Reviewerly/exHarmony huggingface dataset:
Subsynchronous oscillation analysis method based on broad learning system

This paper presents a method to analyze the Sub Synchronous Oscillation (SSO) problem based on broad learning system (BLS), which can quickly analyze the risk of SSO in a system and take measures. The simulation system is constructed by the eigenvalue analysis method to obtain training data, that solves the problem which is difficult to obtain actual data in SSO problem and makes the model more accurate and the conclusion more convincing. Subsequently, BLS is applied for feature extraction and the prediction model is constructed, the model can show the deep relationship between the oscillation source and topology of the system as well as various operating data, which is difficult to be expressed by formula. The system was tested and validated in the 41-bus system. The feature importance analysis method which comes from eXtreme Gradient Boosting (XGboost) algorithm is proposed and combined with the BLS prediction model, which can analyze the influence of each variable on the prediction results in different series compensation levels, and is validated in the 41-bus system, that makes the results of prediction more interpretable. Finally, the results of simulation show that the model can predict data with high accuracy and give adjustment plans for effective control of the system to avoid the risk of SSO.
Vision-Based Robot Arm Control Interface for Retrieving Objects from the Floor

Approximately half of the patients with spinal cord injuries in Japan have a cervical spinal cord injury. Owing to the trunk dysfunction, patients with high-level spinal cord injuries have particular difficulty when searching for or picking up objects from the floor. Recently, welfare robot arms have been developed to help such individuals increase self-reliance. In this study, we propose an operating system that includes an eye-in-hand system with a touchscreen interface for grasping objects from the floor and delivering them to the individual. In the proposed method, the visual information of the target object is shown on a touchscreen interface. The patient specifies the target position for the robot arm by drawing a line on the target object on the interface. We conducted an experiment to compare the proposed interface with an on-screen joystick to demonstrate the proposed system’s efficiency and its ability to reduce physical burden. The results show that the proposed method is both quicker to use and effectively reduces the physical burden on the user compared to the conventional method.
LCVD: Loop-oriented code vulnerability detection via graph neural network

Due to the unique mechanism and complex structure, loops in programs can easily lead to various vulnerabilities such as dead loops, memory leaks, resource depletion, etc. Traditional approaches to loop-oriented program analysis (e.g. loop summarization) are costly with a high rate of false positives in complex software systems. To address the issues above, recent works have applied deep learning (DL) techniques to vulnerability detection. However, existing DL-based approaches mainly focused on the general characteristics of most vulnerabilities without considering the semantic information of specific vulnerabilities. As a typical structure in programs, loops are highly iterative with multi-paths. Currently, there is a lack of available approaches to represent loops, as well as useful methods to extract the implicit vulnerability patterns. Therefore, this paper introduces LCVD, an automated loop-oriented code vulnerability detection approach. LCVD represents the source code as the Loop-flow Abstract Syntax Tree (LFAST), which focuses on interleaving multi-paths around loop structures. Then a novel Loop-flow Graph Neural Network (LFGNN) is proposed to learn both the local and overall structure of loop-oriented vulnerabilities. The experimental results demonstrate that LCVD outperforms the three static analysis-based and four state-of-the-art DL-based vulnerability detection approaches across evaluation settings.
Investigation of Obstacle Prediction Network for Improving Home-Care Robot Navigation Performance

Home-care manipulation robot requires exploring and performing the navigation task safely to reach the grasping target and ensure human safety in the home environment. An indoor home environment has complex obstacles such as chairs, tables, and sports equipment, which make it difficult for robots that rely on 2D laser rangefinders to detect. On the other hand, the conventional approaches overcome the problem by using 3D LiDAR, RGB-D camera, or fusing sensor data. The convolutional neural network has shown promising results in dealing with unseen obstacles in navigation by predicting the unseen obstacle from 2D grid maps to perform collision avoidance using 2D laser rangefinders only. Thus, this paper investigated the predicted grid map from the obstacle prediction network result for improving indoor navigation performance using only 2D LiDAR measurement. This work was evaluated by combining the configuration of the various local planners, type of static obstacles, raw map, and predicted map. Our investigation demonstrated that using the predicted grid map enabled all the local planners to achieve a better collision-free path by using the 2D laser rangefinders only rather than the RGB-D camera with 2D laser rangefinders with a raw map. This advanced investigation considers that the predicted map is potentially helpful for future work in the learning-based local navigation system.
Differential evolution with nearest density clustering for multimodal optimization problems

Multimodal optimization problems (MMOPs) refer to problems with multiple optimal solutions in a given search region. Evolutionary algorithms (EAs) are widely used to search for optimal solutions. To address the multimodal problem, we propose differential evolution with nearest density clustering (NDC-DE), which combines a density clustering-based technique and a differential evolutionary algorithm. NDC-DE includes three mechanisms. First, we use nearest density clustering (NDC) to divide the initial population into multiple subpopulations and identify the best individuals in each ecological niche. Combining niche techniques with evolutionary algorithms significantly improve their ability to solve MMOPs. Clustering algorithms are useful niche techniques divide the population into subpopulations based on different characteristics. Second, we propose two new mutation operators based on nbest. Last, an adaptive species redistribution mechanism based on the opposition-based learning (OBL) mechanism is proposed during the evolutionary iteration of NDC-DE to enhance the diversity of niches. We compare NDC-DE with 15 other algorithms on the CEC2013 benchmark function and show that it outperforms these algorithms in handling MMOPs.
Quantum continual learning of quantum data realizing knowledge backward transfer

For the goal of strong artificial intelligence that can mimic human-level intelligence, AI systems would have the ability to adapt to ever-changing scenarios and learn new knowledge continuously without forgetting previously acquired knowledge. When a machine learning model is consecutively trained on multiple tasks that come in sequence, its performance on previously learned tasks may drop dramatically during the learning process of the newly seen task. To avoid this phenomenon termed catastrophic forgetting, continual learning, also known as lifelong learning, has been proposed and become one of the most up-to-date research areas of machine learning. As quantum machine learning blossoms in recent years, it is interesting to develop quantum continual learning. This paper focuses on the case of quantum models for quantum data where the computation model and the data to be processed are both quantum. The gradient episodic memory method is incorporated to design a quantum continual learning scheme that overcomes catastrophic forgetting and realizes knowledge backward transfer. Specifically, a sequence of quantum state classification tasks is continually learned by a variational quantum classifier whose parameters are optimized by a classical gradient-based optimizer. The gradient of the current task is projected to the closest gradient, avoiding the increase of the loss at previous tasks, but allowing the decrease. Numerical simulation results show that our scheme not only overcomes catastrophic forgetting, but also realize knowledge backward transfer, which means the classifier's performance on previous tasks is enhanced rather than compromised while learning a new task.
Measuring semantic gap between user-generated content and product descriptions through compression comparison in e-commerce

The significance of user-generated content as a source for business intelligence and analytics has been on the rise since the inception of electronic commerce platforms and has been solidified in the wake of the pandemic due to the prominence of electronic commerce as a sales channel. The prevailing approach to harnessing unstructured data involves the utilization of Artificial Intelligence; however, there exist simpler alternatives capable of yielding valuable information. This article introduces a methodology grounded in information theory to quantify the semantic disparity between the consumer community and product descriptions. This disparity can result in potential misunderstandings in the dialogue among consumers, and incidental costs in the dialogue between consumers and vendors. One plausible explanation for this disparity is that the terminology employed by consumers may possess different meanings compared to that utilized by product description writers. Our methodology employs large corpora of consumer reviews and product descriptions to quantify this semantic disparity across multiple electronic commerce domains through the implementation of random word exchanges and compression. Furthermore, we utilize neural word embeddings to identify specific words exhibiting the greatest semantic drift between reviews and descriptions, thereby providing lexical examples of these gaps. Our findings indicate that lower levels of lexical-semantic gap are associated with better consumer satisfaction.
Th e value dimension of schooling through the pianoforte musical instrument in interwar Basarabia

At the beginning of the 19th century, new educational institutions with a musicalartistic profi le were opened, which signifi cantly increased the interest for studying the piano. At the same time, the musical life in Bessarabia is intensifying due to the artistic activity of some professional musicians with studies at conservatories in Russia and Western Europe, creating standards of music education and interpretive mastery, with a signifi cant impact on the professional training of pianists. Th e artistic activity for musical thinking involves performance communication, and the practical study of a musical instrument in its high creative manifestation is intellectually developmental and forms creative artistic consciousness in the student. Th e orientation towards the professional development of musical thinking in pianist students and their intellectual activation requires musical education in a specifi c way of teaching, such as, the penetration into the content of music through analytical interpretation, the provision of rational, priority instruction, in relation to the intuitive one.
Transfer Discriminant Softmax Regression with Weighted MMD

As an efficient classical machine learning classifier, the Softmax regression uses cross-entropy as the loss function. Therefore, it has high accuracy in classification. However, when there is inconsistency between the distribution of training samples and test samples, the performance of traditional Softmax regression models will degrade. A transfer discriminant Softmax regression model called Transfer Discriminant Softmax Regression with Weighted MMD (TDS-WMMD) is proposed in this paper. With this method, the Weighted Maximum Mean Divergence (WMMD) is introduced into the objective function to reduce the marginal distribution and conditional distribution between domains both locally and globally, realizing the cross domain transfer of knowledge. In addition, to further improve the classification performance of the model, Linear Discriminant Analysis (LDA) is added to the label iteration refinement process to improve the class separability of the designed method by keeping the same kind of samples together and the different kinds of samples repeling each other. Finally, after conducting classification experiments on several commonly used public transfer learning datasets, the results verify that the designed method can enhance the knowledge transfer ability of the Softmax regression model, and deliver higher classification performance compared with other current transfer learning classifiers.
The WhyRel Prototype for Modular Relational Verification of Pointer Programs

Abstract Verifying relations between programs arises as a task in various verification contexts such as optimizing transformations, relating new versions of programs with older versions (regression verification), and noninterference. However, relational verification for programs acting on dynamically allocated mutable state is not well supported by existing tools, which provide a high level of automation at the cost of restricting the programs considered. Auto-active tools, on the other hand, require more user interaction but enable verification of a broader class of programs. This article presents WhyRel, a tool for the auto-active verification of relational properties of pointer programs based on relational region logic. WhyRel is evaluated through verification case studies, relying on SMT solvers orchestrated by the Why3 platform on which it builds. Case studies include establishing representation independence of ADTs, showing noninterference, and challenge problems from recent literature.
Transdisciplinary Approach Application in Teaching English Spoken Interaction: Ukrainian University-A Case Study

This paper is a preliminary attempt to analyse the results of implementing the transdisciplinary approach in teaching English professional spoken interaction to mediators at the universities of Ukraine within emergency remote teaching. The study aims to broaden current knowledge of the transdisciplinary approach in Ukrainian university case study. The authors focused on the main characteristics of the transdisciplinary approach and elaborated the Triple Helix: English for specific purposes, mediation field, and stakeholders to ensure penetration of mediation knowledge, skills, and abilities into the knowledge, skills, and abilities of English for Specific Purposes (ESP). There are 42 future mediators who took part in the research. The stages and aims of the transdisciplinary approach in teaching English professional spoken interaction to mediators are identified. They are used to conduct quantitative and qualitative experimental data analysis during ESP teaching. The theoretical and practical analyses were used to stipulate the impact of the transdisciplinary approach on teaching English professional spoken interaction to mediators during emergency remote teaching. This research has shown that applying a transdisciplinary approach allows future mediators to develop professional skills in conflict detection, analysis, and management using different fields of knowledge. The prospect of implementing a transdisciplinary approach is a continuous incentive for future research with students of different specialisms.
Evaluation of an Augmented Reality for Historical Context Experiences of 3D Restored Court Paintings

Court painting is a traditional Korean type of painting that depicts royal events.It should be understood in the context of royal rituals and historical writing.Therefore, it is only possible for visitors of a museum with historical knowledge to interpret the content accurately.This problem is often identified in traditional paintings in other countries, and existing methods have attempted to deal with it using digital media technologies.Most methods aim to reconstruct paintings as 3D virtual objects for visitors to view them.However, these methods prevent visitors from interacting with the historical events depicted in the paintings and do not explain the essential historical facts and stories that the paintings leave out.In this study, we propose an augmented reality system that provides 3D virtual restorations of information necessary for understanding court paintings and supports historical context experiences of royal events.Specifically, we restore the information required for the interpretation of the painting to a virtual 3D object so that visitors can intuitively understand the historical stories missing from the painting.In addition, the system allows visitors to experience royal events through various interactions.The structural design combines experiential learning theory and digital storytelling so that visitors can be exposed to historical knowledge in a stepwise manner.According to the user study results, our system provides visitors with a more immersive experience and gives more accurate information about royal events compared to the existing method.This result suggests that the proposed system effectively communicates historical information and stories of court paintings to visitors who view paintings that require prior historical knowledge.
Improved prostate bioparameter magnetic resonance image segmentation based on Def-UNet

The prostate tissue structure is complex, the shape and size change is relatively large, and the surrounding anatomical structure is complex, so the task of segmenting prostate and prostate cancer is somewhat challenging. In this paper, the idea of deformable convolution is combined with the U-net algorithm widely used in medical image segmentation. By using the deformable convolution module at a specific position in the ordinary U-Net network structure, additional offsets can be added to the convolution operator and the spatial sampling position can be changed by learning the offset of the target segmentation area. The fixed receptive field of the traditional convolution operator is shifted to an adaptive receptive field that can feel the change of features, and the segmentation accuracy of the target area is improved. Experiments show that the algorithm can improve the accuracy of prostate segmentation. In this paper, the segmentation model trained with healthy prostate data is transferred to the prostate cancer data set for secondary training by simulating the way doctors read pictures. Experiments show that the segmentation effect of the lesion area is significantly improved compared with the network model trained directly with small sample prostate cancer data. The research results can provide further exploration ideas for the application of medical domain knowledge in deep learning models.
A real-time framework for dangerous behavior detection based on deep learning

Identifying dangerous behaviors of workers in complex production operation scenarios is an important research field of intelligent monitoring technology. When implementing practical applications, it often has complex and diverse function requirements and business logic. To this end, this paper proposes a unified and simple framework for monitoring unsafe behavior based on deep learning technology. We first conduct data analysis and demand logic disassembly based on six actual production scenarios and decouple the complex task requirement as a coupled problem of object detection function, adaptive and variable scene recognition function, behavior analysis function, and safety logic reasoning function. Then we build a unified detection framework and use four sub-modules for real-time and high-efficiency detection, which are a perceptron-based efficient scene recognition module, a Yolov5s-based real-time object detection module, an area-based behavior judgment module, and a configurable safety rule inference module. While maintaining the characteristics of splitting and combining these modules for different application requirements, their effects on the test set have also reached the best. Among them, the accuracy rate of the scene recognition module has reached 100.00%, and the mAP of the object detection module has reached 99.06%, average FPS of the overall framework reached 72.05.
Runtime Enforcement Using Knowledge Bases

Abstract Knowledge bases have been extensively used to represent and reason about static domain knowledge. In this work, we show how to enforce domain knowledge about dynamic processes to guide executions at runtime. To do so, we map the execution trace to a knowledge base and require that this mapped knowledge base is always consistent with the domain knowledge. This means that we treat the consistency with domain knowledge as an invariant of the execution trace. This way, the domain knowledge guides the execution by determining the next possible steps, i.e., by exploring which steps are possible and rejecting those resulting in an inconsistent knowledge base. Using this invariant directly at runtime can be computationally heavy, as it requires to check the consistency of a large logical theory. Thus, we provide a transformation that generates a system which is able to perform the check only on the past events up to now, by evaluating a smaller formula. This transformation is transparent to domain users, who can interact with the transformed system in terms of the domain knowledge, e.g., to query computation results. Furthermore, we discuss different mapping strategies.
Multi-level feature fusion capsule network with self-attention for facial expression recognition

Different from generic image classification, fine-grained classification, such as facial expression classification, in which multiple expressions share inherently similar underlying facial appearances, may show a small difference between facial expression classes. Unlike lab-controlled data, facial expressions from natural scenes have rich forms of the same expression due to the diversity of subjects and the complexity of real-world conditions, and as a result, facial expressions may have large differences among samples within the same class. Moreover, there is little difference between facial expressions, and facial expressions are displayed simultaneously through various facial regions, which require us to encode the feature of multiple key regions, forming high-order interactive information. To address the aforementioned problems, we design an enhanced capsule network based on multi-level feature fusion attention mechanism, which is comprised of four critical components: multi-level feature extraction module (MFEM), multi-level attention module (MAM), multi-level capsule attention fusion module (MCAFM), and reconstruction module (RM). The MFEM collects the low-level, middle-level, and high-level features from the input image, therefore lowering the high-level convolution layer’s susceptibility to blurred image and the problem of pose variation. The MAM directs the network’s attention to the most significant features in different levels of image features and can assist the network in ignoring blurred, occluded, and irrelevant features and incorporating them into our self-attention center loss function to compress the element distribution in the same class. The MCAFM preserves the attributes of each face region (such as location, size, and direction) by transferring the features into capsules in preparation for the eventual creation of the dynamic routing mechanism, which can resolve the problem of image rotation on FER in the wild. Simultaneously, the capsule features of distinct areas are combined to provide higher-order overall feature information, enhancing the model’s capacity to discriminate between different kinds of expressions. The RM reconstructs the image and calculates the difference between the reconstructed image and the original input image. Our model outperforms a large number of current methods on two public datasets, RAF-DB and SFEW.