Miracle-dz/UniFAR-FTUs huggingface dataset:
Discrete Sliding Mode Control for a Class of WSN Based IoT with Time-delay and Nonlinear Characteristic

This paper applies discrete sliding mode control to the WSN based IoT, to ensure its robust stability under different types of communication time-delay and nonlinear characteristic. Due to the limited sampling frequency, the system will approach a quasi-sliding surface in finite time. Linear matrix inequality (LMI) approach is used to prove the quadratically stability of the sliding surface and suitable Lyapunov function is constructed to obtain the appropriate control law. Two situation will be investigated in this paper, the system with signal time-delay and system with multiple time-delays. Furthermore, nonlinear characteristic of system has been taken into consideration. By employing non-singular transformation and reduced order processing, the proof of sliding surface stability and finite time approaching is simplified. Finally, two numerical simulations are employed to demonstrate the effectiveness and applicability of the proposed approach.
Remember the Good, Forget the Bad, do it Fast - Continuous Learning over Streaming Data

Continuous, dynamic and short-term learning is an effective learning strategy when operating in very fast and dynamic environments, where concept drift constantly occurs. In an on-line, stream learning model, data arrives as a stream of sequentially ordered samples, and older data is no longer available to revise earlier suboptimal modeling decisions as the fresh data arrives. Learning takes place by processing a sample at a time, inspecting it only once, and as such, using a limited amount of memory; stream approaches work in a limited amount of time, and have the advantage of performing a prediction at any point in time during the stream. We focus on a particularly challenging problem, that of continually learning detection models capable to recognize cyber-attacks and system intrusions in a highly dynamic environment such as the Internet. We consider adaptive learning algorithms for the analysis of continuously evolving network data streams, using a dynamic, variable length system memory which automatically adapts to concept drifts in the underlying data. By continuously learning and detecting concept drifts to adapt memory length, we show that adaptive learning algorithms can continuously realize high detection accuracy over dynamic network data streams.
Non-Contact Calibration-Free Eye-Tracker

Commercially available eye-trackers need to be calibrated for each subject by following a personal calibration procedure in which the subject is required to fixate multiple target points. This paper describes a system that can measure eye movements accurately without personal calibration, in the presence of head movements and without contact with the subject. While eliminating the need for personal calibration can be beneficial in general, it is critical for applications in which subjects cannot follow instructions (e.g., infants, mentally challenged individuals, non-human subjects), when subjects cannot maintain an accurate and stable fixation (e.g., Age-related Macular Degeneration), when subjects cannot fixate (e.g., oscillopsia) or when subjects are not willing to cooperate.
Balancing Computation Loads and Optimizing Input Vector Loading in LSTM Accelerators

The long short-term memory (LSTM) is a widely used neural network model for dealing with time-varying data. To reduce the memory requirement, pruning is often applied to the weight matrix of the LSTM, which makes the matrix sparse. In this paper, we present a new sparse matrix format, named rearranged compressed sparse column (RCSC), to maximize the inference speed of the LSTM hardware accelerator. The RCSC format speeds up the inference by: 1) evenly distributing the computation loads to processing elements (PEs) and 2) reducing the input vector load miss within the local buffer. We also propose a hardware architecture adopting hierarchical input buffer to further reduce the pipeline stalls which cannot be handled by the RCSC format alone. The simulation results for various datasets show that combined use of the RSCS format and the proposed hardware requires $2\times $ smaller inference runtime on average compared to the previous work.
Monolithic Column Ion-Pair Chromatography with Indirect Ultraviolet Detection for the Determination of Three Acid Radical Anions Containing Fluorine in Ionic Liquids

Abstract A rapid ion-pair chromatographic method was developed on a monolithic column for the determination of trifluoroacetate, trifluoromethanesulfonate, and tetrafluoroborate. The three anions were successfully detected using indirect ultraviolet detection. These analyte anions were separated using a tetrabutylammonium hydroxide - p-toluenesulfonic acid – acetonitrile mobile phase on a silica-based monolithic column with an octadecyl functional group. The effects of detection wavelength, mobile phase, column temperature and flow rate on the retention of the anions were investigated. The chromatographic conditions were optimized and the retention properties were evaluated. Under the selected optimal conditions, the successful separation of the anions was achieved within 3.5 min. The detection limits were less than 1.0 mg/L for trifluoroacetate, trifluoromethanesulfonate, and tetrafluoroborate. The relative standard deviations for five repeated measurement of retention time and peak area were no more than 0.2%. The linear ranges extended across two orders of magnitude. The three anions in synthetic ionic liquid samples were successfully separated and detected by this method. The results show that the recoveries of this method were between 94.0% and 99.0%. The method was rapid, simple, and easy to use.
Thermal degradation of phytate produces all four possible inositol pentakisphosphates as determined by ion chromatography and 1H and 31P NMR spectroscopy

Abstract Decomposition of phytate has recently been shown to occur under mild conditions in the solid state, giving rise to a complex mixture of lower inositol phosphates. In this study, the reaction products of this thermal, abiotic degradation of phytate were separated using ion chromatography and the most highly phosphorylated products subsequently identified using 1D and 2D NMR spectroscopy. Two late eluting fractions were each shown to be a mixture of two specific inositol pentakisphosphate isomers. The peak with shorter retention time contained Ins(1,2,3,4,6)P5 and DL-Ins(1,2,3,4,5)P5, while the later eluting fraction contained Ins(1,3,4,5,6)P5 and DL-Ins(1,2,4,5,6)P5. The formation of all four possible inositol pentakisphosphate isomers by thermal degradation of phytate contrasts with the selective production of unique inositol pentakisphosphate isomers during enzymatic phytate degradation and therefore suggests a method for differentiating abiotic and biotic processes in environmental samples, including soils and decomposing plant biomass. Graphical Abstract
Optimal the ex-situ biogas biological upgrading to biomethane and its combined application with the anaerobic digestion stage

ABSTRACT Biomethane upgraded from biogas had been proven as a potential renewable energy. Utilizing the metabolic activity of enriched hydrogenotrophic methanogens was regarded as a new way to upgrade biogas. The optimal reaction pH and mixed gas (H2 and CO2) injection time for enriching hydrogenotrophic methanogens were identified, and the performance of upgrading raw biogas produced from food wastes anaerobic digestion was assessed in this study. In the optimal experiment, the results showed that the maximum methane content reached 61.01% when pH was 8 and injection time was 5 min. In all groups, the concentration of acetic acid showed a trend of increasing followed by decreasing during this process. High-throughput sequencing indicated that Methanobacterium and Methanoculleus belonging to hydrogenotrophic methanogens were the dominated genus. The maximum methane content reached 91.43% in the biogas upgrading stage when it was combined with anaerobic digestion stage. At the end of the experiment, the methane content of the upgrading stage was 36.66% higher than that of the raw biogas produced from anaerobic digestion stage. Microbiological analysis showed that hydrogenotrophic methanogens were also the predominant genus.
Reliable Task Offloading for Vehicular Fog Computing Under Information Asymmetry and Information Uncertainty

Vehicular fog computing has emerged as a cost-efficient solution for task processing in vehicular networks. However, how to realize effective server recruitment and reliable task offloading under information asymmetry and uncertainty remains a critical challenge. In this paper, we adopt a two-stage task offloading framework to address this challenge. First, we propose a convex–concave-procedure-based contract optimization algorithm for server recruitment, which aims to maximize the expected utility of the operator with asymmetric information. Then, a low-complexity and stable task offloading mechanism is proposed to minimize the total network delay based on the pricing-based matching. Furthermore, we extend the work to the scenario of information uncertainty and develop a matching-learning-based task offloading mechanism, which takes both occurrence awareness and conflict awareness into consideration. Simulation results demonstrate that the proposed algorithm can effectively motivate resource sharing and guarantee bounded deviation from the optimal performance without the global information.
Development of A Simple and Low Cost Unified Smart Video Surveillance System for Monitoring Hostel Student Attendance

:- Personal security and surveillance is a hot topic in the field of research and computer vision. The CCTV is used for surveillance and security purpose. The crime detection is possible with surveillance but if the face recognition is integrated with surveillance the performance of existing surveillance and security system can be enhanced. The smart surveillance system using face recognition can be used for a verity of application such as attendance system for students and employees at private and government offices, research labs, ATM etc. In this paper, one application of smart surveillance called hostel attendance system is demonstrated.
Entity Disambiguation Algorithm for Literature in Biomedical Field

Based on the requirements of knowledge learning and application in the domain of biomedical, a kind of entity disambiguation algorithm is proposed to solve the problem of entity ambiguity. Entity disambiguation is usually divided into two parts: candidate generation and entity disambiguation. In this paper, candidates of name mention are generated based on the knowledge base method and candidate entities are filtered based on the rule in the candidate generation stage, which ensures the recall rate of the candidate entity set and reduces the computational complexity and noise of the disambiguation stage effectively. In the stage of entity disambiguation, we propose a kind of entity disambiguation method based on probability model, estimating the probability that an entity becomes the target entity through the language model and selecting the entity with the highest probability as the target entity. The result of the method proposed in this paper shows the accuracy rate is 83%, higher than that of other algorithms. The method of entity disambiguation proposed in this paper is the best in the field of biomedical.
Classification and Characterization of Organic Aerosols in the Atmosphere over Seoul Using Two Dimensional Gas Chromatography-time of Flight Mass Spectrometry (GC×GC/TOF-MS) Data

Two approaches were applied for characterization of organic compounds in the atmospheric aerosols. These were chemical and statistical grouping analysis. We applied chemical group analysis as a new approach using the GC×GC data. Based on the principle of GC×GC, analytes in two dimensional data were divided into 6 chemical group depending on their volatility and polarity. Using this classification standards, all analytes were classified as one of 6 chemical groups, HP-VOC, LP-VOC, HP-SVOC, LP-SVOC, HP-NVOC and LP-NVOC. The relationship between the sum of the peak areas of each chemical group with other variables such as the concentrations of OC, EC, OCPOC, OC/EC and OCSOC were studied. However, no apparent relationship was observed. The principle component analysis (PCA) was carried out to find out the relationship among the analytes based on their sources. The OVOCs were dominant compounds in 3 out of 4 factors in summer. In winter, factor 1 was related to secondary organic aerosol and factor 3 was related to primary emission sources. The proposed approach looks promising in characterization of chemical groups but have limitations at current stage which are discussed.
Visual exploration of seismic volume datasets

This paper introduces a novel method supporting the interactive exploration of volumetric subsurface data. To facilitate better insights into the datasets we propose the application of focus and context visualization metaphors. Using these metaphors users can emphasize arbitrary parts of a dataset or remove occluding information interactively to focus on the region of interest. In addition to these visualization issues we will explain how the focus and context metaphors can be combined with VR-based interaction techniques to allow the efficient exploration within more immersive VR environments. In particular, we will discuss how to control the focus and context metaphor to highlight the region of interest in combination with the usage of visual bookmarks to track potentially interesting parts within large volumetric subsurface datasets.
Molecular propellants for ion thrusters

There is no ideal atomic propellant for ion thrusters. Xenon commonly used as propellant becomes resource-critical in light of electric propulsion commercialization. Combining these considerations leads to seeking alternatives to xenon as propellant. In this review, we summarize the current literature on molecular propellants. We define two classes of molecules, group I and II, comprising diatomic molecules and more complex molecules, respectively. We identify basic properties which a candidate molecule belonging to either group, I or II, should possess in order to be suitable as molecular propellant. We discuss the pits and traps in testing such candidate molecules inside a thruster on the basis of our experiences with iodine (a member of group I) and adamantane (a member of group II). The thruster system needs to be individually adopted for each propellant candidate in order to enable a thorough testing inside the thruster. The same holds for optimizing the thruster’s performance when fed with a new propellant because the microscopic processes occurring inside the plasma will differ from molecule to molecule. These circumstances make such testing time-consuming and costly. To accelerate systematic screening of the vast number of molecular species in terms of suitability as propellant, we propose a screening and evolution procedure based on combining chemical engineering and fundamental physical measurements.
Implicit entity linking in tweets: An ad-hoc retrieval approach

Within the context of Twitter analytics, the notion of implicit entity linking has recently been introduced to refer to the identification of a named entity, which is central to the topic of the tweet, but whose surface form is not present in the tweet itself. Compared to traditional forms of entity linking where the linking process revolves around an identified surface form of a potential entity, implicit entity linking relies on contextual clues to determine whether an implicit entity is present within a given tweet and if so, which entity is being referenced. The objective of this paper, while introducing and publicly sharing a comprehensive gold standard dataset for implicit entity linking, is to perform the task of implicit entity linking. The dataset consists of 7,870 tweets, which are classified as either containing implicit entities, explicit entities, both, or neither. The implicit entities are then linked to three levels of entities on Wikipedia, namely coarse-grained level, e.g., Person, Fine-grained level, e.g., Comedian, and the actual entity, e.g., Seinfeld. The proposed model in this work formulates the problem of implicit entity linking as an ad-hoc document retrieval process where the input query is the tweet, which needs to be implicitly linked and the document space is the set of textual descriptions of entities in the knowledge base. The novel contributions of our work include: 1) designing and collecting a gold standard dataset for the task of implicit entity linking; 2) defining the implicit entity linking process as an ad-hoc document retrieval task; and 3) proposing a neural embedding-based feature function that is interpolated with prior term dependency and entity-based feature functions to enhance implicit entity linking. We systematically compare our work with existing work in this area and show that our method is able to provide improvements on a number of retrieval measures.
Resource provisioning under uncertainty in cloud computing

In this thesis, we mainly focus on the resource provisioning in cloud computing. Resources can be provisioned from cloud providers to cloud consumers through two options, i.e., reservation and on-demand. The reservation option is cheaper and able to guarantee the availability and prices of resources. However, a cloud consumer has to purchase the reservation option with prior commitment for specific resources. Due to uncertainties, the common problems encountered in resource provisioning with the two options are overprovisioning and underprovisioning. In this thesis, we consider different uncertainties in the resource provisioning problems, i.e., uncertainties of resource demand, resource price, power price, and availability of resources. For our major contributions, we propose the resource provisioning algorithms and framework to deal with the uncertainties for three cloud stakeholders, namely cloud consumer, cloud provider, and cloud retailer. The contributions are as follows: First, we propose novel algorithms for a cloud consumer to provision resources from cloud providers. The algorithms can minimize the expected resource provisioning cost incurred by overprovisioning and underprovisioning of resources, while the uncertainties are taken into account. We formulate optimization models to obtain the optimal solution for the algorithms. The models are derived by stochastic programming with twoand multi-stage recourse so that the optimal solution from the algorithms can be applied for long-term resource provisioning plans. We also apply the robust optimization to handle the impact of the uncertainties on the optimal solution. The performance evaluation shows that the proposed algorithms have the lowest resource provisioning cost when they are compared with other well-known algorithms. To reduce the computational complexity of the algorithms, we also apply Benders decomposition and sample-average approximation methods. Second, we propose a novel resource provisioning framework for a cloud provider. The framework considers the cloud provider owning datacenters where the smart grid technology is available. Inside the framework, we derive a stochastic programming model with
Orbital mechanism of upright CO activation on Fe(100)

The knowledge of bond activation forms a cornerstone for modern chemistry, wherein symmetry rules of electronic activation lie in the heart of bond activation. However, the question as to how a chemical bond is activated remains elusive. By taking CO activated on Fe(100), herein, we have resolved the long‐standing fundamental question; we have found that excitations in the adsorbate feature the bond activation. We essentially have discovered contrasting electronic processes in respective σ and π electron systems of the adsorbed CO molecule. The σ electron system is involved in reversible hidden excitations/deexcitations between two occupied σ orbitals, whereas the π electron system is subject to irreversible π to π* excitations dispersed along the d‐band region, which is coupled to the rotational 2π electron couplings depending on the strength of molecule‐metal interactions. The σ excitations pertain to the Pauli repulsion mediated quantum nature with energy and entropy marked by the two energy levels, whereas the π to π* excitations fall into a new category of electronic excitations contributing to energy and entropy exchanges in a wide and continuous d‐band region. The findings that the internal states of the adsorbate are excited and that fundamental connections between the frontier orbitals and low‐lying orbitals are established as the molecule comes to the surface may open up new channels to realize more efficient bond activation and renew our thinking on probing the quantum mechanical nature of bond activation at surfaces with further possible impact on manipulation of orbital activation in femtochemistry and attochemistry.
Three dimensional acoustic imaging technology of buried object detection

With the development of 21th century seabed imaging sonar technology, more and more attention is paid to buried object detection technology in the world. In this paper, a low frequency and high resolution three-dimensional acoustic imaging of buried object detection method and its application example are given. Compared with the traditional two-dimensional synthetic aperture imaging, the 3D imaging technology not only solves the problem of the aliasing of the seabed formation echo and the sea floor echo, being able to provide the target buried depth, but also the 3D imaging is more helpful to the image recognition. The 3D acoustic imaging method proposed by this paper has already become the development trend of buried object detection technology. We have noticed that, different from the three-dimensional visualization of the target in the water, the three-dimensional visualization of buried objects has a serious formation image occlusion problem. In addition, the three-dimensional imaging needs to be obtained centimeter-level resolution on three dimensions for better image recognition of small buried objects, in which azimuth resolution is the bottleneck.
Molecular modelling to understand AFM tip functionalisation and imazaquin-AHAS interactions in order to design a new nanobiosensor

ABSTRACT The use of herbicides represents an important risk to environmental health, thus their monitoring is of fundamental importance to prevent and control contamination. The development of nanobiosensors is a powerful alternative because of the speed and accuracy of analysis. In this work, we aimed to present a further development and description of a theoretical model, at the atomic level, that represents an enzyme-herbicide interaction process, in order to apply in the construction of an optimised nanobiosensor based on Atomic Force Microscope (AFM), to detect the herbicide imazaquin. Here, structural data and molecular surface charge distribution were obtained from molecular dynamics (MD) simulations of the acetohydroxyacid synthase enzyme in complex and not with the herbicide molecule. The results were useful to predict the molecular orientation of the AHAS and the direction of unbinding pathway of herbicide from enzyme. Steered Molecular Dynamics (SMD) Simulations, along the previously determined direction of unbinding pathway of the imazaquin from the active site, were carried out to obtain a force value of 1.87 (± 0.20) nN per enzyme. These results are in accurate agreement with experimental data described in the literature.