Miracle-dz/UniFAR-FTUs huggingface dataset:
Socio-Pragmatics and Attention: Contributions to Gesturally Guided Word Learning in Toddlers

It is clear that gestural cues facilitate early word learning. In hopes of illuminating the relative contributions of attentional and socio-pragmatic factors to the mechanisms by which these cues exert their influence, we taught toddlers novel words with the support of a hierarchy of gestural cues. Twenty-eight- to 31-month-olds heard one of two possible referents labeled with a novel word, while the experimenter gazed at or gazed at and pointed to, touched, or manipulated the target. Learning improved with greater redundancy among cues, with the largest improvement evident when pointing was added to gazing. Looking times revealed that attentional factors accounted for only a small fraction of the variance in performance. Indeed, a significant increase in attention driven by manipulation of the target failed to improve learning. The results therefore suggest a strong role for socio-pragmatic factors in supporting the facilitative effect of gestural cues on word learning.
Characterization of lignocellulolytic bacterial strains associated with decomposing wood residues in the Lagos lagoon, Nigeria

Aims: The presence of lignocelluloses, especially sawdust in the Lagos lagoon and the attendant ecological problems warranted studies on their degradation. This study aimed to isolate and identify the indigenous bacterial strains capable of utilizing lignocellulosic wastes under the prevalent tropical estuarine conditions. Methodology and results: Nine bacterial species were obtained by elective culture from decomposing wood residues in the lagoon. They were identified on the basis of morphology, biochemical characteristics and analysis of their 16S rRNA gene sequences as Streptomyces, Bacillus and Paenibacillus species. They were cultured on various ligninrelated lignocellulosic substrates over a period of 7 to 12 days. All the isolates showed moderate to very good growth on sugarcane baggase. Streptomyces albogriseolus strain AOB and Paenibacillus sp. ROB showed good growth on grass while on sawdust, only Streptomyces AOB, and Bacillus megaterium strain NOB showed good growth. High performance liquid chromatographic analysis showed that the Streptomyces species completely utilized coniferyl alcohol, B. megaterium strain NOB utilized 90-100% of all the ligninrelated aromatic compounds. All the bacterial species utilized less than 40% of sinapyl alcohol, Bacillus sp. OOB and Paenibacillus sp. strain ROB failed to utilize vanillic acid. Conclusion, significance and impact of study: The isolates degraded lignocellulosic wastes and lignin-related compounds. The role of fungi in the breakdown of lignocellulose in the Lagos lagoon had been the subject of previous research considerations whilst the role of bacteria spp was unreported. Autochthonous bacterial species may equally play a role in the bio-rehabilitation of the sawdust-polluted water of the Lagos lagoon.
Performance Evaluation of Different Ground Filtering Algorithms for Uav-Based Point Clouds

Digital Elevation Model (DEM) generation is one of the leading application areas in geomatics. Since a DEM represents the bare earth surface, the very first step of generating a DEM is to separate the ground and non-ground points, which is called ground filtering. Once the point cloud is filtered, the ground points are interpolated to generate the DEM. LiDAR (Light Detection and Ranging) point clouds have been used in many applications thanks to their success in representing the objects they belong to. Hence, in the literature, various ground filtering algorithms have been reported to filter the LiDAR data. Since the LiDAR data acquisition is still a costly process, using point clouds generated from the UAV images to produce DEMs is a reasonable alternative. In this study, point clouds with three different densities were generated from the aerial photos taken from a UAV (Unmanned Aerial Vehicle) to examine the effect of point density on filtering performance. The point clouds were then filtered by means of five different ground filtering algorithms as Progressive Morphological 1D (PM1D), Progressive Morphological 2D (PM2D), Maximum Local Slope (MLS), Elevation Threshold with Expand Window (ETEW) and Adaptive TIN (ATIN). The filtering performance of each algorithm was investigated qualitatively and quantitatively. The results indicated that the ATIN and PM2D algorithms showed the best overall ground filtering performances. The MLS and ETEW algorithms were found as the least successful ones. It was concluded that the point clouds generated from the UAVs can be a good alternative for LiDAR data.
Toward the development of a new scale of adolescent sexual risk taking

TOWARD THE DEVELOPMENT OF A NEW SCALE OF ADOLESCENT SEXUAL RISK TAKING by DIANA L. YOUNG (Under the Direction of Adam S. Goodie) ABSTRACT The present research aims to construct a realistic and valid scale of adolescent sexual risk taking. This scale is comprised of four subscales of risk taking relevant to the decision-making literature: perceived risks, expected benefits, history of engagement, and likelihood of engaging in the behavior in the future. In Study 1, undergraduate participants responded to 23 behavioral items specific to an adolescent population in this way. Exploratory factor analyses revealed an easily-interpretable factor structure across all subscales, and hypothesized relationships among the Risks, Benefits, History, and Likelihood subscales emerged. Study 2 results provided strong support for the proposed factor structures and directly tested competing structural models that predicted likelihood to engage in sexual risk. Results are discussed as they relate to adolescent risk taking from a decisional perspective.
Sand dynamics in coastal dune landscapes constrain diversity and life-history characteristics of spiders

Summary 1. In fragmented landscapes where natural disturbance acts at the local level, community composition can be altered either by ‘species-by-species matching’, with communitywide character displacement, or by ‘constrained species sorting’, whereby disturbance favours species with distinct ecological traits. It is necessary to understand such species shifts when aiming to detect bottlenecks associated with changing disturbance dynamics at different spatial levels. Such an understanding also provides insight into the underlying ecological mechanisms in operation and allows identification of species of conservation concern. 2. This approach to understanding species’ shifts was applied to a study of spider diversity and life-history variation in relation to sand dynamics in stabilized and dynamic grey dune landscapes located along the coasts of France, Belgium and the Netherlands. 3. Local ( α ) diversity appeared to decrease with increasing local sand dynamics in both stable and dynamic landscapes. In dynamic dune landscapes, β -diversity decreased significantly and approached regional ( γ ) diversity under local grey dune stabilization; in stabilized landscapes, β -diversity was not affected by local sand dynamics. 4. Comparative analyses of ecological traits revealed shifts in life-history patterns, suggesting that patterns in local diversity resulted from species sorting. Species from fragments characterized by high sand dynamics showed narrower niche breadths, larger body sizes and longer generation times, while summer-active species tended to become residential after sand stabilization. This provides evidence for constrained species sorting in which natural disturbance (through local sand dynamics) allows only species with distinct ecological traits to persist. 5. Synthesis and applications . Species with larger body sizes, longer generation times and a higher degree of habitat specialization (i.e. related to dispersal ability) would be expected to be more vulnerable at dynamic sites and more prone to extinction than their counterparts from stabilized fragments. In particular, species with a burrowing lifestyle would be expected to go extinct locally as a result of increasing soil development and soil hardness. Shifts in species composition were found to be more pronounced in dynamic landscapes. Hence ensuring conservation of sand dynamics at a landscape level rather than at a local level is of prime importance when aiming to conserve typical psammophilous spider species within local assemblages in grey dune habitats.
Any-time clustering of high frequency news streams

a large scale system for clustering a stream of news articles that was developed as part of the Geospace & Media Tool (GMT). The GMT integrates the news feed with geospatial, census, and human network information to provide a research tool for members of Congress and their staffs. News articles covering the same event are summarized for the user through the clustering component. The clustering result is available to the user at any time without additional on-demand clustering steps. The documents are grouped into clusters on-the-fly without any assumptions on the number of clusters and without retrieving previous documents. High efficiency is achieved by utilizing locality sensitive hashing (LSH) as a means to determine a small set of candidate clusters for each document. This way a large number of clusters can be considered while keeping the number of expensive document to cluster comparisons low. Our experiments with the system reveal interesting aspects of large-scale text processing in general and news clustering in particular. We demonstrate how the LSH based approximation achieves a large speedup at the cost of only few and small errors. On a high- frequency benchmark data set a clustering quality comparable to one of the best non-streaming document clustering algorithms is obtained.
Processing of Words Related to the Demands of a Previously Solved Problem

: Earlier research by the author brought about findings suggesting that people in a special way process words related to demands of a problem they previously solved, even when they do not consciously notice this relationship. The findings concerned interference in the task in which the words appeared, a shift in affective responses to them that depended on sex of the participants, and impaired memory of the words. The aim of this study was to replicate these effects and to find out whether they are related to working memory (WM) span of the participants, taken as a measure of the individual’s ability to control attention. Participants in the experimental group solved a divergent problem, then performed an ostensibly unrelated speeded affective classification task concerning each of a series of nouns, and then performed an unexpected cued recall task for the nouns. Afterwards, a task measuring WM span was administered. In the control group there was no problem-solving phase. Response latencies for words immediately following problem-related words in the classification task were longer in the experimental than in the control group, but there was no relationship between this effect and WM span. Solving the problem, in interaction with sex of the participants and, independently, with their WM span, influenced affective responses to problem-related words. Recall of these words, however, was not impaired in the experimental group.
Production of extracellular proteases by Aeromonas , Serratia and Shewanella species and characterization of extracellular proteases using zymogram analysis

Seafood spoilage microorganisms are known to produce extracellular proteases. The current research was aimed at using SDS-PAGE substrate impregnated gel to characterize the proteases secreted by the spoilage bacteria. Extracellular proteases (ECP) of seven spoilage microorganisms namely; Shewanella baltica OS185, Aeromonas sp. HB-6,, A. hydrophila HX 201006-3, Sh. baltica, Sh. baltica OS678, Serratia spp. I-11331 and A. salmonicida subsp. achromogenes isolated from various seafoods were analyzed by sodium dodecylsulfate polyacrylamide gel electrophoresis (SDS-PAGE) using Coomassie brillant blue-stain. Extracellular proteases of the seven spoilage microorganisms were characterized by substrate (gelatin and sheep blood) co-polymerized SDS-PAGE. The proteolytic activity results of the (ECPs) the spoilage microoganisms revealed the presence of many bands ranging in molecular weight from 20 to 100 kDa which represents serine, cysteine, aspartic and metallo proteases. The gelatinase profiles for Aeromonas spp. , Shewanella spp. and Serratia spp. were characterized by the presence of high molecular weight metallo proteases ranging from 69, 70, 100, 110,140 and 220 kDa; 70, 100 and 120 kDa and 100 kDa, respectively. Whereas the sheep-blood profiles for Aeromonas spp. , Shewanella spp. and Serratia spp. were characterized by the presence of serine, cystein, aspartic and high molecular weight metallo proteases ranging from 37, 50 and 100 kDa; 35, 37, 50 and 100 kDa and 44 kDa respectively.
Building Change Detection using Semantic Segmentation on Analogue Aerial Photos

Spatio-temporal urban monitoring in large-scale is critical in various engineering applications. Periodical building change detection is necessary for applications such as urban and rural planning, updating 3D cadastral objects and databases, identification of informal settlements and constructions, 3D city modeling and valuation purposes. Automatic building change detection is a research topic of high interest including several challenges referring to radiometric, geometric and atmospheric corrections, data registration and multi-modal data fusion as well as difficulty in using images of complex building structures derived from different viewpoints. Deep learning techniques have received increased attention for achieving satisfying results in many classification problems. Semantic segmentation is a pixel-wise classification of images by implementing a deep neural network scheme such as Convolutional Neural Networks (CNNs) under a supervised setting. This paper presents an one-shot building change detection procedure using semantic segmentation on scanned analogue aerial photos. An augmented time period feature band vector is firstly created by fusing 3D geospatial information, that is a 3D point cloud extracted from Dense Image Matching (DIM), with the corresponding orthoimage. A small training set for the classes of “new buildings”, “unchanged buildings” and “other” is created from the same dataset. The training set and the augmented time period feature band vector are fed as input into a CNN, which is responsible for the semantic segmentation. The “new buildings” and the “unchanged buildings” masks are then processed to eliminate noise taking into account the spatial coherency properties. To verify the applicability, usability and functionality of the employed procedure, two complex and real-life urban study areas in Greece (Keratea and Perissa) with various building structures, pixel resolutions and types of data are selected. The CNN results evaluated via pixel-wise success rates of completeness, correctness and quality, and compared with conventional and other “shallow” learning paradigms, such as Support Vector Machines (SVMs). The derived results show the effectiveness of the proposed deep scheme.
Determining the impact of some wood characteristics on the performance of a mobile chipper

A study was conducted to determine the effect of some wood characteristics such as species, moisture content and tree part on the performance and product quality offered by a mobile industrial chipper, of the type commonly used for roadside chipping. Two main species, two tree parts and two moisture content levels were combined in a factorial design yielding 8 treatments, each replicated 5 or 6 times. A flow meter was installed on the chipper engine, and all chips produced were weighed and sampled for moisture content and particle size distribution. The results indicated that some wood characteristics such as species and moisture content have a secondary effect on chipper productivity and fuel consumption, which are primarily controlled by piece size. In particular, fuel consumption per unit dry mass seem to be rather constant and in the range of 3.2 l per oven dry ton. Moisture content and tree part may have a significant effect on the particle size distribution of chips. Of course, these results were only verified for the species used in the test and for industrial chippers, and may change if substantially different species or machines are used. ; ;
A new method for community detection in social networks based on message distribution

Social networks are the social structures which are composed of people and their relationships and nowadays, play an important role in data extension. In such networks, the communities are recognized as the groups of users who are often interacting with each other. In this article, a method will be introduced for community detection, which has the capability of adoption with different kinds of social networks and also is synchronized with the actual world. One of the most important defined parameters in this paper is the rate of the transferred messages between the nodes of the network, this parameter would be dynamically investigated. In this strategy, the network is reviewed in different time intervals, and the inter-node relations are enhanced or weakened. Therefore, the topology of the network is continuously changing in response to the behavior of the users. The defined parameters in the proposed algorithm are capable of adopting with different types of the social networks and a weight will be assigned to every parameter which is indicative of the relative importance of that parameter in comparison with the other ones. The obtained results show that this method, in comparison with the similar methods, leads to achievement of the desirable results.
Data Analytics : Techniques and Applications to Transform Army Learning

Data analytics is the scientific process of transforming data into insight for making better decisions and is used in industry to improve organizational decision-making and in the sciences to verify or disprove existing models or theories. Current data analytic models have begun to make an impact on the way that courses are designed, run, and evaluated, although little progress has been made towards the design of a structured method to categorize and implement data measurements as they relate to the Army Learning Model (ALM) goals. The following paper describes ongoing work with the U.S. Army Research Laboratory to examine data analytics as it relates to the design of courses, evaluation of individual and group performances, and the ability to tailor the learning experience to achieve optimal learning outcomes. This paper describes: a) the methodology for research and evaluation; b) the fields of Learning Analytics and Educational Data Mining; c) data analytics methods and techniques relevant to learning systems; and d) a framework for applying data analytic methods and techniques for learning via three illustrative use cases. Ultimately, the goal of this paper will be to provide a vision for successful application of these techniques within the Army learning community and higher education.
Performance evaluation of block acquisition and tracking algorithms using an open source GPS receiver platform

Location technologies have many applications in wireless communications, military and space missions, etc. US Global Positioning System (GPS) and other existing and emerging Global Navigation Satellite Systems (GNSS) are expected to provide accurate location information to enable such applications. While GNSS systems perform very well in strong signal conditions, their operation in many urban, indoor, and space applications is not robust or even impossible due to weak signals and strong distortions. The search for less costly, faster and more sensitive receivers is still in progress. As the research community addresses more and more complicated phenomena there exists a demand on flexible multimode reference receivers, associated SDKs, and development platforms which may accelerate and facilitate the research. One of such concepts is the software GPS/GNSS receiver (GPS SDR) which permits a facilitated access to algorithmic libraries and a possibility to integrate more advanced algorithms without hardware and essential software updates. The GNU-SDR and GPS-SDR open source receiver platforms are such popular examples. This paper evaluates the performance of recently proposed block-corelator techniques for acquisition and tracking of GPS signals using open source GPS-SDR platform.
Experiences Applying Automated Architecture Analysis Tool Suites

In this paper, we report our experiences of applying three complementary automated software architecture analysis techniques, supported by a tool suite, called DV8, to 8 industrial projects within a large company. DV8 includes two state-of-the-art architecture-level maintainability metrics—Decoupling Level and Propagation Cost, an architecture flaw detection tool, and an architecture root detection tool. We collected development process data from the project teams as input to these tools, reported the results back to the practitioners, and followed up with telephone conferences and interviews. Our experiences revealed that the metrics scores, quantitative debt analysis, and architecture flaw visualization can effectively bridge the gap between management and development, help them decide if, when, and where to refactor. In particular, the metrics scores, compared against industrial benchmarks, faithfully reflected the practitioners' intuitions about the maintainability of their projects, and enabled them to better understand the maintainability relative to other projects internal to their company, and to other industrial products. The automatically detected architecture flaws and roots enabled the practitioners to precisely pinpoint, visualize, and quantify the “hotspots” within the systems that are responsible for high maintenance costs. Except for the two smallest projects for which both architecture metrics indicated high maintainability, all other projects are planning or have already begun refactorings to address the problems detected by our analyses. We are working on further automating the tool chain, and transforming the analysis suite into deployable services accessible by all projects within the company.
UvA-DARE (Digital Academic Repository) Manipulating word awareness dissociates feed-forward from feedback models of language-perception interactions

Previous studies suggest that linguistic material can modulate visual perception, but it is unclear at which level of processing these interactions occur. Here we aim to dissociate between two competing models of language–perception interactions: a feed-forward and a feedback model. We capitalized on the fact that the models make different predictions on the role of feedback. We presented unmasked (aware) or masked (unaware) words implying motion (e
Fragility Assessment Model of Building Structures Using Characteristics of Artificial Aftershock Motions

Any additional loads applied to a damaged structure can aggravate its instability and thus, the impact of successive earthquakes need to be considered. This study proposed a quantitative assessment model for the fragility of a damaged structure subjected to aftershock. Mean period and the strong motion duration were considered as characteristics of earthquake motions. Simulation models of two reinforced concrete structures and one steel structure were selected to examine the applicability of the model. Based on the suggested fragility and residual deformation coefficients, critical earthquake sequences for each structure were identified. The proposed model was efficient in selecting critical earthquake sequences by using the limited number of aftershocks, and these sequences are expected to be useful indicators in the establishment of a retrofit plan according to the predicted structural response and target performance levels.