Miracle-dz/UniFAR-FTUs huggingface dataset:
Bidirectional Influences of Information Sampling and Concept Learning

Contemporary models of categorization typically tend to sidestep the problem of how information is initially encoded during decision making. Instead, a focus of this work has been to investigate how, through selective attention, stimulus representations are “contorted” such that behaviorally relevant dimensions are accentuated (or “stretched”), and the representations of irrelevant dimensions are ignored (or “compressed”). In high-dimensional real-world environments, it is computationally infeasible to sample all available information, and human decision makers selectively sample information from sources expected to provide relevant information. To address these and other shortcomings, we develop an active sampling model, Sampling Emergent Attention (SEA), which sequentially and strategically samples information sources until the expected cost of information exceeds the expected benefit. The model specifies the interplay of two components, one involved in determining the expected utility of different information sources and the other in representing knowledge and beliefs about the environment. These two components interact such that knowledge of the world guides information sampling, and what is sampled updates knowledge. Like human decision makers, the model displays strategic sampling behavior, such as terminating information search when sufficient information has been sampled and adaptively adjusting the search path in response to previously sampled information. The model also shows human-like failure modes. For example, when information exploitation is prioritized over exploration, the bidirectional influences between information sampling and learning can lead to the development of beliefs that systematically differ from reality.
Multitask bilateral learning for real‐time image enhancement

Nowadays, deep neural networks (DNNs) for image processing are becoming more complex; thus, reducing computational cost is increasingly important. This study highlights the construction of a DNN for real‐time image processing, training various image processing operators efficiently through multitask learning. For real‐time image processing, the proposed algorithm takes a joint upsampling approach through bilateral guided upsampling. For multitask learning, the overall network is based on an encoder‐decoder architecture, which consists of encoding, processing, and decoding components, in which the encoding and decoding components are shared by all the image processing operators. In the processing component, a semantic guidance map, which contains processing information for each image processing operator, is estimated using simple linear shifts of the shared deep features. Through these components, the proposed algorithm requires an increase of only 5% in the number of parameters to add another image processing operator and achieves faster and higher performance than that of deep‐learning‐based joint upsampling methods in local image processing as well as global image processing.
Examining customer-oriented positive deviance intentions of retail employees

Purpose Occasionally, retail employees “break the rules” in order to help customers. Currently, there is little research on the mechanisms by which a sales assistants’ positive deviance intentions help them attain specific personal and organisational goals. The purpose of this paper is to examine one mechanism, hope, which develops employees’ deviance intentions to provide benefits to the customer, themselves and the organisation. Design/methodology/approach A survey captured responses from 270 frontline employees from the retail and services sector. AMOS 23 was used to conduct measurement, path and mediation analyses. Findings This study highlights the role of employee hope in developing employees’ positive deviance intentions, and improving perceptions of organisational performance. Results demonstrate that the direct positive impact of hope on positive deviance intention was significant. Furthermore, positive deviance intention was found to positively impact employee goal attainment and perceived organisational performance. The authors’ employee hope model offers a better understanding of positive outcomes of employee deviance, suggesting that retail managers should invest resources to build strong employee–organisation relationships. Originality/value This is the first study to empirically demonstrate that employee hope can explain how customer-oriented positive deviance intentions help employee goal attainment and improve their perceptions of organisational performance.
Non-linear Relation between External Debt and Economic Growth in Nigeria: Does the Investment Channel Matter?

Abstract Large external debt stock has been identified as one of the most important factors, which have restricted the development of many poor countries. The consensus in the literature remains that external debt promotes growth to the extent that a country does not exceed its debt carrying capacity. Otherwise, additional debt accumulation would serve as a tax on future investment returns capable of creating disincentive to invest in the highly indebted countries. In the light of these arguments, this study investigated the possible role of domestic investment in the non-linear relation between external debt and economic growth in Nigeria over the period from 1981 to 2015. Based on the results of threshold regression analysis employed in this study, the overall findings showed that the impact of external debt on economic growth is sensitive to both measures of external debt used, and whether or not the role of domestic investment is accounted for. Specifically, this study confirmed the existence of the debt Laffer curve associated with the debt overhang theory arising from excessive external debt accumulation. Similarly, empirical support was obtained for the crowding-out effect of excessive external debt servicing. In addition, accounting for the role of domestic investment in the non-linear relation between external debt and economic growth reduces the optimal debt carrying capacity of the country. It is therefore suggested that the Nigerian government internalizes a maximum ceiling of 6.81% as the share of external debt stock in gross national income (GNI) to enjoy the resulting growth benefits. External debt financing sources that are free of interest charge could also be explored to circumvent the burden imposed by excessive external debt servicing.
UAV-CLOUD: A PLATFORM FOR UAV RESOURCES AND SERVICES ON THE CLOUD

UAVs Unmanned Aerial Vehicles – have gained significant attention recently, due to the increasingly growing range of applications. However, developing collaborative UAV applications using traditional technologies in a tightly coupled design requires a great deal of development effort, time, and budget especially for heterogeneous UAVs. Moreover, monitoring and accessing UAV resources using traditional communication media suffer from several restrictions and limitations. This research aims to simplify the efforts, reduce the time, and lower the costs of developing collaborative applications for distributed heterogeneous UAVs. In addition, the research aims to provide ubiquitous UAV resources access. A platform is proposed for developing distributed UAVs. This platform provides services to simplify application development. In this approach, UAVs are integrated with the Cloud Computing paradigm to provide ubiquitous access to their resources and services. Due to the limited capabilities of UAVs, a lightweight architecture is adopted. UAV resources and services are modeled in a Resource Oriented Architecture which is a new flexible web service design pattern with loosely coupled interaction between services. Hence, they are accessed as Representational State Transfer RESTful services using HTTP. Moreover, the research proposes using a broker architecture to increase efficiency by separating responsibilities. Therefore, it separates the requester’s logic and functionalities from the provider’s. It also takes the responsibility for allocating the issued request to the available and suitable UAV(s). To test the proposed platform, I first developed the UAV resources as a payload subsystem then provided them with Internet connectivity. Then, resource identifiers and uniform interfaces were developed using the RESTful Application Programming Interfaces (APIs). I also developed the broker service along with a database containing the information of the registered UAVs and their resources. The platform system components were tested using a requester interface in a browser by issuing a request for a resource to the broker to find and request the service from a suitable UAV. The test was done for retrieving data from UAVs as well as requesting actions from them. The main contributions of this research are proposing the UAV-Cloud platform for simplifying the development of ubiquitous UAV applications and its vii perspectives, as well as a lightweight loosely coupled design for UAV resources. Another contribution is developing the broker architecture for separating responsibilities in this platform.
GFrames: Gradient-Based Local Reference Frame for 3D Shape Matching

We introduce GFrames, a novel local reference frame (LRF) construction for 3D meshes and point clouds. GFrames are based on the computation of the intrinsic gradient of a scalar field defined on top of the input shape. The resulting tangent vector field defines a repeatable tangent direction of the local frame at each point; importantly, it directly inherits the properties and invariance classes of the underlying scalar function, making it remarkably robust under strong sampling artifacts, vertex noise, as well as non-rigid deformations. Existing local descriptors can directly benefit from our repeatable frames, as we showcase in a selection of 3D vision and shape analysis applications where we demonstrate state-of-the-art performance in a variety of challenging settings.
Passive Task-Prioritized Shared-Control Teleoperation with Haptic Guidance

Robot teleoperation is widely used for several hazardous applications. To increase teleoperator capabilities shared-control methods can be employed. In this paper, we present a passive task-prioritized shared-control method for remote telemanipulation of redundant robots. The proposed method fuses the task-prioritized control architecture with haptic guidance techniques to realize a shared-control framework for teleoperation systems. To preserve the semi-autonomous telerobotic system safety, passivity is analyzed and an energy-tanks passivity-based controller is developed. The proposed theoretical results are validated through experiments involving a real haptic device and a simulated slave robot.
CLASSIFICATION OF AERIAL POINT CLOUDS WITH DEEP LEARNING

Abstract. Due to their usefulness in various implementations, such as energy evaluation, visibility analysis, emergency response, 3D cadastre, urban planning, change detection, navigation, etc., 3D city models have gained importance over the last decades. Point clouds are one of the primary data sources for the generation of realistic city models. Beside model-driven approaches, 3D building models can be directly produced from classified aerial point clouds. This paper presents an ongoing research for 3D building reconstruction based on the classification of aerial point clouds without given ancillary data (e.g. footprints, etc.). The work includes a deep learning approach based on specific geometric features extracted from the point cloud. The methodology was tested on the ISPRS 3D Semantic Labeling Contest (Vaihingen and Toronto point clouds) showing promising results, although partly affected by the low density and lack of points on the building facades for the available clouds.
Prospective memory in non-psychotic first-degree relatives of patients with schizophrenia: a meta-analysis

Abstract Prospective memory (PM) could be impaired in the non-psychotic first-degree relatives of patients with schizophrenia. This meta-analysis systematically examined the PM of non-psychotic first-degree relatives of patients with schizophrenia. Both Chinese and English databases were systematically searched for articles from the inception of the databases through November 13, 2018. Case-control studies of PM in non-psychotic first-degree relatives of patients with schizophrenia were included in the analyses. Confidence intervals (CIs) and standardized mean differences (SMDs) were calculated utilizing the random effects model. Four studies (n=268) that compared PM performance between non-psychotic first-degree relatives of patients with schizophrenia (n=136) and healthy controls (n=132) were included. Three studies were rated as “high quality”, while the quality of evidence of the three outcomes included in this meta-analysis was moderate. Compared with the healthy controls, the non-psychotic first-degree relatives of patients with schizophrenia showed impairments in overall PM (two studies, n=127; SMD: −0.46; 95% CI=−0.82, −0.11, P=0.01; I=0%), event-based PM (EBPM) (four studies, n=268; SMD: −0.56; 95% CI=−0.80, −0.31, P<0.00001; I=0%), and time-based PM (TBPM) (four studies, n=268; SMD: −0.66; 95% CI=−0.90, −0.41, P<0.00001; I=0%). This meta-analysis demonstrated that the overall PM, EBPM, and TBPM might be impaired in the non-psychotic first-degree relatives of patients with schizophrenia.
Noise Calculation Charts and Indoor Environmental Quality for Evaluating Industrial Indoor Environment and Health

Noise, defined as “a sensation of unwanted intensity of a wave,” is perception of a pollutant and a type of environmental stressor. An environmental stressor such as noise may have detrimental effects on various aspects of health. The unwanted intensity of a wave is a propagation of noise due to transmission of waves (viz. physical agents) such as sun, light, sound, heat, electricity, fluid, and fire. The effects of these physical agents on human health as noise-intruding elements in an industrial indoor environment are discussed. Noise characterization is discussed from indoor air quality and health perspective. The noise calculation charts are detailed for interference of noise waves based on a benchmark solution. These charts calculate positive and negative magnitudes of noise based on noise characterization of waves due to power difference of two intensities. The noise interference is calculated from newly devised noise measurement equations and their units. The grades and flag colors are notated to the noise calculation charts. Furthermore, illustrated examples of noise characterization calculations for indoor environment are presented using devised noise measurement equations. Indoor environmental quality and noise instrumentation are discussed. Adverse effects of pollutants on human health are summarized. Ventilation systems for dispersion of pollutants from industrial indoor environment are also elaborated.
Modeling Study of the Air Quality Impact of Record‐Breaking Southern California Wildfires in December 2017

We investigate the air quality impact of record‐breaking wildfires in Southern California during 5–18 December 2017 using the Weather Research and Forecasting model with Chemistry in combination with satellite and surface observations. This wildfire event was driven by dry and strong offshore Santa Ana winds, which played a critical role in fire formation and air pollutant transport. By utilizing fire emissions derived from the high‐resolution (375 × 375 m2) Visible Infrared Imaging Radiometer Suite active fire detections, the simulated magnitude and temporal evolution of fine particulate matter (PM2.5) concentrations agree reasonably well with surface observations (normalized mean bias = 4.0%). Meanwhile, the model could generally capture the spatial pattern of aerosol optical depth from satellite observations. Sensitivity tests reveal that using a high spatial resolution for fire emissions and a reasonable treatment of plume rise (a fair split between emissions injected at surface and those lifted to upper levels) is important for achieving decent PM2.5 simulation results. Biases in PM2.5 simulation are relatively large (about 50%) during the period with the strongest Santa Ana wind, due to a possible underestimation of burning area and uncertainty in wind field variation. The 2017 December fire event increases the 14‐day averaged PM2.5 concentrations by up to 231.2 μg/m3 over the downwind regions, which substantially exceeds the U.S. air quality standards, potentially leading to adverse health impacts. The human exposure to fire‐induced PM2.5 accounts for 14–42% of the annual total PM2.5 exposure in areas impacted by the fire plumes.
Ray-based modeling and imaging in viscoelastic media using graphics processing units

In seismic data processing, the amplitude loss caused by attenuation should be taken into account. The basis for this is provided by a 3D attenuation model described by the quality factor [Formula: see text], which is used in viscoelastic modeling and imaging. We have accomplished viscoelastic modeling and imaging using ray theory and the ray-Born approximation. This makes it possible to take [Formula: see text] into account using complex-valued and frequency-dependent traveltimes. We have developed a unified parallel implementation for modeling and imaging in the frequency domain and carried out the numerical integration on a graphics processing unit. A central part of the implementation is an efficient technique for computing large integrals. We applied the integration method to the 3D SEG/EAGE overthrust model to generate synthetic seismograms and imaging results. The attenuation effects are accurately modeled in the seismograms and compensated for in the imaging algorithm. The results indicate a significant improvement in computational efficiency compared to a parallel central processing unit baseline.
PC-Based Game Features That Influence Instruction and Learner Motivation

PC-based games are currently being used for military training, but the instructional and motivational features of such technology are not well understood. To identify features of training games that influence instruction and motivation, a popular first-person-perspective game with a military theme was analyzed empirically. Twenty-one participants played the “basic training” portion of the game, which included Army background information, virtual marksmanship training, an obstacle course, virtual weapons familiarization, and an urban terrain training mission. The results of this research provide useful information to individuals developing training games, desktop simulations, and interactive multimedia courseware to meet optimal training objectives and strategies.
Exploring Semantic Relatedness in Arabic Corpora using Paradigmatic and Syntagmatic Models

In this paper we explore two paradigms: firstly, paradigmatic representation via the native HAL model including a model enriched by adding word order information using the permutation technique of Sahlgren and al [21], and secondly the syntagmatic representation via a words-by-documents model constructed using the Random Indexing method. We demonstrate that these kinds of word space models which were initially dedicated to extract similarity can also been efficient for extracting relatedness from Arabic corpora. For a given word the proposed models search the related words to it. A result is qualified as a failure when the number of related words given by a model is less than or equal to 4, otherwise it is considered as a success. To decide if a word is related to other one, we get help from an expert of the economic domain and use a glossary 1 of the domain. First we begin by a comparison between a native HAL model and termdocument model. The simple HAL model records a better result with a success rate of 72.92%. In a second stage, we want to boost the HAL model results by adding word order information via the permutation technique of sahlgren and al [21]. The success rate of the enriched HAL model attempt 79.2 %.
시공간 2D 특징 설명자를 사용한 BOF 방식의 동작인식 ☆ BoF based Action Recognition using Spatio-Temporal 2D Descriptor

Since spatio-temporal local features for video representation have become an important issue of modeless bottom-up approaches in action recognition, various methods for feature extraction and description have been proposed in many papers. In particular, BoF(bag of features) has been promised coherent recognition results. The most important part for BoF is how to represent dynamic information of actions in videos. Most of existing BoF methods consider the video as a spatio-temporal volume and describe neighboring 3D interest points as complex volumetric patches. To simplify these complex 3D methods, this paper proposes a novel method that builds BoF representation as a way to learn 2D interest points directly from video data. The basic idea of proposed method is to gather feature points not only from 2D  spatial planes of traditional frames
The Challenges of Making Research Collaboration in Africa More Equitable

Collaborative research has a critical role to play in furthering our understanding of African politics. Many of the most important and interesting questions in the field are difficult, if not impossible, to tackle without some form of collaboration, either between academics within and outside of Africa—often termed North–South research partnerships—or between those researchers and organizations from outside the academic world. In Africa in particular, collaborative research is becoming more frequent and more extensive. This is due not only to the value of the research that it can produce but also to pressures on the funding of African scholars and academics in the Global North, alongside similar pressures on the budgets of non-academic collaborators, including bilateral aid agencies, multilateral organizations, and national and international non-government organizations. Collaborative projects offer many advantages to these actors beyond access to new funding sources, so they constitute more than mere “marriages of convenience. ” These benefits typically include access to methodological expertise and valuable new data sources, as well as opportunities to increase both the academic and “real-world” impact of research findings. Yet collaborative research also raises a number of challenges, many of which relate to equity. They center on issues such as who sets the research agenda, whether particular methodological approaches are privileged over others, how responsibility for different research tasks is allocated, how the benefits of that research are distributed, and the importance of treating colleagues with respect despite the narrative of “capacity-building. ” Each challenge manifests in slightly different ways, and to varying extents, depending on the type of collaboration at hand: North–South research partnership or collaboration between academics and policymakers or practitioners. This article discusses both types of collaboration together because of their potential to overlap in ways that affect the severity and complexity of those challenges. These challenges are not unique to research in Africa, but they tend to manifest in ways that are distinct or particularly acute on the continent because of the context in which collaboration takes place. In short, the legacy of colonialism matters. That history not only shapes who collaborates with whom but also who does so from a position of power and who does not. Thus, the inequitable nature of some research collaborations is not simply the result of oversights or bad habits; it is the product of entrenched structural factors that produce, and reproduce, imbalances of power. This means that researchers seeking to make collaborative projects in Africa more equitable must engage with these issues early, proactively, and continuously throughout the entire life cycle of those research projects. This is true not just for researchers based in the Global North but for scholars from, or working in, Africa as well.