Reviewerly/exHarmony huggingface dataset:
An Incremental Broad-Learning-System-Based Approach for Tremor Attenuation for Robot Tele-Operation

The existence of the physiological tremor of the human hand significantly affects the application of tele-operation systems in performing high-precision tasks, such as tele-surgery, and currently, the process of effectively eliminating the physiological tremor has been an important yet challenging research topic in the tele-operation robot field. Some scholars propose using deep learning algorithms to solve this problem, but a large number of hyperparameters lead to a slow training speed. Later, the support-vector-machine-based methods have been applied to solve the problem, thereby effectively canceling tremors. However, these methods may lose the prediction accuracy, because learning energy cannot be accurately assigned. Therefore, in this paper, we propose a broad-learning-system-based tremor filter, which integrates a series of incremental learning algorithms to achieve fast remodeling and reach the desired performance. Note that the broad-learning-system-based filter has a fast learning rate while ensuring the accuracy due to its simple and novel network structure. Unlike other algorithms, it uses incremental learning algorithms to constantly update network parameters during training, and it stops learning when the error converges to zero. By focusing on the control performance of the slave robot, a sliding mode control approach has been used to improve the performance of closed-loop systems. In simulation experiments, the results demonstrated the feasibility of our proposed method.
Improved Detector Based on Yolov5 for Typical Targets on the Sea Surfaces

Detection of targets on sea surfaces is an important area of application that can bring great benefits to the management and control systems in marine environments. However, there are few open-source datasets accessible for the purpose of object detection on seas and rivers. In this paper, a study is conducted on the improved detection algorithms based on the YOLOv5 model. The dataset for the tests contains ten categories of typical objects that are commonly seen in the contexts of seas, including ships, devices, and structures. Multiple augmentation methods are employed in the pre-processing of the input data, which are verified to be effective in enhancing the generalization ability of the algorithm. Moreover, a new form of the loss function is proposed that highlights the effects of the high-quality boxes during training. The results demonstrate that the adapted loss function contributes to a boost in the model performance. According to the ablation studies, the synthesized methods raise the inference accuracy by making up for several shortcomings of the baseline model for the detection tasks of single or multiple targets from varying backgrounds.
Casting Rubik’s Group into a Unitary Representation for Reinforcement Learning

Abstract Rubik’s Cube is one of the most famous combinatorial puzzles involving nearly 4.3 × 10 19 possible configurations. However, only a single configuration matches the solved one. Its mathematical description is expressed by the Rubik’s group, whose elements define how its layers rotate. We develop a unitary representation of the Rubik’s group and a quantum formalism to describe the Cube based on its geometrical constraints. Using single particle quantum states, we describe the cubies as bosons for corners and fermions for edges. By introducing a set of four Ising-like Hamiltonians, we managed to set the solved configuration of the Cube as the global ground state for all the Hamiltonians. To reach the ground state of all the Hamiltonian operators, we made use of a Deep Reinforcement Learning algorithm based on a Hamiltonian reward. The Rubik’s Cube is successfully solved through four phases, each phase driven by a corresponding Hamiltonian reward based on its energy spectrum. We call our algorithm QUBE , as it employs quantum mechanics to tackle the combinatorial problem of solving the Rubik’s Cube. Embedding combinatorial problems into the quantum mechanics formalism suggests new possible algorithms and future implementations on quantum hardware.
SFBKT: A Synthetically Forgetting Behavior Method for Knowledge Tracing

Knowledge tracing (KT) aims to model students’ knowledge levels based on their historical learning records and predict their future learning performance, which constitutes an essential component of intelligent education. Learning and forgetting are closely related, and forgetting can often interfere with the learning process. Prior research has employed diverse techniques to address the issue of interference caused by forgetting factors in predictions, yet many of these methods fail to fully leverage the forgetting information contained within learning records. This paper proposes a synthetically forgetting behavior knowledge tracing (SFBKT) model that comprehensively models a student’s knowledge level by considering both individual forgetting factors and group status. Specifically, the model initially extracts forgetting information from exercise records in the input module, then updates the student’s knowledge state through an improved continuous-time long short-term memory network (CTLSTM), and finally combines the individual state with the group state using collaborative filtering to predict the student’s ability to correctly answer the next exercise. Our predictive model has been evaluated using four public education datasets. The experimental results indicate that our model’s predictions are effective and outperform other existing methods.
Action Recognition via Adaptive Semi-Supervised Feature Analysis

This study presents a new semi-supervised action recognition method via adaptive feature analysis. We assume that action videos can be regarded as data points in embedding manifold subspace, and their matching problem can be quantified through a specific Grassmannian kernel function while integrating feature correlation exploration and data similarity measurement into a joint framework. By maximizing the intra-class compactness based on labeled data, our algorithm can learn multiple features and leverage unlabeled data to enhance recognition. We introduce the Grassmannian kernels and the Projected Barzilai–Borwein (PBB) method to train a subspace projection matrix as a classifier. Experiment results show our method has outperformed the compared approaches when a few labeled training samples are available.
The effect of usability, information quality, and accessibility on the intensity of the madrasa website use at Madrasah Aliyah level

The majority of academic communities in madrasa access the madrasa website infrequently. It is related to the user’s perception of the madrasa website’s quality. This study seeks to determine how usability, information quality, and accessibility effect the frequency with which madrasa websites are visited. This study proposes a correlational quantitative research methodology. The research cohort consisted of 2401 MAN Surakarta students, and the sample size was 96. The method of sampling is proportional random sampling. Data collection techniques using a questionnaire. Techniques of data analysis utilizing correlation analysis and single regression to test the hypotheses of single correlation, correlation, and multiple regression. The results of the study indicate that: 1) Usability has a positive effect on the intensity of website use; 2) Information quality has a positive effect on the intensity of website use; 3) Accessibility has a positive effect on the intensity of website use; and 4) Usability, information quality, and accessibility all have a positive effect on the intensity of website use. Usability, information quality, and accessibility collectively contribute 51.1% to the website's intensity, while the remaining 48.9% is influenced by unmeasured variables.
An Opcode-Based Vulnerability Detection of Smart Contracts

Aiming at the problem of insufficient technology for detecting smart contract vulnerabilities and the lack of improvement of certain detection tools, a method for the expansion and improvement of the internal module of the Mythril method is proposed. Since the technology of detecting vulnerabilities is not yet mature and there are vulnerabilities in smart contracts, vulnerability detection is particularly important. Since the Mythril tool covers the most types of vulnerabilities, its accuracy rate is also the highest. In order to ensure the effect of smart contract vulnerability detection, this paper proposes the expansion and improvement of the internal module of Mythril, which realizes the operation of automatic vulnerability analysis when performing vulnerability detection by improving the operation efficiency and simplifying the operation of smart contract opcodes while analyzing them. The comparison of experimental results shows that the proposed method is more suitable for smart contract vulnerability detection, and the detection accuracy and efficiency are improved, with an average accuracy rate of 94.07%. It performs better in vulnerability detection and provides an effective guarantee for the security and reliability of smart contracts, which has important application value and research significance.
A reality check on research reproducibility in Open Science students’ projects

Open Science as the foundation of transparent and reproducible science is increasingly being incorporated into curricula. We argue that Open Science education is predestined not to be taught in classical lectures, but to be experienced first-hand as reproduction studies in student projects. The case study of a successful Master’s module is presented from three perspectives: the lecturers’, the students’ and the researchers’ whose published study was reproduced. This illustrates that attempting to reproduce a published study is a very vivid and sustainable learning experience that naturally incorporates many Open Science topics, and that the students’ work contributes to increasing the number of reproduced studies and ensuring the quality of the published body of scientific knowledge.
Machine learning and the James–Stein estimator

Abstract It is now 62 years since the publication of James and Stein’s seminal article on the estimation of a multivariate normal mean vector. The paper made a spectacular first impression on the statistical community through its demonstration of inadmissability of the maximum likelihood estimator. It continues to be influential, but not for the initial reasons. Empirical Bayes shrinkage estimation, now a major topic, found its early justification in the James–Stein formula. Less obvious downstream topics include Tweedie’s formula and Benjamini and Hochberg’s false discovery rate algorithm. This is a short and mainly non-technical review of the James–Stein rule and its effects on the machine learning era of statistical innovation.
Feature-preserving color pencil drawings from photographs

Abstract Color pencil drawing is well-loved due to its rich expressiveness. This paper proposes an approach for generating feature-preserving color pencil drawings from photographs. To mimic the tonal style of color pencil drawings, which are much lighter and have relatively lower saturation than photographs, we devise a lightness enhancement mapping and a saturation reduction mapping. The lightness mapping is a monotonically decreasing derivative function, which not only increases lightness but also preserves input photograph features. Color saturation is usually related to lightness, so we suppress the saturation dependent on lightness to yield a harmonious tone. Finally, two extremum operators are provided to generate a foreground-aware outline map in which the colors of the generated contours and the foreground object are consistent. Comprehensive experiments show that color pencil drawings generated by our method surpass existing methods in tone capture and feature preservation.
DIGITAL SECURITY IN EDUCATIONAL TRAINING PROGRAMS: A STUDY BASED ON FUTURE TEACHERS’ PERCEPTIONS

A key aspect of future teacher training in this highly digitalized period of education requires a specific focus on digital skills, especially in the area of security. This study delves into this issue by means of a quantitative, theoretical and systematic review of future teachers currently in training at the Faculty of Education, Economy and Technology of Ceuta of the University of Granada (Spain). The specific intention is to determine their perception of the instruction they receive on digital matters, concretely on cybersecurity during their phase of pre-service. To carry out the project, the study resorted to a descriptive method by submitting the participants to a specifically designed questionnaire entitled “Digital Security Competence Required for Teachers”. Examinations of the psychometric properties of the questionnaire reveal its relevance and reliability. The intention, after applying descriptive and inferential statistics tests, was to determine whether students in pre-service training really feel competent on digital matters or if they feel need more theoretical-practical training, especially in the area of security. The general findings reveal that future female teachers pursuing a university degree in Early Childhood Education, although competent from the techno-pedagogical viewpoint, indicate that attaining a high level of competence in digital security requires more theoretical and practical training. Future male Primary Education teachers, by contrast, perceive themselves as competent in digital security. This perhaps stems from their training (courses and workshops) in Information and Communication Technologies (ICT) and the theoretical and practical instruction they received at the university during courses or during a practicum. This aspect deserves further research because, as highlighted elsewhere, higher education institutions must prioritize instruction on digital security in their teacher programs.
Extraction of Liver Capsule from High-Frequency Ultrasound Images via Drift Iterative Search Algorithm

Aiming at the prior medical knowledge that hepatic ascites only occurs in the severe period of liver cirrhosis, and the severe rupture of the liver capsule curve, when ascites occurs visually, can easily lead to the wrong location of the liver capsule, a transposed grayscale statistical threshold method is proposed to solve the problem.Realize the identification of liver ascites.By analyzing the visual characteristics of the liver image, the gray value of the upper half of the ultrasound image is counted column by column from a mathematical point of view, the gray distribution curve is drawn, and the relevant threshold is set for corresponding judgment.At the same time, the gray value above the ascites detection boundary is set to zero.The ablation experiment proved that the ascites detection method and post-processing operation proposed in this paper provide effective support for the precise positioning of the liver capsule curve, quantitative analysis and diagnosis of liver cirrhosis in the later stage.The Hessian matrix is sensitive to linear structure to achieve image enhancement.In view of the low accuracy of the existing liver envelope curve detection method and the incomplete quantitative evaluation of liver cirrhosis, it is proposed to use drift iteration under the synergistic effect of multiple filters.A search algorithm extracts the liver capsule.
Data Mining Using Association Rules for Intuitionistic Fuzzy Data

This paper considers approaches to the computation of association rules for intuitionistic fuzzy data. Association rules can provide guidance for assessing the significant relationships that can be determined while analyzing data. The approach uses the cardinality of intuitionistic fuzzy sets that provide a minimum and maximum range for the support and confidence metrics. A new notation is used to enable the representation of the fuzzy metrics. A running example of queries about the desirable features of vacation locations is used to illustrate.
Evaluation of Krueng Tripa River Capacity in Ujung Krueng Village, Nagan Raya Regency, Indonesia

Abstract Flood events are very common in the lowlands as often happens in Ujong Krueng Village, Tripa Makmur District. This study aims to determine the magnitude of the design flood discharge using the Nakayasu method and evaluating the cross-sectional capacity of the Krueng Tripa River in Ujong Krueng Village. The data used are both primary and secondary. Primary data consists of measurements of river slope, river flow velocity, river depth and river width, while secondary data consists of rainfall data, Krueng Tripa watershed maps and Krueng Tripa River situation maps. The results of the design flood discharge calculation using the Nakayasu method show that the 2-year return period (RP) is 3237.46 m 3 /s, the 5-year RP is 4389.53 m 3 /s, the 10-year RP is 5177.08 m 3 /s, the 10-year RP is 5177.08 m 3 /s, the 25-year RP is 6199.32 m 3 /s, the 50-year RP is 6979.04 m/s, and the 100-years RP is 7778.23 m 3 /s. From the results of hydraulic analysis on the section of the Krueng Tripa river in Ujong Krueng Village using the HEC-RAS 6.1.0 software , it shows that in the 2-year return period, on pias 1, 7 and 8 experienced overflows on both sides while on pias 2, 3, 4, 5 and 6 only overflow on the right side. In the return periods of 5 th , 10 th , 25 th , 50 th - year and 100 th , all the streams had overflowed, the highest overflow reached 4.84 m, while the lowest overflow reached 0.30 m.
Grammatical Evolution-Driven Algorithm for Efficient and Automatic Hyperparameter Optimisation of Neural Networks

Neural networks have revolutionised the way we approach problem solving across multiple domains; however, their effective design and efficient use of computational resources is still a challenging task. One of the most important factors influencing this process is model hyperparameters which vary significantly with models and datasets. Recently, there has been an increased focus on automatically tuning these hyperparameters to reduce complexity and to optimise resource utilisation. From traditional human-intuitive tuning methods to random search, grid search, Bayesian optimisation, and evolutionary algorithms, significant advancements have been made in this direction that promise improved performance while using fewer resources. In this article, we propose HyperGE, a two-stage model for automatically tuning hyperparameters driven by grammatical evolution (GE), a bioinspired population-based machine learning algorithm. GE provides an advantage in that it allows users to define their own grammar for generating solutions, making it ideal for defining search spaces across datasets and models. We test HyperGE to fine-tune VGG-19 and ResNet-50 pre-trained networks using three benchmark datasets. We demonstrate that the search space is significantly reduced by a factor of ~90% in Stage 2 with fewer number of trials. HyperGE could become an invaluable tool within the deep learning community, allowing practitioners greater freedom when exploring complex problem domains for hyperparameter fine-tuning.
The Library of the Norwegian University of Science and Technology (NTNU) and its work with Open Science

The article describes the library of the Norwegian University of Science and Technology and its work with Open Science. It gives a brief overview over the university and the library and presents the new organisational structure of the library. Afterwards, important strategy documents and guidelines for Open Science, Open Access, and Research Data Management at NTNU are introduced and the practical work with it is described. In the end, a selection of other projects and focus areas are mentioned.
CLUE-AI: A Convolutional Three-Stream Anomaly Identification Framework for Robot Manipulation

Despite the great promise of service robots in everyday tasks, many roboethics issues remain to be addressed before these robots can physically work in human environments. Robot safety is one of the essential concerns for roboethics which is not just a design-time issue. It is also crucial to devise the required onboard monitoring and control strategies to enable robots to be aware of and react to anomalies (i.e., unexpected deviations from intended outcomes) that arise during their operations in the real world. The detection and identification of these anomalies is an essential first step toward fulfilling these requirements. Although several architectures have been proposed for anomaly detection; identification has not yet been thoroughly investigated. This task is challenging since indicators may appear long before anomalies are detected. In this paper, we propose a ConvoLUtional threE-stream Anomaly Identification (CLUE-AI) framework to address this problem. The framework fuses visual, auditory and proprioceptive data streams to identify everyday object manipulation anomalies. A stream of 2D images gathered through an RGB-D camera placed on the head of the robot is processed within a self-attention-enabled visual stage to capture visual anomaly indicators. The auditory modality provided by the microphone placed on the robot’s lower torso is processed within a designed convolutional neural network (CNN) in the auditory stage. Last, the force applied by the gripper and the gripper state is processed within a CNN to obtain proprioceptive features. These outputs are then combined with a late fusion scheme. Our novel three-stream framework design is analyzed on everyday object manipulation tasks with a Baxter humanoid robot in a semi-structured setting. The results indicate that CLUE-AI achieves an f-score of 94%, outperforming the other baselines in classifying anomalies.
PEJL: A path-enhanced joint learning approach for knowledge graph completion

&lt;abstract&gt;&lt;p&gt;Knowledge graphs (KGs) often suffer from incompleteness. Knowledge graph completion (KGC) is proposed to complete missing components in a KG. Most KGC methods focus on direct relations and fail to leverage rich semantic information in multi-hop paths. In contrast, path-based embedding methods can capture path information and utilize extra semantics to improve KGC. However, most path-based methods cannot take advantage of full multi-hop information and neglect to capture multiple semantic associations between single and multi-hop triples. To bridge the gap, we propose a novel path-enhanced joint learning approach called PEJL for KGC. Rather than learning multi-hop representations, PEJL can recover multi-hop embeddings by encoding full multi-hop components. Meanwhile, PEJL extends the definition of translation energy functions and generates new semantic representations for each multi-hop component, which is rarely considered in path-based methods. Specifically, we first use the path constraint resource allocation (PCRA) algorithm to extract multi-hop triples. Then we use an embedding recovering module consisting of a bidirectional gated recurrent unit (GRU) layer and a fully connected layer to obtain multi-hop embeddings. Next, we employ a KG modeling module to leverage various semantic information and model the whole knowledge graph based on translation methods. Finally, we define a joint learning approach to train our proposed PEJL. We evaluate our model on two KGC datasets: FB15K-237 and NELL-995. Experiments show the effectiveness and superiority of PEJL.&lt;/p&gt;&lt;/abstract&gt;