Miracle-dz/UniFAR-FTUs huggingface dataset:
Unveiling a Paradox: The Persisting Discrimination against Black LGBT Workers amidst Progressive Policies in South Africa

This research report focuses its attention on findings from a qualitative re-search study conducted in South Africa about the persisting discrimination in the workplace against black LGBT workers. This paper unveils an intriguing paradox that exists in South Africa’s workplaces, where the legal framework and political climate promotes anti-discrimination against LGBT workers while a complex social system sets realities of the lived experiences of black LGBT employees within the South African labour market. This research explores deep and untouched areas of the lived experiences of Black LGBTs workers by drawing respondents about a variety of issues re-garding inclusion and exclusion in workplace, but also looks through the lens of the national policies and laws aimed at protecting such groups and the actual facts on ground about people’s perceptions, attitudes and behaviours towards LGBTs workers. The study also looks at the consequences of coming out at work, workplace cultures and policies, institutional challenges and opportuni-ties, and the coping strategies devised by LGBT workers. The research con-cludes that there is a need to look beyond legislation and consider people’s atti-tudes which are embedded in the cultural, religious, institutional beliefs and also the weaknesses with in the institutions aimed at protecting the employees, as a basis for eliminating all forms of discrimination in the workplace.
Association Between Remoteness to a Health Care Facility and Incidence of Ambulance Calls in Rural Areas of Japan

Objectives: Whether traffic remoteness from health care service in rural areas influences usage of ambulance service has not been well investigated. This study aimed to evaluate the relation between remoteness to health care facilities and incidence of ambulance calls in rural areas of Japan. Methods: We analyzed 155 rural communities of Hiroshima. Data were obtained on all ambulance dispatches from 2010 to 2012. Driving time was calculated from each community to the closest primary/secondary and tertiary health care facility (equivalent to tertiary emergency care centers). We estimated the incidence rate and the incidence rate ratio (IRR) of ambulance calls for each 10-minute increase in the driving time, using generalized log-linear regression models, and evaluated the effect among each specific subgroup of emergency level and season. Results: During the study period, the median incidence rate was 436 per 10 000 people in targeted communities. When driving time to the closest primary/secondary facility increased by an increment of 10 minutes, there was a significant increase in the IRR of ambulance calls, especially during colder seasons (IRR: 1.29 [95% confidence interval: 1.11-1.49]), and this relation was also obtained for most emergency levels. In comparison, there was no such increase in IRRs observed for driving time to a tertiary facility. Conclusion: This study indicated a positive association between remoteness to primary/secondary medical facilities and the frequency of ambulance calls. The remoteness to a primary/secondary health care may induce an increase in ambulance calls, particularly during cold seasons.
On computational modeling of human-oriented knowledge in Ambient Intelligence

Recent developments within Ambient Intelligence (AmI) provide new possibilities to contribute to personal care, e.g. [1,2,6,7]. For example, our car may monitor us and warn us when we are falling asleep while driving. Similarly, the workspace of a technical worker may monitor the person’s stress level, and provide support in case it is too high. As another example, an elderly person may wear a device that monitors his or her well-being and generates an action when a dangerous situation is noticed. While such AmI applications can be initially based on the acquisition and fusion of sensory information, more intelligent applications need to depend on the availability of an adequate knowledge base for the analysis of human functioning. In recent years, scientific areas such as cognitive science, psychology, neuroscience and biomedical sciences have made substantial progress in providing increased insight in the physical and mental aspects of human functioning. Although much work still remains to be done, models have been developed for a variety of such aspects and the way in which humans (try to) manage or regulate them. From a biomedical angle, examples of such aspects are (management of) heart functioning, diabetes, and eating regulation disorders. From a psychological and social angle, examples are emotion regulation, attention regulation, addiction management, trust management, stress management, and criminal behavior management. If models of human processes and their management are represented in a formal and computational format, and incorporated in intelligent agents in the human environment [5], then AmI applications are able to perform a more in-depth analysis of the human’s functioning [4]. By endowing devices in the environment with explicit knowledge about humanoriented processes and with the capability to reason about the dynamics of such processes, an environment is created that has a better understanding of humans and how to support them, and is able to show a form of ‘social intelligence’ [3].
Omegabot : Biomimetic inchworm robot using SMA coil actuator and smart composite microstructures (SCM)

Many researchers have developed various robots with novel gaits that can travel on rough terrain where conventional vehicles or other robots cannot. Many of these robots are bio-inspired since there is lots of amazing locomotions in nature that enable movement through various obstacles. This paper presents a robot based on the motion of ascotis selenaria, a type of inchworm with a locomotion that has an omega shape bending motion in between the extension motions. This type of inchworm can travel approximately its body length per stroke on a rough surfaces, leaf edges and branches of trees. The robot is built with smart composite microstructures, a fabrication method that uses laser micromachining to cut composites and assemble them into micro structures. The robot is actuated with a single shape memory alloy coil actuator. This robot can be used for search and rescue or gathering useful information in an area where only small scale robots can penetrate.
A S TUDY OF M ULTIPLE H UMAN T RACKING FOR V ISUAL S URVEILLANCE

A BSTRACT Visual surveillance has become very active research topic in computer vision. This paper deals with the problem of detecting and tracking multiple moving people in a static background. Detection of foreground object is done by background subtraction. Tracking multiple humans in complex situations is challenging. The difficulties are tackled with appropriate knowledge in the form of various models in our approach. Human motion is decomposed into its global motion and limb motion. Our objective in this paper is to segment multiple human objects and track their global motion in complex situations where they may move in small groups, have interocclusions, cast shadows on the ground, and reflections may exist.
Resistance of Streptococcus gordonii to Polymorphonuclear Leukocyte Killing Is a Potential Virulence Determinant of Infective Endocarditis

ABSTRACT Significant differences in virulence among seven representative Streptococcus gordonii strains were observed by using the rat model of infective endocarditis. Five strains, including S. gordonii DL1, caused severe disease, while the other two strains, including S. gordonii SK12, caused minimal or no disease. The differences in virulence were evident from the visible presence of streptococci in the vegetations present on the aortic valves of catheterized rats that were challenged with individual strains and also from the much greater recovery of rifampin-resistant S. gordonii DLl than of streptomycin-resistant S. gordonii SK12 from the hearts of animals coinfected with both organisms. Each S. gordonii strain aggregated with human platelets and bound to polymorphonuclear leukocytes (PMNs), as shown by the stimulation of PMN superoxide anion production. These interactions were reduced or abolished by pretreatment of the platelets or PMNs with sialidase, indicating that there was bacterial recognition of host sialic acid-containing receptors. Adhesin-mediated binding of each S. gordonii strain to PMNs also triggered phagocytosis. However, the subsequent PMN-dependent killing differed significantly for the seven strains. The five virulent strains included three strains that were not killed and two strains whose numbers were reduced by approximately 50%. In contrast, the level of killing of each avirulent strain under the same conditions was significantly greater and approached 90% of the bacteria added. Parallel studies performed with rat PMNs revealed comparable differences in the resistance or susceptibility of representative virulent and avirulent strains. Thus, the ability of S. gordonii to survive in PMNs following adhesin-mediated phagocytosis may be an important virulence determinant of infective endocarditis.
Rapid pulsed microwave propagation

Transit time measurements of the leading edge of pulse modulated microwaves in open space and inside a rectangular waveguide have been performed. The experimental setup used is described. Both measurements show that a part of the energy associated with the leading edge of the pulse propagates with the phase velocity. Calibration techniques and repeated measurements confirm this phenomenon. <<ETX>>
Influence of Parameter Settings in Voxel-based Morphometry 8

Summary Objectives: To investigate whether reproducibility of gray matter volumetry is influenced by parameter settings for VBM 8 using Diffeomorphic Anatomical Registration Through Exponentiated Lie Algebra (DARTEL) with region-of-interest (ROI) analyses. Methods: We prepared three-dimensional T1-weighted magnetic resonance images (3D-T1WIs) of 21 healthy subjects. All subjects were imaged with each of five MRI systems. Voxel-based morphometry 8 (VBM 8) and WFU PickAtlas software were used for gray matter volumetry. The bilateral ROI labels used were those provided as default settings with the software: Frontal Lobe, Hippocampus, Occipital Lobe, Orbital Gyrus, Parietal Lobe, Putamen, and Temporal Lobe. All 3D-T1WIs were segmented to gray matter with six parameters of VBM 8, with each parameter having between three and eight selectable levels. Reproducibility was evaluated as the standard deviation (mm3) of measured values for the five MRI systems. Results: Reproducibility was influenced by ‘Bias regularization (BiasR)’, ‘Bias FWHM’, and ‘De-noising filter’ settings, but not by ‘MRF weighting’, ‘Sampling distance’, or ‘Warping regularization’ settings. Reproducibility in BiasR was influenced by ROI. Superior reproducibility was observed in Frontal Lobe with the BiasR1 setting, and in Hippocampus, Parietal Lobe, and Putamen with the BiasR3*, BiasR1, and BiasR5 settings, respectively. Conclusion: Reproducibility of gray matter volumetry was influenced by parameter settings in VBM 8 using DARTEL and ROI. In multi-center studies, the use of appropriate settings in VBM 8 with DARTEL results in reduced scanner effect.
Reducing overheads of local communications in fine-grain parallel computation

For fine-grain computation to be effective, the cost of communications between the large number of subtasks should be minimised. In this paper we present an optimization technique which reduces overheads of communications between local subtasks by bypassing the network interface and transferring data directly from memory or registers to memory. On average, the optimization results in 35.6% improvement in total execution time on instruction-level simulations with six benchmark programs from 1 to 32 nodes.
Optimizing Super-resolution Reconstruction using a Genetic Algorithm

Super-resolution reconstruction (SRR) is aimed at increasing spatial resolution given a single image or multiple images presenting the same scene. The existing methods are underpinned with a premise that the observed low resolution images are obtained from a hypothetic high resolution image by applying a certain imaging model (IM) which degrades the image and decreases its resolution. Hence, the reconstruction consists in applying an inverse IM to recover the high resolution data. Such an approach has been found effective, if the IM is known and controlled, in particular when the low resolution images are indeed obtained from a high resolution one. However, in a real-world scenario, when SRR is performed from images originally captured at low resolution, finding appropriate IM and tuning its hyperparameters is a challenging task. In this paper, we propose to optimize the SRR hyperparameters using a genetic algorithm, which has not been reported in the literature so far. We argue that this may substantially improve the capacities of learning the relation between low and high resolution images. Our initial, yet highly encouraging, experimental results reported in the paper allow us to outline our research pathways to deploy the developed techniques in practice.
Do Levels of Self-Esteem and Social Support Sources Predict Aggression Among College Students?

This study aimed at investigating the relationship between self-esteem (SES), social support (SOS) and aggression (AG) among a sample of college students (N=304). The study replicated recent studies (Donnellan, Trzesniewski, Robins, Moffitt and Caspi, 2005; Webster, 2006) through examining the hypothesized linear relationship between the different levels of self-esteem and aggression, by adding social support as another variable related to self- esteem. The results validated the negative relationship between (SES), (SOS) and (AG) and verified the linear relationship between self-esteem, social support and aggression. Findings indicated that low self-esteem and low social support were related to greater attitudinal hostility than were high and moderate self-esteem and social support. In particular, low-level family support was a unique predictor of aggression. Additionally, a significant gender effect was found in students' aggression scores. Taken together, the results suggest that students with low self-esteem and family support may be at risk for high levels of hostility and aggression. Implications for counseling interventions, limitations and directions for further research are discussed.
Circuit theory predicts gene flow in plant and animal populations

Maintaining connectivity for broad-scale ecological processes like dispersal and gene flow is essential for conserving endangered species in fragmented landscapes. However, determining which habitats should be set aside to promote connectivity has been difficult because existing models cannot incorporate effects of multiple pathways linking populations. Here, we test an ecological connectivity model that overcomes this obstacle by borrowing from electrical circuit theory. The model vastly improves gene flow predictions because it simultaneously integrates all possible pathways connecting populations. When applied to data from threatened mammal and tree species, the model consistently outperformed conventional gene flow models, revealing that barriers were less important in structuring populations than previously thought. Circuit theory now provides the best-justified method to bridge landscape and genetic data, and holds much promise in ecology, evolution, and conservation planning.
Integrated Refractometric Sensor Utilizing a Triangular Ring Resonator Combined With SPR

In this letter, a triangular resonator-based surface plasmon resonance (SPR) sensor that uses total internal reflection (TIR) mirrors to provide SPR excitation for the detection of refractive index (RI) change is proposed and first demonstrated using SoI substrate without any gain medium. The fabricated triangular resonator sensor with a 35-nm-thick Au film on a TIR mirror shows an output power increment of 1.2 dB and a sensitivity of 102 nm/RIU, while that nonmetallic layer on a TIR mirror shows 30 nm/RIU. The sensitivity enhancement is attributed to a phase change in the SPR resulting in a large shift of the resonance peak. This configuration provides significant advantages by increasing the sensitivity of the RI sensor even with the extremely small sensing area of the SPR mirror.
A Review on Motorway Traffic Clogging System

As we know the population of metropolitan is growing every day. With increasing country population, amount of vehicles is also rising, and this direct enchases traffic of cities which should be manages as early as possible without any loss people. Traffic causes accidents which directly affect an individual life. In the last 10 years the situation has get worse due to quick growth of automobiles and inadequate highways to accommodate them. This work describes methods of real time area based traffic by means of image processing for intelligent traffic control system.
Joint Source and Relay Precoding Designs for MIMO Two-Way Relaying Based on MSE Criterion

Properly designed precoders can significantly improve the spectral efficiency of multiple-input multiple-output (MIMO) relay systems. In this paper, we investigate joint source and relay precoding design based on the mean-square-error (MSE) criterion in MIMO two-way relay systems, where two multiantenna source nodes exchange information via a multiantenna amplify-and-forward relay node. This problem is non-convex and its optimal solution remains unsolved. Aiming to find an efficient way to solve the problem, we first decouple the primal problem into three tractable subproblems, and then propose an iterative precoding design algorithm based on alternating optimization. The solution to each subproblem is optimal and unique, thus the convergence of the iterative algorithm is guaranteed. Second, we propose a structured precoding design to lower the computational complexity. The proposed precoding structure is able to parallelize the channels in the multiple access (MAC) phase and broadcast (BC) phase. It thus reduces the precoding design to a simple power allocation problem. Last, for the special case where only a single data stream is transmitted from each source node, we present a source-antenna-selection (SAS)-based precoding design algorithm. This algorithm selects only one antenna for transmission from each source and thus requires lower signalling overhead. Comprehensive simulation is conducted to evaluate the effectiveness of all the proposed precoding designs.
Paranoid Android : Zero-Day Protection for Smartphones Using the Cloud

Smartphones have come to resemble PCs in software complexity. Moreover, as they are often used for privacy-sensitive tasks, they are becoming attractive targets for attackers. Unfortunately, they are quite different from PCs in terms of resources, so that PC-oriented security solutions are not always applicable. Worse, common security solutions (such as on-access file scanners, system call profilers, etc.) protect against a very limited set of attacks. Comprehensive measures require a far wider and more expensive set of checks some of which are much beyond the capacity of a phone. We propose an alternative solution, where security checks are applied on remote security servers which host exact replicas of the phones in virtual environments. The servers are not subject to the same constraints, allowing us to apply multiple detection techniques simultaneously (including ones that are very heavy-weight). Moreover, as the full execution trace is preserved on the security server, attackers cannot hide their traces. This property ensures that attacks for which a detection method exists at the security server even if the detection method is installed at a later time are detectable eventually. This is a stronger guarantee than existing solutions can give. It allows administrators to trade server resources for security by specifying how far back in time they want to be able to start looking for intrusions with a new detection method. We implemented the security model for Android phones and show that it is both practical and scalable: we generate about 2KiB/s and 64B/s of trace data under high-load and idle operation respectively, and are able to support more than a hundred replicas on a single server.
Factored Markov Translation with Robust Modeling

Phrase-based translation models usually memorize local translation literally and make independent assumption between phrases which makes it neither generalize well on unseen data nor model sentencelevel effects between phrases. In this paper we present a new method to model correlations between phrases as a Markov model and meanwhile employ a robust smoothing strategy to provide better generalization. This method defines a recursive estimation process and backs off in parallel paths to infer richer structures. Our evaluation shows an 1.1‐3.2% BLEU improvement over competitive baselines for Chinese-English and Arabic-English translation.
The Gender Politics of Development: Essays in Hope and Despair

In The Gender Politics of Development Shirin Rai provides a comprehensive assessment of how gender politics has emerged and developed in post-colonial states. In chapters on key issues of nationalism and nation-building, the third wave of democratization and globalization and governance, Rai argues that the gendered way in which nationalist statebuilding occured created deep fissures and pressures for development. She goes on to show how women have engaged with institutions of governance in developing countries, looking in particular at political participation, deliberative democracy, representation, leadership and state feminism. Through this engagement, Rai claims, vital new political spaces have been created. Though Rai focuses in-depth on how these debates have played out in India, the book's argument is highly relevant for politics across the developing world. This is a unique and compelling synthesis of gender politics with ideas about development from an authoritative figure in the field.
Multichannel Sound Event Detection Using 3D Convolutional Neural Networks for Learning Inter-channel Features

In this paper, we propose a stacked convolutional and recurrent neural network (CRNN) with a 3D convolutional neural network (CNN) in the first layer for the multichannel sound event detection (SED) task. The 3D CNN enables the network to simultaneously learn the inter-and intra-channel features from the input multichannel audio. In order to evaluate the proposed method, multichannel audio datasets with different number of overlapping sound sources are synthesized. Each of this dataset has a four-channel first-order Ambisonic, binaural, and single-channel versions, on which the performance of SED using the proposed method are compared to study the potential of SED using multichannel audio. A similar study is also done with the binaural and single-channel versions of the real-life recording TUT-SED 2017 development dataset. The proposed method learns to recognize overlapping sound events from multichannel features faster and performs better SED with a fewer number of training epochs. The results show that on using multichannel Ambisonic audio in place of single-channel audio we improve the overall F-score by 7.5%, overall error rate by 10% and recognize 15.6% more sound events in time frames with four overlapping sound sources.