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In recent years dynamic internet-based start-ups have captured the public imagination through the creation of revolutionary business models. As we are now seeing, many lack the financial stamina to survive and become profitable. Attention has switched to how established companies are trying to clone these new models to their competitive advantage. However, in the rush to do so, many organizations are making quite fundamental strategic mistakes; mistakes that are often then repeated by others. This may be because they have failed to recognize the need to apply advanced risk management thinking to both the strategic and implementation issues surrounding the E-business imperative.

Motivated by reconstruction results by Rubin, we introduce a new reconstruction notion for permutation groups, transformation monoids and clones, called automatic action compatibility, which entails automatic homeomorphicity. We further give a characterization of automatic homeomorphicity for transformation monoids on arbitrary carriers with a dense group of invertibles having automatic homeomorphicity. We then show how to lift automatic action compatibility from groups to monoids and from monoids to clones under fairly weak assumptions. We finally employ these theorems to get automatic action compatibility results for monoids and clones over several well-known countable structures, including the strictly ordered rationals, the directed and undirected version of the random graph, the random tournament and bipartite graph, the generic strictly ordered set, and the directed and undirected versions of the universal homogeneous Henson graphs., Comment: 32 pp; changes to v2: minor modifications, typos corrected, Cor. 5.5, Lem. 5.6, Fact 5.7, Lem. 5.8, Cor. 5.9 are new; Cor. 5.5/5.6 have become 5.10/5.11; some references added and updated

There are somewhere between 200 million and 600 million Pentecostal/ Charismatic Christians in the world today. Most of them live in the “majority world,” and two thirds are women. Pentecostals are proud of being modern and frequently boast of it. Yet “Pentecostal modernity” is not a straightforward clone of the intellectual and political history of Europe and the North Atlantic. It contains paradoxical elements that can be plausibly interpreted as evidence of purposefully moral selectiveness by Pentecostals among the items in the “modern” cultural program. They in effect help to “heal the wounds of modernity.” This account of Pentecostal modernity also seeks to show that in two particular respects Pentecostal modernity might be considered a “correction” of Charles Taylor’s western model of modernity: in regarding human flourishing as spiritually sanctioned; and in retaining a porous model of the self, vertically open to possession by the Spirit or by forces of evil, and horizontally open by retaining some “dividual” characteristics of embeddedness with others.

The critical relations are the building blocks of the relational clone of a relational structure with respect to the relational operations intersection and direct product. In this paper we describe the critical relations of crowns. As a consequence, we obtain that the subpower membership problem for any crown is polynomial-time solvable.

Clone theory plays an important role in studying aggregation functions on bounded posets or bounded lattices. Several important classes of aggregation functions on a bounded lattice L form a clone, particularly the set of all aggregation functions on L, the so-called full aggregation clone on L. For any finite lattice L, this clone is known to be finitely generated and various generating sets and their constructions have been presented in recent papers. The aim of this paper is to extend previous results concerning generating sets of aggregation clones on finite chains. Namely, the objective is to discuss the minimality of certain generating bases, the so-called ( χ , ⊕ ) -generating sets.

The proliferation of edge computing technologies has boosted the development of new applications for a plethora of edge devices. However, many applications face privacy issues and bandwidth limitations. To solve these limitations, we propose a collaborative learning framework on the edges, named CLONE, which is steered by the real-world data sets collected from a large electric vehicle (EV) company and a grocery store of a shopping mall, respectively. We categorize two application scenarios for CLONE, i.e., CLONE in the training stage (CLONE_ training ) and CLONE in the inference stage (CLONE_ inference ). As to CLONE_ training , we choose the failure prediction of EV battery and associated components as the first use case. While as for CLONE_ inference , customer tracking in a grocery store is selected as another case study. In this work, the goal of the CLONE is to support real-time training and inference for connected vehicles and marketing intelligence services. Our experimental results on the EV data show that CLONE is able to reduce model training time without sacrificing algorithm performance. Furthermore, the experimental results on the video data from the grocery store reveal that CLONE is a useful approach to solve the multitarget multicamera tracking problem in a collaborative fashion.

This study describes a method to detect hijacked journals based on the analysis of the archives of clone journals. This approach is most effective in discovering a network of hijacked journals that have the same organizer(s). Analysis of the archives of clone journals allowed to detect 62 URLs of hijacked journals. It also provided the possibility to predict two clone websites before they became operational. This study shows that most detected hijacked journals represent a network of clone journals organized by one or several fraudulent individuals. The information and content of nine legitimate journals were compromised in international and national scientometric databases., 31 pages, 2 figures, list of hijacked journals

A complete and accurate cross-language clone detection tool can support software forking process that reuses the more reliable algorithms of legacy systems from one language code base to other. Cross-language clone detection also helps in building code recommendation system. This paper proposes a new technique to detect and classify cross-language clones of C and C++ programs by filtering the nodes of ANTLR-generated parse tree using a common grammar file, CPP14.g4. Parsing the input files using CPP14.g4 provides all the lexical and semantic information of input source code. Selective filtering of nodes performs serialization of two parse trees. Vector representation using term frequency inverse document frequency (TF-IDF) of the resultant tree is given as an input to cosine similarity to classify the clone types. Filtered parse tree of C and C++ increases the precision from 51% to 61%, and matching based on renaming the input/output expressions provides average precision of 91.97% and 95.37% for small scale and large scale repositories respectively. The proposed cross-language clone detection exhibits the highest precision of 95.37% in finding all types of clones (1, 2, 3 and 4) for 16,032 semantically similar clone pairs of C and CPP codes.

An abundant number of clone detection tools have been proposed in the literature due to the many applications and benefits of clone detection. However, there has been difficulty in the performance evaluation and comparison of these clone detectors. This is due to a lack of reliable benchmarks, and the manual efforts required to validate a large number of candidate clones. In particular, there has been a lack of a synthetic benchmark that can precisely and comprehensively measure clone-detection recall. In this paper, we present a mutation-analysis based benchmarking framework that can be used not only to evaluate the recall of clone detection tools for different types of clones but also for specific kinds of clone edits and without any manual efforts. The framework uses an editing taxonomy of clone synthesis for generating thousands of artificial clones, injects into code bases and automatically evaluates the subject clone detection tools following the mutation analysis approach. Additionally, the framework has features where custom clone pairs could also be used in the framework for evaluating the subject tools. This gives the opportunity of evaluating specialized tools for specialized contexts such as evaluating a tool’s capability for the detection of complex Type-4 clones or real world clones without writing complex mutation operators for them. We demonstrate this framework by evaluating the performance of ten modern clone detection tools across two clone granularities (function and block) and three programming languages (Java, C and C#). Furthermore, we provide a variant of the framework that can be used to evaluate specialized tools such as for large gaped clone detection. Our experiments demonstrate confidence in the accuracy of our Mutation and Injection Framework when comparing against the expected results of the corresponding tools, and widely used real-world benchmarks such as Bellon’s benchmark and BigCloneBench. We provide features so that most clone detection tools that report clones in the form of clone pairs (either in filename/line numbers or filename/tokens) could be evaluated using the framework.

In this research paper, the simulation of a secured & energy efficient clone detection protocols in h-WSNs with improvement in key generation, encryption & decryption using an improvised security algorithm is presented. This protocol is developed in the NS-2 platform & the simulations are performed for a requisite amount of time and the results are observed and conclusions are drawn. The simulation results show the efficacy of the methodology that is being developed in this paper.

In this paper, the review of a secured & energy efficient clone detection protocols in h-WSNs with improvement in key generation, encryption & decryption using an improvised security algorithm is presented. This protocol is developed in the NS-2 platform. The algorithm shows how the proposed methodology is being developed.

We investigate the complexity of the Boolean clone membership problem (CMP): given a set of Boolean functions $F$ and a Boolean function $f$, determine if $f$ is in the clone generated by $F$, i.e., if it can be expressed by a circuit with $F$-gates. Here, $f$ and elements of $F$ are given as circuits or formulas over the usual De Morgan basis. B\"ohler and Schnoor (2007) proved that for any fixed $F$, the problem is coNP-complete, with a few exceptions where it is in P. Vollmer (2009) incorrectly claimed that the full problem CMP is also coNP-complete. We prove that CMP is in fact $\Theta^P_2$-complete, and we complement B\"ohler and Schnoor's results by showing that for fixed $f$, the problem is NP-complete unless $f$ is a projection. More generally, we study the problem $B$-CMP where $F$ and $f$ are given by circuits using gates from $B$. For most choices of $B$, we classify the complexity of $B$-CMP as being $\Theta^P_2$-complete (possibly under randomized reductions), coDP-complete, or in P., Comment: 30 pages

Code clones. In this work, we propose a novel detection framework using machine learning for automated detection of all four type of clones. The features extracted from a pair of code blocks are combined for possible detection of a clone with respect to a reference block. We use AST and PDG features of both code blocks to prepare labelled training samples after fusing the two feature vectors using three different alternatives. We use six state-of-the-art classification models including Deep Convolutional Neural Network to assess the prediction performance of our scheme. To access the effectiveness of our framework we use seven datasets and compare its performance with five state-of-the-art clone detectors. We also compare a large number of algorithms for code clone detection. Comparing the performance of a large number of machine learning techniques, ANN and non-ANN, using such features, and establishing that fusing of AST and PDG features gives competitive results using deep learning as well as boosted tree algorithms, we find that boosted tree algorithms like XGBoost are quite competitive in clone detection. Experimental results demonstrate that our approach outperforms existing clone detection methods in terms of prediction accuracy.

Motivation Proteins are intrinsically dynamic entities. Flexibility sampling methods, such as molecular dynamics or those arising from integrative modeling strategies, are now commonplace and enable the study of molecular conformational landscapes in many contexts. Resulting structural ensembles increase in size as technological and algorithmic advancements take place, making their analysis increasingly demanding. In this regard, cluster analysis remains a go-to approach for their classification. However, many state-of-the-art algorithms are restricted to specific cluster properties. Combined with tedious parameter fine-tuning, cluster analysis of protein structural ensembles suffers from the lack of a generally applicable and easy to use clustering scheme. Results We present CLoNe, an original Python-based clustering scheme that builds on the Density Peaks algorithm of Rodriguez and Laio. CLoNe relies on a probabilistic analysis of local density distributions derived from nearest neighbors to find relevant clusters regardless of cluster shape, size, distribution and amount. We show its capabilities on many toy datasets with properties otherwise dividing state-of-the-art approaches and improves on the original algorithm in key aspects. Applied to structural ensembles, CLoNe was able to extract meaningful conformations from membrane binding events and ligand-binding pocket opening as well as identify dominant dimerization motifs or inter-domain organization. CLoNe additionally saves clusters as individual trajectories for further analysis and provides scripts for automated use with molecular visualization software. Availability and implementation www.epfl.ch/labs/lbm/resources, github.com/LBM-EPFL/CLoNe. Supplementary information Supplementary data are available at Bioinformatics online.

The hundreds of thousands of active users everywhere in the globe use online social community, inclusive of Facebook, Twitter, Tumbler and LinkedIn. This makes it handy for fake profile cloning and compromise user information. This system, uses information to be hidden in a profile photos with a hidden information to detect profiles which are fake and any attacks that’s taken place by botnet. This project presents the detection mechanisms of social network based attacks that takes place online, analysis of the profile with interval in time and sequenced Protocol. This project we have proposed data hiding with the use of discrete wavelet transform algorithm. Thus it will save you from clone attack and provide entire user statistic with private record. Thus we will capable of keeping away from clone attacks in social media networks.

To make up the existing deficiencies of clone attack detection methods of wireless sensor networks, a low resource consumption clone detection method (MSCD) for multi-base station wireless sensor networks is proposed. MSCD has the following characteristics: (1) Running in each ring network with base station as the center and using nodes in non-hotspot area to complete clone attack detection, which reduces the effect of clone attack detection on the network lifetime; (2) Combine the head node rotation mechanism and the backup head node mechanism to ensure the energy balance of the network; (3) The ring head node path can find clone nodes that come from different local networks, which makes the MSCD method be suitable for the whole multi-base station network. Meanwhile, in the detection domain of the head node, local broadcast is used to ensure the encounter of legitimacy verification messages and witness nodes; (4) It is proved theoretically that the clone detection probability of MSCD can reach 1 when the witness is credible. The theoretical analysis and the simulation results show that the MSCD clone detection probability is still above 98% when the number of clone nodes accounts for 10% of the total number of nodes, and the network lifetime and storage requirements are significantly better than the existing similar methods.

The s-box plays the vital role of creating confusion between the ciphertext and secret key in any cryptosystem, and is the only nonlinear component in many block ciphers. Dynamic s-boxes, as compared to static, improve entropy of the system, hence leading to better resistance against linear and differential attacks. It was shown in Easttom (2018) that while incorporating dynamic s-boxes in cryptosystems is sufficiently secure, they do not keep non-linearity invariant. This work provides an algorithmic scheme to generate key-dependent dynamic n×n clone s-boxes having the same algebraic properties namely bijection, nonlinearity, the strict avalanche criterion (SAC), the output bits independence criterion (BIC) as of the initial seed s-box. The method is based on group action of symmetric group Sn and a subgroup S2n respectively on columns and rows of Boolean functions (GF(2n)→GF(2)) of s-box. Invariance of the bijection, nonlinearity, SAC, and BIC for the generated clone copies is proved. As illustration, examples are provided for n=8 and n=4 along with comparison of the algebraic properties of the clone and initial seed s-box. The proposed method is an extension of Hussain et al. (2012); Hussain et al. (2012); Hussain et al. (2018); Anees and Chen (2020) which involved group action of S8 only on columns of Boolean functions (GF(28)→GF(2) ) of s-box. For n=4, we have used an initial 4 × 4 s-box constructed by Carlisle Adams and Stafford Tavares (Adams and Tavares, 1990) to generated (4!)2 clone copies. For n=8, it can be seen (Hussain et al. (2012); Hussain et al. (2012); Hussain et al. (2018); Anees and Chen (2020)) that the number of clone copies that can be constructed by permuting the columns is 8!. For each column permutation, the proposed method enables to generate 8! clone copies by permuting the rows.

Over the past few decades, clone detection and evolution have become a major area of study in software engineering. Clone detection experiments present several challenges to researchers such as accurate data collection, selecting proper code detection algorithms, and understanding clone evolution phenomena. This paper attempts to facilitate clone detection and evolution research by providing a structured and systematic mechanism to conduct experiments. Clone detection experiments usually consist of several tasks such as fetching data from a version control system, performing necessary pre-processing activities, and feeding the data to a clone detection algorithm. Therefore, a particular clone detection experiment can interpret as a meaningful combination of such tasks into a scientific workflow. In this work, the concrete tasks in a code clone detection workflow are referred to as Building Blocks. This paper presents a useful collection of Building Blocks identified based on a systematic literature review, and a conceptual framework of an experimental testbed to facilitate clone detection experiments. The reusability of the Building Blocks was validated using four case studies selected from the literature. The validation results confirm the reusability and the expressiveness of the Building Blocks in new ventures. Besides, the proposed experimental testbed is proven beneficial in conducting and replicating clone detection experiments.

Software developers frequently reuse source code from repositories as it saves development time and effort. Code clones (similar code fragments) accumulated in these repositories represent often repeated functionalities and are candidates for reuse in an exploratory or rapid development. To facilitate code clone reuse, we previously presented DeepClone, a novel deep learning approach for modeling code clones along with non-cloned code to predict the next set of tokens (possibly a complete clone method body) based on the code written so far. The probabilistic nature of language modeling, however, can lead to code output with minor syntax or logic errors. To resolve this, we propose a novel approach called Clone-Advisor. We apply an information retrieval technique on top of DeepClone output to recommend real clone methods closely matching the predicted clone method, thus improving the original output by DeepClone. In this paper we have discussed and refined our previous work on DeepClone in much more detail. Moreover, we have quantitatively evaluated the performance and effectiveness of Clone-Advisor in clone method recommendation. Subjects Data Mining and Machine Learning, Software Engineering

We consider the problem of determining the achievable region of the universal $$1 \rightarrow 2$$ asymmetric quantum cloning problem. This concerns the quantum cloning of any quantum state to two approximate clones of different qualities. Measuring the cloning performance with the probabilities of the mixed clone states as the figure of merit, we show that the physical region is a union of ellipses in the plane. The study of these regions has consequences for the eavesdropping on quantum cryptography, and a wide variety of tasks. Equivalently, we characterize the region of quantum-state compatibility of two possibly different isotropic states, considering, for the first time, negative figures of merit.

A code clone (in short, clone) is a code fragment that is identical or similar to other code fragments in source code. Clones generated by a large number of changes to copy-and-pasted code fragments are called large-variance (modifications are scattered) or large-gap (modifications are in one place) clones. It is difficult for general clone detection techniques to detect such clones and thus specialized techniques are necessary. In addition, with the rapid growth of software development, scalable clone detectors that can detect clones in large codebases are required. However, there are no existing techniques for quickly detecting large-variance or large-gap clones in large codebases. In this paper, we propose a scalable clone detection technique that can detect large-variance clones from large codebases and describe its implementation, called NIL. NIL is a token-based clone detector that efficiently identifies clone candidates using an N-gram representation of token sequences and an inverted index. Then, NIL verifies the clone candidates by measuring their similarity based on the longest common subsequence between their token sequences. We evaluate NIL in terms of large- variance clone detection accuracy, general Type-1, Type-2, and Type- 3 clone detection accuracy, and scalability. Our experimental results show that NIL has higher accuracy in terms of large-variance clone detection, equivalent accuracy in terms of general clone detection, and the shortest execution time for inputs of various sizes (1–250 MLOC) compared to existing state-of-the-art tools.

Two code fragments that are identical to each other and repeatedly being implemented in a software program are called code clones. Code clones are classified into four different type which are clone Type-1, clone Type-2, clone Type-3 and clone Type-4. In the past decades, variegated code clone approaches as well as tools have been utilized for detecting code clones. However, the minimum comprehensiveness or lacking of generality in detecting all four types of code clone, has prompted other researchers to develop a code clone detection model. Generic Code Clone Detection (GCCD) Model is the model that implemented a clone detection for the four types of code clone. GCCD incorporates with process namely Pre-processing, Transformation, Parameterization, Categorization and Match Detection process. This research study intends to ameliorate the code clone detection of GCCD Model through Match Detection process of the respective model. A literature review is done to center a few other models and techniques and distance measure formula that available for calculating the code clones in Match Detection process. Based on the analysis, there are ten distance measure formula will be used to decide on the best method for calculating the code clone in Match Detection process. This study hopes to be used as reference for other researchers in aiding their research regarding code clone detection model.

Code clone detection remains one of the main challenges in maintaining software projects. Recently, state-of-the-art researches have shown that neural models based on abstract syntax trees (ASTs) can better represent code fragment. However, existing tree-based models are prone to gradient vanishing problems due to the large size of ASTs. In this paper, we represent a code fragment as the set of compositional paths in its abstract syntax tree (AST) and use this code representation to train a classifier to detect clone pairs. Unlike the siamese based model that obtains the embeddings of code fragments separately and then computes the similarity in vector space, our compare-aggregate based network takes two code fragments as a whole to obtain the vectors for classification. To validate our model's ability to detect code clones, we evaluated it on the publicly available dataset BigCloneBench, and the experimental results show our model outperforms the state-of-the-art model ASTNN.

We consider consecutive aggregation procedures for individual preferences 𝔠 ∈ ℭr (A) on a set of alternatives A, |A| ≥ 3: on each step, the participants are subject to intermediate collective decisions on some subsets B of the set A and transform their a priori preferences according to an adaptation function 𝒜. The sequence of intermediate decisions is determined by a lot J, i.e., an increasing (with respect to inclusion) sequence of subsets B of the set of alternatives. An explicit classification is given for the clones of local aggregation functions, each clone consisting of all aggregation functions that dynamically preserve a symmetric set 𝔇 ⊆ ℭr (A) with respect to a symmetric set of lots 𝒥. On the basis of this classification, it is shown that a clone ℱ of local aggregation functions that preserves the set ℜ2 (A) of rational preferences with respect to a symmetric set 𝒥 contains nondictatorial aggregation functions if and only if 𝒥 is a set of maximal lots, in which case the clone ℱ is generated by the majority function. On the basis of each local aggregation function f, lot J, and an adaptation function 𝒜, one constructs a nonlocal (in general) aggregation function fJ,A that imitates a consecutive aggregation procesure. If f dynamically preserves a set 𝔇 ⊆ ℭr (A) with respect to a set of lots 𝒥, then the aggregation function fJ,A preserves the set 𝔇 for each lot J ∈ 𝒥. If 𝔇 = ℜ2(A), then the adaptation function can be chosen in such a way that in any profile c ∈ (ℜ2(A))n, the Condorcet winner (if it exists) would coincide with the maximal element with respect to the preferences fJ,A (c) for each maximal lot J and f that dynamically preserves the set of rational preferences with respect to the set of maximal lots.

Code clone is common in software development, which usually leads to software defects or copyright infringement. Researchers have paid significant attention to code clone detection, and many methods have been proposed. However, the patterns for generating the code clones do not always remain the same. In order to fool the clone detection systems, the plagiarists, known as the clone creator, usually conduct a series of tricky modifications on the code fragments to make the clone difficult to detect. The existing clone detection approaches, which neglects the dynamics of the “contest” between the plagiarist and the detectors, is doomed to be not robust to adversarial revision of the code. In this paper, we propose a novel clone detection approach, namely ACD, to mimic the adversarial process between the plagiarist and the detector, which enables us to not only build strong a clone detector but also model the behavior of the plagiarists. Such a plagiarist model may in turn help to understand the vulnerability of the current software clone detection tools. Experiments show that the learned policy of plagiarist can help us build stronger clone detector, which outperforms the existing clone detection methods.

Wireless sensor network has widely used in various applications such as military, environment monitoring where sensor nodes has operated in open, harsh environment. Different type of attack...

A classical theorem of Baker and Pixley states that if [Formula: see text] is a finite algebra with a majority term and [Formula: see text] is an [Formula: see text]-ary operation on [Formula: see text] which preserves every subuniverse of [Formula: see text], then [Formula: see text] is representable by a term in [Formula: see text]. We give a generalizacion of this theorem for the case in which [Formula: see text] is a finite algebra belonging to some relatively congruence distributive quasivariety.

The presented new method for detecting of clone areas in a digital image is effective in conditions of disturbing effects on the image that are additional to the cloning, including cases of a small area of clone area (less than 0.4% of the image). The method is based on the detection of geometric comparability of surface parts, which is compared with digital image, corresponding to areas of the clone and its prototype. The quantitative index was found. This index is characterizing the individual areas of the image and coinciding for areas of clone and its prototype, including under attack conditions. This index is the value of the local (global) minimum of function values that interpolate the elements of the matrix G, which is put into correspondence to the analyzed digital image matrix. The elements G represent the smallest difference between the corresponding block of the digital images matrix q × q from any other of its block. The results of the computational experiment are presented. They are confirmed high efficiency of this method in compare to the existing analogues, regardless of the specifics of attack conducted on cloned image, the relative sizes of the cloned area, block sizes used in the image analysis.

In the first part of this paper we present a new family of finite bounded posets whose clones of monotone operations are not finitely generated. The proofs of these results are analogues of those in the famous paper of Tardos. Another interesting family of finite posets from the finite generability point of view is the family of locked crowns. To decide whether the clone of a locked crown where the crown is of at least six elements is finitely generated or not one needs to go beyond the scope of Tardos’s proof. Although our investigations are not conclusive in this direction, they led to the results in the second part of the paper. We call a monotone operation ascending if it is greater than or equal to some projection. We prove that the clones of bounded posets are generated by certain ascending idempotent monotone operations and the 0 and 1 constant operations. A consequence of this result is that if the clone of ascending idempotent operations of a finite bounded poset is finitely generated, then its clone is finitely generated as well. We provide an example of a half bounded finite poset whose clone of ascending idempotent operations is finitely generated but whose clone is not finitely generated. Another interesting consequence of our result is that if the clone of a finite bounded poset is finitely generated, then it has a three element generating set that consists of an ascending idempotent monotone operation and the 0 and 1 constant operations.

This paper aims at empirically measuring the effect of clone refactoring on the size of unit test cases in object-oriented software. We investigated various research questions related to the: (1) impact of clone refactoring on source code attributes (particularly size, complexity and coupling) that are related to testability of classes, (2) impact of clone refactoring on the size of unit test cases, (3) correlations between the variations observed after clone refactoring in both source code attributes and the size of unit test cases and (4) variations after clone refactoring in the source code attributes that are more associated with the size of unit test cases. We used different metrics to quantify the considered source code attributes and the size of unit test cases. To investigate the research questions, and develop predictive and explanatory models, we used various data analysis and modeling techniques, particularly linear regression analysis and five machine learning algorithms (C4.5, KNN, Naive Bayes, Random Forest and Support Vector Machine). We conducted an empirical study using data collected from two open-source Java software systems (ANT and ARCHIVA) that have been clone refactored. Overall, the paper contributions can be summarized as: (1) the results revealed that there is a strong and positive correlation between code clone refactoring and reduction in the size of unit test cases, (2) we showed how code quality attributes that are related to testability of classes are significantly improved when clones are refactored, (3) we observed that the size of unit test cases can be significantly reduced when clone refactoring is applied, and (4) complexity/size measures are commonly associated with the variations of the size of unit test cases when compared to coupling.

Wider adoption of model-driven engineering leads to an abundance of models and metamodels in academic and industrial practice. One of the key techniques for the management and maintenance of such artifacts is model clone detection, where highly similar (meta-)models and (meta-)model fragments are mined from a typically large set of data. In this paper we have extended the SAMOS framework (Statistical Analysis of MOdelS) to clone detection, exemplified on Ecore metamodels. Our clone detection approach uses and extends the framework’s feature extraction, vector space model, natural language processing and clustering capabilities. We performed three extensive case studies to demonstrate its accuracy both quantitatively and qualitatively. We first compared the sensitivity and accuracy of SAMOS for metamodel changes through mutation and scenario analysis (which simulate clones) with those of NICAD-Ecore and MACH, tools for clone detection on Ecore and UML models respectively. We then compared the precision and recall of SAMOS and of NICAD-Ecore on a real dataset, consisting of conference management metamodels from the ATL Zoo. Finally we performed a repository-wide mining of metamodel clones from GitHub. We conclude that SAMOS stands out with its higher accuracy and yet considerable scalability for further large-scale clone detection and other empirical studies on metamodels and domain specific languages.

This paper presents a novel code clone search technique that is accurate, incremental, and scalable to hundreds of million lines of code. Our technique incorporates multiple code representations (i.e., a technique to transform code into various representations to capture different types of clones), query reduction (i.e., a technique to select clone search keywords based on their uniqueness), and a customised ranking function (i.e., a technique to allow a specific clone type to be ranked on top of the search results) to improve clone search performance. We implemented the technique in a clone search tool, called Siamese, and evaluated its search accuracy and scalability on three established clone data sets. Siamese offers the highest mean average precision of 95% and 99% on two clone benchmarks compared to seven state-of-the-art clone detection tools, and reported the largest number of Type-3 clones compared to three other code search engines. Siamese is scalable and can return cloned code snippets within 8 seconds for a code corpus of 365 million lines of code. Using an index of 130,719 GitHub projects, we demonstrate that Siamese’s incremental indexing capability dramatically decreases the index preparation time for large-scale data sets with multiple releases of software projects. The paper discusses the applications of Siamese to facilitate software development and research with two use cases including online code clone detection and clone search with automated license analysis.

This article explores the Mortis trilogy, a three-episode story arc from the third season of Star Wars: The Clone Wars, as the rhetorical articulation of a spiritual text. As more than one Star Wars scholar has pointed out, some fans draw on the spiritual ideologies scattered throughout the popular franchise and position themselves as real-life followers of a Jedi code. With this in mind, I suggest that the Mortis story arc offers a lucid account of the Force as related to spiritual practice. As Scott Stroud points out in his work examining the intersection of popular culture, rhetoric, and religion, spiritual texts serve two primary functions: (1) to explain the divine nature of the world and (2) to offer the means to achieve spiritual awareness. I contend that the Mortis trilogy fulfills both of these functions. To make my argument concerning The Clone Wars story arc explicit, I explore the relationship between Star Wars and spiritual practice, emphasize the rhetorical nature of such a relationship, and illustrate how the Mortis trilogy offers a coherent spirituality lacking in the other texts of the Star Wars universe.

Proximity-based service (ProSe) provides direct communications among smart sensor nodes in proximity which aims at reserving resource consumption and alleviating the load in base stations, which is a promising solution for smart sensor systems that possess limited computing and energy resources. During the ProSe direct communications, most of the prior art security methods are usually provided by the ProSe function and are based on complex cryptography. However, despite the computing complexity, it is difficult for cryptographic methods to detect clone attack which is a common kind of attack in sensor systems. Clone nodes feature different physical positions but claim colliding IDs with captured nodes. Thus, clone nodes can be detected by spatial differences, in particular, by the surveillance of physical layer channel state information (CSI). However, CSI is not absolute static due to the random noise in wireless propagation environment. Accordingly, the detection accuracy varies with the stability of CSI. To address this challenge, we take the first attempt to introduce physical layer reputation and then elaborate the physical layer reputation based clone detection protocol to detect clone attack in multiple scenarios. The proposed protocol significantly improves the detection rate and false alarm rate and it is validated both by simulations and realizations.

Code cloning have been an issue in these few years as the number of available web application and stand alone software increase nowadays. The major consequences of cloning is that it would risk the maintenance process as there are many duplicated codes in the systems that practically increase the complexity of the system. There are many code clone detection techniques that can be found nowadays which generally can be group into string based, token based. tree based and semantic based. The aim of this project is to find out the possibility of using a technique of ontology mapping technique to solve the problem, but we are not using the real ontology for the clone detection. It has been prove that there is the possibility as it manages to detect clone code. In this thesis the clone detection is using two layers of detection: i.e. structural similarity and string based similarity. The structural similarity is by using subgraph miner where it capable to get the similar subtree between different files. And then we extract all elements of that particular subtree and treat the elements as a string. Two strings from different files then applied with similarity metric to know whether it is a clone pair. From the experimental result we found that the system is language independent but the result is good in precision but not so good recall. It is also capable to detect two main types of clone. i.e identical clones and similar clones.

We study clones on a four-element set related to the clone $\mathsf{DMA}$ of all term functions of the sub\-directly irreducible four-element De~Morgan algebra $\mathbf{DM_{4}}$. We find generating sets for the clones of all functions preserving the subalgebras of $\mathbf{DM_{4}}$, the auto\-morphisms of~$\mathbf{DM_{4}}$, the truth order and the information order on $\mathbf{DM_{4}}$, as well as clones defined by conjunctions of these conditions. We identify the covers of $\mathsf{DMA}$ in the lattice of four-valued clones and describe the lattice of clones above $\mathsf{DMA}$ which contain the discriminator function. Finally, observing that each clone above $\mathsf{DMA}$ defines an expansion of the four-valued Belnap--Dunn logic, we classify these clones by their metalogical properties, specifically by their position within the Leibniz and Frege hierarchies of abstract algebraic logic., Comment: 42 pages, 10 figures

In conventional quantum mechanics, quantum no-deleting and no-cloning theorems indicate that two different and nonorthogonal states cannot be perfectly and deterministically deleted and cloned, respectively. Here, we investigate the quantum deleting and cloning in a pseudo-unitary system. We first present a pseudo-Hermitian Hamiltonian with real eigenvalues in a two-qubit system. By using the pseudo-unitary operators generated from this pseudo-Hermitian Hamiltonian, we show that it is possible to delete and clone a class of two different and nonorthogonal states, and it can be generalized to arbitrary two different and nonorthogonal pure qubit states. Furthermore, state discrimination, which is strongly related to quantum no-cloning theorem, is also discussed. Last but not least, we simulate the pseudo-unitary operators in conventional quantum mechanics with post-selection, and obtain the success probability of simulations. Pseudo-unitary operators are implemented with a limited efficiency due to the post-selections. Thus, the success probabilities of deleting and cloning in the simulation by conventional quantum mechanics are less than unity, which maintain the quantum no-deleting and no-cloning theorems., Comment: 6 pages

Many problems in society and life can be solved by modeling as large-scale global optimization problems. The large-scale global optimization problems are very complex and difficult to solve. At present, for the CEC2013 large-scale test set, the existing algorithms cannot obtain satisfactory results. The large-scale global optimization problems remained to be solved, especially the inseparable problems among them. This paper proposes an algorithm that combines immune cloning algorithm with differential evolution algorithm. In this algorithm, clone immune operation and receptor editing mechanism are introduced to enhance the local searching ability, to improve the global optimization ability and convergence speed. The algorithm is used to solve the inseparable problems in the test set. The experimental results show that the algorithm can not only effectively avoid premature convergence, but also improve the convergence speed.

Cloning has been an integral part of most laboratory research questions and continues to be an essential tool in defining the genetic elements determining life. Cloning can be difficult and time consuming as each plasmid is unique to a particular project and each sequence must be carefully selected, cloned and sequenced to determine correctness. Loop assembly (uLOOP) is a recursive, Golden Gate-like assembly method that allows rapid cloning of domesticated DNA fragments to robustly refactor novel pathways. With uLOOP methodologies, one can clone several sequences directionally to generate a library of transcriptional units (TUs) in plasmids within a single reaction but analysis of the plasmid population has been impeded by current sequencing and analysis methods. Here we develop LoopViz, a quality control tool that quantifies and visualizes results from assembly reactions using long-read Oxford Nanopore Technologies (ONT) sequencing. LoopViz identifies full length reads originating from a single plasmid in the population, and visualizes them in terms of a user input DNA fragments file, and provides QC statistics. This methodology enables validation and analysis of cloning and sequencing reactions in less than a day, determination of the entire plasmid’s sequence, and sequencing through repetitive meta-regions that cannot be meaningfully assembled. Finally, LoopViz represents a new paradigm in determining plasmid sequences that is rapid, cost-effective and performed in-lab. LoopViz is made publicly available at https://gitlab.com/marielelensink325/loopseq

Tumors contain multiple subpopulations of genetically distinct cancer cells. Reconstructing their evolutionary history can improve our understanding of how cancers develop and respond to treatment. Subclonal reconstruction methods cluster mutations into groups that co-occur within the same subpopulations, estimate the frequency of cells belonging to each subpopulation, and infer the ancestral relationships among the subpopulations by constructing a clone tree. However, often multiple clone trees are consistent with the data and current methods do not efficiently capture this uncertainty; nor can these methods scale to clone trees with a large number of subclonal populations. Here, we formalize the notion of a partially-defined clone tree (partial clone tree for short) that defines a subset of the pairwise ancestral relationships in a clone tree, thereby implicitly representing the set of all clone trees that have these defined pairwise relationships. Also, we introduce a special partial clone tree, the Maximally-Constrained Ancestral Reconstruction (MAR), which summarizes all clone trees fitting the input data equally well. Finally, we extend commonly used clone tree validity conditions to apply to partial clone trees and describe SubMARine, a polynomial-time algorithm producing the subMAR, which approximates the MAR and guarantees that its defined relationships are a subset of those present in the MAR. We also extend SubMARine to work with subclonal copy number aberrations and define equivalence constraints for this purpose. Further, we extend SubMARine to permit noise in the estimates of the subclonal frequencies while retaining its validity conditions and guarantees. In contrast to other clone tree reconstruction methods, SubMARine runs in time and space that scale polynomially in the number of subclones. We show through extensive noise-free simulation, a large lung cancer dataset and a prostate cancer dataset that the subMAR equals the MAR in all cases where only a single clone tree exists and that it is a perfect match to the MAR in most of the other cases. Notably, SubMARine runs in less than 70 seconds on a single thread with less than one Gb of memory on all datasets presented in this paper, including ones with 50 nodes in a clone tree. On the real-world data, SubMARine almost perfectly recovers the previously reported trees and identifies minor errors made in the expert-driven reconstructions of those trees. The freely-available open-source code implementing SubMARine can be downloaded at https://github.com/morrislab/submarine. Copyright: © 2021 Sundermann et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited., PLoS Computational Biology, 17 (1), ISSN:1553-734X, ISSN:1553-7358

To facilitate clone maintenance, various automated tools were proposed to detect code clones by identifying similar token sequences or similar program syntactic structures in source code. They achieved different trade-offs between precision and recall. Inspired by prior work, we developed a new approach CCLearner, a solely token-based clone detection approach using deep learning. Given known clone pairs and non-clone pairs, CCLearner extracts features from each code pair and leverages the features to train a classifier. The classifier is then used to compare methods pair-by-pair in a given codebase to detect clones. We evaluated CCLearner by reusing an existing benchmark of real clone code—BigCloneBench. We split the benchmark such that some data was used for classifier training, and some data was used for testing. With the testing data, we evaluated CCLearner’s effectiveness of clone detection, and also assessed three existing popular clone detection tools: SourcererCC, NiCad, and Deckard. CCLearner outperformed existing tools by achieving a better trade-off between precision and recall. To further investigate whether other machine learning algorithms can perform comparatively as deep learning, we replaced deep learning with five alternative machine learning algorithms in CCLearner, and observed that CCLearner worked best when using deep learning.

The Just-in-Time Management Platform (JMP) brings together all the different virtual desktop components such as OS (Horizon View with Instant Clone desktops, applications (App Volumes), and user data and profiles (VMware Dynamic Environment Manager).

Clone detection is one application of measuring the similarity of code. However, clone and plagiarism detectors use very different representations of source code and different techniques to identify similar code fragments. This chapter investigates the impact of source code representation (i.e. tokenisation and renaming of identifiers and literals) and the impact of similarity measurements (e.g. Jaccard index or Kondrak’s distance over n-grams) for measuring source code similarity on two known datasets. A comparison using average precision at k with dedicated clone and plagiarism detectors shows that simple similarity measurements like Kondrak’s distance using n-grams over tokenised source code usually outperform specialised tools for the detection of similar, cloned, plagiarised or duplicated code.

One of the most famous ranking methods for digraphs is the ranking by Copeland score. The Copeland score of a node in a digraph is the difference between its outdegree (i.e. its number of outgoing arcs) and its indegree (i.e. its number of ingoing arcs). In the ranking by Copeland score, a node is ranked higher, the higher is its Copeland score. In this paper, we deal with an alternative method to rank nodes according to their out- and indegree, namely ranking the nodes according to their degree ratio, i.e. the outdegree divided by the indegree. To avoid dividing by zero, we add 1 to both the out- as well as indegree of every node. We provide an axiomatization of the ranking by degree ratio using a clone property, which says that the entrance of a clone or a copy (i.e. a node that is in some sense similar to the original node) does not change the ranking among the original nodes. We also provide a new axiomatization of the ranking by Copeland score using the same axioms except that this method satisfies a different clone property. Finally, we modify the ranking by degree ratio by taking only the out- and indegree, but by definition assume nodes with indegree zero to be ranked higher than nodes with positive indegree. We provide an axiomatization of this ranking method using yet another clone property and a maximal property. In this way, we can compare the three ranking methods by their clone property.