Miracle-dz/UniFAR-FTUs huggingface dataset:
Automated Checking of Scaffold Safety Regulations using Multi-Class 3D Segmentation

Scaffolds, one of the most widely used temporary structures, are prone to safety-related accidents. Despite the fact, checking regulations for a scaffold is manually being conducted, which is inefficient, especially for a large construction site. This paper proposes an automated method to check safety regulations regarding scaffolds on sites. 3D point cloud data obtained from Terrestrial Laser Scanning (TLS) is first processed by a deep learning-based 3D segmentation to automatically identify major entities Then, a simple rule-based algorithm is applied to the segmented data to check for three types of major safety-related regulations. The result of our experiment shows potential for successfully automating scaffold safety checking at a construction site.
Enhancing Player Experience in Asymmetric Virtual Reality Gameplay

Interest in multiplayer games that allow players to connect and play with different technologies, such as virtual or augmented reality (VR/AR) has increased. Research has shown that in asymmetric gaming experiences (eg. where there are differences between the players’ abilities or interfaces) it is possible to achieve an enhanced player experience (PX) through various interdependencies. However, most of the previous work focuses on co-located scenarios, where the space and proximity of the players are local and utilized. With this study, we investigate a remote asymmetric game setup, and we explore how asymmetries of space, user interface (UI), interaction design, role and abilities, and information access impact the PX and performance for users of different technologies (eg. VR, AR, PC).
The long echo of war. Early-life exposure to armed conflict and female experiences of intimate partner violence

While there are reasons to expect a link between armed conflict and victimisation in intimate relationships, empirical evidence on the association is scant and rarely considers the age at exposure to war. This paper examines the legacy of experiencing armed violence in developmental ages on women’s later risk of intimate partner violence (IPV) in four ex-Soviet countries (Armenia, Azerbaijan, Moldova, Tajikistan). I combine cross-national data on IPV from the Demographic and Health Survey and geo-referenced information from the Uppsala Conflict Data Program, and compare the IPV outcomes of cohorts who were exposed to conflict before the end of their teens with same-age non-exposed and older cohorts. Findings show that war in young ages is associated with greater risk of experiencing IPV later in life. Exposure in childhood ages (0-10) matters the most, and is particularly related to suffering physical forms of IPV. Results hold for both lifetime and past- year domestic abuse, and are not driven by migration. I explore some pathways and find that, while attitudes towards IPV are not associated with early-age experience of conflict in women, men exposed to war in late adolescence (16-19) are more likely to condone violence against female partners. Normalisation of the use of violence in future potential perpetrators rather than desensitisation to abuse in victims appears as one plausible mechanism through which armed conflict can have lasting consequences on intimate relationship quality. conflict exposure may have normalised the use of violence in future potential perpetrators, making them more lenient towards violence against their partners. This study adds to theoretical knowledge on the long-term consequences of war on individuals and society at large, which so far overlooked implications for the quality of private relations and for gender attitude formation. It further expands our understanding on the transmissibility of violence over the lifecourse and levels of the social realm. Results are also policy-relevant: they point at conflict as a likely determinant of the high IPV rates in these fragile contexts, and suggest that interventions targeting very young girls and adolescent boys exposed to war can be promising to interrupt cycles of violence.
The MasterPlan: Master without Bachelor. Discussing diversity in higher education within a pilot case study at Brandenburg University of Applied Sciences (Germany)

As a result of the promotion of lifelong learning processes over the past two decades, the focus of educational policy has increasingly been placed on non-formal and informal learning as forms of learning that have equal status with formal learning. The impetuses set by the European Union have caused a development in this regard and it is clearly discernible that strategic considerations at the macro level are increasingly leading to specific actions at the micro level. This paper emerges from research, the amendment of the Brandenburg Higher Education Act and according activities at Brandenburg University of Applied Sciences. A pilot case study is being conducted with focus on access for qualified professionals without a higher education degree in special postgraduate courses universities of applied sciences. All of these policy doc-uments are driven by national need, particularly in relation to the labour market and job activa-tion. The overall aim of the MasterPlan project is first to create, develop and design a target-group-oriented procedure to examine if job-qualified applicants without a first university degree are suitable to enrol in a master's degree course and second to test them in a pilot phase. The project has been specifically established for the purpose of collaboration on enhancing access, transfer and progression opportunities across the region. The paper describes the current process of interlinked project stages, preliminary reflections for the empirical pilot case study and shows first outcomes of the project.
ALKALINE PROTEASE PRODUCING BACTERIA ISOLATED FROM THE SOIL OF MANGO ORCHARD AND IDENTIFIED ON THE BASIS OF 16S rDNA SEQUENCING

Five alkaline protease producing bacteria were isolated from the soil of mango orchards. The total protein was estimated and protease activity was measured against casein in 0.05 M buffer (carbonate: bicarbonate) at different temperature and pH. Alkaline proteases from all five isolates were thermostable and found to retain almost full activity after 1h incubation at 40°C. The protease exhibited good stability in presence of some locally available commercial detergents like Surf Excel, Ariel, Wheel, Rin, Sunlight and Tide and found to retain almost 50% activity after 10 min incubation at 35°C. Indicating that these enzymes could be used in detergent industries. One of the five isolates was identified as Pseudomonas aeruginosa [MTCC 7807] by 16s rDNA gene sequence analysis. Morphologically they are rod shaped revealed by scanning electron microscopy.
Deep Learning Approach Using Long Short Term Memory Technique for Monthly Rainfall Prediction in Chhattisgarh, India

— Rainfall is an essential factor in Chhattisgarh state as the economy is dependent on agriculture here. Time Series forecasting approach for monthly rainfall prediction is done using Long Short Term Memory [LSTM] Model applying on 1404 months data of Chhattisgarh state. The factors taken for the evaluation of the performance and the efficiency of the proposed rainfall prediction model are Mean Absolute Deviation (MAD), Mean Square Error (MSE), Root Mean Square Error (RMSE), Cosine Similarity (CS) and Correlation Coefficient (r). Various learning rate like (α = 0.01, 0.05, 0.001, 0.005) for various epoch(s) such as 200, 400, 600, 800 and 1000 respectively are done for LSTM approach. The experimental results show that Long Short Term Memory gave significant results than ANN for 200 epochs.
Kinematics of a Mechanism with a Triad

The Assuric Structural Groups are the most well-known classification and modulation used in machine and machine theory, and even though other modular classifications adapted to robots have emerged today, they still remain a reference classification in industrial mechanics and engineering. Diada in the mechanics can be studied similarly to the diode in the electronics, the triad is studied in the theory of machine and robot mechanisms similar to triode (or transistor) study in electronics. Further, the theory of the mechanisms is studying: tetrad, pentad . but it cannot go further than for a 12th-order structural group because the efficiency of mechanisms using such very large groups is very small and such a mechanism can be blocked in operation. If the similarity between the mechanisms and the electronics is correct up to the 5-6th class, the larger ones are of no use, the advantages of the electronics being that it can also function in the large or very large group with high yields, without blockages, which is why the integrated circuits and electronic chips were born. The present work is intended presenting a triad kinematics general used only with the kinematic couplings rotational (C5), because such approaches are rare in the area, although triad is a structured group Assuric often used. The calculation method presented is an analytical one.
The spectrum of opportunistic fungal infections in relation with ART in HIV patients

Recent years have witnessed a phenomenal increase in prevalence as well as severity of opportunistic fungal infection (OFIs) despite of availability of different therapeutic options. This could be attributed to various reasons including increased prevalence of immunocomprised patients. Awareness of spectrum of fungal infections in immunocompromised conditions and their epidemiological changes is critical for early diagnosis and proper management. Method: A total of 277 clinical samples were collected from 216 HIV patients suspected of OFI. Sample processing and identification of fungi carried out by standard conventional methods. Result: Out of 216 patients, 30% (65) showed positive fungal etiology with single or multiple system involvement. Oral candidiasis ranked highest (46%) followed by Cryptococcal meningitis (35%), Candidial pneumonia (10%), Oesophageal candidiasis (7%), Dermatophytoses (6 %) , Pulmonary aspergillosis (4%), Systemic candidiasis (4%), Pneumocystis pneumonia (3%) etc. Discussion: As most of opportunistic fungi (OF) are part of the normal human microbial flora, clinical significance of fungal isolates must be confirmed by standard criteria laid down such as repeated isolation, supportive clinical findings and radiographical evidences. Although Antiretroviral Therapy for minimum 6 months duration provides protection against OFI by maintaining CD4 count, compliance to treatment important.
Multi-Agent Reinforcement Learning: Transfer and Algorithms

This manuscript details some of the literature in transfer learning for reinforcement learning tasks and multi-agent systems. In addition, we will explore a new decentralized scalable algorithm for multi agent reinforcement learning. The algorithm is an online actor-critic with a modular action-value function learned using agent inﬂuence measures and a decentralized policy. We evaluate the approach and compare to single agent methods on a multi agent particle environment domain. This work was done during a semester long research course
Regression Modeling Dilution Assays by Generalized Linear Estimation of Their Frequency in Limiting Detection of Suppressor T Lymphocytes and

The estimate of the frequency of suppressor T lymphocytes in unfractionated cell populations remains challenging, mainly because these regulatory cells do not display speciﬁc immunophenotypic markers. In this paper, we describe a novel theoretical approach for quantifying the frequency of suppressor cells. This method is based on limiting dilution data modeling, and allows the simultaneous estimation of the frequencies of both proliferating and suppressor cells. We used previously published biological data, characterizing the inhibiting activity of suppressor T cell clones. Starting from these data, we propose a mathematical model describing the interaction between suppressor and proliferating T cells, and applied to a Poisson process. Limiting dilution data corresponding to this non-single-hit, suppressor two-target Poisson model were artiﬁcially generated, then modeled according to a generalized linear regression procedure. Deviation from the single-hit Poisson model was revealed by a statistical slope test, and a stepwise analysis of the regression appeared to be an efﬁcient method that strongly argued in favor of the presence of suppressor cells. By using the frequency of proliferating T cells calculated in the ﬁrst step of the regression, we demonstrated the possibility to provide a reasonable estimate of the frequency of suppressor T cells. Based on these ﬁndings, a practical decision-making procedure is given to perform standard analyses of limiting dilution data. The Journal of Immunology, 2003, 170: 2884–2894.
Permafrost Dynamics Observatory (PDO) Postprocessing and Calibration Methods of UAVSAR 2 L-band InSAR Data for Seasonal Subsidence 3 Estimation

27 Interferometric Synthetic Aperture Radar (InSAR) has been used to quantify a range 28 of surface and near surface physical properties in permafrost landscapes. Most previous 29 InSAR studies have utilized spaceborne InSAR platforms, but InSAR datasets over per- 30 mafrost landscapes collected from airborne platforms have been steadily growing in re- 31 cent years. Most existing algorithms dedicated towards retrieval of permafrost physical 32 properties were originally developed for spaceborne InSAR platforms. In this paper, which 33 is the ﬁrst in a two part series, we introduce a series of calibration techniques developed 34 to apply a novel joint retrieval algorithm for permafrost active layer thickness retrieval 35 to an airborne InSAR dataset acquired in 2017 by NASA’s Uninhabited Aerial Vehicle 36 Synthetic Aperture Radar (UAVSAR) over Alaska and Western Canada. We demonstrate 37 how InSAR measurement uncertainties are mitigated by these calibration methods and 38 quantify remaining measurement uncertainties with a novel method of modeling inter- 39 ferometric phase uncertainty using a Gaussian mixture model. Finally, we discuss the 40 impact of native SAR resolution on InSAR measurements, the limitation of using few 41 interferograms per retrieval, and the implications of our ﬁndings for cross-comparison 42 of airborne and spaceborne InSAR datasets acquired over Arctic regions underlain by 43 permafrost. 44
The Chameleon Effect: The Relationship Between Imitation and Interdependence

Most chameleon effect studies focus on the relationship between mimicry of nonverbal behaviors and rapport during interactions. However, verbal behaviours and the role of self-construals should be studied. We investigated mimicry of verbal and nonverbal behaviours and interdependence in explaining the relationship between mimicry and rapport. We hypothesized no differences in the magnitude of verbal and nonverbal mimicry and that interdependence was a mediator of mimicry and rapport. Using interdependence self-reports, participants’ level of mimicry (the difference between the monologue and the dialogue), and a rapport questionnaire regarding their interaction with the confederate, significant differences between the monologue and the dialogue emerged. There were no statistically significant results for the association between imitation and rapport. A significant association between interdependence and rapport was obtained. The findings suggest a revision to the chameleon effect, an expansion to the type of imitated behaviours and aid in fostering and understanding social interactions.
Finding Four-Leaf Clovers: A Benchmark for Fine-Grained Object Localization

We present a new experimental framework with the objective of studying ﬁne-grained object localization problems. The Four-Leaf Clover (FLC) dataset was built with the contribution of trained hobbyists, who were assigned the task of spotting four-leaf clovers on a ﬁxed geographical location over two clover seasons, one season for the train set and another for the test set. Each object instance was then annotated for the tasks of object detection, semantic segmentation, instance segmentation, object parsing and semantic boundary detection. Our dataset is composed of more than 100,000 images, containing 2,151 carefully annotated clover instances of four, ﬁve or six leaves. The Four-Leaf Clover Dataset is extremely challenging due to its small inter-class variance and its very large intra-class variation. We perform extensive experiments with state-of-the-art methods in order to establish strong baselines for each of the tasks. The dataset and all its resources will be made publicly available.
Weather Invariant Vehicle Localization by Data Association and Deep Neural Network-based Visual Perception

—This study is envisioned for weather invariant localization, but weather invariant localization also depends on the weather invariant perception. So, as the part of localization brought to perfection, this work planned to accomplish weather invariant visual perception, then use that perception for robust localization. The workflows of this research are divided into several steps. First, collect some available datasets that covered road images containing traffic signs and road objects in various weather conditions, then accumulate those datasets according to their usefulness and characteristics. Completely new data labeling is required for the newly accumulated data storehouse because annotations of the original datasets might differ, and the new repository requires new labeling according to the desired output. Until now, for this work, several related datasets have been collected through online registration into the corresponding websites. An outline of those datasets is discussed in this work according to their characteristics. Then future goals of this preliminary study are outlined and anticipated. After processing the data, a neural network needs to be trained with this accrued dataset through transfer learning using the YOLOv3 method, which is based on the neural network called Darknet-53. Then the trained weights from YOLOv3 and added new layers will help detect objects and traffic signs in a weather-consistent manner. After completing the visual perception, another network will be trained to perform the visual localization [2]. Then the Kalman filter/Particle filter will help execute localization through sensors fusion with Radar data and IMU data besides the camera data. The two types of localization will aid in comparing the robustness of weather invariant localization.
Testing a usability scale for chatbots: The effect of familiarity on satisfaction ratings

Although the use of chatbots as information retrieval assistants becomes more common nowadays, there are many problems that companies encounter when implementing these tools. To find out which facets of those chatbots need improvement to better accommodate the needs of the user, usability scales for chatbots are the most effective tools available. This study aims to perform confirmatory factor analysis on the 15-item Chatbot Satisfaction Scale developed by Borsci et al. (under review). The data of 56 participants, who used an English and a German scale to rate their interaction with 10 different chatbots, were used. The confirmatory factorial analysis suggested a good model fit for the Chatbot Satisfaction Scale that could be further improved by adding a new factor and deleting two items that assessed the same concept as another item. The resulting scale composed of 13 items demonstrated good reliability ( α = 0.93) and showed a strong correlation with the UMUX-Lite. The German translation of the new scale was found to have a good reliability ( α = 0.95) that was comparable to the original English version. While a moderate to high correlation was found between the two versions, a significant difference suggested that the German version fails to measure the same concepts. Moreover, we also investigated the potential effect of familiarity on the new scale, nevertheless, results suggested no significant effects of familiarity on satisfaction after the interaction with chatbots.
On the polar vortex streamer dynamics ∗

Potential vorticity (PV) streamers and cutoﬀs are indicators to the Rossby wave breaking (RWB) near the extratropical tropopause. In the Rossby waves breaking, the formation of elongated high-PV stratospheric air tongues extending to the equator and tropospheric low PV air tongues extending to the pole are obtained. There are two types of wave breaking, which are referred to as equa-torward and poleward types of the RWB. Frequently, the PV tongues are stretched into narrow ﬁlaments, the so-called PV streamers that split to PV cutoﬀ vortices. Here stratospheric PV streamer and cutoﬀ are stratospheric features on isentropic surfaces. In this paper, the conﬁguration of a potential vortex ﬁeld for model data is investigated. The main areas of the RWB for the winter and summer periods are shown.
Lightweight Vehicle Detection Algorithm Based on Improved YOLOv4

—Vehicle detection is the first step and an important part of automatic traffic incident detection systems. It guarantees subsequent vehicle identification and vehicle counting accuracy and has crucial theoretical significance and practical value for traffic safety and control. The model obtained by the original YOLOv4 algorithm is too large to be used in embedded terminals in real time. To overcome this problem, this study replaces the original backbone network of YOLOv4, which is CSPDarknet53, with MobileNetv3 for the feature extraction. To further reduce the number of parameters, deep separable convolution is used to replace the common 3×3 convolution in the original model of the enhanced feature extraction networks SPP and PANet. Because of the imbalance in the object detection data, the loss function is redesigned using a weighting method. The research results show that in comparison to the original YOLO series algorithm, the optimized YOLOv4 algorithm improves the accuracy by 0.53% and reduces the number of model parameters by 78%. In comparison to the other algorithms, the improved YOLOv4 model is smaller and more accurate, which is the basis for realizing intelligent transportation systems.
NEURAL NETWORKS FOR TIME-VARYING DATA Survey presentation for the workshop: Neural Networks for Statistical and Economic Data

This paper reviews some basic issues and methods involved in using neural networks to respond in a desired fashion to a temporally-varying environment. Some popular network models and training methods are introduced. A speech recognition example is then used to illustrate the central di(cid:14)culty of temporal data processing: learning to notice and remember relevant contextual information. Feedforward network methods are applicable to cases where this problem is not severe. The application of these methods are explained and applications are discussed in the areas of pure mathematics, chemical and physical systems, and economic systems. A more powerful but less practical algorithm for temporal problems, the moving targets algorithm, is sketched and discussed. For completeness, a few remarks are made on reinforcement learning.