Miracle-dz/UniFAR-FTUs huggingface dataset:
Evolutionary Multi-task Optimization Introducing Task Assignment Based on a Search Degree

Multi-task optimization problems are to find solutions of multiple optimization problems simultaneously. Conventional evolutionary methods for the problems maintain a population for each task, and some offspring are generated by combining individuals in different tasks. The generated offspring are randomly assigned to tasks. However, the offspring may be assigned to inappropriate tasks. We propose a method that assigns them to appropriate tasks using a new criterion called search degree. The search degree represents how fast the solution search in each task progresses. The proposed method increases the probability of assigning the offspring to a task with a small search degree. Experimental results show the proposed method is superior.
GeFL: Gradient Encryption-Aided Privacy Preserved Federated Learning for Autonomous Vehicles

Autonomous vehicles (AVs) are getting popular because of their usage in a wide range of applications like delivery systems, self-driving taxis, and ambulances. AVs utilize the power of machine learning (ML) and deep learning (DL) algorithms to improve their self-driving learning experiences. The sudden surge in the number of AVs raises the need for distributed learning ecosystem to optimize their self-driving experiences at a rapid pace. Toward this goal, federated learning (FL) benefits, which can create a distributed learning environment for AVs. But, the traditional FL transfers the raw input data directly to a server, which leads to privacy concerns among the end-users. The concept of blockchain helps us to protect privacy, but it requires additional computational infrastructure. The extra infrastructure increases the operational cost for the company handling and maintaining the AVs. Motivated by this, in this paper, the authors introduced the concept of gradient encryption in FL, which preserves the users’ privacy without the additional computation requirements. The computational power present in the edge devices helps to fine-tune the local model and encrypt the input data to preserve privacy without any drop in performance. For performance evaluation, the authors have built a German traffic sign recognition system using a convolutional neural network (CNN) algorithm-based classification system and GeFL. The simulation process is carried out over a wide range of input parameters to analyze the performance at scale. Simulation results of GeFL outperform the conventional FL-based algorithms in terms of accuracy, i.e., 2% higher. Also, the amount of data transferred among the devices in the network is nearly three times less in GeFL compared to the traditional FL.
Linear lateral motion model for urban delivery skid steering robot

This paper deals with the linear lateral motion model for the dynamic skid steering mobile robot system. Previous papers, it doesn’t have any research which considers the lateral target position of the robot but also driving conditions. The proposed model includes a cornering stiffness to contemplate varying road conditions. For driving based on the vision sensors, it has a parameter like look-ahead distance detected with a stereo or depth camera. States of the model consider the yaw-rate state, then make the robot more smooth or more exact. Following the yaw-rate state, lateral position and yaw state are changed. We observe various scenarios by adopting a high-level controller or trajectory on the proposed model. The proposed model’s reliability was validated through computational simulation results and genuine commercial service with a real robot, Neubility-inc’s Neubie.
SLAS: Speed and Lane Advisory System for Highway Navigation

This paper proposes a hierarchical autonomous vehicle navigation architecture, composed of a high-level speed and lane advisory system (SLAS) coupled with low-level trajectory generation and trajectory following modules. Specifically, we target a multi-lane highway driving scenario where an autonomous ego vehicle navigates in traffic. We propose a novel receding horizon mixed-integer optimization based method for SLAS with the objective to minimize travel time while accounting for passenger comfort. We further incorporate various modifications in the proposed approach to improve the overall computational efficiency and achieve real-time performance. We demonstrate the efficacy of the proposed approach in contrast to the existing methods, when applied in conjunction with state-of-the-art trajectory generation and trajectory following frameworks, in a CARLA simulation environment.
Sampling matters: SGD smoothing through importance sampling

Many authors have suggested studying the loss landscape of Deep Neural Networks as a tool to understand their generalisation capabilities, the performance of optimisation algorithms as well as to tailor acceleration schemes. Nonetheless, a complete understanding of the entire learning process is still missing. This is mainly due to the complexity of the loss landscape, characterised by a rich structure with many local minima and saddle points. The purpose of this paper is to study the stochastic nature of Stochastic Gradient Descent and its link with the loss function as well as to propose a data sampling scheme which favours smoothing of the loss landscape to accelerate convergence speed. We validate our sampling scheme on AlexNet and ResNet applied to the CIFAR-10 dataset.
When Prioritization Meets Input-output Linearization: A Prioritized Multi-objective Control and Case Study with Two-link Manipulator

We discuss a control strategy to handle singularity of input gain matrix in the input-output linearization of a dynamical system with multiple outputs. The key idea is to prioritize the outputs and designate available control inputs to the outputs in the order of priority, by which tracking control for higher priority outputs is possibly maintained even in the presence of the singularity. This strategy of prioritization (called the prioritized control) allows to successfully deal with critical control objectives (such as stability and/or collision avoidance) by assigning higher priority to the relevant outputs. In this paper, we first explain how the prioritization can be applied to the process of input-output linearization and provide a definition of the prioritized control law on the framework of the multi-objective optimization with the lexicographical ordering. Then, a simple case study is performed for a two-link manipulator along with simulation results, where we further discuss an intrinsic degenerate property of prioritization that the prioritized control law can be discontinuous.
Compressed Cluster Sensing in Multiagent IoT Control

Traditional multiagent system control relies primarily on inter-agent local communications. In large-scale IoT systems it may appear hard to synthesize local control actions in a simple manner. Cluster control possibilities are thus thoroughly discussed, with possible control goals stated. The relation between multiagent state sparsity and cluster patterns is illustrated, for further utilization of sparsity for cluster control. Consequently, the problem of cluster identification is stated, and a possible solution is proposed in the form of the compressed cluster sensing algorithm. The algorithm utilizes compressed sensing methodology for a compact representation of agent states, which is then used to synthesize compact control actions in a low-dimensional space, without requiring to specify cluster locations.
A Novel Ping-pong Task Strategy Based on Model-free Multi-dimensional Q-function Deep Reinforcement Learning

Deep reinforcement learning has been widely used in table tennis decision-making tasks, but most methods have their own defects, such as relying on high-precision trajectory prediction work or requiring targeted class training, etc. None of the methods can directly get a complete hitting strategy through the initial state of the ball. In this paper, we train a ping-pong hitting policy controller with model-free reinforcement learning. By extending the multi-dimensional Q-function, the prediction part of the table tennis task and the batting strategy part are integrated, the trajectory prediction work and the batting work are completed by a single network, which simplifies the complex prediction process and does not need to build a complex dynamics model or train a neural network to predict the trajectory of ping-pong balls. In this way, the deep reinforcement learning process and supervised trajectory prediction training process are organized into a single process. The experimental results show that the best convergence effect can be basically achieved in 50,000 rounds of training. 10,000 tests were performed as a test set with a success rate of over 99%.
A Low-Profile, Scalable 28-GHz Phased Array Antenna in Fan-Out Wafer-Level Package for 5G Communication

This paper presents a 4-element (2×2) dual-polarized 28-GHz phased array antenna in package (AiP) for 5G communication. The AiP utilizes advanced fan-out wafer-level package (FOWLP) technology embedding a 65-nm CMOS beamformer IC and yields a low profile of 0.47 mm. Despite such an extremely low profile, a practical bandwidth of 2 GHz (27–29 GHz) is achieved by slot-fed antenna design. The antenna element on the AiP exhibits an EIRP of 15.2 dBm with good uniformity due to high fabrication accuracy of FOWLP. The AiPs are designed to be scalable so that they can be arranged in two dimensions to make a larger-scaled array. A 16-elements (2×8) phased array module using the four AiPs exhibits a peak EIRP of 40.5 dBm and is capable of scanning within ±40° close to ideal beam patterns, indicating high scalability of the developed FOWLP-based AiP.
Health for all in Montrouis, Haiti.

Health for all by the year 2000" . everyone's discussing it. in meetings, workshops and conferences. But are we any closer to it now than we were in 1978 when the concept was born at Alma Ata or are we still just discussing it? The author believes that in Montrouis, Haiti, they are closer as the concept has become action in the community health programme of Clinique St. Paul.
Bearing Degradation Prediction by WPD and DPNN: Introducing a Novel Deep Learning Method

In this article, a novel method of deep learning based on wavelet transform and deep perceptron neural networks (DPNNs) is proposed to predict the remaining useful life (RUL) of bearings. The proposed approach first extracts from the recorded signals the energy features by the wavelet packet transform. After training on these data, the DPNN model can be used to predict the RUL of a bearing. To verify the model, the proposed DPNN based on wavelet packet transform is compared with a least-squares support vector machine (LS-SVM) and long short-term memory (LSTM). The experimental results illustrate that DPNN can effectively predict the RUL of the bearing and is superior to the LS-SVM and LSTM in terms of prediction performance.
Current practice and determinants of medication management in long-term care facilities

Background: Medication management is a complex process which involves different phases including prescribing, transcribing, ordering, dispensing, supplying, administering, and storing. This study aimed to assess the medication adherence and its barriers among the residents in long-term care facilities to identify residents who are at risk for medication-related problem and resident`s beliefs toward medication. Methods: A prospective cross-sectional study was conducted in 24 long-term care facilities in Malaysia. The Chi-square test was performed to see any association between the adherence level and medication beliefs. Results: A total number of 185 residents were interviewed and majority of the residents were identified as highly adherent to their medication. There was a significant association found between duration of disease and medication adherence (P = 0.002). With regards to their belief toward medication, most of the residents had weak necessity scores (mean = 16.85, standard deviation [SD] = ±3.41) and strong concern scores (mean = 13.91. SD = ±2.684). There was a significant association between medication beliefs and medication adherence (P = 0.007), and a high number of residents were identified as at high risk for medication-related problems. Conclusion: Residents′ risk for medication-related problems and their overall well-being should be emphasized and related actions should be taken to rectify the problems.
An Efficient Approach for Trajectory Simplification Based on Essential Inflection Point Extraction

The prevalence of GPS positioning software has led to the generation of massive trajectory data, so it is necessary to take measures to compress the data. This paper proposes an efficient trajectory simplification algorithm based on Essential Inflection Point Extraction (EIPE). EIPE algorithm adopts synchronous Euclidean distance and direction angle deviation to preserve the temporal-spatial information while compressing the trajectories. However, considering the direction angle deviation will improve the compression rate for trajectories with high randomness in direction. To address this issue, we set a range threshold for the EIPE algorithm to extract essential inflection points from trajectories. The evaluation results on two real-life datasets indicate that our algorithm can improve compression efficiency and achieve satisfactory performance on both average direction angle deviation error and running time.
Factors Affecting Job Satisfaction of In-Home Child Care Helpers: Focusing on Child-Care Efficacy, Job Efficacy, and Health Promoting Lifestyle

Purpose: The purpose of this study was to investigate the influence of child-care efficacy, job efficacy, and health promoting lifestyle on the job satisfaction of in-home child care helpers who are called Idolbomi. Methods: Participants for this survey were 153 in-home child care helpers who worked in Seoul. Data were collected from May 23 to October 17, 2014 using self-report structured questionnaires. Data were analyzed using IBM/SPSS 18.0 program. Results: There were significantly positive correlations between child-care efficacy, job efficacy, health promoting lifestyle, and job satisfaction. Stepwise multiple regression analysis revealed that the predictors of job satisfaction were spiritual growth, education level, job efficacy, effect of income. Conclusion: Therefore, we need to develop strategies to enhance the job efficacy and health promoting lifestyle of in-home child care helpers to improve their job satisfaction.
Workplace stress and coping strategies among nurses in HIV/AIDS care: Geita District Hospital, Tanzania

The unprecedented increase in HIV and AIDS cases has trickled down to the already impoverished health sector, thus impacting health workers in various ways. In a shrinking health workforce, HIV/AIDS has created an extra demand and workload, emotional burden and stress among health workers. The study aimed to explore and describe nurses’ workplace stressors and coping strategies with regards to HIV/AIDS environment. The exploratorydescriptive study was qualitative in nature. Geita District Hospital was selected as it is the only health facility in the district that provides in-patient care services related to HIV/AIDS. The study population consisted of all nurses who work with HIV and AIDS patients and the managers providing support to nurses. The researcher interviewed twelve nurses and two key informants. Face to face interviews were conducted and a semistructured interview guide was utilised to collect data. Thematic content analysis was utilised and themes were derived from the concepts that emerged during the process. Validity and trustworthiness of the study was established through triangulation and member checking. The findings of the study revealed that nurses in HIV/AIDS experience stress from the workplace. Nurses struggled with issues of death and dying, feared occupational exposure and found it difficult to cope with nursing shortage, increased workload and inadequate training. The nurses were generally disturbed by lack of organisational support and the unavailability of resources such as; basic medical supplies and protective equipment. Nurses seemed to be resorting more to positive reappraisal, planful problem solving and seeking social support strategies.
Optimizing Edge SLAM: Judicious Parameter Settings and Parallelized Map Updates

Edge Simultaneous Localization and Mapping (SLAM) retains only the tracking on the mobile device, while offloading the compute-intensive local mapping and loop close to edge computing. Existing Edge SLAM approaches incur relatively high delays for offloading, resulting in high failure probabilities, i.e., low reliability, for commonly used public SLAM datasets. We discovered that two parameters which had not previously been studied in detail, namely the number of features and the number of keyframes that are bundled for a local map update, play a critical role in the offloading delay. Also, previous approaches updated the local map in the mobile device in a serial manner, incurring map update latencies. We study the numbers of features and bundled keyframes in detail and we parallelize the local map update. We find that judicious parameter settings, namely relatively small numbers of features (750 per frame) and bundled keyframes (1, i.e., effectively no bundling), reduce the map update latency to less than half compared to the previously common settings (1000 features per frame and 6 keyframes used for a map update). For a low network latency of 20ms, these judicious parameter settings in conjunction with our parallelized local map updating, reduce the 79% failure rate of the previous Edge SLAM systems down to 2%.
Certified Control-Oriented Learning: A Coprime Factorization Approach

This paper considers the problem of learning models to be used for controller design. Using a simple example, it argues that in this scenario the objective should reflect the closed-loop, rather than open-loop distance between the learned model and the actual plant, a task that can be accomplished by using a gap metric motivated approach. This is particularly important when identifying open-loop unstable plants, since typically in this case the open-loop distance is unbounded. In this context, the paper proposes a convex optimization approach to learn its coprime factors. This approach has a dual advantage: (1) it can easily handle open-loop unstable plants, since the coprime factors are stable, and (2) it is "self certified", since a simple norm computation on the learned factors indicates whether or not a controller designed based on these factors will stabilize the actual (unknown) plant. If this test fails, it indicates that further learning is needed.
Toward an automatic diagnostic scanning robotic system using ultrasound images

Ultrasonography is widely used for the diagnosis of various diseases. To obtain a clear view of the ultrasound image, an examiner needs to press the ultrasound (US) probe with a constant press and move smoothly and continuously. To mitigate the workload and prepare the remote treatment, there has been a demand for an automatic diagnostic scanning robotic system (ADSRS) using US images. For the complete ADSRS, the target localization, path planning, robot control, and US image analysis need to be addressed. This paper explains mainly the robot control part as the preliminary research toward ADSRS. A simple path was selected within a predefined inspection area as the preliminary work and the selected path was converted from the camera coordinates into the robot coordinates. Both the position and orientation of the US probe were used as the desired path. For the robot control, hybrid force-positon control was implemented to guarantee the quality of US images and the safety of patients. For the position control, the built-in Cartesian impedance control and null space control were utilized to control the 7-degree-of-ffeedom (7-DOF) manipulator. For the force control, the basic proportional control was implemented, and it was integrated with the aforementioned position control system. The experimental evaluation was conducted by using a 7-DOF manipulator with a joint torque sensor at each joint after attaching a US probe and a 3-dimensional (3-D) camera to an end-effector. The simple path over the surface of the skin of the patient was generated successfully, and the desired force during movement was set to 5N. The error is measured to $0.3144\pm 0.4853$ N. The proposed system shows sufficient performance in the robot control part and the path planning part.
The Impact of the United States Air Force Deployment Transition Center on Postdeployment Mental Health Outcomes

The United States Air Force Deployment Transition Center (DTC) operates a 2-day third-location decompression program that commenced operations during the summer of 2010 in Ramstein, Germany, with the aim to assist Air Force service members (AFSMs) who are returning from deployment as they prepare to reintegrate back into their home lives and work stations. The present study evaluated the impact of DTC attendance on later mental health outcomes. Because participants are not randomly assigned to attend the DTC, propensity score weighting was used to compare DTC participants (N = 1,573) to a weighted control group of AFSMs (N = 1,570) in the same job specialties who returned from deployment during the same time period. Rates of endorsement to items on the Postdeployment Health Reassessment were examined and compared, as were rates of mental health diagnoses from AFSMs’ official medical records. Key findings indicate that DTC participants reported lower levels of depressive and posttraumatic stress symptoms and lower levels of relationship conflict following return from deployment, as compared to weighted control participants. Mental health diagnostic rates were comparable for the 2 groups during the first 6 months following return from deployment. These findings suggest that participation in the DTC program had notable benefits for redeploying AFSMs and support the continued use of the program.
Noncontact selective laser transfer printing and assembly of micro-sized semiconductor devices

Laser induced forward transfer (LIFT) technique is widely considered as an efficient way to transfer print and assemble micro-sized semiconductor devices between wafers at a high speed. In this paper we propose a novel noncontact selective laser transfer printing and assembly method based on thermal releasing tape and infrared lasers. Thermal analysis and simulation are conducted to illustrate the mechanism of transfer printing. The experiment results of microchip arrays printing demonstrate that the proposed method has high precision, full selectivity, and compatibility of flexible transfer printing. The proposed method shows great potential to become a very competitive method for transfer printing of micro-devices.