Miracle-dz/UniFAR-FTUs huggingface dataset:
Path-Independent Device-Free Gait Recognition Using mmWave Signals

Device-free gait recognition is a promising technique which could provide insensible identification for smart applications. It leverages the unique influence of the target's gait on surrounding wireless signals to achieve identity recognition in a contact-free and device-free manner. Existing device-free gait recognition methods could achieve good accuracy when a target walks along a predetermined path. However, the accuracy will drop dramatically when a target walks along an arbitrary path. This is due to the fact that different paths will exert different influence on the wireless signals. In order to address this issue, we develop a path-independent device-free gait recognition system, which can recognize the identity of a person no matter what path he/she walks. Specifically, we propose a novel robust path-independent gait spectrogram construction method, which leverages location information and doppler spectrogram to generate corrective velocity spectrogram so as to approximate the actual velocity spectrogram, and utilizes an energy normalization strategy to eliminate the influence of path on spectrogram energy so as to achieve the independence of the walking path. Based on the path-independent gait spectrogram, we use a convolutional neural network to extract deep features and accomplish the gait recognition task. Experimental results conducted on a 77 GHz mmWave testbed, and achieve the average accuracies of 91% and 87% in the non-radial straight path and curve path, respectively, when 10 targets are trained in the radial path.
Conditional natal dispersal provides a mechanism for populations tracking resource pulses after fire

Animals that persist in spatially structured populations face the challenge of tracking the rise and fall of resources across space and time. To combat these challenges, theory predicts that species should use conditional dispersal strategies that allow them to emigrate from patches with declining resources and colonize new resource patches as they appear. We studied natal dispersal movements in the black-backed woodpecker (Picoides arcticus), a species known for its strong association with recent post-fire forests in western North America. We radio-tracked juveniles originating from seven burned areas and tested hypotheses that environmental and individual factors influence dispersal distance and emigration rates—investigating emigration while additionally accounting for imperfect detection with a novel Bayesian model. We found that juveniles were more likely to leave natal areas and disperse longer distances if they were heavier or hatched in older burned areas where resources are increasingly scarce. Juveniles were also more likely to leave their natal burn if they hatched in a nest closer to the fire perimeter. While dispersing across the landscape, black-backed woodpeckers selected for burned forest relative to unburned available habitat. Together, these results strongly support the hypothesis that black-backed woodpecker populations track resource pulses across fire-prone landscapes, with conditional natal dispersal acting as a mechanism for locating and colonizing newly burned areas. Lending empirical support to theoretical predictions, our findings suggest that changes in resource distribution may shape dispersal patterns and, consequently, the distribution and persistence of spatially structured populations.
A novel and efficient method for wood–leaf separation from terrestrial laser scanning point clouds at the forest plot level

With the increasing use of terrestrial laser scanning (TLS) technology in the field of forest ecology, a large number of studies have been carried out on the separation of wood and leaves based on TLS point cloud data. However, most wood–leaf separation methods adopt the point‐wise classification strategy, which is not efficient for processing large‐volume TLS datasets acquired at the forest plot level. In this study, we proposed a segment‐wise classification strategy to improve the efficiency of the wood–leaf separation from large‐volume TLS point cloud datasets collected at the forest plot. The proposed method first decomposes the point cloud into three parts based on the threshold values of its local curvature. Then, the first two parts with lower local curvatures were segmented respectively by a connected component labelling algorithm. Finally, the segmented point clouds were classified into wood or leaf segments according to the segment‐wise geometric features of each segment. We tested our method on both needleleaf and broadleaf forest plots in temperate and tropical forests. We also compared our method with two other state‐of‐the‐art wood–leaf separation methods, that is, the CANUPO and LeWoS. The results showed that our method was more than 10 times faster than the compared methods while maintaining comparable and even higher accuracy. Our study demonstrates that the segment‐wise classification strategy applies to the large‐volume TLS datasets and can greatly improve the efficiency of the classification. The proposed method is simple, fast and universally applicable to the TLS data from various tree species and forest types at the plot level, which may facilitate the adoption of TLS technology by forest ecologists in their studies.
English Language Learning and Teaching in Indonesia in the Period of Covid-19 Out Break (Challenges and Opportunities)

This study explores challenges and opportunities experienced by both teachers and students in the teaching and learning activity during this covid-19 outbreak. It is a literature study by reading the literature sources, and the data were obtained from articles of online journals, e-book, law regulations, and other relevant sources. The study identified some challenges teachers and students encountered in teaching and learning activities as follows 1) Learning Facility to support online learning, 2) Teacher’s ability to use technology, 3) Challenge in preparing the suitable material suits to online learning during the covid-19 pandemic, 4) Students’ difficulties to understand the subject materials. On the other hand, it’s obvious that the implementation of online learning brings some opportunities for teachers and students that can’t be denied. Some important opportunities are 1) Teachers and students engage to learning technology, 2) Students More Engage the Independent Learning, and 3) Changed Practices and Perspectives. Further task that should be undertaken is to take responsibility on keeping a balanced in order that the challenges are reduced and the opportunities are taken maximally to make the learning objectives achieved as planed prior to the teaching and learning activity.
Soil organic carbon stocks in relation to the altitude in Serbia

The research was conducted to determine the soil organic carbon (SOC) stocks in relation to the altitude in Serbia. The database included a total of 1,140 soil profiles. SOC were investigated at different the altitudes (0-200 m, 200-500 m, 500-1,000 m, 1,000 - 2.000 m) and at two soil depths (0-30 cm and 0-100 cm). Statistical correlation was done for five regions where locations were grouped according to the spatial distribution. The results showed that the highest mean values of SOC were measured on the terrain that includes mountains with the altitudes of 1,000-2,000 m and covers an area of 11.5% of the territory of Serbia. The lowest obtained result is related to the lowland areas with the largest number of locations. Greater variability in the results of SOC stocks were found at the higher altitudes and the greatest on the low mountains of 500-1,000 m altitude. There is a medium to strong statistical dependence of the altitude with the SOC stocks at two soil depths (0-30 cm and 0-100 cm). The result indicate that the relationship between SOC stocks and altitude varies between the regions. This study shows that altitude is an important factor affecting SOC stocks.
Mucoadhesive Microsphere Drug Delivery System (MMDDS): A Mini Review

Over the past few decades, broad research work has been carried out to develop a gastro-retentive dosage form, this type of dosage form shows effec-tiveness in delivery of drug due to its presence in the stomach for enough time. The present survey is concern about the review of bioadhesive or mucoadhesive microspheres, for its combined effect of bioadhesion and surface to volume ratio of microspheres in which particle size is range from 1-1,000 µm range in diameter having a core of drug and outer layer of mucoadhesive polymer. Mucoadhesive microspheres have advantage of controlled and sustained release of drug from dosage form with less fluctuation in blood concentration. Evaluation of Mucoadhesive Microspheres has to be carried for FTIR Studies, Scanning Electron Microscopy (SEM), Micrometric Studies, Bulk Density, Tapped Density, Angle of Repose, % Compressibility Index, Hausner’s Ratio, Particle Size, Density Determination, Floating Behavior, Angle of Contact, Swelling Index, Percentage Yield, Percentage Drug Content, The Percentage of Drug Entrapment, Drug Entrapment Efficiency, Ex Vitro Mucoadhesive Study, In Vitro Drug Release Studies, In Vivo X-Ray Imaging Study, In Vitro Wash-Off Test for Microspheres, Mucoadhesive Microspheres Stability in the pH 1.0 HCl Medium, Stability Studies. The present review highlights some of the new drugs that can be benefits from MMDDS.
A Bibliometric Review of Information Systems Research From 1975-2018: Setting an Agenda for IS Research

Information Systems (IS) scholars attempted to explore the intellectual structure of the IS discipline from previous literature. However, the corpus data, analytical method, and sample size are significantly different among these review studies, which lead to rather mixed results. In this bibliometric review, the authors portray and visualize the panorama of the evolutionary process of IS research with two powerful bibliometric tools, including both CiteSpace and VOSviewer. Specifically, based on a series of bibliometric algorithms (e.g., SCI-Map and referene-NLP), the authors analyze 80,147 primary articles and 1,325,376 secondary articles in the IS domain to examine its 1) intellectual output mode, 2) knowledge bases, and 3) the popular and promising topics in the future from the evolutionary and clustering perspectives. By doing so, the authors provide a comprehensive navigation for the IS studies and set up an agenda for future research.
Event-Triggered Model Predictive Adaptive Dynamic Programming for Road Intersection Path Planning of Unmanned Ground Vehicle

Autonomous driving of unmanned ground vehicle (UGV) at road intersection is a challenging task due to the complicated traffic conditions. In this paper, an event-triggered model predictive adaptive dynamic programming (MPADP) algorithm is proposed for path planning of UGV at road intersection. Following the critic-actor scheme of adaptive dynamic programming (ADP), cost function approximation and control policy generation are combined to formulate MPADP. The infinite horizon cost function of ADP is stacked over predictive horizon of model predictive control (MPC), and then the infinite horizon cost function is converted to the finite horizon-stacked cost function in MPADP. By minimizing the approximation error within predictive horizon, the approximation accuracy is enhanced. Considering the limitation of energy consumption, the event-triggered mechanism is designed based on the mismatch of cost function approximation. Three triggering conditions are designed, and the corresponding boundedness of approximation error is proved. Simulation results illustrate the effectiveness, efficiency and feasibility in application of the event-triggered MPADP method for path planning at road intersection.
Metaphorical Argumentation

It is a fact that novel metaphorical utterances appear in natural language argumentation. It seems, moreover, that these put forward metaphorical propositions that can have different roles (data, warrants or claims) in argument structure. There can even be good argumentation which is indispensably metaphorical. However, not all metaphor theories provide an explanation of metaphorical meaning compatible with these claims. In this article, we explain the three main views on metaphorical meaning and show, analysing some examples, their consequences for metaphorical argumentation. Our analysis shows that only the cognitive view can explain that there are arguments which can only be generated using novel metaphors.
Recognition Model of Sideslip of Surrounding Vehicles Based on Perception Information of Driverless Vehicle

At present, many vehicle sideslip driving status estimation approaches based on the inner information of the sideslip vehicle have been studied. However, the method of identifying the sideslip in surrounding vehicles is rarely developed. The surrounding severe sideslip vehicle threatens the safety of driverless vehicles if the driverless vehicle cannot detect the surrounding sideslip vehicle. Therefore, this study proposes a sideslip recognition model that uses the perception information of driverless vehicles to assess the sideslip driving status of the surrounding vehicles. First, the severe sideslip is defined, which may influence the safety of driverless vehicles, and the sideslip recognition problem is described. Second, the severe sideslip is divided into two categories according to different sideslip trajectories. The two types of sideslip progress are analyzed, and the sideslip features of the two types of serious sideslip are extracted. A sideslip recognition model is established using a logical rule method based on the sideslip features. Finally, some simulation experiments are designed to verify the proposed sideslip recognition model. The simulation results show that the true-positive rate and the false-positive rates are 100% and 6.8%, respectively, which demonstrates that the proposed sideslip recognition model has a good performance.
‘Garnem’ and Myrobalan ‘P.2175’: Two Different Drought Responses and Their Implications in Drought Tolerance

One of the challenges in rootstock breeding programs is the combination of tolerances to different abiotic stresses in new interspecific hybrids adapted to a wide range of environmental conditions. In this work, two Prunus L. rootstocks: Myrobalan ‘P.2175’ (P. cerasifera Ehrh.) and the almond × peach hybrid ‘Garnem’ (P. amygdalus Batsch × P. persica (L.) Batsch) were subjected to drought during 24 h to understand their drought response mechanisms. The study was conducted monitoring leaf water potential (LWP), stomatal conductance (gs), relative water content (RWC), and electrolyte leakage (EL); as well as the abscisic acid (ABA) content in roots. The relative expression of five drought-relative genes was also studied. The obtained results allowed examining the drought tolerance potential of ‘Garnem’ and Myrobalan ‘P.2175’, demonstrating the great potential of ‘Garnem’ as drought tolerance source in future selections in breeding. Furthermore, based on the obtained data, the transcription factor Myb25-like could be a good biomarker of drought sensitivity for use in Prunus rootstock breeding programs.
ATwo-StageEstimator forChangePoint in theMeanofPanelData

In this paper, a two-stage consistency estimator for change point in the mean of panel data is given. Firstly, a single sequence is extracted, and the initial estimator and confidence interval of the change point are given by the least square method. Based on the confidence interval, a random interval containing change point with probability tending to 1 is constructed. Secondly, using all panel data falling into the random interval, the final estimator of change point is obtained by least square estimation. (e asymptotic distribution is established. Simulation results show that our method can not only ensure the estimation accuracy but also greatly reduce time complexity.
Tweeting about abusive comments and misogyny in South Korea following the suicide of Sulli, a female K-pop star: Social and semantic network analyses

This study examined the development of the public discussion on Twitter about the abusive comments specific to misogynistic discourse after the suicide of Sulli, a female celebrity in South Korea. Both the pattern of social networking between the users and the semantic representations of user responses were analyzed from a social network perspective using a large-scale Twitter dataset. A total of 37,101 tweets generated by 25,258 users were collected and analyzed. The findings of the network analysis suggest that hubs and authorities on Twitter were closely connected to each other and contributed to promoting the public discussion about abusive comments in response to her death. The results of the semantic network analysis suggested that her death, presumably due in part to continuous hateful comments from trolls, evoked an open discussion about the deeply rooted abusive comments and misogyny that are prevalent in South Korea. Users perceived that sensational news coverage about celebrities and unethical journalistic practices led to abusive comments and her death. The users shared their observations that gendered hate speech contributed to Sulli’s bullying. Dominant words that referred to Sulli’s sexual harassment show the ways in which haters had bullied her, as well as the criticism of online harassment. The results imply that the issue of online misogyny was closely associated with abusive comments in the public consciousness. This study verified the role of celebrities in increasing awareness about social issues and word-of-mouth dissemination even after a death. This study also offers methodological insights by demonstrating how social network analysis can be used to analyze public discussion using big data.
Low Ligation Plus High Dissection Versus High Ligation of the Inferior Mesenteric Artery in Sigmoid Colon and Rectal Cancer Surgery: A Meta-Analysis

Background Whether high or low ligation of the inferior mesenteric artery (IMA) is superior in surgery for rectal and sigmoid colon cancers remains controversial. Although several meta-analyses have been conducted, the level of lymph node clearance was poorly defined. We performed a meta-analysis comparing high and low ligation of the IMA for sigmoid colon and rectal cancers, with emphasis on high dissection of the lymph node at the IMA root in all the included studies. Methods PubMed, MEDLINE, and EMBASE databases were searched to identify relevant articles published until 2020. The patient’s perioperative and oncologic outcomes were analyzed. Statistical analysis was performed using the statistical software RevMan version 5.4. Results A total of 17 studies, including four randomized controlled trials, published between 2011 and 2020 were selected. In total, 1,846 patients received low ligation of the IMA plus high dissection of lymph nodes (LL+HD), and 2,648 patients received high ligation of the IMA (HL). LL+HD was associated with low incidence of anastomotic leakage (p < 0.001), borderline long operative time (p = 0.06), and less yields of total lymph nodes (p = 0.03) but equivalent IMA root lymph nodes (p = 0.07); moreover, LL+HD exhibited non-inferior long-term oncological outcomes. Conclusion In comparison with HL, LL+HD was an effective and safe oncological procedure for sigmoid colon and rectal cancers. Therefore, to ligate the IMA below the level of the left colic artery with D3 high dissection for sigmoid colon and rectal cancers might be suggested once the surgeons are familiar with this technique. Systematic Review Registration INPLASY.com, identifier 202190029.
Towards for Designing Intelligent Health Care System Based on Machine Learning

Health Information Technology (HIT) provides many opportunities for transforming and improving health care systems. HIT enhances the quality of health care delivery, reduces medical errors, increases patient safety, facilitates care coordination, monitors the updated data over time, improves clinical outcomes, and strengthens the interaction between patients and health care providers. Living in modern large cities has a significant negative impact on people’s health, for instance, the increased risk of chronic diseases such as diabetes. According to the rising morbidity in the last decade, the number of patients with diabetes worldwide will exceed 642 million in 2040, meaning that one in every ten adults will be affected. All the previous research on diabetes mellitus indicates that early diagnoses can reduce death rates and overcome many problems. In this regard, machine learning (ML) techniques show promising results in using medical data to predict diabetes at an early stage to save people’s lives. In this paper, we propose an intelligent health care system based on ML methods as a real-time monitoring system to detect diabetes mellitus and examine other health issues such as food and drug allergies of patients. The proposed system uses five machine learning methods: K-Nearest Neighbors, Naïve Bayes, Logistic Regression, Random Forest, and Support Vector Machine (SVM). The system selects the best classification method with high accuracy to optimize the diagnosis of patients with diabetes. The experimental results show that in the proposed system, the SVM classifier has the highest accuracy of 83%.