Miracle-dz/UniFAR-FTUs huggingface dataset:
SOME THOUGHTS ON RF ENERGY HARVESTING

Background. Energy harvesting is the actual topic. The system for energy harvesting has many components which require complex designing. Objective. The aim of the paper is optimal ways of collecting and accumulation of electromagnetic energy. Methods. Analysis of all known publications devoted to the energy harvesting, and induction of optimal elements for the energy harvester. Results. Optimal solutions and some practical implementatio ns were proposed for each element of the energy harvester. Moreover, IR-UWB signals were proposed a s a candidate for energy effective communications. Conclusion. Energy harvesting can be effectively used for powe r supply for some devices (for instance, sensors). Also, energy effective communications (fo r instance, IR-UWB) can be used in those devices.
A Mechanism for Detecting the Intruder in the Network through a Stacking Dilated CNN Model

Rapid advancements in the technology and telecommunication areas have led to a massive expansion of network density and information. As a consequence, numerous intruder assaults are being attempted, making it difficult for cybersecurity to identify intruders effectively. The increasing amount of network traffic data has poses a major problem for conventional intrusion detection systems. Moreover, intruders with the intent of launching various assaults inside the networks could not be overlooked. The classification in the article is based on the DL methodologies used in constructing network-based IDS technologies, and it first describes the idea of intrusion detection system. The effectiveness of extracted features and classifications is closely related to detection accuracy, yet typical extraction of features and classification techniques do not function well in this situation. Basic traffic data is also uneven that has a significant effect on classifications findings. A novel intrusion detection model using stacked dilated convolutional autoencoders is proposed and tests it on two additional intrusion detection databases. Many tests have been conducted to define the effectiveness of the strategy. The use of the concept in extensive and practical network systems has a lot of potentiality and possibility. The CTU-UNB database as well as CTU-UNB database is made up of trafficking data from multiple sources. The suggested efficiency of the algorithm is used to evaluate, three types of classification. The deep learning strategy is compared to other ways that were similar. The implications of a number of key hyperparameters are investigated further. The comparison experimental findings show that the suggested approach can reach significantly high efficiency, fulfilling the needs for network intrusion detection systems (NIDS) with higher accuracy.
System and method of automatic collection of objects in the room

A system and method of automatic collection of objects in a room focused on the minimum energy consumption is proposed in this paper. This result is achieved by the implementation of an improved method of automatic collection of items in the room and mathematical method for calculating the desired motion trajectory; development and implementation of algorithms that implement the proposed method; software and hardware implementation of the system for automatic collection of items with minimal energy consumption, including the small number of system components and an improved movement trajectory. The system’s main component is the Arduino Uno, which acts as a controller. The developed software makes it possible to evaluate the implemented method’s effectiveness in a real-life system. An application example of the proposed method is given.
Waste management in Romania: Current data and application of a decision support tool

The present paper discusses the current situation in the municipal solid management existing in the North-East region of Romania. Furthermore, a Decision Support Tool is implemented in order to compare viable alternative waste management scenarios. The most recent data on the available current infrastructure and the amount of the generated waste have been used so as to determine the baseline status, while the results from the onsite waste analysis were also used as input data. To verify several combinations of technologies, in total five different scenarios were defined at regional level: four hypothetical options and one corresponding to the solution already recommended by the current County Master Plans.
Using Dynamic Responses of Moving Vehicles to Extract Bridge Modal Properties of a Field Bridge

The vehicles moving on a bridge excite bridge vibration and can also serve as response receivers because the vehicle dynamic response contains the vibration information of the bridge. A methodology was proposed in a previous study for extracting bridge modal properties, such as natural frequencies and modal shapes, from the vehicle dynamic responses. A specialized test vehicle consisting of a tractor and two following trailers was developed in which the two trailers towed by the tractor moving along the bridge were used as the dynamic response receivers. The responses of one trailer with a time shift were subtracted from the responses of the other to obtain the residual responses that were then processed with fast Fourier transformation (FFT) and short-time Fourier transformation (STFT) to extract the bridge modal properties. In the present paper, field test data of an existing bridge were adopted to verify the proposed methodology. In the vehicle and bridge system, a bridge finite-element (FE) model was updated using the measured accelerations and strains of the bridge; two types of test vehicle models were proposed for use in simulations of the tractor–trailer test system; and the measured surface-roughness profile was used in the numerical simulation. Parametric studies have been conducted to determine the trailer mass and stiffness. VehicleModel I shows a good capacity for extracting bridge frequencies and the first two modal shapes with a dominant vertical component. However, Vehicle Model II performed better than Vehicle Model I on the extraction of bridge modal shapes that are dominant in lateral bending. DOI: 10.1061/(ASCE)BE.19435592. 0001038. © 2017 American Society of Civil Engineers. Author keywords: Moving vehicle; Vehicle dynamic responses; Field bridge; Bridge modal properties; Tractor and trailer test vehicle;
Modeling Neural Networks for Artificial Vision

Modeling neural networks is a powerful approach to uncover the mechanism of the brain, and the results of the research are ready to use for engineering applications. This paper introduces several models for vision from recent works by the author. (1) Extraction of optic flow: In the MST area of the monkey, there are cells that respond selectively to specific motions of a large area of the visual field, such as rotation or expan- sion/contraction. They respond steadily even when the location of the center of optic flow shifts on the retina. They are thought to analyze optic flows of the retinal images. A neural network model for these cells is designed based on vector field hypothesis. It is a hierarchical multilayered network: retina, layer V1, layers MT and layer MST. (2) Use of blur for robust image processing: In the process of comparing images, the blurring operation increases robustness against deformations and various kinds of noise, and largely reduces computational cost. As an example of usefulness of blurring operation, the paper discusses a neural network model that extracts axes of symmetry from visual patterns. The model is a hierarchical multi-layered network. The model checks conditions of sym- metry, not directly from the oriented edges, but from a blurred version of the response of edge-extracting layer. The input patterns can be complicated line drawings, plane figures or gray-scaled natural images taken by CCD cameras. (3) A model capable of recognizing and restoring partly occluded patterns: Even the identical image is perceived differently by human beings depending on the shape of occlud- ing objects. The model responds in a similar way as human beings. It is a multi-layered hier- archical neural network, in which visual information is processed by interaction of bottom-up and top-down signals. Occluded parts of a pattern are restored mainly by feedback signals from the highest stage of the network, while the unoccluded parts are reproduced mainly by signals from lower stages. The model does not use a simple template matching method. It can recognize and restore even deformed versions of learned patterns.
Dynamic Ultrasound Assessment of the Anterior Tibial Translation for Anterior Cruciate Ligament Tears Diagnostic

The aim of our study was to investigate the accuracy of dynamic ultrasound assessment of the anterior tibial translation, in diagnosing anterior cruciate ligament tears, and to assess its test–retest reliability. Twenty-three patients (32 ± 8.42 years; 69.56% males) with a history of knee trauma and knee instability participated in the study. Knee ultrasound was performed by an experienced orthopedic surgeon. The anterior tibial translation was measured in both knees and differences between the injured and uninjured knee were calculated. Side-to-side differences > 1 mm were considered a positive diagnosis of an ACL tear. The anterior tibial translation values were 3.34 ± 1.48 mm in injured knees and 0.86 ± 0.78 mm in uninjured knees. Side-to-side differences > 1 mm were found in 22 cases (95.65%). The diagnosis accuracy was 91.30% (95%CI: 71.96–98.92%) and sensitivity 95.45% (95%CI: 77.15–99.88%). The intraclass correlation coefficient showed an excellent test–retest reliability (ICC3,1 = 0.97 for the side-to-side difference in anterior tibial translation). The study highlights the accuracy and reliability of the dynamic ultrasound assessment of the anterior tibial translation in the diagnosis of unilateral anterior cruciate ligament tears. Ultrasound assessment is an accessible imaging tool that can provide valuable information and should be used together with physical examination in suspected cases of ACL injuries.
Visual Analytics Tools for Polycode Stimuli Eye Gaze Tracking in Virtual Reality

This paper is devoted to extending the previously created unified pipeline for conducting eye-tracking-based experiments in a virtual reality environment. In the previous work, we proposed using SciVi semantic data mining platform, Unreal Engine and HTC Vive Pro Eye head-mounted display to study reading process in the immersive virtual reality. The currently proposed extension enables to handle so-called polycode stimuli: compound visual objects, which consist of individual parts carrying different semantics for the viewer. To segment polycode stimuli extracting areas of interest (areas, where the informant’s eye gaze is being tracked) we adopt Creative Maps Studio vector graphics editor. To integrate Creative Maps Studio into the existing pipeline we created plugins for SciVi platform to load and handle the segmented stimuli, place them in the virtual reality scenes, collect corresponding eye gaze tracking data and perform visual analysis of the data collected. To analyze the eye gaze tracks, we utilize a circular graph that allows comprehensive visualization of hierarchical areas of interest (mapping them to color-coded graph nodes grouped into the hierarchy with a help of multilevel circular scale) and corresponding eye movements (mapped to the graph edges). We tested our pipeline on two different stimuli: the advertising poster and the painting “The Appearance of Christ Before the People” by A. Ivanov (1857).
Enough With Default Allow in Web Applications!

The default allow deployment model, which is commonly used to implement and deploy web applications, is the cause of numerous security problems. We pro-pose a method of modelling web applications in a platform-agnostic way to adopt a default deny model instead, removing several classes of vulnerability altogether and signiﬁcantly reducing the attack surface of many others. Our approach is best adopted during development, but can be nearly as eﬃcient as an afterthought, or when used at deployment time
The Desire to Remain Childless and its Role in Female Partner Selection Criteria: An Evolutionary Psychology Based Perspective

The present article aims to investigate if there are differences – and which – between partner selection criteria of females in long term relationships that desire and that do not desire children with their current male partners. According to Evolutionary Psychology, these criteria are directly related to the type of parental investment practiced by the partner. The method consisted of virtual data collection of 288 answers to the research questionnaire aimed at cisgender, heterosexual and nulliparous women currently in a relationship, which evaluated the presence or absence of certain traits in their current male partners. Results showed significative differences between partner selection criteria of females that desired and that did not desire children in 14 male traits and no significative differences in the other 13 male traits presented. Traits containing significative differences were, whereas also culturally valued, mostly indicative of parental investment and genetic quality.
Putting Web Audio API to the test: Introducing WebAudioXML as a pedagogical platform

Web technologies in general and Web Audio API in particular have a great potential as a learning platform for developing interactive sound and music applications. Earlier studies at the Royal College of Music in Stockholm have led to a wide range of student projects but have also indicated that there is a high threshold for novice programmers to understand and use Web Audio API. We developed the WebAudioXML coding environment to solve this problem, and added a statistics module to analyze student works. The current study is the ﬁrst presentation and evaluation of the technology. Three classes of students with technical repsectively artistic background participated through online courses by building interactive, sound-based applications. We analysed the source code and self-reﬂective reports from the projects to understand the impact WebAudioXML has on creativity and the learning process. The results indicate that WebAudioXML can be a useful platform for teaching and learning how to build online audio applications. The platform makes mapping between user interactions and audio parameters accessible for novice programmer and supports artists in successfully realizing their design ideas. We show that templates can be a great help for the students to get started but also a limitation for them to expand ideas beyond the presented scope.
Characterization of PISTILLATA-like Genes and Their Promoters from the Distyly Fagopyrum esculentum

Arabidopsis PISTILLATA (PI) encodes B-class MADS-box transcription factor (TF), and works together with APETALA3 (AP3) to specify petal and stamen identity. However, a small-scale gene duplication event of PI ortholog was observed in common buckwheat and resulted in FaesPI_1 and FaesPI_2. FaesPI_1 and FaesPI_2 were expressed only in the stamen of dimorphic flower (thrum and pin) of Fagopyrum esculentum. Moreover, intense beta-glucuronidase (GUS) staining was found in the entire stamen (filament and anther) in pFaesPI_1::GUS transgenic Arabidopsis, while GUS was expressed only in the filament of pFaesPI_2::GUS transgenic Arabidopsis. In addition, phenotype complementation analysis suggested that pFaesPI_1::FaesPI_1/pFaesPI_2::FaesPI_2 transgenic pi-1 Arabidopsis showed similar a flower structure with stamen-like organs or filament-like organs in the third whorl. This suggested that FaesPI_2 only specified filament development, but FaesPI_1 specified stamen development. Meanwhile, FaesPI_1 and FaesPI_2 were shown to function redundantly in regulating filament development, and both genes work together to require a proper stamen identity. The data also provide a clue to understanding the roles of PI-like genes involved in floral organ development during the early evolution of core eudicots and also suggested that FaesPI_1 and FaesPI_2 hold the potential application in bioengineering to develop a common buckwheat male sterile line.
Machine Learning Methods for Computer Vision

Over the last years, deep learning methods proved to outperform previous machine learning techniques, especially in high computational task such as computer vision. This review paper aims to provide a preliminary overview of the machine learning tasks where computer vision in involved. Furthermore, a brief review of their history and state-of-the-art techniques is presented in the fields of image classification and object detection.
Network Intrusion Detection Method Based on FCWGAN and BiLSTM

Imbalanced datasets greatly affect the analysis capability of intrusion detection models, biasing their classification results toward normal behavior and leading to high false-positive and false-negative rates. To alleviate the impact of class imbalance on the detection accuracy of network intrusion detection models and improve their effectiveness, this paper proposes a method based on a feature selection-conditional Wasserstein generative adversarial network (FCWGAN) and bidirectional long short-term memory network (BiLSTM). The method uses the XGBoost algorithm with Spearman's correlation coefficient to select the data features, filters out useless and redundant features, and simplifies the data structure. A conditional WGAN (CWGAN) is used to generate a small number of samples in the dataset, add them to the original training set to supplement the dataset samples, and apply BiLSTM to complete the training of the model and realize the classification. In comparative tests based on the NSL-KDD and UNSW-NB15 datasets, the accuracy of the proposed model reached 99.57% and 85.59%, respectively, which is 1.44% and 2.98% higher than that of the same type of CWGAN and deep neural network (CWGAN-DNN) model, respectively.
Analysis of the Ripple Effect of the US Federal Reserve System's Quantitative Easing Policy on Stock Price Fluctuations

The macroeconomic concept represents the movement of a country's economy, and it affects the overall economic activities of business, government, and households. In the macroeconomy, by looking at changes in national income, inflation, unemployment, currency, interest rates, and raw materials, it is possible to understand the effects of economic actors' actions and interactions on the prices of products and services. The US Federal Reserve System (FED) is leading the world economy by offering various stimulus measures to overcome the corona economic recession. Although the stock price continued to decline on March 20, 2020 due to the current economic recession caused by the corona, the US S&P 500 index began rebounding after March 23 and to 3,694.62 as of December 15 due to quantitative easing, a powerful stimulus for the FED. Therefore, the FED's economic stimulus measures based on macroeconomic indicators are more influencing, rather than judging the stock price forecast from the corporate financial statements. Therefore, this study was conducted to reduce losses in stock investment and establish sound investment by analyzing the FED's economic stimulus measures and its effect on stock prices.
Prediction of Complications and Surgery Duration in Primary Total Hip Arthroplasty Using Machine Learning: The Necessity of Modified Algorithms and Specific Data

Background: Machine Learning (ML) in arthroplasty is becoming more popular, as it is perfectly suited for prediction models. However, results have been heterogeneous so far. We hypothesize that an accurate ML model for outcome prediction in THA must be able to compute arthroplasty-specific data. In this study, we evaluate a ML approach applying data from two German arthroplasty-specific registries to predict adverse outcomes after THA, after careful evaluations of ML algorithms, outcome and input variables by an interdisciplinary team of data scientists and surgeons. Methods: Data of 1217 cases of primary THA from a single center were derived from two German arthroplasty-specific registries between 2016 to 2019. The XGBoost algorithm was adjusted and applied. Accuracy, sensitivity, specificity and AUC were calculated. Results: For the prediction of complications, the ML algorithm achieved an accuracy of 80.3%, a sensitivity of 31.0%, a specificity of 89.4% and an AUC of 64.1%. For the prediction of surgery duration, the ML algorithm yielded an accuracy of 81.7%, a sensitivity of 58.2%, a specificity of 91.6% and an AUC of 89.1%. The feature importance indicated non-linear outcomes for age, height, weight and surgeon. No relevant linear correlations were found. Conclusion: The attunement of input and output data as well as the modifications of the ML algorithm permitted the development of a feasible ML model for the prediction of complications and surgery duration.
Conditions That Simulate the Environment of Atopic Dermatitis Enhance Susceptibility of Human Keratinocytes to Vaccinia Virus

Individuals with underlying chronic skin conditions, notably atopic dermatitis (AD), are disproportionately affected by infections from members of the herpesviridae, papovaviridae, and poxviridae families. Many patients with AD experience recurrent, widespread cutaneous viral infections that can lead to viremia, serious organ complications, and even death. Little is known about how the type 2 inflammatory environment observed in the skin of AD patients impacts the susceptibility of epidermal cells (keratinocytes) to viral pathogens. Herein, we studied the susceptibility of keratinocytes to the prototypical poxvirus, vaccinia virus (VV)—the causative agent of eczema vaccinatum—under conditions that simulate the epidermal environment observed in AD. Treatment of keratinocytes with type 2 cytokines (IL-4 and -13) to simulate the inflammatory environment or a tight junction disrupting peptide to mirror the barrier disruption observed in AD patients, resulted in a differentiation-dependent increase in susceptibility to VV. Furthermore, pan JAK inhibition was able to diminish the VV susceptibility occurring in keratinocytes exposed to type 2 cytokines. We propose that in AD, the increased viral susceptibility of keratinocytes leads to enhanced virus production in the skin, which contributes to the rampant dissemination and pathology seen within patients.