Miracle-dz/UniFAR-FTUs huggingface dataset:
Towards Leveraging AI-based Moderation to Address Emergent Harassment in Social Virtual Reality

Extensive HCI research has investigated how to prevent and mitigate harassment in virtual spaces, particularly by leveraging human-based and Artificial Intelligence (AI)-based moderation. However, social Virtual Reality (VR) constitutes a novel social space that faces both intensified harassment challenges and a lack of consensus on how moderation should be approached to address such harassment. Drawing on 39 interviews with social VR users with diverse backgrounds, we investigate the perceived opportunities and limitations for leveraging AI-based moderation to address emergent harassment in social VR, and how future AI moderators can be designed to enhance such opportunities and address limitations. We provide the first empirical investigation into re-envisioning AI’s new roles in innovating content moderation approaches to better combat harassment in social VR. We also highlight important principles for designing future AI-based moderation incorporating user-human-AI collaboration to achieve safer and more nuanced online spaces.
Patterns of Effective Human-Agent Teams

Intelligent systems are increasingly interacting with people, both in their daily lives and through their use in safety critical systems. Current research is focused on how to use intelligent systems in a collaborative way as a teammate, rather than a tool. This requires a better understanding of what behaviors enable effective human-agent teams. This paper reports an experiment where a human player collaborates with an agent to perform a maneuvering task while concurrently performing a memory task. The player must determine in which contexts the agent requires their input to achieve better combined game plus memory task accuracy scores. We hypothesized that high-performing teams would exhibit different patterns of control inputs when compared to low-performing teams and that these patterns of control would be made more evident with user interfaces that increased operator situation awareness. Preliminary results are inconclusive, but show different patterns of interaction between high- and low-performing teams.
Dynamical behaviors of cavitation bubble under acoustic standing wave field

Considering liquid compressibility，the dynamical behaviors of cavitation bubble in acoustic standing wave field are investigated by regarding water as a work medium.The motion state of cavitation bubble at each position are simulated under standing wave field，the influence of the primary Bjerknes force on the direction of motion of cavitation bubble at each position are also simulated with the related parameters.The results show that the cavitation bubble is steady-state cavitation near pressure antinode, the cavitation bubble is transient cavitation deviate pressure antinode. In standing wave field, acoustic pressure amplitude, frequency and initial radius of cavitation bubble has a corresponding limit, lower limit is conducive to the occurrence of the phenomenon of cavitation
A Data Collection System from the RPL Routing Protocol for Detecting Distributed Denial of Service Attacks in IoT Networks

The work presents a data collection system from the RPL routing protocol for detecting distributed denial-of-service attacks in Internet of Things (IoT) networks operating on the basis of the 6LoWPAN and RPL protocols. The system consists of three modules: a data gathering module, a classification module and a detection module. The main feature of the data collection module was that data collection was provided by several sniffers installed in the network and with subsequent aggregation of the collected data. For the implementation of the classification module, research was carried out on the method of support vector machines (SVM) and a multilayer perceptron (MLP). The detection module was used to broadcast a message about the abnormal behavior to the rest of the IoT network nodes, containing the ID of the compromised node and the path to it. To evaluate the efficiency of the proposed system that is based on the data collected by the data gathering module, a number of experiments were conducted. To obtain the data set for the experiments, an infrastructure based on the Ubuntu operating system and the Cooja simulator was deployed, which allowed to simulate the RPL network. Based on the operation of the deployed network, network traffic was collected that corresponded to both legitimate traffic and traffic during a black hole attack. The total number of test data was 24,023 samples. According to the research results, it was established that the SVM-based model demonstrated better performance level, in particular, the accuracy of detecting denial-of-service attacks was 89.6%, while the rate of false positives was 6%.
Blockchain-based Deep Learning Algorithm for Detecting Malware

Nowadays malware detection is a very important task in information security. Criminals are constantly looking for new ways to attack computer networks and systems, so it is important to have a reliable mechanism to detect and prevent these threats. Existing anti-virus programs are not always effective in detecting new types of viruses or malware, which can compromise your system and steal important information. Therefore, it is critically important to investigate and create new methods for detecting malicious software, especially using modern technologies like Blockchain. One of the ways to detect malicious software is to use deep learning algorithms. For this, a Deep Learning Algorithm using Blockchain technology was developed to detect malicious programs. The basic idea is to use the blockchain to ensure security and accuracy of malware detection. The algorithm proposed in this work is based on the subsystems and Proof-of-Action. The first mechanism provides parallel analysis of potentially dangerous software by different participants. The second mechanism is used to validate the results of analysis and increase the accuracy of detection. The application of the proposed approach allows to detect malicious software with an accuracy of from 98.81% to 99.33%, which is quite a high result.
Exploring domain and task adaptation of LamBERTa models for article retrieval on the Italian Civil Code

This paper is concerned with AI-based NLP solutions to the law article retrieval problem, with application to the Italian legal domain and, particularly, to the Italian Civil Code. Based upon the current state-of-the-art on this topic, we revise our early LamBERTa framework in a twofold way relating its domain-adaptation feature: replacing the general-domain pre-trained model with a legal-specific one to fine-tune for the task of article retrieval, and delving into the injection of out-of-vocabulary legal terms into the models’ tokenizer. Extensive experimental evaluation based on different collections of query sets, along with qualitative analysis on the models’ prediction interpretability, have unveiled interesting findings about the combined effect of domain- and task-adaptation of an Italian BERT model on the task of law article retrieval.
MDPrep: Data Preparation for Metadata Management

With the increased amounts of data production, a significant surge has appeared in metadata generation as a part of the process. This metadata can provide meaningful insights, leading to improved data analytics, data integration, and resource management. However, due to many variations, users and tools do not follow the standards during recording metadata, resulting in various inconsistencies, such as missing attribute information, missing publishing URL, lack of provenance, etc. In addition, even recorded metadata may contain inconsistencies, such as multiple value formats, values with special characters, incorrectly entered values, etc. Preparing metadata can improve the user experience during data management tasks by addressing the above mentioned inconsistencies. This paper discusses the usability and applicability of data preparation techniques for enhancing metadata. We approach the problem by (1) detecting and identifying metadata elements and structural metadata issues, (2) applying a keyword-based approach for preparing metadata elements and syntax-based approach for preparing structural metadata issues, (3) comparing the outcome for improved readability and reusability of prepared metadata files
Adaptive interferometer with a femtometer-band resolution based on volume photorefractive holograms

We present experimental results for a high resolution interferometer based on dynamic volume holography in photorefractive materials. The proposed interferometer is able to detect small angular deviations (up to 10-8 rad) and wavelength shifts in one of the readout beams of 10 fm. Small changes in the recording beams of the hologram are detected by evaluating the corresponding readout Bragg wavelength.
Role of CFD in the Aerodynamic Design and Analysis of the Parametric Inlet

Computational Fluid Dynamics (CFD) played a significant role in the design of the Parametric Inlet – an advanced, external-compression, supersonic inlet. CFD allowed the designers to account for turbulent boundary layers and shock / boundary-layer interactions during the design of the shape of the internal inlet surfaces, define locations of porous bleed regions, optimize porous bleed factors, examine the effect of a vortex generator array, and examine inlet configurations with various cowl and slot geometries. The CFD simulations provided information that allowed the efficient use of costly wind tunnel resources. Preliminary comparisons between CFD and experimental data are encouraging for the validation of CFD methods for supersonic inlet design.
Four new suomilides isolated from the cyanobacterium Nostoc sp. KVJ20 and proposal of their biosynthetic origin

The suomilide and the banyasides are highly modified and functionalized non-ribosomal peptides produced by cyanobacteria of the order Nostocales. These compound classes share several substructures, including a complex azabicyclononane core, which was previously assumed to be derived from the amino acid tyrosine. In our study we were able to isolate and determine the structures of four suomilides, named suomilide B – E (1–4). The compounds differ from the previously isolated suomilide A by the functionalization of the glycosyl group. Compounds 1–4 were assayed for anti-proliferative, anti-biofilm and anti-bacterial activities, but no significant activity was detected. The sequenced genome of the producer organism Nostoc sp. KVJ20 enabled us to propose a biosynthetic gene cluster for suomilides. Our findings indicated that the azabicyclononane core of the suomilides is derived from prephenate and is most likely incorporated by a proline specific non-ribosomal peptide synthetase-unit.
Asymmetry measures for quantification of mechanisms contributing to dynamic stability during stepping-in-place gait

The goal of this study is to introduce and to motivate the use of new quantitative methods to improve our understanding of mechanisms that contribute to the control of dynamic balance during gait. Dynamic balance refers to the ability to maintain a continuous, oscillating center-of-mass (CoM) motion of the body during gait even though the CoM frequently moves outside of the base of support. We focus on dynamic balance control in the frontal plane or medial–lateral (ML) direction because it is known that active, neurally-mediated control mechanisms are necessary to maintain ML stability. Mechanisms that regulate foot placement on each step and that generate corrective ankle torque during the stance phase of gait are both known to contribute to the generation of corrective actions that contribute to ML stability. Less appreciated is the potential role played by adjustments in step timing when the duration of the stance and/or swing phases of gait can be shortened or lengthened to allow torque due to gravity to act on the body CoM over a shorter or longer time to generate corrective actions. We introduce and define four asymmetry measures that provide normalized indications of the contribution of these different mechanisms to gait stability. These measures are ‘step width asymmetry’, ‘ankle torque asymmetry’, ‘stance duration asymmetry’, and ‘swing duration asymmetry’. Asymmetry values are calculated by comparing corresponding biomechanical or temporal gait parameters from adjacent steps. A time of occurrence is assigned to each asymmetry value. An indication that a mechanism is contributing to ML control is obtained by comparing asymmetry values to the ML body motion (CoM angular position and velocity) at the time points associated with the asymmetry measures. Example results are demonstrated with measures obtained during a stepping-in-place (SiP) gait performed on a stance surface that either remained fixed and level or was pseudorandomly tilted to disturb balance in the ML direction. We also demonstrate that the variability of asymmetry measures obtained from 40 individuals during unperturbed, self-paced SiP were highly correlated with corresponding coefficient of variation measures that have previously been shown to be associated with poor balance and fall risk.
An underwater image enhancement model for domain adaptation

Underwater imaging has been suffering from color imbalance, low contrast, and low-light environment due to strong spectral attenuation of light in the water. Owing to its complex physical imaging mechanism, enhancing the underwater imaging quality based on the deep learning method has been well-developed recently. However, individual studies use different underwater image datasets, leading to low generalization ability in other water conditions. To solve this domain adaptation problem, this paper proposes an underwater image enhancement scheme that combines individually degraded images and publicly available datasets for domain adaptation. Firstly, an underwater dataset fitting model (UDFM) is proposed to merge the individual localized and publicly available degraded datasets into a combined degraded one. Then an underwater image enhancement model (UIEM) is developed base on the combined degraded and open available clear image pairs dataset. The experiment proves that clear images can be recovered by only collecting the degraded images at some specific sea area. Thus, by use of the scheme in this study, the domain adaptation problem could be solved with the increase of underwater images collected at various sea areas. Also, the generalization ability of the underwater image enhancement model is supposed to become more robust. The code is available at https://github.com/fanren5599/UIEM.
A validation of the religious and spiritual struggles scale among young people living with HIV in Zimbabwe: Mokken scale analysis and exploratory factor analysis

Introduction Religious/spiritual convictions and practices can influence health- and treatment-seeking behavior, but only few measures of religiousness or spirituality have been validated and used outside of the US. The Religious and Spiritual Struggles scale (RSS) measures internal and external conflict with religion and spirituality and has been validated mainly in different high-income contexts. The aim of this study was the validation of the RSS in the Zimbabwean context and among young people living with human immunodeficiency virus (YPLHIV) aged 14–24. Methods Data collection with an Open Data Kit (ODK) questionnaire with 804 respondents took place in 2021. The validation was performed by confirmatory factor analysis (CFA), using statistical equation modeling (SEM), and Mokken scale analysis (MSA). After the low confirmability of the original scale sub-dimensions exploratory factor analysis (EFA) was applied. Results The EFA resulted in four new sub-domains that were different from the original six domains in the RSS but culturally more relevant. The new sub-domains are significantly related to health. Discussion The findings support the validity and relevance of the RSS and the new sub-domains in this context. As our study was limited to YPLHIV, further validation of the RSS among different population groups and contexts in the sub-Saharan region is encouraged.
Detection of limited-energy α particles using CR-39 in laser-induced p −11 B reaction

Due to the harsh radiation environment produced by strong laser plasma, most of the detectors based on semiconductors cannot perform well. So, it is important to develop new detecting techniques with higher detection thresholds and highly charged particle resolution for investigating nuclear fusion reactions in laser-plasma environments. The Columbia Resin No. 39 (CR-39) detector is mainly sensitive to ions and insensitive to the backgrounds, such as electrons and photons. The detector has been widely used to detect charged particles in laser-plasma environments. In this work, we used a potassium–ethanol–water (PEW) etching solution to reduce the proton sensitivity of CR-39, by raising the detection threshold for the research of laser-induced 11 B(p, α)2α reaction. We calibrated the 3–5 MeV α particles in an etching condition of 60°C PEW-25 solution (17% KOH + 25%C2H5OH + 58%H2O) and compared them with the manufacturer’s recommended etching conditions of 6.25 N NaOH aqueous solution at 98°C in our laser-induced nuclear reaction experiment. The results indicate, with the PEW-25 solution, that CR-39 is more suitable to distinguish α tracks from the proton background in our experiment. We also present a method to estimate the minimum detection range of α energy on specific etching conditions in our experiment.
A preliminary study on the online processing of anticipatory tonal coarticulation – Evidence from eye movements

While the f0 realization of lexical tones vary extensively in contexts, little has been known on how listeners process the variation in lexical tones due to contextual effects such as tonal coarticulation in spoken word recognition. This study thus aims to fill the knowledge gap in tone perception with evidence from two types of anticipatory tonal coarticulation effects in Tianjin Mandarin, i.e., the slope raising effect due to a following low-falling tone and the overall-height raising effect due to a following low-dipping tone. An eye-tracking experiment with the Visual World Paradigm was carried out to compare participants’ eye movements when they heard targets in three types of anticipatory raising conditions, i.e., the Slope Raising condition, the Overall-height Raising condition, as well as the No Raising condition (the baseline). The eye movement results showed significant differences in the proportion of looks to target between the Slope Raising condition versus the other two conditions, whereas the Overall-height Raising condition did not differ significantly from the No Raising condition. The findings thus suggest the facilitatory effect of tonal coarticulation cues in the anticipation of the upcoming tones, but listeners in this study seemed to be only sensitive to the raising in the f0 slope rather than the overall raising in the f0 height.
Active faulting of the Nanhe Fault and relation to the Anninghe Fault zone in the late Quaternary, eastern Tibetan Plateau

Faults along the boundaries of active tectonic blocks are the main structures that are responsible for major earthquakes in mainland China. Investigating the geometric distribution, rupture behavior, and paleoseismic history of these faults is the prerequisite for constraining geodynamic models and regional seismic hazard analyses. The Nanhe Fault, located at the eastern boundary of the Sichuan–Yunnan Block near Mianning County, has been paid less attention so far due to insufficient historical records of major earthquakes. In this paper, we focused on the Nanhe Fault and conducted satellite imagery interpretation, field investigations, and trench excavations. Our findings indicate that the Nanhe Fault initiates north of Mianning County; the north segment of the fault is connected with the Anninghe Fault; and it extends for about 70 km south-westward and terminates southwest of Ermaga Village. The fault has been faulting in the late Late Pleistocene with a left-lateral strike-slip rate of 2.40–2.56 mm/yr, while in the late Holocene, the left-lateral strike-slip and vertical slip rates are 2.50–2.60 mm/yr and about 0.60 mm/yr, respectively. Three paleoseismic events (5373–4525 BC, AD 1193–1576, and AD 1496–1843) were identified by excavating trenches at the Nanhe Fault. A comparative analysis of paleoseismic events between the Nanhe and the Anninghe fault indicates that both faults may have induced cascade rupture or triggered earthquakes—such related events may have occurred in 1496–1627. Additionally, by comparing the kinematic relationship of the faults at the eastern boundary of the Sichuan–Yunnan Block, we propose that the Nanhe Fault takes part in strain partitioning along the boundary. This interpretation reasonably explains the loss of the sliding rate between the Anninghe and Zemuhe faults, which also supports the GPS inversion results, and the discontinuous deformation model for the eastern margin of the Tibetan Plateau.
The impact of structured self-monitoring of blood glucose on clinical, behavioral, and psychosocial outcomes among adults with non-insulin-treated type 2 diabetes: a systematic review and meta-analysis

Background Self-monitoring of blood glucose (SMBG) is considered of little clinical benefit for adults with non-insulin-treated type 2 diabetes, but no comprehensive review of a structured approach to SMBG has been published to date. Purpose To conduct a systematic review and meta-analysis of the impact of sSMBG on HbA1c, treatment modifications, behavioral and psychosocial outcomes, and; examine the moderating effects of sSMBG protocol characteristics on HbA1c. Data sources Four databases searched (November 2020; updated: February 2022). Study selection Inclusion criteria: non-randomized and randomized controlled trials (RCTs) and prospective observational studies; reporting effect of sSMBG on stated outcomes; among adults (≥18 years) with non-insulin-treated type 2 diabetes. Studies excluded if involving children or people with insulin-treated or other forms of diabetes. Data extraction and analysis Outcome data extracted, and risk of bias/quality assessed independently by two researchers. Meta-analysis was conducted for RCTs, and moderators explored (HbA1c only). Data synthesis From 2,078 abstracts, k=23 studies were included (N=5,372). Risk of bias was evident and study quality was low. Outcomes assessed included: HbA1c (k=23), treatment modification (k=16), psychosocial/behavioral outcomes (k=12). Meta-analysis revealed a significant mean difference favoring sSMBG in HbA1c (-0·29%, 95% CI: -0·46 to -0·11, k=13) and diabetes self-efficacy (0.17%, 95% CI: 0.01 to 0.33, k=2). Meta-analysis revealed no significant moderating effects by protocol characteristics. Limitations Findings limited by heterogeneity in study designs, intervention characteristics, and psychosocial assessments. Conclusion A small positive effect of sSMBG on HbA1c and diabetes self-efficacy was observed. Narrative synthesis of sSMBG intervention characteristics may guide future implementation. PROSPERO registration https://www.crd. york.ac. uk/prospero/display_record. php? ID=CRD42020208857, identifier CRD42020208857.