Self-optimizing Feature Generation via Categorical Hashing Representation and Hierarchical Reinforcement Crossing

Feature generation aims to generate new and meaningful features to create a discriminative representation space. A generated feature is meaningful when the generated feature is from a feature pair with inherent feature interaction. In the real world, experienced data scientists can identify potentially useful feature-feature interactions, and generate meaningful dimensions from an exponentially large search space in an optimal crossing form over an optimal generation path. But, machines have limited human-like abilities. We generalize such learning tasks as self-optimizing feature generation. Self-optimizing feature generation imposes several under-addressed challenges on existing systems: meaningful, robust, and efficient generation. To tackle these challenges, we propose a principled and generic representation-crossing framework to solve self-optimizing feature generation. To achieve hashing representation, we propose a three-step approach: feature discretization, feature hashing, and descriptive summarization. To achieve reinforcement crossing, we develop a hierarchical reinforcement feature crossing approach. We present extensive experimental results to demonstrate the effectiveness and efficiency of the proposed method. The code is available at https://github.com/yingwangyang/HRC_feature_cross.git

Self-optimizing Feature Generation via Categorical Hashing Representation and Hierarchical Reinforcement Crossing

Feature generation aims to generate new and meaningful features to create a discriminative representation space.A generated feature is meaningful when the generated feature is from a feature pair with inherent feature interaction. In the real world, experienced data scientists can identify potentially useful feature-feature interactions, and generate meaningful dimensions from an exponentially large search space, in an optimal crossing form over an optimal generation path. But, machines have limited human-like abilities.We generalize such learning tasks as self-optimizing feature generation. Self-optimizing feature generation imposes several under-addressed challenges on existing systems: meaningful, robust, and efficient generation. To tackle these challenges, we propose a principled and generic representation-crossing framework to solve self-optimizing feature generation.To achieve hashing representation, we propose a three-step approach: feature discretization, feature hashing, and descriptive summarization. To achieve reinforcement crossing, we develop a hierarchical reinforcement feature crossing approach.We present extensive experimental results to demonstrate the effectiveness and efficiency of the proposed method. The code is available at https://github.com/yingwangyang/HRC_feature_cross.git

K-Means Based Constellation Optimization for Index Modulated Reconfigurable Intelligent Surfaces

Reconfigurable intelligent surface (RIS) has recently emerged as a promising technology enabling next-generation wireless networks. In this letter, we develop an improved index modulation (IM) scheme by utilizing RIS to convey information. Specifically, we study an RIS-aided multiple-input single-output (MISO) system, in which the information bits are conveyed by reflection patterns of RIS rather than the conventional amplitude-phase constellation. Furthermore, the K-means algorithm is employed to optimize the reflection constellation to improve the error performance. Also, we propose a generalized Gray coding method for mapping information bits to an appropriate reflection constellation and analytically evaluate the error performance of the proposed scheme by deriving a tight upper bound of the average bit error rate (BER). Finally, numerical results verify the accuracy of our theoretical analysis as well as the substantially improved BER performance of the proposed RIS-based IM transmission scheme.Comment: 5 pages, 3 figures, accepted by IEEE C

Modeling and Analyzing User Behavior Risks in Online Shopping Processes Based on Data-Driven and Petri-Net Methods

With the rapid spread of e-commerce and e-payment, the increasing number of people choose online shopping instead of traditional buying way. However, the malicious user behaviors have a significant influence on the security of users' accounts and property. In order to guarantee the security of shopping environment, a method based on Complex Event Process (CEP) and Colored Petri nets (CPN) is proposed in this paper. CEP is a data-driven technology that can correlate and process a large amount of data according to Event Patterns, and CPN is a formal model that can simulate and verify the specifications of the online shopping processes. In this work, we first define the modeling scheme to depict the user behaviors and Event Patterns of online shopping processes based on CPN. The Event Patterns can be constructed and verified by formal methods, which guarantees the correctness of Event Patterns. After that, the Event Patterns are translated into Event Pattern Language (EPL) according to the corresponding algorithms. Finally, the EPLs can be inserted into the complex event processing engine to analyze the users' behavior flows in real-time. In this paper, we validate the effectiveness of the proposed method through case studies

Spatial variation decomposition via sparse regression

In this paper, we briefly discuss the recent development of a novel sparse regression technique that aims to accurately decompose process variation into two different components: (1) spatially correlated variation, and (2) uncorrelated random variation. Such variation decomposition is important to identify systematic variation patterns at wafer and/or chip level for process modeling, control and diagnosis. We demonstrate that the spatially correlated variation can be accurately represented by the linear combination of a small number of “templates”. Based upon this observation, an efficient algorithm is developed to accurately separate spatially correlated variation from uncorrelated random variation. Several examples based on silicon measurement data demonstrate that the aforementioned sparse regression technique can capture systematic variation patterns with high accuracy.Interconnect Focus Center (United States. Defense Advanced Research Projects Agency and Semiconductor Research Corporation)Focus Center Research Program. Focus Center for Circuit & System SolutionsNational Science Foundation (U.S.) (Contract CCF-0915912

Tell Me How to Survey: Literature Review Made Simple with Automatic Reading Path Generation

Recent years have witnessed the dramatic growth of paper volumes with plenty of new research papers published every day, especially in the area of computer science. How to glean papers worth reading from the massive literature to do a quick survey or keep up with the latest advancement about a specific research topic has become a challenging task. Existing academic search engines such as Google Scholar return relevant papers by individually calculating the relevance between each paper and query. However, such systems usually omit the prerequisite chains of a research topic and cannot form a meaningful reading path. In this paper, we introduce a new task named Reading Path Generation (RPG) which aims at automatically producing a path of papers to read for a given query. To serve as a research benchmark, we further propose SurveyBank, a dataset consisting of large quantities of survey papers in the field of computer science as well as their citation relationships. Each survey paper contains key phrases extracted from its title and multi-level reading lists inferred from its references. Furthermore, we propose a graph-optimization-based approach for reading path generation which takes the relationship between papers into account. Extensive evaluations demonstrate that our approach outperforms other baselines. A Real-time Reading Path Generation System (RePaGer) has been also implemented with our designed model. To the best of our knowledge, we are the first to target this important research problem. Our source code of RePaGer system and SurveyBank dataset can be found on here.Comment: 16 pages, 12 figure

Determination of cordycepin content of Cordyceps militaris recombinant rice by high performance liquid chromatography

Purpose: To assess the suitability of high performance liquid chromatography (HPLC) for the determination of cordycepin content of Cordyceps militaris recombinant riceMethods: Cordyceps militaris recombinant rice was made by mixing brown rice with artificial Chinese caterpillar fungus culture medium powder using twin-screw extrusion technology. Cordycepin content was determined by reversed-phase HPLC with water:acetonitrile (95:5, v/v) as mobile phase, detection wavelength of 260 nm, and flow rate of 1.0 mL/min.Results: Cordycepin contents showed good linearity in the range of 1 - 50.0 μg/mL (r2 = 0.9996), and while recovery ranged from 103.2 to 109.9 %. Relative standard deviation (RSD), precision and repeatability RSD was 2.38, 0.76 and 1.46 %, respectively.Conclusion: The HPLC method is simple, fast, accurate and reproducible. It is suitable for determination of cordycepin content of artificial Chinese caterpillar fungus culture medium and brown rice recombinant rice.Keywords: Recombinant rice, Cordycepin, Chinese caterpillar fungus, Aweto, Cordyceps militari

Inhibitory effect of α-cyclodextrin on α-amylase activity

Purpose: To explore the effect of α-cyclodextrin on the activity of α-amylase with a view to expanding its application range.Methods: The concentration of α-cyclodextrin, temperature, pH and interaction time were used as single factors to explore the influence of α-cyclodextrin on the activity of α-amylase and endogenous fluorescence in the enzyme system.Results: The results showed that the concentration, time, pH and temperature affect the interaction of them. The most obvious conditions for inhibition of α-amylase activity are as follows: 10 mmol/L concentration of α-cyclodextrin, pH 6.9, duration of 120 min and temperature at 55 oC. In addition, the fluorescence intensity of α-amylase changed as a result of the addition of α-cyclodextrin.Conclusion: The activity of α-amylase can be inhibited by α-cyclodextrin. At the same time, the addition of α-cyclodextrin will lead to the transfer of tryptophan group in α-amylase, which cause the change of microenvironment and changes the endogenous fluorescence intensity of α-amylase.Keywords: α-Cyclodextrin, α-Amylase, Fluorescence intensity, Inhibitio

Application of multiple methods for reverse flow routing: A case study of Luxi river basin, China

Because of the lack of hydrological monitoring facilities and methods in many areas, basic hydrological elements cannot be obtained directly. In that case, the reverse flow routing method is frequently used, which allows for the simulation of hydraulic elements upstream using downstream data, and is of great significance for river and reservoir joint regulation, flood disaster management, flood control evaluation, and flood forecasting. The hydrological and hydrodynamic methods are the two main approaches to reverse flow routing. The hydrological method is mainly realized by constructing a distributed or lumped hydrological model based on rainfall, soil type, terrain slope, and other data. A distributed hydrological model focuses on the physical mechanism of runoff yield and flow concentration, the spatial variability of model input, and the hydraulic connection between different units. The solution of the hydrological method is relatively simple, but it requires a large amount of measured data, which limits the applicability of this method. The other method builds a hydrodynamic model by solving shallow water equations for reverse flow routing. This method has definite physical significance, higher accuracy, and obvious advantages of simple and fast calculations. It can not only simulate one-dimensional but also two-dimensional flood routing processes. In addition, the slope-area method is frequently used for flood reverse routing in many areas in China without relevant hydrological data, and can calculate the peak discharge, maximum water level, flood recurrence interval, and other information by the hydrodynamic formula, along with the cross-section and the measured flood mark water level. Due to the influence of extreme weather, a heavy rainstorm and flood occurred in the Luxi river basin in China on 16 August 2020, resulting in severe flood disasters in this area and causing significant economic losses. Moreover, due to the lack and damage of hydrological monitoring equipment, hydrological information such as flood hydrographs and peak discharges of this flood could not be recorded. To reduce the uncertainty of a single method for reverse flow routing, we integrated and applied the hydrodynamic, hydrological, and slope-area methods to reverse flow routing in the Luxi river basin on 16 August 2020. The simulation accuracy of the three methods was verified in terms of the measured flood mark water level, and the simulation results of the three methods were analyzed and compared. The results are as follows: 1) The hydrological method can better simulate flood hydrographs and durations, especially for flood hydrographs with multiple peaks, and is more applicable than the other two methods. However, the hydrodynamic and slope-area methods have better accuracy in the reverse simulation of flood peaks. Therefore, through the comprehensive comparative analysis of these three methods, flood elements such as flood hydrographs, peak discharges, and durations can be simulated more accurately, and the problem of large errors caused by a single method can be avoided; 2) The simulation results of the hydrodynamic and slope-area methods are similar, and the maximum error of the peak discharge calculated using the two methods is within 10%. According to the simulation results, the peak discharge reached 2,920 m3/s downstream of Luxi river basin, which is a flood having more than 100-year recurrence interval; 3) The simulation results of the hydrological method show that the flow hydrograph is a double-peak, and the two peaks occurred at 17:00 on August 16 and 6:00 on 17 August 2020, respectively

Modeling and Analyzing User Behavior Risks in Online Shopping Processes Based on Data-Driven and Petri-Net Methods

With the rapid spread of e-commerce and e-payment, the increasing number of people choose online shopping instead of traditional buying way. However, the malicious user behaviors have a significant influence on the security of users’ accounts and property. In order to guarantee the security of shopping environment, a method based on Complex Event Process (CEP) and Colored Petri nets (CPN) is proposed in this paper. CEP is a data-driven technology that can correlate and process a large amount of data according to Event Patterns, and CPN is a formal model that can simulate and verify the specifications of the online shopping processes. In this work, we first define the modeling scheme to depict the user behaviors and Event Patterns of online shopping processes based on CPN. The Event Patterns can be constructed and verified by formal methods, which guarantees the correctness of Event Patterns. After that, the Event Patterns are translated into Event Pattern Language (EPL) according to the corresponding algorithms. Finally, the EPLs can be inserted into the complex event processing engine to analyze the users’ behavior flows in real-time. In this paper, we validate the effectiveness of the proposed method through case studies