Miracle-dz/UniFAR-FTUs huggingface dataset:
Genomics assisted functional characterization of Bacillus velezensis E as a biocontrol and growth promoting bacterium for lily

Lily (Lilium spp.) is one of the most famous ornamental flowers globally. Lily basal rot (also known as root rot or stem rot) and lily gray mold have seriously affected the yield and quality of lily, resulting in huge economic losses. In this study, bacterial strain E was isolated from a continuous lily cropping field. Strain E displayed high control efficiency against lily basal rot and gray mold, caused by Fusarium oxysporum and Botrytis cinerea respectively, and promoted the occurrence of scale bulblets. Strain E displayed strong inhibitory effects against several other plant pathogenic fungi and two pathogenic bacteria in dual culture and disc diffusion assays, respectively. Whole genome sequencing revealed that strain E contained a 3,929,247 bp circular chromosome with 4,056 protein-coding genes and an average GC content of 47.32%. Strain E was classified as Bacillus velezensis using genome-based phylogenetic analysis and average nucleotide identity and digital DNA–DNA hybridization analyses. A total of 86 genes and 13 secondary metabolite biosynthetic gene clusters involved in antifungal and antibacterial activity, plant growth promotion, colonization, nutrient uptake and availability were identified in the genome of strain E. In vitro biochemical assays showed that strain E produced siderophores, proteases, cellulases, biofilms, antifungal and antibacterial substances, and exhibited organic phosphate solubilization and swimming and swarming motility, which were consistent with the results of the genome analysis. Colonization analysis showed that strain E could colonize the root of the lily, but not the leaf. Overall, these results demonstrate that B. velezensis strain E can be used as a potential biofertilizer and biocontrol agent for lily production.
ADD: Accelerator Design and Deploy ‐ A tool for FPGA high‐performance dataflow computing

Dataflow‐based FPGA accelerators have become a promising alternative to deliver energy‐efficient high‐performance computing. However, FPGA programming is still a challenge. This paper presents Accelerator Design and Deploy (ADD), a high‐level framework to specify, to simulate, and to implement dataflow accelerators for streaming applications. The framework includes an open dataflow operator library, and templates are provided to easily design new operators. The framework also provides a high‐level and an accurate simulation at circuit level with short execution times. Moreover, ADD provides software and hardware APIs to simplify the integration process, extending the benefits of portability from low‐cost FPGA boards to high performance datacenter FPGA platforms. Our framework supports coupling with high‐level programming languages, and it has been validated on two FPGA platforms: the Intel high‐performance CPU‐FPGA heterogeneous computing platform and an educational FPGA kit. We show that our simple approach presents competitive performance, both in time and energy, when compared to multi‐core and GPU accelerators.
Vehicular Dynamic Spectrum Access: Using Cognitive Radio for Automobile Networks

Vehicular Dynamic Spectrum Access (VDSA) combines the advantages of dynamic spectrum access to achieve higher spectrum efficiency and the special mobility pattern of vehicle fleets. This dissertation presents several noval contributions with respect to vehicular communications, especially vehicle-to-vehicle communications. Starting from a system engineering aspect, this dissertation will present several promising future directions for vehicle communications, taking into consideration both the theoretical and practical aspects of wireless communication deployment. This dissertation starts with presenting a feasibility analysis using queueing theory to model and estimate the performance of VDSA within a TV whitespace environment. The analytical tool uses spectrum measurement data and vehicle density to find upper bounds of several performance metrics for a VDSA scenario in TVWS. Then, a framework for optimizing VDSA via artificial intelligence and learning, as well as simulation testbeds that reflect realistic spectrum sharing scenarios between vehicle networks and heterogeneous wireless networks including wireless local area networks and wireless regional area networks. Detailed experimental results justify the testbed for emulating a mobile dynamic spectrum access environment composed of heterogeneous networks with four dimensional mutual interference. Vehicular cooperative communication is the other proposed technique that combines the cooperative communication technology and vehicle platooning, an emerging concept that is expected to both increase highway utilization and enhance both driver experience and safety. This dissertation will focus on the coexistence of multiple vehicle groups in shared spectrum, where intra-group cooperation and inter-group competition are investigated in the aspect of channel access. Finally, a testbed implementation VDSA is presented and a few applications are developed within a VDSA environment, demonstrating the feasibility and benefits of some features in a future transportation system.
Cuproptosis key gene FDX1 is a prognostic biomarker and associated with immune infiltration in glioma

Recent studies have found that the protein encoded by the FDX1 gene is involved in mediating Cuproptosis as a regulator of protein lipoylation and related to immune response process of tumors. However, the specific biological function of FDX1 in glioma is currently unclear. To explore the potential function of FDX1, this study explored the correlation between the expression of FDX1 in cancers and survival prognosis by analyzing the public databases of GEPIA and Cbioportal. Immune infiltration was analyzed by the TIMER2.0 database in tumors. The possible biological processes and functions of FDX1-related in glioma were annotated through gene enrichment. Relationship between Cuproptosis and autophagy was explored through gene co-expression studies. Summary and conclusions of this study: (1) FDX1 is highly expressed in gliomas and associated with poor prognosis in low-grade gliomas (LGG). (2) Gene annotation indicates that FDX1 is mainly involved in the tumor protein lipoylation and cell death. (3) FDX1 expression is positively correlated with the infiltration of immune cells. (4) LIPT2 and NNAT, two other genes involved in lipoylation, may be unidentified marker gene for Cuproptosis. And the Cuproptosis genes related to FDX1 were positively correlated with the expression of autophagy marker genes Atg5, Atg12, and BECN-1. This evidence suggests that there may be some interaction between FDX1 mediated Cuproptosis and autophagy. In summary, FDX1 may serve as a potential immunotherapy target and prognostic marker for Glioma.
Feature-Based Mobile Phone Rating Using Sentiment Analysis and Machine Learning Approaches

This paper proposes a model of sentiment analysis of various features of different companies' mobile phones and their overall rating. Before buying a phone, customers usually look for reviews to decide which phone to buy. The model proposed in this paper provides an optimal solution for the customer for making this decision more efficiently. In this model, each feature of a mobile phone is rated based on public opinion and an overall rating for each phone is provided. Amazon is one of the largest Internet retailers, which makes way for most public reviews on their products. These reviews are collected as a form of an open source platform and used as the dataset in this model. The gathered data is preprocessed and then separated into two different sets – Training Set and Testing Set which are used to train and test the supervised machine learning algorithms for classification. 15 most common features of the mobile phones based on public reviews are selected from the training data set and used as the feature set in this model. Different algorithms which include Naïve Bayes, Support Vector Machine, Logistic Regression, and Stochastic Gradient Descent algorithms are used in this model and the comparison of their performance is shown. This model provides a rating of each feature and an average rating of the mobile phone based on sentiment polarity. Thus, this research work can assist potential customers to choose the best product based on the opinion of the other users.
Multi-atlas segmentation using clustering, local non-linear manifold embeddings and target-specific templates

Multi-atlas segmentation (MAS) has become an established technique for the automated delineation of anatomical structures. The often manually annotated labels from each of multiple pre-segmented images (atlases) are typically transferred to a target through the spatial mapping of corresponding structures of interest. The mapping can be estimated by pairwise registration between each atlas and the target or by creating an intermediate population template for spatial normalisation of atlases and targets. The former is done at runtime which is computationally expensive but provides high accuracy. In the latter approach the template can be constructed from the atlases offline requiring only one registration to the target at runtime. Although this is computationally more efficient, the composition of deformation fields can lead to decreased accuracy. Our goal was to develop a MAS method which was both efficient and accurate. In our approach we create a target-specific template (TST) which has a high similarity to the target and serves as intermediate step to increase registration accuracy. The TST is constructed from the atlas images that are most similar to the target. These images are determined in low-dimensional manifold spaces on the basis of deformation fields in local regions of interest. We also introduce a clustering approach to divide atlas labels into meaningful sub-regions of interest and increase local specificity for TST construction and label fusion. Our approach was tested on a variety of MR brain datasets and applied to an in-house dataset. We achieve state-of-the-art accuracy while being computationally much more efficient than competing methods. This efficiency opens the door to the use of larger sets of atlases which could lead to further improvement in segmentation accuracy.
AIS Electronic

s: The massive adoption of the Internet facilitates growth of online social networks, in which information can be exchanged in a more efficient way. Such as products, user accounts, web pages, there may be a variety of objects suitable to structurize this kind of networks. As a result, this gives the networks complexity and dynamics. The work in this paper is aiming to studying the topological property of online social network structure from the aspect of dynamics, and make clear the evolution processes of the networks. This is done by a Mean-Field analysis of network growth based on BA Scale-Free network model. Data resources come from the Chinese online e-commerce platform you.163. com and graphs are modeled through commentator and mutual comments by calculating degree distribution of the networks. We build a growing random model for forecasting dynamics of degree evolution. Finally, we use data set on Sina Weibo to test the model and the results are satisfying.
The Impact of Data Re-Sampling on Learning Performance of Class Imbalanced Bankruptcy Prediction Models

The aim of this paper is to evaluate the effect of data sampling techniques on the performance of learners using real highly imbalanced Spanish bankruptcy dataset. The class imbalance problem refers to the highly uneven distribution of class instances where one class is having most of the instances than others. In the presence of highly skewed data distribution, the performance of classical learners is heavily biased in recognizing the majority class and consequently leads to the performance degradation of quantitative classifier or predictors models. In this paper, six sampling methods such as synthetic minority oversampling technique (SMOTE), Borderline-SMOTE, Safe-level-SMOTE, Random under sampling, random oversampling and condensed nearest neighbor are used with a different individual(SVM, C4.5, and Logistic regression) and ensemble learners(AdaBoostM1, DTBagging, and Random Forests). The different quantitative prediction models are designed by combination data sampling techniques and classical learners. The performance of quantitative prediction models are evaluated using G-Mean and area under the curve (AUC) measures on the real highly imbalanced data set. The result suggest that the performance of oversampling (with LR and DTBagging) and undersampling (with C4.5 and RF) methods are superior as compare to others on this data set.
Actuator Fault Decoupled Residual Generation on Lateral Moving Aircraft

Implementation of time-scheduled maintenance is not suitable if it is applied for systems with many varieties of heavy workload and harsh environment since on that condition components degrade earlier that those under normal condition. Therefore it has been shifted to condition-based maintenance (CBM). One important aspect, among others, toward implementation of CBM method is fault isolation. The problem investigated in this paper is related todecouple residual generation for actuator fault isolation of an aircraft on lateral movement. The proposed solution for that problem is to implement combination of transformation matrix and special filter. Transformation matrix is used to convert feature locations of actuator faults to signature vectors. Moreover, the signature vectors will be processed further by special filter to generate decoupled residuals. It is assumed that the actuator is the only fault when the aircraft is on lateral movement. The result showed that special filter and transformation matrix can be designed so that the residual of aileron actuator fault is decoupled from the residual of rudder actuator fault.
Analysis of Trends in Resistance to Fluoroquinolones and Extended Spectrum Beta-Lactams among Salmonella Typhi Isolates Obtained from Patients at Four Outpatient Clinics in Nairobi County, Kenya

Typhoid fever caused by the bacterium Salmonella enterica serovar Typhi (S. Typhi) causes an estimated 25 million illnesses and approximately 200,000 deaths annually mostly in developing countries. Although the management of typhoid fever has been effectively through antibiotic treatment, S. Typhi is increasingly becoming resistant to the currently recommended drugs. This study utilized a quasi-experimental design focusing on archived samples to describe antimicrobial susceptibility patterns of S. Typhi and determine the genetic basis of resistance to the two most commonly used classes of antimicrobials. A total sample size of 287 isolates of S. Typhi isolates stored in -80°C freezer at the Centre for Microbiology Research was utilized. Isolates were subjected to anti-microbial susceptibility testing to commonly available antimicrobials using disk diffusion method, then analyzed for trends in resistance to fluoroquinolones and extended spectrum beta lactams. Among the 287 isolates 158 (55.5%) were found to be Multi Drug Resistant (MDR). This implied that these isolates were resistant to all first line classes of treatment such as ampicillin, chloramphenicol and sulfamethoxazole-trimethroprim. In addition to this, these isolates were also resistant to at least one of the currently recommended drugs of choice, either a β-lactam or a fluoroquinolone. This study observed resistances at 18.2% and 15.4% to fluoroquinolones and cephalosporins respectively. PCR results revealed presence of blaTEM, blaINT and blaCTX-M genes coding for resistance to β-lactams in 80% of the isolates that had combined resistance to β-lactams and fluoroquinolones. It is likely that recent heavy use of these classes of antimicrobials is driving resistances to these antimicrobials.
Public Cloud Cost Analysis Based on Game Theory

In a hybrid cloud computing environment, how to divide workload reasonably is a problem that every rational user needs to consider. The game model of hybrid cloud computing is constructed. The cost is also taken into consideration when the user usually considers the response time. The function which takes the response time and the cost as the variable is the utility function. By solving the Nash equilibrium method, the user's strategy behavior is analyzed, and the optimal strategy of the user is determined. Through the simulation, the influence of different response time and expense ratio on the user equilibrium strategy is given and compared.
Mapping knowledge risks: towards a better understanding of knowledge management

ABSTRACT This conceptual paper aims to identify, present, and analyze potential knowledge risks organizations might face. With the growing complexity of organizational environments and the plethora of new knowledge risks emerging, this critical but under-researched field of knowledge management (KM) deserves closer attention. The study is based on a critical analysis of the extant literature devoted to knowledge risks, discusses potential outcomes of these risks and proposes a concept map of knowledge risks. The map shows a number of knowledge risks organizations should be aware of. Knowledge risks can be assigned to three categories: human, technological and operational. The research is the first systematic and comprehensive review of knowledge risks at the organizational level. By aggregating and consolidating the knowledge risks covered, the study does not only provide a knowledge risk taxonomy but also promising directions for future research. The study also contributes to a more comprehensive understanding of KM.
Joint energy and latency optimization for upstream IoT offloading services in fog radio access networks

Recently, the emergence of fog computing and big internet of things (IoT) data have been considered as the main representatives identifying fifth‐generation mobile networks. In fifth generation, cloudization is extended from the core to the access tiers, referred to as fog radio access networks. Fog radio access networks provide ultralow latency offloading services to a massive number of IoT devices in their proximity. In this paper, we propose a joint energy and latency optimization (JELO) scheme for upstream IoT offloading services in fog radio access networks. The JELO scheme controls the offloaded task assignment among fog‐enabled eNodeBs (fog nodes) with strict consideration of the systematic resources and specific characteristics of individual tasks. The joint objective function aims at red optimizing the energy red consumption and offload latency for entire networks. Simulation results demonstrate that the JELO scheme outperforms existing approaches in terms of the energy consumption and load balancing while maintaining IoT service satisfaction.
Asymptotically Optimal Prediction for Time-Varying Data Generating Processes

We develop a methodology (referred to as kinetic prediction) for predicting time series undergoing unknown changes in their data generating distributions. Based on Kolmogorov-Tikhomirov’s <inline-formula> <tex-math notation="LaTeX">${\varepsilon }$ </tex-math></inline-formula>-entropy, we propose a concept called <inline-formula> <tex-math notation="LaTeX">${\varepsilon }$ </tex-math></inline-formula>-predictability that quantifies the size of a model class (which can be parametric or nonparametric) and the maximal number of abrupt structural changes that guarantee the achievability of asymptotically optimal prediction. Moreover, for parametric distribution families, we extend the aforementioned kinetic prediction with discretized function spaces to its counterpart with continuous function spaces, and propose a sequential Monte Carlo-based implementation. We also extend our methodology for predicting smoothly varying data generating distributions. Under reasonable assumptions, we prove that the average predictive performance converges almost surely to the oracle bound, which corresponds to the case that the data generating distributions are known in advance. The results also shed some light on the so called “prediction-inference dilemma. ” Various examples and numerical results are provided to demonstrate the wide applicability of our methodology.
Stochastic pretopology as a tool for complex networks analysis

ABSTRACT We are proposing in this work a novel approach for complex network analysis by introducing Stochastic Pretopology, a result of the combination of Pretopology theory and Random Sets. We firstly show how pretopology generalizes the graph theory to deal with complex interactions in complex networks. By connecting with random set theory, we then give the definition of stochastic pretopology and also propose the ways to build this kind of pretopology in many situations. That is to say how proximity with randomness can be delivered to model complex neighbourhoods formation in complex networks. In addition, we also show how stochastic pretopology can be applied for modelling dynamic processes on complex networks by representing classical information diffusion models under stochastic pretopology language and then proposing Pretopology Cascade Model as a general model for information diffusion process that can take place in more complex networks such as multi-relational networks or stochastic graphs.
Privacy protection for spatial trajectories against brute-force attacks

PRIVACY PROTECTION FOR SPATIAL TRAJECTORIES AGAINST BRUTE-FORCE ATTACKS Dorukhan Arslan M.S. in Computer Engineering Advisor: Erman Ayday August 2018 The prevalence of Global Positioning System (GPS) equipped mobile devices and wireless communication technologies have resulted in widespread development of location-based services (LBS). As some typical examples of LBS, routing, tracking, local search, social networking, and context advertising can be given. In terms of update frequency of location, LBS are divided into two categories: snapshot and continuous. Snapshot LBS request a user’s location only once to control features. Continuous LBS, on the other hand, require a user’s location in a dynamically periodic or on-demand manner. In the course of interaction with a continuous LBS application, the user reveals a sequence of location samples, namely, spatial trajectory, to service provider. Trajectory privacy in such services is of great importance, since adversaries may use the spatio-temporal sequential pattern to disclose the user’s personally identifiable information (PII) with high certainty. In order to prevent this from happening, service providers generally encrypt spatial trajectory data under the user’s password, and then store in their databases. However, potential adversaries may decrypt the encrypted database via a brute-force attack. In other words, they try every possible value for a password until success is achieved. Although using high-entropy passwords have caused inconvenience for adversaries, the encryption schemes of service providers are vulnerable to this type of an attack due to the tendency of users to choose weak passwords. Also, if the rapid evaluation of computing technology and algorithmic advances are taken into consideration, even the use of a large password domain with conventional encryption can lead to the success of a brute-force attack that became feasible computationally. Thus it is crucial to assess privacy threats and take security countermeasures for spatial trajectories. We present a system that incorporates honey encryption (HE) scheme that