Reviewerly/exHarmony huggingface dataset:
QAPP: A quality-aware and privacy-preserving medical image release scheme

The direct release of medical image may face the dilemma: the privacy protection of medical images inevitably affects the visual quality of images. To balance medical image quality and privacy, this paper proposes a quality-aware and privacy-preserving medical image release scheme, QAPP, which effectively integrates the discrete cosine transform (DCT) with differential privacy (DP). Specifically, QAPP is composed of three phases. First, DCT is applied to each medical image to obtain its cosine coefficients matrix. Second, the original cosine coefficients matrix is compressed into k*k cosine coefficients matrix, which can retain the main features of each image. Third, the appropriate Laplace noise is injected into the formed k*k matrix to achieve differential privacy, and these noise-added coefficients are used to reconstruct the noise-added medical images through inverse DCT. Especially, considering there two error sources affecting the image quality in our work: the compression error caused by DCT, and the injected noise error caused by DP, Therefore, a selection function is proposed to determine the optimal compression dimension k, which can minimize the influence of these two errors to improve the visualization quality of the medical image. Subjective and objective image quality evaluation, and extensive experiments of image classification and segmentation using the real medical image dataset demonstrate that the proposed method QAPP can better balance medical image quality and privacy than other similar DP-based methods.
3DU–TA: A new multifractal model involving surface topography for separation of geochemical anomalies

A novel anomaly recognition technique was propounded to help reconcile mineralization-related geochemical patterns with topographic non-stationaries within the study area. The topography variations were characterized by elevation and slope factors of grid cells in a digital elevation model (DEM). In the first step of the technique architecture, the spatial U-statistics formulation was expanded for a 3D framework to integrate elevation-related information into geochemical measurements. Slope angles were then modeled across the cell-based geochemical map using DEM information to modulate planimetric areas into topographic attributes. Three-dimensional spatial U-values and topographic attributes were integrated in a multifractal model called 3DU–TA, providing power-law relations that aid in decomposition of geochemical patterns. The mathematical formulation established for the 3DU–TA model was implemented by programming in MATLAB platform and it was applied to geochemical data pertaining to a deposit-scale porphyry copper system. Core drilling data were used as benchmark to appraise the performance of the 3DU–TA model in the recognition and separation of metal enrichment levels from background and it was compared with the well-established concentration–area (C–A) fractal model. We defined mineralization rate Mr and normalized productivity Np as criteria for judging the integrity of anomalous geochemical patterns derived by the 3DU–TA model in reflecting subsurface mineralization. We also adapted a success-rate plot for deposit-scale experiments and the relevant areas under the curve (AUC) as gauges for quantitative evaluation of model relevance in reflecting subsurface metal enrichment. The derived values of Mr and Np imply that the geochemical populations decomposed with the 3DU–TA model, compared with those decomposed with the C–A model, yielded anomaly patterns that were more consistent with subsurface metallogenic realities. The success-rate curve for topography-enhanced results had an AUC = 0.940, which was higher than that for the C–A model (AUC = 0.867), indicating that the former revealed anomalies with substantially stronger spatial correlations with subsurface metal contents. Intuitive geochemical and quantitative metallogenic assessments suggest that the proposed multifractal modeling technique is capable of providing more practical insights into mineralization realities in geosystems with complex topographic variations.
Climate paleogeography knowledge graph and deep time paleoclimate classifications

The climate paleogeography, especially the climate classifications, helps to interpret the global and regional climate changes and intuitively compare the climate conditions in different regions. However, the application of climate classification in deep time (i.e., climate paleogeography) is prohibited due to the usually qualitatively constrained paleoclimate and the inconsistent descriptions and semantic heterogeneity of the climate types. In this study, a climate paleogeography knowledge graph is established under the framework of the Deep-Time Digital Earth program (DDE). The hierarchical knowledge graph consists of five paleoclimate classifications based on various strategies. The classifications are described and their strengths and weaknesses are fully evaluated in four aspects: "simplicity, applicability, quantifiability, and comparability". We also reconstruct the global climate distributions in the Late Cretaceous according to these classifications. The results are compared and the relationships among these climate types in different classifications are evaluated. Our study unifies scientific concepts from different paleoclimate classifications, which provides an important theoretical basis for the application of paleoclimate classifications in deep time.
Text-to-Image Generation Using Deep Learning

Text-to-image generation is a method used for generating images related to given textual descriptions. It has a significant influence on many research areas as well as a diverse set of applications (e.g., photo-searching, photo-editing, art generation, computer-aided design, image reconstruction, captioning, and portrait drawing). The most challenging task is to consistently produce realistic images according to given conditions. Existing algorithms for text-to-image generation create pictures that do not properly match the text. We considered this issue in our study and built a deep learning-based architecture for semantically consistent image generation: recurrent convolutional generative adversarial network (RC-GAN). RC-GAN successfully bridges the advancements in text and picture modelling, converting visual notions from words to pixels. The proposed model was trained on the Oxford-102 flowers dataset, and its performance was evaluated using an inception score and PSNR. The experimental results demonstrate that our model is capable of generating more realistic photos of flowers from given captions, with an inception score of 4.15 and a PSNR value of 30.12 dB, respectively. In the future, we aim to train the proposed model on multiple datasets.
Applying the Metaverse Platform and Contents in Practical Engineering Education

Recently, metaverse is rapidly expanding its area as a platform that can be applied to various fields.In particular, the fu nction that allows many users to interact in a three-dimensional space allows VR/AR-based educational content to be used a s a more advanced concept.Due to the nature of engineering education, it is often based on three-dimensional objects.In t he case of a three-dimensional object, it is difficult to explain through two-dimensional videos or documents, and it become s more difficult to express when the process of changing the object is included.The three-dimensional space of the metaver se can improve this difficulty based on real-time rendering.Another characteristic of engineering education is that there are many invisible elements.Although it is involved in the movement of objects due to electromagnetic fields, magnetic fields, and forces, it is the main reason for increasing learning difficulty because it is invisible.These problems can also help lear ning because they can be visually represented in the metaverse space.In this paper, the results of the establishment of the metaverse platform for engineering education and the real-time lecture contents produced based on it are described, and the applied results and lecture evaluation are discussed.Lectures using a total of 9 metaverse contents were conducted, and 9 0% of the positive lecture evaluation results were obtained.
Knowledge Enhanced Person-Job Fit for Talent Recruitment

As an essential task of talent recruitment, person-job fit aims to measure the matching degree between talent qualifi-cation and the job requirements of a position. Existing studies usually formulate this task as a long text matching problem with a focus on learning effective representations of both job postings and resumes. However, it is commonly known that there exists a semantic gap between textual job postings and textual resumes. Therefore, in this paper, we study how to improve person-job fit by bridging this semantic gap with the help of prior knowledge. To this end, we first design a distantly supervised skill extraction model to identify the skill entities from the given job postings and resumes using only unlabeled data and skill entity dictionaries. The identified skill entities will be used to construct a skill knowledge graph (KG) on the global corpus, which can provide the prior knowledge. Also, we propose a knowledge enhanced person-job fit approach for talent recruitment. Here, we model job postings and resumes as two graphs and fuse the prior external knowledge into the graph representation learning. Specifically, we first build the graphs from job posting and resume text. Then, we design a knowledge-aware graph encoder that can not only capture the contextual word relationships within each job posting or resume, but also incorporate the prior knowledge into node representation learning. In addition, we propose an interactive learning method to perform effective graph matching in both graph-level and node-level, respectively. Meanwhile, a multi-task learning strategy is introduced to facilitate the graph representation learning. Finally, extensive experiments conducted on real-world datasets have clearly validated the effectiveness of our approaches compared with state-of-the-art baselines.
The Stair Sketch: Bringing more Clarity to Memorize Recent Events

Data stream processing has become fundamental in computer science, with a wide range of applications, such as in databases, data mining, and security. Memorizing when an item appears in the data stream is one important task in stream processing. Because the older data is, the less value it has, memorizing recent events with higher accuracy is desirable. To achieve this, we propose a novel data stream processing structure named the Stair sketch. Our key idea is to organize the memory used by different time periods in the shape of stairs. We deploy the Stair sketch on Bloom filters, CM sketches, and CU sketches as case studies. Experiment results show that our approach outperforms state-of-the-art algorithms by more than 5× in accuracy while providing comparable efficiency. The source code of the Stair sketch is available at GitHub.
Enhancing Federated Learning with In-Cloud Unlabeled Data

Federated learning (FL) has been widely applied to collaboratively train deep learning (DL) models on massive end devices (i.e., clients). Due to the limited storage capacity and high labeling cost, there are always insufficient data stored and annotated on each client. Conversely, in cloud datacenters, there exist large-scale unlabeled data, which are easy to collect from public access (e.g., social media). Herein, upon the federated semi-supervised learning (FSSL) technology, we propose the Ada-FedSemi system, which leverages both on-device labeled data and in-cloud unlabeled data to boost the performance of DL models. Given the limited communication and massive quantity of the clients, in each training round, we decide to select partial clients to participate in FL, and their local models are aggregated by the parameter server (PS) to produce pseudo-labels for the unlabeled data, which are utilized to enhance the global model. Considering that the number of participating clients and the quality of pseudo-labels will have a significant impact on the training performance (e.g., efficiency and accuracy), we introduce a multi-armed bandit (MAB) based online algorithm to adaptively determine the participating fraction and confidence threshold during federated model training. Extensive experiments on benchmark models and datasets show that, given the same resource budget, the model trained by Ada-FedSemi achieves 3%-14.8 % higher test accuracy than that of the baseline methods. Besides, when achieving the same test accuracy, Ada-FedSemi saves up to 48% training cost, compared with the baselines.
SMD Anomaly Detection: A Self-Supervised Texture–Structure Anomaly Detection Framework

In electronic manufacturing, anomaly detection of surface mount devices (SMDs) through computer vision is an important task to control the production quality of SMDs. The difficulty of the detection is that some anomalous regions on the surfaces of SMDs are very minor and with variable shapes, which leads to poor detection efficiency. To solve this problem, based on the assumption that normal samples can be reconstructed more accurately than anomalous samples, a self-supervised image anomaly detection framework with a multi-scale two-branch feature fusion strategy is proposed. Specifically, it adopts autoencoder as the basic framework, and to enhance the reconstruction error between input anomalous samples and the reconstructed ones, a self-supervised learning task of reconstructing images is introduced to have the model neglect the encoding of the suspected anomalous regions found by a contextual attention mask module. Meanwhile, a multi-scale feature fusion strategy is developed to fuse texture and structure features in the decoder to reconstruct samples. Moreover, a multi-level anomalous score criterion is proposed to enlarge the scores for the samples with very minor anomalies. At last, an SMD-Capacitor anomaly detection dataset (SMDC-DET) is built to evaluate the proposed method. The experiments show that the proposed method achieves an average AUC accuracy of 98.82%, much better when compared to the start-of-art existing anomaly detection methods.
Toward User-Centric Resource Allocation for 6G: An Economic Perspective

This article presents an economics-inspired solution for realizing user-centric resource allocation for 6G. Particularly, the user-centric criterion is proposed based on users' subjective values, that is, quality of experience (QoE) on services, thereby satisfying the most desired demands of all users. Furthermore, the price is utilized as the indicator of users' values, and the total value of information conveyed to users is prioritized so that the user-centric resource allocation is economically efficient. In addition, a Cybertwin-assisted core network is introduced as a practical solution for implementing user-centric resource allocation. A resource market with appropriate market rules is developed from auction theory. It is demonstrated through simulations that our proposed user-centric resource allocation solution can convey more personalized value to users.
Realization of natural language processing and machine learning approaches for <scp>text‐based</scp> sentiment analysis

Abstract The leading intention of the current paper is to review the research work accomplished by various researchers to achieve sentiment analysis on the text and to elaborate on natural language processing (NLP) and various machine learning algorithms used to evaluate textual sentiments. In this study, primitive cases are considered that used crucial algorithms, and knowledge that can be opted for sentiment analysis. A survey of the work that has been done till now is conducted observing the results and outcomes concerning varying parameters of various researchers who worked on previously existing as well as novel and hybrid algorithms opting legion methodologies. The fundamental algorithms like Support Vector Machine (SVM), Bayesian Networks (BN), Maximum Entropy (MaxEnt), Conditional Random Fields (CRF) and Artificial Neural Networks (ANN) are also discussed to achieve practice percentage and accuracy score in the field of NLP, sentiment analysis and text analytics. Various other novel approaches and algorithms like CNN, LSTM, KNN, K*, K‐means, K‐means++, SOM and ENORA, along with their limitations and the performance metrics providing accuracies for major open data sets are also analyzed.
What factors affect the UX in mobile apps? A systematic mapping study on the analysis of app store reviews

Researchers and practitioners are becoming aware of the importance of User eXperience (UX) in mobile app development. Developing merely usable apps became insufficient to meet users’ needs, requiring developers to focus on promoting pleasurable experiences to get a competitive advantage. To that end, it is crucial to understand what factors can lead to positive or negative UX. In this scenario, app store reviews emerged as a valuable source to address UX issues from analyzing several self-reports of end-users experiences in the wild. Many researchers have proposed approaches to analyze such reviews and investigate the effect of factors related to the user (e.g., gender, culture) and the app (e.g., bugs, features) on UX. However, the fragmentation of the results into various studies makes it difficult to draw conclusions that can support the development process and advance the research in the field. This paper presents a systematic mapping study to address publications that analyze app store reviews and identify the factors affecting UX reflected on users’ ratings and sentiment. From 25 accepted publications, we extracted 31 factors and their associated polarities. We also identified research gaps and future work opportunities with implications for practitioners and researchers.
Time- and Space-Efficient Regular Path Queries

We introduce a time- and space-efficient technique to solve regular path queries over labeled (RDF) graphs. We combine a bit-parallel simulation of the Glushkov automaton of the regular expression with the ring index introduced by Arroyuelo et al., exploiting its wavelet tree representation in order to efficiently reach relevant states of the product graph. Our algorithm is able to simultaneously process several automaton states, as well as several graph nodes/labels. Our experiments show that our approach uses 3–5 times less space than existing state-of-the-art systems, while generally outperforming them in query times (nearly 3 times faster than the next best, on average).
Multi-Attention Fusion and Fine-Grained Alignment for Bidirectional Image-Sentence Retrieval in Remote Sensing

Dear editor, Cross-modal retrieval in remote sensing (RS) data has inspired increasing enthusiasm due to its merit in flexible input and efficient query. In this letter, we address to establish semantic relationship between RS images and their description sentences. Specially, we propose a multi-attention fusion and fine-grained alignment network, termed MAFA-Net, for bidirectional cross-modal image-sentence retrieval in RS. While multiple attention mechanisms are fused to enhance the discriminative ability of visual features for RS images with complex scenes, fine-grained alignment strategy is introduced to study the hidden connection between RS observations and sentences. To validate the capability of MAFA-Net, we leverage four captioning benchmark datasets with paired RS images and descriptions, i.e., UCM-Captions, Sydney-Captions, RSICD and NWPU-Captions. Experimental results on the four datasets demonstrate that MAFA-Net can yield better performance than the current state-of-the-art approaches.
Encrypted Network Traffic Classification using Self-supervised Learning

Network traffic classification is used in many applications including network provisioning, malware detection, resource management, and so on. In modern networks, use of encrypted protocols is a norm rather than an exception. Existing network traffic classification techniques fall short in working with encrypted traffic. Although deep learning based techniques have been shown to perform well in the case of encrypted traffic classification, they require an abundance of labeled data to achieve high accuracy. However, labeled data is rarely available in sufficient volumes in real network settings as they require domain experts to annotate data with labels. Therefore, in this paper, we propose a self-supervised approach that can achieve high accuracy on encrypted network traffic classification with a few labeled data. The proposed method is evaluated on three publicly available datasets. The empirical result shows that our method not only achieves high accuracy on encrypted traffic but also has the ability to apply the acquired knowledge on a different dataset. In our experiments, our method outperforms the state-of-the-art baseline methods by ~3% in terms of accuracy even with a much lower volume of labeled data.
The 4-Adic Complexity of Quaternary Sequences of Even Period With Ideal Autocorrelation

The purpose of this paper is to determine the 4-adic complexity of the balanced quaternary sequences of period 2(2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</sup> −1) with ideal autocorrelation defined by Jang et al. (ISIT, pp. 278-281, 2009). Results show that the 4-adic complexity of such sequences is large enough to resist the attack of the rational approximation algorithm for feedback with carry shift registers.
Proportional Fair Clustered Federated Learning

This paper studies federated learning (FL) with non-identical data generating distributions and under the notion of proportional fairness. Users are partitioned into disjoint clusters based on their data distributions, and a distinct model is learnt for each cluster. Different than previous work that considered clustering to optimize personalized learning performance, here clustering is inspected under the disparate impact doctrine which requires that protected classes (e.g., those of certain race or gender) have representations in every cluster that are approximately equal to their representation in the overall set of users. A family of iterative algorithms that balance the learning performance and proportional fairness through cluster assignments as randomized functions of the learning losses is proposed. The trade-off induced by our algorithms between accuracy of cluster estimation and the introduced randomization level is characterized. The proposed algorithm is examined on a real dataset to evaluate its performance.
Singleton-type bounds for list-decoding and list-recovery, and related results

We prove a new Singleton-type upper bound for list-decodable codes, which improves upon the previously known bound by roughly a factor of 1/L, where L is the list size. We also prove a Singleton-type upper bound for list-recoverable codes, which to the best of our knowledge, is the first such bound. Then, we apply these results to obtain new lower bounds on the list size of list-decodable or recoverable codes with rates approaching capacity, that are optimal up to a multiplicative constant.Moreover, we show that for a wide range of parameters, list-decodable nonlinear codes can strictly outperform list-decodable linear codes. This is achieved by a novel connection between list-decoding and the notion of sparse hypergraphs in extremal combinatorics. Lastly, we show that list-decodability or recover-ability of codes implies in some sense good unique decodability.The full version of the paper is accessible at [1].