Miracle-dz/UniFAR-FTUs huggingface dataset:
A diagnostic system for classifying and segmenting breast cancer based on ultrasound images

Breast cancer is the most common type of cancer among women worldwide. Ultrasound is a type of imaging widely used in the diagnosis and examination of many soft tissues, including abnormalities of the breast because it offers the advantages of being real-time, portable, low-cost, and non-invasive. Ultrasounds suffer from high variability as well as speckle noise, which reduces image quality; therefore, it may be difficult for doctors to detect a cancerous cell. In this paper, we use image enhancement filters to improve image quality then we develop the U-Net model to segment ultrasound images. The accuracy reached was 0.97 and the dice coefficient was 0.95. Furthermore, we classify images as malignant or benign using various traditional techniques such as k nearest neighbors (KNN), Random Forest, Decision Tree and support vector machine (SVM). The highest rate is achieved by KNN. After the image enhancement, KNN obtained an accuracy of 0.86.
Load Profiling via In-Band Flow Classification and P4 With Howdah

Data center traffic management challenges increase with the complexity and variety of new Internet and Web applications. Efficient network management systems are often needed to thwart delays and minimize failures. In this regard, it seems helpful to identify in advance the different classes of flows that (co)exist in the network, characterizing them into different types based on different latency/bandwidth requirements. In this paper, we propose Howdah, a traffic identification and profiling mechanism that uses Machine Learning and a load-aware forwarding strategy to offer adaptation to different classes of traffic with the support of programmable data planes. With Howdah, the sender and gateway elements inject in-band traffic information obtained by a supervised learning algorithm. When a switch or router receives a packet, it exploits this host-based traffic classification to adapt to a desirable traffic profile, for example, to balance the traffic load. We compare our solution against recent traffic engineering proposals and demonstrate the effectiveness of the cooperation between host traffic classification and P4-based switch forwarding policies, reducing packet transmission time in data center scenarios.
Improving Timing-Related Guarantees for Main Memory in Multicore Critical Embedded Systems

Main memory is one of the most complex resources to analyze in multicore-based embedded real-time systems, with contention in the memory controller and the timing constraints of the main memory device as the main contributors to that complexity. One of the main challenges in multicore real-time systems is producing the required evidence on the management of contention delay for the certification. This stems from the fact that current MPSoCs barely provide any event monitors on how tasks interact and delay each other in memory. Besides, even if hardware and software mechanisms are in place to mitigate contention in the memory system, it is hard - if at all possible - to provide evidence about their correctness. In this work, we cover this gap by proposing a lightweight hardware mechanism that tightly tracks inter-core contention in memory. The proposed hardware mechanism, which we evaluate in detail, improves the quality of timing-related evidence that must be provided on how contention in main memory of multicore real-time systems is handled in adherence to applicable safety standards.
A Wireless Tire Pressure and Temperature Monitoring System Based on Software Defined Radio

There are many vehicle accidents on the road, around 18-23% are caused by burst tires. It occurs when the tire pressure and temperature surpass the designated threshold. One potential remedy involves monitoring the tire pressure and temperature of the vehicle, which can be continuously monitored by the driver. If an error occurs in the monitoring device on the vehicle dashboard, the driver can be reminded by other parties who are also monitoring the tire pressure and temperature data remotely. Such a solution can be made if tire pressure data is also sent via wireless communication to other parties using internet technology. In this research, we created a wireless tire pressure monitoring system. This system uses Software Defined Radio (SDR) technology for the communication process between the sensor and controller. Vehicle tire pressure and temperature data can be continuously monitored via a computer or smartphone without installing a special application. This system successfully monitors changes in tire pressure and temperature remotely. Based on the results, the best of the standard deviation for the tire pressure value is 0.544. While the best of the standard deviation for the tire temperature value is 1.899.
Evaluation and Prediction of the Nutritive Value of Underutilised Forages as Potential Feeds for Ruminants

The aim of the chapter was to evaluate and predict the nutritive and feeding value of unknown and underutilised forages. Underutilised forages were collected from various regions. Chemical composition and degradability of forages in the rumen were determined. A dataset was created bearing degradability parameters of feeds from 40 studies. Using the dataset, a step-wise regression procedure was used to develop regression equations to predict rumen degradability. Of the underutilised forages, crude protein content tended to be double for Brassica oleracea var. acephala compared to Colophospermum mopane leaves and pods. Forage grasses tended to have very low crude protein contents compared to legumes and concentrates. Underutilised Brassica oleracea var. acephala tended to have higher crude protein levels compared to commonly used protein sources. The regression model for predicting the soluble fraction accounted for 59% (development) and 71% (validation) of the variation. The regression model for predicting the potential degradability accounted for 65% (development) and 24% (validation) of the variation. In conclusion, the nutritive value of underutilised forages was good, high in crude protein and high potential degradability. After correcting for factors that significantly affected degradability parameters, predicted solubility and effective degradability lay near the ideal prediction line, giving good predictions.
DICE: Dynamic In-Situ Control for Edge-Based Applications

This paper focuses on addressing computational constraints and energy limitations prevalent in edge-based applications through an innovative approach, dynamic in-situ control for edge-based applications (DICE). DICE capitalizes on the burgeoning trend in vehicle sensor technologies, such as camera, Radar, and LiDAR, which are becoming increasingly powerful and capable of performing pre-processing computations. DICE introduces a concept of “downstream offloading”, which distinguishes it from traditional offloading approaches that typically offload computational tasks from edge devices to more powerful Edge Servers. In contrast, DICE offloads part of the computational tasks from the Edge Server to the sensor itself, thereby optimizing data processing at the source and reducing the volume of data transmission required. This approach not only addresses the latency bottleneck frequently encountered in energy-intensive neural networks but also enhances the efficiency of data processing by selectively filtering out non-critical frames based on event-triggering mechanisms. DICE leverages the unique strengths of portable devices such as smartwatches and smartphones, even with their inherent computational and power limitations. The framework consists of an adaptive control layer for dynamic task allocation and an application layer designed to deploy quantized models on System on Chips (SoCs) like TinyML, thereby improving the efficiency of AI-driven applications while conservatively utilizing energy. This system proposes a sustainable, energy-efficient pathway for future edge-based applications.
Evaluation of the efficiency of the system for detecting malicious outgoing traffic in public networks

Detecting malicious outgoing traffic is an urgent task being addressed in both public and private sectors, across corporate and public networks. There are different ways to detect malicious traffic. Each of the methods has its advantages and disadvantages. The article’s authors proposed a method of detecting malicious outgoing traffic based on a fuzzy logical conclusion. Its use makes it possible to increase the efficiency of combating malicious traffic in public networks.The article also offers quality metrics for evaluating the intruder detection system implemented on the basis of the specified method. With the help of the selected metrics, the developed system can be compared both with other software tools for detecting malicious traffic in networks and with its own modifications.
Corporate Financial Literacy at the Hungarian Small and Medium-sized Enterprises Across Pandemic Times

Today, the role and significance of corporate financial awareness is becoming increasingly important, driven by the constantly and dramatically changing business environment on the one hand, and economic and geopolitical changes on the other. Businesses are thus faced with newer and newer challenges which require adequate financial response. This, however, calls for awareness. In the present study, we aim to present how financial literacy affects the outcome of financial decisions and the context corporate risk-taking. To verify these objectives, we use a path model based on zero-order regression.
Integrated Terahertz Beamforming System Based on Micromachined Silicon Interposer

Terahertz communication is approaching the center of the stage in such a date-rate-demanding era. However, there are still several issues to be solved including integration and beam steering before bridging the gap between the generated THz wave and its practical usage. In this paper, three silicon-micromachined THz modules associated with THz wave radiation and transmission and their simulation results are presented. The 4×1 phased array achieves a realized gain of 10.2 dBi and a beam steering range of ± 22°. The CPW-to-hollow waveguide transition structure exhibits a bandwidth of about 50 GHz at -10 dB level and a return loss lower than -45 dB at working frequency. The proposed micromachined silicon platform reveals great scalability and potentials in integrated THz systems.
Improved Performance by Designing and Building a 1.92 Tbps ROF System Using DWDM with External Modulation and DCF

Telecommunication technology is rapidly evolving, accompanied by an increase in service requirements such as higher bandwidth and service accessibility. Transferring data at high speeds and the system performance are second to none. DWDM (Dence Wavelength Division Multiplexing) can be used to meet these requirements, but it is not the only option. Long-distance transmission using DWDM systems often experiences signal degradation, which can degrade the system performance. An EDFA (Erbium-Doped Fiber Amplifier) can be used to overcome signal degradation. OptiSystem 18.0 is the DWDM link model used in this study, with a DWDM design that uses 48 channels at an average bit rate per channel of 40 Gbps. A channel spacing of 200 GHz was used. At a 50-km SMF (single-mode fiber) length, the system quality increased in terms of power and frequency spacing. With the EDFA amplifier, the linkage is 180 km long, 240 km long, and 300 km long. Changes in laser power of 10 dB were used in the study. According to the results of the analysis, power changes affect the performance of the system. DWDM with a laser power of 10 dB complies with the values of the standards for the Q factor (Quality Factor) and the BER Min Bit Error Rate parameters. The highest Q factor at a distance of 180 km is 47.008 and BER is 0 with a wavelength of 1498.213nm. The highest Q factor at a distance of 240 km is 28.19 and BER is 3.32092 * 10-175 and the wavelength is 1554.134nm. The highest Q factor at a distance of 300 km is 24.9, with BER equal to 3.66154 * 10-138, and the wavelength being 1565.495nm. According to this study, the higher the laser power used, the better the performance of the system.
A Local Regression-Based SLA-Aware Energy-Efficient Virtual Machine Consolidation Algorithm in Data Centers

Virtual Machine Consolidation (VMC) is an effective means to improve the resource utilization and reduce energy consumption of data centers. However, the VMC optimization that only considers energy consumption may lead to SLA (Service-Level Agreements) violations and thus fail to guarantee the QoS (Quality of Service) provided. Therefore, it is necessary to consider both energy saving and SLA violations in the process of VMC. In this paper, we propose an energy and SLA-aware VMC algorithm that considers both energy saving and SLA violation optimization in data centers. The proposed algorithm uses the local regression method to predict the utilization of virtual machines, based on which it determines whether the host will be overloaded, so as to make the decision of virtual machine migrations. The proposed VMC algorithm is evaluated with CloudSim toolkit. Simulation results demonstrate that our algorithm has good energy saving performance and can effectively reduce SLA violation in cloud data center. Considering energy consumption and SLA violation, the ESV can be reduced by 7.57% on average and 16.36% at most compared with other algorithms.
Citation Style Classification: a Comparison of Machine Learning Approaches

Citation style classification is a task that aims to detect a citation style according to which a given bibliographic entry is formatted. There are more than a hundred of recognized citation styles available, including popular ones such as ACM, IEEE, MLA, APA, and even more exotic ones. Automatic detection of citation style can be used in document linters, such as those intended for conference and term papers. Using automatic style classification enables people who assess articles in large quantities reduce cognitive load and increase efficiency. Apart from this, automatic detection of citation style can be used for reference parsing, topic classification, and reference extraction.In this paper we propose two novel approaches to citation style classification using both deep and classic machine learning methods. We evaluate them using a specially designed dataset, consisting of 6 million bibliographic records and spanning 91 citation styles. Our experiments showed that the proposed approaches significantly outperform the existing state-of-the-art solution, while also supporting almost five times more citation styles.
HeteroPush: Communication-Efficient Video Analytics by Scheduling Heterogeneous Filters

A large number of cameras have been deployed in different real world scenarios. Due to the limited computing power and bandwidth, a combined device-cloud framework is typically used to process the captured videos. Filtering techniques select a part of video frames to upload to the cloud, aiming to reduce the computational overhead and bandwidth consumption. As the video content may change during time, a filter that can not adjust to these changes lacks robustness during long-term operation. We find that heterogeneous filters based on various modal features have diverse performances in different time periods, which leads to time-varying local optimality of different filters. In this paper, we design an efficient method to adaptively schedule local optimal filters to perform filtering tasks, aiming to maximize the cost-effectiveness of communication overhead (for example, system accuracy improvement per unit upload). Direct application of existing methods, there are two following challenges in real systems: (1) Noisy samples that with conflict filtering labels but the same on-device inference results; (2) Unpredictability of the correct inference result before uploading frames. To address these challenges, we propose HeteroPush, the first reinforcement learning based adaptive filter scheduling framework. HeteroPush selects the local optimal filter at each moment by modeling the performance of filters in real-time. We perform extensive evaluations on two real-world datasets to demonstrate the effectiveness of our design. The results show that HeteroPush reduces the bandwidth cost by 90% with an additional 2.7% computational overhead.
Trend Analysis and Continuous Wavelet Transform for Bearing Fault Detection in Induction Motors

In this research, we studied on fault detection at the inner race of bearing in induction motors using trend analysis and continuous wavelet transform. Vibration signals collected from a test setup for normal and faulty cases are analyzed by the trend analysis method. This method aims to determine intersection points of trend lines that represent fault information carrying frequency. Then, the faulty vibration signal is examined with continuous wavelet transform. By using continuous wavelet transform (CWT), it is aimed to calculate the frequency resulting the highest wavelet coefficients which describe the fault signal frequency.
TagVibra: push the limit of RFID-based vibration sensing

RFID-based mechanical vibration detection is considered a promising method for many Internet of Things (IoT) applications. However, existing methods are affected by ambient interference and leakage from the reader signature, which makes it difficult to obtain high-precision detection results. To overcome this limitation, this paper introduces TagVibra, a high-precision non-contact vibration sensing system based on RFID technology. By leveraging the nonlinear effect inside the tag and the modulation mechanism of RFID, TagVibra can achieve sub-millimeter level vibration detection accuracy. We implement TagVibra on our Universal Software Radio Peripheral (USRP) platform and evaluate its performance via experiments. The experimental results show that TagVibra can detect vibration with amplitude as low as 0.3mm, and the average estimation error below 0.7Hz when the vibration frequency is 100Hz.
Fault Detection and Diagnosis of Air Handling Units Based On Adversarial Multi-task Learning

Thanks to the development of sensor networks and Artificial Intelligence algorithms, modern smart buildings have adopted advanced computer technology to integrate various building control and functional modules to achieve fast and accurate responses to occupancy needs. However, The smart buildings system is complex, and the connection between various outer environmental conditions and inner interactions has not been adequately addressed, resulting in low FDD efficiency. In this paper, we proposed an Adversarial Multi-task Learning-based Fault Detection and Diagnosis (AML-FDD) framework to benefit the learning process by mining data collected under different environmental conditions via a unified model. The proposed AML-FDD adopted multi-task learning principles for collaboratively mining hidden knowledge among various FDD tasks to obtain better FDD effects. The framework-wise common features were utilized for training all tasks, and the task-wise specific features were used to train each task privately. Besides, we adopted an adversarial training manner with orthogonal regulations to guarantee the discrimination between task-shared and task-specific feature embedding. We conducted various experiments in the ASHRAE Research Project 1312 (RP-1312) and ASHRAE Research Project 1020 (RP-1020), and experimental results showed that AML-FDD outperformed state-of-the-art multi-task-based and single-task-based FDD.
Implementation of Intelligent Q&A System for Electric Power Knowledge Based on Knowledge Graph

Aiming at the difficulty of information retrieval of massive electric power data and the difficulty of ordinary retrieval system to accurately return the answers required by users, this paper designs an intelligent Q&A system for electric power based on knowledge graph. According to the characteristics of user data, it is organised and stored as Q&A library, document library, mapping library and structured database, respectively, and the corresponding intelligent Q&A system is designed, which combines the classification results of complex problem understanding and intention recognition to complete the acquisition of answers and their sources. And based on the realisation of intelligent Q&A, the BERT model is added to improve the accuracy of the system response. We conducted experimental validation on the constructed electric power knowledge dataset, and the results show that the system can accurately understand user-input questions and give answers quickly and accurately. This system improves the efficiency of user information acquisition and effectively enhances the work efficiency of power grid practitioners.
Ecological Study of Comprehensive Effects of the Beneficial Elements of Farmland Topsoil in Nanling-Jing County National Commodity Grain Base

The development of characteristic agriculture in Nanling-Jing County requires an in-depth understanding of the surface soil beneficial/heavy metals, elemental distribution characteristics, differences and potential ecological effects. Nanling-Jing County national commodity grain base is a famous Nanling rice production area and has been designated as a national good-quality rice base and national commodity grain base. It is necessary to obtain information on the distribution characteristics and the comprehensive effects of the beneficial elements of the farmland topsoil. In this study, Nanling-Jing County national commodity grain base, Anhui province was chosen as the research object for the determination of soil organic matter (SOM); major elements (N P, and K); and minor elements (B, Mo, Cu, Zn, Fe, and Mn). A total of 541 soil samples were collected in the topsoil layer and subjected to geostatistics, principal composition analysis, membership functions methods, and GIS technology application. The study area was divided into five sections according to the characteristics of the geomorphic unit, surface water drainage basin, and groundwater system, as well as the land-use conditions and crop types. The distribution and comprehensive effects of beneficial elements were different among the five sections; minor elements were the restricting factors for the farmland in the eastern plain, whereas minor and major elements’ synthetic action affected the farmland fertility in the central plain and southern mountain areas. In contrast, the farmland fertility in the western hilly area was controlled by the combined influence of SOM, and minor and major elements. All major elements and the minor elements B and Mo were the restricting factors for farmland soil fertility, the deficiency of which would have negative impacts on soil fertility, and the enrichment of Cu and Zn will enhance soil fertility. There was a significant difference in the distribution of the beneficial elements comprehensive factor among the five sections, the high-value area of comprehensive factor was same as the distribution of the K, B, Mo, Cu and Zn high-value areas. Research results can provide a theoretical basis for the construction of permanent and high standard basic farmland and grain safety and production increase.
Long Time Series Prediction of Meteorological Data Based on Improved Autoformer Model

Aiming at the problem of low prediction accuracy caused by many uncertain factors in the prediction of long-time series of meteorological data, a long-time series prediction model of meteorological data based on improved Autoformer model (Wdformer) is proposed. Change the routine of sequence decomposition pretreatment and reduce the noise and irregularity in the data by introducing dynamic diffusion to simulate the changes of meteorological elements in the atmosphere; The wavelet transform time series decomposition module is designed to improve the gradual decomposition ability of the model for long time series. The experimental results of meteorological data prediction in a certain area show that the accuracy of the proposed Wdformer model is higher than that of the standard Autoformer model.