Reviewerly/exHarmony huggingface dataset:
Mutli-Robot Cooperative Object Transportation with Guaranteed Safety and Convergence in Planar Obstacle Cluttered Workspaces via Configuration Space Decomposition

In this work, we consider the autonomous object transportation problem employing a team of mobile manipulators within a compact planar workspace with obstacles. As the object is allowed to translate and rotate and each robot is equipped with a manipulator consisting of one or more moving links, the overall system (object and mobile manipulators) should adapt its shape in a flexible way so that it fulfills the transportation task with safety. To this end, we built a sequence of configuration space cells, each of which defines an allowable set of configurations of the object, as well as explicit intervals for each manipulator’s states. Furthermore, appropriately designed under- and over-approximations of the free configuration space are used in an innovative way to guide the configuration space’s exploration without loss of completeness. In addition, we coupled methodologies based on Reference Governors and Prescribed Performance Control with harmonic maps, in order to design a distributed control law for implementing the transitions specified by the high-level planner, which possesses guaranteed invariance and global convergence properties, thus avoiding the requirement for synchronized motion as inherently dictated by the majority of the related works. Furthermore, the proposed low-level control law does not require continuous information exchange between the robots, which rely only on measurements of the object’s configuration and their own states. Finally, a transportation scenario within a complex warehouse workspace demonstrates the proposed approach and verifies its efficiency.
Fully Connected Hashing Neural Networks for Indexing Large-Scale Remote Sensing Images

With the emergence of big data, the efficiency of data querying and data storage has become a critical bottleneck in the remote sensing community. In this letter, we explore hash learning for the indexing of large-scale remote sensing images (RSIs) with a supervised pairwise neural network with the aim of improving RSI retrieval performance with a few binary bits. First, a fully connected hashing neural network (FCHNN) is proposed in order to map RSI features into binary (feature-to-binary) codes. Compared with pixel-to-binary frameworks, such as DPSH (deep pairwise-supervised hashing), FCHNN only contains three fully connected layers and incorporates another new constraint, so it can be significantly accelerated to obtain desirable performance. Second, five types of image features, including mid-level and deep features, were investigated in the learning of the FCHNN to achieve state-of-the-art performances. The mid-level features were based on Fisher encoding with affine-invariant local descriptors, and the deep features were extracted by pretrained or fine-tuned CNNs (e.g., CaffeNet and VGG-VD16). Experiments on five recently released large-scale RSI datasets (i.e., AID, NWPU45, PatternNet, RSI-CB128, and RSI-CB256) demonstrated the effectiveness of the proposed method in comparison with existing handcrafted or deep-based hashing methods.
A Comparison of Automated Named Entity Recognition Tools Applied to Clinical Text

Significant part of the data in electronic health records (EHR) is in the form of free text containing valuable information about clinical events. This information needs to be extracted in order to enable further analysis and utilization in daily healthcare setting as well as in research. Clinical named entity recognition is an important natural language processing (NLP) task that is critical for extracting important concepts (named entities) from clinical narratives and encoding them. The aim of this paper was comparison of an automatic entity recognition performance of Amazon Comprehend Medical (ACM), Clinical Language Annotation, Modeling and Processing (CLAMP) toolkit and Spark NLP on a standardized validated dataset and its annotated entities. Recall, precision and F1-score were used to evaluate accuracy and performance of the tools. Spark NLP outperformed ACM and CLAMP in terms of average recall, however CLAMP showed better performance in terms of average precision. Depending on the application, CLAMP and Spark NLP can be used in real world applications.
Life cycle: semantic matrix of process modeling

Acceleration of socio-technological changes increases demands and complexity of process modeling tasks. Developing the appropriate thinking skills is becoming a mass necessity. This paper describes a method for representation of pro- cesses, allowing systematization of such skill based on life cycle. Any process is then represented as a sequence of stag- es: perception, analysis, design, action, coordination, and evaluation. This sequence is formalized as a circular trajecto- ry on a phase space, the Cartesian axes of which are the activity and potency factors of classical semantics. As a result, the scheme receives a simple mathematical expression and is interfaced with standard methods of semantic measure- ments. The experimentally presented structure was revealed in the word2vec English language vector model, which additionally aligns it with machine learning and artificial intelligence algorithms. The developed model is considered as general ontology of processes in Nature, used in natural thinking as a stereotype of building cause-and-effect models of events. The result obtained opens up opportunities for the development of methods of process and causal-sense model- ing for scientific, technical and everyday purposes.
AI-Based Chatbot

Abstract: A chatbot is software used to develop interaction between a user/human and a computer/system in natural language, similar to human chats. Chatbots converse with the customer in a discussion following input from a human and a response to the customer. It makes the user believe that he is chatting with a human while chatting with the computer. The chatbot application helps the student to get information about the college admission process and get quick answers from anywhere with an internet connection. This chatbot system reduces the workload of the admissions department by providing students or parents with the information they need and also reduces the workload of the department, which has to constantly answer all the students' questions.
Implementasi Customer Relationship Management Berbasis Web Pada Toko Baizuri Bunut

Engaged in sales, Baizuri Bunut Store provides all personal needs, such as shoes, wallets and belts. The ordering process made by customers in ordering a product is still manual, namely by visiting the Baizuri Store directly. Another problem is the lack of facilities that help customers to get information about products, and interact with the store to submit suggestions and criticisms. As well as experiencing difficulties in the process of managing and storing customer data that has made transactions. In the services provided by the Baizuri Bunut Store, it is also still not effective, thereby reducing the level of effectiveness of competition in the business, the demand for products is increasing day by day, which can trigger the Baizuri Bunut Store to be able to compete in providing quality products and increase competitive advantage. This can be achieved by managing the flow of information, the flow of the sales system, between suppliers, companies and distributors using Customer Relationship Management. By using qualitative methods with interviews or direct observation, aims to describe the facts of a situation at the research site. The purpose of this research is to provide information about products such as shoes, wallets and belts produced by the Baizuri Bunut Store in an updated manner and to be able to promote their business and business at a low cost but have extraordinary effectiveness and increase the efficiency of product information processing management.
Enhancing Storage Capabilities of Oscillatory Neural Networks as Associative Memory

An Oscillatory Neural Network (ONN) resorts to oscillatory-based computing using emerging nano-oscillators and phase-encoded information for extremely energy-efficient processing. Typically, ONN is trained to learn a set of patterns that are fixed as attractor states, being able to operate as an associative memory that can perform pattern recognition tasks. In this work, Hebbian learning rule limitation is surpassed with different training approaches based on Storkey algorithm. Trained networks are evaluated in terms of capacity and noise robustness. Experiments using 60-neuron and 96-neuron network with training sets of digits and characters are used as case studies.
Research on the construction and application of computer network experimental technology teaching mode

In the development of modern society, the computer network experiment course can train excellent technical talents at the same time, continue to optimize the computer network experiment technology teaching system. Under the background of modern education reform, the traditional sense directly use the technology of network to carry on the teaching management mode, has been unable to meet the demand of students, so the education field according to the outline of colleges and universities and students' learning ability, made more perfect and suitable for the experiment content, only in this way can cultivate talents needed for the social development of new era. Therefore, on the basis of understanding the current development status of computer network experiment technology course, this paper mainly discusses the implementation effect of computer network experiment teaching with Cisco Packet Tracer tool as the core according to the curriculum reform ideas put forward in educational innovation, so as to achieve the expected educational goals.
Active vibration control of flexible beam system based on cuckoo search algorithm

&lt;p&gt;A flexible beam is recognized as a lightweight structure that is prone to excessive vibration, resulting in poor performance. Thus, controlling unwanted vibration is necessary to maintain the system’s performance. Therefore, this study presents a technique to suppress undesired vibration in a flexible beam structure by introducing active vibration control (AVC). However, to develop an effective controller, an appropriate flexible beam model must first be obtained. In recent times, one of the best methods employed to model a flexible beam structure is system identification via a swarm intelligence algorithm. In this study, an intelligent algorithm acknowledged as cuckoo search (CS) was acquainted. The capability of the proposed algorithm was verified using three robustness techniques which were correlation test, pole-zero diagrams and mean square error (MSE). The simulation result showed that the CS algorithm achieved superior performance by achieving the lowest MSE of 6.1547x10&lt;sup&gt;-9&lt;/sup&gt;, a correlation test between a 95% confidence level and high stability. Next, a proportional-integral-derivative (PID) controller tuned by the Ziegler-Nichols method was developed using the transfer function accomplished from the CS model. Two types of interference, namely single and multiple sine waves were introduced to validate the effectiveness of the controller. The controller successfully achieved a 30.2 dB of attenuation level for both disturbances.&lt;/p&gt;
Effect of Wigner rotation on estimating the unitary-shift parameter of a relativistic spin-1/2 particle

We obtain the accuracy limit for estimating the expectation value of the position of a relativistic particle for an observer moving along one direction at a constant velocity. We use a specific model of a relativistic spin-1/2 particle described by a gaussian wave function with a spin down in the rest frame. To derive the state vector of the particle for the moving observer, we use the Wigner rotation that entangles the spin and the momentum of the particle. Based on this wave function for the moving frame, we obtain the symmetric logarithmic derivative (SLD) Cramer-Rao bound that sets the estimation accuracy limit for an arbitrary moving observer. It is shown that estimation accuracy decreases monotonically in the velocity of the observer when the moving observer does not measure the spin degree of freedom. This implies that the estimation accuracy limit worsens with increasing the observer's velocity, but it is finite even in the relativistic limit. We derive the amount of this information loss by the exact calculation of the SLD Fisher information matrix in an arbitrary moving frame.
Heterogeneous graph construction and HinSAGE learning from electronic medical records

Abstract Graph representation learning is a method for introducing how to effectively construct and learn patient embeddings using electronic medical records. Adapting the integration will support and advance the previous methods to predict the prognosis of patients in network models. This study aims to address the challenge of implementing a complex and highly heterogeneous dataset, including the following: (1) demonstrating how to build a multi-attributed and multi-relational graph model (2) and applying a downstream disease prediction task of a patient’s prognosis using the HinSAGE algorithm. We present a bipartite graph schema and a graph database construction in detail. The first constructed graph database illustrates a query of a predictive network that provides analytical insights using a graph representation of a patient’s journey. Moreover, we demonstrate an alternative bipartite model where we apply the model to the HinSAGE to perform the link prediction task for predicting the event occurrence. Consequently, the performance evaluation indicated that our heterogeneous graph model was successfully predicted as a baseline model. Overall, our graph database successfully demonstrated efficient real-time query performance and showed HinSAGE implementation to predict cardiovascular disease event outcomes on supervised link prediction learning.
A Programming Language Characterizing Quantum Polynomial Time

Abstract We introduce a first-order quantum programming language, named foq , whose terminating programs are reversible. We restrict foq to a strict and tractable subset, named pfoq , of terminating programs with bounded width, that provides a first programming language-based characterization of the quantum complexity class fbqp . We finally present a tractable semantics-preserving algorithm compiling a pfoq program to a quantum circuit of size polynomial in the number of input qubits.
Generalized Laplace Particle Filter on Lie Groups Applied to Ambiguous Doppler Navigation

Particle filters are suited to solve nonlinear and non-Gaussian estimation problems which find numerous applications in autonomous systems navigation. Previous works on Laplace Particle Filter on Lie groups (LG-LPF) demonstrated its robustness and accuracy on challenging navigation scenarios compared to classic particle filters. Nevertheless, LG-LPF is applicable when the prior probability density and the likelihood have a predominant mode, which narrows the scope of applications of this method. Thus, this paper proposes a generalized strategy to use LG-LPF while keeping its benefits. The core idea is to compute an accurate multimodal importance function based on local optimizations and resample the particles accordingly. This approach is compared to a Laplace Particle Filter (LPF) designed in the Euclidean space, on a UAV navigation scenario with ambiguous Doppler measurements. The Lie group approach shows improved accuracy and robustness in every case, even with a reduced number of particles.
Does Father Christmas Have a Distinctive Facial Phenotype?

We investigated whether Father Christmas has a distinguishable facial phenotype by performing a cross-sectional cohort study examining the facial feature vectors of all publicly available photographs obtained from a google image search of individuals meeting our eligibility criteria presenting as Father Christmas compared with other adult and elderly bearded men. Facial feature vectors were determined using the open-source OpenFace facial recognition system and assessed by support vector machines (SVM). SVM classifiers were trained to distinguish between the facial feature vectors from our groups. Accuracy, precision, and recall results were calculated and the area under the curve (AUC) of the receiver operating characteristic (ROC) were reported for each classifier. SVM classifiers were able to distinguish the face of Father Christmas from other adult men with a high degree of accuracy and could discriminate Father Christmas from elderly bearded men but with lower accuracy. Father Christmas appears to have a distinct facial phenotype when compared to adult men and elderly bearded men. This will be reassuring to children who may be keen to recognise him but raises some interesting questions about the careful use of two-dimensional facial analysis, particularly when employed to explore the relationships between genotype and facial phenotype in a clinical dysmorphology setting.
Education as an Effective Component of Political Development and Socio-Economic Prosperity in Society

This article shows the importance of education. Education forms a personality that is able to realize its potential in the process of development throughout life. The purpose of the article is to determine the features of the formation of a personality that strives for self-realization, which is possible only in the educational influence on the personality. The relevance of the article is due to the need for social development, which is the result of a combination of many components that form the basic needs of every person. The theoretical and methodological basis of the article is devoted to the justification of socio-economic policy in the coverage of the educational process, the discussion and systematic analysis form the essence of the thesis that the world is an effective component of the political development and socio-economic prosperity of society, which leads to the need to control the processes that occur in education. The research used an integration method of synthesis of the figurative structure of the educational process and analysis, characteristics of education as a political phenomenon and socio-economic development. The methods of synthesis, analysis and interpretation of educational technologies are used. The research findings confirm the thesis about education as a factor of political and socio-economic development. The results of the research do not exhaust all possible options, but they are the basis for proving the main goal.
A Strategy for Enhancing English Learning Achievement, Based on the Eye-Tracking Technology with Self-Regulated Learning

Owing to the global promotion of e-learning, combining recognition technology to facilitate learning has become a popular research topic. This study uses eye-tracking to analyze students’ actual learning situations by examining their attention during the learning process and to provide timely support to enhance their learning performance. Using cognitive technology, this study can analyze students’ real-time learning status, which can be utilized to provide timely learning reminders that help them achieve their self-defined learning goals and to effectively enhance their interest and performance. Accordingly, we designed a self-regulated learning (SRL) mechanism, based on eye-tracking technology, combined with online marking and note-taking functions. The mechanism can aid students in maintaining a better reading state, thereby enhancing their learning performance. This study explores students’ learning outcomes, motivation, self-efficacy, learning anxiety, and performance. The experimental results show that students who used the SRL mechanism exhibited a greater learning performance than those who did not use it. Similarly, SRL mechanisms could potentially improve students’ learning motivation and self-efficacy, as well as increase their learning attention. Moreover, SRL mechanisms reduce students’ perplexities and learning anxieties, thereby enhancing their reading-learning performance to achieve an educational sustainability by providing a better e-learning environment.
Exploring English Teachers’ Perception on the Teaching of Using Story Telling Viewed from Phonology Awareness to Improve Students’ Speaking Skills amid Covid-19 Pandemic

Storytelling is a learning strategy of communication that occur a date earlier in writing history to teach lessons. It gives the fundamental structure of how learners create information out of their everyday lives. In practice, the students’ brain is tied to manage and organize information through story and that every relationship experience is recorded in their mind as an informative story. Storytelling is one of the technique that helps the students master English, especially in speaking skills. Therefore, this study is aimed at exploring English teachers’ perception on the teaching of storytelling viewed from phonology awareness to improve students’ speaking skills. To achieve the research aims, researchers employed descriptive qualitative study. Descriptive qualitative study is used to investigate natural condition of teachers’ perception of using storytelling viewed from phonology awareness in improving students’ speaking skills. The instruments are divided into two, observation sheets and interview guidelines. To support the research instrument, researchers use mobile phones. It is needed for recording the conversation both visual and audio. Based on the data analysis, Storytelling helps students to focus on story structure by paying more attention to vocabulary and grammar. When this happens, positive feedback is given by the teacher to improve students' communication methods and use the right English vocabulary with good pronunciation. Storytelling can not only improve students' speaking skills but can also help students acquire a lot of vocabulary and phonology awareness. It can be concluded that English teachers felt that the story-telling technique can improve students' speaking skills in the midst of the Covid-19 pandemic because teachers are supported by some applications such as: Google-meets, Zoom, and school Moodle.
An Uncalibrated Image-Based Visual Servo Strategy for Robust Navigation in Autonomous Intravitreal Injection

Autonomous intravitreal injection in ophthalmology is a challenging surgical task as accurate depth measurement is difficult due to the individual differences in the patient’s eye and the intricate light reflection or refraction of the eyeball, often requiring the surgeon to first preposition the end-effector accurately. Image-based visual servo (IBVS) control does not rely on depth information, exhibiting potential for addressing the issues mentioned above. Here we describe an enhanced IBVS strategy to achieve high performance and robust autonomous injection navigation. The radial basis function (RBF) kernel with strong learning capability and fast convergence is used to globally map the uncertain nonlinear strong coupling relationship in complex uncalibrated IBVS control. The Siamese neural network (SNN) is then used to compare and analyze the characteristic differences between the current and target poses, thus making an approximation of the mapping relationships between the image feature changes and the end-effector motion. Finally, a robust sliding mode controller (SMC) based on min–max robust optimization is designed to implement effective surgical navigation. Data from the simulation and the physical model experiments indicate that the maximum localization and attitude errors of the proposed method are 0.4 mm and 0.18°, exhibiting desirable accuracy with the actual surgery and robustness to disturbances. These results demonstrate that the enhanced strategy can provide a promising approach that can achieve a high level of autonomous intravitreal injection without a surgeon.