Reviewerly/exHarmony huggingface dataset:
A Hybrid Quantum-Classical Algorithm for Underwater Target Classification

Offshore channel clearance is an essential underwater task to protect vessels and divers effectively, but current underwater target classification relies heavily on operator identification. Machine learning provides highly accurate methods for image classification as well as detection. In this paper, a new hybrid quantum-classical classification algorithm is proposed. It uses quantum devices to reduce dimension and denoise data sets, greatly reducing the difficulty of classical computer processing data. Using abundant classical classification algorithms, the classification problem of different scenarios can be processed, improving the classification efficiency. Using two kinds of underwater object data sets as examples, the numerical simulation results show that the quantum algorithm can accurately achieve dimensionality reduction. This hybrid algorithm has polynomial acceleration in dimension reduction than classical methods, even considering the classical readout of quantum data. The results also show that the classification accuracy of the training set improves from 0.772 to 0.821 compared to the original dataset. Furthermore, different classical classifiers can be selected in the case of different objects, so this hybrid algorithm has broad application prospects in different fields.
Neural Network based College English Informatization Learning Environment Construction with ESP Teaching Mode

In order to improve the effect of sports strategy formulation, this paper combines artificial intelligence assisted agile Internet of Things to conduct research on sports simulation scheme, and builds an intelligent sports simulation system.The sports simulation model designed in this paper can change the mechanism parameters according to the actual situation of the moving object, establish the fixed coordinate system and local coordinate system of the parallel platform model, and establish the mathematical model of the parallel platform.Moreover, according to the principle of vector closure, the inverse kinematics position solution is analyzed, and the inverse kinematic velocity solution is derived by using the equation of the inverse position solution.The experimental research results show that the sports simulation system based on artificial intelligence-assisted agile Internet of Things proposed in this paper has a good performance in sports simulation.
Confronting value-based argumentation frameworks with people’s assessment of argument strength

We reported a series of experiments carried out to confront the underlying intuitions of value-based argumentation frameworks (VAFs) with the intuitions of ordinary people. Our goal was twofold. On the one hand, we intended to test VAF as a descriptive theory of human argument evaluations. On the other, we aimed to gain new insights from empirical data that could serve to improve VAF as a normative model. The experiments showed that people’s acceptance of arguments deviates from VAF’s semantics and is rather correlated with the importance given to the promoted values, independently of the perceptions of argument interactions through attacks and defeats. Furthermore, arguments were often perceived as promoting more than one value with different relative strengths. Individuals’ analyses of scenarios were also affected by external factors such as biases and arguments not explicit in the framework. Finally, we confirmed that objective acceptance, that is, the acceptance of arguments under any order of the values, was not a frequent behavior. Instead, participants tended to accept only the arguments that promoted the values they subscribe.
Blockchain-Based Privacy-Preserving Sensor Data Sharing with Fine-Grained Authorization in Microgrid

Microgrid is a power system that includes various energy sources (e.g., solar panels and wind turbines), where a number of device status and sensing data are collected and transmitted by smart sensors. Based on sensing-as-a-service in microgrid, sensor owners and sensor data consumers can effectively perform data sharing operations. However, the state-of-the-art sensor data sharing works in microgrid have the following two limitations: (i) cannot support fine-grained authorization for sensor owners and sensor data consumers and (ii) fail to simultaneously consider confidentiality and authenticity for sensor data sharing. To address the problems, in this article, we propose a lightweight privacy-preserving sensing data sharing system with fine-grained authorization in microgrid. Technically, we employed attribute-based signature methodology to design a fined-grained authorization mechanism for sensor data users. Moreover, a lightweight hyper elliptic curve-based signcryption scheme is employed to provide confidentiality and authenticity for sensor data sharing. To clarify the feasibility of our proposed system, we implement the system and evaluate the performance. The experimental results show that the system achieves small communication and time overhead, as well as highly acceptable gas consumption of smart contract.
QuantuMoonLight: A low-code platform to experiment with quantum machine learning

Nowadays, machine learning is being used to address multiple problems in various research fields, with software engineering researchers being among the most active users of machine learning mechanisms. Recent advances revolve around the use of quantum machine learning, which promises to revolutionize program computation and boost software systems’ problem-solving capabilities. However, using quantum computing technologies is not trivial and requires interdisciplinary skills and expertise. For such a reason, we propose QuantuMoonLight, a community-based low-code platform that allows researchers and practitioners to configure and experiment with quantum machine learning pipelines, compare them with classic machine learning algorithms, and share lessons learned and experience reports. We showcase the architecture and main features of QuantuMoonLight, other than discussing its envisioned impact on research and practice.
FASTune: Towards Fast and Stable Database Tuning System with Reinforcement Learning

Configuration tuning is vital to achieving high performance for a database management system (DBMS). Recently, automatic tuning methods using Reinforcement Learning (RL) have been explored to find better configurations compared with database administrators (DBAs) and heuristics. However, existing RL-based methods still have several limitations: (1) Excessive overhead due to reliance on cloned databases; (2) trial-and-error strategy may produce dangerous configurations that lead to database failure; (3) lack the ability to handle dynamic workload. To address the above challenges, a fast and stable RL-based database tuning system, FASTune, is proposed. A virtual environment is proposed to evaluate configurations which is an equivalent yet more efficient scheme than the cloned database. To ensure stability during tuning, FASTune adopts an environment proxy to avoid dangerous configurations. In addition, a Multi-State Soft Actor–Critic (MS-SAC) model is proposed to handle dynamic workloads, which utilizes the soft actor–critic network to tune the database according to workload and database states. The experimental results indicate that, compared with the state-of-the-art methods, FASTune can achieve improvements in performance while maintaining stability in the tuning.
The Influence of the SQ4R Learning Model with Digital Schoolbooks on Students' Understanding of Biology Concepts

The low understanding of concepts can be caused because the learning process is not maximized which makes students inactive, unable to think independently, more practical to find answers on the internet, and unable to express what they have learned. Digital School Books as a medium and learning model Survey, Question, Read, Reflection and Review (SQ4R) is an alternative that can activate students and make it easier to remember concepts so that learning is more meaningful. The purpose of this study was to describe the effect of the SQ4R learning model with digital school books on students' understanding of biology concepts in class VII at the Madrasah Tsanawiyah Nurussa'adah Sangkulirang Islamic Boarding School. This research is a quasi-experimental study with a pretest-posttest control group design. The study population was students of class VII, using cluster random sampling technique which produced class VII.B as the experimental class and class VII.A as the control class. The results of the data analysis showed that there was an influence of the SQ4R learning model with digital school books on the understanding of biology concepts for class VII students at the Madrasah Tsanawiyah Nurussa'adah Sangkulirang Islamic Boarding School.
Wedge Video: Supporting Remote Participants in a Mixed-Mode Videoconference Meeting

Abstract As global COVID-19 pandemic response has moved from full lockdowns and partial lockdowns in most parts of the world to a post-COVID era, an interesting new phenomenon that has emerged is the increased prevalence of hybrid meetings with a mixture of online and in-person attendees. The opportunity for remote participants to observe the responses and interactions of others in the meeting is generally accepted as being limited. An experimental prototype system, called Wedge Video, has been constructed as an attempt to improve the experience of remote participants in hybrid in-person/remote meetings. Wedge Video uses standard screen and camera equipment with existing video conferencing software (Zoom). An evaluation of the prototype system was conducted based on three simple games that each required players to interact rapidly and with some use of body language or gaze direction. Encouraging results led to the examination of the geometry of screen and camera placement in detail. A system that has a somewhat ‘virtual reality’ feeling to it has now been developed. The remote user is given a view of the in-person part of the meeting with participants at the same scale and location as they would be if the remote user were at the table themselves. Similarly, the local participants see the remote person in place at their table, at a realistic scale and with close to accurate gaze direction. A very preliminary evaluation of these concepts has been promising.
FANet: Improving 3D Object Detection with Position Adaptation

Three-dimensional object detection plays a crucial role in achieving accurate and reliable autonomous driving systems. However, the current state-of-the-art two-stage detectors lack flexibility and have limited feature extraction capabilities to effectively handle the disorder and irregularity of point clouds. In this paper, we propose a novel network called FANet, which combines the strengths of PV-RCNN and PAConv (position adaptive convolution). The goal of FANet is to address the irregularity and disorder present in point clouds. In our network, the convolution operation constructs convolutional kernels using a basic weight matrix, and the coefficients of these kernels are adaptively learned by LearnNet from relative points. This approach allows for the flexible modeling of complex spatial variations and geometric structures in 3D point clouds, leading to the improved extraction of point cloud features and generation of high-quality 3D proposal boxes. Compared to other methods, extensive experiments on the KITTI dataset have demonstrated that the FANet exhibits superior 3D object detection accuracy, showcasing a significant improvement in our approach.
Artificial Rivers: Conservation of Cultural Heritage in the Context of Environmental, Regulation and Community Culture Issues (Case Study in Banjarmasin)

Abstract Artificial rivers are one of the objects of cultural heritage that has not been studied, in both archaeology and its preservation. Preservation of artificial rivers is related to environmental issues, namely waste disposal in rivers, settlements on river banks, and flooding. Both studies are beneficial for the preservation of the artificial river. In this regard, this paper intended to gain the causes of unsustainability and its solutions. This paper uses a qualitative-descriptive method. The data were collected through field observations, interviews, and literature studies. The analysis is descriptive to find out the causes of unsustainability. As a result, the causes of unsustainability and steps for its preservation include implementing regulations, supporting stakeholders, and reinventing the culture of river use. The application of regulations is related to the use of the border and disposal of waste in the river. Meanwhile, cultural reinvention of river utilization (not only as a transportation route but also as a river tourism activity) is obligatory to be executed to provide profit to the community and support river conservation.
A Disentangled-Attention Based Framework with Persona-Aware Prompt Learning for Dialogue Generation

Endowing dialogue agents with personas is the key to delivering more human-like conversations. However, existing persona-grounded dialogue systems still lack informative details of human conversations and tend to reply with inconsistent and generic responses. One of the main underlying causes is that pre-defined persona sentences are generally short and merely superficial descriptions of personal attributes, making appropriate persona selection and understanding non-trivial. Another challenge is that it is crucial to consider the context and the conversation flow to dynamically determine when to invoke different types of persona signals. To address these problems, we propose a disentangled-attention based pre-training architecture, which incorporates persona-aware prompt learning to bridge the connection between the selected persona and response generation. Our model first exploits the conversation flow to select context-relevant personas, and subsequently enriches the superficial persona descriptions with extra personality traits through persona-aware prompting. Finally, the decoder leverages a disentangled-attention mechanism to flexibly control the reliance on personas and dialogue contexts, and incorporates A*-like keyword-based heuristic estimates for controllable generation. Extensive experiments show that our approach can outperform strong baselines and deliver more consistent and engaging responses on the PERSONA-CHAT dataset.
Bilinear Exponential Family of MDPs: Frequentist Regret Bound with Tractable Exploration &amp; Planning

We study the problem of episodic reinforcement learning in continuous state-action spaces with unknown rewards and transitions. Specifically, we consider the setting where the rewards and transitions are modeled using parametric bilinear exponential families. We propose an algorithm, that a) uses penalized maximum likelihood estimators to learn the unknown parameters, b) injects a calibrated Gaussian noise in the parameter of rewards to ensure exploration, and c) leverages linearity of the bilinear exponential family transitions with respect to an underlying RKHS to perform tractable planning. We provide a frequentist regret upper-bound for our algorithm which, in the case of tabular MDPs, is order-optimal with respect to H and K, where H is the episode length and K is the number of episodes. Our analysis improves the existing bounds for the bilinear exponential family of MDPs by square root of H and removes the handcrafted clipping deployed in existing RLSVI-type algorithms.
CLIP Driven Few-Shot Panoptic Segmentation

This paper presents CLIP Driven Few-shot Panoptic Segmentation (CLIP-FPS), a novel few-shot panoptic segmentation model that leverages the knowledge of Contrastive Language-Image Pre-training (CLIP) model. The proposed method builds upon a center indexing attention mechanism to facilitate knowledge transfer, which entails representing objects in an image as centers along with their pixel offsets. The model comprises a decoder responsible for generating object center-offset groups and a self-attention module tasked with producing a feature attention map. Subsequently, the object centers index the map to acquire the corresponding embeddings, paving the way for matrix multiplication and SoftMax operation to facilitate text embedding matching and the computation of the final panoptic segmentation masks. Quantitative evaluation on datasets such as COCO and Cityscapes shows that our method outperforms existing panoptic segmentation techniques in terms of Panoptic Quality (PQ) metrics.
ERDBF: Embedding-Regularized Double Branches Fusion for Multi-Modal Age Estimation

Human age information is present in the face image and speech, but most age estimation methods focus on single-modal data only. Although multi-modal approaches have achieved promising performance in other fields by leveraging complementarity between modalities, the development of age estimation has hardly been more inspired by them because of the problem of modality missing in age data. In this paper, we propose an Embedding-Regularized Double Branches Fusion (ERDBF) framework that can simultaneously handle single-modal and multi-modal age estimation. To address the modality missing, we design a double branches fusion network which consists of an embedding regularization module and an information interaction module. The former aims to enhance the representational capacity of single-modal features. The latter learns inter-modal complementary information. Through their collaboration in the fusion process, the network can extract discriminative age representations, and even if a certain modality is missing, robust age estimation can be achieved using enhanced single-modal information. To our best knowledge, the proposed framework is the first deep learning-based multi-modal age estimation framework that can easily utilize the achievements made in the single-modal and have broader applications. Experimental results show that our best method achieves state-of-the-art results on AgeVoxCeleb. The code is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/Daretowin/ERDBF</uri> .
Extension of constraint-procedural logic-generated environments for deep Q-learning agent training and benchmarking

Abstract Autonomous robots can be employed in exploring unknown environments and performing many tasks, such as, e.g. detecting areas of interest, collecting target objects, etc. Deep reinforcement learning (RL) is often used to train this kind of robot. However, concerning the artificial environments aimed at testing the robot, there is a lack of available data sets and a long time is needed to create them from scratch. A good data set is in fact usually produced with high effort in terms of cost and human work to satisfy the constraints imposed by the expected results. In the first part of this paper, we focus on the specification of the properties of the solutions needed to build a data set, making the case of environment exploration. In the proposed approach, rather than using imperative programming, we explore the possibility of generating data sets using constraint programming in Prolog. In this phase, geometric predicates describe a virtual environment according to inter-space requirements. The second part of the paper is focused on testing the generated data set in an AI gym via space search techniques. We developed a Neuro-Symbolic agent built from the following: (i) A deep Q-learning component implemented in Python, able to address via RL a search problem in the virtual space; the agent has the goal to explore a generated virtual environment to seek for a target, improving its performance through a RL process. (ii) A symbolic component able to re-address the search when the Q-learning component gets stuck in a part of the virtual environment; these components stimulate the agent to move to and explore other parts of the environment. Wide experimentation has been performed, with promising results, and is reported, to demonstrate the effectiveness of the approach.
Knowledge injection of Datalog rules via Neural Network Structuring with KINS

Abstract We propose a novel method to inject symbolic knowledge in form of Datalog formulæ into neural networks (NN), called Knowledge Injection via Network Structuring (KINS). The idea behind our method is to extend NN internal structure with ad-hoc layers built out of the injected symbolic knowledge. KINS does not constrain NN to any specific architecture, neither requires logic formulæ to be ground. Moreover, it is robust w.r.t. both lack of data and imperfect/incomplete knowledge. Experiments are reported, involving multiple datasets and predictor types, to demonstrate how KINS can significantly improve the predictive performance of the neural networks it is applied to.
On the study of acceptability in weighted argumentation frameworks through four-state labelling semantics

Abstract Computational Argumentation provides tools for both modelling and reasoning with controversial information. Abstract Argumentation Frameworks represent the building blocks in this field and allow one to model the relationships between arguments with the ultimate goal of establishing their acceptability. Arguments can be evaluated through sets of criteria, called semantics, which distinguish among various justification states. For example, an argument may be accepted, rejected, ignored or even marked as undecided. This paper considers Weighted Argumentation Frameworks and proposes a novel labelling semantics that differentiates four states and generalizes existing approaches.
RESEARCH ON STRATEGIES FOR IMPROVING THE GOVERNANCE EFFICIENCY OF INTERNATIONAL COOPERATIVE EDUCATION

Governance includes the state, the private sector, and civil society, all of which play an important role in sustained human development. The state constructs a feasible political and legal environment, the private sector creates employment and income opportunities, civil society assists in political and social interaction, and mobilizes various forces to participate in economic, social, and political activities. Vocational education is a product of social development, the development of human civilization, and can also be said to be a product of human self-development. And it is a product of development to a special period. Vocational education benefits society, and society can also benefit from vocational education. Promoting social development is a necessary and sacred duty of vocational education. Building an international cooperation platform for vocational education based on regional advantages, development strategies, pillar industries, and talent needs is a key component of the reform of the vocational education system construction. In this context, how to improve the governance system of vocational undergraduate cooperative education, enhance governance efficiency, and provide internal support for the high-quality development of vocational education has become an urgent problem to be solved. This article is based on Sino foreign cooperative education for vocational undergraduate students, studying the basic connotation, specific characterization, and improvement strategies of governance efficiency, providing theoretical support for building a high-quality Sino foreign cooperative education system for vocational education.