Miracle-dz/UniFAR-FTUs huggingface dataset:
An Internet Water Army Detection Supernetwork Model

The emergence of Internet water armies has strongly affected the information quality of online communication platforms, thus disrupting the order of the Internet. Accurate detection of Internet water armies is therefore of great significance. Based on the supernetwork theory, a new Internet water army detection model is proposed in this paper, in which a supernetwork with four layers is established, including social subnetwork, information subnetwork, psychological subnetwork, and negative keyword subnetwork. Then, personal information of users, dissemination process of information, the transformation process of different psychologies, the similarity between different keywords, and the connections between different subnetworks are considered in the model. Thus, nine composite indexes are proposed, the majority of which are used for the first time in detecting Internet water armies. A dataset selected from the largest online communication platform in China, the Weibo website, is used to test the performance of the model. Four existing water army detection models introduced in previous studies are used to provide a comparison analysis. The results show that our proposed model has better performance in terms of accuracy and stability than the other four existing models, which thanks to the employment of the supernetwork theory. We believe that our proposed model could be helpful for information researchers to further understand the complex nature of Internet water armies, as well as for the government to better manage the Internet.
Successful aerial survey using thermal camera to detect wild orangutans in a fragmented landscape

Asia’s only great ape, the orangutan, builds nests for the night in the upper rainforest canopy. Due to the location in the upper canopy, aerial surveys of orangutans rely primarily on counting nests. This method has come under increasing criticism, because nest decay rates can vary greatly over space and time and is depended on e.g. local climatic conditions, tree species and presence of termites and other animals. Whereas empty nests may not provide a good measure of presence, live orangutans in their nests does. Assuming the orangutan is the only animal species of the size that can be found in the upperrainforest canopy at night, we used thermal camera fitted to a drone to successfully detect and identify orangutan in Kumai Estate, Central Kalimantan, Indonesia. Our results provide an encouraging new census platform for conservationists and park managers.
Community structure of tenebrionid beetles in the Ulan Buh Desert (Inner Mongolia, China) (Coleoptera: Tenebrionidae)

Tenebrionids are a conspicuous faunal component of Central Asian deserts, but little is known about their community ecology. We investigated how tenebrionid community structure varied along a vegetational gradient in the Ulan Buh Desert (Gobi Desert). Sampling was done with pitfall traps in three sites with different vegetation cover. Species abundance distributions were fitted by the geometric series model, which expresses the “niche pre-emption” hypothesis. Community structure was investigated using different measures of diversity (number of species, Margaleff richness and Shannon-Weaner index), dominance (Simpson and Berger-Parker indexes) and evenness (Pielou’s index). The observed tenebrionid species richness was similar to that known from other Gobi Desert sites. The three investigated sites have similar species-abundance patterns, but the most dominant species varied among them. This suggests that the local environment operates a filtering action on the same basic fauna, allowing different species to dominate under different conditions. Overall, the highest total abundance was observed in the true desert site, however this site had a community structure similar to that observed in the site with more vegetation. By contrast, the investigated site with intermediate conditions showed a higher diversity and evenness, and a lower dominance. Thus, intermediate conditions of plant cover favour tenebrionid diversity, whereas a dense cover or a very sparse cover increases the dominance.
Anomaly Detection in Orthogonal Metal Cutting based on Autoencoder Method

The choice of appropriate cutting conditions is widely acknowledged as a key performance indicator for efficient machining, since it allows to mitigate tools wear. In precision machinery, tool wear can indeed lead to poor surface quality and even affect the dimensions of the final product. In such a context, the cutting and feed forces, along with other parameters are affected and are not optimal. It is therefore necessary to detect any anomaly and provide the operators in the plant with a decision support, allowing to monitor the machining conditions in order to predict the outputs values and anticipate the wear's damage on the whole cutting process. In this article, cutting speed, cutting angle, cutting width and feed depth are the input parameters. Using the extended-Oxley analytical model of orthogonal metal cutting, a sample of cutting conditions has been simulated in order to analyze the optimality of the cutting force, the feed force and the internal temperature, based on the autoencoder method. Given a threshold for decision, the results allow to identify abnormal parameters and provide significant insights for operators, allowing them to avoid error and make the best choices of the inputs for optimal cutting conditions.
Formation of the adaptive fuzzy model of the rock geological structure for exploratory drilling

The authors suggest an approach aimed at improving the technology, increasing energy efficiency and quality of controlling borehole drilling in mining various mineralogical-technological types of iron ore through applying a geological structure model based on data of measuring drilling parameters of a drilling rig (drilling rate, torque, energy characteristics) and rock properties on the basis of studying regularities of changing parameters of the specifically formed ultrasonic field. In rapidly changing conditions of borehole drilling, it is offered to apply a two-level adaptive optimization strategy implying a simultaneous investigation into drilling and improvement of control over this process by means of the adaptive fuzzy system.
A GIS Analysis of the Environmental Variables Related to Rift Valley Fever Outbreaks

A GIS ANALYSIS OF THE ENVIRONMENTAL VARIABLES RELATED TO RIFT VALLEY FEVER OUTBREAKS Jacqueline Florette Jackson Old Dominion University, 2010 Director: Dr. Holly Gaff Rift Valley fever is a mosquito-borne disease that causes widespread febrile illness and mortality in domestic animals as well as humans (Gaff, 2007). Rift Valley fever virus was first isolated in 1931 (Daubney, 1931), and since then, outbreaks have occurred in sub-Saharan Africa, southern Africa, Egypt, Saudi Arabia, Yemen and Madagascar, proving it to be a virus able to invade ecologically diverse regions (Gaff, 2007). The potential introduction of Rift Valley fever into the United States suggests the potential for human infection and major economic disruption. It is important to understand the role environmental variables have played in historical outbreaks to further understand the disease and the possibility of translocation of the virus. This study examines the relationship between both temperature and rainfall rates and Rift Valley fever outbreaks in Kenya, Madagascar, and South Africa. Datasets employed in the analysis are several including a long term dataset (1982-2004), short term datasets (1999-2005; 1999-2007) and datasets covering the Rift Valley fever outbreaks in Kenya (2006, 2007), Madagascar (2008, 2009), and South Africa (2008, 2009). Geographic information systems analysis, time series analysis, and statistical analyses are used to gauge the relationships among temperature, rainfall, and Rift Valley fever outbreak events. Results of this study show a relationship between rainfall and Rift Valley fever in Kenya, but not in Madagascar or South Africa. Although a positive rainfall anomaly was found at the beginning of the Rift Valley fever outbreak in Kenya, further analysis finds above average rainfall anomalies prior to the outbreak with no Rift Valley fever activity reported. No significant differences are found among the historical temperature ranges and temperature ranges during Rift Valley fever outbreaks in Kenya, Madagascar or South Africa. By better understanding these two important variables, disease transmission models for Rift Valley will later be able to predict future outbreaks and spread of disease. Studies about variables related to disease transmission models like this further strengthen these models, thus providing policymakers the ability to design systems to enhance preventative and control measures. Co-Directors of Advisory Committee: Dr. Anna Jeng Dr. Joshua Behr
A Hierarchical Quasi-Recurrent approach to Video Captioning

Video captioning has picked up a considerable attention thanks to the ability of Recurrent Neural Networks to extrapolate an encoded representation of the input video, and then use it to generate a description. We propose a recurrent encoding approach able to find and exploit the layered design of the video. Differently from the established encoder-decoder procedure, in which a video is repeatedly encoded by a recurrent layer, we employ revised Quasi-Recurrent Neural Networks. We further extend their basic cell with a boundary detector in order to recognize discontinuous segments boundaries and likewise correct the temporal connections of the encoding layer accordingly. Experiments, on the Montreal Video Annotation dataset, demonstrate that our approach can find suitable levels of representation of the input information, while reducing the computational requirements.
Piecewise linear perceptual quantizer as a non-uniform histogram equalization adjustment for contrast enhancement of tone mapped HDR images

This paper proposes a High Dynamic Range (HDR) image Tone Mapping (TM) approach converting HDR images into Low Dynamic Range (LDR) images so as the tone mapped HDR images ensure good visual quality on LDR display devices in accordance with the observers’ assessment. The proposed algorithm adjusts the logarithm luminance distribution of the HDR image according to a perceptual quantizer with respect to the Human Visual System (HVS) using a piecewise linear function. The parameters’s approximation are induced by LM-norms (ℓ1, ℓ2, ℓ∞) and the performance of the proposed algorithm is discussed. Simulation results provide good results, both in terms of visual quality and TMQI metric, compared to existing competitive TM approaches.
Geosite Potential as Tourism Destination in Jebus West Bangka Regency Bangka Belitung Archipelago Province

Bangka Island was formed by collition of some block geology (terraine) in hundreds million years ago. Earth plate amalgamation driven by tectonic force that caused increasing intensity of magmatism, tin mineralation, structural geology dan metamorphic rock formed. The geological condition has attractiveness to be developed as tourism destination, and many geosite in Bangka Island that interesting and can be claimed. The best geosite that collect the highest unique geology in aspect of rocks, structural geology dan landform variety, was done. The study focused on Bembang Beach and its surrounding area in Jebus District West Bangka Region about 120 km far from Pangkalpinang City as Province Capital and it can be reached in 2,5 hours. In Bembang Beach and its surrounding area found at least 4 of 5 types of all rock formation and alluvial deposited that formed in Bangka Island are Pemali Complex Metamorphic, igneous rock Diabas Penyabung, ancient sedimentary rock Tanjung Genting Formation, plutonic rock Granit Klabat that all rock has million years old age, also young river and beach alluvial deposite. Structural geology has developed as symmetrical or asssymetrical fold and trust fault. The landscape unit in geosite is wavy flat and hilly area that gives natural attraction. Beautiful beach area covered by white colour quartz sand, with other condition in the form of villages, culture, hospitality of the population with a strong history of tin, also availability of forest and plantation around the driveway of geosite increasing the potential value for developing the tourism destination as geotourism, natural education tour and geopark. Keywords–Geosite , potensial, tourism
Fuzzy 2-Partition Kapur Entropy for Image Segmentation Using Teaching-Learning-Based Optimization Algorithm

Image segmentation has been used widely for detection and extraction of objects in digital images. Thresholding is one of the effective image segmentation techniques. Fuzzy 2-partition entropy is used effectively for the selection of threshold value. Fuzzy 2-partition entropy required the optimization of some parameters for the selection of threshold value. Teaching-Learning-Based Optimization (TLBO) algorithm has been applied on fuzzy 2-partition entropy to find these parameters and subsequent threshold value. Normally, fuzzy 2-partition Shannon entropy is used for thresholding. In the proposed research work, fuzzy 2-partition Kapur entropy is explored for the evaluation of its potential for selecting optimal threshold value using TLBO algorithm. The present work measures the performance comparison of fuzzy 2-partition Kapur entropy using TLBO with fuzzy 2-partition Shannon entropy using TLBO. The experiments are carried out on standard test images from benchmark dataset. Peak signal to noise ratio, uniformity and structural similarity index are the three different measures which have been used to compare the performance. Results demonstrate that the performance of fuzzy 2-partition Kapur entropy using TLBO is quite promising.
3D Simulator for Wind Interferometer Data-Model Comparison

The connection between earth and space weather has numerous impacts on spacecraft, radio communications, and GPS signals. Thus, predicted & modeling this region is essential, yet models (both empirical and first principles) do a poor job of characterizing the variability of this region. One of the main objectives of the NASA ICON mission is to measure the variability of the ionosphere and thermosphere at low-mid latitudes. The MIGHTI instrument on ICON is a Doppler Interferometer that measures the horizontal wind speed and direction with 2 discrete MIGHTI units, separated by 90°, mounted on the ICON Payload Interface Plate. This work focuses on building a simulation of wind interferometer data, similar to MIGHTI, using a first-principles model as the input dataset, which will be used for early validation and comparison to the MIGHTI data. Using a ray-tracing approach, parameters like O, O2, O+, O2+, T, wind, will be read for every point along every ray from the model and brightness and Line of Sight (LOS) wind will be calculated as functions of altitude and time. These data will be compared to the MIGHTI observations to both to establish the limitation of such models, and to validate the ICON data. ICON will help determine the physics of our space environment and pave the way for mitigating its effects on our technology, communications systems, and society. However, ICON is yet to launch and due to the unavailability of MIGHTI data, we have selected another instrument called WINDII (Wind Imaging Interferometer) from a different mission UARS (Upper Atmosphere Research Satellite) to demonstrate the utility of this data-model comparison. Similar to MIGHTI, WINDII measures Doppler shifts from a suite of visible region airglow and measures zonal and meridian winds, temperature, and VER (Volume Emission rate) in the upper mesosphere and lower thermosphere (80 to 300 km) from observations of the Earth's airglow. We will use a similar approach discussed for MIGHTI to calculate the vertical profile of Redline airglow, Wind velocity, emission rate and compare them with our simulated results to validate our algorithm. We initially thought asymmetry calculation along the Line of Sight (LOS) would be the limiting factor. We believe other things are going on such as variability in the winds associated with natural fluctuations in the thermosphere, atmospheric waves, inputs from the sun and the atmosphere below etc., that appear to be a more significant factor than just asymmetry along the line of sight.
High Resolution Air Quality Modeling for Improved Characterization of Exposures and Health Risk to Traffic-Related Air Pollutants (TRAPs) in Urban Areas

2 3 Shih Ying Chang: High Resolution Air Quality Modeling for Improved Characterization of 4 Exposures and Health Risk to Traffic-Related Air Pollutants (TRAPs) in Urban Area 5 (Under the direction of Saravanan Arunachalam and William Vizuete) 6 7 Exposure to traffic-related air pollutants in health studies is often obtained from air 8 quality models at a relatively coarse spatial resolution that is unable to capture concentration 9 hotspots near roadways thus has the potential to underestimate the risk. The goal of this work is 10 to improve the characterization of exposure and health risk to traffic-related air pollutants. The 11 hypothesis is that dispersion models can reduce the error for large-scale exposure and risk 12 assessment because of the capability for fine-resolution modeling. This overall hypothesis was 13 verified by the following 3 studies. 14 The first study describes the development of a modeling framework that combined space15 time kriging and Gaussian dispersion to inform exposure estimates for traffic-related air 16 pollutants with a high spatial resolution. This framework reduces CPU-time by 88-fold by 17 reducing the required meteorological data, while retaining the accuracy of exposure estimates. 18 With this work, air quality models can be used to achieve fine-resolution modeling. 19 The second study compared a series of six different hourly-based exposure metrics 20 including ambient background concentration from space-time ordinary kriging (STOK), ambient 21 on-road concentration from research line source dispersion model (R-LINE), a hybrid 22 concentration combining STOK and R-LINE, and their associated indoor concentrations from an 23 indoor infiltration mass balance model. Using a hybrid-based indoor concentration as the 24 standard, outdoor STOK metrics yielded large error at both population (67% to 93%) and 25
Routing Selection With Reinforcement Learning for Energy Harvesting Multi-Hop CRN

This paper considers the routing problem in the communication process of an energy harvesting (EH) multi-hop cognitive radio network (CRN). The transmitter and the relay harvest energy from the environment use it exclusively for transmitting data. In a relay on the path, a limited data buffer is used to store the received data and forward it. We consider a real-world scenario where the EH node has only local causal knowledge, i.e., at any time, each EH node only has knowledge of its own EH process, channel state, and currently received data. An EH routing algorithm based on Q learning in reinforcement learning (RL) for multi-hop CRNs (EHR-QL) is proposed. Our goal is to find an optimal routing policy that can maximize throughput and minimize energy consumption. Through continuous intelligent selection under the partially observable Markov decision process (POMDP), we use the Q learning algorithm in RL with linear function approximation to obtain the optimal path. Compared with the basic Q learning routes, the EHR-QL is superior for longer distances and higher hop counts. The algorithm produces more EH, less energy consumption, and predictable residual energy. In particular, the time complexity of the EHR-QL is analyzed and its convergence is proved. In the simulation experiments, first, we verify the EHR-QL using six EH secondary users (EH-SUs) nodes. Second, the performance (i.e., network lifetime, residual energy, and average throughput) of the EHR-QL is evaluated under the influences of different the learning rates $\alpha $ and discount factors $\gamma $ . Finally, the experimental results show that the EHR-QL obtains a higher throughput, a longer network lifetime, less latency, and lower energy consumption than the basic Q learning routing algorithms.
Modular HF Link Inverter Topology

This paper proposes an inverter topology with high frequency link, for conversion of low voltage DC at about 30V to 415V, 50Hz, three phase AC for standalone inverter systems. To establish suitability, the proposed topology is compared with another widely used topology based on practically relevant considerations. Devices and components have been identified for each of the topologies and actual device parasitics have been considered for loss estimation and efficiency computation. In addition, the paper also proposes and studies a proportional-resonant (PR) voltage control structure for the proposed topology for mitigating effects of non-ideal operating conditions such as unbalanced and non-linear loading.
Single Stage SEGS Tied to the Three Phase Grid

This paper presents a multi-objective novel ESASMA (Exponential Sparsity-Aware Set-Membership Adaptive) based control strategy for grid tied single stage SEGS (Solar Energy Generating System). The ESASMA method is utilized for fundamental load component harvesting, when load current is non-linear. Moreover, ESASMA approach is also used for power quality improvement at PCC in both conditions either with solar power generation or absence. In this way, power converters is used at its extreme level, consequently, payback time is reduced, and proposed system becomes economical. For validation of proposed approach, a system is examined in various test condition on developed hardware prototype.
Robust Authentication Using Dorsal Hand Vein Images

This paper presents a robust dorsal hand vein authentication system. A new method is proposed for the region of interest extraction using fingertips and finger valley key points. Some new features and a new classifier are proposed based on information set theory. Information set stems from a fuzzy set on representing the uncertainty in its attribute/information source values using the information-theoretic entropy function. The new feature types include vein effective information, vein energy feature, vein sigmoid feature, Shannon transform feature, and composite transform feature. A classifier called the improved Hanman classifier is formulated from training and test feature vectors using Frank t-norm and the entropy function. The performance and robustness are evaluated on GPDS and BOSPHORUS palm dorsal vein database under both the constrained and unconstrained conditions.
Bending the Curve of Global Freshwater Biodiversity Loss: An Emergency Recovery Plan

Abstract Despite their limited spatial extent, freshwater ecosystems host remarkable biodiversity, including one-third of all vertebrate species. This biodiversity is declining dramatically: Globally, wetlands are vanishing three times faster than forests, and freshwater vertebrate populations have fallen more than twice as steeply as terrestrial or marine populations. Threats to freshwater biodiversity are well documented but coordinated action to reverse the decline is lacking. We present an Emergency Recovery Plan to bend the curve of freshwater biodiversity loss. Priority actions include accelerating implementation of environmental flows; improving water quality; protecting and restoring critical habitats; managing the exploitation of freshwater ecosystem resources, especially species and riverine aggregates; preventing and controlling nonnative species invasions; and safeguarding and restoring river connectivity. We recommend adjustments to targets and indicators for the Convention on Biological Diversity and the Sustainable Development Goals and roles for national and international state and nonstate actors.