Miracle-dz/UniFAR-FTUs huggingface dataset:
Defining Benthic Organism Exposure: Bioavailability and Effects of Non-Polar Organics

In situ toxicity and bioaccumulation tests with Ceriodaphnia dubia (48-h), Chironomus tentans (96-h), Hyalella azteca (96-h) and Lumbriculus variegatus (96-h) were conducted at three stations on a river that was contaminated primarily with chlorobenzenes (CBs) and results were compared to a nearby reference site. Exposures were characterized by: 1) using mini-piezometers for contaminant profiling and determination of hydraulic heads and vertical flow direction within the sediments, and 2) measuring contaminants in sediment, surface water and exposure chamber water samples. Localized zones of upwelling and downwelling existed in the exposure areas at contaminated sites 5 and 18, while site 23 was downwelling at all measurement positions. Pore water samples from mini-piezometers contained CBs at the three contaminated sites which were highest at site 23. However, sediment and water samples from 251 exposure chambers at site 23 contained the lowest levels of CBs among the contaminated sites. CBs were not detected at the reference site, but other organic contaminants and metals were detected at all sites with the highest concentrations occurring at sites 5 and 18. In water column exposures, there were no significant (p > 0.05) differences in species survival between the contaminated sites and the reference. Mean percent survival of H. azteca, C. dubia and C. tentans exposed to surficial sediments (SS) at sites 5 and 18 was significantly (p < 0.05) reduced compared to the reference, whereas only C. tentans survival was significantly reduced at site 23. Body residues of total CB congeners in L. variegatus exposed to SS were highest at site 18 (618 μmol/kg lipid) and lowest at site 23 (21 μmol/kg lipid). The data suggest that downwelling reduced the bioavailability, of CBs in surficial sediments most likely by mobilizing the freely-dissolved and colloid-bound fractions to deeper sediments. Overall, downwelling conditions reduced the in situ exposure of organisms in surficial sediments, and hence, the toxicity and bioaccumulation of CBs. Hydrologic and chemistry data from nested mini-piezometers improved the interpretation of exposure-effects relationships.
Integrated Photonics for multi-channel RF scanning receivers

Electronic warfare (EW) receivers face strict requirements, balancing high performance with small size. To this extent, photonics offers a solution by providing wide bandwidth, flexible tuning, and immunity to electromagnetic interference. Recent advances in integrated photonics also offer potential reductions in weight and size, while demonstrating microwave photonics application capabilities. In this paper, we present the design and simulation results of a 6-channel EW receiver. It covers a wide frequency range from 0.5 to 40 GHz, with an instantaneous bandwidth of 1.333 GHz, suitable for modern radars. Our approach is based on hybrid integration, combining silicon nitride (SiN) and indium phosphide (InP) photonics platforms.
Classification of Alphabetic Handwriting of Pre-toddlers Using Support Vector Machine Method

Writing is an important aspect of literacy and an important learning foundation in addition to reading for children. The main challenge is to handle the unique characteristics and writing styles that are not yet fully formed in children’s handwriting. Therefore, technology is needed that can help children learn to write, especially alphabetic characters. This research aims to build a model based on machine learning for recognizing the alphabetic handwriting of pre-toddler children. This research uses the support vector machine (SVM) method and the directional element feature (DEF) feature extraction in grayscale images to effectively classify children’s alphabet handwriting. In this study, four kernels in the SVM model were tested. The best results of classification using the linear kernel obtained an average accuracy of 86.33% with a cost optimization parameter of 0. In the use of the RBF kernel, the best average accuracy is 84%, with a cost optimization parameter of 50. With the polynomial kernel, the best average accuracy is 68%, with the cost optimization parameter being 10. In the sigmoid kernel, the best average accuracy is 85.67% with a cost optimization parameter of 50. As a conclusion, the SVM model has proven effective for optimizing handwriting recognition, especially in children’s handwriting recognition.
Systematic Literature Review of Monetization in Mobile App: Strategies, Trends, Challenges, and Best Practices

In the digital age, the concept of monetization has undergone significant transformations, particularly within the mobile app industry. For mobile app developers, the identification of an optimal monetization strategy is crucial for achieving success. This study aims to thoroughly investigate monetization strategies, trends, challenges, and best practices in mobile application development. The ultimate goal is to assist firms in selecting the most suitable revenue stream while ensuring the delivery of high-quality applications to users. To accomplish this, the author conducted a comprehensive systematic literature review encompassing a wide range of topics, including monetization types such as freemium, in-app purchases, and in-app advertising, as well as the potential challenges and opportunities associated with each strategy. The findings reveal that among the three types of monetization strategies—free, premium, and freemium—the latter proves to be the most advantageous. However, the successful implementation of freemium requires careful consideration of various factors. To address this, the author proposed a diagram consisting of six levels of monetization strategies. This diagram serves as a practical tool that enhances firms’ awareness and enables them to make informed decisions when selecting the appropriate strategy for their mobile applications.
Edge Computing Sleep Mode Task Scheduling Based on Deep Reinforcement Learning

In edge computing, computing, storage, and network services are provided at the edge of the network. If the edge server is configured with the appropriate virtualization services, end-device requests can be processed at the edge to save network bandwidth and reduce response time. It is necessary for edge devices to run for a long period of time in order to receive user requests, however, users do not always send requests, which may result in some edge devices being idle for a long time and causing a significant amount of idle energy consumption.In this paper, we design a sleep-mode based edge computing architecture that uses containers as resource units. Also, based on the proximal policy optimization algorithm (PPO), we propose a task scheduling algorithm using this edge computing architecture, namely Proximal Policy Optimization Task Scheduling based on Sleep Mode (PPO-TSSM). Our goal is to minimize the total utility of the task; where the total utility is the tradeoff between completion time, energy consumption and deadline. The experimental results show that our proposed algorithm PPO-TSSM reduces the total cost by at least 3.33% (up to 62.92%) compared with the baseline algorithm, and reduces the minimum completion time by 5.37% to 157.7%, and the energy consumption by 2.95% to 8.97% compared with other baseline algorithms. Meanwhile, we set up PPO-TS-NS without sleep mode to compare with the environment using sleep mode, and find that PPO-TSSM reduces the energy consumption by 74.76% compared with PPO-TS-NS, while the minimum completion time only increases by 5.33%. Therefore, introducing sleep mode can effectively reduce the cost of the whole edge computing system.
A Rule-based Evaluation Method of Local Explainers for Predictive Process Monitoring

One of the main challenges in using machine learning (ML) models is to ensure the interpretability of their predictions. Addressing this challenge becomes increasingly important as ML models are adopted in business scenarios and applications. One approach to explaining predictions is by the application of intrinsically interpretable methods, such as rule-based explainers. However, when missing crucial interpretability criteria, an explaining rule turns out to be complex to understand for any user. Consequently, a complex explanation might leave the user doubtful about the predictions generated by an ML model for unforeseen new data. Certain criteria should be satisfied by an interpretable explanation before being introduced to human subjects. Nonetheless, a few research studies are concerned with providing quantitative evaluation of rule-based local explanations in the context of predictive process monitoring use cases. In this paper, we propose a set of measurements that quantify the interpretability and completeness of explanation rules. These measurements are based on characteristics and criteria that are advisable to be satisfied by a rule to be considered interpretable. The approach is evaluated using real-life event logs in order to compare the explanations of two local rule-based explainers, i.e., LORE and Anchor. Our experiments show that LORE generates more concise and more interpretable rules compared to Anchor. The proposed approach can be extended to include more local rule-based explainers for further evaluation.
Birds of a Feather Purchase Together: Accurate Social Network Inference using Transaction Data

Social networks play a crucial role in providing valuable contextual information across disciplines and applications. However, the unobservable nature of physical-world social connections has led to the development of social network inference. Existing approaches rely on co-occurrences and universal thresholds to infer social networks from spatiotemporal data. Yet, these methods suffer from two limitations: disregarding individual social preferences and failing to address “familiar strangers”. Our analysis reveals that relying solely on common spatiotemporal data is inadequate for accurate social network inference. Fortunately, the availability of extensive transaction data, encompassing spatiotemporal and consumption information, presents an opportunity. Our approach involves integrating individuals’ lifestyles with co-occurrences, driven by the fact that different lifestyles entail distinct social preferences and that true friends share similar lifestyles. However, we face two significant challenges: flexible extraction of lifestyle features and personalized threshold setting. To overcome these challenges, we propose nonparametric methods applicable to various scenarios and leverage domain knowledge for threshold determination. Evaluation on a real dataset of over 2, 000 individuals demonstrates an impressive improvement of over 20% in F1-score compared to the baselines.
Resting state cerebral networks in mouse lemur primates: from multilevel validation to comparison with humans

Measures of resting-state functional connectivity allow the description of neuronal networks in humans and provide a window on brain function in normal and pathological conditions. Animal models are critical to further address experimentally the function of brain networks and their roles in pathologies. Here we describe for the first time brain network organization in the mouse lemur (Microcebus murinus), a small primate attracting increased attention as a model for neuroscience. Resting-state functional MR images were recorded at 11.7 Tesla. Forty-eight functional regions were identified and used to identify networks using graph theory, dictionary learning and seed-based analyses. Comparison of results issued from these three complementary methods allowed the description of the most robust networks from mouse lemurs. Large scale networks were then identified from resting-state functional MR images of humans using the same method as for lemurs. Strong homologies were outlined between cerebral networks in mouse lemurs and humans.
Automatic Personalized Health Insurance Recommendation Based on Utility and User Feedback

Health insurance is a fundamental type of insurance that is essential to every person. Due to the increasing requests for personalized health insurance recommendation, traditional ways of obtaining recommended health insurance products from experts or agents are insufficient, and thus automatic personalized health insurance recommendation is needed. However, existing approaches to addressing this problem either need heavy input from users to obtain the utility of health insurance products for recommendation, or directly rely on user feedback and neglect the utility in recommendation. In this paper, we propose an automatic personalized health insurance recommendation system that incorporates both utility-based recommendation and user feedback based recommendation. To achieve that, we propose a two-component architecture in our system, where the first component is for daily operations to provide utility-based personalized recommendation for users with simple input of health risks, and the second component is to refine the first component by learning dynamic user preferences from user feedback data and combine the learnt preferences into utility-based recommendation. On a complex real-world insurance dataset, our proposed system achieves 2x more effectiveness than the baseline approaches for improving the recommendation quality.
Rehabilitation robot with a new diagnostic and control protocol

Using a connected robot for wrist and forearm rehabilitation, a new rehabilitation system is developed. Forearm and wrist biomechanics are studied as part of a rehabilitation protocol. An integrated wrist-forearm joint mathematical model is developed and integrated into the main controller. The proposed new rehabilitation protocol is composed of three main sessions: the first is dedicated to the extraction of the passive components of the dynamic model of the wrist-forearm biomechanics while the active components are extracted in the second session. The third session consists of performing the continuous exercises using the determined dynamic model of the forearm-wrist joints taking into account the torque due to muscle fatigue. In addition to determining the wrist condition and capturing the torque produced by the robot and that provided by the patient, this protocol aims to identify the level of fatigue faced by the wrist. An EMG signal is used to estimate muscle fatigue using a fuzzy classifier designed and implemented for this purpose. The results show that the rehabilitation system developed allows a good progression of the articular amplitudes as well as the resistive-active couple.
Ensemble Soft-Voting Model for Classification Optimization of Medicinal Plants Leaves

Growing public awareness of the value of going “back to nature’’ encourages the use of natural substances in pharmaceuticals. Chemical medications are thought to pose a great risk to the human body if used repeatedly, which has raised awareness of the importance of getting back to nature. Another issue is that not everyone in the community has access to information on therapeutic plants. Visible identification is thought to be ineffectual because of human mistakes, multiple sample testing, inadequate equipment, waiting times, and reading errors. Because of this, machine learning is crucial in finding patterns and plants more rapidly and accurately. The ensemble voting method is being used in this study to increase the accuracy of individual classifier findings. Additionally, this study analyzes various machine-learning techniques utilizing validation and test data. The process begins with feature extraction, which is followed by data normalization. Support Vector Machine (SVM), Random Forest (RF), and k-Nearest Neighbor (k-NN) are the three machine learning algorithms (individual classifiers) that are used to train the dataset. The prediction outcomes from the individual classifier algorithm are then entered into the ensemble voting method using soft voting. a person who uses a soft vote. 640 training data, 200 test data, and 160 validation data were divided randomly for the investigation. Individual classifier accuracy in this study was 91%, 94%, and 93% for SVM, RF, and k-NN, respectively, while accuracy for the ensemble soft-voting approach was 96%. Comparing the results of separate classifiers with the soft voting approach revealed an improvement in accuracy.
Intrusion Detection System Based on Machine Learning Techniques: A Survey

The volume of network traffic data has become so big and complicated as a result of the development in Internet-based services that it is extremely difficult to process using typical data processing techniques. Due to the enormous and complicated nature of network traffic data, fast and effective cybersecurity intrusion detection is a highly difficult task. In order to detect hostile traffic as soon as feasible, a realistic cyber security intrusion detection system should be able to handle a huge volume of network traffic data as quickly as possible.This paper studies a classification-based intrusion detection algorithms based on Machine Learning for speedy and effective intrusion detection in huge network traffic, including Support Vector Machines (SVM), Random Forests(RF), Decision Trees(DT), Naïve Bayes (NB), Deep Neural Network (DNN), Extreme Gradient Boosting (XG Boost), Nondominated Sorting Genetic Algorithm (NSGA2), as well as Deep Belief Network (DBN) and artificial neural network(AI). It has been applied to ACTUAL TIME The KDD dataset, KDD Cup 99 dataset, NSL-KDD dataset, CICIDS-2017 dataset, CICIDS-2018 dataset, ISCX Dataset, CICAndMal2017 dataset and UNSW-NB15 to compare performance based on rating Intrusion detection systems which are assessed in terms of training time, prediction time, accuracy.
From ERS-1/2 to Sentinel-1: two decades of subsidence monitored through A-DInSAR techniques in the Ravenna area (Italy)

Land subsidence due to underground resources exploitation is a well-known problem that affects many cities in the world, especially the ones located along the coastal areas where the combined effect of subsidence and sea level rise increases the flooding risk. In this study, 25 years of land subsidence affecting the Municipality of Ravenna (Italy) are monitored using Advanced Differential Interferometric Synthetic Aperture Radar (A-DInSAR) techniques. In particular, the exploitation of the new Sentinel-1A SAR data allowed us to extend the monitoring period till 2016, giving a better understanding of the temporal evolution of the phenomenon in the area. Two statistical approaches are applied to fully exploit the informative potential of the A-DInSAR results in a fast and systematic way. Thanks to the applied analyses, we described the behavior of the subsidence during the monitored period along with the relationship between the occurrence of the displacement and its main driving factors.
Challenges Towards VR Technology: VR Architecture Optimization

This paper provides an exhaustive exploration of the challenges faced in the development and application of Virtual Reality (VR) technology. Despite VR’s revolutionary potential across various sectors, its full-fledged realization is impeded by technical, financial, and ethical hurdles. Technical issues involve the development of high-quality hardware and software, seamless integration with existing infrastructures, and the demand for extensive computational resources. Financial constraints arise from high production and maintenance costs, which limit accessibility. Ethically, VR’s immersive nature presents the potential for addiction, antisocial behavior, and data privacy concerns. The paper further discusses the trade-off decisions, complexity, technological limitations, hardware-software co-optimization, future-proofing, testing, and validation associated with VR development. Furthermore, a survey of resolutions to these challenges, as proposed in recent scientific literature, is presented. The article underscores the need for continued exploration, innovation, and refinement to overcome these hurdles and unlock the transformative power of VR technology.
Neural Style Transfer of Text Supervision

Style transfer technology has been widely applied in the field of image processing. Most of the current style transfer methods obtain style information from a single mode. This will cause the model to lose or incomplete information during style transfer, which will affect the visual effect of the resulting image. Multimodal technology is a method that combines the information of different modes together to realize a more comprehensive understanding and processing of data. It has the advantages of rich information expression, cross-modal interconnection, complementing and complementing each other, which provides a new idea for style transfer method. Based on CNN, this paper proposes a style transfer method using text information as constraint. On the one hand, the style information provided by the text makes up for the deficiency of the visual modal information, which helps the model to learn more style information. On the other hand, the semantic structure and spatial relationship provided by the text playa constraining role in maintaining the semantic structure of the content image. The experiment shows that compared with the traditional style transfer method, the style transfer method with text constraint can solve the problem of information loss and incomplete by using a single mode style transfer, and get better experimental results.
Graph Neural Networks with Non-Recursive Message Passing

Graph neural networks (GNNs) have become the de-facto standard for learning on graphs. GNNs involve a recursive message passing mechanism to recursively aggregate messages from adjacent nodes. It is in line with the topological structures and has dominated the implementation of existing GNN models. However, it causes a critical issue, i.e., messages from high-order neighbors must be transmitted layer by layer. Important high-order neighbors of a node could be trivial to its low-order neighbors, which corrupts long-range messages. In this paper, we propose a simple but effective non-recursive message passing model (nrecGNN) to enable each node to access its multiple-hop neighbors directly. nrecGNN considers neighbors with the same order as a hop set and combines messages within each set to obtain hop-level representations. The final embedding representation of a node is explicitly obtained by aggregating all hop representations in a non-recursive manner. We theoretically prove that nrecGNN has the same expression capacity as its recursive counterpart. Experiments on multiple benchmark datasets of various scales and types demonstrate the superiority of nrecGNN against the state-of-the-art GNNs.