Miracle-dz/UniFAR-FTUs huggingface dataset:
Phosphorus in UK Rivers: The Impact of Urban Waste Water Treatment Directive

Excessive phosphorus levels leading to eutrophication in natural waters as a result of growing population, urbanisation and intensified agriculture has long been a major environmental concern at a global scale. Many remediation strategies and actions have been undertken since the implementation of Urban Waste Water Treatment Directive (UWWTD) in 1992. The UWWTD was implemented to reduce direct phosphorus inputs into rivers from effluents of sewage treatment works. Nevertheless, the long-term outcomes and effectiveness of theses actions still remain unknown. An understanding of the prospective results and effectiveness of these implementations is only possible with a retrospective analysis of riverine phosphorus dynamics. Therefore, this thesis explored the evolution of P concentration and flux from across the UK with datasets available from 230 river sites between the period of 1974 and 2012. These datasets were examined with the purpose of detecting the traces of the events that are likely to be governing the changes in phosphorus levels over the this time period i.e. the implementation of the UWWTD. Trend and change point analyses conducted on Total reactive phosphorus (TRP) and Total phosphorus (TP) data indicated that the concentrations and fluxes have been declining since the mid-1980s correlatingwith declining phosphate-fertilizer usage. The sites with the largest declines were from the Midlands and South East regions of England, whereas Scotland has seen almost no change. Significant step changes were encountered in most of the TRP concentration records, and most of these step changes were detected in the period 1993-1997 suggesting that the UWWTD was the key factor leading to these step changes. To validate this hyphothesis, Principal component analysis (PCA) was conducted to be able to track down the urban source contributor. Water quality (TRP, BOD, Suspended solids, Nitrate concentration and Conductivity) data from 5 example sites were analysed however, in none of the case examples was there an explicit principle component that could be construed with an urban or any other source contributor. Therefore, the PCA technique was not found to be a suitable technique to analyse this type of datasets.
Crowdfunding With Cognitive Limitations

To achieve the desirable funding target in a crowdfunding campaign, the project creator needs to accurately anticipate the pledging behaviors of contributors with practical cognitive limitations. In this paper, we present a study on how the contributors’ cognitive bounded rationality affects the creator’s campaign decisions. Specifically, we consider a two-stage crowdfunding model, where a creator first announces the project decisions (i.e., price and the minimum number of required contributors), and then the contributors choose their pledging behaviors (whether and how to contribute) as they arrive stochastically. We consider the cognitive hierarchy model, where contributors are classified into different levels according to their capability of anticipating other contributors’ behaviors. Surprisingly, we show that the cognitive limitation may improve the chance of project success, especially when the crowdfunding target is high. We prove that with a high average contributor cognitive level, a low funding target, and a low difficulty of motivating the lowest cognitive level contributors, the creator can achieve a close-to-optimal revenue by simply assuming that contributors are fully rational. Otherwise, ignoring the contributors’ cognitive limitations can lead to a significant revenue loss.
A Spatio-temporal Adaptive Personalized Meta-recommender for Next Location

As a popular location-based service, next point-of-interest (POI) recommendation predicts users’ next movements based on recent visits. Existing works mostly focus on individual sequential preference mining, ignoring the crucial crowd transition patterns under the effects of corresponding spatio-temporal (ST) contexts. Intuitively, users also follow crowd transition rules. In order to better exploit the extensive ST-specific collaborative signals among users, we propose a novel next POI recommender, including both ST-adaptive crowd-enhanced preference and user-specific multi-semantic interest modeling. We firstly introduce user-agnostic region-and timeslot-level graphs to capture ST-specific crowd transition patterns. As they imply fine-grained general preferences and function as prior meta knowledge, we employ a meta gated recurrent network to make an adaptive prediction for specific ST-context in a meta-learned unified way. Moreover, in modeling user-specific interest, we extract multi-semantic correlations from graph-augmented personal trajectories to obtain high-quality preference representation. Extensive experiments on real-world datasets show the superiority of our proposed model against seven state-of-the-art methods.
Lightweight Network-Based IoT Device Authentication in Cloud Services

Internet of Things (IoT) devices can be divided into two main categories: resource-rich devices such as smart TVs, fitness machines and connected vehicles; and resource-constrained such as low-battery body sensors, pacemakers or bridge monitoring sensors. This work focuses on the second type of devices, which usually have low computing power and/or demanding energy-consumption restrictions (e.g. unable to be charged frequently). It presents three lightweight approaches to provide constrained IoT devices with strong authentication by leveraging the trust in the cellular network and associated standard authentication mechanisms. Furthermore, leveraging the secure communication between cellular devices and the core network, two proposed solutions introduce as well a core network broker that secures the communication of constrained IoT devices that don't have capabilities establish/use secure channels.
Formal Analysis of IBC Protocol

Since the inception of Bitcoin in 2008, blockchain technology has had a significant impact on many fields. The lack of effective communication between heterogeneous and isolated blockchains, however, restricts the promotion and ecological development of blockchain industry. In this context, crosschain technology has rapidly developed and become a new research hotspot. Due to the decentralized nature of blockchain and the complexity of crosschain scenarios, crosschain technology faces huge security risks. In this paper, we propose formal analysis of the IBC protocol, one of the most popular crosschain communication protocols, aiming to help developers design and implement crosschain technologies more securely. We formalize three main components of the IBC protocol with TLA+, a temporal logic specification language, and verify some important requirements with model checking tool TLC. The verification results are analyzed comprehensively. Issues found through our formal analysis have been reported to the community, most of which have been acknowledged. We also propose some recommendations for removing potential risks.
Poster: mmBox: mmWave Bounding Box for Vehicle and Pedestrian Detection Under Outdoor Environment

Millimeter-wave technology's unique advantages, in-cluding low-light functionality, cost-effectiveness, and penetration of small objects, make it perfect for outdoor object detection. But traditional methods like likelihood clustering have faced challenges in determining target objects' extent and distance. This work presents mmBox, a two-stage system tailored for precise bounding boxes of vehicles and pedestrians outdoors. We assess mmBox's effectiveness through extensive testing in outdoor street scenes using multiple metrics.
Integral AI-based planning for management of WSNs in military operations

Military tactical scenarios have been shifting to more often consider combat situations in urban environments. Threats in these environments are generally more dynamic in nature, imposing new requirements on sensors and communications systems that support military operations. Wireless sensor networks (WSNs) with a large number of small and mobile computing nodes became the typical solution. However, WSNs demand additional complexity to dynamically manage their tasks, resource allocation, mobility, power consumption, and communication. This paper illustrates the integration of AI techniques into a Battle Management System (BMS) to support military operations in urban environments. The BMS is enhanced with an AI-based planner able to plan tasks, allocate resources, and monitor the WSN operation. The planner takes into consideration energy harvesting capabilities, secure data transfer, and authorization procedures. It generates plans using the information received from the sensors. In case new situations emerge, based on data fusion information, it automatically replans to adapt to the uncertainty in the environment. Finally, it takes into account the coverage between the different components to optimize the communications and better support WSN’s operator(s) and their activities.
Online learning for obstacle detection in construction for a multi-robot setting

Learning from sequential data as it becomes available opens up a diverse set of applications that would otherwise require large datasets to accurately represent rapidly changing environments, such as those found in construction environments. Due to labor shortages and hard manual labor, the construction industry is increasingly looking for automated solutions to increase efficiency. In this work, we present a model-agnostic online learning pipeline that utilizes the parameter estimation method RANSAC to generate ground truth labels which are used to iteratively update a deep learning model. The pipeline includes a dynamic dataset updated based on the amount of potential information to be learned. Specifically, each entry in the dataset is sampled with a probability inversely proportional to the model performance on those entries. Additionally, we utilize this same method to replace out-of-date entries as more data becomes available, ensuring a max size of the dataset with high learning potential. Each data entry in the dataset includes images with a temporal distance δ into the past. The images are stacked channel-wise to factor in the implicit temporal property of sequential video streams. Our experiments show that even with only retaining a modest dataset size this sampling strategy performs up to 15% better than a standard first-in-first-out dataset with a uniform sampling strategy.
A Knowledge Representation Framework for Evolutionary Simulations with Cognitive Agents

We propose a generic knowledge representation framework that supports evolutionary game-theoretic simulations using cognitive agents. The framework allows an experimenter to test a population of such agents via generations to study how specific population behaviours evolve over time. A generation is composed of rounds which can be further divided into encounters according to model-specific requirements. As agents in the population interact, events (caused either by agent actions or by separate environment processes) take place. These events change the environment, changes are then perceived by agents that, in turn, decide to take new actions that affect the environment. This process continues until it is time to evolve a new generation, when strategies of the fittest players are selected for the next generation to start evolving. This evolutionary loop continues until all the terminating conditions of the simulation are met. We use the framework to show how to successfully repeat existing experiments from evolutionary simulations of agent cooperation. Our results validate our framework and pave the way for EVOCOGNISIM, a simulation platform that implements the key aspects of the framework in a systematic manner.
Population structure and geographically structured reproductive strategies of the haplodiplontic seaweed Dictyota dichotoma

Both mating system variation and the propensity of many seaweeds to reproduce both sexually and asexually, leave a strong imprint in the genetic structure of species. In this respect, we study the population genetic structure of Dictyota dichotoma, a common haplodiplont brown subtidal seaweed. This benthic species is widespread in the NE-Atlantic, from the Canary Islands and Mediterranean Sea to southern Norway, but lately populations have been reported from Argentina and South Africa. Phenology and reproduction of D. dichotoma was monitored year-round in four populations to investigate how the species has adapted to the steep thermal gradient in southern and northern ranges of its distribution. Thirteen microsatellites are developed in order to assess patterns of population diversity and structure across the biogeographic range, as shaped by past and present processes. Last, we assess the genetic structure of South African and South American populations and their relationship to the northern hemisphere populations. Throughout its range, D. dichotoma shows a varying reproductive effort, with sexual reproduction being more abundant in the northern range. In contrast, the Mediterranean populations show a clear sporophyte dominance, suggesting that sexual reproduction is not the prime mode of reproduction, and indicating that the species potentially resorts to other modes of propagation as for instance fragmentation or apospory. Genetic diversity is highest in the southern population decreasing gradually northward, indicative for a recolonization pattern after the demise of the last glacial maximum where these areas served as glacial refugia. European mainland populations show an isolation by distance pattern, while the population in the Canary Islands has its own genetic identity, being significantly diverged from the mainland population. Populations in South Africa and Argentina are seemingly introduced from mainland Europe, but no conclusion can be made on the exact timing of these introductions.
Identification Method Based on Improved Hierarchical Multi-innovation Stochastic Gradient Algorithm

An improved hierarchical multi-innovation stochastic gradient algorithm was proposed for the parameter identification problem of input nonlinear closed-loop control autoregressive systems. In the existing literature, a hierarchical multi-innovation stochastic gradient algorithm was used to identify model parameters, but there are some problems such as slow convergence speed and low identification accuracy. In order to deal with these issues, the paper proposed an algorithm that combines the hierarchical multi-innovation stochastic gradient algorithm with momentum methods. Among them, the momentum method can help the stochastic gradient accelerate and suppress the oscillation in the related direction. Simulation results showed that the algorithm is effective.
SyncCast: Real-Time Self-Adaptive and Fault-Tolerant All-to-All Data-Sharing in IoT

Advancements in the domain of Synchronous- Transmission (ST) have brought a drastic improvement in the all-to-all data-sharing strategies in low-power wireless distributed systems. However, in the current design of the ST-based protocols, the inability of overhearing from the neighbors, and the lack of simultaneous system-wide commencement of the target process act as major bottlenecks in the faster percolation of data. The physical layer phenomena Capture-Effect (CE) is considered to be one of the fundamental strengths of ST. However, since the scope of CE largely depends on network topology, an ST-based protocol also mostly fail to exploit the full benefit of CE. Existing strategies try to exploit the topology information implicitly through on-the-fly exploration which, however, results in only partial advantage. In this work, we first introduce a few fundamental design ingredients to accelerate the percolation of data. Next, we propose a TDMA-based self-adaptive all-to-all data-sharing strategy SyncCast which explicitly learns the topology of the underlying network in real-time, and then on-the-fly optimizes the transmission plans to maximize the advantage from CE. Through extensive experiments in IoT-testbeds we demonstrate that despite being simple, SyncCast is upto 65 % faster and consumes 74% less Radio-on time compared to the existing state-of-the-art solution for all-to-all data-sharing.
Short-Term Wind Power Interval Prediction Method Based on Deep Convolution Neural Network

Wind power forecast is of great importance to the grid planning and dispatching. In view of the fact that traditional wind power prediction is mostly point prediction, the results generally have different degrees of error, and the wind speed signal used is mostly the wind speed signal at one point, ignoring the relativity of different place, resulting in insufficient forecast accuracy. This paper proposes a new forecast method on the basis of deep convolution neural network(DCNN). This method combines the DCNN and short-term memory network(STMN), uses the DCNN to gain the long-term wind speed spatial distribution characteristics, and uses the STMN model to obtain the short-term time series characteristics, which can simultaneously gain the forecast limits of the wind power considering the wind speed at multiple points, and can efficiently describe the incertitude of wind energy output. By comparing and analyzing with other methods, it can be seen that the design proposed in this paper can obtain better prediction precision and shorter response time, which can predict the intervals of wind power more excellent, and at the same time provide decision-making support for long-term reliable operations of the grid connection of wind energy.
Optimal Scheduling of Integrated Energy Systems Considering Master-slave Games and User Comfort

Integrated energy system has significant benefits in improving energy utilisation, and gradually becomes a research hotspot, how to establish the optimal operation model of the integrated energy operator (IEO) and the users is an urgent problem to be solved nowadays. Firstly, this paper constructs a framework for the operation of regional integrated energy system and analyses the coupling relationship between the subjects in the system. Secondly, a demand response flexible load model is established to consider the response comfort, and the flexible load incentive strategy is dynamically adjusted according to the energy comfort. Then, the game relationship between IEO and user aggregators is analysed, the Stackelberg game model between IEO and users is proposed, and the existence and uniqueness of the Stackelberg equilibrium solution is proved. Finally, the master-slave game objective is regarded as a nonlinear optimisation problem, and the dynamic operator adjustment genetic algorithm is used to solve the problem, and the experimental study through Matlab to establish the arithmetic simulation shows that this scheduling strategy can realise the joint improvement of the benefits of the management side and the user side.
SURVEY AND TEMPORAL AND EVOLUTIONARY ANALYSIS OF LINEAR WATER EROSION ON A SAMPLE HILLSLOPE OF THE LEOPOLDO RIVER

The article presents a temporal and evolutionary analysis of linear water erosion, as from a survey of a sample hillslope of the Leopoldo river, located in the municipality of Agudos (SP). The survey was elaborated from satellite images made available by Google Earth, which allowed to identify the main characteristics of erosive features. The analyzed characteristics are: slope position, length, width, line of inclination, shape, evolutionary stage, incision system, possible origin, curvature longitudinal and transversal. So, the article made it possible to study the erosions’ behavior and the relationship with local morphology.
JPEG-LS Compression on FPGA: A Solution for Wireless Capsule Endoscopy

In Wireless Capsule Endoscopy (WCE), image compression can be used to reduce the necessary bandwidth and power consumption for the wireless transmission, allowing for a longer battery life for a given image resolution and frame rate. In this paper an FPGA implementation of the JPEG-LS compression algorithm is developed for use in Wireless Capsule Endoscopy with the aim of investigating the hardware resource utilization and power consumption. The pipelined HDL implementation is designed to interface directly with a camera module at its input and a transmitting device at its output, where the compressed bitstream is stored in a buffer. The full system is intended to run on the Lattice iCE40UP5K low power FPGA and the considered camera module is the OV7670 from OmniVision. Tests performed on a gastro-intestinal image dataset, representative of the use case, showed an average compression ratio of 2.84. An estimated power consumption of 10.35 mW is reported along with a utilization of 27860 4-bit Look-Up Tables (LUT4) and 136.5 KiB block-RAM. The most resource demanding module is the output buffer for the bitstream, using more than 50 percent of the consumed LUT4. The Look-Up Table consumption exceeds that of the intended target FPGA and further work is needed to optimize the design.
Finding the appropriate harvest time of coffee fruits using convolutional neural networks

Coffee pricing is strongly influenced by fruit quality. High-quality coffee fruits are achieved when developed under adequate nutritional, climatic, and health conditions but also by finding the appropriate harvest time. This paper aimed to present a methodological approach based on Convolutional Neural Networks (CNN) to identify the ripening process of coffee fruits in order to support farmers in the harvest decision. The study was conducted using images of coffee plants from farms located in Brazilian cities which were further labelled by several experts regarding its maturation stage as well as the appropriateness to harvest or not. Such images were later used in the learning and evaluation of state-of-the-art CNNs architecture models. The computer simulations results were satisfactory, with the model surpassing 92% accuracy, thereby achieving values higher than those of some current models. This approach can significantly improve harvest management by increasing the precision of fruit ripening classification systems and the predictability of crop evolution, thereby increasing both the added value of the product and reducing costs through quality management.
OV2SLAM on EuRoC MAV Datasets: a Study of Corner Detector Performance

Theoretically, SLAM (Simultaneous Localization and Mapping) systems acquire information from its environment with sensors, extract landmarks from the received data and estimate its location on a map based on the sensor measurements. EuRoC datasets is a batch of visual-inertial measurements from embedded stereo camera and inertia measurement unit in a Micro Aerial Vehicle (MAV). The MAV flights include eleven itineraries and took place in indoor environments: an industrial environment and two motion capture rooms. OV2SLAM (Online and Versatile Visual SLAM) is an open-source visual feature points-based SLAM methods that is remarkably efficient. A feature points-based method extracts and tracks keypoints because they represent stable features. The native keypoint detection method in OV2SLAM tested in the EuRoC MAV datasets is a well-known KLT (Kanade-Lucas-Tomasi) corner detector. Nevertheless, no other detector was experimented on this SLAM method. This paper enables the investigation of which corner detector is optimum for OV2SLAM method on the EuRoC MAV datasets. Overall, the experiments are led on 10 itineraries containing 28 058 stereo-pair images in all. Thus, by varying the parameter of the Gaussian influencing the detection of the keypoints, a global score based on different statistics is calculated in relation to the ground truth to classify which pair detector/parameter is optimal on these datasets.