Reviewerly/exHarmony huggingface dataset:
A Distributed Architecture for Secure Delegated Quantum Computation

In this paper, we propose a distributed secure delegated quantum computation protocol, by which an almost classical client can delegate a (dk)-qubit quantum circuit to d quantum servers, where each server is equipped with a 2k-qubit register that is used to process only k qubits of the delegated quantum circuit. None of servers can learn any information about the input and output of the computation. The only requirement for the client is that he or she has ability to prepare four possible qubits in the state of (|0⟩+eiθ|1⟩)/2, where θ∈{0,π/2,π,3π/2}. The only requirement for servers is that each pair of them share some entangled states (|0⟩|+⟩+|1⟩|-⟩)/2 as ancillary qubits. Instead of assuming that all servers are interconnected directly by quantum channels, we introduce a third party in our protocol that is designed to distribute the entangled states between those servers. This would simplify the quantum network because the servers do not need to share a quantum channel. In the end, we show that our protocol can guarantee unconditional security of the computation under the situation where all servers, including the third party, are honest-but-curious and allowed to cooperate with each other.
SLAM: Efficient Sweep Line Algorithms for Kernel Density Visualization

Kernel Density Visualization (KDV) has been extensively used in a wide range of applications, including traffic accident hotspot detection, crime hotspot detection, disease outbreak detection, and ecological modeling. However, KDV is a computationally expensive operation, which is not scalable to large datasets (e.g., million-scale data points) and high resolution sizes (e.g., 1920 x 1080). To significantly improve the efficiency for generating KDV, we develop two efficient Sweep Line AlgorithMs (SLAM), which can theoretically reduce the time complexity for generating KDV. By incorporating the resolution-aware optimization (RAO) into SLAM, we can further achieve the lowest time complexity for generating KDV. Our extensive experiments on four large-scale real datasets (up to 4.33 million data points) show that all our methods can achieve one to two-order-of-magnitude speedup in many test cases and efficiently support KDV with exploratory operations (e.g., zooming and panning) compared with the state-of-the-art solutions.
Zero-shot unsupervised image-to-image translation via exploiting semantic attributes

Recent studies have shown remarkable success in unsupervised image-to-image translation. However, if there is no access to enough images in target classes, learning a mapping from source classes to the target classes always suffers from mode collapse, especially the zero shot case, which limits the application of the existing methods. In this work, we propose a zero-shot unsupervised image-to-image translation framework to address this limitation, by effectively associating categories with their side information like attributes. To generalize the translator to previously unseen classes, we introduce two strategies for exploiting the semantic attribute space. First, we propose to preserve semantic relations to the visual space for effective guidance on where to map the input image. Second, expanding attribute space is introduced by utilizing attribute vectors of unseen classes, which alleviates the mapping bias for unseen classes. Both of these strategies encourage the translator to explore the modes of unseen classes. Quantitative and qualitative results on different datasets validate the effectiveness of our proposed approach. Moreover, we demonstrate that our framework can be applied to fashion design task. The code is available at https://github.com/cyq373/ZUNIT.
Exploring Approaches for Heterogeneous Transfer Learning in Dynamic Networks

Maintaining machine-learning models for prediction of service performance is challenging, especially in dynamic network and cloud environments where route changes occur, and execution environments can be scaled and migrated. Recently, transfer learning has been proposed as an approach for leveraging already learned knowledge in a new environment. The challenge is that the new environment may be significantly different from the one the model is trained in, and transferred from, with respect to data distributions and dimensionality.In this paper, we introduce heterogeneous transfer learning in the context of dynamic environments and show its efficiency in predicting service performance. We propose two heterogeneous transfer-learning approaches and evaluate them on several neural-network architectures and scenarios. The scenarios are a natural consequence of network and cloud infrastructure reorchestration. We quantify the transfer gain, and empirically show positive gain in a majority of cases for both approaches. Furthermore, we study the impact of neural-network configurations on the transfer gain, providing tradeoff insights. The evaluation of the approaches is performed using data traces collected from a cloud testbed that runs two services under multiple realistic load conditions.
Persian Stance Detection with Transfer Learning and Data Augmentation

With increasing public access to social media, many dubious and inaccurate content is being generated and shared for profitable targets. This content is generated to attract audiences, increase revenue, impact people’s decisions, and influence important events such as political elections. Manually, detecting this news is very time-consuming, costly, and tedious, so the automatic detection of this content has attracted the attention of many natural language processing researchers. In this research, we have investigated the effect of using EDA data augmentation methods and the ParsBERT pre-training model to solve the problem of scarcity and lack of annotated datasets in the Persian stance detection task The results of this study indicate that we can identify the stance of news to the specific claim better than previous works with the help of data augmentation methods, content-based representation of the data, and the ParsBERT model.
Image Steganalysis based on Pretrained Convolutional Neural Networks

the process of identifying the presence of secret information in cover images is known as image steganalysis. As a result, classifying an image as a cover image or a stego image might be considered a classification task. The majority of steganalysis approaches that rely on deep learning are effective. Deep learning technology can identify and extract features mechanically using deep networks, allowing steganalysis technology to eliminate the need for specialist knowledge. However, Deep learning model training is tough and takes a large amount of processing time and information. Therefore, pre-trained CNN such as AlexNet model were used as feature extractors to save time during training. Therefore, this research presented an image steganalysis method based on AlexNet CNN Model. There are 3 steps make up the proposed image steganalysis method: Firstly, Data collection and preparation. Secondly, AlexNet model are used for extract Distinctive features. Lastly, the feature vector is then utilized to train the Random forest (RF) classifier in order to detect the binary classification (Cover/Stego). The experimental results under IStego100K database show that the proposed method accuracy is 99%. The properties of AlexNet models can be deduced to be useful and concise to classify using RF. In compared to previous techniques, the presented method outperformed them.
Hybrid Recommender System using K-means Clustering

Recommender architectures imitate people's decisions in order to advise goods to purchase or investigate. They've evolved into critical programs in digital commerce and information access, disseminating clues that successfully trim large statistical regions so that buyers are guided to the things that best fit their needs and preferences. For acting guidance, a variety of methodologies have been proposed, including content-based, collaborative, information-based, and other approaches. Those strategies have been blended in hybrid recommenders on occasion to improve performance. This project examines the landscape of genuine and practical hybrid recommenders and offers a very specific hybrid device for movie recommendation that blends content-based with collaborative filtering using kmean clustering algorithm. Furthermore, it has been demonstrated that meaningful rankings obtained from the device's content-based database improve the efficacy of collective filtering.
A Secure IoT and Cloud Computing-Enabled e-Health Management System

Utilization of the Internet of Things (IoT) technology is virtually ubiquitous across various life disciplines. When the IoT is integrated into e-health system to enable more real-time on-demand services, it brings significant convenience to the physicians and patients. However, the risk of encountering unreliable information and potential security threat is promptly raised at the same time. Therefore, in this research, we establish a secure IoT and cloud computing-enabled e-health management system in which a distinctive authentication scheme is adopted. The proposed system ensures all major security requirements, such as confidentiality, entity anonymity, repudiation tracking, data integrity, and innate resistance to man-in-the-middle, location spoofing, and replay attacks. Moreover, our system can effectively be executed on low-powered processing units and simultaneously guarantees greater security than historically available alternatives.
Knitting Access: Exploring Stateful Textiles with People with Disabilities

Wearables are often a primary means of collecting data on the body and in-situ. The data collected upon wearables can shape or record interactions in real time, prompting practices like self-care and reflection. In this work, we became intrigued by textile structures that were non-digital but in themselves “stateful”. We explored how these textile interfaces can fit meaningfully into the lives of people with disabilities as sensors and display. Our study revealed interesting practices that emerged for self-tracking that were qualitatively unique in their close relationship to the body and deeply physical modes of engagement. Our findings offer insights into (1) qualities of textile interfaces that are important to people with disabilities, (2) new forms of data that people found to be worthwhile in tracking, and (3) knitted interfaces for sensing and display.
A Delphi Study to Develop Scenario-Based Assessment Framework of Mathematical Attitude and Practice

In this study, a Delphi survey was conducted to generate the framework of a mathematics competency, called Attitude and Practice. Sixteen participants in the two consecutive rounds of the Delphi survey included mathematics education expert in student affect or educational psychology, and in-service mathematics teachers with a master’s degree or higher. We first established the definition of Attitude and Practice and identified their components while securing validity evidence from the Delphi survey results. Attitude and Practice are the competency to engage in mathematical activities with the joy and passion of doing mathematics as an individual or a group. Next, we characterized the components of Attitude and Practice as follows: (1) pleasure of learning mathematics, including interest, self-efficacy, and meta-affect (2) passion for mathematics including perseverance and seeking challenges, (3) doing mathematics together, including resolving conflicts and motivation for collaboration, and (4) global citizenship and enjoying mathematics culture. We defined and validated the specific components with the literature review and the Delphi survey responses. The results of this study will serve as a foundation of research to develop scenario-based assessments for students’ attitudes and practices. Furthermore, the framework established in this study can provide insights into the evaluation of mathematics competency of Attitude and Practice regarding Korean mathematics educators who have experienced difficulties in defining and assessing Attitude and Practice.
Visual Drone Detection In Static Complex Environment

Drone applications are gaining traction in both lawful and unlawful fields. Different techniques have been introduced to monitor the sky horizon and provide protection for high-risk areas. Electro-Optical (EO) sensors for drone detection are promising from a cost-to-quality perspective. However, visual drone detection faces several difficulties such as the differentiation between the other flying objects and the necessity of real-time detection. This paper introduces a visual drone detection approach based on captured video camera for a static background. The proposed method is divided into two phases that are detection phase and the enhanced detection by tracking phase. For the detection phase, it employs the background subtraction method followed by the CNN classifier. Moreover, the tracking phase handles the missed detection tracks. The experiment results show that the proposed method has a significant accuracy and low computational power performance.
On Scalable Computation of Graph Eccentricities

Given a graph, eccentricity measures the distance from each node to its farthest node. Eccentricity indicates the centrality of each node and collectively encodes fundamental graph properties: the radius and the diameter --- the minimum and maximum eccentricity, respectively, over all the nodes in the graph. Computing the eccentricities for all the graph nodes, however, is challenging in theory: any approach shall either complete in quadratic time or introduce a 1/3 relative error under certain hypotheses. In practice, the state-of-the-art approach PLLECC in computing exact eccentricities relies heavily on a precomputed all-pair-shortest-distance index whose expensive construction refrains PLLECC from scaling up. This paper provides insights to enable scalable exact eccentricity computation that does not rely on any index. The proposed algorithm IFECC handles billion-scale graphs that no existing approach can process and achieves up to two orders of magnitude speedup over PLLECC. As a by-product, IFECC can be terminated at any time during execution to produce approximate eccentricities, which is empirically more stable and reliable than KBFS, the state-of-the-art algorithm for approximately computing eccentricities.
Form Follows Mental Models: Finding Instantiations of Image Schemas using a Design Research Approach

Applying image schema theory to the design of user interfaces has been shown to lead to more intuitive, inclusive, and innovative interaction. We propose that this success can be transferred to the design of data physicalisations (dataphys). But currently designers didn't use image schemas purposefully because the documentation of image schemas is too bulky to apply in practice. Here we propose an image schema toolkit as a more concise instrument to support dataphys design processes. In this work we report on a research-through-design approach aimed at finding appropriate instantiations of image schemas to include in the toolkit. In an iterative process including analysis, synthesis, and evaluation, we created image schema icons, tokens, and interactive objects. Through the design and evaluation of these artefacts we generated knowledge about image schemas, how to instantiate them in visual and physical ways and how to organize them in a toolkit.
A Probabilistic Chaotic Image Encryption Scheme

This paper proposes a probabilistic image encryption scheme that improves on existing deterministic schemes by using a chaining mode of chaotic maps in a permutation-masking process. Despite its simplicity, the permutation phase destroys any correlation between adjacent pixel values in a meaningful image. The masking phase, however, modifies the pixel values of the image at hand using pseudorandom numbers with some other initiated random numbers so that any slight change in the plain image spreads throughout the corresponding cipher image. These random numbers ensure the generation of distinct cipher images for the same plain image encryption, even if it is encrypted multiple times with the same key, thereby adding some security features. Simulations show that the proposed scheme is robust to common statistical and security threats. Furthermore, the scheme is shown to be competitive with existing image encryption schemes.
Economic Development Analysis of China’s Coastal Industries Based on Deep Learning and SDA Model Weighting

China’s coast is the main spatial carrier and important geographical unit for future industrial development in China. We propose a weighted average decomposition method based on three basic problems in the theory of SDA model decomposition method, investigate the relevant properties of this method, and prove that two decomposition methods widely used in the literature—bipolar decomposition method and midpoint power decomposition method—are the approximate solutions of this decomposition method. Based on the idea of the Wasserstein distance algorithm for machine learning, the Wasserstein distance algorithm and its solution are improved by using the matrix expansion Sinkhorn algorithm and the entropy regularization constraint method, and the industry copolymerization index is constructed through hypothesis testing and Monte Carlo simulation to measure the level of industry copolymerization in coastal China. The results show that the level of industry copolymerisation within the same quantile industry is greater than that between industries across quantile industries in China’s coasts.
Quantum Bit Retransmission Using Universal Quantum Copying Machine

Quantum internet, which is expected to be a combination of quantum and classical networks, promises to provide information-theoretic security for data exchange. Classical networks have well-established protocols for reliable end-to-end transmission that implicitly make use of duplicating classical bits. However, quantum bits (qubits) cannot be copied due to the no-cloning theorem. In this paper, we take advantage of the principle of creating imperfect clones using a Universal Quantum Copying Machine (UQCM) and propose the Quantum Automatic Repeat Request (QARQ) protocol, inspired by its classical equivalent. A simulation platform has been developed to study the feasibility of QARQ. Results show that our proposal is well suited for applications that are compatible with low fidelity requirements.
UV3D: Underwater Video Stream 3D Reconstruction Based on Efficient Global SFM

With the increasing demand for underwater resource exploration, three-dimensional (3D) reconstruction technology is important for searching for lost underwater civilizations, underwater shipwrecks, or seabed structures. However, faced with the limitations of underwater unmanned systems in terms of energy, bandwidth, and transmission delay, 3D reconstruction technology based on video streams as direct data will not work well. We propose a terminal image processing strategy to save data transmission time and cost and to obtain 3D scene information as soon as possible. Firstly, we propose an adaptive threshold key frame extraction algorithm based on clustering, which extracts key frames from the video stream as structure from motion (SFM) image sequences. On this basis, we enhance the underwater images with sufficient and insufficient illumination to improve the image quality and obtain a better visual effect in the 3D reconstruction step. Additionally, we choose global SFM to construct the scene and propose a faster rotation averaging method, the least trimmed square rotation averaging (LTS-RA) method, based on the least trimmed squares (LTS) and L1RA methods. It is proven to reduce 19.97% of the time through experiments. Finally, our experiments demonstrate that the dense point cloud saves about 70% of the transmission cost compared to video streaming.
Finite Semi-group Modulo and Its Application to Symmetric Cryptography

In this paper, the application of finite semigroup structure S is studied.In particular, the study uses finite semi-group modulo n         , p n FS to generate Mutually Orthogonal Latin Squares (MOLS) and applied same to symmetric cryptography.It is shown in the study that for any 1  n , the maximum number of mutually orthogonal Latin squares in a finite semi-group modulo n          , p n FS  , 1 .
Clinical time-to-event prediction enhanced by incorporating compatible related outcomes

Accurate time-to-event (TTE) prediction of clinical outcomes from personal biomedical data is essential for precision medicine. It has become increasingly common that clinical datasets contain information for multiple related patient outcomes from comorbid diseases or multifaceted endpoints of a single disease. Various TTE models have been developed to handle competing risks that are related to mutually exclusive events. However, clinical outcomes are often non-competing and can occur at the same time or sequentially. Here we develop TTE prediction models with the capacity of incorporating compatible related clinical outcomes. We test our method on real and synthetic data and find that the incorporation of related auxiliary clinical outcomes can: 1) significantly improve the TTE prediction performance of conventional Cox model while maintaining its interpretability; 2) further improve the performance of the state-of-the-art deep learning based models. While the auxiliary outcomes are utilized for model training, the model deployment is not limited by the availability of the auxiliary outcome data because the auxiliary outcome information is not required for the prediction of the primary outcome once the model is trained.
DRISHYAM: An Interpreter for Deaf and Mute using Single Shot Detector Model

There have been extensive studies in several fields that indicate hearing impairment and inability to verbally communicate lead to inequality of opportunity and even difficulties in everyday life. Despite being a very useful medium of communication for deaf and mute people, sign language has no meaning for someone who does not understand it. Identifying these hand gestures can be accomplished in two ways. One method of identifying is by static images, and another is by dynamic gestures. Upon reviewing the previous research, many limitations became apparent. In the existing system, high accuracy rates could be obtained when static images were fed, but accuracy rates decreased significantly when dynamic input was fed. With dynamic input, some systems achieved good accuracy rates, but their scope was inadequate. Hence as a solution to these difficulties, a system is proposed which acts as a medium for the deaf and mute communities to communicate with ordinary people who are unable to understand sign language. As an input method, the system offers two options. First, video frames are retrieved from the camera, and then processed and passed through the model to generate the text output. Second, the input is fetched as a word from a naive user without any prior knowledge of sign language. Using the segmented words from the database, the system will search for relevant video frames matching the segmented words and combine these video frames to produce a single video feed as an output. Thus the communication gap between the deaf-mute and a person with normal comprehension will be reduced.