Reviewerly/exHarmony huggingface dataset:
Intelligent system for Distributed Quality Monitoring of Sewage Management based on Wastewater Treatment Procedure and Data Mining

Wastewater treatment procedures (WWTP) rely heavily on accurate forecasting of treatment results to keep oxygenation levels under control. Conventional biochemical mechanism-driven approaches provide poor results, mainly due to complicated and redundant system factors. As sewage treatment operations expand fast, automated operational solutions are needed to achieve this goal. In the research, data mining was used to model the WWTP to predict the outcomes based on input circumstances and the amount of oxygenation provided to the system. Combined Sustainability Research for Wastewater Treatment procedures (CSR-WWTP) is proposed in this research. Data-driven approaches to modeling WWTP have already been developed but do not consider long-term treatment procedures and structure features. Forecasting and management for the WWTP are described in this article using a combination of convolutional neural networks (CNN) and recurrent neural networks (RNN). The first stage utilizes the CNN structure to dynamically learn and encrypt the local features of each WWTP timestamp in the first phase. The RNN model is applied to the WWTP to express global sequence characteristics using local feature encryption. For this purpose, it conducts a huge number of tests to assess the performance and accuracy of the proposed forecasting framework.
Research on Building City-Area Industry-Education Consortium

Since 2022, municipal industry-education consortium has become an important practice to promote local industrial upgrading and vocational education development. It aims to explore how to build an education model that meets the needs of municipal industry development, meets the diversified growth needs of students, and realizes the integration of industry and education. Starting from the concept, characteristics and functions of the city-level industry-education consortium, this paper summarizes and discusses the problems involved in the construction of the city-level industry-education consortium, such as the collaborative development among multiple subjects, the updating and upgrading of education and teaching content, and the transformation and realization of training objectives. It analyzes the necessity of building the city-level industry-education consortium, as well as the pain points and obstacles in the construction process. The construction strategy and optimization scheme of the city-level industry-education consortium are put forward, so as to provide reference for promoting the construction of the city-level industry-education consortium and the reform of vocational education.
VIRTUAL ASSISTANT USING ARTIFICIAL INTELLIGENCE

Voice assistants are programs on digital devices that listen and respond to verbal commands. A user can say, “What’s the weather?” and the voice assistant will answer with the weather report for that day and location. They could say, “Tell me a story,” and the assistant will jump into a tale. Many devices are becoming smarter in their own way to interact with human in an easy language. The Desktop based voice assistant are the programs that can recognize human voices and can respond via integrated voice system. Voice assistants are the great innovation in the field of AI that can change the way of living of the people in a different manner. The voice assistant was first introduced on smartphones and after the popularity it got. We use artificial intelligence python as a programming language because it have a major libraries which is use to execute commands. By using python installer packages our personal virtual assistant recognize the user voice and process on it. It takes voice as input. The system is being designed in such a way that all the services provided by the mobile devices are accessible by the end user on the user's voice commands. We reviewed studies that used voice assistants for various tasks in this context. Our study highlighted the usability measures currently used for voice assistants. This paper will define the working of a voice assistants, their main problems and limitations. In this paper it is described that the method of creating a voice assistant without using cloud services, which will allow the expansion of such devices in the future.
LitCQD: Multi-hop Reasoning in Incomplete Knowledge Graphs with Numeric Literals

Most real-world knowledge graphs, including Wikidata, DBpedia, and Yago are incomplete. Answering queries on such incomplete graphs is an important, but challenging problem. Recently, a number of approaches, including complex query decomposition (CQD), have been proposed to answer complex, multi-hop queries with conjunctions and disjunctions on such graphs. However, these approaches only consider graphs consisting of entities and relations, neglecting literal values. In this paper, we propose LitCQD—an approach to answer complex, multi-hop queries where both the query and the knowledge graph can contain numeric literal values: LitCQD can answer queries having numerical answers or having entity answers satisfying numerical constraints. For example, it allows to query (1) persons living in New York having a certain age, and (2) the average age of persons living in New York. We evaluate LitCQD on query types with and without literal values. To evaluate LitCQD, we generate complex, multi-hop queries and their expected answers on a version of the FB15k-237 dataset that was extended by literal values.
RBNets: A Reinforcement Learning Approach for Learning Bayesian Network Structure

Bayesian networks are graphical models that are capable of encoding complex statistical and causal dependencies, thereby facilitating powerful probabilistic inferences. To apply these models to real-world problems, it is first necessary to determine the Bayesian network structure, which represents the dependencies. Classic methods for this problem typically employ score-based search techniques, which are often heuristic in nature and have limited running times and performances that do not scale well for larger problems. In this paper, we propose a novel technique called RBNets, which uses deep reinforcement learning along with an exploration strategy guided by Upper Confidence Bound for learning Bayesian Network structures. RBNets solves the highest-value path problem and progressively finds better solutions. We demonstrate the efficiency and effectiveness of our approach against several state-of-the-art methods in extensive experiments using both real-world and synthetic datasets.
Leveraging Free Labels to Power up Heterophilic Graph Learning in Weakly-Supervised Settings: An Empirical Study

Graph learning on heterophilic graphs is challenging for classic graph neural network models. Recent research addresses this issue by using adaptive graph filters that consider signals with different frequencies. Although such models provide insightful design patterns for heterophilic graph analysis, their practical effect has been overlooked. Previous studies have evaluated adaptive graph filters with a large proportion of training data to demonstrate their effectiveness. However, such dense labeling is often impractical. Empirically, we observed that typical adaptive filters perform badly in weakly-supervised settings, making them easily outperformed by fixed filters. With empirical evidence, we demonstrate that the key reason is that sparse node labels make it difficult to learn effective filters. Fortunately, although labeled nodes are sparse in weakly-supervised settings, graph structures provide substantial supervision by indicating whether an edge is present. Through theoretical analysis on contextual Stochastic Block Models, we show that a good link predictor can imply the knowledge needed by a good node classifier. Therefore, we propose to use the “free labels” from the graph structure to form link prediction tasks and obtain an effective graph filter, which can be used to initialize the node classification model. Experimental results on both synthetic and real-world heterophilic graphs demonstrate the effectiveness of our approach. We also provide an in-depth analysis of the learned filters, which sheds light on the underlying mechanisms of our proposed approach. Codes are available at https://github.com/lucio-win/PKDD2023.
MuSE: A Multi-scale Emotional Flow Graph Model for Empathetic Dialogue Generation

The purpose of empathetic dialogue generation is to fully understand the speakers’ emotional needs in dialogues and to generate appropriate empathetic responses. Existing works mainly focus on the overall coarse-grained emotion of the context while neglecting different utterances’ fine-grained emotions, which leads to the inability to detect the speakers’ fine-grained emotional changes during a conversation. However, in real-life dialogue scenarios, the speaker usually carries an initial emotional state that changes continuously during the conversation. Therefore, understanding a series of emotional states can help to better understand speakers’ emotions and generate empathetic responses. To address this issue, we propose a Multi-Scale Emotional flow model called MuSE, which simulates speakers’ emotional flow. First, we introduce a fine-grained expansion strategy to transform context into an emotional flow graph that combines multi-scale coarse and fine-grained information. This emotional flow graph captures speakers’ constant emotional changes at each turn of a conversation. And then, the emotion node and the situational node are introduced to the emotional flow graph respectively in order to extend the speakers’ initial emotion into the ensuing conversation. Finally, we conduct experiments on the public EMPATHETIC DIALOGUES dataset. The experimental results demonstrate that the MuSE model achieves superior performance under both automatic evaluation and human evaluation metrics compared with the existing baseline models. Our code is available at https://github.com/DericZhao/MuSE .
REST: Enhancing Group Robustness in DNNs Through Reweighted Sparse Training

The deep neural network (DNN) has been proven effective in various domains. However, they often struggle to perform well on certain minority groups during inference, despite showing strong performance on the majority of data groups. This is because over-parameterized models learned bias attributes from a large number of bias-aligned training samples. These bias attributes are strongly spuriously correlated with the target variable, causing the models to be biased towards spurious correlations (i.e., bias-conflicting). To tackle this issue, we propose a novel reweighted sparse training framework, dubbed as REST, which aims to enhance the performance of biased data while improving computation and memory efficiency. Our proposed REST framework has been experimentally validated on three datasets, demonstrating its effectiveness in exploring unbiased subnetworks. We found that REST reduces the reliance on spuriously correlated features, leading to better performance across a wider range of data groups with fewer training and inference resources. We highlight that the REST framework represents a promising approach for improving the performance of DNNs on biased data, while simultaneously improving computation and memory efficiency. By reducing the reliance on spurious correlations, REST has the potential to enhance the robustness of DNNs and improve their generalization capabilities. Code is released at https://github.com/zhao1402072392/REST .
Negative Prototypes Guided Contrastive Learning for Weakly Supervised Object Detection

Weakly Supervised Object Detection (WSOD) with only image-level annotation has recently attracted wide attention. Many existing methods ignore the inter-image relationship of instances which share similar characteristics while can certainly be determined not to belong to the same category. Therefore, in order to make full use of the weak label, we propose the Negative Prototypes Guided Contrastive learning (NPGC) architecture. Firstly, we define Negative Prototype as the proposal with the highest confidence score misclassified for the category that does not appear in the label. Unlike other methods that only utilize category positive feature, we construct an online updated global feature bank to store both positive prototypes and negative prototypes. Meanwhile, we propose a pseudo label sampling module to mine reliable instances and discard the easily misclassified instances based on the feature similarity with corresponding prototypes in global feature bank. Finally, we follow the contrastive learning paradigm to optimize the proposal’s feature representation by attracting same class samples closer and pushing different class samples away in the embedding space. Extensive experiments have been conducted on VOC07, VOC12 datasets, which shows that our proposed method achieves the state-of-the-art performance.
Anomaly Detection in Online Data Streams Using Deep Belief Neural Networks

Internet technologies are now utilized in almost every domain to gather data streams and also to monitor important events in an organization. However, these data streams are affected by abnormal or unusual pattern widely known as anomalies, which is responsible for malicious attacks, hardware failure, software failure, and reading errors. Hence, a dynamic, effective anomaly detection model is established in this research to enhance the quality of data gathered by the networks. The deep belief neural network is employed to obtain promising results in anomaly detection. The classifier performance is improved by reducing data dimensionality through the process known as feature extraction. The experimental analysis demonstrates the effectiveness of the proposed methodology by comparing it with existing techniques such as RNN, BNN, CNN, and Autoencoders. The experimental outcome such as accuracy, precision, F1-score, and error of about 80.5714%, 94.5440%, and 95.3% for the HDFS dataset reveals the effectiveness of the DBN model.
Survey: Trust Management in IoT

Internet of Things, also known as IoT, is a prominent and emerging domain in today’s technological world where physical objects are connected to Internet using wired or wireless technologies for information retrieval and performing various tasks. Since last decade, a large number and a huge variety of IoT applications have been developed throughout. With this tremendous increase, there has occurred so many challenges regarding privacy and security of IoT devices; it’s very important to have trust among various entities of an IoT system so that secure connections can be established among them and an efficient transfer of information could take place. This paper addresses a detailed review of different trust calculation and trust management models in an IoT system. Further, some research challenges has also been explored for giving directions for future works.
Medical Image Authentication and Restoration Based on mCloud Computing: Towards Reliant Medical Digitization Era

The protection of multimedia data, particularly images, has become crucial during the past few years. Over encryption or data-hiding technologies, image data, in particular medical image (MedImg) data, can be secured. Medical data compression is required to cut down on transmission time. Several years ago, a brand-new issue emerged combining single-step, encryption, compression, and data hiding (DtaHng). Few techniques, for example, have been put out so far to combine image compression and encryption. Today, Image-based embedding of data is a new difficulty. The encryption image is of the maximum entropy, so conventional data-hiding cannot be utilized for the stage of embedding, also called noise, because the entropy is the maximum. A novel concept, Invertible Data Hiding, is utilized for MedImgs decryption containing embedded data. High-capacity reversible data-hiding techniques have recently been described, but they do not work for MedImgs protected through encryption. This study proposed analyzing the annotated encrypted MedImgs’ local standard deviations (LSD) to exclude embedded data during decryption. The utilized method was implemented in a variety of MedImgs, and the results were presented and analyzed. The suggested system also makes utilize of cloud computing technologies. Here, the client is providing the service named Data Security from the medical cloud (mCloud) server. Additionally, with a PSNR of 60 dB and above, MedImg quality is improving.
Application Analysis and Research of Electronic Tags Based on RFID Technology in the Construction of the New Academic Library for University System

RFID is the product of the development of automatic identification technology, which realizes two-way data interaction through wireless connection. The working principle of RFID is when the tag enters the reading area, it receives the radio frequency signal sent by the reader and sends out the product information stored in the chip by virtue of the energy obtained by the induced current. At present, the introduction of RFID tags in various industries is basically UHF. During the construction of the new library, the library of Nanchang Institute of Technology investigated the current situation and frequency selection of RFID electronic tags in some university libraries and local libraries in the province. Through the practical test, the label frequency selection method and advantage analysis for intelligent channels, self-service book borrowing, and returning machines and self-service inventory machines are analyzed. It is found that in the process of introducing RFID electronic tags in the current university library, because the frequency of the tags is too high, there are certain errors when the number of readers is dense and the number of books borrowed is large. Through later practice, the team in this article found out the form of using "segmented" combined tags, which effectively alleviated the error of single tags in the traditional mode and improved the efficiency of equipment from book borrowing and returning, book shelves, and smart access control. The creation of this article has effectively improved the misjudgment on the type and quantity of the target due to the uniqueness of the label in the traditional mode. Use the "segmentation" management mode to switch and combine label types according to the actual situation, effectively improving the utilization efficiency and detection rate of labels. Recognition rate. Under the expected influence of the innovative management model in this paper, the work of “smart collection” and “smart service” in the “smart” construction of Nanchang Institute of Science and Technology will play an important role in the efficient use of RFID electronic tags, and at the same time improve reader services The impact of the activity.
Blockchain Technology-Based Framework for Anti-Counterfeiting and Traceability

Blockchain technology has received extensive attention and is generating new business forms, becoming a new driving force for economic development. Blockchain has been applied to intelligent manufacturing, supply chain management, the Internet of Things, digital currency, medical health, and other fields. How to use blockchain technology to provide better solutions for various industries and how to solve problems encountered in blockchain applications have become the focus of innovation and research. This study focuses on the anti-counterfeiting & traceability framework in the supply chain, and puts forward three research questions: how to improve the overall efficiency of the anti-counterfeiting traceability system? How to improve the efficiency and user experience of honest block producers (BP) or contributors in the system? How to prevent source data fraud in the system? At the same time, the research objectives are formulated for the three proposed research issues. A framework of a traceability system for anti-counterfeiting based on a meta-model is proposed. Improve the PBFT algorithm, improve consensus efficiency and enhance user experience. The blacklist mechanism is added to the algorithm to solve the source data fraud problem.
Blockchain-Based Mechanism for Secured Information Exchange in Digital Governance Systems

The information infrastructure for data exchange in cryptography for digital governance must be protected. Digital governance refers to using information and communication technologies at different levels of governance, inside and outside the community and in the public sector, to improve accountability in governance. Once information sharing, responsiveness, transparency, and conflicting responsibilities in organizational structures in distributed ledger became a challenge in digital governance systems. The methodology aims to train the implementation of digital governance standards, strategy, and policy in developing a basic blockchain framework by using blockchain technology to ensure efficiency, effectiveness, transparency, and increased governance security systems. Blockchain technology is an exposure area of research that provides exceptional research occasion, specifically the standard of blockchain in digital governance. Blockchain creates and establishes data for various departments in governance environments, including insurance, higher education, supply chain, wealth management, and banking.
Mean-Median Smoothing Backpropagation Neural Network to Forecast Unique Visitors Time Series of Electronic Journal

Sessions or unique visitors is the number of visitors from one IP who accessed a journal portal for the first time in a certain period of time. The large number of unique daily average subscriber visits to electronic journal pages indicates that this scientific periodical is in high demand. Hence, the number of unique visitors is an important indicator of the accomplishment of an electronic journal as a measure of the dissemination in accelerating the journal accreditation system. Numerous methods can be used for forecasting, one of which is the backpropagation neural network (BPNN). Data quality is very important in building a good BPNN model, because the success of modeling at BPNN is very dependent on input data. One way that can be carried out to improve data quality is by smoothing the data. In this study, the forecasting method for predicting time series data for unique visitors to electronic journals employed three models, respectively BPNN, BPNN with mean smoothing, and BPNN with median smoothing. Based on the findings, the results of the smallest error were obtained by the BPNN model with a mean smoothing with MSE 0.00129 and RMSE 0.03518 with a learning rate of 0.4 on 1-2-1 architecture which can be used as a forecast for unique visitors of electronic journals.