Miracle-dz/UniFAR-FTUs huggingface dataset:
Development of a Coupled Land Surface and Groundwater Model

Abstract Traditional land surface models (LSMs) used for numerical weather simulation, climate projection, and as inputs to water management decision support systems, do not treat the LSM lower boundary in a fully process-based fashion. LSMs have evolved from a leaky-bucket approximation to more sophisticated land surface water and energy budget models that typically have a specified bottom layer flux to depict the lowest model layer exchange with deeper aquifers. The LSM lower boundary is often assumed zero flux or the soil moisture content is set to a constant value; an approach that while mass conservative, ignores processes that can alter surface fluxes, runoff, and water quantity and quality. Conversely, groundwater models (GWMs) for saturated and unsaturated water flow, while addressing important features such as subsurface heterogeneity and three-dimensional flow, often have overly simplified upper boundary conditions that ignore soil heating, runoff, snow, and root-zone uptake. In the present stud.
Alphacoronavirus Protein 7 Modulates Host Innate Immune Response

ABSTRACT Innate immune response is the first line of antiviral defense resulting, in most cases, in pathogen clearance with minimal clinical consequences. Viruses have developed diverse strategies to subvert host defense mechanisms and increase their survival. In the transmissible gastroenteritis virus (TGEV) as a model, we previously reported that accessory gene 7 counteracts the host antiviral response by associating with the catalytic subunit of protein phosphatase 1 (PP1c). In the present work, the effect of the absence of gene 7 on the host cell, during infection, was further analyzed by transcriptomic analysis. The pattern of gene expression of cells infected with a recombinant mutant TGEV, lacking gene 7 expression (rTGEV-Δ7), was compared to that of cells infected with the parental virus (rTGEV-wt). Genes involved in the immune response, the interferon response, and inflammation were upregulated during TGEV infection in the absence of gene 7. An exacerbated innate immune response during infection with rTGEV-Δ7 virus was observed both in vitro and in vivo. An increase in macrophage recruitment and activation in lung tissues infected with rTGEV-Δ7 virus was observed compared to cells infected with the parental virus. In summary, the absence of protein 7 both in vitro and in vivo led to increased proinflammatory responses and acute tissue damage after infection. In a porcine animal model, which is immunologically similar to humans, we present a novel example of how viral proteins counteract host antiviral pathways to determine the infection outcome and pathogenesis.
Proposal of Proof-of-Lucky-Id(PoL) to Solve the Problems of PoW and PoS

With Bitcoin, the legitimacy of a block of transaction data is verified by randomly selected nodes (hereinafter, verifiers) and notified to all nodes. To ensure the random selection of the verifier of each block, Proof of Works (PoW) is used. However, there is a serious problem with PoW, in that the amount of computation becomes huge. Although Proof of Stake (PoS) has been proposed to reduce the amount of computation, there remains the problem that if there is collusion among nodes, it is possible to control the probability of being the verifier. In this paper, a novel technique, Proof-of-Lucky-ID (PoL), is proposed. PoL is a kind of lottery that guarantees the randomness of the selection of the verifier, even if the node is collaborating unfairly, by using multiple digital signatures created by other nodes as input. Since this technique can determine the verifier with about two hash calculations per node, it significantly reduces the amount of processing compared with PoW, which requires more than 10^19 hash calculations as of 2017. In addition, since the input value cannot be controlled even if there is node collusion, it is impossible to control the probability of a win. Furthermore, compared to PoW and PoS, the certainty of consensus can be estimated with high accuracy in a short time. Numerical calculations show that both the randomness of verifier selection by the proposed method and the accuracy of the certainty of consensus range estimation are sufficiently high.
Drugledger: A Practical Blockchain System for Drug Traceability and Regulation

Drug traceability system is essentially important for public drug security and business of pharmaceutical companies, which aims to track or trace where the drug has been and where it has gone along the drug supply chain. Traditional centralized server-client technical solutions have been far from satisfying for their bad performances in data authenticity, privacy, system resilience and flexibility. In this paper, we propose a scenario-oriented blockchain system for drug traceability and regulation called Drugledger, which reconstructs the whole service architecture by separating service provider into three independent service components and ensures the authenticity and privacy of traceability data. Drugledger is more resilient than traditional solutions with its p2p architecture. Furthermore, Drugledger could efficiently prune its storage, achieving a finally stable and acceptable blockchain storage. Besides, algorithms reflecting the real drug supply chain logic (e.g, package, repackage, unpackage, etc.) are designed based on the expanded UTXO workflow in Drugledger. To our knowledge, it is the first systematic work from both a technical and practical perspective on how blockchain system could be designed for drug traceability and regulation.
Chain Veri: Blockchain-Based Firmware Verification System for IoT Environment

The number of deployed Internet of Things (IoT) devices has been increasing rapidly, and security has become significantly important in IoT environments. However, security issues remain challenging due to constraints of IoT devices. One critical security issue is that attacks tamper the firmware of IoT devices. Once the firmware is tampered, it is difficult to notice and recover the tampered firmware. For the integrity of firmware, this paper proposes a new blockchain-based firmware verification system, called Chain Veri. We overcome vulnerabilities of previous studies such as fudging firmware verification. Furthermore, unlike previous studies that presented only the concepts and ideas, we implement Chain Veri from scratch and carry out performance evaluation on storage consumption and CPU cycles to run Chain Veri on IoT device. Our results show that only 1.2MB of storage is used to verify 2,845 devices. Also, 1,692 CPU cycles are to generate a palette in ChainVeri.
Be Right Beach: A Social IoT System for Sustainable Tourism Based on Beach Overcrowding Avoidance

The coastal erosion is becoming of paramount importance for many countries. Many studies demonstrated that in some cases the beach overcrowding is the primary cause of coastal erosion. The goal of this work was to design, implement and test a system (BRB-Be Right Beach) that foster beach overcrowding avoidance and allows anyone to choose the right beach to go for having the best experience. The major requirement of our system was to have maximum accuracy (no errors, that is real-time data only are used) in the information provided to the users. The system exploits the Social Internet of Things paradigm to implement a classifier trained by a community of smartphones brought by the owners to the beaches. The BRB sensor network consists of control units equipped with a UV sensor, a thermometer, a humidity sensor and a camera for crowdedness estimation. Data are collected by a cloud platform that provide any user with information about beaches and suggestions where to go, based on users preferences like weather, crowdedness, time of travel, and so on.
Classification of Programming Problems based on Topic Modeling

Programming skill is one of the most important and demanding skill in the current generation. In order to enable learners and programmers to practice programming and gain problem-solving skills, many Online Judge (OJ) systems exist. Most of these OJ systems have to be operated solely by students and learners. These students and novice programmers sometimes compete against each other or solve the programming problems by themselves in offline mode. But, most OJ systems have their problems arranged simply into volumes and various contests events. This arrangement system does not have any clear indication of the difficulties and categories of problems. Thus, in this paper, we have studied reliable techniques on the extraction of keywords and features which can categorize these OJ system's programming problems into their respective types and skills. We have leveraged two popular topic modeling algorithms, Latent Dirichlet Allocation (LDA) and Non-negative Matrix Factorization (NMF) to extract relevant features. Afterward, six classifiers were trained on these topic modeling features and Naive TF-IDF features. From our studies, we discovered that topic modeling features were relatively smaller in dimensionality, yet matched the performance when trained on high dimensional naive TF-IDF features. Our main goal was to understand the precise trade-off between accuracy and dimensionality of the textual data of programming problem statements. This experiment has enabled us to obtain important tags, hint, and classification of Online Judge programming problems.
Estimated Parameters of 6 x 6 Latin Square Design Consisting of Two Missing Values

A design of experiment is a computational technique which is used to select the significant independent variables. The well-designed experiments should reduce the experiment error. We referred to the blocking technique to prevent the error from other variables. This paper focused on the incomplete latin square design (ILSD) based on the blocking technique. The missing values from experiments caused the unbalance design in which the instant formulae were not provided for the analysis of the variance (ANOVA). By means of the exact approach, this paper considered the incomplete latin square design of order 6 x 6 with the two missing values to develop the mathematical formulae for the estimated parameters for full effect model. Finally, it is easy to make the ANOVA. It is noted that the mathematical formulae in this paper can be used to solve all indexes.
A Privacy-Preserving Incentive Framework for the Vehicular Cloud

In this paper, we introduce a promising secure and privacy-preserving incentive mechanism in the vehicular cloud (VC). Such an incentive will convince vehicles with excess sensing data to join in the VC without the risk of privacy disclosure. The proposed scheme uses the Bargain Game theory to model the interactions between a VC server (VCS) and vehicles. With the proposed incentive mechanism, the VCS can select competent vehicles with which to collaborate for the announced task, while the vehicles can earn payment for participation. Further, our mechanism ensures fairness between all participants in terms of reward. We also exploit the signcryption technique to achieve mutual authentication between the VCS and vehicles as well as to prevent the private information of these vehicles from being disclosed. Simulation results are provided to show that the proposed privacy-preserving incentive mechanism is beneficial to both the VCS and vehicles, resulting in a win-win situation.
Fog Computing Security Architecture for the Internet of Things Using Blockchain-Based Social Networks

Despite that the cloud computing is considered as the panacea of processing and analyzing IoT data, it shows drawbacks in many other aspects like latency, bandwidth, and mobility when transferring data from connected devices to the cloud. The fog computing paradigm extends the cloud and refers to a geographically distributed computing paradigm at the edge of loT networks. However, realizing fog computing still has a long way to go, especially when it comes to security in the context of loT unconventional characteristics such as scalability, heterogeneity, mobility and limited resources. In addition, applying social network principles to the loT seems to be appealing to build the Internet of Things as a network of peer-to-peer networks. In this paper, we introduce a hybrid architecture for the Internet of things, combing fog computing to ensure security in the trustless loT environment. By enabling our fog computing architecture with blockchain-based social networks, users could easily manage smart objects via establishing tamper-proof digital identities in a trustless environment and build a new class of authentication and authorization mechanisms for the loT. We also demonstrate and analyze the feasibility of our architecture with a prototype.
Malaria morbidity and mortality following introduction of a universal policy of artemisinin-based treatment for malaria in Papua, Indonesia: A longitudinal surveillance study

Background Malaria control activities can have a disproportionately greater impact on Plasmodium falciparum than on P. vivax in areas where both species are coendemic. We investigated temporal trends in malaria-related morbidity and mortality in Papua, Indonesia, before and after introduction of a universal, artemisinin-based antimalarial treatment strategy for all Plasmodium species. Methods and findings A prospective, district-wide malariometric surveillance system was established in April 2004 to record all cases of malaria at community clinics and the regional hospital and maintained until December 2013. In March 2006, antimalarial treatment policy was changed to artemisinin combination therapy for uncomplicated malaria and intravenous artesunate for severe malaria due to any Plasmodium species. Over the study period, a total of 418,238 patients presented to the surveillance facilities with malaria. The proportion of patients with malaria requiring admission to hospital fell from 26.9% (7,745/28,789) in the pre–policy change period (April 2004 to March 2006) to 14.0% (4,786/34,117) in the late transition period (April 2008 to December 2009), a difference of −12.9% (95% confidence interval [CI] −13.5% to −12.2%). There was a significant fall in the mortality of patients presenting to the hospital with P. falciparum malaria (0.53% [100/18,965] versus 0.32% [57/17,691]; difference = −0.21% [95% CI −0.34 to −0.07]) but not in patients with P. vivax malaria (0.28% [21/7,545] versus 0.23% [28/12,397]; difference = −0.05% [95% CI −0.20 to 0.09]). Between the same periods, the overall proportion of malaria due to P. vivax rose from 44.1% (30,444/69,098) to 53.3% (29,934/56,125) in the community clinics and from 32.4% (9,325/28,789) to 44.1% (15,035/34,117) at the hospital. After controlling for population growth and changes in treatment-seeking behaviour, the incidence of P. falciparum malaria fell from 511 to 249 per 1,000 person-years (py) (incidence rate ratio [IRR] = 0.49 [95% CI 0.48–0.49]), whereas the incidence of P. vivax malaria fell from 331 to 239 per 1,000 py (IRR = 0.72 [95% CI 0.71–0.73]). The main limitations of our study were possible confounding from changes in healthcare provision, a growing population, and significant shifts in treatment-seeking behaviour following implementation of a new antimalarial policy. Conclusions In this area with high levels of antimalarial drug resistance, adoption of a universal policy of efficacious artemisinin-based therapy for malaria infections due to any Plasmodium species was associated with a significant reduction in total malaria-attributable morbidity and mortality. The burden of P. falciparum malaria was reduced to a greater extent than that of P. vivax malaria. In coendemic regions, the timely elimination of malaria will require that safe and effective radical cure of both the blood and liver stages of the parasite is widely available for all patients at risk of malaria.
Deep Association: End-to-end Graph-Based Learning for Multiple Object Tracking with Conv-Graph Neural Network

Multiple Object Tracking (MOT) has a wide range of applications in surveillance retrieval and autonomous driving. The majority of existing methods focus on extracting features by deep learning and hand-crafted optimizing bipartite graph or network flow. In this paper, we proposed an efficient end-to-end model, Deep Association Network (DAN), to learn the graph-based training data, which are constructed by spatial-temporal interaction of objects. DAN combines Convolutional Neural Network (CNN), Motion Encoder (ME) and Graph Neural Network (GNN). The CNNs and Motion Encoders extract appearance features from bounding box images and motion features from positions respectively, and then the GNN optimizes graph structure to associate the same object among frames together. In addition, we presented a novel end-to-end training strategy for Deep Association Network. Our experimental results demonstrate the effectiveness of DAN up to the state-of-the-art methods without extra-dataset on MOT16 and DukeMTMCT.
A Practical Algorithm for Multiplayer Bandits when Arm Means Vary Among Players

We study a multiplayer stochastic multi-armed bandit problem in which players cannot communicate, and if two or more players pull the same arm, a collision occurs and the involved players receive zero reward. We consider the challenging heterogeneous setting, in which different arms may have different means for different players, and propose a new and efficient algorithm that combines the idea of leveraging forced collisions for implicit communication and that of performing matching eliminations. We present a finite-time analysis of our algorithm, giving the first sublinear minimax regret bound for this problem, and prove that if the optimal assignment of players to arms is unique, our algorithm attains the optimal $O(\ln(T))$ regret, solving an open question raised at NeurIPS 2018.
VIRET: A Video Retrieval Tool for Interactive Known-item Search

Known-item search in large video collections still represents a challenging task for current video retrieval systems that have to rely both on state-of-the-art ranking models and interactive means of retrieval. We present a general overview of the current version of the VIRET tool, an interactive video retrieval system that successfully participated at several international evaluation campaigns. The system is based on multi-modal search and convenient inspection of results. Based on collected query logs of four users controlling instances of the tool at the Video Browser Showdown 2019, we highlight query modification statistics and a list of successful query formulation strategies. We conclude that the VIRET tool represents a competitive reference interactive system for effective known-item search in one thousand hours of video.
Featuring Real-Time Imbalanced Network Traffic Classification

Recently, imbalanced traffic classification has attracted more attention due to the fact that most internet traffic exhibits imbalance behavior. However, few works only have considered real-time imbalanced traffic classification. In this project, we propose a comparative study comprising several machine learning algorithms for nine different scenarios. We vary dataset and flow sizes following an under-sampling approach, in order to establish an objective evaluation of the best parameters for classification. The results showed that: 1) Combined with packet length, inter-arrival time and maximum segment size, features related to TCP session signalization enhance imbalanced traffic classification performances; 2) Ensemble approaches, especially Bagged Random Forest, achieve the best results for real-time imbalanced traffic classification; 3) Increasing flow sizes while reducing (to a certain level) training set sizes, enhances classification performances as we learn more about each individual instance. The best classification scenario includes 500 samples in each class with 8 packets flows.
DietLens-Eout: Large Scale Restaurant Food Photo Recognition

Restaurant dishes represent a significant portion of food that people consume in their daily life. While people are becoming health-conscious in their food intake, convenient restaurant food tracking becomes an essential task in wellness and fitness applications. Given the huge number of dishes (food categories) involved, it becomes extremely challenging for traditional food photo classification to be feasible in both algorithm design and training data availability. In this work, we present a demo that runs on restaurant dish images in a city of millions of residents and tens of thousand restaurants. We propose a rank-loss based convolutional neural network to optimize the image features representation. Context information such as GPS location of the recognition request is also used to further improve the performance. Our experimental results are highly promising. We have shown in our demo that the proposed algorithm is near ready to be deployed in real-world applications.
Take Goods from Shelves: A Dataset for Class-Incremental Object Detection

Object detection for automatic visual checkout in self-service vending machines is attracting significant attention in the retail industry. However, several critical challenges have not received enough attention. First, large-scale, high-quality retail image datasets are urgently demanded to train and evaluate the detection models. Second, the trained models should be able to cope with the frequently added new goods at low cost, while most cutting-edge models cannot. In this paper, we propose a new hierarchical large-scale object detection dataset, called Take Goods from Shelves (TGFS), containing 38K images of 24 fine-grained and 3 coarse classes. A preliminary method for solving the goods-adding problem, called Faster R-CNN Class-incremental Object Detector (FCIOD), is also described and evaluated. In addition, several popular methods are benchmarked on the TGFS dataset.
Continuous Control for Automated Lane Change Behavior Based on Deep Deterministic Policy Gradient Algorithm

Lane change is a challenging task which requires delicate actions to ensure safety and comfort. Some recent studies have attempted to solve the lane-change control problem with Reinforcement Learning (RL), yet the action is confined to discrete action space. To overcome this limitation, we formulate the lane change behavior with continuous action in a model-free dynamic driving environment based on Deep Deterministic Policy Gradient (DDPG). The reward function, which is critical for learning the optimal policy, is defined by control values, position deviation status, and maneuvering time to provide the RL agent informative signals. The RL agent is trained from scratch without resorting to any prior knowledge of the environment and vehicle dynamics since they are not easy to obtain. Seven models under different hyperparameter settings are compared. A video showing the learning progress of the driving behavior is available22Video is available at https://youtu.be/UvMGwqfYYY3k. It demonstrates the RL vehicle agent initially runs out of road boundary frequently, but eventually has managed to smoothly and stably change to the target lane with a success rate of 100% under diverse driving situations in simulation.