Miracle-dz/UniFAR-FTUs huggingface dataset:
Optimal RRT* Planning and Synchronous Control of Cable-Driven Parallel Robots

Cable-driven parallel robots (CDPRs) can provide high speed and heavy payload over large workspace by using multiple flexible cables. The main challenge of CDPRs stems from the motion control in which cables should avoid interferences and coordinate each other during the whole workspace. To solve it, a comprehensive motion control scheme including the optimal path planning and the synchronization control is proposed. Based on the improved Rapidly-exploring Random Tree (RRT*) algorithm, the path generation process with a novel cost function is presented to ensure the optimal path and the obstacle avoidance. Moreover, the Potential Field Guided (PFG) method is designed to further improve the efficiency of the planning algorithm. In order to validate that the moving platform can track the feasible path well, a synchronization controller is developed on the basis of the dynamic model. The simulations and experiments are implemented on a self-built 4–3 (4 cables with 3 DOF) CDPR. The results indicate that the proposed planning algorithm has much better efficiency than the RRT* algorithm, and the synchronization controller can get high tracking accuracy.
A Chatbot-supported Smart Wireless Interactive Healthcare System for Weight Control and Health Promotion

People who are overweight and obese have a greater risk of developing serious diseases and health conditions. A steadily increasing trend of obesity is not only limited to developed countries, but to developing nations as well. As smartphones have rapidly gained mainstream popularity, mobile applications (apps) are used in public health as intervention to keep track of diets, activity as well as weight, which is deemed more accurate than relying on user’s self-report measure, for the sake of weight management. A solution called “Smart Wireless Interactive Healthcare System” (SWITCHes) is developed to facilitate objective data reception and transmission in a real-time manner. Based on the user data acquired from SWITCHes app and the auxiliary data from medical instruments, not only SWITCHes app can engage user with tailored feedback in an interactive way, in terms of artificial intelligence-powered health chatbot, but the healthcare professional can provide the more accurate medical advice to user also. This paper presents an overview of development and implementation of SWITCHes.
Fusion of Local Descriptors for Multi-view Facial Expression Recognition

Facial expressions can be seen as a form of non-verbal communication as well as a primary means of conveying social information among humans.Automatic facial expression recognition (FER) can be applied to a wide range of scenarios in human-computer interaction, facial animation, entertainment, and psychology studies. For feature representation in a FER system, various texture descriptors have been employed to derive an effective solution for this system. However, these individual texture descriptor-based FER systems have often failed to achieve effective performance in the recognition of facial expressions. In this sense, it is necessary to further improve the general performance of a facial expression recognition system, evaluating different feature representations. In this paper, a novel local descriptor for a facial expression recognition system is proposed, designated the level of difference descriptor (LOD). The main goal is to use this descriptor as a supplement to state-of-the-art local descriptors to further improve the performance of a FER system in terms of classification accuracy. Furthermore, the fusion of various texture features for devising a robust feature representation for multi-view facial expression recognition is presented.
Data Analysis in Engineering Change Management – Improving Collaboration by Assessing Organizational Dependencies Based on Past Engineering Change Information

Successful communication and collaboration within companies is crucial for competitive product development. Hence, this paper presents an approach to identify organizational dependencies based on past engineering change information using structural complexity management and graph-based analysis. It therefore introduces a meta-model describing the interrelations in change information and defines rules to identify dependencies. Furthermore, it applies the method on a data set of engineering change requests to evaluate the approach.
Engineering Management Qualification: A Comparative Study for South African Universities

This study reviews a leading South African University delivering Engineering Managers in Africa, with students from South Africa, Africa and the globe. This research facilitates a review of skills development via teaching and learning for Engineering Management. This paper provides an African, Universities 21, and ASEM comparative. The current trend in industry is for engineers to be promotion to managers. Experience is a cause for succession. In this study, the modules offered at the University of Johannesburg is the baseline, with which to test/align the review of Engineering Management delivery methods offered globally. This research used a Python code as an engine to automatically gather global data. The results indicate the curriculum at the University of Johannesburg to be relevant and in line with the worlds’ best Engineering Management universities.
Multi-Modal Media Retrieval via Distance Metric Learning for Potential Customer Discovery

As social media grown to become an integral part of many people's daily life, brands are quick to launch targeted social media marketing campaign to acquire new potential customers online. To facilitate the potential customer discovery process, a costly and labor intensive manual selection process is done to build a brand portfolio consisting of multimedia data relevant to the brand. To automate this process in a cost-effective way, in this paper, we propose a novel Multi-Modal Distance Metric Learning (M2DML) method, which learns a data-dependent similarity metric from multi-modal media data, aiming at assisting the brands to retrieve appropriate media data from social networks for potential customer discovery. To comprehensively model the supervised information of multi-modal data, M2DML aims to learn both the intra-modality and inter-modality distance metrics simultaneously. To further explore the unsupervised information of the dataset, M2DML aims to preserve the manifold structure of the multi-modal data. The proposed method is then formulated as a standard eigen-decomposition problem and the closed form solution is efficiently computed. Experiments on a standard multi-modal media dataset and a self-collected dataset validate the effectiveness of the proposed method.
Design and Reflex Control for a Series Elastic Actuator Based Ankle Joint Emulator

Human body is a highly efficient stable system, although the center of mass is high. While quiet standing ankle joint has the function of resisting disturbance to maintain balance of human body. Studying the control mechanism of ankle joint helps to improve relative technologies such as bipedal robot, powered prosthesis and exoskeleton. In this paper, a series elastic actuator (SEA) based ankle joint emulator and reflex-like control algorithm are purposed to test human push-recovery mechanism. The SEA element provides an easy way for ankle joint torque control. Proportion-Derivative (PD) based reflex-like control algorithm calculates reference torque for SEA element. Experimental results indicate that this proposed emulator has the ability to mimic human ankle joint quiet standing push-recovery performance. The emulator is a useful platform to test further human ankle joint torque control function.
Design and Modeling of Omni-directional Bending Pneumatic Flexible Arm

Unlike traditional rigid robots, soft robots can deform in a wide range to suit environment and contact compliantly with operating objects. Therefore, soft robots have wide potential application prospects in terms of physical rehabilitation, minimally invasive surgery and other fields. In this paper, we present an omni-directional flexural inflatable flexible arm model, which consists of three independent controllable pneumatic components. The components have different elongations under the effect of pressure and flexible arm is bending deformation. Under ideal conditions, it can be achieved 90° bending and 360° twisting. Based on the Yeoh model of hyperelastic rubber material and the geometric analysis method, we present the mathematical model of the flexible arm and FEM capabilities. We use finite element analysis to simulate the actuation characteristics of these modules. We compared the analytical and computational results to experimental results and can be used for the future design and control of soft robotic actuators.
Localizing Heterogeneous Access Points using Similarity-based Sequence

Positioning of Wifi access points (APs) is important to understand the nature of the deployed IEEE 802.11 network, for example, coverage, connectivity, and density. More importantly, using Wifi to perform indoor localization is a promising solution, and understanding the Wifi AP location could potentially improve the indoor localization accuracy. A common approach is to use an explicit model, which describes the signal propagation over a distance. But predicting the signal propagation is challenging due to the AP diversity, different antenna gains, occlusion and multi-path issue in indoor environments. Therefore, we propose to use sequence-based approach for the localization of heterogeneous APs. In particular, we represent a position with location sequence and the measurement with RSS (received signal strength) sequence. The localization of an AP is achieved by measuring the similarity between the location and RSS sequence. Moreover, we design a new similarity measure that considers the quality of a match to improve the localization accuracy. The proposed approach can be used to localize diverse APs under different antenna propagation characteristics without modeling the propagation of the radio signal. Experiments were carried in an indoor environment with 8 APs and our results show that our approach outperforms the propagation model-based approach and the sequence-based approach by 32.2% and 19.5%, respectively.
Consolidating Orders in a Crowdsourcing Delivery Network

In this study, we explore the effects of consolidating orders on a crowdsourcing baggage delivery company. We use an empirical study of customers’ preferences, a benchmarking study of pricing models, and an optimization tool for consolidating orders to show that a crowdsourcing strategy that includes order consolidation provides a competitive advantage to companies offering baggage delivery services. As order consolidation enhances the efficiency of the transportation network, drivers can increase revenue, and customers can save on the cost of delivering goods. Statistical analysis and numerical experiments are conducted to support these findings.
Deep reinforcement learning using compositional representations for performing instructions

Abstract Spoken language is one of the most efficientways to instruct robots about performing domestic tasks. However, the state of the environment has to be considered to plan and execute actions successfully. We propose a system that learns to recognise the user’s intention and map it to a goal. A reinforcement learning (RL) system then generates a sequence of actions toward this goal considering the state of the environment. A novel contribution in this paper is the use of symbolic representations for both input and output of a neural Deep Q-network (DQN), which enables it to be used in a hybrid system. To show the effectiveness of our approach, the Tell-Me-Dave corpus is used to train an intention detection model and in a second step an RL agent generates the sequences of actions towards the detected objective, represented by a set of state predicates. We show that the system can successfully recognise command sequences fromthis corpus aswell as train the deep- RL network with symbolic input.We further show that the performance can be significantly increased by exploiting the symbolic representation to generate intermediate rewards.
Efficient Allocation and Pricing of Multifeatured Items

We introduce feature valuations, a new class of valuations that compactly capture preferences of agents who value items based on the features they possess. Such preferences are relevant in many important practical settings, such as Internet advertising markets (where impressions have associated attributes), labor markets (where workers’ skills/titles correspond to features), and long-term bond markets (where coupon payments can be associated with features). We focus on settings where features can be organized in a tree network, and features possessed by items are captured by paths on the tree. In such settings, under appropriate consistency conditions, we establish that Walrasian equilibria exist. In addition, we provide a computationally tractable price update algorithm that terminates with market-clearing prices that support an efficient outcome. The e-companion is available at https://doi.org/10.1287/mnsc.2017. 2917. This paper was accepted by Yinyu Ye, optimization.
Study on Incentive Mechanism of Knowledge Sharing in Supply Chain Based on Evolutionary Game Theory

Lack of knowledge sharing is the main reason for the poor performance of supply chain management. The purpose of this study is to promote knowledge sharing between on enterprises in the supply chain. Based on the evolutionary game theory, the basic game model of knowledge sharing and the game model of incentive mechanism are established. Through analysis, after introducing incentive mechanism the evolution rule of strategic choice of enterprises is revealed. The results show that the introduction of incentive mechanism significantly improves the probability of knowledge sharing between on enterprises in the supply chain, and the minimum incentive intensity is obtained which make the enterprises to select the sharing knowledge strategy.
Conservative compact difference scheme for the Zakharov–Rubenchik equations

ABSTRACT In this article, a compact difference scheme is investigated to solve the Zakharov–Rubenchik equations in one dimension. The new scheme is proved to conserve the total mass and energy in the discrete sense. Rigorous error estimates are established for the new method with the help of an induction argument in energy space which show that the new scheme has second-order accuracy in time and fourth-order accuracy in space. Extensive numerical results are provided to verify our theoretical analysis, and show the accuracy and efficiency of the new scheme.
Adaptive Control of Mobile Manipulators with Nonlinear Friction Cone and Switching Constraints

A non-minimal order model for mobile manipulation subject to many nonlinear constraints can handle a multi-objective control problem. In this paper, the task space controller is designed based on a reduced dynamic model. We present the the fundamental properties of non-minimal order dynamic model. According to this dynamics the adaptive control of mobile manipulation subject to these constraints is proposed, which could deal with uncertain dynamics. The input saturation and unknown dynamics are solved by adaptive control which used neural network. Moreover, the non-minimal order dynamics does not need to redesign other controllers while switching from one task to another. We evaluate our method by several experiments, the results demonstrate the validity of the robustness and effectiveness.
Brain stimulation for epilepsy.

DOI: 10.3760/cma.j.issn.0366-6999.20141257 Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing 100053, China (Wang YP and Lin YC) Beijing Key Laboratory of Neuromodulation, Beijing 100053, China (Wang YP and Lin YC) Correspondence to: Dr. Wang Yuping, Department of Neurology, Xuanwu Hospital, Capital Medical University; Beijing Key Laboratory of Neuromodulation, Beijing 100053, China (Tel: 86-1083198273. Fax: 86-10-83157841. Email: wangyuping01@sina.cn) Editorial
Design and Optimization of Movable Cable-Driven Lower-Limb Rehabilitation Robot

In order to help patients with lower-limb dysfunction, a movable cable-driven lower-limb rehabilitation robot was designed to restore their motor function and improve their quality of life. Through controlling the position / posture and force of the lower limb, the robot can achieve comprehensive trainings of the lower limbs. The robot can assist patients in different rehabilitation stages to carry out rehabilitation training with passive, assistant and active mode. The kinematics modeling, statics modeling and workspace analysis were worked out, and the robot configurations were analyzed and optimized based on these. Continuous cable tension was worked out based on the 2-norm of the cable tension optimization algorithm as well. The foundation for subsequent research work of parallel cable-driven lower-limb rehabilitation robot was laid in this paper, which had reference significance for the research of flexible medical rehabilitation equipment.
The Effects of Underlying Mono and Multilingual Representations for Text Classification

With the exponential growth of multimedia datasets comes the need to combine multiple data representations to create "conceptual" vector spaces in order to use all available sources of information. Following previous experiments [1], in this paper we explore how two different languages can be combined to better represent information. Methods to create textual representations, such as Word2Vec and GloVe, are already well-established in academia and, usually, a single representation method is used in Machine Learning tasks. In this paper, we investigate the effects of different combinations of textual representations to perform classification tasks on a multilingual dataset composed of international news in Portuguese and English. This paper aims to analyze the differences between combinations, and how these representations perform in a small dataset with multiple data inputs.
Beyond the hashtag: Circumventing content moderation on social media

Social media companies make important decisions about what counts as “problematic” content and how they will remove it. Some choose to moderate hashtags, blocking the results for certain tag searches and issuing public service announcements (PSAs) when users search for troubling terms. The hashtag has thus become an indicator of where problematic content can be found, but this has produced limited understandings of how such content actually circulates. Using pro-eating disorder (pro-ED) communities as a case study, this article explores the practices of circumventing hashtag moderation in online pro-ED communities. It shows how (1) untagged pro-ED content can be found without using the hashtag as a search mechanism; (2) users are evading hashtag and other forms of platform policing, devising signals to identify themselves as “pro-ED”; and (3) platforms’ recommendation systems recirculate pro-ED content, revealing the limitations of hashtag logics in social media content moderation.
Towards a Referenceless Visual Quality Assessment Model Using Binarized Statistical Image Features

In many practical multimedia applications, the visual content is modified during transmission, enhancement, modification, and compression stages. These modifications often create visible distortions that may be perceived by humans. Therefore, the development of algorithms that are able to assess the visual quality as perceived by a human viewer can lead to significant progress in multimedia applications. Many researchers have developed algorithms that estimate visual quality. These algorithms can either make use of the full pristine content (full-reference metrics), partial aspects of the pristine content (reduced-reference metrics) or only the assessed content (referenceless or no-reference metrics). These three approaches have advantages and drawbacks. Nevertheless, although the design of a referenceless metric is more challenging, they have greater applicability in different scenarios. This paper introduces a novel referenceless image quality assessment (RIQA) metric. The proposed metric uses statistics of the Binarized Statistical Image Features descriptor (BSIF) to analyze the textures of an image. These statistics are mapped into subjective quality scores using a Random Forest Regression approach. Results show that the proposed metric is robust and accurate, outperforming other state-of-the-art RIQA methods.
A Customizable, Compact Robotic Manipulator for Assisting Multiple Dental Procedures

In this paper, we propose a manipulator with 4 degrees of freedom, actuated by tendon-sheath mechanism and can be tele-operated by a haptic device, the end-effector of which is inner-changeable. The manipulator we proposed consists of two cylindrical joints and two revolute joints, the workspace of which can be customized by adding or removing any of these joint modules or modifying the dimensions. In our case, we optimized the workspace of the manipulator to fit into human oral cavity in order to assist in multiple dental procedures, by holding different dental tools at certain positions or along trajectories under remote control. The performance of our manipulator is presented by figures, quantified and validated by motion and force transmission experiments. Different from current manipulators used in dental surgeries, which were simply adapted from industrial manipulators with workspace in meters, our proposed manipulator is specifically designed for dental applications. Its compact structure, proper workspace, as well as low cost make it promising for improving dental treatments efficiency and realizing automatic dental surgeries in the future.
Side Scan Sonar Image Super Resolution via Region-Selective Sparse Coding

Side scan sonar using low frequency can quickly search a wide range, but the images acquired are of low quality. The image super resolution (SR) method can mitigate this problem. The SR method typically uses sparse coding, but accurately estimating sparse coefficients incurs substantial computational costs. To reduce processing time, we propose a region-selective sparse coding based SR system that emphasizes object regions. In particular, the region that contains interesting objects is detected for side scan sonar based underwater images so that the subsequent sparse coding based SR process can be selectively applied. Effectiveness of the proposed method is verified by the reduced processing time required for image reconstruction yet preserving the same level of visual quality as conventional methods. key words: side scan sonar, super resolution, sparse coding, object detection