Information Processing and Management of Uncertainty in Knowledge-Based Systems, 2020
Using interval-valued data and computing, researchers have reported significant quality improveme... more Using interval-valued data and computing, researchers have reported significant quality improvements of the stock market annual variability forecasts recently. Through studying the entropy of intervalvalued datasets, this work provides both information theoretic and empirical evidences on that the significant quality improvements are very likely come from interval-valued datasets. Therefore, using intervalvalued samples rather than point-valued ones is preferable in making variability forecasts. This study also computationally investigates the impacts of data aggregation methods and probability distributions on the entropy of interval-valued datasets. Computational results suggest that both min-max and confidence intervals can work well in aggregating point-valued data into intervals. However, assuming uniform probability distribution should be a good practical choice in calculating the entropy of an interval-valued dataset in some applications at least.
For decades, research in natural language processing (NLP) has focused on summarization. Sequence... more For decades, research in natural language processing (NLP) has focused on summarization. Sequence-to-sequence models for abstractive summarization have been studied extensively, yet generated summaries commonly suffer from fabricated content, and are often found to be near-extractive. We argue that, to address these issues, summarizers need to acquire the co-references that form multiple types of relations over input sentences, e.g., 1-toN , N-to-1, and N-toN relations, since the structured knowledge for text usually appears on these relations. By allowing the decoder to pay different attention to the input sentences for the same entity at different generation states, the structured graph representations generate more informative summaries. In this paper, we propose a hierarchical graph attention networks (HGATs) for abstractive summarization with a topicsensitive PageRank augmented graph. Specifically, we utilize dual decoders, a sequential sentence decoder, and a graph-structured decoder (which are built hierarchically) to maintain the global context and local characteristics of entities, complementing each other. We further design a greedy heuristic to extract salient users' comments while avoiding redundancy to drive a model to better capture entity interactions. Our experimental results show that our models produce significantly higher ROUGE scores than variants without graph-based attention on both SSECIF and CNN/Daily Mail (CNN/DM) datasets.
Private information can either take the form of key phrases that are explicitly contained in the ... more Private information can either take the form of key phrases that are explicitly contained in the text or be implicit. For example, demographic information about the author of a text can be predicted with above-chance accuracy from linguistic cues in the text itself. Letting alone its explicitness, some of the private information correlates with the output labels and therefore can be learned by a neural network. In such a case, there is a tradeoff between the utility of the representation (measured by the accuracy of the classification network) and its privacy. This problem is inherently a multi-objective problem because these two objectives may conflict, necessitating a trade-off. Thus, we explicitly cast this problem as multi-objective optimization (MOO) with the overall objective of finding a Pareto stationary solution. We, therefore, propose a multiple-gradient descent algorithm (MGDA) that enables the efficient application of the Frank-Wolfe algorithm [10] using the line search. Experimental results on sentiment analysis and part-of-speech (POS) tagging show that MGDA produces higher-performing models than most recent proxy objective approaches, and performs as well as single objective baselines.
2021 IEEE International Conference on Big Knowledge (ICBK), 2021
Summarization of long sequences into a concise statement is a core problem in natural language pr... more Summarization of long sequences into a concise statement is a core problem in natural language processing, which requires a non-trivial understanding of the weakly structured text. Therefore, integrating crowdsourced multiple users' comments into a concise summary is even harder because (1) it requires transferring the weakly structured comments to structured knowledge. Besides, (2) the users comments are informal and noisy. In order to capture the long-distance relationships in staggered long sentences, we propose a neural multi-comment summarization (MCS) system that incorporates the sentence relationships via graph heuristics that utilize relation knowledge graphs, i.e., sentence relation graphs (SRG) and approximate dis-course graphs (ADG). Motivated by the promising results of gated graph neural networks (GG- NNs) on highly structured data, we develop a GG-NNs with sequence encoder that incorporates SRG or ADG in order to capture the sentence relationships. Specifi-cally, we employ the GG- NNs on both relation knowledge graphs, with the sentence embeddings as the input node features and the graph heuristics as the edges' weights. Through multiple layer-wise propagations, the GG- NNs generate the salience for each sentence from high-level hidden sentence features. Consequently, we use a greedy heuristic to extract salient users' comments while avoiding the noise in comments. The experimental results show that the proposed MCS improves the summarization performance both quantitatively and qualitatively.
Concentrating active Pt atoms in the outer layers of electrocatalysts is a very effective approac... more Concentrating active Pt atoms in the outer layers of electrocatalysts is a very effective approach to greatly reduce the Pt loading without compromising the electrocatalytic performance and the total electrochemically active surface area (ECSA) for the oxygen reduction reaction (ORR) in hydrogen-based proton-exchange membrane fuel cells. Accordingly, a facile, low-cost, and hydrogen-assisted two-step method is developed in this work, to massively prepare carbon-supported uniform, small-sized, and surfactant-free Pd nanoparticles (NPs) with ultrathin ā¼3-atomic-layer Pt shells (Pd@Pt3L NPs/C). Comprehensive physicochemical characterizations, electrochemical analyses, fuel cell tests, and density functional theory calculations reveal that, benefiting from the ultrathin Pt-shell nanostructure as well as the resulting ligand and geometric effects, Pd@Pt3L NPs/C exhibits not only significantly enhanced ECSA, electrocatalytic activity, and noble-metal (NM) utilization compared to commercial Pt/C, showing 81.24 m2/gPt, 0.710 mA/cm2, and 352/577 mA/mgNM/Pt in ECSA, area-, and NM-/Pt-mass-specific activity, respectively; but also a much better electrochemical stability during the 10,000-cycle accelerated degradation test. More importantly, the corresponding 25-cm2 H2-air/O2 fuel cell with the low cathodic Pt loading of ā¼ 0.152 mgPt/cm2geo achieves the high power density of 0.962/1.261 W/cm2geo at the current density of only 1,600 mA/cm2geo, which is much higher than that for the commercial Pt/C. This work not only develops a high-performance and practical Pt-based ORR electrocatalyst, but also provides a scalable preparation method for fabricating the ultrathin Pt-shell nanostructure, which can be further expanded to other metal shells for other energy-conversion applications.
We present an enhanced and highly sensitive impedance cytosensor on the basis of folate conjugate... more We present an enhanced and highly sensitive impedance cytosensor on the basis of folate conjugatedpolyethylenimine-carbon nanotubes (folate-PEI-CNT). Zeta potential measurements and UV-vis spectroscopy have been employed to characterize the surface modifications of folate-PEI-CNT. We next investigated the efficiency of this probe for screening HeLa cells whose surfaces over-express folate receptors. The detection results have been further demonstrated by using scanning electron microscopy (SEM). Finally, we investigated the sensitivity of this impedance cytosensor with a detection limit of approximately 90 cells mL ā1 according to 3Ī“ rule, as well as a linear detection range from 2.4Ć 10 2 to 2.4 Ć10 5 cells mL ā1 .
The use of a novel cytosensor, comprised of bio-mimetically synthesized Ag@BSA composite microsph... more The use of a novel cytosensor, comprised of bio-mimetically synthesized Ag@BSA composite microspheres, for the detection of KB cells (a model system) is described. The Ag@BSA composite microspheres were immobilized on Au electrodes via Au-thiol bonds. Scanning electron microscopy (SEM), atomic force microscopy (AFM) and transmission electron microscopy (TEM) images revealed that the Ag@BSA were well-dispersed microspheres with an average diameter of 500 nm, including the monolayer of BSA. The immobilization of Ag@BSA composite microspheres onto Au electrodes is thought to increase the electrode surface area and accelerate the electron transfer rate while providing a highly stable matrix for the convenient conjugation of target molecules (such as folic acid) and the prolonged incubation of cells. Cyclic voltammetry (CV) and electrochemical impedance spectroscopy (EIS) studies showed that the fabricated cytosensor was able to detect KB cells ranging from 6.0 Ć 10 1 to 1.2 Ć 10 8 cells mL Ć 1 with a lower detection limit of 20 cells mL Ć 1. Due to its facile synthesis, high stability and reproducibility and cytocompatibility, the novel cytosensor described here could find multifarious uses in applications, such as cancer diagnosis, drug screening and cell adhesion studies.
A novel electrochemiluminescence (ECL) strategy based on the sandwich-type immunosensor for sensi... more A novel electrochemiluminescence (ECL) strategy based on the sandwich-type immunosensor for sensitive detection of retinol-binding protein (RBP) was developed. The primary antibody anti-RBP was immobilized onto multiwalled carbon nanotubes (MWCNTs), which have large surface area and high electrical conductivity. The RBP antigen and Ru-Nafion@SiO 2-labeled secondary antibody were then successively conjugated to form sandwich-type immunocomplexes through the specific interaction between antigen and antibody. The ECL signal amplification was significantly improved due to the synergistic effect of MWCNTs and mesoporous silica nanospheres (mSiO 2). The developed ECL immunosensor exhibited high sensitivity and specificity for the detection of RBP and responded linearly to the clinically-relevant concentration of RBP from 78 to 5000 ng mL Ć 1. Moreover, the MWCNT-based ECL immunosensor displayed excellent stability and reproducibility, as well as successfully achieved the detection of RBP in patient urine samples with desirable results. The present work provided a promising technique for the clinical screening of RBP and point-of-care diagnostics.
The level of urinary retinol-binding protein (RBP) can be estimated as a significant index of ren... more The level of urinary retinol-binding protein (RBP) can be estimated as a significant index of renal tubular injury. In this work, we used Ag@BSA microspheres as a sensing interface to cross-link RBP monoclonal antibody (RBP mAb) via glutaraldehyde for sensitive detection of RBP. The Ag@BSA microspheres covered on a Au electrode could provide a larger surface area and multifunctional substrate for the effective immobilization of RBP mAb, and the outside BSA layer acted as a biocompatible support to maintain the bioactivity and stability of immobilized immunogen. Electrochemical measurements containing electrochemical impedance spectroscopy (EIS) and differential pulse voltammetry (DPV) were employed to evaluate the analytical performance of the fabricated immunosensor and a higher detection sensitivity was obtained by DPV attributed to the excellent electrical conductivity of Ag@BSA which could enhance the peak current response. This immunosensor had a best detection limit (DL) of 18 ng mL(-1) and a linear response range between 50 and 4500 ng mL(-1). The proposed approach showed high specificity for RBP detection, acceptable reproducibility with an RSD of 5.6%, and good precision with the RSD of 4.5% and 6.3% at the RBP concentrations of 500 and 1500 ng mL(-1). Compared with the ELISA method by analyzing real urine samples from a patient, this immunosensor revealed acceptable accuracy with a relative deviation lower than 6.5%, indicating a potential alternative method for RBP detection in clinical diagnosis.
Electrocatalytic reactions of glucose oxidation based on enzymelabeled electrochemical biosensors... more Electrocatalytic reactions of glucose oxidation based on enzymelabeled electrochemical biosensors demand a high enzymatic activity and fast electron transfer property to produce the amplified signal response. Through a "green" synthesis method, Pt@BSA nanocomposite was prepared as a biosensing interface for the first time. Herein we presented a convenient and effective glucose sensing matrix based on Pt@BSA nanocomposite along with the covalent adsorption of glucose oxidase (GOD). The electrocatalytic activity toward oxygen reduction was significantly enhanced due to the excellent bioactivity of anchored GOD and superior catalytic performance of interior platinum nanoparticles, which was gradually restrained with the addition of glucose. A sensitive glucose biosensor was then successfully developed upon the restrained oxygen reduction peak current. Differential pulse voltammetry (DPV) was employed to investigate the determination performance of the enzyme biosensor, resulting in a linear response range from 0.05 to 12.05 mM with an optimal detection limit of 0.015 mM. The as-proposed sensing technique revealed high selectivity against endogenous interfering species, satisfactory storage stability, acceptable durability, and favorable fabrication reproducibility with the RSD of 3.8%. During the practical application in human blood serum samples, this glucose biosensor obtained a good detection accuracy of analytical recoveries within 97.5 to 104.0%, providing an alternative scheme for glucose level assay in clinical application.
2021 IEEE Symposium Series on Computational Intelligence (SSCI), Dec 5, 2021
With inputs from human crowds, usually through the Internet, crowdsourcing has become a promising... more With inputs from human crowds, usually through the Internet, crowdsourcing has become a promising methodology in AI and machine learning for applications that require human knowledge. Researchers have recently proposed interval-valued labels (IVLs), instead of commonly used binary-valued ones, to manage uncertainty in crowdsourcing [19]. However, that work has not yet taken the crowd worker's reliability into consideration. Crowd workers usually come with various social and economic backgrounds, and have different levels of reliability. To further improve the overall quality of crowdsourcing with IVLs, this work presents practical methods that quantitatively estimate worker's reliability in terms of his/her correctness, confidence, stability, and predictability from his/her IVLs. With worker's reliability, this paper proposes two learning schemes: weighted interval majority voting (WIMV) and weighted preferred matching probability (WPMP). Computational experiments on sample datasets demonstrate that both WIMV and WPMP can significantly improve learning results in terms of higher precision. accuracy. and F1 -score than other methods.
We present the techniques we have used to bound the range of the arcsine, arccosine, arctangent, ... more We present the techniques we have used to bound the range of the arcsine, arccosine, arctangent, arccotangent, and hyperbolic sine functions in our portable FORTRANā77 library INTLIB. The design of this library is based on a balance of simplicity and eciency, subject to rigor and portability.
2017 IEEE International Conference on Big Knowledge (ICBK), 2017
This paper proposes an approach KeyRank to extract high quality keyphrases from a document in Eng... more This paper proposes an approach KeyRank to extract high quality keyphrases from a document in English. It firstly searches all keyphrase candidates from the document, and then ranks them for selecting top-N keyphrase candidates as final keyphrases. Based on a common sense that words do not repeat-edly appear in an effective keyphrase in English, a novel keyphrase candidate search algorithm applying sequential pat-tern mining with gap constraints (called KCSP) is proposed to search keyphrase candidates for KeyRank. An effectiveness eval-uation measure pattern frequency with entropy (called PF-H) is then proposed to rank these keyphrase candidates for KeyRank. Our experimental results show that KeyRank performs better than existing popular approaches do, such as TextRank and KeyEx. Besides, KCSP is much more efficient than a closely re-lated approach SPMW, and PF-H can be applied to improve the performance of TextRank.
Information Processing and Management of Uncertainty in Knowledge-Based Systems, 2020
Applying interval-valued data and methods, researchers have made solid accomplishments in informa... more Applying interval-valued data and methods, researchers have made solid accomplishments in information processing and uncertainty management. Although interval-valued statistics and probability are available for interval-valued data, current inferential decision making schemes rely on point-valued statistic and probabilistic measures mostly. To enable direct applications of these point-valued schemes on interval-valued datasets, we present point-valued variational statistics, probability, and entropy for interval-valued datasets. Related algorithms are reported with illustrative examples.
... However, the decision as to what basis to use would require a fore-knowledge of the pattern o... more ... However, the decision as to what basis to use would require a fore-knowledge of the pattern one wants to identify ... the problem of generalization can be alleviated using neural network implemen-tations of the Sammon mapping; de Ridder and Duin (1997) present a comparison. ...
Information Processing and Management of Uncertainty in Knowledge-Based Systems, 2020
Using interval-valued data and computing, researchers have reported significant quality improveme... more Using interval-valued data and computing, researchers have reported significant quality improvements of the stock market annual variability forecasts recently. Through studying the entropy of intervalvalued datasets, this work provides both information theoretic and empirical evidences on that the significant quality improvements are very likely come from interval-valued datasets. Therefore, using intervalvalued samples rather than point-valued ones is preferable in making variability forecasts. This study also computationally investigates the impacts of data aggregation methods and probability distributions on the entropy of interval-valued datasets. Computational results suggest that both min-max and confidence intervals can work well in aggregating point-valued data into intervals. However, assuming uniform probability distribution should be a good practical choice in calculating the entropy of an interval-valued dataset in some applications at least.
For decades, research in natural language processing (NLP) has focused on summarization. Sequence... more For decades, research in natural language processing (NLP) has focused on summarization. Sequence-to-sequence models for abstractive summarization have been studied extensively, yet generated summaries commonly suffer from fabricated content, and are often found to be near-extractive. We argue that, to address these issues, summarizers need to acquire the co-references that form multiple types of relations over input sentences, e.g., 1-toN , N-to-1, and N-toN relations, since the structured knowledge for text usually appears on these relations. By allowing the decoder to pay different attention to the input sentences for the same entity at different generation states, the structured graph representations generate more informative summaries. In this paper, we propose a hierarchical graph attention networks (HGATs) for abstractive summarization with a topicsensitive PageRank augmented graph. Specifically, we utilize dual decoders, a sequential sentence decoder, and a graph-structured decoder (which are built hierarchically) to maintain the global context and local characteristics of entities, complementing each other. We further design a greedy heuristic to extract salient users' comments while avoiding redundancy to drive a model to better capture entity interactions. Our experimental results show that our models produce significantly higher ROUGE scores than variants without graph-based attention on both SSECIF and CNN/Daily Mail (CNN/DM) datasets.
Private information can either take the form of key phrases that are explicitly contained in the ... more Private information can either take the form of key phrases that are explicitly contained in the text or be implicit. For example, demographic information about the author of a text can be predicted with above-chance accuracy from linguistic cues in the text itself. Letting alone its explicitness, some of the private information correlates with the output labels and therefore can be learned by a neural network. In such a case, there is a tradeoff between the utility of the representation (measured by the accuracy of the classification network) and its privacy. This problem is inherently a multi-objective problem because these two objectives may conflict, necessitating a trade-off. Thus, we explicitly cast this problem as multi-objective optimization (MOO) with the overall objective of finding a Pareto stationary solution. We, therefore, propose a multiple-gradient descent algorithm (MGDA) that enables the efficient application of the Frank-Wolfe algorithm [10] using the line search. Experimental results on sentiment analysis and part-of-speech (POS) tagging show that MGDA produces higher-performing models than most recent proxy objective approaches, and performs as well as single objective baselines.
2021 IEEE International Conference on Big Knowledge (ICBK), 2021
Summarization of long sequences into a concise statement is a core problem in natural language pr... more Summarization of long sequences into a concise statement is a core problem in natural language processing, which requires a non-trivial understanding of the weakly structured text. Therefore, integrating crowdsourced multiple users' comments into a concise summary is even harder because (1) it requires transferring the weakly structured comments to structured knowledge. Besides, (2) the users comments are informal and noisy. In order to capture the long-distance relationships in staggered long sentences, we propose a neural multi-comment summarization (MCS) system that incorporates the sentence relationships via graph heuristics that utilize relation knowledge graphs, i.e., sentence relation graphs (SRG) and approximate dis-course graphs (ADG). Motivated by the promising results of gated graph neural networks (GG- NNs) on highly structured data, we develop a GG-NNs with sequence encoder that incorporates SRG or ADG in order to capture the sentence relationships. Specifi-cally, we employ the GG- NNs on both relation knowledge graphs, with the sentence embeddings as the input node features and the graph heuristics as the edges' weights. Through multiple layer-wise propagations, the GG- NNs generate the salience for each sentence from high-level hidden sentence features. Consequently, we use a greedy heuristic to extract salient users' comments while avoiding the noise in comments. The experimental results show that the proposed MCS improves the summarization performance both quantitatively and qualitatively.
Concentrating active Pt atoms in the outer layers of electrocatalysts is a very effective approac... more Concentrating active Pt atoms in the outer layers of electrocatalysts is a very effective approach to greatly reduce the Pt loading without compromising the electrocatalytic performance and the total electrochemically active surface area (ECSA) for the oxygen reduction reaction (ORR) in hydrogen-based proton-exchange membrane fuel cells. Accordingly, a facile, low-cost, and hydrogen-assisted two-step method is developed in this work, to massively prepare carbon-supported uniform, small-sized, and surfactant-free Pd nanoparticles (NPs) with ultrathin ā¼3-atomic-layer Pt shells (Pd@Pt3L NPs/C). Comprehensive physicochemical characterizations, electrochemical analyses, fuel cell tests, and density functional theory calculations reveal that, benefiting from the ultrathin Pt-shell nanostructure as well as the resulting ligand and geometric effects, Pd@Pt3L NPs/C exhibits not only significantly enhanced ECSA, electrocatalytic activity, and noble-metal (NM) utilization compared to commercial Pt/C, showing 81.24 m2/gPt, 0.710 mA/cm2, and 352/577 mA/mgNM/Pt in ECSA, area-, and NM-/Pt-mass-specific activity, respectively; but also a much better electrochemical stability during the 10,000-cycle accelerated degradation test. More importantly, the corresponding 25-cm2 H2-air/O2 fuel cell with the low cathodic Pt loading of ā¼ 0.152 mgPt/cm2geo achieves the high power density of 0.962/1.261 W/cm2geo at the current density of only 1,600 mA/cm2geo, which is much higher than that for the commercial Pt/C. This work not only develops a high-performance and practical Pt-based ORR electrocatalyst, but also provides a scalable preparation method for fabricating the ultrathin Pt-shell nanostructure, which can be further expanded to other metal shells for other energy-conversion applications.
We present an enhanced and highly sensitive impedance cytosensor on the basis of folate conjugate... more We present an enhanced and highly sensitive impedance cytosensor on the basis of folate conjugatedpolyethylenimine-carbon nanotubes (folate-PEI-CNT). Zeta potential measurements and UV-vis spectroscopy have been employed to characterize the surface modifications of folate-PEI-CNT. We next investigated the efficiency of this probe for screening HeLa cells whose surfaces over-express folate receptors. The detection results have been further demonstrated by using scanning electron microscopy (SEM). Finally, we investigated the sensitivity of this impedance cytosensor with a detection limit of approximately 90 cells mL ā1 according to 3Ī“ rule, as well as a linear detection range from 2.4Ć 10 2 to 2.4 Ć10 5 cells mL ā1 .
The use of a novel cytosensor, comprised of bio-mimetically synthesized Ag@BSA composite microsph... more The use of a novel cytosensor, comprised of bio-mimetically synthesized Ag@BSA composite microspheres, for the detection of KB cells (a model system) is described. The Ag@BSA composite microspheres were immobilized on Au electrodes via Au-thiol bonds. Scanning electron microscopy (SEM), atomic force microscopy (AFM) and transmission electron microscopy (TEM) images revealed that the Ag@BSA were well-dispersed microspheres with an average diameter of 500 nm, including the monolayer of BSA. The immobilization of Ag@BSA composite microspheres onto Au electrodes is thought to increase the electrode surface area and accelerate the electron transfer rate while providing a highly stable matrix for the convenient conjugation of target molecules (such as folic acid) and the prolonged incubation of cells. Cyclic voltammetry (CV) and electrochemical impedance spectroscopy (EIS) studies showed that the fabricated cytosensor was able to detect KB cells ranging from 6.0 Ć 10 1 to 1.2 Ć 10 8 cells mL Ć 1 with a lower detection limit of 20 cells mL Ć 1. Due to its facile synthesis, high stability and reproducibility and cytocompatibility, the novel cytosensor described here could find multifarious uses in applications, such as cancer diagnosis, drug screening and cell adhesion studies.
A novel electrochemiluminescence (ECL) strategy based on the sandwich-type immunosensor for sensi... more A novel electrochemiluminescence (ECL) strategy based on the sandwich-type immunosensor for sensitive detection of retinol-binding protein (RBP) was developed. The primary antibody anti-RBP was immobilized onto multiwalled carbon nanotubes (MWCNTs), which have large surface area and high electrical conductivity. The RBP antigen and Ru-Nafion@SiO 2-labeled secondary antibody were then successively conjugated to form sandwich-type immunocomplexes through the specific interaction between antigen and antibody. The ECL signal amplification was significantly improved due to the synergistic effect of MWCNTs and mesoporous silica nanospheres (mSiO 2). The developed ECL immunosensor exhibited high sensitivity and specificity for the detection of RBP and responded linearly to the clinically-relevant concentration of RBP from 78 to 5000 ng mL Ć 1. Moreover, the MWCNT-based ECL immunosensor displayed excellent stability and reproducibility, as well as successfully achieved the detection of RBP in patient urine samples with desirable results. The present work provided a promising technique for the clinical screening of RBP and point-of-care diagnostics.
The level of urinary retinol-binding protein (RBP) can be estimated as a significant index of ren... more The level of urinary retinol-binding protein (RBP) can be estimated as a significant index of renal tubular injury. In this work, we used Ag@BSA microspheres as a sensing interface to cross-link RBP monoclonal antibody (RBP mAb) via glutaraldehyde for sensitive detection of RBP. The Ag@BSA microspheres covered on a Au electrode could provide a larger surface area and multifunctional substrate for the effective immobilization of RBP mAb, and the outside BSA layer acted as a biocompatible support to maintain the bioactivity and stability of immobilized immunogen. Electrochemical measurements containing electrochemical impedance spectroscopy (EIS) and differential pulse voltammetry (DPV) were employed to evaluate the analytical performance of the fabricated immunosensor and a higher detection sensitivity was obtained by DPV attributed to the excellent electrical conductivity of Ag@BSA which could enhance the peak current response. This immunosensor had a best detection limit (DL) of 18 ng mL(-1) and a linear response range between 50 and 4500 ng mL(-1). The proposed approach showed high specificity for RBP detection, acceptable reproducibility with an RSD of 5.6%, and good precision with the RSD of 4.5% and 6.3% at the RBP concentrations of 500 and 1500 ng mL(-1). Compared with the ELISA method by analyzing real urine samples from a patient, this immunosensor revealed acceptable accuracy with a relative deviation lower than 6.5%, indicating a potential alternative method for RBP detection in clinical diagnosis.
Electrocatalytic reactions of glucose oxidation based on enzymelabeled electrochemical biosensors... more Electrocatalytic reactions of glucose oxidation based on enzymelabeled electrochemical biosensors demand a high enzymatic activity and fast electron transfer property to produce the amplified signal response. Through a "green" synthesis method, Pt@BSA nanocomposite was prepared as a biosensing interface for the first time. Herein we presented a convenient and effective glucose sensing matrix based on Pt@BSA nanocomposite along with the covalent adsorption of glucose oxidase (GOD). The electrocatalytic activity toward oxygen reduction was significantly enhanced due to the excellent bioactivity of anchored GOD and superior catalytic performance of interior platinum nanoparticles, which was gradually restrained with the addition of glucose. A sensitive glucose biosensor was then successfully developed upon the restrained oxygen reduction peak current. Differential pulse voltammetry (DPV) was employed to investigate the determination performance of the enzyme biosensor, resulting in a linear response range from 0.05 to 12.05 mM with an optimal detection limit of 0.015 mM. The as-proposed sensing technique revealed high selectivity against endogenous interfering species, satisfactory storage stability, acceptable durability, and favorable fabrication reproducibility with the RSD of 3.8%. During the practical application in human blood serum samples, this glucose biosensor obtained a good detection accuracy of analytical recoveries within 97.5 to 104.0%, providing an alternative scheme for glucose level assay in clinical application.
2021 IEEE Symposium Series on Computational Intelligence (SSCI), Dec 5, 2021
With inputs from human crowds, usually through the Internet, crowdsourcing has become a promising... more With inputs from human crowds, usually through the Internet, crowdsourcing has become a promising methodology in AI and machine learning for applications that require human knowledge. Researchers have recently proposed interval-valued labels (IVLs), instead of commonly used binary-valued ones, to manage uncertainty in crowdsourcing [19]. However, that work has not yet taken the crowd worker's reliability into consideration. Crowd workers usually come with various social and economic backgrounds, and have different levels of reliability. To further improve the overall quality of crowdsourcing with IVLs, this work presents practical methods that quantitatively estimate worker's reliability in terms of his/her correctness, confidence, stability, and predictability from his/her IVLs. With worker's reliability, this paper proposes two learning schemes: weighted interval majority voting (WIMV) and weighted preferred matching probability (WPMP). Computational experiments on sample datasets demonstrate that both WIMV and WPMP can significantly improve learning results in terms of higher precision. accuracy. and F1 -score than other methods.
We present the techniques we have used to bound the range of the arcsine, arccosine, arctangent, ... more We present the techniques we have used to bound the range of the arcsine, arccosine, arctangent, arccotangent, and hyperbolic sine functions in our portable FORTRANā77 library INTLIB. The design of this library is based on a balance of simplicity and eciency, subject to rigor and portability.
2017 IEEE International Conference on Big Knowledge (ICBK), 2017
This paper proposes an approach KeyRank to extract high quality keyphrases from a document in Eng... more This paper proposes an approach KeyRank to extract high quality keyphrases from a document in English. It firstly searches all keyphrase candidates from the document, and then ranks them for selecting top-N keyphrase candidates as final keyphrases. Based on a common sense that words do not repeat-edly appear in an effective keyphrase in English, a novel keyphrase candidate search algorithm applying sequential pat-tern mining with gap constraints (called KCSP) is proposed to search keyphrase candidates for KeyRank. An effectiveness eval-uation measure pattern frequency with entropy (called PF-H) is then proposed to rank these keyphrase candidates for KeyRank. Our experimental results show that KeyRank performs better than existing popular approaches do, such as TextRank and KeyEx. Besides, KCSP is much more efficient than a closely re-lated approach SPMW, and PF-H can be applied to improve the performance of TextRank.
Information Processing and Management of Uncertainty in Knowledge-Based Systems, 2020
Applying interval-valued data and methods, researchers have made solid accomplishments in informa... more Applying interval-valued data and methods, researchers have made solid accomplishments in information processing and uncertainty management. Although interval-valued statistics and probability are available for interval-valued data, current inferential decision making schemes rely on point-valued statistic and probabilistic measures mostly. To enable direct applications of these point-valued schemes on interval-valued datasets, we present point-valued variational statistics, probability, and entropy for interval-valued datasets. Related algorithms are reported with illustrative examples.
... However, the decision as to what basis to use would require a fore-knowledge of the pattern o... more ... However, the decision as to what basis to use would require a fore-knowledge of the pattern one wants to identify ... the problem of generalization can be alleviated using neural network implemen-tations of the Sammon mapping; de Ridder and Duin (1997) present a comparison. ...
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