Miracle-dz/UniFAR-FTUs huggingface dataset:
A spatiotemporal reconstruction of the C. elegans pharyngeal cuticle reveals a structure rich in phase-separating proteins

Roughly 4.5 million species of ecdysozoan animals repeatedly shed their old cuticle and construct a new one underneath to accommodate growth. How cuticles are constructed is not well understood. Here, we systematically mine gene expression datasets to uncover the spatiotemporal blueprint for how the chitin-based pharyngeal cuticle of the nematode Caenorhabditis elegans is built. We demonstrate that the blueprint correctly predicts expression patterns and functional relevance to cuticle development. We find that as larvae prepare to molt, catabolic enzymes are upregulated and the genes that encode chitin synthase, chitin cross-linkers, and homologs of amyloid regulators subsequently peak in expression. 48% of the gene products secreted during the molt are predicted to be intrinsically disordered proteins (IDPs), many of which belong to four distinct families that are expressed in overlapping waves. These include the IDPAs, IDPBs, and IDPCs that are introduced for the first time here. We find that all four families have sequence properties known to drive phase separation and show in vitro phase separation for one of these proteins. This systematic analysis reveals the massive contribution that IDPs make to the cuticle and highlights how reversibly phase-separating materials may facilitate cuticle disassembly and reassembly during the molt.
Parameterising Bayesian networks: a case study in ecological risk assessment

Most documented Bayesian network (BN) applications have been built through knowledge elicitation from domain experts (DEs). The difficulties involved have led to growing interest in machine learning of BNs from data. There is a further need for combining what can be learned from the data with what can be elicited from DEs. In previous work, we proposed a detailed methodology for this combination, specifically for the parameters of a BN. In this paper, we illustrate the techniques using a case study of an ecological risk assessment problem.
A Dynamic Video Switching Mechanism for Distributed Crane Control System

The automatic storage yard is an essential component in the construction of the intelligent harbor. In the process of constructing an automatic storage yard, the distributed crane control system is critical. However, during the operation process of the distributed crane control system, the video switching mechanism randomly assigns tasks to operators by switching surveillance videos, which limits the efficiency of the distributed crane control system. This paper solves the video switching problem as a task assignment problem, and a mixed-integer linear programming model is established. The branch and bound algorithm is adopted to solve the model on Lingo17.0. The experimental results show that the dynamic video switching mechanism proposed in this paper is efficient.
Graph Laplacian in ℓ2 – ℓq regularization for image reconstruction

The use of the Laplacian of a properly constructed graph for denoising images has attracted a lot of attention in the last years. Recently, a way to use this instrument for image deblurring has been proposed. Even though the previously proposed method was able to provide extremely accurate reconstructions, it had several limitations, namely it was only applicable when periodic boundary conditions were employed, the regularization parameter had to be hand-tuned, and only convex regularization terms were allowed. In this paper, we propose two automatic methods that do not need the tuning of any parameter and that can be used for different imaging problems. Moreover, thanks to the projection into properly constructed subspaces of fairly small dimension, the proposed algorithms can be used for solving large scale problems.
A Fast and Effective Panorama Stitching Algorithm on UAV Aerial Images

Nowadays, UAV(Unmanned Aerial Vehicle) has been widely used in many areas, such as agriculture, rescue and disaster relief, video shooting. In some applications, the whole environment needs to be perceived, so image stitching comes in handy. It is time-consuming for present methods to stitch aerial images, whose bottleneck is to generate dense point clouds. Besides, traditional image stitching algorithm may encounter accumulative error which makes the result distortion. In this paper, a fast UAV image stitching algorithm is proposed by re-placing generation of dense point clouds with image composition algorithm and taking full advantages of GPS information to initialize cameras' rotation to weaken the impact of accumulative error. The proposed model is much simpler than classical models in UAV image stitching, it can handle large scene very quickly. Furthermore, the effect of stitching is better than the current commercial software(Pix4D). When overlap ratio between two images is low, the proposed algorithm can generate a good stitching result, while commercial software may generate some black areas.
A Meta Multi-agent Reinforcement Learning Algorithm for Multi-intersection Traffic Signal Control

Multi-agent deep reinforcement learning (MDRL) has made remarkable progress in the multi-intersection traffic signal control, and meta-learning further promotes its learning capability. However, most existing MDRL algorithms seem to have some drawbacks. 1) These algorithms only employ the information at each step (i.e., short-term information) to train the models, which may lead to induce the non-optimal traffic-signal policies due to the long-term information (e.g., tasks of each agent) ignored. 2) These algorithms based on meta-learning cannot effectively tackle diversity among the tasks due to the shared parameters describing the ‘average’ source tasks. Motivated by the above observations, in this paper, we propose an algorithm, referred to as Meta Multi-agent Advantage Actor-critic (ME-MA2C) algorithm, for multi-intersection traffic signal control. The proposed ME-MA2C algorithm has two components: 1) It conducts meta-learning (ME) using a proposed task-neighbor encoder, which is a meta-learning algorithm. ME algorithm encodes both the short-term information and the long-term information to learn the meta embedding and meta-knowledge, which helps to induce optimal traffic-signal policies. 2) It also conducts policy learning using a Multi-agent Advantage Actor-Critic (MA2C), which is a decentralized multi-agent framework. MA2C framework makes use of the learned meta embeddings and meta-knowledge, and optimizes the whole algorithm to derive transferable traffic-signal policies. Experimental results on different datasets illustrate that ME-MA2C algorithm outperforms the state-of-the-art algorithms in terms of multiple metrics, which seems to be a new promising algorithm for multi-intersection traffic signal control.
An Analysis of Cloud Certifications' Performance on Privacy Protections

: Cloud computing is an evolving paradigm that changes the way humans share, store, and access their information in digital form. Although cloud computing offers tremendous benefits, it also brings security and privacy challenges. Certifications have been developed by governments and authorized organizations as a new approach to protecting users’ information in the cloud. While the security controls in the certifications have been well established and widely applied, the privacy protections provided by certifications are still ambiguous and yet to be examined. In this study, we identified and selected four cloud certifications that are commonly used for certifying the security and privacy of cloud computing, and we evaluated their performance on privacy protections specifically to understand how privacy is treated in these certifications according to their existing controls. Our research reveals a lack of privacy controls in the current certifications and inadequate privacy-related content; even when present, such content is not clear or is difficult to distinguish from security controls. Results demonstrate that without having a set of baseline privacy protection criteria or standards, it is very challenging to determine cloud certifications’ performance and adequacy for privacy protections. It also points to the urgent need for the development of a consistent and comprehensive privacy framework that can be utilized for such evaluations.
Bias Bubbles: Using Semi-Supervised Learning to Measure How Many Biased News Articles Are Around Us

The proliferation of web 2.0 technology allows us to easily create and share online content, but also leads to the rapid spread of mis-information and biased media, which has considerable negative effects on society. Deep learning-based classifiers are one common way of identifying media bias, but they suffer from a lack of large-scale labelled datasets. In this paper, we first explore the use of pseudo-labelling technology to mitigate this problem. Second, we exploit a masking method to identify biased sentences in news articles by iteratively masking each sentence from an article and observing the change in output of a bias detection model. These identified sentences not only contribute to evaluating the proposed model, but also enable end-users to understand where media bias arises in an article. Finally, we apply our well-trained bias detection model to a well-known news article dataset to show how widespread media bias is—the results show that it is rampant and has become a serious social problem that we cannot ignore.
On Tracking Ransomware on the File System

: Ransomware detection is gaining growing importance in the scientiﬁc literature because of widespread and economic impact of this type of malware. A successful ransomware detection system must identify a malicious behaviour as soon as possible while reducing false positive detection. To this end, different strategies have been explored. Recently, a promising approach has risen. It consists in looking for possible running ransomware by measuring the different activities every process does on the ﬁlesystem. Such measurements are represented with quantitative “indicators”. Indicators selection and their interpretation, is a critical and challenging task. In this paper we survey some of most representative ﬁle-system centered ransomware detectors and describe their chosen behavioural indicators and strategies used to measure them. Then we compare the different solutions and discuss pros, cons and open issues of every approach.
Development of a Raspberry Pi-based Underwater Camera System for Inland Freshwater Aquaculture

Optical vision-based system is one of the tools for non-invasive monitoring of freshwater fish ecosystems. This system can be utilized to gather visual data from underwater scenes (in the form of images and videos) that contains information about the freshwater fish environment. Currently, action camera with waterproof housing is the most common choice for underwater visual data acquisition, due to its low cost and accessibility. However, this type of underwater camera is limited by its on-board battery and storage capacities, which makes the operations of placement and retrieval of and the extraction of data from this camera being appended to the many manual processes of freshwater aquaculture management. In contrast, the proposed system automates the data acquisition process by developing an underwater camera system that automatically captures and stores data into local and remote storages. The proposed system is based on Raspberry Pi, an open-source computing platform that allows rapid prototyping, with low-cost, modularity, flexibility, and accessibility as the definite benefits. Upon several simulated runs, the proposed system was able to capture series of 8 MP underwater images during each scheduled acquisition instance at 6. 244-6.528 frames per second. Moreover, proposed system was able to upload underwater images to the remote storage at 0.388-0.469 Mbps at 0-5.55% error.
Prediction of Surface Roughness and Incidence Effects on Turbine Performance

The use of a Navier-Stokes analysis to predict the change in turbine efficiency resulting from changes in blade surface roughness or incidence flow angles is discussed. The results of a midspan Navier-Stokes analysis are combined with those from a quasi-three-dimensional flow analysis code to predict turbine performance. A quasi-three-dimensional flow analysis code was used to determine turbine performance over a range of incidence flow angles. This analysis was done for a number of incidence loss models. The change in loss due to changes in incidence flows computed from the Navier-Stokes analysis is compared with the results obtained using the empirical loss models. The Navier-Stokes analysis was also used to determine the effects of surface roughness using a mixing length turbulence model, which incorporated the roughness height. The validity of the approach used was verified by comparisons with experimental data for a turbine with both smooth and rough blades tested over a wide range of blade incidence flow angles.Copyright © 1993 by ASME
A Demand Response Trade Model Considering Cost and Benefit Allocation Game and Hydrogen to Electricity Conversion

With the widespread utilization of hydrogen in industry and the development of hydrogen-to-electricity (H2E) technology, how to design an efficient market-based demand response trade mechanism draws lots of attention for incentivizing industrial users to promote their demand response quantities and willingness with rational use of hydrogen. This article proposes an auction model of buyers and sellers considering the cost and benefit allocation game among electric transaction manager (ETM), electric response agents (ERAs), and electric-hydrogen industrial users (EHIUs) in demand response trade. First, a participant-led distributed trading framework of electricity demand response (EDR) with ETM, ERAs, and EHIUs is proposed. Second, in order to inspire ERAs to facilitate EDR trade and enhance EHIUs’ willingness to participate in the EDR, a bargain game of the cost and benefit allocation among ETM, ERAs, and EHIUs is established. Then, a co-optimization model with electricity and hydrogen considering production constraints is formed for promoting the ability and economy of EDR of EHIUs. Finally, a modified IEEE-69 node system is employed to illustrate the validity of the proposed method.
Sentiment Polarization in Online Social Networks: The Flow of Hate Speech

The influence of sentiment polarization and ex-change in online social networks has been growing and studied by many researchers and organizations worldwide. For example, the sentiments expressed in a text concerning a topic in the discussion tend to influence a community when a Twitter user retweets the original text, causing a chain of reactions within a network. This paper investigates sentiment polarization in Twitter, focusing on tweets with the hashtags #Coronavirus, #ClimateChange #Immigrants, and #MeToo. Specifically, we collect the tweets mentioned above and classify them into five categories: hate speech, offensive, sexism, positive, and neutral. In this context, we address the problem as a multiclass classification problem by using the pre-trained language models ULMFiT and AWD-LSTM, which achieved a Fmicro of 0.85. Finally, we use the classified dataset to conduct a case study in which we capture the sentiment orientation during the network evolution.
Secure Access Service Edge: A Multivocal Literature Review

Over the last decade, corporate networks have undergone significant changes and have been increasingly reliant on cloud-based services to run their businesses. The Covid-19 pandemic has expedited this pattern. In this scenario, there is a need to provide security to infrastructures in an advanced and integrated way. Gartner invented the term "Secure Access Service Edge (SASE)" to meet the above-mentioned goal. SASE is a single framework cloud-native architecture that integrates various network and security functions. SASE is seen as a new cybersecurity solution that impacted established vendors but has received little academic attention. Therefore, in this paper, we present a Multivocal Literature Review (MVLR) aiming to gather an illustration of SASE, including definition, key characteristics, reported benefits and challenges and, finally, critiques of this new term. SASE is adopting the benefits of cloud approaches adding security to the service, however, challenges remain in several setups, including legacy systems.
Automated Detection of Lung Cancer Using MRI Images

Among the type of cancer diseases, lung cancer is the leading cause of death for both men and women. Considering the new cases, it's the 2nd commonest type of cancer. Detection of lung cancer at the very early stage will assist physicians to provide proper medications to the patients to limit the cancer. A number of existing screening options such as CT, X-ray, MRI and numerous computerized approaches are available to detect lung cancer at the very early stage. But, due to cell structure it is still a challenging task. Additionally, its interpretation is still difficult and time consuming for the radiologists. An automated computerized approach can assist to make this process easier. This study delineates an automated approach for lung cancer detection using MRI images of lung. This study approach tends to provide an accuracy rate of 96.28% using conventional image processing approach and 96.55% using CNN based classification approach which possesses an acceptable accuracy rate.
Improved dynamic recurrent-based ELM neural network for fault prediction

With the increase of the complexity of industrial system, the demand of real time of accuracy of fault prediction is higher. Therefore, an improved dynamic recurrent-based extreme learning machine(DR-ELM) neural network is proposed for fault prediction. For the network structure, a feedback layer is added to memorize the hidden output. And the trend characters of data variation is extracted from the feedback information, so as to update the output weight of feedback layer dynamically. Through the prediction of the next-time output for the nonlinear dynamic system, the diagnosis is made for the prediction output, then the fault prediction is realized. A numerical study(Sinc test) and the complicated Tennessee Eastman(TE) benchmark process show the superiority of the proposed approach not only in prediction accuracy but also in dynamic adaptability, and the better prediction ability for non-linear sequential systems.
Time Difference of Arrival Estimation of Multiple Simultaneous Speakers Using Deep Clustering Neural Networks

A novel multiple acoustic source localization approach is presented that is capable of providing spatial information about concurrent active speakers from a mixture signal captured by a microphone array. The proposed method first separates the observed array mixture signal into single speaker array signals using deep clustering (DC), which is a deep neural network (DNN) based method that maps source signal information into an embedding space, in which a clustering algorithm can be then used to separate the sources. Spatial information in terms of time difference of arrival (TDoA) can be then extracted from each separated signal. This approach is novel for TDoA estimation of multiple sources, since the state-of-the-art method first localizes multiple sources and then performs the separation. The inherent advantage of the proposed approach is that there is no need for data association of the measurements and the sources. The results with data from an actual room show that the proposed approach outperforms the current state-of-the-art in extracting the spatial information from two concurrent speakers mixture signal.
Precision Agriculture Detecting NPK Level Using a Wireless Sensor Network with Mobile Sensor Nodes

Agriculture is one of the most important things to be considered in a nation because it is one of its backbones. The nutrients plants need most and will not survive are referred to as the primary macronutrients are Nitrogen, Phosphorus, and Potassium. These nutrients are necessary for healthier crops and higher yields, so NPK fertilizers are usd to supplement the soil’s lack of nutrients. To detect the amount of NPK in soil, this paper proposed a wireless sensor network using mobile sensor nodes. For detecting soil nutrients, the device used a lux sensor. To prepare the soil for sensor calibration, the sensor is calibrated using controlled NPK. To ensure that the nutrient in the soil sample is correct, the soil-controlled NPK is checked with the UPLB Soil Test Kit. Soil samples are used to calibrate and determine the thresholds for low, medium, and high nutrient levels in each soil. Different forms of soil and lighting conditions will be used in the tests. The average percent difference between different soil type’s NPK values is as follows: 6.30%, 16.88%, and 13. 65%. The values are higher than 5%. While, for the different lighting conditions, and the average values are as follows: 054%, 1.21%, and 1. 11%. The following percentages are less than 5%. The system is only limited to following a straight line, is not waterproof, and cannot be used in muddy or extremely rough conditions. In addition, the device should be calibrated for each use under various lighting conditions and soil types.
A Real-Time Human Mobility Visualization of Covid-19 Spread from East Asian Countries

Human mobility using different modes of transportation is constantly increasing. The culture of posting activities while travelling has gained attention on social networks immensely. The evolution of social networking platforms has resulted in an engaging user base multiplying across the globe. The combination of data and information created by these online platforms is massive in terms of the volume and variety of topics. The real-time existence of user-produced information has inspired researchers to analyze material to obtain real-time insight into current affairs. The focus is on collecting tweets of real-time travelling activities for the first 100 days of Covid-19 keeping Chinese Airports as the source. This paper illustrates the multidimensional visualization of real-time covid-19 spread from China & neighbouring East Asian countries to the rest of the world. The visualization tools used are python folium, matplotlib & networks/graphx, Carto, Tableau, Google Data Studio, and MS Excel.
Educational Robotics in the Service of the Gestalt Similarity Principle

Today, many technological approaches, including educational robotics (ER), enrich the teaching process through gamification. Several studies have shown that educational activities with robots lead to increased student interest, a positive and more effective learning process, and several different skills. This paper adopts a content-based image retrieval mechanism to automate the Gestalt similarity testing process and evaluates the impact of the involvement of a humanoid robot. The proposed framework aims to improve participants' visual perception, cultivate their creativity, and improve their visual working memory. During a pilot study, the participants communicate with the proposed framework either by using a tablet or by interacting with a humanoid NAO robot. The experimental results showed that the participation of NAO significantly increased the interest, attention, and commitment of the students.