Miracle-dz/UniFAR-FTUs huggingface dataset:
The Laser Vegetation Imaging Sensor (LVIS): An Airborne Laser Altimeter for Mapping Vegetation and Topography

The Laser Vegetation Imaging Sensor (LVIS) is an airborne laser altimeter designed to quickly and extensively map surface topography as well as the relative heights of other reflecting surfaces within the laser footprint. Since 1997, this instrument has primarily been used as the airborne simulator for the Vegetation Canopy Lidar (VCL) mission, a spaceborne mission designed to measure tree height, vertical structure and ground topography (including sub-canopy topography). LVIS is capable of operating from 500 m to 10 km above ground level with footprint sizes from 1 to 60 m. Laser footprints can be randomly spaced within the 7 degree telescope field-of-view, constrained only by the operating frequency of the ND:YAG Q-switched laser (500 Hz). A significant innovation of the LVIS altimeter is that all ranging, waveform recording, and range gating are performed using a single digitizer, clock base, and detector. A portion of the outgoing laser pulse is fiber-optically fed into the detector used to collect the return signal and this entire time history of the outgoing and return pulses is digitized at 500 Msamp/sec. The ground return is then located using software digital signal processing, even in the presence of visibly opaque clouds. The surface height distribution of all reflecting surfaces within the laser footprint can be determined, for example, tree height and ground elevation. To date, the LVIS system has been used to monitor topographic change at Long Valley caldera, CA, as part of NASA's Topography and Surface Change program, and to map tree structure and sub-canopy topography at the La Selva Biological Research Station in Costa Rica, as part of the pre-launch calibration activities for the VCL mission. We present results that show the laser altimeter consistently and accurately maps surface topography, including sub-canopy topography, and vegetation height and structure. These results confirm the measurement concept of VCL and highlight the benefits of airborne prototypes of spaceborne instruments.
WriMouCon: Wrist-Mounted Haptic Controller for Rendering Physical Properties in Virtual Reality

For immersive Virtual Reality (VR), the need for effective and precise haptic feedback is increasing. This paper proposes a wrist-mounted haptic controller that provides direct feedback to the palm for rendering various physical properties. We conducted various experiments that demonstrated the effectiveness and accuracy of our device. First, we found that our device renders negligible force (0.02N) to the user in the free-hand situation. Second, we confirm that our device renders physical properties with high accuracy (within 6%) by using Force Sensing Resistor (FSR). Finally, we present the minimum force feedback threshold and difference threshold of the proposed device. From the results of perceptual experiments, we suggest a design guideline for using the proposed prototype.
Muscle Synergy-Based Human-in-the-Loop Optimization for Personalized Hip Exoskeleton Control

Exoskeletons are promising devices used for walking training in patients with weak muscle strength. However, it is currently challenging to provide personalized assist profiles for better human-exoskeleton interaction. Human in-the-loop (HIL) optimization has been used by many studies to optimize assist torque by minimizing energy expenditure, but there are currently no studies on HIL optimization using muscle synergy as an evaluation index. In this paper, a muscle synergy-based HIL optimization framework is proposed for the hip exoskeleton to provide personalized torque assistance and reduce the effort of walking at the same time. Firstly, we put forward a muscle synergy similarity index to quantify the similarity of synergies during walking with and without exoskeleton assistance. In addition, by introducing sEMG signals to calculate the evaluation index of muscle synergy and iteratively optimizing the assist parameters in real time, muscle synergy-based HIL optimization of assist torque profile was proposed and tested on a portable hip exoskeleton. The optimal and sub-optimal torque profiles were explored through 35 iterations. The muscle synergy similarity index and the degree of muscle activation under 4 different assist profiles were tested by the validation experiment. The muscle synergy similarity during walking with exoskeleton assistance can be improved as much as possible by using the assist parameters generated by HIL optimizing, which reached 0.7113 in our experiment. This method can also reduce muscle activations of vastus medialis, gluteus maximus, tibialis anterior, soleus muscle, which proves that the HIL optimization based on muscle synergy proposed can reduce the effort of walking and ensure a “natural‟ walking as much as possible.
Analysis of Student Learning Outcomes in Data Structures and Algorithms

Student learning outcomes (SLOs) are the specified knowledge, skills, and abilities that students are expected to achieve by the end of a course or program. Student learning outcomes describe what educators expect students to know and be able to do. SLO's specify an action of a student that must be observable, measurable and demonstrable. On the other hand, grades are objectives measure. This study attempts to quantify SLOs of a graduate level foundation course named Algorithms and Data Structures. Three key aspects of the course are defined to address learning outcomes: a) Algorithm Design Skill, b) Data Structure Selection and Design Skill and c) Algorithm Analysis Ability. For each aspect five achievement classes are specified in order to observe and quantify their knowledge, skills and ability. According to self-assessment, 86% students can a) determine the correct time and space complexities in an elegant and effective way, and b) correctly figure out time and space complexities of the designed algorithm but more elegant analysis exists. The study also shows 86% students can a) design elegant and robust algorithms and b) the designed algorithm is correct and efficient in most cases but it does not scale well in extreme cases. And finally, 79.7% students mentioned that a) data structure is well chosen (i.e., efficient) and well justified, and b) data structure is appropriate, but operations are not described sufficiently clearly. This clearly shows better success because of appropriate course modality, teaching pedagogy and engagement among many others.
An Interactive Control Strategy Based on A Wheelchair Exoskeleton

In order to solve the problem of home-based rehabilitation for the elderly with severe stroke and lower limb movement disorders, an adaptive interaction force-based assist-as-needed (AIFAAN) control strategy is proposed, where the problem of force trajectory tracking is considered. The structure of a novel wheelchair exoskeleton is described briefly, and the mechanical analysis is presented firstly. Then, the core idea of the AIFAAN control strategy is described in detail, where the force threshold profile is introduced, and some numerical examples are given based on the proposed wheelchair exoskeleton. Finally, the efficiency of the proposed control strategy and some discussions are presented based on the experiment results performed on the wheelchair exoskeleton. The results show that the proposed interactive control strategy is easy to implement in applications, with excellent stability and adaptability to different environments, and it can be better applied to the rehabilitation needs of elderly patients with lower limb movement disorders at home.
Eyes-Free Fingertip Guidance Based on Tactile Cues, an Extension of the Steering Law

The use of a modern human-machine interface involves a large amount of possible interactions. In order to allow users to navigate through a large number of available operations, interface designers often use drop-down menus that offer many options in a constrained area. This kind of menu shows good performance for selecting quickly from a large amount of choices. However, they require a high visual attention which is not always possible for the user. Here, we investigate if one can navigate through paths made of orthogonal tunnels, simulating drop-down menus, relying only on tactile cues on a haptic touchscreen. We found that subjects were able to follow the path with a success rate of $\sim$90% for 1 tunnel which decreased linearly to $\sim$40% for 5 tunnels. Four types of friction-modulated haptic feedback were tested and showed no major differences in terms of success rate. Nevertheless, participants were slightly faster with slipping path feedback. The user trajectories presented robust regularities that could be well described by the steering law model. Hence, we propose a novel definition of path difficulty for non-visual conditions based on path width, length and number of orthogonal tunnels. These findings pave the way toward eyes-free guidance on surface haptic interfaces.
Design and Control of Soft Exoskeleton Based on Human Motion Intention

Exoskeletons have been utilized to enhance human movement capabilities; however, most of exoskeletons are typically limited by the small-range stiffness of actuators. Furthermore, these exoskeletons are not comfortable to wear due to the rigid structures. This letter proposes a soft exoskeleton with twisted string actuators to assist knee joint movement. The mechanical structure and dynamics model of the exoskeleton robotic system is introduced, where the variable-stiffness actuator with twisted strings is able to provide controllable walking assistance and improve users' comfort. In addition, the users motion intention is detected through biomedical signal, and an assist-as-need control strategy is proposed to enhance assistance efficiency. Finally, the effectiveness of the proposed exoskeleton is verified empirically.
Exploring Pre-Trained Neural Audio Representations for Audio Topic Segmentation

Recent works have shown that audio embeddings can improve automatic topic segmentation of formats such as radio shows. In this work we expand the work in that direction by showing how and which publicly available, pre-trained neural audio embeddings can perform the task, without the need of any further fine-tuning of the audio encoders. The ranking of the encoders suggest that neural encoders pre-trained for speaker diarization and general purpose audio classification are the best suited to be used as features, beating non-neural baselines. We show that we can obtain perfect results on a newly created random dataset similar to the one used in previous work. We also show for the first time results on real-world data, proving that our method can be applied to actual radio shows with good results, but the choice of audio encoders is extremely important in order to achieve those. Finally, by releasing the datasets we used we make the contribution of providing the first (to our knowledge) publicly available, free of charge datasets for audio topic segmentation of media products.
Survivability of Battery Energy Storage Systems During Shipboard Power Events

Battery applications in the marine industry have attracted significant attention over the last decade. Its relevance is often related to sustainable operations, grid reliability, efficiency, integration of renewable energy sources, and dc-grids onboard implementation. Despite these numerous advantages, designing an appropriate storage system for the shipboard ac network can prove not straightforward - even the simplest application, like providing additional grid ‘spinning reserve,’ can present a significant challenge. The difficulty stems from integrating a stationary energy source (with multiple controlling options) into a classical power system dominated by rotating synchronous generators and induction motors. This paper investigates the qualitative design issues of integrating a battery system through a grid converter into a dynamic position-type vessel. In each section, a comprehensive review of the main technical aspects affecting the storage system design is carried out. Finally, qualitative elements and required improvements for battery ‘spinning reserve’ mode integration are briefly discussed.
A Model Predictive Control Based Robust Gait Generation with Turning Ability

To enhance the anti-disturbance ability of biped robots and improve their full range of motion ability, this paper proposes a gait generation method based on model predictive control with turning ability, which can automatically generate gait trajectories with the given reference speed and turning speed. We integrate footstep positions into the objective function and use the change of center of pressure (CoP) as the optimal input to minimize the objective. The contribution of this paper is considering various feasible optimization constraints to generate gait trajectories online with anti-disturbance ability, adjustable stride, and turning ability. Simulation results show that this method can generate robust gait trajectories online and turn in any direction.
Statistical relationship between internet data indicators and the Brazilian stock exchange

This work presents a statistical analysis between indicators obtained from internet data in Portuguese - news sentiment and Google Trends - and data on the Brazilian stock market through Spearman's rank correlation coefficient. The methodology used to collect, pre-process and obtain each indicator is detailed. Data from the years 2019 to 2021 were obtained. For the sentiment analysis of the news, a CNN model (Convolutional Neural Network) was adopted, which obtained an F1-score of 96%. As a result, some interesting correlations were obtained, among which, an inverse correlation characterized as “moderate” (according to the Cohen scale) between news sentiment and adjusted closing price in 2019; between search volume and closing price, a negative and “very large” and a positive and “large” correlation between trade volume and search volume. In both 2020 and 2021, negative coefficients defined as “large” were found, taking into account the closing price and trading volume.
Detection and Isolation Malware by Dynamic Routing Moving Target Defense with Proxies

In recent years, many companies and organizations have introduced internal networks. While such internal networks propose availability and convenience, there have been many cases in which malicious outsiders have intruded on these local networks, and leaked customer information through cyber attacks. In addition, there have recently been reports of a type of attack called “Advanced Persistent Threats (APT)”. Unlike conventional cyber attacks, these attacks target specific objectives. And they use sophisticated techniques to penetrate the target's system. Once malware successes to intrude into the system, malware does not immediately attack the target but hides for a long time to investigate the system and gather information. Moving Target Defense, MTD is a technology that dynamically changes the configurations of systems targeted by cyber attacks. In this study, we implemented a model using a proxy-based network-level MTD to detect and quarantine malware in internal networks. And we can confirm that the proposed method is effective in the detection and quarantine of malware.
Transistor Compact Model Based On Gradient Constrainted Convolution Neural Network

In semiconductor research, the characteristic curves of analog devices under different combinations of process and device structure parameters are very important. Traditional TCAD simulation plays a very important role, but building a compact model based on quantum physics requires a lot of effort and time, and shows relatively weak technical adaptability. The derivative of the model output may also lose continuity. In order to solve these problems, this work proposes a gradient supervised convolution neural network model with adaptive dynamic adjustment of learning rate. It is employed to model the I-V characteristics of GAA and PLANAR devices and achieves the continuity of higher-order derivatives. In addition, this work designs effective data preprocessing and new weighted loss function to enhance model expressivity. The I-V curves and physical verification of GAA and PLANAR devices by the proposed model show that the model can obtain high-precision fitting and achieve high-order differentiable continuity.
A Wearable System Integrating Force Myography and Skin Stretch Feedback toward Force Skill Learning

We propose a wearable system integrating force myography (FMG) and skin stretch toward force skill learning, and describe its basic evaluation. The efficacy of learning sports, rehabilitation, and vocational skills is expected to be improved through the use of motion learning systems. Among the skills, force coordination skills, in particular, often require long-term experience to master because visual information may not be available. The system proposed in this study integrates body motion estimation by FMG and skin stretch haptic feedback to provide feedback cues from the current force output to the target force output. After evaluating the performance of the system in terms of sensing and haptic presentation, an initial user study was conducted to assess the feasibility of force guidance in practice. The system was found to output force intensity relative to varied static target forces, indicating its efficacy. Based on the results of the experiments, we discuss future issues and potential applications.
Gender Differences in the Perceptions of Information Security, Usefulness, and Enjoyment of Metaverse

Metaverse is leading new markets and industries as a virtual space. Users trade goods and make payments on the metaverse. They also introduce it to be useful in their work. Some consumers use the metaverse because it is fun. In this context, information quality, usefulness, and enjoyment may be very important determinants for the sustainable development of the metaverse. On the other hand, demographic information has a significant influence on the behavior of information technology users. In particular, gender plays a key role in the adoption and use of information technology. It would provide useful guidance for academia and industry to identify how the perception of information quality, usefulness, and enjoyment of metaverse differs according to gender. Therefore, this study examines the differences in cognition towards metaverse targeting metaverse users. Data were collected from 180 users. To make comparisons between groups, we performed an analysis of variance (ANOVA). The study result shows that there is a significant difference in the perception of information security between men and women. The analysis partially supports differences in perceptions of usefulness by gender. The findings partially support the significant differences in the enjoyment by gender. The results of this paper will suggest meaningful implications for researchers and practitioners.
Social Network Analysis of Biomedical Research Growth at a Primarily Undergraduate Institution

The National Institutes of Health Institutional Development Award Programs support the establishment and growth of biomedical research infrastructure in states that receive a low level of federal funding for biomedical research. The purpose of this investigation was to analyze the growth in research productivity over time. This program fostered an environment in which a biomedical research program could be developed and allowed to grow at Boise State University, a primarily undergraduate institution. The growth of the biomedical research community can be visualized through social network analysis.
Identifying and Classifying Fake COVID-19 Tweets using Transformer Models

COVID-19 pandemic has significantly impacted daily life around the world during the past few years. According to government measures, people used to prevent spreading the virus by quarantine, social distancing, staying at home and distance learning. The problem of fake news in social media is dramatically increasing during this pandemic, especially when it refers to fake news about Covid-19. The objective of this research is to propose a transformer model to classify and identify fake covid-19 tweets. BERT, RoBERTa, and DistilBERT transformer models are applied. The results showed that RoBERTa outperforms the other two transformer models by achieving 97% accuracy and ROC AUC Score of 0.961.
Gateway Scholars Program - Reducing Barriers to STEM for Undergraduate Students Through Scholarship and Supportive Programs

This report presents the Gateway Scholars Program, an NSF-S-STEM supported program that recruited academically talented undergraduate students with demonstrated financial need. The objectives of our program included establishing a mentored cohort program, implementing enhanced risk-based advising, integrating evidence-based instructional practices in the curriculum, engaging students in co-curricular experiences, and generating new knowledge about the effect of activities on retention, student success, and degree attainment. Knowledge about broadening participation and effectiveness of evidence-based practices in STEM curricular and co-curricular activities and systems developed through this program have the potential to impact all STEM departments.
Aqua-Farm fish behavior analysis using machine learning

Aquaculture production has been shown to represent approximately 46% of the total fish production in 2018 from FAO. This percentage shows how aquaculture contributes to global fish production, and how the demand is increasing for fish and seafood products. Understanding Fish behavior is crucial for better fish production. Fishes showing abnormal behavior can serve as early warning signs of health issues, therefore monitoring behaviors such as feeding is important to ensure the growth rate of fishes. Machine learning provides a way to be used for classifying behaviors, then the results will be passed to the manager to notify about the status of the fish farm. In this paper we proposed a system which used RNN architectures(RNN-LSTM-BILSTM-GRU) and LRCNN for classifying the behaviors based on trajectories, and the architecture that achieved the biggest average accuracy and lowest validation loss with 100% and 0.00275 respectively using LRCNN was tested on Self collected fish Dataset.
Classify Brain Tumors from MRI Images: Deep Learning-Based Approach

Brain tumors pose a significant health threat and cause severe damage to the body and its physiological functions. Traditional diagnostic methods for brain tumors involve expensive medical imaging scans and invasive surgical procedures, resulting in prolonged waiting times and recovery periods. Thus, we explore the potential of deep learning techniques and magnetic resonance imaging (MRI) for the diagnosis of brain tumors. With these technologies, we develop a diagnostic method that is faster, more accurate, and more reliable than current approaches. The proposed model employs preprocessing techniques and convolutional neural network (CNN) methods with the Adam optimizer. An average accuracy reaches 99.8% on the training set and 94.4% on the testing set. These results indicate that the classification of brain MRI is stable and reliable with the proposed method. This proposed approach outperforms four previous methods, demonstrating its superiority and potential for various applications in medical image analysis. In the future, improving overall performance and developing more advanced deep-learning models enables the medical community to diagnose diseases faster and more accurately.