Reviewerly/exHarmony huggingface dataset:
Extended Kalman Filter Calibration Method Based on Lightweight Robotic Arm

Most researches on position-based visual servo system pay more attention to control method rather than calibration. However, the impact of calibration on performance cannot be ignored, and its accuracy needs to be improved as much as possible. At the same time, some calibration methods rely heavily on high-precision sensors. In practical applications, some small devices such as lightweight robotic arm does not require high accuracy during the task, and it is not cost-effective to use high-precision sensors. Aiming at the above situation, a hand-eye calibration algorithm based on extended Kalman filter is designed to achieve the desired accuracy without relying on high-precision measuring devices. First, state equation and observation equation are constructed according to camera model and translation-rotation. Then use extended Kalman filter to eliminate accumulated error in state parameters. The result proves feasibility of the algorithm.
Human-Sensing Technologies for Business Solutions

Detection of human presence can be used for intelligently switching on and off devices. This will save a lot of electricity as well as help in building an intuitive experience for smart homeowners. In the future, technologies like artificial intelligence may also benefit from this information by taking smart decisions and providing more contextual responses. One can find many research proposals and commercial products for efficiently counting humans. However, these systems are very costly, extremely difficult to install, and many a time obstructs people. It is the reason why they have still not made it to residential homes yet. In this paper, we have discussed some of the most cutting-edge technologies that can be used for sensing humans. We analyze them based on six factors and determine how economic and easy to deploy they are. Some of the solutions deliver promising results and have the potential of becoming a mass-market product.
Exploring the Design of Streaming Data Interfaces for Emergency Medical Contexts

In this late-breaking work, we describe the design of an interface for displaying streaming vital sign data on a digital checklist used in the emergency medical setting of pediatric trauma resuscitation. We used feedback from interviews and participatory design workshops with clinicians to develop two prototypes of the streaming vital sign interface. We evaluated these prototypes in design-walkthroughs, finding that clinicians preferred the design displaying trend graphs for all four vital signs at once. We discuss how streaming data interfaces on interactive mobile devices can be used to provide situational awareness while unobtrusively supporting different levels of clinical experience.
Formal Specification of Fault-Tolerant Multi-agent Systems

Multi-agent systems (MAS) are increasingly used in critical applications. To ensure dependability of MAS, we need to formally specify and verify their fault tolerance, i.e., to ensure that collaborative agent activities are performed correctly despite agent failure. In this paper, we present a formalisation of fault tolerant MAS and use it to define specification and refinement patterns for modelling MAS in Event-B.
The Application of Personalized Cloud Service and Grey System Theory in Network Platforms

In order to better practice the purpose of cloud computing to provide users with cheap on-demand services and meet the personalized needs of service requesters, a dynamic trust model for personalized cloud services is proposed. Personalized cloud service is defined based on the idea of fine-grained service. The direct trust value is modified by introducing time decay factor and establishing an efficient incentive mechanism. The evaluation similarity between entities is calculated based on the grey system theory, and the evaluation similarity and the recommender's recommendation credibility are important factors to synthesize the recommendation trust value, At the same time, a method of self-confidence factor assignment based on Evaluation similarity is proposed to improve the accuracy of synthetic trust value. The experimental results show that, compared with GM-Trust model and CCIDTM model, the interaction success rate of this model is increased by 4% and 11% respectively.
THE DEMODULATION ALGORITHM OF OPTICAL FIBER FABRY–PEROT SENSOR USING THE NONLINEAR DYNAMIC CHARACTERISTICS OF THE OPTICAL FIBER BROADBAND LIGHT SOURCE

The purpose is to optimize the optical fiber Fabry–Perot (F-P) sensor’s performances to provide a realistic basis for the optical fiber’s development. First, the classification of the optical fiber F-P sensors is summarized. The optical fiber F-P sensors’ fundamental principles are analyzed, focusing on the phase demodulation method that converts the optical fiber F-P sensing system’s input light source into a broadband light source. The broadband light source’s lightwave changes are analyzed, and the Fourier transform method and the fringe counting method are proposed to optimize the phase demodulation method’s function and analyze the light source’s nonlinear dynamic characteristics. According to the proposed demodulation method, the optical fiber F-P sensor with a cavity length change of [Formula: see text][Formula: see text][Formula: see text]m is simulated, and the cavity length selected for the simulation spectrum is the change range’s center value, 185[Formula: see text]um. The wavelength division multiplexer (WDM) decomposes the two optical signals, and an equation directly calculates the wavelength value orthogonal to its phase. The results show that the light intensity’s initial value of the actual spectrum is 0.04, and that of the standard function is 0.06. The light intensities for the maximum peak values are the same, 0.12, with the wavelength in [Formula: see text]–[Formula: see text][Formula: see text]m. When the wavelength exceeds [Formula: see text][Formula: see text]m before the next peak, the light intensity difference between them is large. The two orthogonal optical signals’ peaks change between the cavity length 0.18[Formula: see text]nm and 0.19[Formula: see text]nm after the wavelength selected, and the two signals’ peak values are orthogonal with the cavity length changes. The two signals’ initial and end peak values are different. The curve obtained by the two signals division calculation shows a tangent trend with the highest peak value of 87 and the lowest of [Formula: see text]. The peak curve changes obtained by the two signals’ arctangent calculation are in an orthogonal state, with the maximum peak value of 1.3 and the minimum of [Formula: see text]. The reflected light peak value changes of the optical fiber F-P sensor are in an orthogonal state, with the highest value of [Formula: see text] and the lowest [Formula: see text], and the adjacent peak changes in the wavelengths of 1500–1580 are the same. The curve’s fitting linearity is 1, and the linearity in the dynamic demodulation is 0.9776. The sampling frequency in the phase demodulation is [Formula: see text][Formula: see text]Hz, and the sampling frequency in the dynamic demodulation is [Formula: see text][Formula: see text]Hz, showing that upgrading the interface can increase the dynamic sampling frequency continuously to improve the device performance.
Multi-Label Classification of Voice Calls from Power Plant Operation Centers

This paper presents a machine learning-based pipeline to classify, in six labels, voice calls from power plant operation centers from ENGIE Brasil Energia (private power producer). The pipeline consists of a customized speech-to-text model from Amazon Web Services (AWS) followed by a multi-label text classification model. Our experiments showed how to leverage the performance of Amazon Transcribe with a custom vocabulary, as well as the predictive performance for different tree-based machine learning models. The work aims to facilitate the audit of internal actions and increase operational efficiency from the post-operation activities from ENGIE Brasil Energia (EBE). We achieved great predictive results, high accuracy, and Fl-score for all six labels.
Real-time motion planning of self-driving vehicle on closed structured road

Automated driving motion planning algorithms require a high degree of real-time performance. In a closed structured road environment, making full use of the road information helps to reduce the computational effort and improve the real-time performance of the algorithm. Therefore, this paper proposes a real-time automated driving motion planning process suitable for closed structured road environments. Based on the environmental information of the structured road, the key factors affecting the motion decision are extracted. The scenarios are classified according to the key factors, and decision rules are established for the vehicle based on the headway. In addition, based on a pure tracking algorithm, a motion planning method based on dynamic target points is proposed, which takes into account both single-cycle trajectory planning and vehicle motion continuity. Finally, a real-vehicle test is conducted under two typical driving scenarios. The results show that the proposed motion planning process has a good real-time performance and adaptability in real traffic scenarios.
Analysis of factors of software development effort and productivity

The goal of this paper was to identify factors of development effort and productivity and investigate the nature of these relationships using the current release of the ISBSG dataset. In particular, statistical measures of correlations and associations, single-predictor linear regression models, and, most importantly, a moderation analysis were used. Performed analysis demonstrated which attributes are in strong relationships with effort and productivity, investigated the explainability of single-predictor models, and discussed if and how particular attributes moderate the strongest relationships reflected in these single-predictor models.
Preserving Privacy in Personalized Models for Distributed Mobile Services

The ubiquity of mobile devices has led to the proliferation of mobile services that provide personalized and context-aware content to their users. Modern mobile services are distributed between end-devices, such as smartphones, and remote servers that reside in the cloud. Such services thrive on their ability to predict future contexts to pre-fetch content or make context-specific recommendations. An increasingly common method to predict future contexts, such as location, is via machine learning (ML) models. Recent work in context prediction has focused on ML model personalization where a personalized model is learned for each individual user in order to tailor predictions or recommendations to a user's mobile behavior. While the use of personalized models increases efficacy of the mobile service, we argue that it increases privacy risk since a personalized model encodes contextual behavior unique to each user. To demonstrate these privacy risks, we present several attribute inference-based privacy attacks and show that such attacks can leak privacy with up to 78% efficacy for top-3 predictions. We present Pelican, a privacy-preserving personalization system for context-aware mobile services that leverages both device and cloud resources to personalize ML models while minimizing the risk of privacy leakage for users. We evaluate Pelican using real world traces for location-aware mobile services and show that Pelican can substantially reduce privacy leakage by up to 75%.
Unsupervised-learning financial reconciliation

Financial reconciliation (cross-checking independent sources of data) is a time-honored and widespread function in finance and audit. Its objectives are to ensure completeness, timeliness, and accuracy of recording of transactions.
Data Assisted Smart System During Epidemic: Application to Online Entrepreneurial Work Environment of Colleges

Data assisted smart system during epidemic with application to the online entrepreneurial work environment of colleges is studied in this study. Under the support of the big data model, the communication infrastructure and the management system will be integrated to strengthen various aspects. Firstly, this research paper designs a novel data analytic algorithm to process the data with efficiency and then, the smart system is implemented with the integration of the complex information systems, later, the online business models are studied and tested to validate the performance of the model designed.
Normal Distribution Function on Descriptor Extraction

The triplet-loss function is widely used in fields of descriptor extraction in recent years and owing to its good performance in various databases. However, some recent works make less effort on the relationship of adjacent descriptors from the same sample, which leads to the instability of descriptors and results in the mismatching problem in practical applications. To solve this problem, we introduce the topological relationship with the Normal Distribution Function (NDF) into the triplet loss function. The loss function establishes the relationship of descriptors from the same sample and considers the interrelation of descriptors from different types of samples. Furthermore, to increase the calculation speed, we normalize the algorithm NDF. Finally, we propose the triplet-loss function on three databases. These results demonstrate that our algorithm obtains better performance than state-of-the-art methods.
A Robust Rail Positioning Method for Turnout and Non-turnout Sections

Rail positioning is an essential task for railway track defect detection. The turnout is one of the main components of railway tracks. In order to realize the positioning of rails in turnout sections and non-turnout sections, this paper proposes a robust rail positioning method based on double sliding windows. Firstly, extract lines from the preprocessed image. Secondly, lines are discretized into points, and then all points are projected to the x-axis of the image. Finally, a double sliding window method is used to search for the rail area. The experimental results show that the proposed method can effectively identify the rail’s location in the turnout area, and the detection accuracy can reach 99%. What’s more, it has strong adaptability to complex detection environments such as curved rail or broken rail.
Simultaneous estimation of unknown input and state for measurement delay systems based on Kalman filter

In this paper, for the linear discrete-time system with measurement delay, a research scheme is proposed to take the unknown input and state estimation algorithm as the limit of Kalman filter. Firstly, the existing recursive filters of state and input are refined and summarized. Secondly, under the assumption that the unknown input is Gaussian white noise, the Kalman filter theory is used to design recursive filter for unknown input and state. Then, under the specific condition that the variance of unknown input tends to infinity, it is proved that the simultaneous estimation of unknown input and state is a standard Kalman filtering algorithm with specific unknown input model. Finally, a numerical example is given to prove the effectiveness of the research scheme.
Hyperbolic Knowledge Graph Embedding with Logical Pattern Learning

Recently, knowledge graph embedding approaches which aim to learn latent semantic representations for entities and relations in the knowledge graph, have become an active research topic. In this paper, we focus on hyperbolic knowledge graph embedding with logical pattern learning, based on the following observations: (i) the hyperbolic embedding methods have shown promising performance for knowledge graphs, which always exhibit hierarchical patterns; (ii) the Givens rotation has an excellent effect on modeling the logical pattern relations in the knowledge graph; (iii) ontology axioms are important extra information that can derive a variety of logical patterns (rules), and should be taken into consideration when learning embeddings. Futhermore, embedding learning and rule learning will complement each other and can be integrated into a unified framework. Therefore, we propose a novel hyperbolic knowledge graph embedding model named HyperLP, which can simultaneously learn embeddings and logical patterns. With extensive expriments on benchmark datasets, we find that our proposed HyperLP model achieves superior perfomence compared with a line of state-of-the-art baselines.
Still Crazy After All These Years. 60 Years of Hypertext and Counting?

This year marks the 30th anniversary of the World Wide Web. But the notion of hypertext as a form of non-sequential writing dates back to the early 1960s. Hypertext and hypermedia systems as useful tools for creative thought have almost been forgotten along with several other flavors of pioneering systems. Even the Web went through several stages from a cross-platform information medium, to e-commerce, to Web 2.0, to the mobile Web, to the so-called Social Networks. Today, several trending online platforms are successful despite the fact that they are mobile-only or even app-only without providing decent cross-linking mechanisms to content on other servers. According to business goals it seems to be mandatory to make users spend as much time as possible on their own pages and let them swipe through endless content streams. The keynote will present some of the lost cyberspaces to revive the original motivation for future development. The urgent and complex problems of the world can only be tackled collectively with a powerful, and connected online medium for personal benefit and collective commons.
Image Super-Resolution Based on Additional Self-Loop Supervision

Abstract Relying on the prevalence of deep neural networks, only one image super-resolution (SR) research has taken a big step forward. Many network models use the difference between the real image and the generated super-resolution image as a loss function, ignoring the difference between the small-scale image and the super-resolution generated image. We adopt an end-to-end network model with two-way supervision, which can ensure that the image content is similar in both large and small scales. Only real natural images are used for supervision. As the scale of the image becomes larger, the difference becomes larger, and the effect of correcting image details becomes smaller. Adding the loss between the input image and the degraded image of the generated image can not only keep the content of the image similar on a small scale, but also ensure the image details. Compared with several state-of-the-arts, our method obtains the best results.
Various Auto-Correlation Functions of m-Bit Random Numbers Generated from Chaotic Binary Sequences

This paper discusses the auto-correlation functions of m-bit random numbers obtained from m chaotic binary sequences generated by one-dimensional nonlinear maps. First, we provide the theoretical auto-correlation function of an m-bit sequence obtained by m binary sequences that are assumed to be uncorrelated to each other. The auto-correlation function is expressed by a simple form using the auto-correlation functions of the binary sequences. This implies that the auto-correlation properties of the m-bit sequences can be easily controlled by the auto-correlation functions of the original binary sequences. In numerical experiments using a computer, we generated m-bit random sequences using some chaotic binary sequences with prescribed auto-correlations generated by one-dimensional chaotic maps. The numerical experiments show that the numerical auto-correlation values are almost equal to the corresponding theoretical ones, and we can generate m-bit sequences with a variety of auto-correlation properties. Furthermore, we also show that the distributions of the generated m-bit sequences are uniform if all of the original binary sequences are balanced (i.e., the probability of 1 (or 0) is equal to 1/2) and independent of one another.
Integration of new trends in musicology as a way to reform the children's art school

The paper discusses ways of solving the problem of fragmentation of the educational process in the modern Children's School of Arts due to subject segregation. The author proposes ways to solve this problem by implementing the principles of integration in the context of mastering the innovative course “Musical content”. The technologies of a holistic integrative approach have been developed, combining the cycle of theoretical disciplines with each other, as well as with performing and artistic specialties.
On the Relationship Between Disentanglement and Multi-task Learning

One of the main arguments behind studying disentangled representations is the assumption that they can be easily reused in different tasks. At the same time finding a joint, adaptable representation of data is one of the key challenges in the multi-task learning setting. In this paper, we take a closer look at the relationship between disentanglement and multi-task learning based on hard parameter sharing. We perform a thorough empirical study of the representations obtained by neural networks trained on automatically generated supervised tasks. Using a set of standard metrics we show that disentanglement appears naturally during the process of multi-task neural network training.
University of Guadalajara: Transforming and Innovating Through Stronger Collaboration Between Higher and Upper-Secondary Education During the Pandemic

Abstract This chapter examines the context of higher education and upper-secondary education in Mexico, focusing on the role of the University of Guadalajara – the second largest in the country – and its response to the pandemic by strengthening collaboration with the upper-secondary system. During the pandemic, the lack of articulation between higher education and upper-secondary education posed a challenge for the transition to online education in the University of Guadalajara. This chapter discusses how the different initiatives advanced by the University to face this unprecedented situation helped to reduce the barriers between these levels and led to academic innovation, resulting in valuable discussions on the educational model and teaching practices for the post-Covid-19 world. Finally, the authors reflect on the views of faculty regarding the need for an innovative educational model, concluding that a closer collaboration between systems is needed for the benefit of students and faculty.
Implementation of the Differentiated Instructional Approach among Public Secondary Schools in Mwanga District, Tanzania

The Differentiated Instructional approach (DI) is a very important strategy to meet the diverse needs of the learner in improving the teaching and learning process. This study aimed at investigating the implementation of the Differentiated Instructional approach among the public secondary schools in Mwanga District, Tanzania. The study employed a mixed research approach with the convergent parallel design. The targeted population of this study was 7800 people and the study sample involved 98 participants including 1 District Quality Assurance Officer, 4 head of schools, 20 teachers as well as 73 students from 25 public secondary schools who were selected using purposive, stratified random sampling and simple random sampling. Data were analyzed through descriptive statistics with the help of the SPSS version 20 and content analysis. The findings indicate that teachers were familiar with the DI although they were not implementing it effectively to meet the diverse needs of the learners due to several work related hindrances. The study recommends that there should be the provision of adequate teaching and learning facilities in order to keep teachers updated on inclusive instructional approaches.