Reviewerly/exHarmony huggingface dataset:
Content-Driven Layout for Visualization Design

Multi-view visualizations are typically presented in a grid layout with elements positioned according to their bounding rectangles. These rectangles often contain unused white space. In cases where Tufte's Shrink Principle can be applied to reduce non-data-ink without impairing the communication of information, unused white space can be utilized for the placement of other elements. This is often done in manually "hand-crafted" layouts by designers. However, upon changes to individual elements, this design process has to be repeated. To reduce non-data-ink and repetitive manual design, we contribute a method for automatically turning a grid layout into a content-driven layout, where elements are positioned with respect to their contents. Existing approaches have explored the use of a force simulation in conjunction with proxy geometries to simplify collision handling for irregular shapes. Such customized force directed layouts are usually unstable, and often require additional constraints to run properly. In addition, proxy geometries become less accurate and effective with more irregular shapes. To solve these shortcomings, we contribute an approach for identifying central elements in an original grid layout in order to set up corresponding attractive forces. Furthermore, we utilize an image-based approach for collision detection and avoidance that works accurately for highly irregular shapes. We demonstrate the utility of our approach with three case studies.
AI-Driven User Interface Design for Solving a Rubik’s Cube: A Scaffolding Design Perspective

In this paper, we describe an AI-driven platform, ALLURE, with an embodied chatbot that teaches the user how to solve a Rubik’s Cube. Our AI algorithm consists of macro-actions, which refer to a set of moves that are not transparent or usable to users as a black box. Through the integration of explainable AI and conversational user interface designs, we created a novel AI-driven visual user interface inspired by scaffolding design paradigms to engage users. We conducted a set of initial usability testing with ten users, and usability findings imply some important AI-driven user interface designs to engage users for problem solving.
Bandwidth-Aware Adaptive Codec for DNN Inference Offloading in IoT

The lightweight nature of IoT devices makes it challenging to run deep neural networks (DNNs) locally for applications like augmented reality. Recent advances in IoT communication like LTE-M have significantly boosted the link bandwidth, enabling IoT devices to stream visual data to edge servers running DNNs for inference. However, uncompressed visual data can still easily overload the IoT link, and the wireless spectrum is shared by numerous IoT devices, causing unstable link bandwidth. Mainstream codecs can reduce the traffic but at the cost of severe inference accuracy drops. Recent works on differentiable JPEG train the codec to tackle the damage to inference accuracy. But they rely on heuristic configurations in the loss function to balance the rate-accuracy tradeoff, providing no guarantee to meet the IoT bandwidth constraint. This paper presents AutoJPEG, a bandwidth-aware adaptive compression solution that learns the JPEG encoding parameters to optimize the DNN inference accuracy under bandwidth constraints. We model the compressed image size as a closed-form function of encoding parameters by analyzing the JPEG codec workflow. Furthermore, we formulate a constrained optimization framework to minimize the original DNN loss while ensuring the image size strictly meets the bandwidth constraint. Our evaluation validates AutoJPEG on various DNN models and datasets. In our experiments, AutoJPEG outperforms the mainstream codecs (like JPEG and WebP) and the state-of-the-art solutions that optimize the image codec for DNN inference.
Graph Partitioning-based Query Acceleration of Power Graph Database

As the topology of the power grid continues to increase, the time complexity of querying the shortest path in the grid GIS graph database increases dramatically, which restricts the topological analysis performance of the grid GIS graph database and makes the risk of power system paralysis increase considerably. In order to accelerate the query speed, we propose a Shortest-Path (SP) algorithm based on graph partition and routing nodes. The SP algorithm first divides the overall grid topology, followed by specifying the two nodes that form the interacting edges of the subgraph in the topology graph as routing nodes and making the routing nodes store the shortest paths between them and other nodes in the subgraph they belong to, and finally converting the overall shortest path query into a routing node query to optimize the shortest path query efficiency. The effectiveness of the proposed algorithm is verified by simulation calculations on a real dataset in a province.
Medical Image Zooming by Using Rational Bicubic Ball Function

This chapter deals with image processing in the specific area of image zooming via interpolation. The authors employ bivariate rational cubic ball function defined on rectangular meshes. These bivariate spline have six free parameters that can be used to alter the shape of the surface without needed to change the data. It also can be used to refine the resolution of the image. In order to cater the image zomming, they propose an efficient algorithm by including image downscaling and upscaling procedures. To measure the effectiveness of the proposed scheme, they compare the performance based on the value of peak signal-to-noise ratio (PSNR) and root mean square error (RMSE). Comparison with existing schemes such as nearest neighbour (NN), bilinear (BL), bicubic (BC), bicubic Hermite (BH), and existing scheme Karim and Saaban (KS) have been made in detail. From all numerical results, the proposed scheme gave higher PSNR value and smaller RMSE value for all tested images.
Multi-model Comparison for Classification of Medical Records using the BioBERT Models

The contents of medical records are extremely crucial for clinical diagnosis and medication, but there is often too much information to accurately find the key message when medical decision making. Therefore, the NLP (Natural Language Processing) and BERT (Bidirectional Encoder Representations from Transformers) models can be used to extract the main characteristics of the medical records from each patient. This study compares the performance of BERT, BioBERT and PubMedBERT for classification of four clinical divisions from medical records. After text preprocessing, modeling and testing, PubMedBERT model shows the best result with an accuracy 0.9, precision 0.81 and the weighted F1-score 0.8. The results of this study verify that PubMedBERT is the best classification model, which can be used to extract patient features to fully understand the key background information of patients, and to improve the medical decision making, and reduce medical disputes and unessential costs.
Security Evaluation of Compressible Image Encryption for Privacy-Preserving Image Classification Against Ciphertext-Only Attacks

The security of learnable image encryption schemes for image classification using deep neural networks against several attacks has been discussed. On the other hand, block scrambling image encryption using the vision transformer has been proposed, which applies to lossless compression methods such as JPEG standard by dividing an image into permuted blocks. Although robustness of the block scrambling image encryption against jigsaw puzzle solver attacks that utilize a correlation among the blocks has been evaluated under the condition of a large number of encrypted blocks, the security of encrypted images with a small number of blocks has never been evaluated. In this paper, the security of the block scrambling image encryption against ciphertext-only attacks is evaluated by using jigsaw puzzle solver attacks.
Art Rich: Place Your AR Artwork

We propose Art Rich, an artwork augmentation service that helps users choose artworks to decorate their personal space without vis-iting the art fairs or galleries. We recommend artworks that match the color scheme of their rooms. By developing it in a Unity environment, augmented services could be implemented, and using the k-means algorithm, the primary color in the user's room is extracted. The extracted color is compared to artworks' colors, and artworks with similar or complementary colors are recommended. In addition, by measuring the length of the area to place the artwork, users can determine the size of the artwork. This service allows users to place artworks in their space and even purchase them. This could meet the art tech needs of the MZ generation, who want to quickly select artworks within their budget without visiting places in person. From an artist's point of view, it functions as a platform by inserting a link to information about their artwork and exhibition history.
Comparing Twitter Data for Topic Modling, Clustering, and Predictive Analysis Using LSTM Model

Extraction topics to find the difference between the two datasets of information, as a rule, seems unused. Machine learning and the calculation of Natural language Process correction are used to analyze the ever-changing statistics of Twitter accessible online, including modelling processes for a longer time. Two datasets of content information were selected to evaluate comparisons based on specific statistical tests, such as quality and response, accuracy, and statistical tests for particular measurements, such as Portion F and Title. Twitter has become amongst the most often used online network site because anyone can easily publish information about their thoughts on a specific issue via a public message called a tweet. Twitter play an essential part in the prioritization of public life. It is necessary to know about the topic and domain on social media sites. This research work performs a topic modelling on Twitter data related to covid19. Two different data sets are discussed, and tweets are clustered through the k-mean clustering algorithm. Topics are also found in each cluster using the LDA technique. The clustered data sets are predictively analyzed through the LSTM model. The results show that the model achieves 96% accuracy.
Learning while Respecting Privacy and Robustness to Adversarial Distributed Datasets

Massive datasets are typically distributed geographically across multiple sites, where scalability, data privacy and integrity, as well as bandwidth scarcity typically discourage uploading these data to a central server. This has propelled the so-called federated learning framework where multiple workers exchange information with a server to learn a “centralized” model using data locally generated and/or stored across workers. This learning framework necessitates workers to communicate iteratively with the server. Although appealing for its scalability, one needs to carefully address the various data distribution shifts across workers, which degrades the performance of the learnt model. In this context, the distributionally robust op-timization framework is considered here. The objective is to endow the trained model with robustness against adversarially manipulated input data, or, distributional uncertainties, such as mismatches between training and testing data distributions, or among datasets stored at different workers. To this aim, the data distribution is assumed unknown, and to land within a Wasserstein ball centered around the empirical data distribution. This robust learning task entails an infinite-dimensional optimization problem, which is challenging. Leveraging a strong duality result, a surrogate is obtained, for which a primal-dual algorithm is developed. Compared to classical methods, the proposed algorithm offers robustness with little computational overhead. Numerical tests using image datasets showcase the merits of the proposed algorithm under several existing adversarial attacks and distributional uncertainties.
StyleVC: Non-Parallel Voice Conversion with Adversarial Style Generalization

Voice conversion converts the voice while maintaining the language information. It uses two samples to synthesize speech: the source sample is used for content, the target sample is used for style representation. Therefore, VC has been progressed to design information flow to disentangle content and style in a speech. However, separated representations are damaged while passing sparse subspace. Besides, VC models suffer from the training-inference mismatch problem: they only use one sample in training. Accordingly, the model extracts inappropriate content and style representation and generates low-quality speech during inference. To address the mismatch scenario problem, we propose a StyleVC, which utilizes adversarial style generalization. First, we propose style generalization, which captures global style representation and restricts the model from copying information. Second, we use a pitch predictor to estimate pitch information according to content and style representation. Third, we further use adversarial training to make the model generate more realistic speech. Finally, we demonstrate our proposed model can generate high-quality speech. The experimental results also show that the proposed StyleVC significantly outperforms to extract the desired features and improve audio quality during inference.
Sentiment Analysis on Tripadvisor Hotel Review using Named Entity Recognition

Sentiment analysis is useful for understanding and grouping emotions including positive and negative in writing using natural language processing techniques. One form of sentiment analysis can be seen from people's reviews of products or services provided by a company. Reviews are excerpts, interpretations, and comments. Review can be interpreted as a form of assessment someone to something. regular review given to a product or service. good and bad reviews, may affect the reputation of the hotel. Good review indicates that the quality of service products must be maintained by the hotel. while the reviews are bad signifies that the quality of the service product must be evaluated and repaired by the hotel. Tripadvisor is a website that focuses on in the tourism sector such as tourist attractions, hotels, and restaurants. everyone can see the service rating a tourist attraction through Tripadvisor. Data hotel visitor reviews found on Tripadvisor, only classified by rating only stars from a scale of 10 to 50. Hotel visitors also provide various reviews to share their experiences during visit the hotel. Like experience dine, stay overnight, swim in the pool, etc. To facilitate the classification process based on review of the visitor's experience, then this research was made. In this research carried out sentiment analysis to distinguish negative reviews and positive using named entity method recognition. In previous research, the accuracy generated from the named entity recognition method reach 90%.
A Systematic Review on Sentiment Analysis for The Depression Detection During Covid-19 Pandemic

Social media platforms such as Facebook, Twitter and Instagram are powerful tools to express sentiments and emotions across the globe. Researchers use sentiment analysis and its evaluation to reveal the positive, negative and neutral opinions associated with an individual or group. In this study, we have analysed the literature available on sentiment analysis based on various parameters such as publication count, year of publication, country and university wise production and keyword progression. Results reveal that sentiment analysis is a pre-owned field and researchers are contributing to this field since 2008. Collaboration among different countries and universities have also seen during this study with maximum contributions received during 2019. Further this study shows that 149 unique keywords are used by different researchers in their literature. 65 universities have contributed to the literature with the highest number of authors from India.
Establishing Cooperative Computation with Hardware Embassies

While recent cryptographic techniques enable cooperative multi-party client-server computations under mutual distrust, they also introduce an efficiency tradeoff. Hosting all of the computation from the different parties involved on one set of servers requires everyone to agree on which servers are trustworthy. On the other hand, keeping the computations truly distributed introduces significant delays because of the inherently latency-sensitive nature of the protocols involved. In this paper, we explore the architectural impact of a possible middle path to this problem: resource-poor but physically secure devices interacting with significant (but not mutually trusted) compute and storage resources. The idea is that a small and well-protected “Embassy” can serve as a plot of sovereign soil in an otherwise untrusted environment. Building on techniques from multiparty computation (MPC) we show how such an architecture, even when extremely limited in size, can leverage local network capabilities and asymmetries in cryptographic operations to perform more efficient interactive secure computations. Even with a client-side device $5\times$ slower, we show that common MPC applications can still be accelerated by $3\times$ on average. Moreover, we explore the potential for architectural changes to further support multi-party evaluation through the addition of dedicated evaluator hardware further improving performance $1.52\times$.
Graph Intention Neural Network for Knowledge Graph Reasoning

Reasoning over knowledge graph explores valuable information for amounts of tasks. However, most methods adopt the coarse-grained and single representation of each entity for reasoning, ignoring simultaneously processing various semantics contained in internal information and external information. On the one hand, the surrounding nodes and relations existing in the graph structure express the internal information of the entity, which contains abundant graph context information, but the extracted internal features are still limited. On the other hand, different scenarios as the external information focus on different aspects of the certain entity, meanwhile the external information should have message interaction with the internal information to learn the adaptive embedding, both of which are seldom considered by the existing methods. In this paper, we propose a Graph Intention Neural Network (GINN) for knowledge graph reasoning to explore fine-grained entity representations, which use external-intention and internal-intention simultaneously. For external-intention, a novel constructed matrix is used to calculate the triple-attention that determines the aggregated information to learn different embeddings adapting to the different scenarios. Furthermore, a communication bridge is leveraged to have message interaction between the external information and the internal information. For the internal-intention, the surrounding nodes and relations are integrated to update the entity embedding with the consideration of the interaction features between the external and internal information. The triple-attention can capture relevancy among the reasoning hops, which contributes to figuring out reasonable paths. We evaluate our approach on real-world datasets, achieving better performance compared to the state-of-the-art methods and showing plausible interpretability for the results.
A Novel Approach for Cross-Selling Insurance Products Using Positive Unlabelled Learning

Successful cross-selling of products is a key goal of companies operating within the insurance industry. Choosing the right customer to approach for cross-purchase opportunities has a direct effect on both decreasing customer churn rate and increasing revenue. Unlike sales data of general products, insurance sales data typically contains only a few products (e.g., private medical insurance, life insurance, etc), it is highly imbalanced with a vast majority of customers with no cross-purchasing information, highly noisy due to varying purchase behaviour between different customers, and has no ground truth for knowing if the majority customers are truly non-cross-sell customers or they are missed opportunities. These data challenges render the building of machine learning models for accurately identifying potential cross-sell customers extremely difficult. This paper proposes a novel approach to solve this challenging problem of cross-sell customer identification using Positive Unlabelled (PU) learning in conjunction with advanced feature engineering on customer demographic data and unstructured customer question-response texts through topic modelling. We implement a bagging approach to iteratively learn the positive samples (the confirmed cross-sells) alongside random sub-samples of the unlabelled set. The proposed approach is extensively evaluated on real insurance data that has been newly collected from a leading insurance company for this study. Evaluation results demonstrate that our approach can successfully identify new potential opportunities for likely cross-sell customers.
Improving Pre-trained Masked Autoencoder via Locality Enhancement for Person Re-identification

Person Re-identification (ReID) is a computer vision task of retrieving a person of interest across multiple non-overlapping cameras. Most of the existing methods are developed based on convolutional neural networks trained with supervision, which may suffer from the problem of missing subtle visual cues and global information caused by pooling operations and the weight-sharing mechanism. To this end, we propose a novel Transformer-based ReID method by pre-training with Masked Autoencoders and fine-tuning with locality enhancement. In our method, Masked Autoencoders are pre-trained in a self-supervised way by using large-scale unlabeled data such that subtle visual cues can be learned with the pixel-level reconstruction loss. With the Transformer backbone, global features are extracted as the classification token which integrates information from different patches by the self-attention mechanism. To take full advantage of local information, patch tokens are reshaped into a feature map for convolution to extract local features in the proposed locality enhancement module. Both global and local features are combined to obtain more robust representations of person images for inference. To the best of our knowledge, this is the first work to utilize generative self-supervised learning for representation learning in ReID. Experiments show that the proposed method achieves competitive performance in terms of parameter scale, computation overhead, and ReID performance compared with the state of the art. Code is available at https://github.com/YanzuoLu/MALE .