CN110889643A - Paper quality evaluation method and system - Google Patents
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Abstract
The invention discloses a method and a system for evaluating thesis quality, wherein the method comprises the following steps: acquiring published thesis data in the field to be evaluated; preprocessing the paper data, and constructing a graph structure according to the reference relationship among the papers; establishing a weighted independent cascade probability model according to the graph structure; and acquiring the influence ranking of all papers to be evaluated by utilizing an ASNI-RR algorithm based on the SNI centrality and an ASV-RR algorithm based on the Sharpley centrality according to the weighted independent cascade probability model. The paper quality evaluation method provided by the invention utilizes the ASNI-RR algorithm based on the SNI centrality and the ASV-RR algorithm based on the Sharpley centrality to evaluate the paper quality, so that the evaluation result of the paper quality is more accurate.
Description
Technical Field
The invention relates to the field of thesis influence evaluation, in particular to a thesis quality evaluation method and system.
Background
With the increase of the number of scientific researchers, the number of academic papers is increased rapidly, the evaluation on the influence of the academic papers is limited to the number of times of the cited academic papers, the index has many defects, so that the influence of the academic papers is difficult to accurately evaluate, the traditional academic paper evaluation index is basically limited to the number of times of the cited academic papers, the index has many defects, so that the influence of the academic papers is difficult to accurately evaluate, wherein the IMM algorithm is the simplest algorithm based on a reverse reachable set concept, a greedy method is used for searching multiple nodes which can enable reverse reachable sets covered by the academic papers to be as large as possible and sorting the nodes according to the multiple nodes, and the accuracy of the current algorithm cannot meet the evaluation on the quality of the scientific research papers by scientific research institutions or academic evaluation institutions.
Disclosure of Invention
Therefore, the invention provides a method and a system for evaluating the quality of a paper, which overcome the defect of poor accuracy in evaluating the quality of the paper in the prior art.
In order to achieve the purpose, the invention provides the following technical scheme:
in a first aspect, an embodiment of the present invention provides a method for evaluating quality of a paper, including the following steps: acquiring published thesis data in the field to be evaluated; preprocessing the paper data, and constructing a graph structure according to the reference relationship among the papers; establishing a weighted independent cascade probability model according to the graph structure; and acquiring the influence ranking of all papers to be evaluated by utilizing an ASNI-RR algorithm based on the SNI centrality and an ASV-RR algorithm based on the Sharpley centrality according to the weighted independent cascade probability model.
In one embodiment, the paper data includes a paper title, author, publication time, and cited paper.
In an embodiment, the step of preprocessing the paper data and constructing a graph structure according to reference relationships among the papers includes: error elimination, redundancy and data noise elimination are carried out on the thesis data, and the obtained thesis data are numbered uniformly according to a preset rule; and determining edges in the structure of the construction graph according to the reference relation among the papers.
In an embodiment, the weight of each edge in the weighted independent cascading probability model is an inverse degree of an outgoing node.
In an embodiment, the step of obtaining the influence ranks of all papers to be evaluated according to the weighted independent cascade probability model by using an SNI centrality-based ASNI-RR algorithm and a Sharpley centrality-based ASV-RR algorithm includes: calculating the SNI values of all nodes of the weighted independent cascade probability model by using an ASNI-RR algorithm, wherein the SNI values are used for evaluating the influence of a single node; calculating Shapley values of all nodes of the weighted independent cascade probability model by using an ASV-RR algorithm, wherein the Shapley values are used for evaluating the group influence; selecting a larger node in the Shpley value and the SNI value as a seed node in the influence propagation process; and acquiring the influence ranking of all papers to be evaluated according to the influence propagation range of the seed nodes.
In one embodiment, the step of calculating SNI values of all nodes of the weighted independent cascade probability model by using the ASNI-RR algorithm includes: calculating a lower bound estimation value LB of the OPT of the maximum influence approximate optimal solution; estimating the number theta of the required reverse reachable sets by using the boundary estimation value LB; initializing SNI values of all nodes, randomly generating a reverse reachable set R for theta times in a circulating mode, and calculating SNI estimated values est of nodes u in the reverse reachable set Ru'＝estu' + 1; acquiring a kth large value in the SNI estimated values, and calculating a lower bound estimated value LB if a preset condition is met; calculating the number theta of reverse reachable sets by using the lower bound estimated value LB to obtain the final SNI estimated value of each node v
In an embodiment, the step of calculating sharley values of all nodes of the weighted independent cascade probabilistic model by using the ASV-RR algorithm includes: calculating a lower bound estimation value LB of the OPT of the maximum influence approximate optimal solution; estimating the number theta of the required reverse reachable sets by using the boundary estimation value LB; initializing Shapley values of all nodes, and randomly generating a reverse direction for theta times in a cycleAn reachable set R calculates a Shapley estimated value est of a node u in the reverse reachable set Ru＝estu+1/| R |; acquiring a kth large value in the Shapley estimated values, and calculating a lower bound estimated value LB if a preset condition is met; calculating the number theta of the reverse reachable sets by using the lower bound estimated value LB to obtain the final Shapley estimated value of each node v as
In a second aspect, an embodiment of the present invention provides a system for evaluating paper quality, including: the evaluation target data acquisition module is used for acquiring published thesis data in the field to be evaluated; the paper data preprocessing module is used for constructing a graph structure according to the reference relationship among papers; the weighted independent cascade probability model module is used for establishing a weighted independent cascade probability model according to the graph structure; and the influence ranking module of the evaluation papers is used for acquiring the influence ranking of all papers to be evaluated by utilizing an ASNI-RR algorithm based on SNI centrality and an ASV-RR algorithm based on Sharpley centrality according to the weighted independent cascade probability model.
In a third aspect, an embodiment of the present invention provides a terminal, including: the system comprises at least one processor and a memory which is in communication connection with the at least one processor, wherein the memory stores instructions which can be executed by the at least one processor, and the instructions are executed by the at least one processor, so that the at least one processor executes the method for evaluating the quality of the paper according to the first aspect of the embodiment of the invention.
In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium, where computer instructions are stored, and the computer instructions are configured to cause a computer to execute the method for evaluating quality of a paper according to the first aspect of the embodiment of the present invention.
The technical scheme of the invention has the following advantages:
the paper quality evaluation method provided by the invention comprises the following steps: acquiring published thesis data in the field to be evaluated; preprocessing the paper data, and constructing a graph structure according to the reference relationship among the papers; establishing a weighted independent cascade probability model according to the graph structure; and acquiring the influence ranking of all papers to be evaluated by utilizing an ASNI-RR algorithm based on the SNI centrality and an ASV-RR algorithm based on the Sharpley centrality according to the weighted independent cascade probability model. The thesis quality evaluation method provided by the embodiment of the invention solves the problem of inaccurate thesis quality evaluation through an ASNI-RR algorithm based on SNI centrality and an ASV-RR algorithm based on Sharpley centrality.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and other drawings can be obtained by those skilled in the art without creative efforts.
FIG. 1 is a flowchart illustrating a method for evaluating quality of a paper according to an embodiment of the present invention;
FIG. 2 is a flowchart illustrating a specific example of obtaining influence rankings of all papers to be evaluated based on an SNI centrality-based ASNI-RR algorithm and a Sharpley centrality-based ASV-RR algorithm in the embodiment of the present invention;
fig. 3 is a flowchart of an influence index model based on Sharpley and SNI centrality provided in the embodiment of the present invention and other conventional thesis evaluation ranking algorithms;
FIG. 4 is a histogram of the number of matches of the ASNI-RR algorithm and ASV-RR algorithm provided in the present embodiment to excellent papers compared to the Precision index and Recall index of the conventional quote number method;
FIG. 5 is a block diagram of a system for evaluating quality of a paper according to an embodiment of the present invention;
fig. 6 is a block diagram of a terminal according to an embodiment of the present invention.
Detailed Description
The technical solutions of the present invention will be described clearly and completely with reference to the accompanying drawings, and it should be understood that the described embodiments are some, but not all embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
In the description of the present invention, it should be noted that the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc., indicate orientations or positional relationships based on the orientations or positional relationships shown in the drawings, and are only for convenience of description and simplicity of description, but do not indicate or imply that the device or element being referred to must have a particular orientation, be constructed and operated in a particular orientation, and thus, should not be construed as limiting the present invention. Furthermore, the terms "first," "second," and "third" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance.
In the description of the present invention, it should be noted that, unless otherwise explicitly specified or limited, the terms "mounted," "connected," and "connected" are to be construed broadly, e.g., as meaning either a fixed connection, a removable connection, or an integral connection; can be mechanically or electrically connected; the two elements may be directly connected or indirectly connected through an intermediate medium, or may be communicated with each other inside the two elements, or may be wirelessly connected or wired connected. The specific meanings of the above terms in the present invention can be understood in specific cases to those skilled in the art.
In addition, the technical features involved in the different embodiments of the present invention described below may be combined with each other as long as they do not conflict with each other.
Example 1
The embodiment provides a quality evaluation method for a paper, which can be applied to quality evaluation of papers published in various fields, as shown in fig. 1, and includes the following steps:
and step S1, obtaining published paper data of the field to be evaluated.
In the embodiment of the present invention, published paper data of the field to be evaluated may be collected from aps (american physical society) to obtain data of all published physical domain papers, including paper titles, authors, publication times, cited papers, etc., which are only used as examples and not limited thereto, and the paper data is adaptively selected according to actual requirements in practical applications.
And step S2, preprocessing the paper data and constructing a graph structure according to the reference relationship among the papers.
In the embodiment of the invention, data are preprocessed, and the data are specifically divided into data cleaning and data integration, wherein the data cleaning is mainly used for eliminating errors, redundancy and data noise, the obtained thesis data are uniformly numbered according to rules, and the data integration is used for converting the reference relation among the thesis into edges in a graph structure and exporting the edges into a data file with a uniform format.
And step S3, establishing a weighted independent cascade probability model according to the graph structure.
In the embodiment of the present invention, the establishing of the weighted independent cascade probability model is to set the weight, i.e., the propagation probability, of each edge as the inverse of the outgoing degree of the outgoing node on the basis of the independent cascade model (independentcascodel), and the back propagation probability of the edge is set as 0.
And step S4, acquiring influence ranks of all papers to be evaluated according to the weighted independent cascade probability model by utilizing an ASNI-RR algorithm based on SNI centrality and an ASV-RR algorithm based on Sharpley centrality.
In the embodiment of the invention, the processed data sets are used for respectively operating the ASNI-RR algorithm based on the SNI centrality and the ASV-RR algorithm based on the Sharpley centrality, and all the paper influence rankings ordered according to different ordering algorithms are obtained.
In the embodiment of the present invention, the process of step S4 is executed, as shown in fig. 2, and includes:
step S41: and calculating the SNI values of all nodes of the weighted independent cascade probability model by using an ASNI-RR algorithm, wherein the SNI values are used for evaluating the influence of the single node.
In the embodiment of the invention, the weighted independent level is calculated by using the ASNI-RR algorithmThe step of connecting SNI values of all nodes of the probability model comprises the following steps: calculating a lower bound estimation value LB of the OPT of the maximum influence approximate optimal solution; estimating the number theta of the required reverse reachable sets by using the boundary estimation value LB; initializing SNI values of all nodes, randomly generating a reverse reachable set R for theta times in a circulating mode, and calculating SNI estimated values est of nodes u in the reverse reachable set Ru'＝estu' + 1; acquiring a kth large value in the SNI estimated values, and calculating a lower bound estimated value LB if a preset condition is met; calculating the number theta of reverse reachable sets by using the lower bound estimated value LB to obtain the final SNI estimated value of each node v
Step S42: shapley values of all nodes of the weighted independent cascade probabilistic model are calculated by using an ASV-RR algorithm, and the Shapley values are used for evaluating the group influence.
In the embodiment of the invention, the step of calculating the Shapley values of all nodes of the weighted independent cascade probability model by using the ASV-RR algorithm comprises the following steps: calculating a lower bound estimation value LB of the OPT of the maximum influence approximate optimal solution; estimating the number theta of the required reverse reachable sets by using the boundary estimation value LB; initializing Shapley values of all nodes, randomly generating a reverse reachable set R for theta times in a circulating mode, and calculating Shapley estimated values est of nodes u in the reverse reachable set Ru＝estu+1/| R |; acquiring a kth large value in the Shapley estimated values, and calculating a lower bound estimated value LB if a preset condition is met; calculating the number theta of the reverse reachable sets by using the lower bound estimated value LB to obtain the final Shapley estimated value of each node v as
Step S43: the larger node of the Shpley value and the SNI value is selected as a seed node in the influence propagation process.
The method and the device sort the calculation results of the Shapley values and the SNI values of all nodes in the model from high to low respectively, and consider that the Shapley and SNI centrality of the nodes are high when the Shpley values and the SNI values of the nodes are larger, so that the nodes with the large Shpley values and SNI values can be respectively selected as seed nodes in the influence propagation process, and the influence propagation ranges of the seed nodes selected by the two centrality methods are compared.
Step S44: and obtaining the influence ranks of all papers to be evaluated according to the influence propagation range of the seed nodes, and ranking according to the size of the influence propagation range.
In the embodiment of the invention, the proposed ASV-RR algorithm and ASNI-RR algorithm respectively introduce the centrality of an SNI value and the centrality of a Shapley value on the basis of an IMM algorithm. The method adopting the SNI value refers to the centrality of the influence of a single node, the natural centrality of a node is the influence propagation range of the node which is only used as a seed node, the method adopting the Shapley value refers to the method adopting the Shapley value in a large cooperative alliance, an optimal benefit distribution scheme is obtained according to given contribution functions of different cooperative modes corresponding to different collaborators, the optimal 'fair' solution of benefit distribution is achieved, the method introducing the Shapley value can better explore the role of the node in social influence, and the influence of the node in a group is considered.
In the embodiment of the invention, in order to verify the effectiveness and feasibility of the algorithm, the influence index model based on Sharpley and SNI centrality in the project is compared with other traditional thesis evaluation ranking algorithms (degree-based evaluation algorithm, iterative influence sorting algorithm and interactive multi-model algorithm), the specific steps are shown in FIG. 3, and results generated by different evaluation algorithm models are used for matching an excellent thesis catalog. Wherein:
the evaluation algorithm based on the degree reads all the citation relation data in the degree sorting algorithm, records the number of times of citation for each cited paper, and then sorts all the articles in a descending order according to the count, wherein the input data format is that a first column is a paper number, a second column is preprocessed data of the number of times of citation, and the order is the ranking of the evaluation algorithm based on the degree.
Based on an iterative influence sorting algorithm (BiRank algorithm), firstly, randomly initializing a sorting vector; then, an iterative process is performed until convergence; and finally, sorting according to the returned score arrays of v and u to obtain the score of item, and sorting according to the score.
The interactive multi-model algorithm (IMM) structure is divided into two steps: firstly, estimating the number of required reverse reachable sets and generating the reverse reachable sets (Sampling subfunctions), and storing the reverse reachable sets in a data structure R; then, using greedy method to find k nodes in reverse reachable set R to make their covered reverse reachable sets as many as possible (node selection subfunction); and finally, returning the found k nodes as seed nodes.
In the embodiment of the invention, the matching situation of different ranking algorithm models to the papers in the excellent paper catalogue can be obtained through experiments, the first 1%, 1.5%, 2%, 3%, 4%, 5%, 6%, 7%, 8%, 9%, 10%, 11%, 12%, 13%, 14%, 15%, 16%, 17%, 18%, 19%, 20% of the ranking result of each algorithm is respectively taken, and the matching number of the papers in the catalogue in different proportions before the ranking of each algorithm is calculated. The matching data obtained by the experiment is shown in fig. 4, and it can be seen that the Precision index and the Recall index of the method provided by the embodiment also have advantages compared with other algorithms. The number of matches to the superior papers is better than the traditional quote method.
According to the method for evaluating the quality of the thesis, the quality of the thesis is evaluated by utilizing an ASNI-RR algorithm based on the SNI centrality and an ASV-RR algorithm based on the Sharpley centrality, so that the evaluation result of the quality of the thesis is more accurate.
Example 2
The present embodiment provides a system for evaluating the quality of a paper, as shown in fig. 5, including:
the evaluation target data acquisition module 1 is used for acquiring published thesis data in the field to be evaluated; this module executes the method described in step S1 in embodiment 1, and is not described herein again.
The paper data preprocessing module 2 is used for constructing a graph structure according to the reference relationship among papers; this module executes the method described in step S2 in embodiment 1, and is not described herein again.
The weighted independent cascade probability model module 3 is used for establishing a weighted independent cascade probability model according to the graph structure; this module executes the method described in step S3 in embodiment 1, and is not described herein again.
The influence ranking module 4 of the evaluation papers is used for acquiring the influence ranking of all papers to be evaluated by utilizing an ASNI-RR algorithm based on SNI centrality and an ASV-RR algorithm based on Sharpley centrality according to the weighted independent cascade probability model; this module executes the method described in step S4 in embodiment 1, and is not described herein again.
The embodiment of the invention provides an evaluation system of a paper quality, which evaluates the paper quality by utilizing an ASNI-RR algorithm based on SNI centrality and an ASV-RR algorithm based on Sharpley centrality, so that the evaluation result of the paper quality is more accurate.
Example 3
An embodiment of the present invention provides a terminal, as shown in fig. 6, including: at least one processor 401, such as a CPU (Central Processing Unit), at least one communication interface 403, memory 404, and at least one communication bus 402. Wherein a communication bus 402 is used to enable connective communication between these components. The communication interface 403 may include a Display (Display) and a Keyboard (Keyboard), and the optional communication interface 403 may also include a standard wired interface and a standard wireless interface. The Memory 404 may be a RAM (random Access Memory) or a non-volatile Memory (non-volatile Memory), such as at least one disk Memory. The memory 404 may optionally be at least one memory device located remotely from the processor 401. Wherein the processor 401 may perform the method of evaluating the quality of the paper in embodiment 1. A set of program codes is stored in the memory 404, and the processor 401 calls the program codes stored in the memory 404 for executing the evaluation method of the quality of the paper in embodiment 1. The communication bus 402 may be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus. The communication bus 402 may be divided into an address bus, a data bus, a control bus, and the like. For ease of illustration, only one line is shown in FIG. 6, but it is not intended that there be only one bus or one type of bus.
The memory 404 may include a volatile memory (RAM), such as a random-access memory (RAM); the memory may also include a non-volatile memory (english: non-volatile memory), such as a flash memory (english: flash memory), a hard disk (english: hard disk drive, abbreviation: HDD), or a solid-state drive (english: SSD); the memory 404 may also comprise a combination of memories of the kind described above.
The processor 401 may be a Central Processing Unit (CPU), a Network Processor (NP), or a combination of a CPU and an NP.
The processor 401 may further include a hardware chip. The hardware chip may be an application-specific integrated circuit (ASIC), a Programmable Logic Device (PLD), or a combination thereof. The aforementioned PLD may be a Complex Programmable Logic Device (CPLD), a field-programmable gate array (FPGA), a General Array Logic (GAL), or any combination thereof.
Optionally, the memory 404 is also used to store program instructions. The processor 401 may call program instructions to implement the method for evaluating the quality of a paper according to embodiment 1.
The embodiment of the present invention further provides a computer-readable storage medium, where a computer-executable instruction is stored on the computer-readable storage medium, and the computer-executable instruction can execute the method for evaluating the quality of a paper in embodiment 1. The storage medium may be a magnetic Disk, an optical Disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a Flash Memory (Flash Memory), a Hard Disk (Hard Disk Drive, abbreviated as HDD), a Solid-State Drive (SSD), or the like; the storage medium may also comprise a combination of memories of the kind described above.
It should be understood that the above examples are only for clarity of illustration and are not intended to limit the embodiments. Other variations and modifications will be apparent to persons skilled in the art in light of the above description. And are neither required nor exhaustive of all embodiments. And obvious variations or modifications of the invention may be made without departing from the spirit or scope of the invention.
Claims (10)
1. A method for evaluating the quality of a paper is characterized by comprising the following steps:
acquiring published thesis data in the field to be evaluated;
preprocessing the paper data, and constructing a graph structure according to the reference relationship among the papers;
establishing a weighted independent cascade probability model according to the graph structure;
and acquiring the influence ranking of all papers to be evaluated by utilizing an ASNI-RR algorithm based on the SNI centrality and an ASV-RR algorithm based on the Sharpley centrality according to the weighted independent cascade probability model.
2. The method of claim 1, wherein the paper data includes paper title, author, publication time and citation.
3. The method for evaluating the quality of the papers according to claim 2, wherein the step of preprocessing the data of the papers and constructing a graph structure according to the reference relationship among the papers comprises the following steps:
error elimination, redundancy and data noise elimination are carried out on the thesis data, and the obtained thesis data are numbered uniformly according to a preset rule;
and determining edges in the structure of the construction graph according to the reference relation among the papers.
4. The method of evaluating the quality of an thesis according to claim 3, wherein the weight of each edge in the weighted independent cascade probability model is the inverse degree of the outgoing node.
5. The method for evaluating the quality of papers according to claim 4, wherein the step of obtaining the influence ranking of all papers to be evaluated by utilizing the SNI centrality-based ASNI-RR algorithm and Sharpley centrality-based ASV-RR algorithm according to the weighted independent cascade probability model comprises:
calculating the SNI values of all nodes of the weighted independent cascade probability model by using an ASNI-RR algorithm, wherein the SNI values are used for evaluating the influence of a single node;
calculating Shapley values of all nodes of the weighted independent cascade probability model by using an ASV-RR algorithm, wherein the Shapley values are used for evaluating the group influence;
selecting a larger node in the Shpley value and the SNI value as a seed node in the influence propagation process;
and acquiring the influence ranking of all papers to be evaluated according to the influence propagation range of the seed nodes.
6. The method of evaluating the quality of paper according to claim 5, wherein the step of calculating SNI values of all nodes of the weighted independent cascade probability model using the ASNI-RR algorithm comprises:
calculating a lower bound estimation value LB of the OPT of the maximum influence approximate optimal solution;
estimating the number theta of the required reverse reachable sets by using the boundary estimation value LB;
initializing SNI values of all nodes, randomly generating a reverse reachable set R for theta times in a circulating mode, and calculating SNI estimated values est of nodes u in the reverse reachable set Ru'＝estu'+1；
Acquiring a kth large value in the SNI estimated values, and calculating a lower bound estimated value LB if a preset condition is met;
calculating the number theta of reverse reachable sets by using the lower bound estimate LB to obtain eachThe final SNI estimate for each node v is
7. The method of evaluating the quality of paper according to claim 5, wherein the step of calculating sharley values of all nodes of the weighted independent cascade probabilistic model using the ASV-RR algorithm comprises:
calculating a lower bound estimation value LB of the OPT of the maximum influence approximate optimal solution;
estimating the number theta of the required reverse reachable sets by using the boundary estimation value LB;
initializing Shapley values of all nodes, randomly generating a reverse reachable set R for theta times in a circulating mode, and calculating Shapley estimated values est of nodes u in the reverse reachable set Ru＝estu+1/|R|；
Acquiring a kth large value in the Shapley estimated values, and calculating a lower bound estimated value LB if a preset condition is met;
calculating the number theta of the reverse reachable sets by using the lower bound estimated value LB to obtain the final Shapley estimated value of each node v as
8. A system for evaluating the quality of an article, comprising:
the evaluation target data acquisition module is used for acquiring published thesis data in the field to be evaluated;
the paper data preprocessing module is used for constructing a graph structure according to the reference relationship among papers;
the weighted independent cascade probability model building module is used for building a weighted independent cascade probability model according to the graph structure;
and the paper influence ranking acquisition module is used for acquiring the influence rankings of all papers to be evaluated by utilizing an ASNI-RR algorithm based on SNI centrality and an ASV-RR algorithm based on Sharpley centrality according to the weighted independent cascade probability model.
9. A terminal, comprising: at least one processor, and a memory communicatively coupled to the at least one processor, wherein the memory stores instructions executable by the at least one processor to cause the at least one processor to perform the method of evaluating the quality of a paper recited in any one of claims 1-7.
10. A computer-readable storage medium storing computer instructions for causing a computer to perform the method of evaluating the quality of an article of any one of claims 1 to 7.
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