Miracle-dz/UniFAR-FTUs huggingface dataset:
The Identification of Supplier Selection Criteria Within a Risk Management Framework Towards Consistent Supplier Selection

The aim of the study is to evaluate the consistency of supplier risk assessment performed during the supplier selection process. Existing literature indicates that current supplier selection processes yield inconsistent results. Consistent supplier selection cannot be accomplished without stable risk assessment performed during the process. A case study was conducted in a train manufacturer in South Africa, and document analysis, interviews and questionnaires were employed to source information and data. Triangulation and pattern matching enabled a comparative study between literature and practice from which findings were derived. The study suggests selection criteria that may be considered when performing supplier risk assessment during the selection process. The findings indicate that structured supplier risk assessment with predefined supplier selection criteria may eliminate inconsistencies in supplier assessment and selection.
Research on Multi-feature Fusion Algorithm for Facial Expression Recognition System

Facial expression recognition has played a significant role in harmonious human-computer interaction, and feature extraction is the key step for the final recognition result. In the part of feature extraction, a fusion information generally has a number of advantages over a single feature information. The traditional serial features fusion technique is the most common fusion method, but there is a constant problem existing in the above fusion method: the curse of the dimension. For solving the problem, a new fusion method is proposed. Extensive experiments illustrate that the new image fusion method has higher recognition rate and better realtime.
American Productivity Center Method for Measuring Productivity in Palm Oil Milling Industry

The main products of palm oil milling in this case are Crude Palm Oil and Kernels. This industry shows production targets are not achieving that cause fluctuations in profits due to not optimal use of resources in production activities. The aim of this study is to measure the productivity of palm oil milling industry and identify its factors. Integration of American Productivity Center (APC) method and Failure Modes and Effect Analysis (FMEA) was used to measure productivity. The finding in year 2015 to 2016 showed that there were decreasing of productivity index (9.81% to 0.68%) and profitability index (-7.23% to 20.27%) which both of them were caused by variable input of labor. Then, focus of improvements should be done in the sterilizer process, boiler process and sorting process. Further research is suggested to direct in maintenance case in order to know the effect of each machine on industry.
Control of Gene Regulatory Networks Basin of Attractions with Batch Reinforcement Learning

Basin of attraction contains biological functions and channels cell behavior, so when a gene network is in an unhealthy basin it may cause diseases. Control techniques can support the design of therapies that promote the transition of a biological system from diseased to healthier basins. Most control methods first infer a gene network and then derive a control strategy to avoid diseased states. However, this approach is limited to few genes and may cause other diseases, as the biological side of the problem is not considered. While changing between basins may change a diseased biological function for a healthier one, state avoidance can change functions in an unexpected way. We propose to extend a batch reinforcement learning method FQI-Sarsa, to change basin of attractions in a partial observable network. Using a batch reinforcement learning technique avoids the most time consuming phases that are the inference and control of the gene network. Results demonstrate that our method, BOAFQI-Sarsa, is more effective than previous studies that do not consider basins in their computations.
Multi-armed Bandit Based Hyper-Heuristics for the Permutation Flow Shop Problem

In this work, we propose MAB variants as selection mechanisms of a hyper-heuristic running on the multi-objective framework named MOEA/D-DRA to solve the Permutation Flow Shop Problem (PFSP). All the variants are designed to choose which of low-level heuristic components (for crossover and mutation operators) should be applied to each solution during execution. FRRMAB is the classical MAB, RMAB is restless and LinUCB is contextual (its context is based on side information). The proposed approaches are compared with each other and the best one, MOEA/D-LinUCB, is compared with MOEA/DDRA using the hypervolume indicator and nonparametric statistical tests. The results demonstrate the robustness of MAB-based approaches, especially the contextual-based one.
Process monitoring and industrial informatics for online optimization of Welding Procedure Specifications (WPS) in Gas Tungsten Arc Welding (GTAW) – Industry 4.0 for robotic additive remanufacturing of aeroengine components

Industry 4.0, the scheme that drives the fourth industrial revolution, has been, since its conception, reshaping the manufacturing industry. To advance current industrial chains into the smart factories of the future, cyber-physical systems are monitored and communicate with each other to ensure transparent interoperability, giving birth to the emerging field of industrial informatics. To enable the repair and recycling of high value jet engine compressor blades, additive manufacturing is utilized. The complex geometries and asymmetrical wear of the blades require robotic welding systems to be trained by experienced human welding engineers in order to be able to adapt to differing components. Demonstrated in this paper, process monitoring and industrial informatics are introduced to the adaption of Welding Procedure Specifications (WPS) utilized by a developing robotic system for the additive remanufacturing of aeroengine components. Using a novel variant of Gas Tungsten Arc Welding (GTAW), the robotic welding system under development is a product of an industry-academia collaboration between the Enabling Sciences for Intelligent Manufacturing Group (ESIM) of the University of Sheffield and VBC Instrument Engineering Ltd.
Risk Identification Practice in Patient Safety Context

Risk identification has gained an increased interest in healthcare to improve patient safety and quality within the scope of risk management. While past studies evaluated the evolvement of the risk management practice in healthcare in general, they also suggested that healthcare can learn and adapt from other safety-critical industries, such as aviation, chemical, nuclear and others. In this study, we particularly reviewed the current risk identification practice in patient safety context. Moreover, we compared healthcare with other industries to shed light on what can particularly be learned on risk identification to accelerate improvements in patient safety and enhance quality of care in healthcare delivery.
Study on stretch-retractable single-section continuum manipulator

ABSTRACT This paper proposes a stretch-retractable single-section (SRSS) continuum manipulator. Its backbone is composed by a series of springs. Thus, this continuum manipulator owns feature of a large-scale stretch-retractable ability. With such a feature, different from traditional single-section continuum manipulator which distal end moves in a curved surface, the presented manipulator’s distal end has a solid work space. The kinematic model including the variable parameter of backbone length was established, and the stretch-retractable workspace was analyzed, with which the driving strategy was proposed. The prototype was made out based on the concept of the large-scale stretch-retractable backbone. The driving patterns with single parameter input and the driving patterns with multi parameter inputs were carried out. The tests verified the presented SRSS continuum manipulator. GRAPHICAL ABSTRACT
Multi-Objective Design Space Exploration for the Integration of Advanced Analytics in Cyber-Physical Production Systems

The integration of advanced data analytics in manufacturing systems has shown impressive results in various fields, including fault diagnosis, predictive maintenance, energy management, and manufacturing system control. However, due to the distributed nature of analytics algorithms and the growing complexity of modern production systems, the performance and the cost of such systems highly depends on the underlying system architecture. Therefore, it is mandatory that system architects systematically explore and evaluate all architectural alternatives of the highly constrained design space defined by the systems functional and economical objectives. This paper presents a design-space-exploration method that not only generates different implementation alternatives, but also provides a formal performance analysis of the generated solutions. By analyzing the architecture of a manufacturing system as well as the data flow graph model of a data analytics algorithm, we automatically allocate, synthesize, and generate different simulatable software solutions to efficiently compute and visualize data analytics algorithms on the shop floor. This approach allows the user to evaluate different architectural implementation during the design phase, to select a solution according to its requirements and to analyze the performance of the resulting system. The applicability of this method is also demonstrated by means of a real world example.
Object tracking via the dynamic velocity Hough transform

Motion tracking is an important task in computer vision. A new technique, the dynamic velocity Hough transform (DVHT), for tracking of parametric objects is described that extends the velocity Hough transform (VHT) to cater for arbitrary motion. Like the VHT, the new technique processes the whole image sequence, gathering global evidence of motion and structure. However, we do not assume constant linear velocity but rather allow arbitrary velocity. The method tries to find an optimal, smooth trajectory in the parameter space with maximum energy, where the latter incorporates both the structure of the moving object and the smoothness of motion. The constrained optimisation problem is solved using a temporal (time-delay) dynamic programming algorithm. Tracking in noise is much superior to the standard Hough transform.
Robot Localization Using Classification with Reject Option from Structural Co-occurrence Matrix in Omnidirectional Images

Location represents a crucial issue for mobile robotics since it is directly related to the performance of the robot in the execution of the activity for which it was designated. In this paper, a new navigation and localization approach for mobile robots in topological maps using classification with reject option in attributes obtained from a Structural Co-occurrence Matrix (SCM) in omnidirectional images is proposed. A study among several feature extractors and classifiers consolidated in the literature is performed and characteristics such as robustness and efficiency related to the hit rate and processing time are highlighted since these parameters are essential in recognition systems aimed at real world applications. According to the proposed approach, SCM produced relevant results and stood out among other extractors, reaching an average accuracy of 99% and the shortest extraction time, with 0.1518s, consolidating itself as a suitable method in the location and navigation task.
Information Privacy Practices in Organizations: Activities, Knowledge and Skill Requirements for Information Technology Professionals

This paper focuses on governance and daily operation activities information technology (hereafter IT) professionals carry out to protect information privacy in organizations. Its objective is to identify these activities and the knowledge and skills they require. Content analysis of 120 relevant job postings is used to provide a picture of such practices. Findings help assess and update an existing body of knowledge available to IT professionals working in the area of information privacy protection. Findings help also identify the relative importance of the different domains of knowledge and skills hence required. Implications for practice and research are discussed.
Laser-aided Infrared Visual Inertial Odometry for Unmanned Aerial Vehicle with Multi-plane Constraint

In this work, we propose an infrared visual inertial odometry (VIO) algorithm for unmanned aerial vehicles (UAV) for both daytime and nighttime. It utilizes images from a single downward-looking thermal camera, and uses a laser scanner to measure relative distance to determine the scale factor. The scale recovery process consists of a comprehensive logic flow to try out measurements into a homography decomposition with a multi-plane constraint, obtaining the results with minimum error, making the algorithm more robust. Moreover, other modifications, such as keyframe idea and pruning KLT tracker with sub-regions further improves the performance. Besides, the results are fused with an on-board IMU through an EKF-based sensor fusion framework. This infrared visual inertial odometry algorithm can still navigate UAVs during the missions when GPS is down or not reliable or under poor illumination condition.
ON-OFF: a reactive routing algorithm for dynamic thermal management in 3D NoCs

Dynamic thermal management (DTM) techniques of three-dimensional (3D) Network-on-Chips (NoCs) are employed to rescue the chip from thermal difficulties. Reactive routing algorithms, which utilise router throttling technique as a popular DTM, disregard distribution of heat generation of routers resulting in more throttled routers as well as long packet delays in throttled processing elements. This study proposes a reactive routing algorithm for 3D NoCs to (i) dynamically detour packets from hot zones containing throttled routers and (ii) minimise the number of required router throttling in the network. The proposed routing algorithm defines two virtual networks to enhance the path diversity for packets in each layer of 3D NoCs. The selection of diverse paths distributes heat generation to alleviate the thermal variance. The proposed routing algorithm is analysed by turn model to achieve deadlock freedom. Access Noxim simulator is also used to evaluate the performance and the thermal behaviour of the proposed routing algorithm in the variety of conditions. Results show that the proposed routing algorithm improves temperature variance by 9-39% and reduces number of throttled routers by 16-86%, which is achieved at the cost of one extra virtual channel per each physical channel in the XY -plane.
Closed-Loop System for Myoelectric Hand Control Based on Electrotactile Stimulation

Prostheses are important for amputees to accomplish basic tasks in normal life. Due to lack of feedback, most prosthetic hands are easily rejected by amputees. Electrotactile feedback is a good way to close the loop of prosthetic control and improve human-computer interaction. In this work, we designed a closed-loop system for a myoelectric hand, which includes a driver module for controlling the prosthesis via electromyography (EMG), a force sensor module for measuring contact force of prosthesis, and an elcetrotactile device for delivering sensory feedback to subjects. Pilot experiments involving four amputees were carried out to test the performance on discrimination of stiffness and prosthetic grasping of bottles. Our preliminary results showed that electrotactile feedback played an important role in closed-loop control for the prosthetic hand.
On Context, Issues, and Pitfalls of Expert Judgement Process in Risk Assesment of Arctic Offshore Installations and Operations

Decisions to be made in the Arctic offshore operations rely extensively on risk assessment outputs, which require a great deal of historical data and information. However, at the current stage of operating in the Arctic offshore – compared to normal-climate regions – such data is scarce due to the limited industrial activities to date. Lack of data on the probability of the occurrence of an unwanted event and, given severe Arctic environmental conditions, the extent of potential severe consequences pose a great deal of challenges and issues for decision-makers. A widely acceptable alternative is the use of expert judgement process. However, this is faced with some issues and pitfalls, which may raise questions regarding the objectivity and level of uncertainty of risk assessment outputs. In this paper, we discuss such issues and pitfalls associated with expert judgement application in risk assessment of Arctic offshore operations.
Perception-based image transcoding for universal multimedia access

We propose a novel scheme for generating transcoded (e.g. scaled and cropped) image to fit the size of the respective client display when an image is transmitted to a variety of client devices with different display sizes. The scheme has two key components: (i) perceptual hint for each image block, and (ii) an image transcoding algorithm. For a given semantically important block in an image, the perceptual hint provides the information on the minimum allowable spatial resolution reduction. The image transcoding algorithm that is basically a content adaptation process selects the best image representation to meet the client capabilities while delivering the largest content value. The content value is defined as a quantitative measure of the information on importance and spatial resolution for the transcoded version of an image. Experimental results demonstrate the feasibility of the proposed scheme with a variety of client devices having different display sizes.
Real-time Semantic Segmentation for Road Scene

Semantic segmentation is challenging for diverse scenes. In this paper, we propose a new model for road complex scene. Our model use encoder-decoder structure with auxiliary loss. Based on ResNet bottleneck block, we proposed dilated bottleneck block and tiny block. These block applied in the encoder and decoder. The dilated bottleneck block enlarges the field-of-view and the tiny block maintains the model as small as possible. We train our model end-to-end from scartch, and the image and segmentation map in network is pixel-to-pixel. With the help of auxiliary loss, our model yields 56.9% mean IoU on CamVid dataset, it is smaller and faster than ENet and SegNet.
Robust Control of a Serial Variable Stiffness Actuator Based on Nonlinear Disturbance Observer (NDOB)

This paper focuses on the difficulty of designing control strategy for a Serial Variable Stiffness Actuator (SVSA) because of the disturbances in such as spring deflection, model uncertainty and load disturbance. A nonlinear disturbance observer based composite controller (NDOBC) is developed to reject the load disturbance and improve the performance in disturbance rejection. This NDOBC is proposed to control both stiffness and position of the SVSA. Compared to other nonlinear controller for SVSA, the proposed control method can guarantee the robust stability and performance against load disturbance. The effectiveness of the proposed controller is experimentally verified on the SVSA.
Robot Manipulators Control with Parameters Adaptation and State Estimation

Robot manipulator control considering parameter uncertainty and velocity estimation is address in this paper. An adaptive controller using state estimation feedback is provided in this paper, which can adapt the dynamic parameters and estimate the velocity with imprecise system model simultaneously. The stability of the proposed controller is discussed and a 2-links robot manipulator is adopted as an example to illustrate and validate the provided control scheme. In the simulation, all the observer performance, parameter adaptation, and the trajectory tracking are validated.
Cold-standby Redundancy Optimization for Multi-Type Production Systems Using NSGA-II

This paper develops a new model for investigating cold-standby redundancy allocation problem (RAP) of a multi-type production system. The system consists of several independent linearly ordered subsystems which undertaking different tasks. The system can process different types of production. Each type of production requires a unique manufacturing line: required subsystems should be turned on, while others powered off. A binary matrix is used to describe the structure of multi-type production system. Cold-standby redundancy on subsystems is considered for achieving higher reliabilities of the different manufacturing lines. The RAP for the multi-type production system is essentially multi-objective subject to resource constraints. The paper formulates the multi-objective problem and solves it by using a well-known algorithm called NSGA-II. Finally, illustrative examples are presented.
Stair-Climbing Auxiliary Device With Self-Adaption

With the aging of the population becoming more and more serious, it is necessary to design a device to reduce the risk of low-back injury during stair-climbing process. Most of the existing devices maybe inferior in case the application scenario lacks space, so a more compact and user-friendly device is needed. A power-assisting device is introduced to support elder people lacking in physical strength during stair-climbing process. This study presents a new device suitable for daily application. Firstly, the structure of the device is presented and tested in simulation environment. Then, an adaptive controlling algorithm is developed to keep the output in accordance with motion pattern of the user. Finally, the robustness of the algorithm is verified through simulation.
Cyberbullying in WhatsApp classmates’ groups: Evaluation of an intervention program implemented in Israeli elementary and middle schools

Social networking platforms, such as WhatsApp, constitute a major part of adolescents’ social lives. Alongside the positive aspects of such platforms, there is a risk of using them as a means for cyberbullying. Schools have become increasingly aware of this risk and are prioritizing fighting cyberbullying through intervention programs, yet few interventions have been studied for their effectiveness. This study reports the results of a wide-scale school-based intervention designed to reduce cyberbullying and improve usage norms in WhatsApp classmates’ groups. Data were collected from 52 classes in 12 public schools in Israel: n = 47 elementary school classes, 4th to 6th grades, and n = 5 middle school classes, 8th grade. A total of 1402 students answered questionnaires pre-intervention (51% females), while 90% of them (n = 1268, 52% females) answered questionnaires 1 month post-intervention. Results indicated a significant decrease in WhatsApp cyberbullying and a significant improvement in classroom climate. In addition, improvement in WhatsApp usage norms was positively correlated with a decrease in WhatsApp cyberbullying.
Increasing Convolutional Neural Networks Training Speed by Incremental Complexity Learning

Convolutional Neural Networks have been successfully applied in several image related tasks. On another hand, there are some overhead costs in most of the real applications. Often, the Deep Learning techniques demand a huge amount of data for training and also a crescent need for handling high definition images. For this reason, late network architectures are getting even more complex and deeper. These factors lead to a long training time even when specific hardware is available. In this paper, we present a novel incremental training procedure which is able to train faster with small performance losses, based on measuring and ordering the relative complexity of subsets of the training set. The findings reveal an expressive reduction in the number of training steps, without critical performance losses. Experiments showed that the proposed method can be about 40% faster, with less than 10% of accuracy loss.