Miracle-dz/UniFAR-FTUs huggingface dataset:
Silence, Voice, and Resilience: An Examination of Workplace Sexual Harassment

ABSTRACT Whether victims of workplace sexual harassment voice their abuse or remain silent has implications for both organizations and victims. This study surveyed victims and observers of workplace sexual harassment to assess how medium of harassment, and voicing behaviors impact victims’ resilience and perceived vulnerability to future harassment. The study also examined the impact of organizational (in)tolerance of sexual harassment on both victims’ and observers’ organizational withdrawal and perceived vulnerability to future harassment. Findings show that victims’ resilience decreases when victims formally report their sexual harassment and when victims withdraw from their organizations, which is more likely to occur in organizations that are perceived as tolerant of sexual harassment. Results also show that observing sexual harassment had no effect on perceptions of organizational tolerance of sexual harassment. Theoretical and practical implications for organizations attempting to address the problem of sexual harassment are offered.
Size-Dependent Magnetization Switching in Magnetoelectric Heterostructures for Self-Biased MRAM Applications

Straintronic magnetic random access memory (MRAM) devices based on magnetoelectric heterostructures are gaining much attention because of their ability to perform energy-efficient, nonvolatile magnetization switching. However, practical applications of such devices are often restricted by the requirement of biasing magnetic field to provide the initial magnetization state. This work reports the self-biased, in-plane 180° magnetization switching of FeGaB nanomagnets on PMN-PT piezoelectric substrate. By varying the thickness of the nanomagnets with different aspect ratio, the self-biased operation is obtained for a particular range where the lower limit is imposed by the thermal stability and the upper limit is given by the shape anisotropy energy. We also demonstrate that in-plane 180° magnetization switching for larger aspect ratio nanomagnets is also limited because of the maximum achievable stress from the piezoelectric layer. The underlying physics, including the relationship between the critical switching time and energy, is delineated using finite difference method (FDM) micromagnetic model coupled with elastodynamic and electrostatic conditions.
ROT-Harris: A Dynamic Approach to Asynchronous Interest Point Detection

Event-based vision sensors are a paradigm shift in the way that visual information is obtained and processed. These devices are capable of low-latency transmission of data which represents the scene dynamics. Additionally, low-power benefits make the sensors popular in finite-power scenarios such as high-speed robotics or machine vision applications where latency in visual information is desired to be minimal. The core datatype of such vision sensors is the ‘event’ which is an asynchronous per-pixel signal indicating a change in light intensity at an instance in time corresponding to the spatial location of that sensor on the array. A popular approach to event-based processing is to map events onto a 2D plane over time which is comparable with traditional imaging techniques. However, this paper presents a disruptive approach to event data processing that uses a tree-based filter framework that directly processes raw event data to extract events corresponding to interest point features, which is then combined with a Harris interest point approach to isolate features. We hypothesise that since the tree structure contains the same spatial information as a 2D surface mapping, Harris may be applied directly to the content of the tree, bypassing the need for transformation to the 2D plane. Results illustrate that the proposed approach performs better than other state-of-the-art approaches with limited compromise on the run-time performance.
Surface analysis of iron and steel nanopowder

High sinter density is desired in powder metallurgy components as the requirement for performance is increasing day‐by‐day. One of the promising ways to achieve improved densification during sintering is through the addition of nanopowder to the conventional micrometer sized metal powder. It is well known that the surface chemistry of the powder has a decisive effect on sintering and consequently the properties of the components produced. Extensive research has hence been conducted to elucidate the surface chemistry and its influence on sintering for powder used in conventional powder metallurgy. Nanopowder, owing to high surface to volume ratio, can contribute to the activation of sintering at lower temperatures and enhance the sinter density. In this context, the surface chemistry of the nanopowder is also expected to exhibit substantial influence on sintering. The present investigation is aimed at establishing a methodology to study the surface chemistry and oxide thickness of nanopowder. For this purpose, iron nanopowder of 3 different size fractions: 35 to 45, 40 to 60, and 60 to 80 nm with core‐shell structure were studied. Different approaches were adopted to evaluate the shell thickness of the iron nanoparticles. The methodology was developed and tried on low alloy steel nanopowder to measure oxide thickness. X‐ray photoelectron spectroscopy, thermogravimetry, and high‐resolution scanning electron microscopy techniques were used to study the nanopowder. Results from different core‐shell models for iron nanopowder were found to be consistent except in the case where depth profiling was taken into account. The results were in agreement with the values obtained from thermogavimetry‐surface area correlation.
Construction of Therapy-Disease Knowledge Graph (TDKG) Based on Entity Relationship Extraction

The knowledge graph of treatment-disease relationship can be a benefit not only to understand, inquire, and learn the relations between treatments and diseases from a macro level, but also to obtain the differences between treatments to the same disease through the comparison of different treatments; with the aid of commonalities of some treatments, a treatment to a disease that has not been discovered may be recognized; and with the aid of the commonalities of some diseases, a treatment to a disease that has not been discovered may be recognized too. At present, the therapy-disease knowledge graph has become the focus of medical informatics fields. International and domestic Internet giants have deployed some products in this area. The basic method of knowledge graph research is relation extraction in natural language processing. This paper uses semantic technology to discover several sentence patterns expressed in the therapy-disease sentence, and uses a method similar to BERT, that is, using the known part of the sentence to learn the unknown part of the sentence. An unsupervised learning algorithm is designed that extracts the relationship between therapies and diseases. By extracting the relationship between diseases and therapies entities from the abstracts of 300844 literature on diseases’ treatments, 203,238 entity relationships were identified, of which 180,675 entity relationships were valid. The results show that the sentence patterns expressing entity relationships in the literature on disease-treatment are relatively fixed. The entity relationship extraction is performed by dividing the sentence structure by subject, predicate and object. And the accuracy of extraction is 88.98%, and the recall rate is 60.05%.
Research and Implementation of Beidou Receiver Digital Correlator

In this paper, a Beidou correlator chip based on FPGA (Field Programmable Gate Array) is developed.Using the top-down design method, combined with the hardware programming language Verilog HDL, the function simulation of each module in the tracking channel of Beidou correlator is emphasized.The simulation results show that the theoretical analysis is consistent with the experimental results, and each module can be realized correctly and efficiently, in which the code tracking frequency resolution is 0.00908MHz, and the code phase error is 1/2 chip.It lays a good foundation for the development of Beidou receiver correlator chip.
Investigating the Tradeoff Between Transparency and Efficiency in Semitransparent Bifacial Mesosuperstructured Solar Cells for Millimeter-Scale Applications

This article thanks to recent advancements in nanofabrication and 3-D packaging, typical Internet of Things devices can now be wirelessly controlled using millimeter-scale sensors known as Internet of Tiny Things devices. Since these low-power devices may be exposed to low and indirect solar irradiation, we demonstrate a novel mesosuperstructured solar cell (MSSC) that allows low flux light to be harvested from both its top and bottom sides. Our cell is based on either a dye-sensitized solar cell (DSSC) or a perovskite solar cell (PSC). The active layer in the proposed MSSCs was tuned to allow semitransparent behavior. Moreover, we developed an experimentally validated model that enables optimization of the active layer thickness for different semitransparent MSSC applications. In MSSCs, such optimization is necessary to balance the tradeoff between transparency and efficiency for various active layer thicknesses. Fabricated DSSCs and PSCs cells were used to validate the simulation results. The fabricated DSSC achieved a harvesting ratio of 1:10 with a conversion efficiency of around 2% at one Sun. We demonstrate that the optimum thickness of the mesoporous TiO2 active layer in DSSCs was 800 nm, enabling a maximum power density of 7 mW/cm2.
Shaped Egypt Flag Superstrate Based Antenna for 5G Sub-6GHz Application

This paper introduces a multi-layered Egypt flag-superstrate-based antenna design with high gain for X band application. For the sake of improving the performance, two multilayer superstrates of $\varepsilon=\mathrm{4.4}$ are used to form the flag and eagle shape and added above the patch antenna. The proposed antenna consists of three substrates layers with an overall dimension of $44\times 41\times \mathrm{1.6}\ \mathrm{mm3}$. The proposed patch antenna is mounted in the first layer of the RO5880 board. The antenna operates at 7.15 GHz (From 7 GHz to 7.3 GHz) and 8.61 GHz (From 8.4 GHz to 8.75 GHz) with a 5.68/6.45 dB and 86%/89% gain and radiation efficiency. A very good agreement can be noticed between the simulation analysis using a CST Studio Suite Electromagnetics (EM) solver and experimental validation using ROHDE & SCHWARZ ZVB20 network analyzer. In conclusion, this work presents a new way to design a multiband antenna with an artistic perspective using multilayer substrate and Defected Ground Structure (DGS).
Organic and conservation agriculture promote ecosystem multifunctionality

Considering agroecosystem multifunctionality is essential for designing sustainable cropping systems. Ecosystems provide multiple services to humans. However, agricultural systems are usually evaluated on their productivity and economic performance, and a systematic and quantitative assessment of the multifunctionality of agroecosystems including environmental services is missing. Using a long-term farming system experiment, we evaluated and compared the agronomic, economic, and ecological performance of the most widespread arable cropping systems in Europe: organic, conservation, and conventional agriculture. We analyzed 43 agroecosystem properties and determined overall agroecosystem multifunctionality. We show that organic and conservation agriculture promoted ecosystem multifunctionality, especially by enhancing regulating and supporting services, including biodiversity preservation, soil and water quality, and climate mitigation. In contrast, conventional cropping showed reduced multifunctionality but delivered highest yield. Organic production resulted in higher economic performance, thanks to higher product prices and additional support payments. Our results demonstrate that different cropping systems provide opposing services, enforcing the productivity–environmental protection dilemma for agroecosystem functioning.
Continual Learning of Human-like Arm Postures

Inspired from established human motor control theories, our HUMP algorithm plans upper-limb collisions-free movements for anthropomorphic systems, which show kinematic human-like features [1]. Related cognitive issues can be further resolved when robots act as they are familiar with their workspace and can take initiative faster than in the early onsets of a task. Here, a continual learning technique is proposed to improve the performance of the HUMP under uncertainties of the items in a given scenario. Given the locality of the optimization-based HUMP algorithm, a meaningful initial guess, predicted from similar past motion experiences, can significantly reduce the computational cost and put the robot into action arguably faster than in the first attempts of planning with inexperienced initial guesses. This prediction is proposed to be incrementally refined by an optimal locally weighted regression method that operates on datasets of situational features that are regularly updated as new movements are planned by the robot in similar scenarios. The proposed cyclic experiential learner is tested on the selection of optimal human-like target postures in a reaching task with a large obstacle obstructing the straight-line path towards a given target. Results demonstrate the capability of extracting meaningful situational features in few sessions of online learning with a very limited size of the datasets. Comparisons with simple Euclidean locally weighted regression and random initializations showed the capability of planning target configurations of better quality with less computational cost. The proposed approach also exhibits to be robust against the interferences of new incoming samples depicting slightly changed situations of the same task.
A Robust Blind Source Separation Algorithm Based on Non-negative Matrix Factorization and Frequency-Sliding Generalized Cross-Correlation

In this paper, a blind source separation algorithm based on time delay estimation (TDE) and non-negative matrix factorization (NMF) is proposed. In the TDE module, sub-band generalized cross correlation (GCC), frequency-sliding and singular value decomposition (SVD) techniques are used to get more accurate estimation. The time differences of arrival (TDOA) estimation of sources can be obtained from weighted low-rank approximation of the Frequency-Sliding GCC (FS-GCC) matrix. Then the sound sources are reconstructed in the NMF module by grouping the dictionary atoms according to their spatial information. The experiment uses utterances from SiSEC2018 database. Performance is quantified using the BSS Eval toolkits. Results prove that the proposed algorithm outperforms the compared ones in the noisy environments.
Transitions Into and Out of Work: Stay-at-Home Fathers’ Thoughts and Feelings: A Brief Report

This qualitative study explored how stay-at-home fathers (SAHFs) think and feel about transitioning back into paid employment. Findings from a thematic analysis on interviews with 21 SAHFs in the United Kingdom revealed that many of the SAHFs expected to return to work. However, most of the fathers suggested that this would be part-time, or self-employment, which would allow them to remain highly involved in caregiving, representing their commitment to moving away from the traditional gender roles that are largely evident in parenting. Some fathers expressed a desire to return to the paid workforce whilst others showed apprehension, indicating tensions over negotiating work and care. These findings have practical implications for fathers who are highly involved in caregiving.
Preservation of Eye Movements in Parkinson's Disease Is Stimulus- and Task-Specific

Parkinson's disease (PD) is a neurodegenerative disease that includes motor impairments, such as tremor, bradykinesia, and postural instability. Although eye movement deficits are commonly found in saccade and pursuit tasks, preservation of oculomotor function has also been reported. Here we investigate specific task and stimulus conditions under which oculomotor function in PD is preserved. Sixteen PD patients and 18 healthy, age-matched controls completed a battery of movement tasks that included stationary or moving targets eliciting reactive or deliberate eye movements: pro-saccades, anti-saccades, visually guided pursuit, and rapid go/no-go manual interception. Compared with controls, patients demonstrated systematic impairments in tasks with stationary targets: pro-saccades were hypometric and anti-saccades were incorrectly initiated toward the cued target in ∼35% of trials compared with 14% errors in controls. In patients, task errors were linked to short latency saccades, indicating abnormalities in inhibitory control. However, patients' eye movements in response to dynamic targets were relatively preserved. PD patients were able to track and predict a disappearing moving target and make quick go/no-go decisions as accurately as controls. Patients' interceptive hand movements were slower on average but initiated earlier, indicating adaptive processes to compensate for motor slowing. We conclude that PD patients demonstrate stimulus and task dependency of oculomotor impairments, and we propose that preservation of eye and hand movement function in PD is linked to a separate functional pathway through the superior colliculus-brainstem loop that bypasses the fronto-basal ganglia network. Our results demonstrate that studying oculomotor and hand movement function in PD can support disease diagnosis and further our understanding of disease progression and dynamics. SIGNIFICANCE STATEMENT Eye movements are a promising clinical tool to aid in the diagnosis of movement disorders and to monitor disease progression. Although Parkinson's disease (PD) patients show some oculomotor abnormalities, it is not clear whether previously described eye movement impairments are task-specific. We assessed eye movements in PD under different visual (stationary vs moving targets) and movement (reactive vs deliberate) conditions. We demonstrate that PD patients are able to accurately track moving objects but make inaccurate eye movements toward stationary targets. The preservation of eye movements toward dynamic stimuli might enable patients to accurately act on the predicted motion path of the moving target. These results can inform the development of tools for the rehabilitation or maintenance of functional performance.
Verifying the Mathematical Library of a UAV Autopilot with Frama-C (cid:63)

Ensuring safety of critical systems is crucial and is often attained by extensive testing of the system. Formal methods are now commonly accepted as powerful tools to obtain guarantees on such systems, even if it is generally not possible to formally prove the safety and correctness of the whole system. This paper presents an ongoing work on the formal veriﬁcation of the Paparazzi UAV autopilot using the Frama-C veriﬁcation platform. We focus on a Paparazzi mathematical library providing diﬀerent UAV state representations and associated conversion functions and manage to prove the absence of runtime errors in the library and some interesting functional properties on ﬂoating-point conversion functions.
Mixed-Scale Unet Based on Dense Atrous Pyramid for Monocular Depth Estimation

Monocular depth estimation is an undirected problem, so constructing a network to predict better image depth information is an important research topic. This paper proposes a mixed-scale Unet network (MAPUnet) with a dense atrous pyramid based on the coder-decoder structure widely used in computer vision. We innovatively introduce the Unet++ structure of the image segmentation network for depth estimation. We reset the number of convolutional layers of the network under the framework of the Unet++ network and innovatively connect the decoders densely. Moreover, by choosing the appropriate size of the atrous radius, we form a dense atrous pyramid based on different feature layers to better connect the features in the deep and shallow layers of the network. To verify the effectiveness of the proposed algorithm, we test the network on the KITTI dataset and the NYU Depth V2 dataset. We compare the network with the current state-of-the-art methods. The proposed method has higher accuracy and has steadily improved relative to the threshold of accuracy and root-mean-square error. We also conduct ablation studies, studies targeting the effectiveness of the network framework, and discussions on the convergence time and parameter complexity of the network. We will open-source the code at https://github.com/yang-yi-fan/MAPUnet.
Internet of things device recognition method based on natural language processing and text similarity

Effective identification of Internet of things devices in cyberspace is of great significance to the protection of Cyberspace Security. However, there are a large number of such devices in cyberspace, which can not be identified by the existing methods of identifying IoT devices because of the lack of key information such as manufacturer name and device name in the response message. Their existence brings hidden danger to Cyberspace Security. In order to identify the IoT devices with missing key information in these response messages, this paper proposes an IoT device identification method, IoTCatcher. IoTCatcher uses HTTP response message and the structure and style characteristics of HTML document, and based on natural language processing technology and text similarity technology, classifies and compares the IoT devices whose response message lacks key information, so as to generate their device finger information. This paper proves that the recognition precision of IoTCatcher is 95.29%, and the recall rate is 91.01%. Compared with the existing methods, the overall performance is improved by 38.83%.
Harmonizing Artificial Intelligence with Radio Access Networks: Advances, Case Study, and Open Issues

Driven by the demands of efficient network operation and high service availability, the convergence of artificial intelligence (AI) with radio access networks (RANs) has drawn considerable attention. However, current academic research mainly focuses on applying AI into optimizing RANs with a few discussions on architecture design. This article surveys the recent progress achieved by industry in integrating AI into RANs, and proposes an AI-driven fog RAN (F-RAN) paradigm. Specifically, being wrappers of Al-re-lated functionalities, AI capsules are presented as new network functions in the F-RAN domain. With AI capsules, computation and cache resources at various fog nodes can be utilized to facilitate real-time AI-based F-RAN optimization and alleviate the transmission burden incurred by network data collection. At the edge cloud, a centralized AI brain for F-RANs is deployed, which incorporates a wireless-oriented auto-AI platform and a digital colon of the network environment for offline AI model training and evaluation. By the interplay among AI capsules and the AI brain, universal and endogenous intelligence can be fully realized within F-RANs, which in turn enhances system performance. Furthermore, we demonstrate the effectiveness of a scalable deep-reinforcement-learning-based method in minimizing energy consumption for a computation offloading use case. At last, open issues are identified in terms of interface standardization, federated learning, and transfer learning.