Miracle-dz/UniFAR-FTUs huggingface dataset:
A Pseudo-3D Convolutional Neural Network based Framework for Short-term Mixed Passenger Flow Prediction in Large-scale Public Transit

1 Globally, many metropolitan areas tend to be promoting the multi-modal public transit which in2 terconnects different modes to move large quantities of commuters in the urban area. Therefore, 3 the accurate short-term passenger flow prediction can contribute to improve the reliability, re4 sponsiveness and the service quality of the multi-modal public transit. In this paper, we propose 5 an end-to-end deep learning framework, called ST-Pseudo3D Net to collectively predict different 6 types of public transit passenger flow at each region of city in the near future. The framework 7 employs the deep Pseudo-3D residual architecture to model the network-wide spatial-temporal 8 correlations among different types of passenger flow. Based on smart card data collected from 9 Singapore’s multi-modal public transit, considering metro passenger flow, bus passenger flow as 10 well as the transfer passenger flow between these metro and bus, totally we construct 7 different 11 types of passenger flow to be collectively predicted, where the experiments results demonstrate 12 that the proposed model synthetically outperforms other baselines. To the best knowledge of the 13 authors, this is the first attempt to investigate the integrated prediction for multi-modal passenger 14 flow leveraging deep learning techniques. 15 16
Benchmarking automated GUI testing for Android against real-world bugs

For ensuring the reliability of Android apps, there has been tremendous, continuous progress on improving automated GUI testing in the past decade. Specifically, dozens of testing techniques and tools have been developed and demonstrated to be effective in detecting crash bugs and outperform their respective prior work in the number of detected crashes. However, an overarching question "How effectively and thoroughly can these tools find crash bugs in practice? " has not been well-explored, which requires a ground-truth benchmark with real-world bugs. Since prior studies focus on tool comparisons w.r. t. some selected apps, they cannot provide direct, in-depth answers to this question. To complement existing work and tackle the above question, this paper offers the first ground-truth empirical evaluation of automated GUI testing for Android. To this end, we devote substantial manual effort to set up the Themis benchmark set, including (1) a carefully constructed dataset with 52 real, reproducible crash bugs (taking two person-months for its collection and validation), and (2) a unified, extensible infrastructure with six recent state-of-the-art testing tools. The whole evaluation has taken over 10,920 CPU hours. We find a considerable gap in these tools finding the collected real bugs --- 18 bugs cannot be detected by any tool. Our systematic analysis further identifies five major common challenges that these tools face, and reveals additional findings such as factors affecting these tools in bug finding and opportunities for tool improvements. Overall, this work offers new concrete insights, most of which are previously unknown/unstated and difficult to obtain. Our study presents a new, complementary perspective from prior studies to understand and analyze the effectiveness of existing testing tools, as well as a benchmark for future research on this topic. The Themis benchmark is publicly available at https://github.com/the-themis-benchmarks/home.
A Review on Present Technologies for Fraud Detection Using Data Mining

In Todays era of Internet lots of data are stored and transferred from one location to another. Data transferred online is most likely vulnerable to attack. As there is striking increase of fraud that is leading to loss of many billions of dollars worldwide every year; various modern ways in detecting fraud are regularly proposed and applied to several business fields. The main task of Fraud detection is to observe the actions of tons of users to detect unwanted behavior. To detect this various kinds of data mining methods have been proposed and implemented to lessen down the attacks. In this paper a deep literature survey is presented on different techniques for fraud detection applying data mining
Magnetic resonance of bismuth donors in silicon detected by spin dependent recombination(本文)

Realization of quantum information processing devices including quantum computers is one of the most highlighted projects physicists are pursuing today. When realized, quantum computers will be composed of multiple elements such as processors, memories, communication lines, etc. just like today’s computers. While superconducting qubits are excellent candidates as quantum processors, hydrogenic donors in silicon are considered very promising as quantum memory qubits. Among them, bismuth (Bi) donors in silicon are important since they demonstrate long coherence (quantum memory) time and, in the low enough magnetic field needed by the superconducting qubits to operate, the energy separation between |0〉 and |1〉 states is compatible with that of superconducting qubits making them connectible to each other. The present thesis shows the successful manipulation and the electrical detection of Bi donor spins in silicon at arbitrarily chosen magnetic fields, establishing a novel way to readout electron and nuclear spins of Bi in the zero-field limit. Moreover, the hyperfine clock transitions of Bi that are extremely robust against external electric field noise are identified experimentally for future applications to quantum memories. The present thesis is composed of six chapters. Chapter 1 introduces the motivation of the research. Chapter 2 describes the basics of spin systems utilized in this work. Chapter 3 summarizes the experimental techniques including sample preparations and measurements. Chapter 4 describes the low-field (6 − 110 mT) magnetic resonance spectroscopy of Bi in silicon. It is performed by monitoring the change in photoconductivity of the sample induced by the spin dependent recombination. Spectra at various resonance frequencies show signal intensity distributions drastically different from those observed by the conventional electron paramagnetic resonance spectroscopy. A theoretical model considering the recombination rates for
Sludge Blanket Response to Storm Surge in an Activated‐Sludge Clarifier

Aspects of operation and design for storm flows at a 190‐m3/h (1.2‐mgd) dry weather design flow activated‐sludge plant were tested. A significant plant enlargement allowed recycle of test clarifier effluent to the main plant versus discharge to a stream. Thus it was possible to test high overflow rates without risk of a permit violation. On‐line monitoring showed that the return sludge concentration increase lagged approximately 1 hour behind the start of the storm flow. This required an adjustment of the power equation model used. Furthermore, when the overflow rate exceeded the settling velocity of the feed solids, the sludge blanket rose faster than the model prediction. This phenomenon required developing a new model to predict blanket behavior under various loading conditions. The new model allows for plant design to more accurately include a clarifier zone for storing solids during peak flows associated with rain storms. An exponential equation, fitted to settling data, was also used in the new model with test data for comparison. The exponential equation gave a low blanket prediction. Results are compared with those of other research for solids storage during peak flow.
Stock Price Forecast Based on CNN-BiLSTM-ECA Model

Financial data as a kind of multimedia data contains rich information, which has been widely used for data analysis task. However, how to predict the stock price is still a hot research problem for investors and researchers in financial field. Forecasting stock prices becomes an extremely challenging task due to high noise, nonlinearity, and volatility of the stock price time series data. In order to provide better prediction results of stock price, a new stock price prediction model named as CNN-BiLSTM-ECA is proposed, which combines Convolutional Neural Network (CNN), Bidirectional Long Short-term Memory (BiLSTM) network, and Attention Mechanism (AM). More specifically, CNN is utilized to extract the deep features of stock data for reducing the influence of high noise and nonlinearity. Then, BiLSTM network is employed to predict the stock price based on the extracted deep features. Meanwhile, a novel Efficient Channel Attention (ECA) module is introduced into the network model to further improve the sensitivity of the network to the important features and key information. Finally, extensive experiments are conducted on the three stock datasets such as Shanghai Composite Index, China Unicom, and CSI 300. Compared with the existing methods, the experimental results verify the effectiveness and feasibility of the proposed CNN-BILSTM-ECA network model, which can provide an important reference for investors to make decisions.
Collective Bi-Directional Pedestrian Dyna mies

This paper describes the application of an Artificial Life cellular automata (CA) miurosimulation to model the emergent collective behavior of hi-directional pedestrian flows. Since pedestrian flow is inherently complex, even more so than vehicular flow, previous CA models developed for vehicle flow are noi directly applicable. Itis shown that a relatively small rule Set is capable of effectively capturing the collective behaviors of pedestrians who are autonomous at the micro-level. The model provides for simulating three modes of hi-directional pedestrian flow: (a) tiows in directionally separated lanes, (h) interspersed flow, and (cl dynamic multi-lane flow. The emergent behavior that arises from the mode! is consistent with well-established fundamental properties of pedestrian flows.
Virtual Coach: Monitoring Exercises and Aerobic Dance Generation

1Student, Dept. of Computer Engineering, Datta Meghe College of Engineering, Navi Mumbai, Maharashtra, India 2Professor, Dept. of Computer Engineering, Datta Meghe College of Engineering, Navi Mumbai, Maharashtra, India ---------------------------------------------------------------------***--------------------------------------------------------------------Abstract Virtual Coach was developed to play the role of a personal fitness trainer. The main aim was to prevent the trainee from injury caused by incorrect exercise form. This was achieved using Pose Estimation. The sequence of poses captured while performing a particular exercise was mapped to the correctness of the form. This information about correctness was then used to provide real-time captions, audio and visual feedback to the user. This sequential model was trained with data comprising weightlifting and muscle building exercises. Since yoga involves holding the body in a particular position, the assessment for yoga was done using a geometric model which compared the user pose to a reference pose. The second aim of Virtual Coach was to make cardio fun by generating aerobic dance moves dynamically on beats of the user's choice. The LSTM model mapped music features to dance pose key points extracted by Pose Estimation. These 2D dancing skeletons were then rendered on the screen for the user to follow along. The dance moves generated were natural and harmonious. Results demonstrated that Virtual Coach is reliable and capable of assisting the trainee in maintaining the correct exercise form during workout.
Hegemony of two avian species: Black kites and Cattle egrets on two distant landfills during COVID-19 pandemic in Gujranwala, Pakistan

The pandemic of COVID -19 has brought many changes to wildlife globally positively or negatively. During lock downs the abundance of many wild species has increased due to decease in human disturbance in area. This study is designed to compare two landfills situated 22 km apart in the Gujranwala, Punjab in terms of avifauna diversity, abundance and foraging behavior during COVID-19 pandemic. The field observations using the total count method of population census revealed the abundance of contrasting avian species at both sites with a very low values of the diversity index (H= 1.146 Chianwali & H=1.697 Gondalawala ) and species evenness (E= 0.423 Chianwali & E= 0.587 Gondalawala). A large variation in bird’s population was recorded at both landfills, i.e. N > 17,300 at one landfill (Chianwali) and N> 26,200 at the other (Gondalawala). Time dependent variation in population was also observed. Cattle egret was dominant species (~75% of population) at the Chianwali landfill site while the Black kite (~55% of population) was the dominant species at Gondalawala landfill. House crow was the second dominant species at both sites. Birds were grouped into categories based on ecological behaviors such as dominance, opportunists, insectivory etc. The study finds that each landfill has its own specific character depending upon the geographical location, accessibility of birds to landfills, and management practices such as presence of boundary wall and artificial lights. Moreover, it also finds that COVID -19 lockdown provided an opportunity to wild avian species to exploit human rehabilitated areas abundantly where there presence was very few because of dominance of human beings and their activities.
DHPS_A_280442 257..268

Department of Pharmacy, College of Medicine and Health Sciences, Wollo University, Dessie, Ethiopia Background: Surgical site infections are global healthcare problems. Although surgical site infections are preventable, they still cause significant morbidity, high death rates, and financial stress on national budgets and individual patients. Inappropriate uses of surgical antimicrobial prophylaxis are increasing and worsening patients’ quality of life. This study determined the incidence and risk factors of surgical site infections. Methods: Institution-based retrospective cross-sectional study was conducted using a structured data abstraction format on patients who were attending at the surgical ward of Borumeda hospital from April 1, 2017, to March 31, 2019. The data were collected during July 15–30, 2019. A systematic random sampling technique was employed to select 227 surgical cases. Multivariate logistic regression was computed using the statistical package for social sciences version 23. Results: The incidence of surgical site infections was 46.7%. Prophylaxis was administered to 188 (82.8%) surgical cases. Prophylaxis was recommended for 151 (66.5%). Out of these, only 143 (94.7%) received prophylaxis. One hundred seventy-four (78.4%) of the procedures had appropriate indication. The compliance of surgical antimicrobial prophylaxis use was 13.7%. The predictors of surgical site infections were receiving prophylaxis more than 24 h after surgery (AOR=3.53, 95% CI: 1.22–10.17), clean-contaminated wounds (AOR=4.54, 95% CI: 1.33–15.53), surgical procedure of thyroidectomy (AOR=5.2, 95% CI: 0.9–21.4), appendectomy (AOR = 29, 95% CI: 6.2–141.7), cholecystectomy (AOR = 21, 95% CI: 3.5 −126.7), hernia (AOR= 8.8, 95% CI: 1.2–62.2), skin and deep tissue (AOR = 125, 95% CI: 7.8–196.7), and orthopedic (AOR=57, 95% CI: 1.6–209.5). Conclusion: There was high inconsistency between surgical antimicrobial prophylaxis practice and international surgical site infections prevention guideline. Wrong selection of antimicrobial agents was the most noncompliant to the guidelines. The incidence of surgical antimicrobial prophylaxis was high and requires due attention. The duration of postoperative prophylaxis should be kept to less than 24 h.
The Prognostic Value of CD206 in Solid Malignancies: A Systematic Review and Meta-Analysis

Simple Summary The role of innate immune cells in the tumor microenvironment (TME), more specifically the presence of the tumor associated macrophages (TAMs), is becoming more important in the prognosis and treatment of patients diagnosed with malignancies. The aim of this systematic review and meta-analysis was to assess the potential prognostic value of CD206-expressing TAMs, a subclass of macrophages, which were previously proposed to negatively impact the patient’s prognosis. We identified 27 manuscripts describing the role of CD206 in patient prognosis for 14 different tumor types. Despite a large heterogeneity in the results, we identified a significantly worse overall and disease-free survival for patients with increased CD206-expressing TAMs in the TME. The use of CD206-expressing TAMs could therefore be used as a prognostic marker in patients diagnosed with solid malignancies. Abstract An increased presence of CD206-expressing tumor associated macrophages in solid cancers was proposed to be associated with worse outcomes in multiple types of malignancies, but contradictory results are published. We performed a reproducible systematic review and meta-analysis to provide increased evidence to confirm or reject this hypothesis following the Preferred Reporting Items for Systematic Reviews and Meta-analyses statement. The Embase, Web of Science, and MEDLINE-databases were systematically searched for eligible manuscripts. A total of 27 papers studying the prognostic impact of CD206 in 14 different tumor types were identified. Meta-analyses showed a significant impact on the overall survival (OS) and disease-free survival (DFS). While no significant differences were revealed in progression-free survival (PFS) and disease-specific survival (DSS), a shift towards negative survival was correlated with increased CD206-expresion. As a result of the different tumor types, large heterogeneity was present between the different tumor types. Subgroup analysis of hepatocellular carcinoma and gastric cancers revealed no heterogeneity, associated with a significant negative impact on OS in both groups. The current systematic review displays the increased presence CD206-expressing macrophages as a significant negative prognostic biomarker for both OS and DFS in patients diagnosed with solid cancers. Because a heterogenous group of tumor types was included in the meta-analysis, the results cannot be generalized. These results can, however, be used to further lead follow-up research to validate the specific prognostic value of CD206 in individual tumor types and therapeutic approaches.
The relationship of biceps brachii muscle cross- sectional area with chronic obstructive pulmonary disease and its severity

Introduction: Chronic obstructive pulmonary disease (COPD) is a multi-dimensional disease and one of the leading causes of mortality worldwide. In patients with COPD, extra-pulmonary effects of this disease such as muscle atrophy are often seen. This study performed to investigate relationship of biceps brachii muscle cross-sectional area (CSA) with chronic obstructive pulmonary disease and ıts severity. Materials & Methods: In this cross-sectional study, 50 patients with COPD and 50 non-COPD outpatient subjects referred to Razi Hospital in Rasht (northern Iran) in 2018 were investigated. Basic demographic characteristics were recorded in checklist. The upper limb biceps brachii muscle CSA was measured by ultrasonography in all subjects. Intravenous use of steroids, severe acute exacerbation over the last year, dyspnea severity (mMRC dyspnea scale), FEV1 and FEV1 / FVC (% of predicted) (in Spirometry), the airflow limitation severity (GOLD criteria) in COPD patients were evaluated. The collected data were analyzed using SPSS-24. Results: The groups were matched for age, sex, BMI, smoking and occupation type (P > 0.05). Mean biceps brachii CSA in COPD group (388.106 ± 50.26 mm2) was significantly lower than the control group (465.90 ± 12.97 mm2). In linear regression analysis, COPD was the predictor affecting the muscle CSA. In COPD patients, muscle CSA was directly correlated with FEV1 (r = 0.589) and FEV1 / FVC (r = 0.396). There was an inverse correlation between the muscle CSA and the frequency of severe acute exacerbation (r = -0.380). No significant difference was found between the groups based on mMRC dyspnea scale in terms of the mean biceps brachii CSA (P = 0.070). However, this difference was significant between the groups of GOLD2 (448.01 ± 12.42 mm2), GOLD3 (420.91 ± 0.72 mm2), and GOLD4 (281.57 ± 54.60 mm2) (P < 0.001). In linear regression analysis, patient's age and GOLD criteria (airflow limitation severity) were the predictive factors affecting the muscle CSA in COPD patients. Conclusions: The results of this study showed that chronic obstructive pulmonary disease (COPD) affects the biceps brachii muscle of the upper limbs and leads to a decrease in muscle CSA.
A Novel User and Antenna Selection Techniques in Massive MIMO 5G Wireless Communication System

In this paper, we have proposed a new paradigm for user scheduling in large-scale multiple-input multipleoutput (MIMO) time division duplexing (TDD) system. In this paper, we have selected the users from different groups with semi-orthogonal (SO) and random criterion. We separate the users in different groups with the K-means clustering algorithm which assigns the users into different groups. After user groups are so determined, we use two new user selection paradigms where users are selected in two methods firstly we select the users in intra-group those are SO with each other along with it also SO with other groups’ users. Secondly, users are selected from inter-group those are SO with each other also SO with other group users. In both the selection schemes antennas are scheduled based on the maximum gain of the channel. In the results, it is noticed that in intra-group with semiorthogonal user selection (SUS) and antenna selection (AS) using the zero-forcing (ZF) precoding shown the highest systems rate. We also evaluated the computation cost of our modified proposed algorithm which is exposed in table1. We explored the efficiency of the proposed schemes through MATLAB simulations.
Trajectory Analysis of Multi-domain Knowledge Convergence for Strategic Emerging Industries Based on Citation Networks and Text Information

The analysis of the technology convergence process for strategic emerging industries is helpful for deeply understanding the generation process and development law of industrial technology, thereby capturing the development trend in the field and promoting the healthy development of the industry. To identify the trajectory and degree of technology convergence of strategic emerging industries, we conduct a multi-case study on four fields that exhibit a trend of convergence and attract social attention: high-end equipment manufacturing, new-generation information technology, new medicine, and new energy. This study adopts a trajectory analysis method for knowledge convergence based on citation networks and text information. It utilizes a graph neural network model and encodes the citation network and the titles and abstracts of the publications as vectors. After the data from the four selected technical fields are analyzed, five knowledge convergence trajectories are identified. The results show that information technology and numerical control equipment, biomedicine, and solar photovoltaic technology have shown a trend of strong convergence; the convergence of information technology and numerical control equipment is stronger. The numerical control equipment and solar photovoltaic technology pair and that of information technology and solar photovoltaic technology have shown a converging trend. However, due to the late start of convergence, the current degree of convergence is still insufficient. Numerical control equipment and biomedicine have not shown any convergence trend.