Reviewerly/exHarmony huggingface dataset:
No Grammar to Rule Them All: A Survey of JSON-style DSLs for Visualization

There has been substantial growth in the use of JSON-based grammars, as well as other standard data serialization languages, to create visualizations. Each of these grammars serves a purpose: some focus on particular computational tasks (such as animation), some are concerned with certain chart types (such as maps), and some target specific data domains (such as ML). Despite the prominence of this interface form, there has been little detailed analysis of the characteristics of these languages. In this study, we survey and analyze the design and implementation of 57 JSON-style DSLs for visualization. We analyze these languages supported by a collected corpus of examples for each DSL (consisting of 4395 instances) across a variety of axes organized into concerns related to domain, conceptual model, language relationships, affordances, and general practicalities. We identify tensions throughout these areas, such as between formal and colloquial specifications, among types of users, and within the composition of languages. Through this work, we seek to support language implementers by elucidating the choices, opportunities, and tradeoffs in visualization DSL design.
Enhancing 3D Reconstruction Model by Deep Learning and Its Application in Building Damage Assessment after Earthquake

A timely and accurate damage assessment of buildings after an earthquake is critical for the safety of people and property. Most of the existing methods based on classification and segmentation use two-dimensional information to determine the damage level of the buildings, which cannot provide the multi-view information of the damaged building, resulting in inaccurate assessment results. According to the knowledge of the authors, there is no related research using the deep-learning-based 3D reconstruction method for the evaluation of building damage. In this paper, we first applied the deep-learning-based MVS model to reconstruct the 3D model of the buildings after an earthquake using multi-view UAV images, to assist the building damage assessment task. The method contains three main steps. Firstly, the camera parameters are calculated. Then, 3D reconstruction is conducted based on CasMVSNet. Finally, a building damage assessment is performed based on the 3D reconstruction result. To evaluate the effectiveness of the proposed method, the method was tested in multi-view UAV aerial images of Yangbi County, Yunnan Province. The results indicate that: (1) the time efficiency of CasMVSNet is significantly higher than that of other deep learning models, which can meet the timeliness requirement of post-earthquake rescue and damage assessment. In addition, the memory consumption of CasMVSNet is the lowest; (2) CasMVSNet exhibits the best 3D reconstruction result in both high and small buildings; (3) the proposed method can provide detail and multi-view information of damaged buildings, which can be used to assist the building damage assessment task. The results of the building damage assessment are very similar to the results of the field survey.
Advances in Explainable Artificial Intelligence and Edge Computing Applications

Artificial Intelligence (AI) and its applications have undergone remarkable experimental development in the last decade and are now the basis for a large number of decision support systems [...]
Automated design of quantum-optical experiments for device-independent quantum key distribution

Device-independent quantum key distribution (DIQKD) reduces the vulnerability to side-channel attacks of standard quantum key distribution protocols by removing the need for characterized quantum devices. The higher security guarantees come, however, at the price of a challenging implementation. Here, we tackle the question of the conception of an experiment for implementing DIQKD with photonic devices. We introduce a technique combining reinforcement learning, an optimization algorithm, and a custom efficient simulation of quantum optics experiments to automate the design of photonic setups maximizing a given function of the measurement statistics. Applying the algorithm to DIQKD, we get unexpected experimental configurations leading to high key rates and to a high resistance to loss and noise. These configurations might be helpful to facilitate a first implementation of DIQKD with photonic devices and for future developments targeting improved performances.
Comparative analysis on deep neural network models for detection of cyberbullying on Social Media

Social media usage has been increased and it consists of both positive and negative effects. By considering the misusage of social media platforms by various cyberbullying methods like stalking, harassment there should be preventive methods to control these and to avoid mental stress. These extra words will expand the size of the vocabulary and influence the performance of the algorithm. Therefore, we come up with variant deep learning models like LSTM, BI-LSTM, RNN, BI-RNN, GRU, BI-GRU to detect cyberbullying in social media. These models are applied on Twitter, public comments data and performance were observed for these models and obtained improved accuracy of 90.4%.
Comparative performance assessment of deep learning based image steganography techniques

Increasing data infringement while transmission and storage have become an apprehension for the data owners. Even the digital images transmitted over the network or stored at servers are prone to unauthorized access. However, several image steganography techniques were proposed in the literature for hiding a secret image by embedding it into cover media. But the low embedding capacity and poor reconstruction quality of images are significant limitations of these techniques. To overcome these limitations, deep learning-based image steganography techniques are proposed in the literature. Convolutional neural network (CNN) based U-Net encoder has gained significant research attention in the literature. However, its performance efficacy as compared to other CNN based encoders like V-Net and U-Net++ is not implemented for image steganography. In this paper, V-Net and U-Net++ encoders are implemented for image steganography. A comparative performance assessment of U-Net, V-Net, and U-Net++ architectures are carried out. These architectures are employed to hide the secret image into the cover image. Further, a unique, robust, and standard decoder for all architectures is designed to extract the secret image from the cover image. Based on the experimental results, it is identified that U-Net architecture outperforms the other two architectures as it reports high embedding capacity and provides better quality stego and reconstructed secret images.
Meditation in Motion

Tai Chi is an essential concept of Chinese philosophy which refers to the universe's most primitive state of order. Tai Chi Chuan is a martial art and meditative practice that incorporates the Tai Chi philosophy. With the advent of the digital media age, this traditional martial art is falling into disuse, and most contemporary youths are losing interest. "Meditation in Motion" is an interactive media art installation inspired by the Tai Chi Chuan forms. It aims to convey the central concepts of balance, narrative, and universe of Tai Chi Chuan by breaking down its movements into overlapping circles, building a visual representation of the energy flows in the body.
Multi-Attention Network for Compressed Video Referring Object Segmentation

Referring video object segmentation aims to segment the object referred by a given language expression. Existing works typically require compressed video bitstream to be decoded to RGB frames before being segmented, which increases computation and storage requirements and ultimately slows the inference down. This may hamper its application in real-world computing resource limited scenarios, such as autonomous cars and drones. To alleviate this problem, in this paper, we explore the referring object segmenta- tion task on compressed videos, namely on the original video data flow. Besides the inherent difficulty of the video referring object segmentation task itself, obtaining discriminative representation from compressed video is also rather challenging. To address this problem, we propose a multi-attention network which consists of dual-path dual-attention module and a query-based cross-modal Transformer module. Specifically, the dual-path dual-attention module is designed to extract effective representation from compressed data in three modalities, i.e., I-frame, Motion Vector and Residual. The query-based cross-modal Transformer firstly models the corre- lation between linguistic and visual modalities, and then the fused multi-modality features are used to guide object queries to generate a content-aware dynamic kernel and to predict final segmentation masks. Different from previous works, we propose to learn just one kernel, which thus removes the complicated post mask-matching procedure of existing methods. Extensive promising experimental results on three challenging datasets show the effectiveness of our method compared against several state-of-the-art methods which are proposed for processing RGB data. Source code is available at: https://github.com/DexiangHong/MANet.
Fusing ToF‐SIMS Images for Spatial‐Spectral Resolution Enhancement using a Convolutional Neural Network

Abstract Hyperspectral data sets generated by time‐of‐flight secondary ion mass spectrometry (ToF‐SIMS) contain valuable spatial‐spectral information characterizing the distribution of atomic and molecular species across a sample surface. Modern ToF‐SIMS instruments have high spatial resolution (in the order of tens of nanometers) relative to most other mass spectrometry imaging (MSI) techniques. However, there is generally a trade‐off between spatial and mass resolution when using different instrument modes. In this study, a convolutional neural network (CNN) fusion method is used to fuse correlated high spatial and high mass resolution ToF‐SIMS hyperspectral data sets. This process generates resolution‐enhanced data, which exhibit both high spatial and mass resolution. The CNN fusion method is applied to ToF‐SIMS images of a simple, well‐characterized gold mesh sample and a significantly more complex biological (tumor) tissue section. The method is compared to another linear fusion method used in the broader MSI community and a substantial improvement is found. This comparison focuses on both visual quality observations as well as statistical similarity measures. This work demonstrates the utility of the CNN fusion method for ToF‐SIMS data, enabling investigation of the atomic and molecular characteristics of surfaces at high spatial and mass spectral resolution.
Artificial Intelligence and Human Psychology in Online Transaction Fraud

Banking operations have changed due to technological advancement. On one hand, modernization in technology has facilitated the daily operation of banks; on the other hand, this has also resulted in an increase in the number of cyber-attacks. Artificial Intelligence has introduced new models to detect and prevent cybercrimes. Some fraud has also occurred due to the involvement of employees inside particular organizations. So, this study has focused on both sides: the machine as well as the human. Firstly, the research has focused on fraud diamond theory and has analyzed factors such as rationalization, capabilities, perceived pressure, and perceived opportunities to understand the psychology of the fraudster. Secondly, Artificial Intelligence characteristics, threat exposure, big data management, explainability, cost effectiveness, and risk prediction are evaluated to explore their use in fraud reduction in banks. The research data have been collected from 15 Banks in Pakistan with the help of a questionnaire using five-item Likert scales. The data have been analyzed using IBM SPSS Software. The results gained after correlation and regression analysis proved that Fraud diamond theory and AI characteristics have positive and significant effects on cybercrimes. This study is a great contribution to the banking industry of Pakistan as it provides a complete analysis to control fraud inside organizations by understanding the mindset of fraudsters with the help of fraud diamond theory. At the same time, outside fraud will be handled with the help of Artificial Intelligence. This will result in banks growth, which ultimately boosts the economy of a country.
Exploring indirect entity relations for knowledge graph enhanced recommender system

Knowledge graph (KG)-based recommendation models generally explore auxiliary information to alleviate the sparsity and cold-start problems in recommender systems. Previous approaches enhance representations of users and items by exploring the influence of multi-hop neighbors. However, existing works fail to consider the indirect feedback for improving user representation and the diversity of the multi-hop neighbors for enriching item representation. To this end, we present a novel recommender system, called Entity Relation Similarity and Indirect Feedback-based Knowledge graph enhanced Recommendation (ERSIF-KR) to enhance representation learning in KG-based recommender systems. In addition, our model exploits indirect feedback of items that are not directly interacted with users to alleviate the exposure bias while enhancing user similarity computation when learning user representation. Moreover, our method directly incorporates representation of multi-hop neighbors into the target item embedding with weights determined by the correlations between high-order and low-order relations, which can significantly boost the item representation learning. Extensive experiments on three real-world datasets demonstrate that our model achieves remarkable gains in terms of recommendation performance and model convergence time, and effectively alleviates the sparsity and cold start problems.
Robust Extreme Learning Machine Using New Activation and Loss Functions Based on M-Estimation for Regression and Classification

This paper provides an analysis of the combining effect of novel activation function and loss function based on M-estimation in application to extreme learning machine (ELM), a feed-forward neural network. Due to the computational efficiency and classification/prediction accuracy of ELM and its variants, they have been widely exploited in the development of new technologies and applications. However, in real applications, the performance of classical ELMs deteriorates in the presence of outliers, thus, negatively impacting the precision and accuracy of the system. To further enhance the performance of ELM and its variants, we proposed novel activation functions based on the psi function of M and redescend the M-estimation method along with the smooth <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mi mathvariant="script">l</mi> </math> 2-norm weight-loss functions to reduce the negative impact of the outliers. The proposed psi functions of several M and redescending M-estimation methods are more flexible to make more distinct features space. For the first time, the idea of the psi function as an activation function in the neural network is introduced in the literature to ensure accurate prediction. In addition, new robust <math xmlns="http://www.w3.org/1998/Math/MathML" id="M2"> <mi mathvariant="script">l</mi> </math> 2 norm-loss functions based on M and redescending M-estimation are proposed to deal with outliers efficiently in ELM. To evaluate the performance of the proposed methodology against other state-of-the-art techniques, experiments have been performed in diverse environments, which show promising improvements in application to regression and classification problems.
MCSCSet: A Specialist-annotated Dataset for Medical-domain Chinese Spelling Correction

Chinese Spelling Correction (CSC) is gaining increasing attention in recent years. Despite its extensive use in many applications, such as search engine and optical character recognition system, little has been explored in medical scenarios in which complex and uncommon medical entities are easily misspelled. Correcting the misspellings of medical entities is arguably more difficult than those in the open domain due to its requirements of specific domain knowledge. In this work, we define the task of Medical-domain Chinese Spelling Correction (MCSC) and propose MCSCSet, a large-scale specialist-annotated dataset that contains about 200k samples. In contrast to existing open-domain CSC datasets, MCSCSet involves: i) extensive real-world medical queries collected from Tencent Yidian, ii) corresponding misspelled sentences manually annotated by medical specialists. Our work further offers a medical-domain confusion set consisting of the common error-prone characters in medicine and their corresponding misspellings. Extensive empirical studies have shown significant gaps between the open-domain and medical-domain spelling correction, highlighting the need to develop high-quality datasets that allow for CSC in specific domains. Moreover, our work benchmarks several representative methods, establishing baselines for future work.
Spatiotemporal-aware Session-based Recommendation with Graph Neural Networks

Session-based recommendation (SBR) aims to recommend items based on user behaviors in a session. For the online life service platforms, such as Meituan, both the user's location and the current time primarily cause the different patterns and intents in user behaviors. Hence, spatiotemporal context plays a significant role in the recommendation on those platforms, which motivates an important problem of spatiotemporal-aware session-based recommendation (STSBR). Since the spatiotemporal context is introduced, there are two critical challenges: 1) how to capture session-level relations of spatiotemporal context (inter-session view), and 2) how to model the complex user decision-making process at a specific location and time (intra-session view). To address them, we propose a novel solution named STAGE in this paper. Specifically, STAGE first constructs a global information graph to model the multi-level relations among all sessions, and a session decision graph to capture the complex user decision process for each session. STAGE then performs inter-session and intra-session embedding propagation on the constructed graphs with the proposed graph attentive convolution (GAC) to learn representations from the above two perspectives. Finally, the learned representations are combined with spatiotemporal-aware soft-attention for final recommendation. Extensive experiments on two datasets from Meituan demonstrate the superiority of STAGE over state-of-the-art methods. Further studies also verify that each component is effective.
Cross-Network Social User Embedding with Hybrid Differential Privacy Guarantees

Integrating multiple online social networks (OSNs) has important implications for many downstream social mining tasks, such as user preference modelling, recommendation, and link prediction. However, it is unfortunately accompanied by growing privacy concerns about leaking sensitive user information. How to fully utilize the data from different online social networks while preserving user privacy remains largely unsolved. To this end, we propose a Cross-network Social User Embedding framework, namely DP-CroSUE, to learn the comprehensive representations of users in a privacy-preserving way. We jointly consider information from partially aligned social networks with differential privacy guarantees. In particular, for each heterogeneous social network, we first introduce a hybrid differential privacy notion to capture the variation of privacy expectations for heterogeneous data types. Next, to find user linkages across social networks, we make unsupervised user embedding-based alignment in which the user embeddings are achieved by the heterogeneous network embedding technology. To further enhance user embeddings, a novel cross-network GCN embedding model is designed to transfer knowledge across networks through those aligned users. Extensive experiments on three real-world datasets demonstrate that our approach makes a significant improvement on user interest prediction tasks as well as defending user attribute inference attacks from embedding.
Infrared image contrast enhancement using adaptive histogram correction framework

To enhance infrared images' contrast and visual effect, this paper proposes a contrast enhancement method based on adaptive histogram correction and equalization. Unlike the previous histogram equalization method, this method combines adaptive histogram correction and histogram equalization to effectively reduce artifacts and insufficient local detail enhancement caused by traditional methods. In our method, a more uniform histogram is first obtained by recursive separation weighted histogram equalization, then a weighted averaging method is used to establish a connection between this histogram and the original histogram to correct the histogram, and finally, the corrected histogram is used to establish a mapping relationship between the input gray levels and the output gray levels. The experimental results show that the infrared image contrast enhancement using this method is better in terms of detail representation in dark areas, overall image brightness maintenance, contrast enhancement, and suppression of artifacts, and is better than or similar to existing methods.
Restoring Quantum Communication Efficiency over High Loss Optical Fibers

In the absence of quantum repeaters, quantum communication proved to be nearly impossible across optical fibers longer than ≳20 km due to the drop of transmissivity below the critical threshold of 1/2. However, if the signals fed into the fiber are separated by a sufficiently short time interval, memory effects must be taken into account. In this Letter, we show that by properly accounting for these effects it is possible to devise schemes that enable unassisted quantum communication across arbitrarily long optical fibers at a fixed positive qubit transmission rate. We also demonstrate how to achieve entanglement-assisted communication over arbitrarily long distances at a rate of the same order of the maximum achievable in the unassisted noiseless case.
TrackItPipe: A Fabrication Pipeline To Incorporate Location and Rotation Tracking Into 3D Printed Objects

The increasing convergence of the digital and physical world creates a growing urgency to integrate 3D printed physical tangibles with virtual environments. A precise position and rotation tracking are essential to integrate such physical objects with a virtual environment. However, available 3D models commonly do not provide tracking support on their composition, which requires modifications by CAD experts. This poses a challenge for users with no prior CAD experience. This work presents TrackItPipe, a fabrication pipeline supporting users by semi-automatically adding tracking capabilities for 3D printable tangibles tailored to environmental requirements. TrackItPipe integrates modifications to the 3D model, produces the respective tangibles for 3D printing, and provides integration scripts for Mixed Reality. Using TrackItPipe, users can rapidly equip objects with tracking capabilities.
Thumble: One-Handed 3D Object Manipulation Using a Thimble-Shaped Wearable Device in Virtual Reality

Conventional controllers or hand-tracking interactions in VR cause hand fatigue while manipulating 3D objects because repetitive wrist rotation and hand movements are often required. As a solution to this inconvenience, we propose Thumble, a novel wearable input device worn on the thumb for modifying the orientation of 3D objects. Thumble can rotate the 3D objects depending on the orientation of the thumb and using the thumb pad as an input surface on which the index finger rubs to control the direction and degree of rotations. Therefore, it requires minimal motion of the wrist and the arm. Through the informal user study, we collected the subjective feedback of users and found that Thumble has less hand movement than a conventional VR controller.