Miracle-dz/UniFAR-FTUs huggingface dataset:
International Conference on Intelligent Computing and its Emerging Applications ( ICEA ) An Animal Classification based on Light Convolutional Network Neural Network

Abstract— Designing an accurate and efficient model for animal recognition is a challenging task. It needs to consider many aspects including the accuracy of the model, the number of parameters, the complexity of calculation and so on. Therefore, we propose a novel convolutional network, called Bilateral Convolutional Network (BCNet), which aims at achieving a trade-off between recognition accuracy and model size, so that it can be better feasible to the mobile devices. It consists of two components, namely the feature extraction module and the classification module,respectively. The feature extraction module adopts a bilateral structure, which contains two sub-networks. A feature network for extracting image features and a location network for identifying object locations. The classification modeule adopts a two fully connected layer as a classifier. We tested on the Animals with Attributes animal (AWA) dataset, and the top-1 accuracy of the model was 85.6%. The model size of Bilateral Convolutional Network is 67.14 M, and computation complexity is only 1.12 GFLOPs. Compared with other methods, it achieves a good balance between recognition accuracy and model size.
Learning of Robotic Throwing at a Target using a Qualitative Learning Reward

Autonomous learning and adaptation of actions is critical for robots to operate in unstructured, everyday environments. Reinforcement learning (RL) methods are often applied for this. However, efficient RL requires the determination of an appropriate reward function, which is a complex problem even for domain experts. In this paper we investigate if a standard robotics reinforcement learning method called PoWER can be effectively utilized with a simple, qualitatively determined reward, instead of with a complex reward function. Our use-case example is robotic throwing at a target. However, for increased complexity, we perform throwing with a 7 degree of freedom arm of a humanoid robot, and a two-dimensional target space, i.e., the target is placed arbitrarily on the plain in front of the robot, which needs to learn the direction and the distance. Results show that learning with a simplified reward function that practically assigns a qualitative reward, just as a person would, can still be effectively used for RL using PoWER.
Exploiting the Intertemporal Structure of the Upper-Limb sEMG: Comparisons between an LSTM Network and Cross-Sectional Myoelectric Pattern Recognition Methods

The use of natural myoelectric interfaces promises great value for a variety of potential applications, clinical and otherwise, provided a computational mapping between measured neuromuscular activity and executed motion can be approximated to a satisfactory degree. However, prevalent methods intended for such decoding of movement intent from the surface electromyogram (sEMG) based on pattern recognition typically do not capitalize on the inherently time series-like nature of the acquired signals. In this paper, we present the results from a comparative study in which the performances of traditional cross-sectional pattern recognition methods were compared with that of a classifier built on the natural assumption of temporal ordering by utilizing a long short-term memory (LSTM) neural network. The resulting evaluation indicate that the LSTM approach outperforms traditional gesture recognition techniques which are based on cross-sectional inference. These findings held both when the LSTM classifier operated on conventional features and on raw sEMG and for both healthy subjects and transradial amputees.
Segmentation of 3D meshes combining the artificial neural network classifier and the spectral clustering

3D mesh segmentation has become an essential step in many applications in 3D shape analysis. In this paper, a new segmentation method is proposed based on a learning approach using the artificial neural networks classifier and the spectral clustering for segmentation. Firstly, a training step is done using the artificial neural network trained on existing segmentation, taken from the ground truth segmentation (done by humane operators) available in the benchmark proposed by Chen et al. to extract the candidate boundaries of a given 3D-model based on a set of geometric criteria. Then, we use this resulted knowledge to construct a new connectivity of the mesh and use the spectral clustering method to segment the 3D mesh into significant parts. Our approach was evaluated using different evaluation metrics. The experiments confirm that the proposed method yields significantly good results and outperforms some of the competitive segmentation methods in the literature.
Próba zastosowania modelu TALC i teorii chaosu w analizie rozwoju karpackiego uzdrowiska Żegiestów-Zdrój

The article describes the local environment and natural treatments available in Żegiestów-Zdrój, a small spa resort in the Carpathian mountains. Analysis of the development of the spa resort was conducted using R.W. Butler’s tourism area life cycle model (TALC). The analysis showed that the life cycle of Żegiestów-Zdrój is characterised by unpredictable changes, and that the life cycle 180 Z. Kruczek, A.R. Szromek process is difficult to foresee, as shown by the sporadic unstable changes that have taken place. Chaos theory is better able to explain the resort’s development, as this employs non-linear mapping of events. Supplementing the life cycle analysis with chaos theory allowed for a better description of the development of Żegiestów-Zdrój. The conclusion presents the opportunities for the revival of the resort and the beginning of a new cycle in its development. The theories described may also be used for the interpretation and forecasting of changes in other spa resorts.
Scaling Bulk Data Analysis with Mapreduce

Between 2005 and 2015, the world population grew by 11% while hard drive capacity grew by 95%. Increased demand for storage combined with decreasing costs presents challenges for digital forensic analysts working within tight time constraints. Advancements have been made to current tools to assist the analyst, but many require expensive specialized systems, knowledge and software. This thesis provides a method to address these challenges through distributed analysis of raw forensic images stored in a distributed file system using opensource software. We develop a proof-of-concept tool capable of counting unique bytes in a 116 TiB corpus of drives in 1 hour 41 minutes, demonstrating a peak throughput of 18.33 GiB/s on a 25-node Hadoop cluster. Furthermore, we demonstrate the ability to perform email address extraction on the corpus in 2 hours 5 minutes, for a throughput of 15.84 GiB/s, a result that compares favorably to traditional email address extraction methods, which we estimate to run with a throughput of approximately 91MiB/s on a 24-core production server. Primary contributions to the forensic community are: 1) a distributed, scalable method to analyze large data sets in a practical timeframe, 2) a MapReduce program to count unique bytes of any forensic image, and 3) a MapReduce program capable of extracting 233 million email addresses from a 116 TiB corpus in just over two hours.
Design and Analysis of Efficient Inter-core Communication in GPUs

Graphics Processing Unit (GPU) architectures are becoming an inevitable part of every computing system [1] because of their ability to provide orders of magnitude faster execution. They have become the default choice for accelerating innovations in various fields [2]–[10] such as high-performance computing (HPC), artificial intelligence, deep learning, and virtual/augmented reality. Traditionally, GPUs have relied on bandwidth to achieve high throughput [11]–[14]. The sources of bandwidth have been local/shared caches, scratchpad, and memory. Additionally, high bandwidth interconnect is required to support the data flow between caches/memory and cores. In this work, we focus on dynamically identifying and exploiting an additional source of bandwidth in GPUs, which we call as remote-core bandwidth. As also observed by previous works [15]–[17], the source of this additional bandwidth stems from inter-core locality. Specifically, the data required by one of the GPU cores (i.e., L1 misses) can also be found in the local L1 caches of other remote GPU cores. We find that this additional source of bandwidth leads to significant improvement in performance, however, can only be leveraged if an efficient inter-core communication is enabled. There are several challenges towards designing an efficient inter-core communication, which have not been addressed by prior works. In particular, this work systematically addresses the following research questions: 1) How to determine which data can also be found in remote cores, 2) How to determine which cores have the data of the requester core, and 3) How to get the data as soon as possible without congesting the interconnect bandwidth? Figure 1 shows the architectural diagram of our proposal. To the best of our knowledge, this is the first work that addresses these questions in a systematic manner. Specifically, this work makes the following contributions: • We observe a bi-modal distribution of inter-locality across different instructions – some instructions use data that is shared across cores and some do not. We leverage the observation and use the program counter (PC) to predict which L1 misses are likely to be found in the L1 caches of other cores ( A ). • We develop a low-overhead mechanism that can locally predict which cores are likely to have the shared data ( B ). It is based on our key observation that the data required by a core is generally shared across only a few cores, which can be detected via sampling a limited number of core replies. • We develop a novel two-level probing mechanism that searches the identified cores in parallel while considering the network bandwidth consumption ( C ). This ensures that the searching overhead does not hamper inter-core locality as well as performance. • Our combined schemes take advantage of the unutilized Selected
Football Match Scene Text Detection Based on Convolutional Neural Network

With the development of deep learning, text detection based on neural network has gained more in-depth research and more extensive application. Considering the characteristics of the text in the football match scene, a novel neural network architecture is proposed based on the TextBoxes. The proposed algorithm performs well in the task of text detection in the football maes which fit the text boxes in football match scene. In order to solve the sample imbalance problem that interference model optimization, we propose Focal Loss as a loss function for classification. Finally, the Non-maximal suppression is applied to eliminate redundant bounding boxes and aggregate outputs. We make a dataset for training, and verify the effectiveness of the algorithm.
A Multi-Target Detection Algorithm for Infrared Image based on Retinex and LeNet5 Neural Network

Object detection in infrared video images is an important and challenging work. Due to low resolution, poor contrast, and low visual quality, target detection in infrared images is inefficient and prone to having higher false positive and lower precision rates. To improve detection efficiency, according to the characteristics of infrared images, we proposed a multi-target detection algorithm based on image enhancement and the LeNet5 deep neural network. In our method, we used the Retinex image enhance algorithm to protrude the edge contour and contrast, highlight the detailed features, and enhance the overall visibility of infrared images. In particular, the LeNet5 convolution neural network and CVC vehicle-assisted driving database were used to train the interesting target in the infrared image to generate the target data model, and the selective search algorithm was used to segment the candidate detect object regions in the image. The separated candidate regions were sent to the trained data model to classify the type and locate the position of objects in the image. The simulation results in CVC infrared image subset datasets show that our algorithm has higher detection speed and accuracy than the traditional HOG-based and LBP-based detection algorithms.
Pedestrian Movement Graph Analysis

Development of sustainable urban environments assumes processing of large amount of data from various sources. It could be field study observations, results of simulations or information provided by modeling. This paper focuses on processing modeled data of pedestrian movement based on existed axial maps of particular environment. Introduced component allows further analysis by calculation of set of metrics based on inverted graph, which is built from given paths.
Adapting Swarm Applications: A Systematic and Quantitative Approach

The swarm refers to the vast collection of networked sensors and actuators installed in our connected world. Many swarm applications generate, transport, distill, and process large streams of data across a wide area in real time. The increasing volume of data generated at the edge challenges the existing approaches of directly connecting devices to the cloud.This thesis begins with an overview of emerging swarm and the architecture design trends for swarm applications. The swarm faces challenges from the scarce and variable WAN bandwidth and the heterogeneous compute environment (from low-power microcontrollers to powerful compute units). When network resources or compute resources are insufficient, non-adaptive applications will suffer from increased latency or degraded accuracy. Existing approaches that support adaptation either require extensive developer effort to write manual policies or are limited to application-specific solutions.This thesis proposes a systematic and quantitative approach to build adaptive swarm applications. The solution has three stages: (1) a set of programming abstractions that allow developers to express adaptation options; (2) an automatic profiling tool that learns an accurate profile to characterize resource demands and application performance; (3) a runtime system responsive to environment changes, maximizing application performance guided by the learned profile. We evaluate our methodology with adaptations to network resources and compute resources.For network resources, we present a complete design and implementation of the framework AWStream and several swarm applications: pedestrian detection, augmented reality, and monitoring log analysis. Our experiments show that all applications can achieve sub-second latency with nominal accuracy drops. For compute resources, we focus on improving the profiling efficiency—using Bayesian Optimization to address the large parameter space and profile transfer to address device heterogeneity.
board game to enhance teaching and learning in the Radiographic Technique module

Gamification is used encourage the learner to participate in a competitive activity, to encourage engagement and desire to learn. In the Radiographic Practice module, students learn three core radiography performance standards; professionalism, technique and clinical performance. To address these standards, a pilot radiography board game was developed to determine its effectiveness at improving engagement and interest with learning material and its use as a teaching and learning tool. A qualitative, explorative descriptive research design was used, involving focus group interviews with the radiography students. The students recommended some changes and adjustments on the game design and dynamics. They further described the board game as a fun activity and demonstrated that it was possible to learn whilst engaging with the subject material and group discussions. The board game enabled the students to apply critical thinking skills and be introduced the concept of professionalism in the clinical setting.
Sediment-carrying capacity prediction using extreme learning machine

Sediment-carrying capacity prediction is an important part in the river sediments movement simulation. In this paper, a novel machine learning approach was proposed to predict the sediment-carrying capacity by using the Extreme Learning Machine (ELM). The evaluation indexes and the average errors of these prediction models were analysed in detail. The result shows that the ELM model has a good generalization performance for the sediment-carrying capacity prediction, and the ELM approach could be an effective tool for the sediment movement simulation.
Changes in vegetation seasonality and livestock stocking rate in La Pampa Province (Argentina) = Cambios en la estacionalidad de la vegetación y la carga animal en la provincia de La Pampa (Argentina)

Crop production is traversing expansion and intensification processes all over the planet and in consequence the large scale cattle production is being displaced to marginal lands with lower stocking capacity. The objective of this work was to assess the seasonality of vegetation in La Pampa province located in a semiarid region in Argentina and to explore if the variations in seasonality are related to the stocking rate. The hypothesis is the changes in stocking rate of rangelands and its grazing pressure alter the proportion of different vegetation functional groups and so the vegetation seasonality. It is predicted that overgrazing of seasonal grasses will alter the proportion of woody species having consequences over spectral indices. It was analyzed satellite data, particularly the MODIS Enhanced Vegetation Index (EVI) and related it to stocking rate records from SENASA. This work evidences the relationship between the stocking rate and the spectral index EVI, indicator of the primary productivity of vegetation, at departmental scale in the natural areas of Monte and Espinal of La Pampa. Results indicate that in western region (Monte) there was an increment in the stocking rate at department level and a decrease in vegetation seasonality. It is posed that the higher grazing pressure led to the overgrazing of the most palatable herbaceous species, increasing the shrub proportion in landscape.
The Imataca Complex, NW Amazonian Craton, Venezuela: Crustal evolution and integration of geochronological and petrological cooling histories

dence ages indicate that the Imataca Complex developed from an Archean (≥3.2 Ga) continental protolith which has undergone considerable isotopic disturbance plus and juvenile accretion during late-Archean (~ 2.8 Ga) times. Transamazonian granulites experienced peak metamorphic conditions of 750 – 800 °C, 6 – 8 kbar with associated transpressive thrusting and tectonic imbrication. Geochronology on zircon, pyroxene and garnet constrains the timing of peak metamorphism at 1.98 – 2.05 Ga. Diffusion modeling of Fe-Mg exchange between biotite inclusions and host garnet yields (near metamorphic peak) cooling rates of 50 – 100 °C/Ma, with petrological cooling rates being generally consistent with cooling rates determined from geochronology. Combining the retrograde P-T path with cooling rates suggests that after the metamorphic peak, large portions of the Imataca Complex were exhumed from 30 to 17 km at a rate of 7 – 2 km/Ma. After this, exhumation rates progressively decreased as the rocks approached the surface. Rapid overall uplift/erosion had ceased when the rocks passed below 600 – 550 °C at 2.01 – 1.96 Ga ago. Observed variations in mineral cooling ages are interpreted as to reflect episodic differential tectonic exhumation within major fault systems. Inferred (maximum) ages of fault re-activation generally coincide with major continental accretion events in the Amazonian Craton and reflect long-term thermal evolution of the Imataca terrane, as conditioned by variable response to continued continental convergence during the Proterozoic. Introduction
Converting Time Series Data into Images: An Innovative Approach to Detect Abnormal Behavior of Progressive Cavity Pumps Deployed in Coal Seam Gas Wells

Progressive Cavity Pumps (PCPs) are the predominant form of artificial lift method deployed by Australian operators in Coal Seam Gas (CSG) wells. With over five thousand CSG wells [1] operating in Queensland's Bowen and Surat Basins, managing and maintaining PCP supported production becomes a significant challenge for operators. Especially when these pumps face regular failures due to the production of coal fines. It is possible to gauge the holistic production performance of PCPs with the aid of real-time data, as this allows for pro-active and informed management of artificially lifted CSG wells. Based on data obtained from two (2) CSG operators, this paper will discuss in detail how features extracted from time series data can be converted to images, which can then aid in autonomously detecting abnormal PCP behavior.
Open Ecosystems: Ecology and Evolution Beyond the Forest Edge

Review of: William Bond. 2019, Oxford University Press, Great Clarendon Street, Oxford, OX2 6DP, UK 192 pages, Hardcover, Paperback, Ebook ISBN-13: 978-0198812456DOI:10.1093/oso/9780198812456.001. 0001$78 (~ R 1 135, Hardcover); $85 (~R 1 240, Ebook); $46 (~R 660, Paperback)
Biogeography of N
 2
 Fixation Influenced by the Western Boundary Current Intrusion in the South China Sea

The N 2 fixation and primary production rates were measured simultaneously using 15 N 2 and 13 C incubation assays in the northern South China Sea influenced by the Kuroshio intrusion (KI) seasonally. The degree of KI (KI index, range from 0 to 1) was assessed by applying an isopycnal mixing model. The water column integrated N 2 fixation and primary production for stations with KI index larger than 0.5 were 463 ± 260 μmol N·m −2 ·day −1 and 62 ± 19 mmol C·m −2 ·day −1 , respectively, significantly higher than those for stations with KI index lower than 0.5 (50 ± 10 μmol N·m −2 ·day −1 and 28 ± 10 mmol C·m −2 ·day −1 , respectively). Trichodesmium was the dominant diazotroph at stations with KI index larger than 0.5, with 2 orders of magnitude higher nifH gene abundance than that at stations with KI index lower than 0.5. However, the highest N 2 fixation rates were found in waters with moderate KI index around 0.6, suggesting that frontal zone mixing might stimulate N 2 fixation. Our results demonstrated that diazotrophs (mainly Trichodesmium) were tightly associated with the KI, which modulated the biogeographic distribution of N 2 fixers. In summary, we found the transportation of Trichodesmium by KI, then, we quantified the fraction of KI and N 2 fixation rates in the northern South China Sea. The results suggested that KI generated a new biogeographic regime which could significantly influence the carbon and nitrogen cycles far away from the main stream.