Reviewerly/exHarmony huggingface dataset:
Watermark Removal Scheme Based on Neural Network Model Pruning

In recent years, due to the rapid development of information technology, machine learning is widely used in various fields. Training deep neural network models is a very expensive process, which requires a lot of training data and hardware resources. Therefore, DNN models can be considered the intellectual property rights of model owners and need to be protected. More and more watermarking algorithms have been studied to embed into neural network models to protect the ownership of the models. At the same time, to test the robustness of the watermark, watermarking attack algorithms have emerged. In this paper, we firstly find the unexpected sensitivity of watermarked models, that is, they are more susceptible to adversarial disturbances than unwatermarked models, and then propose a model repair method based on neural network model pruning. By pruning some sensitive neurons to remove the watermark, the success rate of the watermark can be reduced to a certain extent, and on this basis, it verifies that it can effectively avoid model ownership detection.
Bidirectional Knowledge-Aware Attention Network over Knowledge Graph for Explainable Recommendation

Now recommendation systems are introduced into various online applications to help users find the content they want from massive data. Although the recommendation method based on collaborative filtering can adapt to the change of recommendation scenarios, the recommendation heavily depends on interaction data, so the recommendations are seriously affected by data sparsity. To alleviate the above problem researchers introduce knowledge graph as side information into the recommendation system. By exploring the rich entity information and relation information in knowledge graph, we can enrich the representations of user and item and enhance interpretability of recommendation system. However, some recommendation methods only carry out unidirectional knowledge propagation when mining knowledge information, which makes it difficult to capture higher-order knowledge information when the knowledge graph is sparse. Meanwhile, most recommendation models do not make full use of the relations between users, items and entities to enhance the interpretability of recommendations. Based on the reasons above, we design a novel bidirectional knowledge-aware attention network framework for explainable recommendation named BKANE, which integrates interaction information and high-order knowledge information, completing the recommendation in an end-to-end manner. The experimental results on three real datasets show that BKANE is significantly better than the state-of-the-art baselines in terms of recommendation performance. Also, the graphical explanation form can provide developers and users with a reasonable explanation of the model and recommendations.
Measuring citizen satisfaction with e-government services by using sentiment analysis technology

The state provides various services to citizens. Whereas the provision of these services electronically is an essential part of the e-government strategy, it is not enough. Thus, ensuring citizen satisfaction from these services is one of the significant responsibilities of the state. Considering it, in the paper we propose a method for measuring citizen satisfaction with e-services provided by government agencies and determining the rating of these agencies in society. In the proposed method, the citizen comments written for each service were sentimentally analysed, and consequently, the polarity score for each comment was evaluated. The average satisfaction score of the citizens for each service is evaluated according to the polarity score of the comments and the influence of the citizens who made these comments. The influence weight of the citizen in society is determined on the basis of the analysis of his social media accounts. Finally, using the citizens' average satisfaction score for e-services, the average satisfaction score of each government agency is evaluated and the rating of these institutions in society is determined.
Community Colleges Perspectives

Community and Technical Colleges are two-year college institutions in the US that have been at the intersection of many educational initiatives and partnerships, such as K-12, four-year colleges, and workforce/industry partnerships. Community Colleges are notable for their diversity and heterogenous student population, but also for the workforce's influence to disseminate specific curriculum requirements. Therefore, community colleges offer a large variety of computing programs, including computer science degrees, notably Associate in Science (AS) and Arts (AA) (a.k.a. academic transfer degrees). Ideally, these transfer degrees align learning outcomes, objectives, and competencies to the ACM/IEEE curricular guidelines, mainly to create two-year programs, align curricula and ease transferability to four-year colleges. Due to the nature of these degrees, computer science education at the lower-division undergraduate level faces curriculum challenges and emerging opportunities while adopting curricular guideline recommendations. Some of these include the number of transfer hours, the scope of topics in computer science, and the most recent ABET accreditation efforts at the community college level. This discussion aims to identify and recognize additional significant challenges that computer science programs in community colleges face, particularly while adopting curricular guidelines recommendations, such as CS 2023. Moreover, the discussion on this matter will influence the recognition of curricular guidelines and considerations for two-year programs in computer science. Compilation of community colleges' viewpoints and feedback during this conversation will be considered part of the curricular practices "Community Colleges Perspectives: From Challenges to Considerations in Curricula Development" document, featured in the new version of the CS2023: ACM/IEEE-CS/AAAI Computer Science Curricula.
Strype

Python is a very popular programming language in high schools for teaching programming. It is often used as the first text-based programming language after earlier teaching with blocks. This shift to textual syntax can prove to be a bump in the road for learners. Frame-based editing is a hybrid between blocks and text, which can thus smooth this transition. Strype is a frame-based editor for Python that can now be used to program entirely within the web browser. Strype is free and publicly available (at https://strype.org/), and in this demo we will show how it can be used to write and execute code.
FedCLUS: Federated Clustering from Distributed Homogeneous Data

An emerging trend in the field of machine learning presently is Federated Learning (FL) that enables to build a global model without sharing private data distributed among multiple data owners. Extensive research conducted on FL is mostly restricted to deep learning methods utilizing labeled data. However, voluminous big data generated today is unlabeled. The paper presents a federated clustering method, called FedCLUS, that allows to learn clusters from distributed homogeneous data without sharing it. The distinguishing characteristic of the FedCLUS method is its ability to retain, merge and discard original clusters sent by data owners. Also, FedCLUS can identify any number of arbitrary shaped clusters that are robust to outliers. The feasibility and performance of the FedCLUS is tested on many diverse datasets.
SemKnowNews: A Semantically Inclined Knowledge Driven Approach for Multi-source Aggregation and Recommendation of News with a Focus on Personalization

The availability of digital devices has increased throughout the world exponentially owing to which the average reader has shifted from offline media to online sources. There are a lot of online sources which aggregate and provide news from various outlets but due to the abundance of content there is an overload to the user. Personalization is therefore necessary to deliver interesting content to the user and alleviate excessive information. In this paper, we propose a novel semantically inclined knowledge driven approach for multi-source aggregation and recommendation of news with a focus on personalization to address the aforementioned issues. The proposed approach surpasses the existing work and yields an accuracy of 96.62%
Feature Selection Impact on Time Series Problems for Solar Radiation Forecasting

Solar Radiation (Rs) received on our planet is measured in billions of watts. It is introduced into power grid networks and used for various applications. Even though it is still challenging to maintain a perfect balance between the production and consumption of electricity because of the radiation's intermittent and unpredictable nature. Accurate prediction of sun rays is thus critical in the solar industry. Many Machine Learning (ML) approaches and preprocessing models are used for Rs forecasting and complexity reduction to reduce these concern. In this paper, we present a model that shows the impact of feature selection approaches on Rs forecasting based on time series lag values. We use Recursive Feature Elimination (RFE) with random forest (RF), Decision Tree (DT), Logistic Regression (LR), Classification and Regression Tree (CART), Person (Pr) and Gradient Boosting models (GBM). The results show that our proposed approach gives reliable results in terms of some relevant parameters.
A Combined Approach of Computer Vision and NLP for Documents Data Extraction

In recent years, computer vision and NLP became a major topic in computer studies drawing attention from researchers and computer scientists from the entire world. Deep learning technologies present the most outstanding performance in computer vision and NLP issues. In this article, we present an approach based on computer vision and NLP, for documents data extraction, we start from collecting data to predicting the documents objects, while using the NLP, meanwhile, we train the model based on NER technologies, to make the system intelligent.
An Image Encryption Scheme Based on DNA Sequence Operations and Chaotic System

This work presents a new method for encrypting medical and color images. This technique is based on the use of genetic operations acting on DNA sequences controlled by chaotic maps. To overcome the problem of uniform images, the original image is confused with a chaotic image. Then to prepare the image for genetic operations, a passage to the nucleotide sequence is established. The second phase of image encryption consists of applying the mutation and inversion operations. Several simulations performed on a large number of images of different sizes and formats ensure that our method is not subject to any known attacks.
Assessing the Governance of Digital Contact Tracing in Response to COVID-19: Results of a Multi-National Study

This paper describes the results of a multi-country survey of governance approaches for the use of digital contact tracing (DCT) in response to the COVID-19 pandemic. We argue that the countries in our survey represent two distinct models of DCT governance, both of which are flawed. The "data protection model" emphasizes privacy protections at the expense of public health benefit, while the "emergency response model" sacrifices transparency and accountability, prompting concerns about excessive governance surveillance. The ethical and effective use of DCT in the future requires a new governance approach that is better suited to this novel use of mobile phone data to promote public health."
Demonstrating TRAinAR

We demonstrate TrainAR, an augmented reality (AR)-based tool that is designed to improve sim2real reinforcement learning (RL) for robots. Users with TRAinAR can tailor a virtual training environment with constraints to match the real-world, visualize training data to gain insights into an agent's learning process, and animate a robot's future actions before execution. The system described here enabled a robotic arm manipulator to learn how to navigate its end-effector toward a target object. The novelty of our AR application will hopefully enable users to better train a robot by quickly prototyping complex environments that are difficult to model.
Precision synchronization for free-space quantum networking

Precision synchronization is vital for robust long-distance quantum networking over fiber and free-space channels for which high-fidelity entanglement swapping between separate sources via an optical Bell state measurement requires temporal overlap of photonic qubits arriving from either source. This challenge is particularly distinct in satellite-based entanglement distribution in which relative motion, channel effects, and propagation delay must be addressed. This work presents a precision synchronization method for free space entanglement distribution, and reports on risk reduction testing in a quantum networking testbed at MIT Lincoln Laboratory. Primary consideration is for a dual-uplink architecture in which photons from entanglement sources at two ground locations interact in an optical Bell-state measurement implemented on a satellite in a low-earth orbit. The control approach uses independent entanglement sources at each ground location supplemented with a synchronization signal for feedback control from a timing discriminant measured at the spacecraft. The approach is being implemented in a laboratory testbed using 1-GHz repetition rate 1550-nm band entanglement sources generating ~10-MHz source entanglement rates with few-ps photon pulse lengths. The paper describes both fundamental architectural considerations and practical implementation details.
A scientometric evaluation of &lt;i&gt;International Journal of Business Innovation and Research&lt;/i&gt;: (2006-2019)

International Journal of Business Innovation and Research (IJBIR) is a journal in the field of business, management, and accounting, which was founded in 2006. This paper provides a comprehensive scientometric overview of the journal and 664 publications from 2006 to September 23, 2019. A total of 664 articles published were carefully reviewed and subjected to scientometric data analysis techniques. The study focuses on contribution of IJBIR to knowledge domain through publishing trends; citation analysis, co-occurrence analysis, authorship pattern, nature of articles, statistical tools and techniques adopted in research, institutional collaboration of authors, influential authors, influential articles, and industry wise analysis. The number of articles being published in IJBIR has shown an upward trend. Authors from 62 countries contributed for the growth of literature. The study shows that 42% of the articles were contributed by Indian authors and 17.50% of the total studies contributed by centrally funded technical institutes from India.
SoK on Blockchain Evolution and a Taxonomy for Public Blockchain Generations

Blockchain has been broadly recognized as a breakthrough technology of the world. Web3, recently, is emerging as a buzzword, indicating the next generation of Internet based on Blockchain, envisioning the Internet of Money to store and transfer value. However, when people want a comprehensive view throughout advancements in the Blockchain space, there is a missing in the academic domain and scientific publications regarding distributed ledger technology (DLT) classification and taxonomy for the evolution of public Blockchain generations. In this research, the author attempts to classify DLTs in terms of data structure (ledger type), governance and accessibility. Furthermore, based on the well-known problems and the most technical challenges in Blockchain space, the author studies breaking and significant inventions of various blockchain protocols to give a taxonomy for the evolution of four public Blockchain generations, blockchain layers (0, 1, 2, 3). The first and second generations are dominated by Bitcoin and Ethereum, respectively. The latest state-of-the-art blockchain protocols are developing and shaping the third and fourth generations, where several ``Ethereum-killer" candidates are trying to solve major problems, to offer fantastic functions and capacity, by their own outstanding innovations and distinguished architectural designs. This work helps readers quickly capture historical evolution and innovations of Blockchain, envisioning the next advancements of Web3 as well as the Internet of Value (Internet 2.0).
Matrix Completion for the Prediction of Yearly Country and Industry-Level CO$$_2$$ Emissions

In the recent past, yearly CO $$_2$$ emissions at the international level were studied from different points of view, due to their importance with respect to concerns about climate change. Nevertheless, related data (available at country-industry level and referred to the last two decades) often suffer from missingness and unreliability. To the best of our knowledge, the problem of solving the potential inaccuracy/missingness of such data related to certain countries has been overlooked. Thereby, with this work we contribute to the academic debate by analyzing yearly CO $$_2$$ emissions data using Matrix Completion (MC), a Statistical Machine Learning (SML) technique whose main idea relies on the minimization of a suitable trade-off between the approximation error on a set of observed entries of a matrix (training set) and a proxy of the rank of the reconstructed matrix, e.g., its nuclear norm. In the work, we apply MC to the prediction of (artificially) missing entries of a country-specific matrix whose elements derive (after a suitable pre-processing at the industry level) from yearly CO $$_2$$ emission levels related to different industries. The results show a better performance of MC when compared to a simple baseline. Possible directions of future research are pointed out.
Three-Stage Bidirectional Interaction Network for Efficient RGB-D Salient Object Detection

The addition of depth maps improves the performance of salient object detection (SOD). However, most existing RGB-D SOD methods are inefficient. We observe that existing models take into account the respective advantages of the two modalities but do not fully explore the roles of cross-modality features of various levels. To this end, we remodel the relationship between RGB features and depth features from a new perspective of the feature encoding stage and propose a three-stage bidirectional interaction network (TBINet). Specifically, to obtain robust feature representations, we propose three interaction strategies: bidirectional attention guidance (BAG), bidirectional feature supplement (BFS), and shared network, and use them for the three stages of feature encoder, respectively. In addition, we propose a cross-modality feature aggregation (CFA) module for feature aggregation and refinement. Our model is lightweight (3.7 M parameters) and fast (329 ms on CPU). Experiments on six benchmark datasets show that TBINet outperforms other SOTA methods. Our model achieves the best performance and efficiency trade-off.
LatentGaze: Cross-Domain Gaze Estimation Through Gaze-Aware Analytic Latent Code Manipulation

Although recent gaze estimation methods lay great emphasis on attentively extracting gaze-relevant features from facial or eye images, how to define features that include gaze-relevant components has been ambiguous. This obscurity makes the model learn not only gaze-relevant features but also irrelevant ones. In particular, it is fatal for the cross-dataset performance. To overcome this challenging issue, we propose a gaze-aware analytic manipulation method, based on a data-driven approach with generative adversarial network inversion’s disentanglement characteristics, to selectively utilize gaze-relevant features in a latent code. Furthermore, by utilizing GAN-based encoder-generator process, we shift the input image from the target domain to the source domain image, which a gaze estimator is sufficiently aware. In addition, we propose gaze distortion loss in the encoder that prevents the distortion of gaze information. The experimental results demonstrate that our method achieves state-of-the-art gaze estimation accuracy in a cross-domain gaze estimation tasks. This code is available at https://github.com/leeisack/LatentGaze/ .
2D2PS: A demand-driven privacy-preserving scheme for anonymous data sharing in smart grids

In smart grid services, electricity consumption data sharing may lead to privacy breaches and the potential for misuse of personal data. Existing data aggregation approaches mainly rely on homomorphic encryption techniques to achieve privacy protection in data sharing, however, lack consideration of controlled anonymity and fine-grained access control. This paper proposes a demand-driven privacy-preserving scheme for anonymous data sharing in smart grids to solve these problems. Specifically, an authenticable pseudonym assignment method is first proposed to achieve controlled anonymity. In addition, a proxy re-encryption technique and a data separation algorithm are implemented to achieve fine-grained access control. Finally, we strictly prove that our scheme achieves privacy protection and authentication, and demonstrate its feasibility and effectiveness through simulations.
A Deep Reinforcement Learning Algorithm Based on Short-Term Advantage for Air Game Decision-Making

Aiming at the problem of difficult convergence of three-dimensional space UAV air game, a deep reinforcement learning air game decision-making algorithm based on improved action strategy is proposed. The main contributions of the paper are as follows: First, a heuristic reward function is designed by introducing situational information such as angle and speed, which alleviates the problem of difficult convergence caused by sparse rewards. Second, an action selection strategy based on short-term advantage is designed to avoid a large number of meaningless repetitions data. Simulation experiments show that the decision-making algorithm based on deep reinforcement learning can avoid risks and occupy a favorable position in different initial situations. The proposed improvement mechanism can effectively improve the convergence efficiency of the algorithm and the success rate of the game.
Multi-obstacle Avoidance of UAV Based on Improved Q Learning Algorithm

Aiming at the autonomous obstacle avoidance problem of UAV in multi-obstacle map environment, a UAV obstacle avoidance algorithm based on the improved Q learning method is proposed. By analyzing the UAV dynamics principle, the UAV kinematic model is built, and the Markov jump system model is further obtained. Considering the safe distance from the obstacle and the position of the target point, an improved immediate reward function is presented, and a Q learning algorithm of UAV obstacle avoidance is proposed by adopting the $$\varepsilon $$ -greedy strategy, which can improve the learning efficiency, realize autonomous obstacle avoidance and optimize the route to the target position. In the simulation experiment, the UAV can track with down different environments and the accumulative rewards are compared and analyzed, which show the effectiveness and advantages of the UAV self-learning algorithm proposed in this paper.