Miracle-dz/UniFAR-FTUs huggingface dataset:
Challenging the Anomaly Detection Paradigm: CNN-CRF Approach

Since intrusion detection has gained so much popularity, scientists continue to dedicate a lot of time and energy to studying it. Even after all these years of research, the intrusion detection community still has problems. Recognizing previously unseen attack patterns is hampered by a high rate of false positives, which is an ongoing pro blem. However, new research has shown that there are exits from this impasse. Intrusion detection relies heavily on the ability to spot anomalies, or unexpected behavior, because they indicate the presence of malicious activity. Data preparation, feature selection, and model training are all parts of the suggested approach. Machine learning algorithms can analyze the data for the development of a predictive model thanks to data preparation methods. For feature selection, the proposed approach relied on principal component analysis and iterative grading. After collecting the features, CNN -CRF is utilized to train the models. The proposed method is superior to the popular algorithms CNN and CRF. There is a 98.63% success rate when using this strategy.
Ultra-Compact E-Plane Waveguide Filtering Antenna Based on Meander-Loop Resonator for 5G mmWave Applications

In this paper, an ultra-compact mmW E-plane waveguide filtering antenna using a pair of metal strips and a meander ring resonator is presented, in which two transmission zeros (TZs) is located at the lower stopband and the other one TZ at the upper stopband. The proposed filtering antenna with stable radiation patterns in the passband and high-frequency selectivity characteristic is only 12 mm in the length of the E-plane substrate. The proposed filtering antenna has an endfire radiation pattern that shows superior performance including cross-polarization discrimination higher than 42dB in the main beam direction, front-to-back greater than 7.84dB and a high gain of 7.8 dBi.
A Multi-Dimensional Resource Cooperative Allocation Scheme Based on Wireless Digital Twin Network

In the future, 6G will have the characteristics of “network can reach everything, computing power is everywhere, and intelligence is everywhere”. Computing network convergence is the development trend of future mobile communication networks. Communication and computing will achieve deeper integration at the 6G network architecture and protocol level, and will bring potential problems of energy conservation. 6G wireless networks need communication and computing collaborative allocation scheme. At the same time, 6G has the characteristics of “high system, high complexity, high dynamic and high reliability and quality assurance”. The communication and computing collaborative allocation scheme needs to adapt to the dynamics and complexity of the 6G network, reduce the impact of allocation decisions on the network and improve the system performance while reducing the system energy consumption. In this paper, we propose a collaborative allocation scheme of computing resources based on the digital twin network, design the short-term and long-term prediction mechanism, the pre-verification and optimization mechanism of resource allocation scheme, and carry out simulation verification. This scheme can improve business performance, reduce system energy consumption, adapt to network changes faster, and reduce decision-making risk.
Holotranscobalamin and total transcobalamin in human plasma: determination, determinants, and reference values in healthy adults.

BACKGROUND We developed microbiological assays (MBAs) to identify determinants and to establish reference values for cobalamin bound to transcobalamin [holotranscobalamin (holoTC)] and total TC in plasma. METHODS We captured holoTC with magnetic beads with TC antibodies and used a conventional MBA for cobalamin measurements. Total TC was determined as holoTC after TC was saturated with cyanocobalamin. The new assays were compared with published methods. Determinants and reference values were determined in 500 blood donors, ages 18-69 years. RESULTS Determination of cobalamin, holoTC, and TC by MBA required <150 microL. HoloTC and TC by MBA correlated with holoTC by RIA (r = 0.95) and TC by ELISA (r = 0.79), respectively. Between-day CVs for holoTC and total TC were 4%-9%. Women had lower holoTC than men, but only at age < or = 45 years. In multivariate regression analyses, holoTC was positively associated with age (in women only), creatinine (in men only), and plasma concentrations of total TC, folate, and cysteine, but inversely correlated with homocysteine and methylmalonic acid. For all study participants, total TC was associated with holoTC and number of TCN2 766C alleles; in female participants only, total TC was also associated with age, homocysteine, and cysteine. Reference values were 670-1270 pmol/L for TC and 42-157 pmol/L for holoTC, but they differed according to age and sex. CONCLUSIONS Our MBAs for TC and holoTC required low plasma volume and performed acceptably compared with other methods. Determinants of holoTC and TC differed between men and women and according to age. Separate reference intervals for holoTC should be considered in younger women.
Anomaly Detection in Smart Homes Based on Kitchen Activities and Machine Learning

Anomaly detection of user behavior in smart homes is vital due to its potential impact on improving the quality of life. Many anomaly detection systems proposed in the literature rely on a wide range of user activities performed at different parts of the smart home environment. This paper presents an anomaly detection framework focused on kitchen activities, a specific area that is not sufficiently addressed by similar studies. The two activities are 'eating' and 'making simple food'. This paper exploits the capability of machine learning algorithms, namely: Artificial Neural Network (ANN), Extreme Gradient Boosting (XGBoost), and Support Vector Machine (SVM). The performance of those algorithms was evaluated and ranged between 80% and 100% for best cases, Demonstrating their high anomaly detection potential despite the relatively small size of the scenario and dataset.
Key Technologies, Applications, Trends of Visual Analysis and Prediction in Healthcare System

With increasing disease-related complexities, the challenges in the healthcare sector are growing at an unprecedented rate. The implications of visual tools and prediction models are playing a significant role in the field of medical and health to overcome the challenges in this globalized technological era, and promote clinical decision-making and personalized care delivery plans. The aim of the present research is to systematically assess the analysis and assessment of the key technologies, applications and trends of visual analysis and prediction in medical and healthcare data making use of machine learning (ML), deep learning (DL), virtual reality (VR), augmented reality (AR), and 3D visualization.
Comprehensive Insight into the Operational Dynamics of TCM-Based Zero-Voltage Switching (ZVS) Two-Level Three-Phase Inverters for Electric Vehicle (EV) Motor-Drive Applications

This paper introduces a novel triangular-current mode (TCM), zero-voltage switching (ZVS) two-level three-phase inverter, specifically designed to enhance the performance of the electric vehicle (EV) drive system. The primary objective is to enhance the inverter efficiency by minimizing turn-on and turn-off switching losses while mitigating electromagnetic interference (EMI) by generating sinusoidal output waveforms. The distinctive feature of this inverter lies in its gate driver, which executes the current modulation scheme. Achieving ZVS during turn-on involves the gate driver sensing the switch voltage and turning it on at zero voltage, utilizing TCM. For turn-off ZVS, the gate driver monitors the switch current, turning it off when it exceeds a predefined reference value. With a carefully placed snubber capacitor, turn-off ZVS is achieved. The implemented current modulation scheme yields an efficiency exceeding 99% for a 10 kW power rating. The sinusoidal output waveforms not only enhance motor lifespan by safeguarding against sharp-edge voltages but also offer benefits like reduced switch stress and simplified thermal management.
Ultra-Compact On-Chip Filtering Power Divider Using GaAs Technology

In this paper, an ultra-compact filtering power divider (FPD) is presented using an integrated passive device (IPD) technology on a GaAs substrate. An LC resonant block is utilized to generate a bandpass filtering response with two transmission zeros (TZs) at zero frequency and 4 GHz. A parallel LC resonator employed at the input port is used to generate another TZ at 5.8 GHz, which further enhances the upper stopband rejection. In addition, an L C impedance transformer is introduced between the L C resonant block and the parallel L C resonator to improve the matching performance. The proposed FPD is working at 2.4 GHz with a fractional bandwidth of $41.6 \%\left(\left|\mathrm{~S}_{11}\right| \lt -15 \mathrm{~dB}\right)$. The minimum insertion loss is 1.2 dB, while the upper stopband rejection achieving better than 20 dB is up to 16 GHz. The chip is ultra-compact with an overall size of only $1.4 \times 1.5 \mathrm{~mm}^{2}$.
Effect of the Dual Attention Suppression Attack on the Performance of Self-Driving Car Models - A Preliminary Study

Compared to human-driven cars, full self-driving cars (SDCs) offer greater safety and convenience. However, the liability shifts from the human driver to the car manufacturer, who has to ensure that the SDCs are robust in withstanding any security attack. The Dual Attention Suppression (DAS) attack is a recent visual adversarial attack proposed in the literature that may significantly degrade the performance of an SDC. In this preliminary work, we simulate the DAS attack on SDCs in the CARLA self-driving simulator framework and study the attack's effect on SDC performance. The SDC performance is measured in terms of success rate and driving score. To the best of our knowledge, this is the first work to perform such a study. We apply a visual static patch at a fixed location on the backside of an ambulance vehicle type to induce the DAS attack. The DAS attack reduces the attention of the “car” class from deep learning models for object classification and object detection. We perform several SDC simulations wherein ambulances with and without modification are spawned randomly as neighboring traffic. Experimental results demonstrate that SDC performance (success rate and driving score) is largely unaffected by the DAS attack. While this initial result is promising, further study with different vehicle types and dynamic visual patch locations is required to generalize the result.
Cost-Efficient Network Planning for Quantum Communication Infrastructure

Quantum communication infrastructure (QCI) is gradually becoming a favorable choice for transmitting secure data across the globe. Network operators often prefer to utilize the existing network infrastructure to reduce network setup costs to build a QCI. This implies transforming traditional network nodes to quantum nodes (QNs) by equipping them with quantum processors. Trusted repeater nodes (TRNs) are used to connect distant QNs. However, the network infrastructure cost increases with the number of TRNs due to the usage of dark fibers. Therefore, it is challenging for the network planning to build a QCI that connects all QNs with a minimum number of TRNs to reduce the infrastructure cost. In this work, we construct a minimum cost spanning tree for the QCI to connect all QNs with a minimum number of links and TRNs. The proposed approach considers node distance and attenuation cost of the physical connections. Compared to the traditional spanning tree approach, our proposed approach reduces the cost of QCI deployment by 10.90%.
Ecosystem Service capacity is higher in areas of multiple designation types

The implementation of the Ecosystem Service (ES) concept into practice might be a challenging task as it has to take into account previous “traditional” policies and approaches that have evaluated nature and biodiversity differently. Among them the Habitat (92/43/EC) and Bird Directives (79/409/EC), the Water Framework Directive (2000/60/EC), and the Noise Directive (2002/49/EC) have led to the evaluation/designation of areas in Europe with different criteria. In this study our goal was to understand how the ES capacity of an area is related to its designation and if areas with multiple designations have higher capacity in providing ES. We selected four catchments in Greece with a great variety of characteristics covering over 25% of the national territory. Inside the catchments we assessed the ES capacity (following the methodology of Burkhard et al. 2009) of areas designated as Natura 2000 sites, Quiet areas and Wetlands or Water bodies and found those areas that have multiple designations. Data were analyzed by GLM to reveal differences regarding the ES capacity among the different types of areas. We also investigated by PCA synergies and trade-offs among different kinds of ES and tested for correlations among landscape properties, such as elevation, aspect and slope and the ES potential. ‡ § § ‡ § § © Nikolaidou C et al. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Our results show that areas with different types or multiple designations have a different capacity in providing ES. Areas of one designation type (Protected or Quiet Areas) had in general intermediate scores in most ES but scores were higher compared to areas with no designation, which displayed stronger capacity in provisioning services. Among Protected Areas and Quiet Areas the latter scored better in general. Areas that combined both designation types (Protected and Quiet Areas) showed the highest capacity in 13 out of 29 ES, that were mostly linked with natural and forest ecosystems. We found significant synergies among most regulating, supporting and cultural ES which in turn display tradeoffs with provisioning services. The different ES are spatially related and display strong correlation with landscape properties, such as elevation and slope. We suggest that the designation status of an area can be used as an alternative tool for environmental policy, indicating the capacity for ES provision. Multiple designations of areas can be used as proxies for locating ES “hotspots”. This integration of “traditional” evaluation and designation and the “newer” ES concept forms a timeand cost-effective way to be adopted by stakeholders and policy-makers in order to start complying with new standards and demands for nature conservation and environmental management.
Fine-Grained Elasticity for Big Data Stream Processing of IoT Applications

In an era when technology is developing rapidly like nowadays, there will be a large amount of data generated from technological devices, especially Internet of Things (IoT) devices, and so the problem raised is how to make effective use of that amount of information. To do this, it is necessary to build a system that can handle big data with the ability to scale resources depending on the amount of information received, which is able to bring both work and economic efficiency as well as avoid wasting resources. IoT smart homes are becoming more and more popular and these houses are equipped with many IoT devices to improve people's quality of life. Every second these smart devices generate a lot of data, so a real-time processing system will be needed to monitor and control them to suit different needs. Ensuring optimal resource usage in this context may require fine-grained elasticity, a characteristic of Cloud computing. Therefore, this paper focuses on solving the big data processing and the implementation of fine-grained elasticity on the Apache Storm platform as a solution to improve the efficiency of resource consumption during the operation of the IoT applications. We also tested and evaluated the pros and cons of the proposed solution when applied to Storm worker nodes. Experiments are based on the DEBS 2014 practical problem to find an appropriate solution to process energy usage data sent over MQTT protocol from large number of smart plugs placed at 40 smart buildings in a German city.
Campus Admin: an Integrated Architecture for Automating Data-Related Processes in Education

Managing courses and students at a university or other educational institutions includes manifold administrative tasks to be performed by different persons, e.g. secretaries, lecturers and system administrators. Most tasks are performed manually sometimes assisted by stand-alone software solutions that are not designed for collaboration and reuse of existing data. On this account, today’s processing of administrative tasks in education is time-consuming, labor intensive and error-prone. In this paper we present an approach and an implementation integrating several data-related processes of students at our university with hundreds of students starting each year. Our implementation is composed of several modules distributed over the campus. Extensive tasks like creating personal login accounts and email-addresses are performed automatically without any human interaction leading to a higher quality of data and completion in time. Frequently occurring processes like managing the results of exams and exercises, admitting students and creating individual certificates are integrated seamless reusing the existing data of the students.
Search-based determination of refactorings for improving the class structure of object-oriented systems

A software system's structure degrades over time, a phenomenon that is known as software decay or design drift. Since the quality of the structure has major impact on the maintainability of a system, the structure has to be reconditioned from time to time. Even if recent advances in the fields of automated detection of bad smells and refactorings have made life easier for software engineers, this is still a very complex and resource consuming task.Search-based approaches have turned out to be helpful in aiding a software engineer to improve the subsystem structure of a software system. In this paper we show that such techniques are also applicable when reconditioning the class structure of a system. We describe a novel search-based approach that assists a software engineer who has to perform this task by suggesting a list of refactorings. Our approach uses an evolutionary algorithm and simulated refactorings that do not change the system's externally visible behavior. The approach is evaluated using the open-source case study JHotDraw.
The Smart Improving of Translation Models Using Recurrent Neural Networks

In current years, Recurrent Neural Networks have been gaining increasing attention within the subject of natural language processing. That is because of their potential to method sequences of information and their utility to a ramification of obligations related to natural language processing. In this paper, we propose a version that improves the overall performance of translation fashions using Recurrent Neural Networks (RNNs). Our model starts evolving with a base translation model that uses an encoder-decoder architecture, in which the input and output languages are separate. We then observe an RNN-primarily based method to the enter and output layers of the translation model. This technique lets the community cope with the absence of parallel data by studying the context of a sentence from the information across languages. Additionally, we experimentally demonstrate that adding a recurrent layer to the output of the translation version creates a massive development in the ordinary translation great. We conduct experiments on an enormous multi-lingual corpus containing numerous language pairs, showing that our proposed model outperforms the baseline translation machine.
Diabetic Retinopathy Detection using Deep Learning Framework and Explainable Artificial Intelligence Technique *

Diabetic retinopathy (DR) is a serious eye condition induced by diabetes that can result in eyesight. In order to overcome the vision impairment of diabetes mellitus patients, it is essential for early age prevention by medical practitioners. In the traditional approach, ophthalmologists conduct various screening tests to detect DR but fail to achieve accurate and conclusive diagnosis due to the associated time consuming processes. In order to eliminate such burden on the ophthalmologists, deep learning and machine learning techniques have evolved rapidly playing a significant role in the classification of diabetic and non diabetic patients. In this paper, the proposed framework implements a publicly available Benchmark APTOS 2019 Gaussian-filtered DR image dataset using a customized CNN model, yielding an enhanced accuracy of 98 %. In addition, the framework also employs a LIME explainer visualization model to provide enhanced transparency and interpretability to the generated predictions. This eliminates the “blackbox” -ed nature of the predicted results from the traditional ML process and enables healthcare providers to take clinical decisions with confidence providing visual representations of the significant features that contribute towards an outcome.
On the Two-user Interference Channel with Partial Codebook Knowledge at one Receiver

In multi-user information theory it is often assumed that every node in the network possesses all codebooks used in the network. This assumption may be impractical in distributed ad-hoc, cognitive or heterogeneous networks. This work considers the two-user Interference Channel with one Oblivious Receiver (IC-OR), i.e., one receiver lacks knowledge of the interfering codebook while the other receiver knows both codebooks. The paper asks whether, and if so how much, the channel capacity of the IC-OR is reduced compared to that of the classical IC where both receivers know all codebooks. A novel outer bound is derived and shown to be achievable to within a constant gap for the class of injective semideterministic IC-ORs; the gap is shown to be zero for injective fully deterministic IC-ORs. An exact capacity result is shown for the general memoryless IC-OR when the non-oblivious receiver experiences very strong interference. For the linear deterministic IC-OR that models the Gaussian noise channel at high SNR, non i.i. d. Bernoulli(1/2) input bits are shown to achieve points not achievable by i.i. d. Bernoulli(1/2) input bits used in the same achievability same. For the real-valued Gaussian IC-OR the gap is shown to be at most 1/2 bit per channel use, even though the set of optimal input distributions for the derived outer bound could not be determined. Towards understanding the Gaussian IC-OR, an achievability strategy is evaluated in which the input alphabets at the non-oblivious transmitter are a mixture of discrete and Gaussian random variables, where the cardinality of the discrete part is appropriately chosen as a function of the channel parameters. Surprisingly, as the oblivious receiver The authors are with the Electrical and Computer Engineering Department of the University of Illinois at Chicago, Chicago, IL 60607 USA (e-mail: odytso2, danielat, devroye @uic.edu). The results in this paper were presented in part at the 2013 IEEE International Symposium on Information Theory [1] and at the 2014 IEEE Information Theory and Applications Workshop [2]. The work of the authors was partially funded by NSF under award number 1017436. The contents of this article are solely the responsibility of the authors and do not necessarily represent the official views of the NSF. Copyright (c) 2013 IEEE. Personal use of this material is permitted. However, permission to use this material for any other purposes must be obtained from the IEEE by sending a request to pubs-permissions@ieee.org. May 8, 2014 DRAFT ar X iv :1 40 5. 11 17 v1 [ cs . I T ] 6 M ay 2 01 4