Miracle-dz/UniFAR-FTUs huggingface dataset:
ManiWare: An Easy-to-Use Middleware for Cooperative Manipulator Teams

Manipulator teams are frequently employed in various industrial applications to handle challenging cooperative tasks. The complicated interaction between manipulators makes it difficult to design applications from scratch. Although robotics middleware has emerged as the key to lowering the development complexity of manipulator applications, existing works still have limitations in controlling multiple manipulators to carry out tasks cooperatively. To overcome the limitations, middleware should provide programming abstraction support, coordination mechanism, and dynamic reconfiguration of motion controllers so that a team of manipulators can work together efficiently. This work proposes ManiWare, an easy-to-use middleware that provides the team-level programming abstraction and the manipulator-level plugin mechanism for programming and configuring manipulator applications. The team-level programming abstraction can facilitate the development process by invoking the functions from the fundamental cooperation components, which drives the developers to focus on designing application logic. Besides, the plugin mechanism dynamically configures and manages the motion controller of different parts of manipulators, making the reconfiguration feasible. This work implements ManiWare and evaluates the task execution performance with three case studies in the high-fidelity simulation platform. The experimental results demonstrate that ManiWare facilitates cooperative tasks with a high success rate, efficient completion time, and marginal runtime overhead. The source code is at https://github.com/sundycoder/maniware.
Walking Control Framework of a Radial-Type Quadruped Robot

This paper deals with a walking control framework for a radial-type quadruped robot. Our goal is to achieve stable blind walking using a minimum number of sensors. Therefore, this work presents a robot control method using three estimation methods. The first is the contact estimation algorithm which calculates whether the robot's foot has landed. The second is the body state estimation algorithm. The third is the slope estimation algorithm for calculating ground slopes. We generate gait patterns based on contact estimation and user command. In addition to the gait pattern, the robot controls the posture while walking through the force control of the stance feet. Finally, we verify the algorithm using GAZEBO simulation and apply it to ST-Quad, a radial-type quadruped robot, to test walking stability.
HiMFP: Hierarchical Intelligent Memory Failure Prediction for Cloud Service Reliability

In large-scale datacenters, memory failure is one of the leading causes of server crashes, and uncorrectable error (UCE) is the major fault type indicating defects of memory modules. Existing approaches tend to predict UCEs using Correctable Errors (CE). However, bit-level CE information has not been completely discussed in previous works and CEs with error bit patterns are strongly correlated with UCE occurrences. In this paper, we present a novel Hierarchical Intelligent Memory Failure Prediction (HiMFP) framework which can predict UCEs on multiple levels of the memory system and associate with memory recovery techniques. Particularly, we leverage CE addresses on multiple levels of memory, especially bit-level, and construct machine learning models based on spatial and temporal CE information. Results of algorithm evaluation using real-world datasets indicate that HiMFP significantly enhances the prediction performance compared with the baseline algorithm. Overall, Virtual Machines (VM) interruptions caused by UCEs can be reduced by around 45% using HiMFP.
Sparse Bayesian Learning with Atom Refinement for mmWave MIMO Channel Estimation

In this paper, we introduce a novel estimation method for the downlink millimeter-wave (mmWave) multiple-input multiple-output (MIMO) channel. The proposed method is able to determine the angles, time delays, and gains of the multi-path components by using the spatially sparse nature of mmWave channels. We first use on-grid sparse Bayesian learning (SBL) to coarsely estimate the channel parameters in the beamspace domain. We then develop a refinement method based on Newton–Raphson and Least Square-based atomic tuning to generate a mismatch-free basis. Finally, we finely reconstruct the channel by SBL using the basis found in the previous step. Simulation results show that the proposed channel estimation method outperforms the traditional ones in terms of mean square error and algorithmic complexity.
Digital Twin for Automated Industrial Optimization: Intelligent Machine Selection via Process Modelling

Digital Twin (DT) is an emerging paradigm that enables a virtual model to effectively represent a physical process. In this paper, we present the adoption of the DT scheme by an offset printing company towards industrial optimization. The considered DT model is a virtual representation that serves as the digital copy of the physical printing process within an industrial unit. A virtual model for selecting the optimal machine line was developed to ensure cost-efficient printing. The machine line selection process was modeled as a decision process and then analyzed through simulations in a safe and cost-efficient digital environment, provided by the DT. Moreover, Machine Learning (ML) models were exploited to extract knowledge for the machine selection task, taking full advantage of the DT experiment. Based on real data and selection policies of a printing enterprise, the results revealed an improvement during the selection process, followed by a 5% cost reduction on the examined dataset.
On the Way to Improving Experimental Protocols to Evaluate Users’ Trust in AI-Assisted Clinical Decision Making

The spread of AI-embedded systems involved in medical decision making makes it critical to build these systems according to trustworthiness standards. However, empirically investigating trust is challenging. One reason is the lack of standard protocols to design trust experiments. To get an overview of the current practices in the experimental protocols for studying trust in the context of AI-assisted decision making, we conducted a systematic review of such papers. We annotated, categorized
Coupled Aperture VCSELs Suitable for 100 GHz Intensity Modulation

In this paper we report high frequency (50 – 100GHz range) optical field intensity oscillations in a laterally-coupled-cavity vertical cavity surface-emitting laser. The oscillating frequency is defined by the photon energy splitting of the coupled states, each oscillating at a frequency defined by the related photon energy. As a result of constructive or destructive interference the optical field intensity reaches maximum either in the left or in the right aperture. As the frequency difference is small as compared to the averaged photon frequency, the cavities are quasi-resonant and the interference effect is strong, causing an intense resonance frequency with integrated intensity of at least 3-fold higher as compared to the resonance oscillation frequency feature. The effect is observed at low and at high current destinies. With proper design of the cavities, one should enable a simple and reliable solution for high-speed data transmission at data rates ~200 Gb/s and beyond. The effect coexists with spin-related frequency resonances allowing its combination with novel concepts in data transmission.
Reinforcement Learning for Autonomous Traffic Flow Capacity Management

As the dynamicity of the traffic increases, the need for self-network operation becomes more evident. One of the solutions that might bring cost savings to network operators, is that of the dynamic capacity management of large packet flows, especially in the context of packet over optical networks. Machine Learning, and particularly Reinforcement Learning (RL), seem to be an enabler for autonomicity, as a result of its inherent capacity to learn from experience. In this tutorial, we introduce RL and review its application for autonomous capacity management of traffic flows.
A Probabilistic Model of Meter Perception : Simulating Enculturation

© 2017 van der Weij, Pearce and Honing This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
Convolutional Knowledge Graph Link Prediction with Reshaped Embeddings

While the majority of current knowledge graphs remain incomplete, a plethora of models have been proposed with the intent of predicting the missing facts within them. In recent years, multi-layer models that leverage Convolutional Neural Networks (CNNs), such as Convolutional 2D Knowledge Graph Embeddings (ConvE), have exhibited superior performance over their shallower counterparts. The key to their success is largely due to the intricate interplay between entity and relation embeddings. In this study, we present ConvRE, a refined version of the ConvE model, and disclose our state-of-the-art results across multiple datasets. We further introduce a quantifiable measure of interaction, termed ’interaction length,’ and propose an innovative approach to reshaping both relation and entity embeddings with the aim of intensifying their interaction. Upon employing these re-shaping techniques, we successfully enhance the MRR score of the ConvE model by up to 3.28% (FB15K-237) and 3.72% (WN18RR). In addition, we conduct an in-depth analysis of the correlation between the degree of feature interaction and model performance. This exploration substantiates our assertion that augmenting the number of interactions between embeddings facilitates superior link prediction performance.
Voltage Control by Smart Sustainable Buildings: Data-Driven vs OPF-based Techniques

The concept of smart sustainable buildings (SSBs) is an important part of the smart city. It includes a variety of controllable devices that provide flexibility to the grid and support its sustainable development. The best way to control these buildings is an undertreated question, with some going for centralized model-based approaches and others opting for local control schemes derived from data. In this paper, through AC-Optimal Power Flow (OPF) modeling of the distribution network, the optimal solution of each controllable equipment is obtained based on historical data. Next, we use machine learning algorithms to design data-driven local control schemes for each controllable device, which can approximate the optimal solution of AC-OPF. Then, the control scheme is applied to specific cases to verify its feasibility and viability as compared to the traditional OPF control scheme, which is popular within the research community. This scheme can promote the safe operation of distribution networks with higher penetration rate (30% to 50%) of controllable devices.
Hardened Processor Architecture

Processors for terrestrial applications are protected by Earth's atmosphere and magnetic field, so the probability of single event effects (SEE) is significantly lower. Shrinking of the technology nodes allows higher operational frequency and performance, but their susceptibility to SEE is increasing. Nowadays satellites and safety critical automotive applications require more and more computational power. The hardening by design has been implemented mainly by redundancy of the whole cores. The consequences are lower frequency, larger area, and higher power consumption. We propose a novel hardening technique, which enables high frequency operation and doesn't cause large power consumption and area overhead. The protection is based redundancy and separation of the pipeline into two main sections. It provides a fast detection of faults, simple recovery by flush of the pipeline, and allows a large prediction unit to be unprotected. The whole protection scheme can be implemented at the Register Transfer Level. We present the protection scheme at the core with RISC-V instruction set. Simulations confirm the protection can handle the injected faults. Synthesis shows, the protection lowers the maximum frequency by only about 4.7%. The area increase is only about 118% and can be even lower.
Generation Scheduling of Grid Connected Microgrid using GAMS

The integrated form of distributed energy resources make a small-scale power grid called microgrid. Based on the production of electrical energy renewable energy sources are integrated especially wind energy which gives various features to the power system. Here a grid-connected mode of microgrid with wind energy resources is used which offers a positive impact on quality of life, climate change and energy efficiency. In this paper an integrated scheduling model is used to find an optimal solution of distributed energy resource network and depository devices in such a way that both cost and emission are reduced by using General Algebraic Modelling System(GAMS). The results show that this system can efficiently synchronize the power generation of distributed energy system and the fluctuations of wind power production is reduced due to the presence of Energy Storage System (ESS). Such that the system is handling uncertainties correctly in electrical microgrid.
An Extended System for External Sensors Data Acquisition and Validation During Conducting Polysomnography

This paper proposes an extended system for acquiring external sensor data during polysomnography (PSG) tests. The proposed method intends to provide means for integrating external sensor devices with existing PSG systems in the sleep clinic without modification to the original system. The acquired external sensor data is temporally synchronized with the PSG data through a trigger signal and can be used in subsequent applications. Using our proposed system, we acquire ten additional channels via Polar Verity Sense. We validate the acquired data qualitatively through signal visualization. Then, we compare the derived heart rate (HR) from PPG with those from an electrocardiogram (ECG) in the PSG device to validate our system quantitatively. The result reveals a fair error between PPG and ECG HR, demonstrating an acceptable performance. In addition to the PPG data acquisition, the proposed method can be employed on different external sensors to produce various databases for sleep studies.
A Neural Network-Based Lower Extremity Joint Angle Estimation from Insole Data

Human pose estimation is among the emerging fields which are being actively researched and attracting interest for various purposes. Applications could range from healthcare to robotics, from human augmentation to virtual reality. Especially, in aging societies like Japan, it could be a great help in monitoring the status of elders in elderly-care centers to notify and prevent injuries. Existing methods vary in sensor type and sensor quantity and utilized algorithms. Optical motion capture systems and multiple inertial sensor systems have been the popular ones. However, both systems suffer from high maintenance and setup costs, space limitations, and drifting errors. This restricted most existing studies to only indoor activities. In this research, we developed a deep learning-based lower limb joint angle estimator using only two insole sensors placed in each shoe. Insole sensors are convenient for attachment and are less prone to skin artifacts. A 45-minute walking insole and optical data were collected in a $6\mathrm{m}\times 3\mathrm{m}$ indoor space. As both systems have different sampling frequencies, synchronization was performed in a semi-automatic way. The optical motion capture was used to validate the estimation system. Two neural network algorithms namely convolutional neural network (CNN) and bi-directional long short-term memory (BLSTM) neural network were investigated in this study. After the training, CNN achieved better results with an average mean absolute error (MAE) of 3.17° for four sagittal plane joint angles, these include the extension/flexion of both hip and knee joints. Finally, the trained model was tested using unseen datasets which showed a promising result that can be used as a foundation for further analysis.
Research on The Design of An International Talent Evaluation System Based on Data Mining

In order to boost the quality of international talent evaluation and promote the rapid development of the internationalization of higher education, data mining techniques were applied to international talent evaluation. The implementation standard of international talent evaluation is proposed, and an international talent evaluation system is constructed based on data mining technology. In this research, the index analysis and the establishment of the index frame were systematically carried out for modeling by extracting the index from data mining and applying it to real-time evaluation. The weight of each index was obtained by using the analytic hierarchy process and the expert consultation method. To ensure the accuracy of the index scores, the different grade options under each index were normalized, and the construction of the scientific research ability evaluation model was completed. This method broadens students’ international horizons through applied analysis and strengthens students’ international-oriented professional knowledge.
Design of Virtual Impedance Control Loop using the Complex Frequency Approach

In this paper the dynamic response of the virtual impedance control loop for power electronic converters is studied using the recently proposed concept of complex frequency. This quantity captures bus frequency variations due to changes in both the phase angle and the amplitude of the voltage and current. The paper analytically derives the link between complex power and complex frequency, allowing the incorporation of the virtual impedance control equations. First, the impact of the virtual impedance control on the complex frequency is considered independently of other control loops by simplifying the overall control structure. Then, the equations are generalised to include the interaction between the virtual impedance and the proportional-integral voltage controller. The paper shows that the proposed theoretical formulation allows identifying the dynamic effect of the virtual impedance and simplifies its design. These features are demonstrated by using simulations based on a modified version of the WSCC 9-bus system.
Identification of Household Electric Load Based on Harmonic Parameters Using SVM, RF, and DT Algorithms

This study designed an identification electrical loads/devices system using 3 machine learning models which are Support Vector Machine, Random Forest, and Decision Tree algorithms. The system was applied for monitoring the use of electrical devices that are operating in order to find out the indications of waste. Data collection and testing of electrical devices was carried out using 7 electronic devices, namely rice cookers, laptops, lamps, hair dryers, fans, dispensers, and phone chargers. This study integrated EMG25, Current Transformer MSQ-30, electrical devices, USB Module RS-485 and Raspberry Pi3 for data processing, forming system models by algorithms and testing system identification. This research produced a system model of three algorithm Support Vector Machine, Random Forest, and Decision Tree with an accuracy of 93.5%, 95,5% and 92.5% respectively and wall time 0.489, 0.337, and 0.0278 second it was proven to be able to identify electrical devices that were operating correctly based on data characteristics.
Compiler-Implemented Differential Checksums: Effective Detection and Correction of Transient and Permanent Memory Errors

The detection of memory errors is common practice in safety-critical software, for example in the automotive and avionics industry. International safety standards recommend using checksums for protecting critical data in computer memories. Typical implementations verify the checksum before data access and recompute it after modification using the same algorithm. However, we show that this approach can sometimes dramatically worsen the reliability of computer systems with regard to transient memory faults, and also permanent faults remain undetected. A solution with significant conceptual advantages is constituted by differential checksum algorithms, which update the respective checksum without full recomputation on data modification. We present a compiler-based solution that inserts differential checksums into C/C++ data structures automatically to cope with their increased complexity. An extensive fault-injection campaign with the TACLeBench benchmark collection shows that differential checksums reduce silent data corruptions by 95% on average whereas non-differential checksums turn out to be mostly ineffective because they introduce a window of vulnerability.
Towards Objectives-Based Process Redesign

Continuously growing and changing multinational companie s oftentimes struggle with heterogeneous degrees of standa rdization. Especially in case of redesigning business processes that h ave been historically grown over decades, the capability of handling semi-structures process is central. Nevertheless, for com petitive advantages, it is essential for a company to work on the optimization of all processes. Existing redesign techniques either focus on completely unstructured or structured proc esses. The Redesign Model presented in this paper transforms processe s with any level of structuredness into processes with an inc reased degree of standardization. Our technique consists of four m ain steps: (i) we extract the objectives for an efficient busi ne s process redesign from existing literature; (ii) we formulate a list of requirements an innovative redesign model has to ful fill; (iii) we present a design science based Business Process Redesign Fr amework including our Redesign model; (iv) we evaluate our m odel showing its applicability and completeness.
Who ’ s more creative , experts or the crowd ?

Prior literature has indicated a remarkable change in the role of the crowd. Gradually, the crowd, in the process of interacting with firms, is no longer limited to providing demand information alone; rather, the crowd sometimes start participating either partially or fully in product innovation. Such change is based on an underlying assumption that the crowd (or customers) would provide creative thinking and thus help firms with product innovation. However, since this assumption has not been formally tested, this paper explores the difference between experts and the crowd in terms of generating creative ideas for products. By launching two experiments on Amazon‟s Mechanical Turk platform and analyzing the resulting data, I found that a difference does exist and is significantly moderated by the extent of information asymmetry embedded across different products.