Search

In this paper, an end-to-end meta imitation learning method supplemented with target recognition (TaR-MIL) is proposed for one-shot learning. This approach divides the procedure of imitating from demonstrations into two parts: distinguishing the target object from distractors and executing the correct actions. Accordingly, the objective of imitation is defined as the combination of target recognition and behavior cloning. Specifically, a target recognition module is adopted in the model architecture, which helps to extract useful information about tasks from observations during training. After training with demonstrations of various tasks via meta learning, a policy capable of solving new tasks given one demonstration is obtained. The real-world experiments on a UR10e robot arm illustrate that, the derived policy manages to perform placing tasks in new scenarios or with new objects after one video demonstration is given, which verify the effectiveness of the proposed method.

The needs to reduce costs on cloning and adapting software leads to the emerging of Software Product Line Engineering (SPLE), which enables developers to create different software with common requirements and defined variabilities. To support web application generation based on the SPLE approach, a dedicated engine is necessary. We built the SPLELive Engine as our alternative approach to that problem. However, we also need to address another issue of SPLE web generation engine which is multi-product access control. This paper explains how the multi-product access control issue was tackled by doing experimental research using the single sign-on approach with external OAuth 2 authentication services and a decentralized authorization mechanism. This paper also explains and demonstrates how the SPLELive Engine handles authorization configuration variability using delta-oriented programming.

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.

In the processing of photographic images, it is common to manipulate them to include objects that are not present in the original image, to make them more appealing or to change the environment. Therefore, they are manually edited and the object to be included is cut out of a source image, to join it to the destination and then, through the use of filters, the contour is softened to make the union between both images less noticeable. Perez et al. introduced a technique, called perfect cloning, based on processing the image gradient to integrate them, using sparse matrices, allowing to automate this process. However, its implementation in languages such as Java is complex due to memory limitations for large matrices. Hence, this paper introduces a method of implementing sparse arrays that allow their use in Java. It has been implemented as a plugin for the free software ImageJ. In addition, the technique is compared with the Multiresolution method, developed by Burt and Adelson, which is considered a reference in the field.

Deep reinforcement learning (RL) has demonstrated success in challenging decision-making/control tasks. However, RL methods, which solve tasks through maximizing the expected reward, may generate undesirable behaviors due to inferior local convergence or incompetent reward design. These undesirable behaviors of agents may not reduce the total reward but destroy the user experience of the application. For example, in the autonomous driving task, the policy actuated by speed reward behaves much more sudden brakes while human drivers generally don’t do that. To overcome this problem, we present a novel method named Reward-Constrained Behavior Cloning (RCBC) which synthesizes imitation learning and constrained reinforcement learning. RCBC leverages human demonstrations to induce desirable or human-like behaviors and employs lower-bound reward constraints for policy optimization to maximize the expected reward. Empirical results on popular benchmark environments show that RCBC learns significantly more human-desired policies with performance guarantees which meet the lower-bound reward constraints while performing better than or as well as baseline methods in terms of reward maximization.

This article proposed a new protocol "untraceable and unclonable sensor movement in the distributed IoT environment". The proposal has the following several advantages: (1) the new protocol using PUF achieved the main contributions of both untraceable and unclonable sensors. (2) the new protocol also achieved standard security features such as mutual authentication, perfect forward and backward secrecy. (3) The proposed authentication protocol can withstand from various kinds of attacks, such as to reply, DoS, impersonation, and cloning attack. (4) Analysis formally using BAN Logic has been conducted and denoted that the protocol achieved a secure mutual authentication. In addition, analysis formally using the RoR model and Scyther tool denoted that the proposed protocol withstands from various attacks. (5) A comparison of security features and computational complexity ensures that the proposal is secure and has low computational complexity

In this work, we automatically distinguish between different clones of the same bacteria species (Klebsiella pneumoniae) based only on microscopic images. It is a challenging task, previously seemed unreachable due to the high clones' similarity. For this purpose, we apply a multi-step algorithm with attention-based deep multiple instance learning, which returns parts of the image crucial to the prediction. Except for obtaining high accuracy, we introduce extensive explainability based on persistence homology, increasing the understandability and trust in the model. Our work opens a plethora of research pathways towards cheaper and faster epidemiological management.

We consider the problem of verifying complex learned controllers using distillation. In contrast to previous work, we require that the distilled model maintains behavioural fidelity with an oracle, defining the notion of non-divergent path length (NPL) as a metric. We demonstrate that current distillation approaches with proven accuracy bounds do not have high expected NPL and can be out-performed by naive behavioural cloning. We thus propose a distillation algorithm that typically gives greater expected NPL, improved sample efficiency, and more compact models. We prove properties of NPL maximization and demonstrate the performance of our algorithm on deep Q-network controllers for three standard learning environments that have been used in this context: Pong, CartPole and MountainCar.

the ongoing lockdown where the accessibility to people you want to interact with or who could provide to you the much needed relief has drastically reduced, be it doctors or therapists. Keeping this in mind, along with the huge amounts being spent by people on consultation, we thought of coming up with a system which would be relevant to people going through a tough time. There is always a need for people to talk with their loved ones even when they are not around and our voice cloning system would serve the purpose. There is a need for real-time artificial human conversation that is customized and personalized according to a person's needs. In addition, there is a requirement of security for preventing voice spoofing which might lead to malpractices. This led us to materialise a solution which would allow a person to interact with people. The system uses NLP methods of chatbot to identify the emotion/problem that the user is trying to express. It uses CNN and RNN to respond to the user in the voice of a loved one. Using the LibriSpeech and VCTK datasets, a Tacotron-based architecture was generated by us which resulted in a similarity percentage of 91% along with a response time of 4 seconds which came out to be better than the various methods studied during the literature survey.

Text to speech (TTS) has been evolving rapidly these years. Researchers have successfully converted English text into speech which sounds like natural speaker, proposing numerous models from RNN to non-autoregressive network. However, the migration of these models to Chinese TTS is still an issue because of its prosodic phrasing problems and large character set, not to mention the disappointing outcomes of those successfully-migrated models, most of which are autoregressive. In this paper, we successfully migrate FastSpeech2 to the field of Chinese TTS with generative adversarial network (GAN) as its discriminator for training to enhance the outcome. Postnet of Tactron2 is also applied to fine-tune the mel-spectrogram. We also use x-vector-based voiceprint extraction model to extract voiceprint to achieve voice cloning. The experiment is operated on both models which offers results of 3.83 mean opinion score (MOS) in terms of naturalness and 3.82 MOS in terms of similarity.

We explore an approach to behavioral cloning in video games. We are motivated to pursue a learning architecture that is data efficient and provides opportunity for interpreting player strategies and replicating player actions in unseen situations. To this end, we have developed a generative model that learns latent features of a game that can be used for training an action predictor. Specifically, our architecture combines a Variational Autoencoder with a discriminator mapping the latent space to action predictions (predictor). We compare our model performance to two different behavior cloning architectures: a discriminative model (a Convolutional Neural Network) mapping game states directly to actions, and a Variational Autoencoder with a predictor trained separately. Finally, we demonstrate how we can use the advantage of generative modeling to sample new states from the latent space of the Variational Autoencoder to analyze player actions and provide meaning to certain latent features.

In this paper, we introduce the low resource text-to-speech system from the ThinkIT team submitted to Multi-Speaker Multi-Style Voice Cloning Challenge (M2VoC). The challenge has two tasks: few-shot track1 provides 100 samples for each person and one-shot track2 offers 5 samples only. Each track contains two sub-tracks A and B. Instead of sub-track A, sub-track B can use extra public data besides the released data. But we participate in the sub-track A only. We choose the finetune as our backbone strategy. Our submitted systems include BERT based prosody boundary prediction module, FastSpeech based acoustic model to generate acoustic features from text input, and HIFIGAN based vocoder to generate waveform from acoustic features. Among them, acoustic models are susceptible to low resource speakers. To prevent over-fitting, we modified the acoustic model and split out validation set to assist the manual model selection. Evaluation results provided by the challenges organizers demonstrate the effectiveness of our system.

The Multi-speaker Multi-style Voice Cloning Challenge (M2VoC) aims to provide a common sizable dataset as well as a fair testbed for the benchmarking of the popular voice cloning task. Specifically, we formulate the challenge to adapt an average TTS model to the stylistic target voice with limited data from target speaker, evaluated by speaker identity and style similarity. The challenge consists of two tracks, namely few-shot track and one-shot track, where the participants are required to clone multiple target voices with 100 and 5 samples respectively. There are also two sub-tracks in each track. For sub-track a, to fairly compare different strategies, the participants are allowed to use only the training data provided by the organizer strictly. For sub-track b, the participants are allowed to use any data publicly available. In this paper, we present a detailed explanation on the tasks and data used in the challenge, followed by a summary of submitted systems and evaluation results., has been accepted to ICASSP 2021

The American television show Rick and Morty, an animated science fiction sitcom, critiques speciesism in the context of bleak existentialist philosophy. Though the show focuses primarily on human characters, it also depicts various forms of artificial sentience, such as robots or clones, undergoing existential crises. It explicitly effaces any distinction between human sentience and artificial sentience, forcefully treating all sentient life with an equivalent respect (or disrespect). The show also problematizes human speciesism in relationship to terrestrial and extra-terrestrial life.

On November 19, 2020, the digital twin platform company 51World officially launched the Earth Cloning Project 4(EC4) in Shenzhen, releasing the latest AES2021 digital twin platform products and WDP Developer Ecology 2.0, and demonstrating dozens of successful landing cases. EC4 has been transformed into an industry summit on the basis of the product launch event, showing more possibilities of next-generation digital twin platforms in different industries under the new infrastructure and digital economy wave.

Autonomous driving systems need to handle complex scenarios such as lane following, avoiding collisions, taking turns, and responding to traffic signals. In recent years, approaches based on end-to-end behavioral cloning have demonstrated remarkable performance in point-to-point navigational scenarios, using a realistic simulator and standard benchmarks. Offline imitation learning is readily available, as it does not require expensive hand annotation or interaction with the target environment, but it is difficult to obtain a reliable system. In addition, existing methods have not specifically addressed the learning of reaction for traffic lights, which are a rare occurrence in the training datasets. Inspired by the previous work on multi-task learning and attention modeling, we propose a novel multi-task attention-aware network in the conditional imitation learning (CIL) framework. This does not only improve the success rate of standard benchmarks, but also the ability to react to traffic lights, which we show with standard benchmarks., Comment: Accepted to CVPR 2021 Workshop on Autonomous Driving

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.

The problem of path generation for the autonomous vehicle in environments with infinite number obstacles is considered. Generally, the problem is known in the literature as the path planning. This chapter treated that problem using the algorithm, named MKBC, which is based on the behavioral cloning and Kohonen rule. In the behavioral cloning, the system learns from control traces of a human operator. Kohonen rule connected with the weighting coefficients, while the MKBC algorithm does not use the weighting values as values from the previous time, but permanentlly uses the training values as weighting values. That is something which enables an intelligent system to learn from the examples (operator’s demonstrations) to control a vehicle in the process of the obstacles avoiding, like the human operator does. Like that, the very important MKBC characteristic is the symplicity. The MKBC simplicity is something which is so obviously, specialy according to the RBF neural network and the machine learnig algorithm which is used the previously. Following the MKBC given context the problem narrow passage avoiding and the goal position reaching fundamentally is observed. Namely, defining if – then rule, according to the named cases is treated as destroying of the consistency of the methodology. In that sense, using MKBC neural network the solution was found. A the end, the autonomous vehicle mathematical model which is given by nonlinear equations describing a 12 state dynamical system is used and in that case the MKBC algorithm is applied successfully. Eventually, as it has been illustrated the previously, the advantage of the entire methodology lies in the fact that a complete path of the vehicle can be defined off-line, without using sophisticated symbolical models of obstacles. These are facts that MKBC algorithm and the given methodology substantially differ from the others. In the next phase it is expected to confirm results in on – line simulation process.

International audience; We introduce the Software Heritage filesystem (SwhFS), a user-space filesystem that integrates large-scale open source software archival with development workflows. SwhFS provides a POSIX filesystem view of Software Heritage, the largest public archive of software source code and version control system (VCS) development history.Using SwhFS, developers can quickly "checkout" any of the 2 billion commits archived by Software Heritage, even after they disappear from their previous known location and without incurring the performance cost of repository cloning. SwhFS works across unrelated repositories and different VCS technologies. Other source code artifacts archived by Software Heritage-individual source code files and trees, releases, and branches-can also be accessed using common programming tools and custom scripts, as if they were locally available.A screencast of SwhFS is available online at dx.doi.org/10.5281/zenodo.4531411.

This study proposes a preprocessing framework for expert examples based on behavior cloning (BC) to solve the problem that inverse reinforcement learning (IRL) is inaccurate due to the noises of expert examples. In order to remove the noises in the expert examples, we first use supervised learning to learn the approximate expert policy, and then use this approximate expert policy to clone new expert examples from the old expert examples, the idea of this preprocessing framework is BC, IRL can obtain higher quality expert examples after preprocessing. The IRL framework adopts the form of maximum entropy, and specific experiments demonstrate the effectiveness of the proposed approach, in the case of expert examples with noises, the reward functions that after BC preprocessing is better than that without preprocessing, especially with the increase of noise level, the effect is particularly obvious.

Characterizing and cataloging genetic parts are critical to the design of useful genetic circuits. Having well-characterized parts allows for the fine-tuning of genetic circuits, such that their function results in predictable outcomes. With the growth of synthetic biology as a field, there has been an explosion of genetic circuits that have been implemented in microbes to execute functions pertaining to sensing, metabolic alteration, and cellular computing. Here, we show a cost-effective and rapid method for characterizing genetic parts. Our method utilizes cell-free lysate, prepared in-house, as a medium to evaluate parts via the expression of a reporter protein. Template DNA is prepared by PCR-amplification using inexpensive primers to add variant parts to the reporter gene, and the template is added to the reaction as linear DNA without cloning. Parts that can be added in this way include promoters, operators, ribosome binding sites, insulators, and terminators. This approach, combined with the incorporation of an acoustic liquid handler and 384-well plates, allows the user to carry out high-throughput evaluations of genetic parts in a single day. By comparison, cell-based screening approaches require time-consuming cloning and have longer testing times due to overnight culture and culture density normalization steps. Further, working in cell-free lysate allows the user to exact tighter control over the expression conditions through the addition of exogenous components, or by titrating DNA concentrations rather than relying on limited plasmid copy numbers. Because this method retains a cell-like environment, the function of the genetic part will typically mimic its function in whole cells.SUMMARYWell-characterized genetic parts are necessary for the design of novel genetic circuits. Here we describe a cost-effective, high-throughput method for rapidly characterizing genetic parts. Our method reduces cost and time by combining cell-free lysates, linear DNA to avoid cloning, and acoustic liquid handling to increase throughput and reduce reaction volumes.

This study proposes a novel hybrid imitation learning (HIL) framework in which behavior cloning (BC) and state cloning (SC) methods are combined in a mutually complementary manner to enhance the efficiency of robotic manipulation task learning. The proposed HIL framework efficiently combines BC and SC losses using an adaptive loss mixing method. It uses pretrained dynamics networks to enhance SC efficiency and performs stochastic state recovery to ensure stable learning of policy networks by transforming the learner’s task state into a demo state on the demo task trajectory during SC. The training efficiency and policy flexibility of the proposed HIL framework are demonstrated in a series of experiments conducted to perform major robotic manipulation tasks (pick-up, pick-and-place, and stack tasks). In the experiments, the HIL framework showed about a 2.6 times higher performance improvement than the pure BC and about a four times faster training time than the pure SC imitation learning method. In addition, the HIL framework also showed about a 1.6 times higher performance improvement and about a 2.2 times faster training time than the other hybrid learning method combining BC and reinforcement learning (BC + RL) in the experiments.

The article examines the phenomenon of Chinese replication. An attempt is made to identify the causes and consequences of such a large-scale replication as one of the leading areas of development of modern cultural industries with the support of the state policy of the PRC. Reproduction is considered as the initial stage of creativity in a new space, where originality and novelty are opposed to completeness.

Research on autonomous vehicle system has risen in the past couple of years. It has posed challenges to the researchers to develop an understanding about the real time scenarios. Deep Learning has demonstrated its extraordinary computational potential by transcending its abilities into more complex areas, where pattern matching, image recognition and behavioral cloning plays a vital role. The system consists of Image Processing and analyzing of the training data into behavioral cloning of the vehicle in a simulated environment. A control algorithm responsible for consolidating the sub systems calculations of the correct steering angle is used to keep the vehicle within the lane markings of the road. The solution proposed requires a better data collection and data interpretation. Addition of cloud computing fastens the data calculation and hence improves the performance of the system.

El presente artículo, trata de reflexionar sobre las implicancias éticas y porque no decirlo morales que comporta el progreso biotecnológico en la vida del ser humano y por ende el impacto que se produce en la cultura, en la sociedad. Para ello es necesario que se tome conciencia de lugar de la importancia que tiene la ética en la sociedad, solo reconociendo el verdadero valor y de la real dignidad que posee la persona la técnica y la ciencia tendrán el lugar adecuado y con ello las fronteras de la ética en las aplicaciones biotecnológicas serán claras. Hoy en día es de suma importancia hacer esta reflexión ya que el avance científico y técnico se da con mayor rapidez y una vez descubierto, el siguiente paso sería aplicarlo, el detalle es que esa aplicación no se hace en algo, sino en “alguien”, razón por la cual el diálogo y debate ético, debe estar regido fundamentalmente por los principios: del respeto a la vida humana y de autodeterminación de la persona humana; además de las normas, en cuestión de la investigación científica y en el cuidado de la salud radica en la dignidad humana; esta dignidad del hombre radica en su ser personal, posee entendimiento, voluntad, libertad y conciencia de sus actos, autodeterminación, autorrealización y autoconciencia. Teniendo en cuenta estos principios y sus normas se podrá emitir juicios éticos en temas de actualidad como son todo lo referente a la manipulación genética en la que entran en juego la manipulación de los datos genéticos, la terapia génica, los productos biológicos de uso terapéutico con la correspondiente farmacología que se va desarrollando, la clonación, los embriones híbridos, los alimentos transgénicos, entre otros. Por otra parte tenemos los temas en cuanto al medio ambiente y su ecología para combatir, reducir y eliminar la contaminación ambiental y que de una u otra forma puede afectar la vida del ser humano.

Analyzing human motion is a challenging task with a wide variety of applications in computer vision and in graphics. One such application, of particular importance in computer animation, is the retargeting of motion from one performer to another. While humans move in three dimensions, the vast majority of human motions are captured using video, requiring 2D-to-3D pose and camera recovery, before existing retargeting approaches may be applied. In this paper, we present a new method for retargeting video-captured motion between different human performers, without the need to explicitly reconstruct 3D poses and/or camera parameters. In order to achieve our goal, we learn to extract, directly from a video, a high-level latent motion representation, which is invariant to the skeleton geometry and the camera view. Our key idea is to train a deep neural network to decompose temporal sequences of 2D poses into three components: motion, skeleton, and camera view-angle. Having extracted such a representation, we are able to re-combine motion with novel skeletons and camera views, and decode a retargeted temporal sequence, which we compare to a ground truth from a synthetic dataset. We demonstrate that our framework can be used to robustly extract human motion from videos, bypassing 3D reconstruction, and outperforming existing retargeting methods, when applied to videos in-the-wild. It also enables additional applications, such as performance cloning, video-driven cartoons, and motion retrieval., SIGGRAPH 2019. arXiv admin note: text overlap with arXiv:1804.05653 by other authors

In an environment where cyber attacks are increasing, both in frequency and complexity, novel ways to shield data, users, and procedures have to be envisioned. Physical unclonable functions (PUFs) are the physical equivalent of one-way mathematical transformations with the exception that their inherent physical complexity renders them resilient to cloning. One interesting deployment scenario includes PUFs as random key generators. The deterministic nature of their operation alleviates the necessity to store the keys in non-volatile means. Along the same lines, blockchain is inherently resistant to modification of the data once stored while their overall security depends on the quality and secrecy of users’ keys. Here, the authors propose a novel optical PUF implementation that can be combined with private blockchain modalities in order to cyber-harden Internet of things ecosystems. PUF-related experimental results are presented, alongside implementation scenarios.

Speaker adaptation techniques can be classified as intra-lingual or cross-lingual depending on whether or not the source model and the target speaker employ the same language. Most of the work in this field has been focused on the first case, while the second one has been less explored. In this paper we address the cross-lingual paradigm in the framework of a HMM-based speech synthesis system by further developing a formerly proposed approach. This method is able to clone a given speaker into a different language by combining the linguistic structure and the acoustic characteristics of two HTS models. In this work, we discuss the extension of the adaptation procedure to some other source model parameters that were kept unmodified in the initial version, and compare the performance of both versions by means of subjective and objective tests. These results are also contrasted with those obtained by a KLD-based technique proposed in the literature for a similar purpose. While no significant preference for any of the versions of our method is observed, our approach clearly outperforms the KLD-based technique.

We present a new video-based performance cloning technique. After training a deep generative network using a reference video capturing the appearance and dynamics of a target actor, we are able to generate videos where this actor reenacts other performances. All of the training data and the driving performances are provided as ordinary video segments, without motion capture or depth information. Our generative model is realized as a deep neural network with two branches, both of which train the same space-time conditional generator, using shared weights. One branch, responsible for learning to generate the appearance of the target actor in various poses, uses \emph{paired} training data, self-generated from the reference video. The second branch uses unpaired data to improve generation of temporally coherent video renditions of unseen pose sequences. We demonstrate a variety of promising results, where our method is able to generate temporally coherent videos, for challenging scenarios where the reference and driving videos consist of very different dance performances. Supplementary video: this https URL.

This article presents an algorithm for integrated timing-driven latch placement and cloning. Given a circuit placement, the proposed algorithm relocates some latches while circuit timing is improved. Some latches are replicated to further improve the timing; the number of replicated latches along with their locations are automatically determined. After latch cloning, each of the replicated latches is set to drive a subset of the fanouts that have been driven by the original single latch. The proposed algorithm is then extended such that relocation and cloning are applied to some latches together with their neighbor logic gates. Experimental results demonstrate that the worst negative slack and the total negative slack are improved by 24% and 59%, respectively, on average of test circuits. The negative impacts on circuit area and power consumption are both marginal, at 0.7% and 1.9% respectively.

Device authentication is an essential security feature for Internet of Things (IoT). Many IoT devices are deployed in the open and public places, which makes them vulnerable to physical and cloning attacks. Therefore, any authentication protocol designed for IoT devices should be robust even in cases when an IoT device is captured by an adversary. Moreover, many of the IoT devices have limited storage and computational capabilities. Hence, it is desirable that the security solutions for IoT devices should be computationally efficient. To address all these requirements, in this paper, we present a lightweight and privacy-preserving two-factor authentication scheme for IoT devices, where physically uncloneable functions have been considered as one of the authentication factors. Security and performance analysis show that our proposed scheme is not only robust against several attacks, but also very efficient in terms of computational efficiently.

In this paper, we propose the use of the Data Cloning (DC) approach to estimate parameter-driven zero-inflated Poisson and Negative Binomial models for time series of counts. The data cloning algor...

Code cloning refers to the duplication of source code. It is the most common way of reusing source code in software development. If a bug is identified in one segment of code, all the similar segments need to be checked for the same bug. Consequently, this cloning process may lead to bug propagation that significantly affects the maintenance cost. By considering this problem, code clone detection (CCD) appears as an active area of research. Consequently, there is a strong need to investigate the latest techniques, trends, and tools in the domain of CCD. Therefore, in this paper, we comprehensively inspect the latest tools and techniques utilized for the detection of code clones. Particularly, a systematic literature review (SLR) is performed to select and investigate 54 studies pertaining to CCD. Consequently, six categories are defined to incorporate the selected studies as per relevance, i.e., textual approaches (12), lexical approaches (8), tree-based approaches (3), metric-based approaches (7), semantic approaches (7), and hybrid approaches (17). We identified and analyzed 26 CCD tools, i.e., 13 existing and 13 proposed/developed. Moreover, 62 open-source subject systems whose source code is utilized for the CCD are presented. It is concluded that there exist several studies to detect type1, type2, type3, and type4 clones individually. However, there is a need to develop novel approaches with complete tool support in order to detect all four types of clones collectively. Furthermore, it is also required to introduce more approaches to simplify the development of a program dependency graph (PDG) while dealing with the detection of the type4 clones.

Software Product Line Engineering (SPLE) is a promising paradigm for reusing knowledge and artifacts among similar software products. However, SPLE methods and techniques require a high up-front investment and hence are profitable if several similar software products are developed. Thus in practice adoption of SPLE commonly takes a bottom-up approach, in which analyzing the commonality and variability of existing products and transforming them into reusable ones (termed core assets) are needed. These time-consuming and error-prone tasks call for automation. The literature partially deals with solutions for early software development stages, mainly in the form of variability analysis. We aim for further creation of core requirements—reusable requirements that can be adapted for different software products. To this end, we introduce an automated extractive method, named CoreReq, to generate core requirements from product requirements written in a natural language. The approach clusters similar requirements, captures variable parts utilizing natural language processing techniques, and generates core requirements following an ontological variability framework. Focusing on cloning scenarios, we evaluated CoreReq through examples and a controlled experiment. Based on the results, we claim that core requirements generation with CoreReq is feasible and usable for specifying requirements of new similar products in cloning scenarios.

A hybrid path planning algorithm based on membrane pseudo-bacterial potential field (MemPBPF) is proposed. Membrane-inspired algorithms can reach an evolutionary behavior based on biochemical processes to find the best parameters for generating a feasible and safe path. The proposed MemPBPF algorithm uses a combination of the structure and rules of membrane computing. In that sense, the proposed MemPBPF algorithm contains dynamic membranes that include a pseudo-bacterial genetic algorithm for evolving the required parameters in the artificial potential field method. This hybridization between membrane computing, the pseudo-bacterial genetic algorithm, and the artificial potential field method provides an outperforming path planning algorithm for autonomous mobile robots. Computer simulation results demonstrate the effectiveness of the proposed MemPBPF algorithm in terms of path length considering collision avoidance and smoothness. Comparisons with two different versions employing a different number of elementary membranes and with other artificial potential field based algorithms are presented. The proposed MemPBPF algorithm yields improved performance in terms of time execution by using a parallel implementation on a multi-core computer. Therefore, the MemPBPF algorithm achieves high performance yielding competitive results for autonomous mobile robot navigation in complex and real scenarios.

Cloning in software represents similar program structures having its own benefits and drawbacks. Proper clone analysis is required to exploit the benefits of clones. A study of software clone evolution serves the purpose of understanding the maintenance implications of clones, which leads to their appropriate management. Structural clones (recurring patterns of simple clones) represent design level similarities in software. Evolutionary characteristics of clones can assess the relevance of those clones for software developers and maintainers. Although the evolution of simple clones has been thoroughly studied by researchers, the evolution of structural clones is still to be explored. In this paper, we study the evolution of structural clones by performing a longitudinal study on multiple versions of five Java systems. To perform a systematic study of the structural clone evolution, we define structural clones and their evolution patterns in a formal notation. Our results show that structural clones are more likely to change inconsistently, however, less frequently than simple clones, whereas the lifetime of the structural clones is similar to that of the simple clones. Evolutionary characteristics of structural clones suggest that they require more attention in their management. Analysis of structural clone evolution reveals similar reasons for changes, and similar trends in evolution patterns, for all subject systems. These trends reveal evolutionary characteristics of structural clones that can help in devising appropriate strategies for managing them, hence devising better clone management systems.

In many research literatures, the dynamical behaviour of cellular neural networks (CNNs) is simplified by using cloning template. However, the flaws of cloning template are obvious, because the cor...

Nowadays, cyber crime is a serious problem in the world including banking industries wherein card cloning is the greatest concern. In various countries, banks are giving magnetic stripe card to their customers, which is not secure because card cloning of magnetic stripe card is very easy and cyber criminals can make cloned card easily using an electronic device known as skimmer. There is strong need to eliminate magnetic stripe cards from banking industries and issue chip based card to the bank customers so that their money can be secured in the banks. In this research, it is suggested that banks should use chip based card to fight against cyber fraud which happens using cloned card.

Nowadays, Information and Communication Technology (ICT) infrastructures play a crucial role for human beings, providing essential services at astonishing speed. Nevertheless, such a centrality of those infrastructures attracts the interest of ill-motivated actors that target such infrastructures with cyberattacks that are every day more sophisticated and more disruptive. In this alarming context, selecting the optimal set of countermeasures represents a primary need to react against the appearance of potentially dangerous threats effectively. With the motivation to contribute to developing faster and more effective response capabilities against them, the paper at hand introduces a novel cybersecurity reaction methodology based on Artificial Immune Systems (AIS), for which the evolutionary computing paradigm has been adopted. By leveraging the outstanding properties of these bio-inspired techniques, the selected countermeasures to defeat cyberthreats through cloning and mutation phases in an effort to improve the quality of the solution from a quantitative perspective, being able to adjust the risk to which the assets of the protected system are exposed. Exhaustive experiments demonstrate the feasibility of the proposed approach, reducing the risk in a more than acceptable time lapse.

At present, assignment plagiarism is common in higher education at home and abroad especially in the coursework of computer majors. How to detect code plagiarism in programming course assignments effectively is a significant problem. The classic methods of code plagiarism detection mainly use the attribute or structure information to measure the similarity of source codes. In this dissertation, the definition and general process of clone plagiarism detection, different code clone detection technologies and the related tools are introduced.

In a recent article Thomas Douglas and Katrien Devolder propose a theory of genetic parenthood according to which a human child can have only two genetic human parents. I argue this theory is arbitrarily narrow and fails to account for cases such as hybrids, cloning, chimerism, twinning, parthenogenesis, mitochondrial replacement techniques, and more. I propose an alternate theory of genetic parenthood, one that is prima facie consistent with our commonsense intuitions about genetic parenthood and relevant to a right to procreative autonomy.

Background and aim: Most of the Mixed Reality models used in the surgical telepresence are suffering from discrepancies in the boundary area and spatial-temporal inconsistency due to the illumination variation in the video frames. The aim behind this work is to propose a new solution that helps produce the composite video by merging the augmented video of the surgery site and the virtual hand of the remote expertise surgeon. The purpose of the proposed solution is to decrease the processing time and enhance the accuracy of merged video by decreasing the overlay and visualization error and removing occlusion and artefacts. Methodology: The proposed system enhanced the mean value cloning algorithm that helps to maintain the spatial-temporal consistency of the final composite video. The enhanced algorithm includes the 3D mean value coordinates and improvised mean value interpolant in the image cloning process, which helps to reduce the sawtooth, smudging and discolouration artefacts around the blending region. Results: As compared to the state of the art solution, the accuracy in terms of overlay error of the proposed solution is improved from 1.01mm to 0.80mm whereas the accuracy in terms of visualization error is improved from 98.8% to 99.4%. The processing time is reduced to 0.173 seconds from 0.211 seconds. Conclusion: Our solution helps make the object of interest consistent with the light intensity of the target image by adding the space distance that helps maintain the spatial consistency in the final merged video., 27 pages

International audience; The practice of writing smart contracts for the Ethereum blockchain is quite recent and still in development. A blockchain developer should expect constant changes in the security software field, as new bugs and security risks are discovered, and new good practices are developed. Following the security practices accepted in the blockchain community is not enough to ensure the writing of secure smart contracts. The paper aims to study the practice of code cloning among the smart contracts by analyzing two corpora. The first corpus, the "Smart-Corpus", includes smart contracts already deployed in the Ethereum blockchain. The second corpus, the "Open-Zeppelin's Solidity Library", is supervised by a community of developers who constantly take care to increase the security and efficiency of the smart contracts included in the corpus. From the comparative analysis of the corpora, we observe that the smart contracts developers frequently duplicate the code by cloning already existing smart contracts which are not part of the "OpenZeppelin corpus". In particular, we found that 79.1% of smart contracts contain duplicated code and only 18.4% of smart contracts reuse the code by implementing a smart corpus belonging to the OpenZeppelin repository. The paper discusses the advantages and the disadvantages of code duplication in the Ethereum blockchain ecosystem, and suggests to refer to the smart contracts of the OpenZeppelin's Solidity Library. The Ethereum blockchain community can indeed benefit from using the tested code presented in OpenZeppelin's Solidity Library to increase its security.