Miracle-dz/UniFAR-FTUs huggingface dataset:
Accounting Intelligent Monitoring System Based on Data Analysis Algorithm

With the continuous increase of the number of risk points in accounting, how to improve accounting risk control has become an urgent problem to be studied and solved under the new situation. This paper designs an intelligent monitoring system for accounting risks. In the system analysis and design stage, this paper builds an intelligent accounting information management system based on SAP HANA to reduce accounting risks through real-time monitoring. In the implementation phase of the accounting risk monitoring system, an accounting risk monitoring model based on CNN-LSTM was established. Experimental analysis shows that the accuracy, accuracy, recall and F1 values of the CNN-LSTM accounting risk monitoring model are higher than traditional machine learning algorithms such as SVM and K-mean clustering analysis. Therefore, the accounting risk intelligent monitoring system constructed in this paper has stronger predictive analysis capabilities and can break through the bottleneck of traditional business intelligence systems.
ChattSpeed: Toward a New Dataset for Single Camera Visual Speed Estimation for Urban Testbeds

Accurate speed estimation of vehicles is crucial for applications like congestion management and speed limit enforcement to reduce accidents. Despite its relevance, datasets for visual speed estimation are limited. In this study, we propose a deep learning-based framework for vehicle tracking and visual speed estimation, implemented on the MLK Smart Corridor. Pretrained YOLOV7 was used for vehicles detection. Furthermore, we enhanced the DeepSort tracking model by replacing its feature extractor with a Siamese network trained on the UADETRAC dataset. This modification resulted in a 71% decrease in the trajectories IDSW rate when evaluated against the KITTI benchmark. Our framework can also estimate vehicle speeds using reference object scaling and perspective transformation, achieving a commendable error rate of 0.516 mph. Recognizing the potential of our speed estimator, we identified the need to build a new dataset for visual speed estimation. For this task, a vehicle equipped with an OBU is used to collect speed ground-truth data, and videos were captured simultaneously and passed to the framework for estimating speed readings to be validated. The final dataset contains a total of 1326 annotated instances stamped with their speed readings and grouped into 78 trajectories. This dataset is intended to be used as a benchmark to facilitate research in speed estimation from visual data obtained from urban testbeds.
Line-of-Sight Probability Estimation Formula for an Urban Environment

This paper discusses a line-of-sight (LOS) probability estimation formula in an urban environment. We developed a LOS estimation formula that adapts to changes of the road width and the building length of a uniform grid pattern city. The basic part of the formula is constructed on the basis of the theory of the d1d2 model, which has been standardized for the development of 5G. In addition, the LOS spread in intersection areas is represented by an empirical formula and is added to the basic formula. As a result, we obtain an overall LOS probability estimation formula that is adaptable to changes of the two parameters.
FNDaaS: Content-agnostic Detection of Websites Distributing Fake News

Automatic fake news detection is a challenging problem in misinformation spreading, and it has tremendous real-world political and social impacts. Past studies have proposed machine learning-based methods for detecting such fake news, focusing on different properties of the published news articles, such as linguistic characteristics of the actual content, which however have limitations due to the apparent language barriers. Departing from such efforts, we propose Fake News Detection-as-a-Service (FNDaaS), the first automatic, content-agnostic fake news detection method, that considers new and unstudied features such as network and structural characteristics per news website. This method can be enforced as-a-Service, either at the ISP-side for easier scalability and maintenance, or user-side for better end-user privacy. We demonstrate the efficacy of our method using more than 340K datapoints crawled from existing lists of 637 fake and 1183 real news websites, and by building and testing a proof of concept system that materializes our proposal. Our analysis of data collected from these websites shows that the vast majority of fake news domains are very young and appear to have lower time periods of an IP associated with their domain than real news ones. By conducting various experiments with machine learning classifiers, we demonstrate that FNDaaS can achieve an AUC score of up to 0.967 on past sites, and up to 77-92% accuracy on newly-flagged ones.
Research on Identification of Geological Hazards in Wide Area Transmission Lines Using Time-series InSAR

High-voltage power transmission equipment is often deployed in inaccessible wild areas covered with vegetation and complex terrain conditions. Geological disasters often occur in these areas where line corridors and towers locations need to be paid attention to. Therefore, it is of great practical significance to ensure the safe and stable operation of power transmission equipment and facilities. Aiming at the high-voltage transmission line network widely erected within the jurisdiction of Kunming, this paper uses the time-series InSAR technique to identify the early geological hazards along the corridor. This technique could provide guarantee for the safe and stable operation of the transmission line.
An Overview of Design and Implementation of Various Ordered Statistical Methods for Target Detection in RADAR

In case of target detection, the background environment is not always homogeneous. Rather, most of the times, it is filled with non-homogeneities like multiple targets and reverberation edges. Cell Averaging Constant False Alarm Rate (CA-CFAR) gives better performance in a homogeneous environment, but its performance decreases in the presence of non-homogeneities and in the presence of multiple targets. No other CFARs give the best performance in both homogeneous and Non- homogeneous environments at the same time. Variability Index CFAR (VI-CFAR) is a combined CFAR approach that adapts itself to CA-CFAR, GO-CFAR, or SO-CFAR depending on whether the background environment is homogeneous, has reverberation edges, or has multiple targets, respectively. This adaptability is based on the values of the Mean Ratio (MR) and the Variability index (VI) calculated from the leading or lagging CFAR reference windows. This paper discusses various CFAR processing techniques with their advantages and disadvantages.
Compact Boundary Matrix from a Polytopal Complex for Computing Persistent Homology

Persistent Homology (PH) is a tool of Topological Data Analysis (TDA) that records the persistence of homologies in data. The persistence of the homologies is computed from a filtration of the data created at a set of increasing connectivity distances $\left(0=\epsilon_{0}, \epsilon_{1}, \cdots, \epsilon_{\max } \leq \infty\right)$. Unfortunately the time and space complexity of computing PH from simplicial complexes (Vietoris-Rips, Cech, or Alpha) or cubical complexes limits its scope of use to small and low dimensional data. This paper examines the use of a Polytopal Complex to represent the data using maximal polytopes as elements of the filtered complexes. Furthermore, the approach further reduces size of the polytopal complex with an externally supplied approximation factor $\delta$. The approximation preserves the homology of the space that extends beyond $\delta$. Experimental results shows a significant reduction in space requirements. The reduction in space complexity and the compromise in PH computation is proportional to $\delta$. In addition to efficient construction of polytopal complexes, this work computes the homology of space using a compact boundary matrix representation made possible by the polytopal complex. The polytopal complex also provides a significant reduction in topological representations of high dimensional data.
Design of a Wide Input Power Dual-Frequency Rectifying Antenna

A Hilbert dual-frequency rectifying antenna is proposed to harvest RF energy in space environment, which can recover wireless energy effectively. The rectifier circuit consists of impedance matching network, Cockcroft-walton voltage doubling rectifier circuit and DC-pass filter. The receiving antenna adopts the symmetrical dipole antenna mode, and the main body is composed of Hilbert fractal structure and helical structure to ensure the omnidirectional antenna radiation. Antenna operating center frequencies are 0.94GHz and 2.45GHz. The impedance matching network is designed based on $\lambda$/4 dual-frequency coupler, which can reduce RF signal reflection and improve energy utilization efficiency. The rectifier circuit uses HMSS-2852 Schottky diodes with low starting conditions and suitable for ambient RF energy with low energy density. The rectifier topology adopts Cockcroft-walton voltage doubling rectifier circuit which has the highest rectifier efficiency at the operating frequency. DC-pass filter adopts fan stub structure to suppress fundamental wave and high order harmonics and improve the efficiency of RF energy rectification. ADS simulation results show that the maximum rectifying efficiency of the rectifying antenna at 0.94GHz and 2.4GHz are 70.84% and 56.77%, respectively. At the 0.94GHz and 2.4GHz frequencies, In the wide input power range of -4.4dBm $\sim$ 17.4dBm and 2.4dBm $\sim$ 16.8dBm, the rectification efficiency is greater than 40%. The rectifier circuit has the characteristics of dual-frequency and wide input power, which can be applied to power to low-power devices such as wireless sensors.
Deep Learning Approaches for Automatic Drum Transcription

Drum transcription is the task of transcribing audio or music into drum notation. Drum notation is helpful to help drummers as instruction in playing drums and could also be useful for students to learn about drum music theories. Unfortunately, transcribing music is not an easy task. A good transcription can usually be obtained only by an experienced musician. On the other side, musical notation is beneficial not only for professionals but also for amateurs. This study develops an Automatic Drum Transcription (ADT) application using the segment and classify method with Deep Learning as the classification method. The segment and classify method is divided into two steps. First, the segmentation step achieved a score of 76.14% in macro F1 after doing a grid search to tune the parameters. Second, the spectrogram feature is extracted on the detected onsets as the input for the classification models. The models are evaluated using the multi-objective optimization (MOO) of macro F1 score and time consumption for prediction. The result shows that the LSTM model outperformed the other models with MOO scores of 77.42%, 86.97%, and 82.87% on MDB Drums, IDMT-SMT Drums, and combined datasets, respectively. The model is then used in the ADT application. The application is built using the FastAPI framework, which delivers the transcription result as a drum tab.
Design of Beam Steering Resonator Antenna for 5G using Switch-Coupling Parasitic Element

This paper proposed beam steering MIMO antenna for 5G application at 3.5 GHz of mid-band. The beam steering performance is based on the integration of the driven parasitic element and switches. The RT Duroid 5880 is used as substrate with thickness 1.57mm. The geometry of the center rectangular patch antenna to achieve the desired resonance frequency which 3.5GHz. The integration of the HPNP 4005 PIN Diode with the parasitic element is tested which the antenna manages to steer the direction at -30°, 0° and +30° with gain of 6.4dBi. In the proposed design, a simple biasing circuit is employed to control all the PIN Diodes switches. The proposed MMO antenna could be potential for 5G application.
Suppressing the Interference Within a Datacenter: Theorems, Metric and Strategy

As the paradigm of cloud computing, a datacenter accommodates many co-running applications sharing system resources. Although highly concurrent applications improve resource utilization, the resulting resource contention can increase the uncertainty of quality of services (QoS). Previous studies have shown that achieving high resource utilization and high QoS simultaneously is challenging. Moreover, quantifying the intensity of interference across multiple concurrent applications in a datacenter, where applications can be either latency-critical (LC) or best-effort (BE), poses a significant challenge. To address these issues, we propose Ah-Q, which comprises two theorems, a metric, and a scheduling strategy. First, we present the necessary and sufficient conditions to precisely test whether a datacenter is both QoS guaranteed and high-throughput. We also present a theorem that reveals the relationship between tail latency and throughput. Our theoretical results are insightful and useful for building datacenters that have desirable performance. Second, we propose the “System Entropy” (E<inline-formula><tex-math notation="LaTeX">${_{S}}$</tex-math><alternatives><mml:math><mml:msub><mml:mrow/><mml:mi>S</mml:mi></mml:msub></mml:math><inline-graphic xlink:href="liu-ieq1-3354418.gif"/></alternatives></inline-formula>) to quantitatively measure the interference within a datacenter. Interference arises due to resource scarcity or irrational scheduling, and effective scheduling can alleviate resource scarcity. To assess the effectiveness of a resource scheduling strategy, we introduce the concept of “resource equivalence”. We evaluate various resource scheduling strategies to demonstrate the correctness and effectiveness of the proposed theory. Third, we introduce a new resource scheduling strategy, ARQ, that leverages both isolation and sharing of resources. Our evaluations show that ARQ significantly outperforms state-of-the-art strategies PARTIES and CLITE in reducing the tail latency of LC applications and increasing the IPC of BE applications.
Use of deep learning for the classification of hyperplastic lymph node and common subtypes of canine lymphomas: a preliminary study

Artificial Intelligence has observed significant growth in its ability to classify different types of tumors in humans due to advancements in digital pathology technology. Among these tumors, lymphomas are quite common in dogs, despite studies on the application of AI in domestic species are scarce. This research aims to employ deep learning (DL) through convolutional neural networks (CNNs) to distinguish between normal lymph nodes and 3 WHO common subtypes of canine lymphomas. To train and validate the CNN, 1,530 high-resolution microscopic images derived from whole slide scans (WSIs) were used, including those of background areas, hyperplastic lymph nodes (n = 4), and three different lymphoma subtypes: diffuse large B cell lymphoma (DLBCL; n = 5), lymphoblastic (LBL; n = 5), and marginal zone lymphoma (MZL; n = 3). The CNN was able to correctly identify 456 images of the possible 457 test sets, achieving a maximum accuracy of 99.34%. The results of this study have demonstrated the feasibility of using deep learning to differentiate between hyperplastic lymph nodes and lymphomas, as well as to classify common WHO subtypes. Further research is required to explore the implications of these findings and validate the ability of the network to classify a broader range of lymphomas.
Free and Forced Vibrations of the Carrier Beam of the Vehicle Chassis

The article is dealing with the stress-strain state of the carrier beam of the two-axle vehicle chassis for a dynamic perturbing load that occurs in the process of driving along the unevenness of the road, taking into account the elastic characteristics of the spring suspension. When analyzing the ride smoothness, it is necessary to take into account low-frequency forced vibrations caused by road irregularities, as well as free low-frequency vibrations. The dynamic calculation was carried out by the force method. In this case, the expansion of a given arbitrary dynamic load is performed in terms of the main modes of vibration. The calculation for free and forced vibrations was made by an exact analytical method, taking into account the elastic pliability of the axial supports of the beam. In the course of the study, the external dynamic forces acting on the structure under consideration, reduced to three-point masses, were determined and three forms of free vibrations were obtained. The results of the study can be used to optimize the design of the truck chassis and to improve their reliability and safety in operation. By understanding the behavior of a suspension system under dynamic loads, engineers can design stronger, more reliable designs that can withstand the stress of transporting heavy loads over rough roads. In general, this study contributes to the development of more efficient and safer transport systems
Relation of eastern Asian summer monsoon with the equat orial eastern Pacific spring SSTA

Defined is a new east Asian monsoon index , which can perfectly describe the seasonal and interannual change of the wester n Pacific subtropical high (WPSH). On the basis of the index, the relations are discussed among the equatorial eastern Pacific spring SSTA, the summer WPSH and east Asian summer monsoon. Related to the warm (cold) spring SSTA in the equator ial eastern Pacific, the summer WPSH position lies to the south (north) of its m ean, the west (east) of its normal, and is slightly intensified (weakened); the east Asian summer monsoon is also weak (strong), the mid/low reaches of the Yang tze River are hit by floods (droughts), accompanied by droughts (floods) in the south China and the Yellow River valleys.
Movie Recommendation System for Cold-Start Problem Using User's Demographic Data

with the exponential growth of online movies, effective recommender systems have become crucial for aiding users in discovering relevant content. However, the surge in new users and movies has led to the cold-start problem, where accurate recommendations are hindered by insufficient data. In this study, we propose the Enhanced scaled-CER model, which integrates user metadata with the MovieLens-10M dataset to address this issue. Bayesian optimization techniques are employed to optimize the model's parameters, resulting in enhanced performance. Experiments conducted on the MovieLens-10M dataset demonstrate impressive achievements, including an item coverage rate of over 95% and increased Shannon entropy, indicating higher diversity and relevance in recommended movie selections. Our research offers a comprehensive and diverse set of movie recommendations to users, even in the face of limited data availability, presenting a robust solution for the cold-start problem in movie recommendation systems.
Network Pharmacology Study of Huangqi-Huangjing in the Treatment of Diabetic Nephropathy and Diabetic Cardiomyopathy

Objective: To systematically investigate the mechanism of Huangqi-Huangjing in the treatment of Diabetic Nephropathy (DN) and Diabetic Cardiomyopathy (DC) using network pharmacology and molecular docking analysis. To provide a theoretical basis for the development of new drugs for the treatment of DN and DC. Methods: The active ingredients and therapeutic targets of Huangqi-Huangjing, DN and DC were predicted and screened using TCMSP, GeneCards, DisGeNet and OMIM databases. Networks of active ingredients and targets were mapped using Cytoscape 3.8. 2, Protein-Protein Interactions (PPI) were analyzed using the STRING database, and enrichment analysis of key targets was performed using “clusterProfiler” in R. Molecular docking of active ingredients and key targets was performed by Autodock vina. Results: A total of twenty-six active drug compounds, including diosgenin, formononetin, 7-O-methyl isomucronulatol, and 207 potential targets of Huangqi-Huangjing were obtained. PPI network analysis showed that targets such as AKT1, JUN, TP53, HSP90AA1 and RELA were associated with both huangqi-huangjing and DN-DC. GO and KEGG pathway analysis showed that most of these targets were involved in pathways such as Th17 cell differentiation, IL-17 signaling pathway, and AGE- RAGE signaling pathway in diabetic complications. Docking studies showed that diosgenin has ideal binding activity to TP53, RELA and AKT1. Conclusion: The active ingredients of Huangqi-Huangjing such as diosgenin may act on DN and DC through different targets such as TP53, RELA and AKT1, which can help to develop innovative drugs for effective treatment of DN and DC.
AQUA: A Framework for Spatiotemporal Analysis and Visualizations of Water Quality Data at Scale

Spatia1 data volumes have grown exponentially alongside the proliferation of sensing equipment and networked observational devices. In this study, we describe our framework aQua for performing visualizations and exploration of spatiotemporally evolving phenomena at scale. We validate our ideas in the context of data from the National Hydrology Database (NHD) and the Environmental Protection Agency (EPA) to support longitudinal analysis (53 years of data) for the vast majority of water bodies in the United States. Our methodology addresses issues relating to preserving interactivity, effective analysis, GPU accelerated visualizations, dynamic query generation, and scaling. We consider optimizations and refinements at the server-side, client-side, and how information exchange occurs between the client and server-side. We report both quantitative and qualitative assessments of several aspects of our tool to demonstrate its suitability. Finally, our methodology is broadly applicable to domains where visualization-driven explorations of spatiotemporally evolving phenomena are needed.
Mitigating Confounding and Selection Biases in Personalized Recommendation: A Causal Approach

Recommender systems usually face confounding bias and selection bias. The former arises when hidden variables determine user/item features and an outcome variable simultaneously while the latter happens due to some biased selection mechanisms, e.g., choosing users based on a specific time or location. How to alleviate such biases has attracted a lot research attention in recent years, but existing approaches mainly focus on one specific source of bias, rather than handle both confounding and selection biases. To this end, we formulate the causal personalized recommendation problem based on the structural causal model (SCM) and a generalization of the notion of backdoor adjustment to account for both biases. Our approach leverages external data of some variables that are also measured without selection bias and uses an adjustment pair based on the derived graphical conditions for identifying conditional causal effects. We present a statistical estimation procedure based on inverse probability weighting to calculate conditional causal effects when training samples are limited. In the presence of confounding and selection biases, we also show how to derive path-specific effects and counterfactual effects, both of which are important for recommendation analysis. We demonstrate the effectiveness of our approach through empirical evaluations.