Reviewerly/exHarmony huggingface dataset:
Cognitive Computation in Ideological and Political Classroom Teaching Based on Digital Sensor Technology

Numerous new ideas and concepts have changed the behavior and value orientation of university students as a result of the internet's rising popularity on college campuses. This study performs research on digital sensor technologies in order to enhance the intelligent effect of ideological and political classroom instruction. In addition, this study combines the fast Fourier transform principle to enhance digital sensor technology, digital sensor and cognitive computation technology to investigate the ideological and political classroom teaching process, and the actual situation of the ideological and political teaching to digitally process the ideological and political teaching process. In addition, this study employs sensor technology to convey data and digital sensor technology to increase the quality of ideological and political classroom instruction by enhancing the traditional teaching paradigm. In addition, on this premise, this study conducts a performance evaluation of the system, primarily focusing on the digital effect and the enhancement of ideological and political teaching quality. In conclusion, this study proves its teaching system through test research. According to the test results, the intelligent teaching method described in this study has a certain practical effect.
RV4JaCa – Runtime Verification for Multi-Agent Systems

This paper presents a Runtime Verification (RV) approach for Multi-Agent Systems (MAS) using the JaCaMo framework. Our objective is to bring a layer of security to the MAS. This layer is capable of controlling events during the execution of the system without needing a specific implementation in the behaviour of each agent to recognise the events. MAS have been used in the context of hybrid intelligence. This use requires communication between software agents and human beings. In some cases, communication takes place via natural language dialogues. However, this kind of communication brings us to a concern related to controlling the flow of dialogue so that agents can prevent any change in the topic of discussion that could impair their reasoning. We demonstrate the implementation of a monitor that aims to control this dialogue flow in a MAS that communicates with the user through natural language to aid decision-making in hospital bed allocation.
Enhancing the information of nonlinear SU(1, 1) quantum systems interacting with a two-level atom

Abstract The effect of nonlinearity, initial atomic state, and different resonance cases on the interaction between nonlinear SU (1, 1) quantum states and a two-level atom is discussed. The optimal behaviours of decoherence, entanglement and quantum coherence are predicted via using the skew information, tomographic entropy, and the relative entropy of coherence, respectively. It is shown that the detuning parameter has a destructive effect on the coherence and consequently on the entanglement if the quantum system is regulated in the ideal SU (1, 1) quantum systems. For the nonlinear SU (1, 1) quantum systems, the ability to suppress the decay of entanglement induced by the detuning may be increased by preparing the initial atomic state in its excited state.
Future Energy Using Blockchain Systems

A fundamental shift is underway from electrical en-ergy to renewable energy. Renewable energy sources are beyond the push and simple decentralization of the electricity market. This transfer process is based on the energy sector&#x0027;s relationship between old technology and new technology. It connects it with other sectors, such as information and communication technology (ICT), so that it can be reached by technology transfer carried out by information technology players into the market. Blockchain technology is playing a vital role in this shift by offering a decentralized system and an alternative approach to the current organizational form of the energy market. This paper describes the role of blockchain systems in the energy sector, which is described using the term &#x201C;energy blockchain.&#x201D; In this study, this research aims to utilize blockchain energy that includes and passes through all aspects by looking at organizations that emphasize the interrelationship of technical dimensions and social dimensions in the energy sector based on the benefits and princi-ples of blockchain for energy trading, information storage, and or increasing transparency of energy flows and energy service. In the following chapters, the authors provide an overview of the current changes in the electricity market, a discussion of the different possible uses for blockchain technology in the energy sector, and a discussion of the barriers and potentials of blockchain energy using different learning perspectives. Finally, the possible opportunities and risks of blockchain energy are discussed. Worldwide significance is mainly based on an integrated world-view-several views are brought together to provide electricity capacity to countless buyers using a predefined global, public and local organization as power transmission: grid power. To impact this course, educational partners and blockchain energy experts need to get further imaginative work together with grounded execution and application in local settings, showing the utility and performance of thoughts before long key accomplices and pioneers. From a specific and IT perspective, this incorporates, specifically, contemplating the possibility of&#x201D; security by the plan.&#x201D; Online security is a subject that ought not to be disre-garded, particularly in the blockchain energy field.
Selective Differential Privacy for Language Modeling

With the increasing applications of language models, it has become crucial to protect these models from leaking private information.Previous work has attempted to tackle this challenge by training RNN-based language models with differential privacy guarantees.However, applying classical differential privacy to language models leads to poor model performance as the underlying privacy notion is over-pessimistic and provides undifferentiated protection for all tokens in the data.Given that the private information in natural language is sparse (for example, the bulk of an email might not carry personally identifiable information), we propose a new privacy notion, selective differential privacy, to provide rigorous privacy guarantees on the sensitive portion of the data to improve model utility.To realize such a new notion, we develop a corresponding privacy mechanism, Selective-DPSGD, for RNN-based language models.Besides language modeling, we also apply the method to a more concrete application -dialog systems.Experiments on both language modeling and dialog system building show that the proposed privacy-preserving mechanism achieves better utilities while remaining safe under various privacy attacks compared to the baselines.The data and code are released to facilitate future research 1 .
Knowing the intention behind limb movements of a partner increases embodiment towards the limb of joint avatar

We explored a concept called "virtual co-embodiment", which enables users to share their virtual avatars with others. Co-embodiment of avatars and robots can be applied for collaboratively performing complicated tasks, skill training, rehabilitation, and aiding disabled users. We conducted an experiment where two users could co-embody one "joint avatar" in first person view and control different arms to collaboratively perform three types of reaching tasks. We measured their senses of agency and ownership towards the two arms of the avatar and changes in skin conductance levels in response to visual stimuli threatening the two virtual arms. We found that sense of agency, ownership, and skin conductance were significantly higher towards the virtual arm with control compared to the arm controlled by the partner. Furthermore, the senses of agency and ownership towards the arm controlled by the partner were significantly higher when the participant dyads shared a common intention or when they were allowed to see their partner's target, compared to when the partner's target was invisible. These results show that while embodiment towards partner-controlled limbs is lower compared to limbs with control, visual information necessary for predicting the partner's intentions can significantly enhance embodiment towards partner-controlled limbs during virtual co-embodiment.
BTrust: A New Blockchain-Based Trust Management Protocol for Resource Sharing

The emergence of blockchain technology and cryptocurrencies opened the possibility for building novel peer-to-peer (P2P) resource allocation and sharing models. However, the trustless nature of these P2P models creates the need for reliable and effective trust and reputation mechanisms to minimize the risk of accessing or interacting with malicious peers. Blockchain technology, which is renowned for ensuring trust in trustless environments, provides us with new mechanisms to overcome the weaknesses of the existing reputation and trust management protocols. This paper proposes BTrust, an innovative decentralized and modular trust management system based on blockchain technology for evaluating trust in large-scale P2P networks. To quantify and assess the trustworthiness of peers and identify malicious peers, BTrust introduces a multi-dimensional trust and reputation model to represent trust and reputation scores in a single value derived from multiple parameters with appropriate weightings. Other contributions of this paper include the combination of recommendation and evidence-based approaches into a single system to provide a reliable and versatile way to compute trust in the network, an optimized trustless bootstrapping process to select trustworthy peers among neighbour peers and an incentive mechanism to encourage truthful feedback. We implement and evaluate the BTrust protocol using simulations and show that BTrust is highly resilient to failures and robust against malicious nodes.
Three-Dimensional Path Planning for Unmanned Helicopter Using Memory-Enhanced Dueling Deep Q Network

Unmanned helicopter (UH) is often utilized for raid missions because it can evade radar detection by flying at ultra-low altitudes. Path planning is the key technology to realizing the autonomous action of UH. On the one hand, the dynamically changing radar coverage area and the mountains in the low airspace environment will seriously affect the flight safety of UH. On the other hand, the huge state space of the three-dimensional (3D) environment will also make traditional algorithms difficult to converge. To address the above problems, a memory-enhanced dueling deep Q-network (ME-dueling DQN) algorithm was proposed. First, a comprehensive reward function was designed, which can guide the algorithm to converge quickly and effectively improve the sparse reward problem. Then, we introduced a dual memory pool structure and proposed a memory-enhanced mechanism, which can reduce invalid exploration, further improve the learning efficiency of the algorithm, and make the algorithm more stable. Finally, the path planning ability of the proposed algorithm in multiple experimental environments was verified. Experiments showed that the proposed algorithm has good environmental adaptability and can help UH to accurately identify dangerous areas and plan a safe and reliable flight path.
Boosting Neural Image Compression for Machines Using Latent Space Masking

Today, many image coding scenarios do not have a human as final intended user, but rather a machine fulfilling computer vision tasks on the decoded image. Thereby, the primary goal is not to keep visual quality but maintain the task accuracy of the machine for a given bitrate. Due to the tremendous progress of deep neural networks setting benchmarking results, mostly neural networks are employed to solve the analysis tasks at the decoder side. Moreover, neural networks have also found their way into the field of image compression recently. These two developments allow for an end-to-end training of the neural compression network for an analysis network as information sink. Therefore, we first roll out such a training with a task-specific loss to enhance the coding performance of neural compression networks. Compared to the standard VVC, 41.4% of bitrate are saved by this method for Mask R-CNN as analysis network on the uncompressed Cityscapes dataset. As a main contribution, we propose LSMnet, a network that runs in parallel to the encoder network and masks out elements of the latent space that are presumably not required for the analysis network. By this approach, additional 27.3% of bitrate are saved compared to the basic neural compression network optimized with the task loss. In addition, we are the first to utilize a feature-based distortion in the training loss within the context of machine-to-machine communication, which allows for a training without annotated data. We provide extensive analyses on the Cityscapes dataset including cross-evaluation with different analysis networks and present exemplary visual results. Inference code and pre-trained models are published at https://github.com/FAU-LMS/NCN_for_M2M.
Machine learning and shallow groundwater chemistry to identify geothermal prospects in the Great Basin, USA

This study discovers various geothermal prospects in the Great Basin, USA based on shallow groundwater chemical (geochemical) data. The geochemical data are expected to include hidden (latent) information that is a proxy for geothermal prospectivity. We processed the sparse geochemical data in the Great Basin at 14,341 locations including 18 attributes. Next, a non-negative matrix factorization with customized k-means clustering is applied to the geochemical data matrix that automatically finds three hidden geothermal signatures representing modestly, moderately, and highly confident geothermal prospects. The algorithm also evaluated the probability of occurrence of these types of resources through the studied region. There is a consistency between regional geothermal prospectivity as estimated by our ML methodology and the traditional play fairway analysis conducted over a portion of the study area. We also identify the dominant data attributes associated with each signature. Finally, our ML analyses allow us to reconstruct attributes from sparse into continuous over the study domain. The predicted continuous attributes can be used for future detailed geothermal explorations in the Great Basin.
The application of artificial intelligence assistant to deep learning in teachers' teaching and students' learning processes

With the emergence of big data, cloud computing, and other technologies, artificial intelligence (AI) technology has set off a new wave in the field of education. The application of AI technology to deep learning in university teachers' teaching and students' learning processes is an innovative way to promote the quality of teaching and learning. This study proposed the deep learning-based assessment to measure whether students experienced an improvement in terms of their mastery of knowledge, development of abilities, and emotional experiences. It also used comparative analysis of pre-tests and post-tests through online questionnaires to test the results. The impact of technology on teachers' teaching and students' learning processes, identified the problems in the teaching and learning processes in the context of the application of AI technology, and proposed strategies for reforming and optimizing teaching and learning. It recommends the application of software and platforms, such as Waston and Knewton, under the orientation of AI technology to improve efficiency in teaching and learning, optimize course design, and engage students in deep learning. The contribution of this research is that the teaching and learning processes will be enhanced by the use of intelligent and efficient teaching models on the teachers' side and personalized and in-depth learning on the students' side. On the one hand, the findings are helpful for teachers to better grasp the actual conditions of in-class teaching in real time, carry out intelligent lesson preparations, enrich teaching methods, improve teaching efficiency, and achieve personalized and precision teaching. On the other hand, it also provides a space of intelligent support for students with different traits in terms of learning and effectively improves students' innovation ability, ultimately achieving the purpose of "artificial intelligence + education."
An automated solid waste detection using the optimized YOLO model for riverine management

Due to urbanization, solid waste pollution is an increasing concern for rivers, possibly threatening human health, ecological integrity, and ecosystem services. Riverine management in urban landscapes requires best management practices since the river is a vital component in urban ecological civilization, and it is very imperative to synchronize the connection between urban development and river protection. Thus, the implementation of proper and innovative measures is vital to control garbage pollution in the rivers. A robot that cleans the waste autonomously can be a good solution to manage river pollution efficiently. Identifying and obtaining precise positions of garbage are the most crucial parts of the visual system for a cleaning robot. Computer vision has paved a way for computers to understand and interpret the surrounding objects. The development of an accurate computer vision system is a vital step toward a robotic platform since this is the front-end observation system before consequent manipulation and grasping systems. The scope of this work is to acquire visual information about floating garbage on the river, which is vital in building a robotic platform for river cleaning robots. In this paper, an automated detection system based on the improved You Only Look Once (YOLO) model is developed to detect floating garbage under various conditions, such as fluctuating illumination, complex background, and occlusion. The proposed object detection model has been shown to promote rapid convergence which improves the training time duration. In addition, the proposed object detection model has been shown to improve detection accuracy by strengthening the non-linear feature extraction process. The results showed that the proposed model achieved a mean average precision (mAP) value of 89%. Hence, the proposed model is considered feasible for identifying five classes of garbage, such as plastic bottles, aluminum cans, plastic bags, styrofoam, and plastic containers.
Credit Risk Evaluation in Enterprise Financial Management by Using Convolutional Neural Network under the Construction of Smart City

Smart cities are the forward goals of future cities, and the development of small- and medium-sized enterprises (SMEs) plays a key role in it. To solve the financing difficulties of SMEs, it is necessary to scientifically, objectively, and accurately assess the credit risk of SMEs. Based on the current research and analysis of scholars in related fields, taking corporate financial data as the object, the SMEs’ credit risk is assessed through an adaptive and self-learning convolutional neural network (CNN) method. The GoogleNet method is further improved to reconstruct the credit risk evaluation model of SMEs. Finally, the influence of the convolution kernel depth on the accuracy is discussed through the optimization of the structure and the selection of the initial value of the learning rate. The results are verified by using the data in the 2016–2021 SME sector in Shanghai and Shenzhen in the wind database. It can be found that when the learning rate is finally set to 0.4 and the convolution kernel depth is set to 8 and 32 in turn, the evaluation accuracy of the test dataset is the highest, with a total accuracy of 96.8%. Therefore, it is believed that the newly constructed model has higher accuracy and more practical value than the other three typical risk assessment models.
On the Design of Secured and Reliable Dynamic Access Control Scheme of Patient E-Healthcare Records in Cloud Environment

Traditional healthcare services have changed into modern ones in which doctors can diagnose patients from a distance. All stakeholders, including patients, ward boy, life insurance agents, physicians, and others, have easy access to patients’ medical records due to cloud computing. The cloud’s services are very cost-effective and scalable, and provide various mobile access options for a patient’s electronic health records (EHRs). EHR privacy and security are critical concerns despite the many benefits of the cloud. Patient health information is extremely sensitive and important, and sending it over an unencrypted wireless media raises a number of security hazards. This study suggests an innovative and secure access system for cloud-based electronic healthcare services storing patient health records in a third-party cloud service provider. The research considers the remote healthcare requirements for maintaining patient information integrity, confidentiality, and security. There will be fewer attacks on e-healthcare records now that stakeholders will have a safe interface and data on the cloud will not be accessible to them. End-to-end encryption is ensured by using multiple keys generated by the key conclusion function (KCF), and access to cloud services is granted based on a person’s identity and the relationship between the parties involved, which protects their personal information that is the methodology used in the proposed scheme. The proposed scheme is best suited for cloud-based e-healthcare services because of its simplicity and robustness. Using different Amazon EC2 hosting options, we examine how well our cloud-based web application service works when the number of requests linearly increases. The performance of our web application service that runs in the cloud is based on how many requests it can handle per second while keeping its response time constant. The proposed secure access scheme for cloud-based web applications was compared to the Ethereum blockchain platform, which uses internet of things (IoT) devices in terms of execution time, throughput, and latency.
The Remapping of Peripersonal Space in a Real but Not in a Virtual Environment

One of the most surprising features of our brain is the fact that it is extremely plastic. Among the various plastic processes supported by our brain, there is the neural representation of the space surrounding our body, the peripersonal space (PPS). The effects of real-world tool use on the PPS are well known in cognitive neuroscience, but little is still known whether similar mechanisms also govern virtual tool use. To this purpose, the present study investigated the plasticity of the PPS before and after a real (Experiment 1) or virtual motor training with a tool (Experiment 2). The results show the expansion of the PPS only following real-world tool use but not virtual use, highlighting how the two types of training potentially rely on different processes. This study enriches the current state of the art on the plasticity of PPS in real and virtual environments. We discuss our data with respect to the relevance for the development of effective immersive environment for trainings, learning and rehabilitation.