Miracle-dz/UniFAR-FTUs huggingface dataset:
Workplace Climate for First-Generation Engineering Faculty: Intersectional Analyses with Gender, Race/Ethnicity, and Socioeconomic Background

The ongoing lack of diversity in the engineering higher education workforce remains a grand challenge with far-reaching implications for universities and the U.S. economy. Institutional social norms and identity are rooted in upper-class, elite culture. Although all individuals may struggle to conform to some aspects of a university society, first-generation (1stGen) faculty members might have exceptional challenges. Faculty perceptions of workplace climate were measured using a scale to assess ten constructs that may impact faculty persistence. In addition, demographic questions were generated to capture respondents' multiple intersecting social identities in a climate survey. The sample size was 846 tenure and tenure track engineering faculty in the U.S. For this study, we compared the perceptions of engineering faculty using the category of 1stGen Faculty intersecting with gender, race/ethnicity, and socioeconomic background (SEB). We employed multiple regression modeling to analyze the data. Findings revealed a significant difference in leadership self-efficacy when 1stGen status was intersected with socioeconomic background. This study demonstrates how the workplace is experienced by faculty members differently based on their entwined social identities, which builds upon our prior research. These new findings contribute to the validity evidence of the climate survey and provide insight into the 1stGen faculty identity.
Comparison and evaluation of IPPG methods for HR estimation under different face regions

Non-contact heart rate measurement reaches a higher level in scientific research; this field presents important advantages in our life, such as human-machine interaction, medical applications, especially in the current situation of the world suffering from COVID-19 pandemic. In recent years, several techniques of extracting the imaging photoplethysmography (ippg) signals from facial videos have been proposed and developed. Based on this, we performed a study and evaluation of the four most well known heart rate estimation methods such as Green, ICA, POS and CHROM in two accessible public datasets MAHNOB-HCI and UBFC-Phys under two different facial regions to enable researchers to develop and use them in real applications. The results show that the video imaging condition and the correct face region detection step play an important role in the accuracy of heart rate estimation.
Review of Immunotherapy Classification: Application Domains, Datasets, Algorithms and Software Tools from Machine Learning Perspective

Immunotherapy treatments can be essential some-times and a waste of valuable resources at other times, de-pending on the diagnosis results. Therefore, researchers in immunotherapy need to be updated on the current status of research by exploring: application domains e.g. warts, datasets e.g. immunotherapy, classifiers or algorithms e.g. kNN and software tools. The research objectives were: 1) to study the immunotherapy-related published literature from a supervised machine learning perspective. In addition, to reproduce im-munotherapy classifiers reported in research papers. 2) To find gaps and challenges both in publications and practical work, which may be the basis for further research. Immunotherapy, b-cell data, cryotherapy, exasens data and sample serum are explored. The results are compared with published literature. To address the found gaps in further research: novel experiments, unbalanced studies, focus on effectiveness and a new classifier algorithm are suggested.
Hierarchical Segmentation of Human Manipulation Movements

This paper introduces a segmentation algorithm which splits complex human manipulation movements into movement segments and automatically groups these into labeled actions. With this hierarchical algorithm the basic movement identities, which we call building blocks, as well as their concatenation to more complex actions can be identified in one handed as well as dual arm human manipulation movements. The algorithm can be used, e.g., in robotic applications such as imitation learning, in which human movement examples are directly used to generate robotic behavior. In this paper, we present two variants of the hierarchical segmentation algorithm, one supervised approach which requires a small number of pre-labeled movements as training data, as well as an approach which uses unsupervised algorithms to group building block segments which belong to the same movement. In both variants, the building block movements are detected based on the velocity of the hand(s), using the velocity-based multiple change-point inference algorithm. We evaluate both methods on human manipulation movements recorded from several participants with a marker-based motion tracking system. The first evaluations are done on simple one-handed point-to-point movements, followed by an evaluation on a complex dual arm manipulation task. The results show, that the presented approaches are able to identify basic movements as well as their concatenations into more complex, labeled actions.
Sparse Branch-and-Bound 2D-3D Registration Based on Prior Correspondences of Dense Points

In Image-Guided Intervention, 2D-3D registration between pre-operative 3D Computed Tomography (CT) or Magnetic resonance imaging (MRI) images and intra-operative X-ray 2D images is a key step for accurate surgical navigation. The goal of 2D/3D registration is to find the best rigid transformation parameters that best align the 3D offline CT and 2D online X-ray images. The multi-modality 2D/3D registration is a challenging problem, partly due to the different dimensions and modalities. To handle the issues, we propose a novel globally Branch-and-Bound (BnB) method incorporating point set neural network, to speed up the estimation of transformation. The pre-trained Deep BlindPnP model can provide global and local prior information for the rotation and translation search in the BnB framework. And different densities of point sets are used in different cases, balancing the capture global information and computation time to find the correspondence. The proposed registration framework combing learning-based and optimization-based methods have been comprehensively validated on real-world datasets, which demonstrate significantly improved accuracy and speed.
Malicious Software Detection Based on Improved Convolution Neural Network

Malicious software detection is the initial and primary step for any network-based security system. Machine learning algorithm has become one of the main methods. Due to the high computational complexity and low computational accuracy of traditional machine learning algorithms, an improved deep learning method is proposed in this paper, Convolution Neural Network (CNN) with Batch Normalization and Inception-Residual network (BIR-CNN). Firstly, a new method is designed to feature extraction. Each vector value of each sample is aggregated, using the functions mean, median, standard deviation, skew, and kurtosis, resulting in a 5-dimension feature for each vector. Secondly, the supervised neural BIR-CNN method is adopted to detect malicious software. Convolution layers and an Inception-Residual network are used in the model to improve its capacity for learning. Batch normalization helps hasten model training while preventing over-fitting. Finally, research is done using network traffic that is accessible to the general public data set CICAndMal2017 and evaluated against three conventional algorithms for CNN and algorithms, and the BIR-CNN model obtains an accuracy of 0.97 and the AUC (Area Under Curve) of 0.99. Therefore, utilizing deep learning for the detection of dangerous software, BIR-CNN can be employed.
On the Possibility of Two-Dimensional Slip Rendering Using Two Cross Placed Belts

Haptic feedback plays a key role in robot teleoperation and enables operators to feel what remote robots touch. To enhance the telepresence, this paper proposes a novel haptic feedback device for slip rendering, which is the primary cause of failure in robotic grasping. The slip feedback is rendered by two cross placed belts driven by individual motors. The upper belt is in direct contact with the operator’s skin to induce a stretch. It is thin enough to allow the operator to also perceive the lower belt, which has bumps on its surface to induce a pressing stimulation. When the belts are actuated, the operator can distinguish their motions and estimate both the direction and speed of a lateral slip rendered from their resultant motion. The proposed device can provide a continuous slip rendering without motion limits. It is also suited for applications in virtual reality. User studies have been conducted to evaluate its effectiveness, showing that the users’ success rate of recognizing eight slip directions and four slip speeds reaches 84.6% and 72.0%, respectively. Furthermore, we demonstrate that its usage in teleoperation helps reduce the force applied by a teleoperated robotic gripper in object grasping.
Design and Experiment of Multiple Terrains Adaptable Mobile Hexapod Robot with Foot-flipper

Aiming at the problem that a single propulsion mode is difficult to adapt to the switching of multiple terrains in the existing mobile hexapod robots, a mobile hexapod robot is designed that can adapt to multiple terrains, and the motion performance of the robot is tested. In this paper, a two-stage planetary gear train mechanism is designed to realize the motion separation of foot motion and two-way deformation of foot-flipper structure. Then the dynamic deformation experiment of foot-flipper structure is carried out to verify the effectiveness of the structure. Finally, the performance experiments of the robot on multi terrains are carried out, the results show that the movement speed of the robot on the grassland is the fastest, up to 83.92 cm/s and the robot can climb a 150 slope, which means that the hexapod robot has good motion performance.
An All-Passive Compound Current Sensor for Fast Switching Current Monitoring

In this paper, a compound current sensor suitable for fast switching current (or similar applications) measurement is presented. The compound sensor consists of two current transformers (CT) with (very) different operating working frequency bandwidth (BW) values. The outputs of each transformer are conditioned and aggregated together through an all-passive impedance network. Hence, a wideband (10Hz-10MHz) current sensor is obtained, having a gain of -20 dB or the sensitivity of 100 mV/ A without any amplification. Therefore, the proposed sensing solution requires little to no secondary active circuits compared to regular current transformers, Rogowski coils, Hall-effect sensors, etc. Having an all-passive circuitry brings certain advantages for space, aerospace, and avionic applications. The operating and design principles of the proposed sensing solution are presented. Furthermore, an analysis of the gain, accuracy, and consistency of the proposed sensor in a switching converter is provided to confirm the validity of the proposed passive topology.
A Blackbox Modeling Approach for DC-DC Converters with Strong Nonlinear Dynamics Using an Improved Strategy for the Transition among Local Models

The use of commercial DC-DC converters is an attractive solution when building power distribution systems, but the reduced information provided by manufacturers makes the modeling a difficult task. Blackbox models have been proposed to solve this problem, but the existing models struggle to reproduce harsh nonlinear dynamics. Previous approaches to tackle this issue involve approximating the global dynamic behavior by weighting local models according to the operating point of the converter. However, their accuracy diminishes as the nonlinearities grow stronger. This paper presents a blackbox modeling approach also based on using local models but introduces an improved method for transitioning among them. The key aspects are the use of electrical RLC networks to identify local models and the transfer of initial conditions when switching from one to another. The technique is validated experimentally with a buck converter subjected to steep load steps.
Detection of Free-Standing Conversational Groups with Graph Convolutional Networks

Automatically detecting conversational groups from video footage is an important research area with applications in social robotics, human-computer interaction, and video analysis. In this paper, we use a novel approach based on Graph Convolutional Networks (GCN) for the social group detection problem. We base our approach on a method from the community detection domain called Deep Modularity Networks (DMoN). As an input to the DMoN method, we construct a graph representation using body view frustums, which indicates affinities among the individuals. In addition to the frame-level group detector, the proposed system utilizes temporal information to improve the group detection accuracy. Experiments conducted on the standard benchmark dataset SALSA confirm that our GCN-based approach improves the group detection quality over state-of-the-art group detection methods.
Gait Prediction and Assist Control of Lower Limb Exoskeleton Based on Inertia Measurement Unit

The lower limb exoskeleton is a wearable robot with wide application, such as helping the elderly and the disabled, military, etc. This paper focuses on the design and control of the lower exoskeleton based on Inertia Measurement Unit (IMU), which can realize trajectory control, active and passive follow-up control and predictive control, thus realizing the assistance to the wearer and reducing the wearer's sense of load. The kinematic mechanism of human lower limbs is analyzed and the kinematic model is established by using standard D-H coordinate transformation method. The mechanical structure of exoskeleton is designed and its hardware system is built. LabVIEW software is selected as upper computer. The physical model of knee joint motor is established and its transfer function is derived. PID control strategy is selected and target trajectory for closed-loop control of external skeleton is set to achieve the specified trajectory. Gait movements of three participants were collected and analyzed using inertial sensors. Machine learning method is used to predict gait data through ARIMA model.
First-principles study of quantum tunneling from nanostructures: Current in a single-walled carbon nanotube electron source

We present a first-principles calculation of the emission current in a single-walled carbon nanotube electron source. We have employed the nonequilibrium Green's function and Fisher-Lee's transmission formulation to describe electronic transport through the system. The simulation results reproduce the trends observed in experimental data closely and, in particular, the current saturation and deviation from the Fowler-Nordheim behavior. The proposed numerical approach is useful whenever a region of vacuum is present in the system Hamiltonian.
Sum-Product Decoding based Simple Hamming Code for Low Power Mobile Devices

Since IoT requires low power transmission and ultra-small devices, a strong error correction codes with simple encoders are needed. Hamming code is considered as an useful simple error correction code. Hamming code with hard decision has a low complexity scheme, but its error correction capability is degraded. Therefore, in this paper, we consider a low complexity decoding method to improve error correction property with applying Sum-product algorithm. Sum-product decoding is a kind of brief propagation (BP) decoding, and can efficiently determine the posterior probability. In addition, using messages in the LLR domain, decoding operation will be much easier. However, it is not an exact MAP decoding because the Tanner graph of the Hamming code has the loops of length 4, and its decoding performance is not predictable. Therefore, we verified the effectiveness of the proposed scheme by computer simulation.
Task Scheduling in Cloud Computing Based on FPA Metaheuristic Algorithm

In this paper, we propose to address the multi-objective task scheduling issue in cloud computing system in order to find the best assignment of the user-submitted tasks to the cloud resources provided by the cloud service providers. To achieve a high overall performance for computational cloud resources, a good scheduler algorithm is required to meet customer requirements and ensure efficient QoS. To this end, the Flower Pollination Algorithm metaheuristic has been used to orchestrate the trade-offs relationship between three end-user QoS-based criteria, namely reliability, time makespan and execution cost. The results obtained from the experiment conducted with the CloudSim simulator clearly show the interest of our approach based on FPA.
Light-Weight Design and Implementation of Deep Learning Accelerator for Mobile Systems

Deep neural network (DNN) has been widely used in various fields along with the great attention of artificial intelligence (AI). Accordingly, the study and development of deep learning accelerators have been actively performed for working the DNN fast and efficiently. The accelerators usually consume high computing power and require large memory footprint. However, low power and memory-efficient accelerators with light-weight design are essential for mobile systems due to the limitations of hardware resources. Mobile system is necessary, in particular, in an era when internet of things (IoT) becomes important. Therefore, we proposed and implemented the light-weight design of deep learning accelerator for mobile systems. Implemented results prove that the proposed design can be sufficiently embedded in mobile systems with over 50% smaller memory size than existing accelerator design for mobile deep learning applications.
Multi-Class Hypersphere Anomaly Detection

Machine learning-based classification algorithms typically operate under assumptions that assert that the underlying data generating distribution is stationary and draws from a finite set of categories. In some scenarios, these assumptions might not hold, but identifying violating inputs - here referred to as anomalies - is a challenging task. Recent publications propose deep learning-based approaches that perform anomaly detection and classification jointly by (implicitly) learning a mapping that projects data points to a lower-dimensional space, such that the images of points of one class reside inside of a hypersphere, while others are mapped outside of it. In this work, we propose Multi-Class Hypersphere Anomaly Detection (MCHAD), a new hypersphere learning algorithm for anomaly detection in classification settings, as well as a generalization of existing hypersphere learning methods that allows incorporating example anomalies into the training. Extensive experiments on competitive benchmark tasks, as well as theoretical arguments, provide evidence for the effectiveness of our method. Our code is publicly available1.
Topology Derivation of Transformerless Inverter without Leakage Current Based on Graph Theory

This paper proposes a topology derivation method based on graph theory, which can obtain topologies that meet specified power requirements given the port type, the number of components, and the target function of the circuit. The method uses graph theory to represent the circuit topology with an adjacency matrix. All closed loops can be obtained using a breadth/depth-first search method. Following this, initial topologies that match the required type and number of devices can be found. Circuit rules and the target circuit function can be converted into matrix constraints. Matrices that violate the constraints are then deleted to remove undesired topologies. A transformerless inverter without leakage current is explored as an example of the target function. The method yields several basic topologies with the minimum number of switches that meet the requirements. Expanding on this, working mode analysis and simulation of an integrated boost inverter are carried out to verify the validity of the method and the inverter topology result.
Low-voltage Ride-through Response of Renewable-Penetrated Distribution Networks

Low voltage ride-through (LVRT) is a grid code that enables distributed energy resources (DERs) to stay connected under voltage sags. However, a DER is exposed to being tripped if it does not meet the LVRT requirement. For a renewable-penetrated distribution network (RPDN), the lost amount of DER capacity over a voltage sag is the so-called LVRT response of an RPDN. Besides the behavior of DERs during the transient undervoltage condition, their trip may have a negative impact on transmission system security. This paper addresses a general mathematical model for analyzing the DERs' behavior during the transient condition. The proposed model is implemented to obtain the RPDN's LVRT response to different voltage sags. This LVRT response is defined as such that concerns the uncertainty in the output power of renewable resources. Furthermore, the expected ride-through capability index is introduced to capture the generation availability due to meeting the LVRT requirements. The studied RPDN shows the lowest capability is for voltage sags with depth and duration greater than 0.70 pu and 0.4 sec, respectively. Also, with 40% and 20% reductions in solar and wind generation compared to their nominal values, the maximum LVRT response of the RPDN decreases from 2420 kW to 1852 kW. Also, the ERC of 66% is achieved for the RPDN, stating the expected loss of 34% of the total DER generation.
A CBPWM Strategy with Flexible Zero-Sequence Voltage Injection for Three-Level TNPC Converters in Aircraft Electric Starter/Generator System

In this paper, a flexible zero-sequence voltage (ZSV) injection method is presented for the silicon carbide (SiC)-based three-level electric starter/generator (ESG) system. The proposed algorithm can automatically adapt based on the op-erating conditions of the studied ESG system, thus not only capacitor voltages are kept balanced, but also the switching loss is reduced over a wide-speed range operation. Moreover, the abrupt commutation phenomenon that is a common issue for the state-of-the-art discontinuous pulse-width-modulation (DPWM) scheme operating in a low modulation index, is effectively avoided. The results of the simulation and experimental test validate the effectiveness and significant improvement of the introduced modulation technique for a next-generation high power-density aircraft three-level power generation unit.
Engineering Identity, Perceptions of Sociotechnical Education, and Views of Engineering Practice in Undergraduate Students

Engineering education is widely perceived as focusing entirely on difficult technical analysis at the exclusion of consideration for the social contexts associated with such work. However, in reality, engineering practice is concerned with defining and solving problems which are both technical and social in nature and which require professional engineers to understand the complex connections between technical and social considerations. In recent years, there have been efforts across a number of research projects and curriculum development efforts to integrate sociotechnical thinking into students’ engineering courses. This paper leverages one such project to explore the connection between sociotechnical thinking and the development of an engineering identity in students. It presents the results from analysis of student focus groups using inductive coding to explore the connections between students’ beliefs about engineering practice, their perceptions of sociotechnical thinking in their engineering education, and their self-reported engineering identities. We identify and describe four clusters of students which capture the most common interactions between these three dimensions and discuss the implications of these clusters for broadening participation in engineering and engineering education.