Miracle-dz/UniFAR-FTUs huggingface dataset:
Exponential Stability of Formations for Reduced Attitudes: A Coordinates Free Approach

In this work, a methodology based on differential geometry techniques is proposed to investigate exponential stability of a formation for reduced attitudes. By the proposed method, there is no need in finding any relative coordinates, which is typically needed but shown to be difficult when the formation problem is evolving in a non-Euclidean space. In this paper, the desired formation is treated as an embedding submanifold in (S2)N and by using the rotation symmetries owned by the attitude dynamics its stability is directly examined. Moreover, such a method turns out to be coordinates free, namely, exponential stability of a formation can be completely determined by just investigating anyone equilibrium which can result in the formation under any local chart of (S2)N. This greatly simplifies the stability analysis for the formation problems.
Modeling Method for Robot Servo System Based on IGSA-RBFNN

To solve the modeling problem for robot servo system, an ensemble approach based on Radial Basis Function Neural Network (RBFNN) and Improved Gravitational Search Algorithm (IGSA) called RBFNN- IGSA, is presented in this paper. Firstly, RBFNN is used to build the servo system model and identify parameters of the system, and the recursive least squares is used in learning process, then during training time, IGSA is used to optimize the thresholds of RBFNN to simplify the complexity of counting process, and reduce the time of servo system modeling.
Tightly Coupled Directional Coupler Using Slotted-Microstrip Line

This paper presents the theory and implementation of a novel microstrip directional coupler (DC) with a high coupling coefficient that can achieve the value of 0 dB. The coupling region is a slotted-microstrip line that supports two modes, even and odd. The geometry of the slotted-microstrip line facilitates the phase velocity compensation, which improves the isolation and directivity. A 3-dB coupler is fabricated and characterized. Measurements and electromagnetic simulations are in a good agreement with the theoretical predictions. They show that the proposed DC has 52% ±1 dB coupling coefficient fractional bandwidth centered around 2.4 GHz with isolation and return loss better than 25 dB.
Nonlinear State Estimation Based on Continuous-Discrete Cubature Kalman Filter for Fermentation Process

To realize on-line detection of biological parameters in fermentation process, a method of nonlinear state estimation for fermentation process based on continuous-discrete Cubature Kalman Filter (CD-CKF) is proposed. The nonlinear state estimation is realized by analyzing the model of fermentation process and applying the CD-CKF which takes use of Gaussian approximation filter approach to update system state and covariance. Then, the implementation steps of the algorithm are given. The experimental results show that the proposed method has high estimation accuracy when dealing with the on-line estimation of fermentation process.
An Automatically Designed Recombination Heuristic for the Test-Assignment Problem

A way of minimizing the opportunity of cheating in exams is to assign different tests to students. The likelihood of cheating then depends on the proximity of the students' desks, and the similarity of the tests. The test-assignment problem is to find an assignment of tests to desks that minimizes that total likelihood of cheating. The problem is a variant of a graph coloring problem and is NP-hard. We propose a new heuristic solution for this problem. Our approach differs from the usual way of designing heuristics in two ways. First, we reduce test-assignment to the more general unconstrained binary quadratic programming. Second, we search for a good heuristic using an automatic algorithm configuration tool that evolves heuristics in a space of algorithms built from known components for binary quadratic programming. The best hybrid heuristics found repeatedly recombine elements of a population of elite solutions and improve them by a tabu search. Computational tests suggest that the resulting algorithms are competitive with existing heuristics that have been designed manually.
BLE Beacon-based Activity Monitoring System toward Automatic Generation of Daily Report

As the world's population of senior citizens continues to grow, the burdens on the professionals who care for them (carers) are also increasing. In nursing homes, carers need to make a daily report for each resident aiming to improve his/her quality of life. However, in the present understaffed situation, it is difficult and burdensome for carers to record the resident's activity in detail since each carer needs to take care of several residents at the same time. In this paper, we propose an automatic daily report generation system which can monitor the activity of multiple residents in nursing homes. Knowing that important activities such as toilet, bathing, rehabilitation and so on take place in specific areas in a nursing home, it is possible to record residents' activities by tracking their stay areas and movement between the areas within the day. Our proposed system estimates stay areas of multiple residents by machine learning for RSSI values that are sent from BLE beacons attached to residents and received at BLE scanners deployed over multiple areas, and records activities of the residents determined based on their estimated stay areas. The proposed system can also output a daily report of each resident based on the recorded data. We carried out a five-day experiment with four elderly participants in a nursing home and evaluated activity estimation accuracy by leave-one-person-out cross-validation. As a result, our proposed system achieved the weighted average F-measure of 81.6%.
Iterative Reconstrained Low-Rank Representation via Weighted Nonconvex Regularizer

Benefiting from the joint consideration of geometric structures and low-rank constraint, graph low-rank representation (GLRR) method has led to the state-of-the-art results in many applications. However, it faces the limitations that the structure of errors should be known a prior, the isolated construction of graph Laplacian matrix, and the over shrinkage of the leading rank components. To improve GLRR in these regards, this paper proposes a new LRR model, namely iterative reconstrained LRR via weighted nonconvex regularization, using three distinguished properties on the concerned representation matrix. The first characterizes various distributions of the errors into an adaptively learned weight factor for more flexibility of noise suppression. The second generates an accurate graph matrix from weighted observations for less afflicted by noisy features. The third employs a parameterized rational function to reveal the importance of different rank components for better approximation to the intrinsic subspace structure. Following a deep exploration of automatic thresholding, parallel update, and partial SVD operation, we derive a computationally efficient low-rank representation algorithm using an iterative reconstrained framework and accelerated proximal gradient method. Comprehensive experiments are conducted on synthetic data, image clustering, and background subtraction to achieve several quantitative benchmarks as clustering accuracy, normalized mutual information, and execution time. Results demonstrate the robustness and efficiency of IRWNR compared with other state-of-the-art models.
Accelerated Decentralized Optimization with Local Updates for Smooth and Strongly Convex Objectives

In this paper, we study the problem of minimizing a sum of smooth and strongly convex functions split over the nodes of a network in a decentralized fashion. We propose the algorithm $ESDACD$, a decentralized accelerated algorithm that only requires local synchrony. Its rate depends on the condition number $\kappa$ of the local functions as well as the network topology and delays. Under mild assumptions on the topology of the graph, $ESDACD$ takes a time $O((\tau_{\max} + \Delta_{\max})\sqrt{{\kappa}/{\gamma}}\ln(\epsilon^{-1}))$ to reach a precision $\epsilon$ where $\gamma$ is the spectral gap of the graph, $\tau_{\max}$ the maximum communication delay and $\Delta_{\max}$ the maximum computation time. Therefore, it matches the rate of $SSDA$, which is optimal when $\tau_{\max} = \Omega\left(\Delta_{\max}\right)$. Applying $ESDACD$ to quadratic local functions leads to an accelerated randomized gossip algorithm of rate $O( \sqrt{\theta_{\rm gossip}/n})$ where $\theta_{\rm gossip}$ is the rate of the standard randomized gossip. To the best of our knowledge, it is the first asynchronous gossip algorithm with a provably improved rate of convergence of the second moment of the error. We illustrate these results with experiments in idealized settings.
Heart Rate Measurement Using Face Detection in Video

Measuring the heart-rate (HR) of people has multiple applications in telemedicine, Internet-of-Things (IoT), sports, security, etc. However, sometimes it is difficult to use a classic method for measuring HR or the classical method does not scale. This paper presents a solution that works on live video streams and can measure the HR of multiple people at the same time. Face detection combined with object tracking is used to produce a set of face rectangles, which are sampled in the later stages of the pipeline for color variations. The average of the color, in a region of interest (ROI) chosen on the face, represents a signal which corresponds to the heart rate. Using signal processing, a heart rate frequency can be extracted from this signal. The method is quite precise and stable. With this algorithm, four people's faces were detected and their heart rates were measured at the same time, and an error rate of 3-5% can be obtained for the HR. By utilizing object tracking combined with face detection, our method reduces the processing power needed and allows better scaling.
Robust Coordinated Formation Control of Multiple Vessels with Disturbance and Time Delay

When considering disturbance, the coordinated formation control of multiple vessels is studied. And a coordinated formation control approach is proposed. And a disturbance estimator is used to estimating the disturbance. Moreover, the undirected communication topology is constructed to represent the relationvessel of vessels. Meanwhile, the stability of the disturbance estimator and the coordinated formation controller are proved by the Lyapunov theory. Finally, four marine vessels with unknown time-variant disturbance and communication delay are simulated by computer simulation tests. It is shown that the proposed control strategy can solve the coordinated of multiple vessels.
Consensus in Placebo Studies: Lessons from The Philosophy of Science

ABSTRACT:In the relatively nascent field of placebo studies, empirical studies have burgeoned. Yet debate about how to define the terms placebo and "placebo effect" has not abated. A number of prominent scholars (drawn from medical practice, as well as philosophy, psychology, and anthropology) continue to propose and defend different conceptual models for these terms, and the perception that conceptual debate persists is often given as one justification for new definitions. Paradoxically—in spite of this lively debate—this article finds considerable underlying agreement about definitional matters within placebo studies. Drawing on key insights from philosophy of science, and by exploring the nature of scientific consensus and normal scientific research, this paper argues that well-developed placebo concepts form the basis for a placebo paradigm and that conceptual disagreement is overstated.
A Feasibility of Respiration Prediction Based on Deep Bi-LSTM for Real-Time Tumor Tracking

In radiotherapy, the position of thoracic-abdominal tumor is changing due to respiratory motion. Real-time tracking of thoracic-abdominal tumors is of great significance in improving the treatment effect of radiotherapy. The accurate prediction of thoracic-abdominal tumor motion is required to compensate for system latency in image-guided adaptive radiotherapy systems. The purpose of this paper is to identify an optimal prediction model to improve the treatment effect of radiotherapy. A seven-layer bidirectional long short term memory (Deep Bi-LSTM) and one output layer deep neural network is proposed to predict respiration motion for a latency about 400 ms. 103 malignant lung tumor patients’ respiratory motion data is used to train model. Mean absolute error (MAE), root mean square error (RMSE), and normalized mean square error are introduced to evaluate the performance of predictive results. Deep Bi-LSTM has great performance in the cases with relative long latency, average MAE of 0.074 mm, RMSE of 0.097 mm, and normalized root mean square error (nRMSE) of 0.081 with latency about 400 ms are obtained from predictive results of Deep Bi-LSTM. It demonstrates that the prediction accuracy of our proposed Deep Bi-LSTM is about five times better than traditional autoregressive integrated moving average model and about three times better than adaptive boosting and multi-layer perceptron neural network when the latency of 400 ms. The method can be applied to improve tracking accuracy and increase efficiency in thoracic-abdominal radiotherapy, which is practical and attractive for clinical application in the near future.
Optimizing Makespan and Ergonomics in Integrating Collaborative Robots Into Manufacturing Processes

As collaborative robots begin to appear on factory floors, there is a need to consider how these robots can best help their human partners. In this paper, we propose an optimization framework that generates task assignments and schedules for a human–robot team with the goal of improving both time and ergonomics and demonstrate its use in six real-world manufacturing processes that are currently performed manually. Using the strain index method to quantify human physical stress, we create a set of solutions with assigned priorities on each goal. The resulting schedules provide engineers with insight into selecting the appropriate level of compromise and integrating the robot in a way that best fits the needs of an individual process. Note to Practitioners—Collaborative robots promise many advantages on the shop and factory floor, including low-cost automation and flexibility in small-batch production. Using this technology requires engineers to redesign tasks that are currently performed by human workers to effectively involve human and robot workers. However, existing quantitative methods for scheduling and allocating tasks to multiple workers do not consider factors, such as differences in skill between human and robot workers or the differential ergonomic impact of tasks on workers. We propose a method to analyze how the inclusion of a collaborative robot in an existing process might affect the makespan of the task and the physical strain the task places on the human worker. The method enables the engineer to prioritize and weigh makespan and worker ergonomics in creating schedules and inspect the resulting task schedules. Using this method, engineers can determine how the addition of a collaborative robot might improve makespan and/or reduce job risk and potential for occupational hazard for human workers, particularly in tasks that involve high physical strain. We apply our method to six real-world tasks from various industries to demonstrate its use and discuss its practical limitations. In our future work, we plan to develop a software tool that will assist engineers in the use of our method.
Administrative ethics: Good intentions, bad decisions

The Canadian healthcare system is costly. Each day, health leaders must make decisions about what healthcare services will be offered, how they will be funded, to whom they will be made available, and within what administrative and clinical structure they will be managed and delivered. These decisions, their justification, and the ethics framework employed can vary greatly across the Canadian landscape. These high-stakes decisions must not only draw upon healthcare science but the science of business finance, risk management, and organizational design. However, in equal measure and often overlooked, these decisions must draw upon our values, upon our ethics. Sometimes we get it right, and other times, decidedly less so. When timely and fair access to effective and efficient healthcare services is not rendered, matters of justice, fairness, rights, and a host of other constructs are often cited. However, these important constructs are commonly misunderstood, contributing, in part, to well-intentioned but ultimately unethical decisions.
Research on the Majority Decision Algorithm based on WeChat sentiment classification

： Sentiment analysis mainly studies the emotional tendencies of texts from grammar, semantic rules and other aspects. The texts from social network are characterized by less words, irregular grammar, data noise and so on, which have increased the difficulty of emotion analysis. In order to improve the performance of machine learning in sentiment analysis, this study proposed the Majority Decision Algorithm to classify the emotional tendentious of the text in WeChat, combined the characteristics of five classifiers and integrated the classification results of five classifiers, eventually the text can be classified in WeChat. Firstly, this study utilized the BlueStacks to crawl the cache of WeChat Moment developed by Tencent company. Secondly, the cache was processed by Python to get the WeChat dataset. After the Chinese word segmentation, data cleaning and segmentation, the sentiment classification experiment were carried out using different classifiers. Finally, a Majority Decision Algorithm composed of five classifiers was established. It included, Naive Bayes (sklearn), Naive Bayes (SnowNLP), SVM (linear), SVM (RBF) and SGD. Then, the comparison was carried out between the performance of the algorithm and the five classifiers. Results show that the precision rates of the five classifiers are 0.8598,0.8154,0.8511,0.8739 and 0.8678; the recall rates are 0.8544,0.8482,0.9380,0.9226 and 0.9349; F1 scores are 0.8571,0.8315,0.8924,0.8975 and 0.9001, respectively. The algorithm of the Precision rate, Recall rate and F1 score were 0.8804,0.9349 and 0.9069, respectively, indicating that algorithm in current study significantly improved the performance, which can be effectively applied into the new text form of WeChat Moment. The conclusions of the study can provide theoretical reference for sentiment classification of Chinese text based on machine learning.
Critical review: Grand challenges in assessing the adverse effects of contaminants of emerging concern on aquatic food webs

Much progress has been made in the past few decades in understanding the sources, transport, fate, and biological effects of contaminants of emerging concern (CECs) in aquatic ecosystems. Despite these advancements, significant obstacles still prevent comprehensive assessments of the environmental risks associated with the presence of CECs. Many of these obstacles center around the extrapolation of effects of single chemicals observed in the laboratory or effects found in individual organisms or species in the field to impacts of multiple stressors on aquatic food webs. In the present review, we identify 5 challenges that must be addressed to promote studies of CECs from singular exposure events to multispecies aquatic food web interactions. There needs to be: 1) more detailed information on the complexity of mixtures of CECs in the aquatic environment, 2) a greater understanding of the sublethal effects of CECs on a wide range of aquatic organisms, 3) an ascertaining of the biological consequences of variable duration CEC exposures within and across generations in aquatic species, 4) a linkage of multiple stressors with CEC exposure in aquatic systems, and 5) a documenting of the trophic consequences of CEC exposure across aquatic food webs. We examine the current literature to show how these challenges can be addressed to fill knowledge gaps. Environ Toxicol Chem 2019;38:46–60. © 2018 SETAC
Towards Socially Assistive Robots for Elderly: An End-to-end Object Search Framework

The lack of technology integration in highly interdisciplinary fields such as robotics and ambient assisted living (AAL) is preventing the diffusion of automated solutions for elderly care at home. This claim holds for the task of object search with a socially assistive robot as well, where fragmented self-contained technologies are not related to a high-level framework. In this paper, we present an end-to-end, modular framework to fulfill the object search task with a mobile robot that takes into account the needs of older users. In particular, we describe the modules composing the task and their interconnections, with special attention to the interaction with the user. Finally, we indicate the existing works that can be used to implement the different functionalities to boost the deployment of this technology and improve the autonomy of senior citizens in their own homes.
Flexible Hybrid Energy Harvesting System to Power Wearable Electronics

This paper contains the design procedure of flexible dual band rectenna along with a solar cell. The textile rectenna element harvests the electromagnetic energy at 4.65GHz and 6.85 GHz of INSAT frequencies. In addition to rectenna, a single flexible solar cell is used to harvest solar energy which makes the design hybrid harvesting system. This hybrid system could be integrated into clothing and accessories to charge the wearable electronic devices. A DC-DC step-up converter with maximum power point control (MPPT) is used to control over the charging.
Distributed Optimization on Unbalanced Time-Varying Topologies: Theories and Experiments

This paper considers distributed optimization problems on both fixed and time-varying directed communication structures within a continuous-time framework. Different from most existing results in literature on continuous-time optimization, the lower bound of local convexity constants are unknown, and the requirement of weight-balanced communication structures is removed. An augmented Lagrangian function is designed to analyze the properties of optimal solutions over an asymmetric Laplacian matrix. Based on this analysis, a consensus-based algorithm is proposed to solve the distributed optimization problem on unbalanced directed graphs such that the algorithm asymptotically converges to optimal solutions. Furthermore, an algorithm is proposed to solve the distributed optimization problem on unbalanced time-varying communication topologies. A novel Lyapunov function including a semi-positive definite term is designed to establish the convergence analysis of this algorithm. By exploring certain features of positive invariance sets and asymmetric Laplacian matrices, sufficient conditions for the convergence are established. Two experiments are carried out on a distributed microcomputer platform, thus validating the proposed algorithms.
A Framework for Semantic Video Content Indexing Using Textual Information

In these last years, many works have been published in the video indexing and retrieval field. However, except some specific cases such ass port video where i ts possible t o estimate the set of important events and concepts in the document, this research is generally limited to analyzing low level content. In this paper, we introduce an approach for semantic video indexing that combines two levels of descriptions. First, we extract automatically textuel information from video frames. The second part of our approach consists to exploit linguistic techniques and semantic network in order to extract semantic concepts such as person identity, location name, event type etc. These informations are then used for semantic description of video content. Our proposed approach was tested on video collection of Arabic TV news and experimental results have been satisfying.