a maximally entangled two-qubit state. Our findings indicate that the fidelity of a quantum system in contact with a non-zero temperature thermal bath can be enhanced by varying the squeezing parameters. These parameters include the squeezing phase of the channel [Formula: see text] and the amount of squeezing of the channel r.