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In sequential decision-making, imitation learning (IL) trains a policy efficiently by mimicking expert demonstrations. Various imitation methods were proposed and empirically evaluated, meanwhile, their theoretical understandings need further studies, among which the compounding error in long-horizon decisions is a major issue. In this paper, we firstly analyze the value gap between the expert policy and imitated policies by two imitation methods, behavioral cloning (BC) and generative adversarial imitation. The results support that generative adversarial imitation can reduce the compounding error compared to BC. Furthermore, we establish the lower bounds of IL under two settings, suggesting the significance of environment interactions in IL. By considering the environment transition model as a dual agent, IL can also be used to learn the environment model. Therefore, based on the bounds of imitating policies, we further analyze the performance of imitating environments. The results show that environment models can be more effectively imitated by generative adversarial imitation than BC. Particularly, we obtain a policy evaluation error that is linear with the effective planning horizon w.r.t. the model bias, suggesting a novel application of adversarial imitation for model-based reinforcement learning (MBRL). We hope these results could inspire future advances in IL and MBRL.

The introduction of Physical Unclonable Functions (PUFs) has been originally motivated by their ability to resist physical attacks, particularly in anti-counterfeiting scenarios. In these one-way functions, machine learning, cryptanalysis, and side-channel attacks are common attack vectors threatening the promised PUFs property of unclonability. These attacks often emulate a PUF by employing a large number of Challenge-Response Pairs (CRPs). Some solutions to defeat such attacks are based on a protocol, where a model of the underlying PUF primitives should be extracted during the enrollment phase. In this paper, we introduce a novel physical cloning approach applicable to FPGA-based implementations, which allows extracting the PUFs unique physical characteristics with a few number of CRPs, that increases only linearly for a higher number of PUF components. Indeed, our proposed approach significantly accelerates the enrollment phase and makes complex enrollment protocols feasible. Our core idea relies on an on-chip delay sensor, which can be realized by ordinary FPGA components, measuring the unique characteristic of the PUF elements. We demonstrate the feasibility of our introduced technique by practical experiments on different FPGA platforms, cloning a couple of (complex) PUF constructions, i.e., XOR APUF, iPUF, composed of delay-based Arbiter PUFs.

It is indispensable for professional traffic signal engineers to perform manual operations of traffic signal control (TSC) to mitigate traffic congestion, especially with complicated scenarios. However, such a task is time-consuming, and the level of congestion mitigation heavily relies on individual expertise in engineering practice. Therefore, it is cost-effective to learn traffic engineers' knowledge to enhance the problem-solving skills for a large-scale urban traffic network. In this paper, a data augmented deep behavioral cloning (DADBC) method is proposed to imitate the problem-solving skills of traffic engineers. The method is under a conceptual framework, parallel learning (PL) framework, that incorporates machine learning techniques for solving decision-making problems in complex systems. The DADBC method enhances a hybrid demonstration by exploiting a generative adversarial network (GAN) and then uses the deep behavioral cloning (DBC) model to learn traffic engineers' control schemes. According to the validation results using the real manipulation data from Hangzhou, China, our method can imitate complex human behaviors in intervening traffic signal control operations to improve traffic efficiency in urban areas.

With the development of deep representation learning, the domain of reinforcement learning (RL) has become a powerful learning framework now capable of learning complex policies in high dimensional environments. This review summarises deep reinforcement learning (DRL) algorithms and provides a taxonomy of automated driving tasks where (D)RL methods have been employed, while addressing key computational challenges in real world deployment of autonomous driving agents. It also delineates adjacent domains such as behavior cloning, imitation learning, inverse reinforcement learning that are related but are not classical RL algorithms. The role of simulators in training agents, methods to validate, test and robustify existing solutions in RL are discussed., Comment: Accepted for publication at IEEE Transactions on Intelligent Transportation Systems

Deep Reinforcement Learning (DRL) has proven its capability to learn optimal policies in decision-making problems by directly interacting with environments. Meanwhile, supervised learning methods also show great capability of learning from data. However, how to combine DRL with supervised learning and leverage additional knowledge and data to assist the DRL agent remains difficult. This study proposes a novel Supervised Assisted Deep Reinforcement Learning (SADRL) framework integrating deep Q-learning from dynamic demonstrations with a behavioral cloning model (DQfDD-BC). Specifically, the proposed DQfDD-BC method leverages historical demonstrations to pre-train a behavioral cloning model and consistently update it by learning the dynamically updated demonstrations. A supervised expert loss function is designed to compare actions generated by the DRL model with those obtained from the BC model to provide advantageous guidance for policy improvements. Experimental results in several OpenAI Gym environments show that the proposed approach accelerates the learning processes, and meanwhile, adapts to different performance levels of demonstrations. As illustrated in an ablation study, the dynamic demonstration and expert loss mechanisms using a BC model contribute to improving the learning convergence performance compared with the baseline models. We believe that SADRL provides an elegant framework and the proposed method can promote the integration of human experience and machine intelligence.

Standardisation of genetic parts has become a topic of increasing interest over the last decades. The promise of simplifying molecular cloning procedures, while at the same time making them more predictable and reproducible has led to the design of several biological standards, one of which is modular cloning (MoClo). The Yeast MoClo toolkit provides a large library of characterised genetic parts combined with a comprehensive and flexible assembly strategy. Here we aimed to (1) simplify the adoption of the standard by providing a simple design tool for including new parts in the MoClo library, (2) characterise the toolkit further by demonstrating the impact of a BglII site in promoter parts on protein expression, and (3) expand the toolkit to enable efficient construction of gRNA arrays, marker-less integration cassettes and combinatorial libraries. These additions make the toolkit more applicable for common engineering tasks and will further promote its adoption in the yeast biological engineering community.

This letter proposes a scalable authentication protocol to address the security vulnerabilities in UAV-base station communication. UAVs are particularly vulnerable to multiple cyberattacks such as physical capture, cloning attacks, eavesdropping, and man-in-middle attacks. Current solutions propose to address these issues using one-to-one authentication. However, the approach is not scalable, especially in scenarios where a group of UAVs is deployed to provide services to end-users. The scalability is achieved using the K-Means clustering algorithm. The proposed protocol outperforms state-of-the-art approaches in terms of total authentication time.

Copy-move image forgery detection has become a significant research subject in multimedia forensics and security due to its widespread use and its hard detection. In this type of image forging, a region of the image is copied and pasted elsewhere in the same image. Keypoint-based forgery detection approaches use local visual features to identify the duplicated regions. The performance of keypoint-based methods degrades in those cases when the duplicated regions are near to each other and when handling highly textured area. The clustering algorithm that mostly used in keypoint- based methods suffer from high complexity. In this paper, an improved approach for keypoint- based copy-move forgery detection is proposed. The proposed method is based on density-based clustering and Guaranteed Outlier Removal algorithm. Experimental results carried out on various benchmark datasets exhibit that the proposed method surpasses other similar state-of-the-art techniques under different challenging conditions, such as geometric attacks, post-processing attacks, and multiple cloning.

If a developer uses trademarked content from other game such as images and music, it will be illegal. But it is fine for the developer to copy the gameplay and make innovative changes to improve the game. This is a good practice that boosts innovation in the video game industry that relies on creativity. The problem is when a developer decided to use the legality to simply copy other works with no spirit of innovation. This act of legal plagiarism is called video game clone. We need to have a more understanding of Indonesia game developer’s perception toward game cloning and plagiarism. Based on the survey, it can be concluded that Indonesia game developer, in general, has no problem with the act of game cloning. Although, more effort and focus could be given to protecting both creators and consumers from the negative intent of using game clone legality

At present, specific voice control has gradually become an important means for 5G-Internet-of-Things-aided industrial control systems, such as controlling the operation and adjustment of industrial Internet of Things equipment through telephone voice of the controller. However, the security of specific voice control system needs to be improved, because the voice cloning technology based on transfer learning can easily simulate the voice of the controller, which may lead to industrial accidents and other potential security risks. Therefore, this article mainly aims to study and understand the principle of voice cloning attack technology, putting forward a voice clone attack method, in order to prepare for the construction of a specific voice recognition system in the future. At present, the key technology of voice cloning attack is how to solve the problem that the target speaker's personalized speech with high quality cannot be synthesized under small samples. In fact, voice cloning is a very challenging problem because speech is more difficult to be represented in the hidden space of the model. We propose a transductive voice transfer learning method to learn the predictive function from the source domain and fine-tune in the target domain adaptively. The target learning task and the source learning task are both synthesizing speech signals from the given audio, while the datasets of both domains are different. By adding different penalty values to each instances and minimizing the expected risk, an optimal precise model can be learned. In addition, an evaluation method to verify the audio similarity of the target speaker was given to show the similarity between the synthesized audio and the original audio. Many details of the experimental results show that our method can effectively synthesize the speech of the target speaker with small samples.

Construction of DNA-encoded programs is central to synthetic biology and the chosen method often determines the time required to design and build constructs for testing. Here we describe and summarise key features of the available toolkits for DNA construction for mammalian cells. We compare the different cloning strategies based on their complexity and the time needed to generate constructs of different sizes, and we reflect on why Golden Gate toolkits now dominate due to their modular design. We look forward to future advances, including accessory packs to cloning toolkits that can facilitating editing, orthogonality, advanced regulation, and integration into synthetic chromosome construction.

In this paper, we explore the recent creation of "iBlastoids," which are 3-D structures that resemble early human embryos prior to implantation which formed via self-organization of reprogrammed adult skin cells. We explore some of the ethical, philosophical, social, and regulatory issues related to this research, with focus particularly on what it means to "anticipate" research outcomes when using novel methods or when serendipitous discoveries are made. We defend the need for reflexive, anticipatory, and deliberative ethical and conceptual work by researchers working in emerging and contentious research domains, in collaboration with interdisciplinary scholars, as well as regulators, funders, and publics.

Index tracking and hedge fund replication aim at cloning the return time series properties of a given benchmark, by either using only a subset of its original constituents or by a set of risk facto...

Many items have impacts on recombinant protein production in making it affordable, the most important of which is host with its characteristics such as compatibility, yields, expression system, costs, environmental effects, etc. Humans have been using spirulina (Arthrospira sp.) algae for centuries. It enjoys features which propose it as a golden host cell. Although several researches have been conducted to develop a stable and reliable transformation system, only a few projects meet the need. The aim of current paper was to focus on spirulina transformation researches. So reason to select the host and procedures to optimize a transformation system were given.

Behavioral cloning from observation (BCO) allows the robot to learn the policy without the expert's action information. However, it requires a few interactions with the environment to infer expert'...

This paper investigates the physical and metaphorical meanings of nausea in Michel Houellebecq’s The Possibility of an Island. Through the trope of cloning, Houellebecq likens the human body to a ship, and conflates existential nausea with nausea caused by inhabiting a body. The future clones of the narrator Daniel inhabit a world of ‘neohumans’ that are clones like themselves, and old-style, barbaric humans. Neohumans change their bodies through cloning, which after a while give them ship-sickness, or nausea. Daniel’s nausea is shaped by his relationship with the Mediterranean throughout. The novel asks the question ‘What happens to human consciousness when the body keeps changing and the white male body is propagated into the future?’ Thus, the novel works as an allegory for the way the Mediterranean functions today both as a curative and lethal space for European endeavor.

Vibrio natriegens is known as the world’s fastest growing organism with a doubling time of less than 10 minutes. This incredible growth speed empowers V. natriegens as a chassis for synthetic and molecular biology, potentially replacing E. coli in many applications. While first genetic parts have been built and tested for V. natriegens, a comprehensive toolkit containing well-characterized and standardized parts did not exist. To close this gap, we created the Marburg Collection – a highly flexible Golden Gate Assembly-based cloning toolbox optimized for the emerging chassis organism V. natriegens. The Marburg Collection overcomes the paradigm of plasmid construction – integrating inserts into a backbone – by enabling the de novo assembly of plasmids from basic genetic parts. This allows users to select the plasmid replication origin and resistance part independently, which is highly advantageous when limited knowledge about the behavior of those parts in the target organism is available. Additional design highlights of the Marburg Collection are novel connector parts, which facilitate modular circuit assembly and, optionally, the inversion of individual transcription units to reduce transcriptional crosstalk in multigene constructs. To quantitatively characterize the genetic parts contained in the Marburg Collection in V. natriegens, we developed a reliable microplate reader measurement workflow for reporter experiments and overcame organismspecific challenges. We think the Marburg Collection with its thoroughly characterized parts will provide a valuable resource for the growing V. natriegens community.

In 2012, David Hontiveros revisited and expanded the world of his Carlos Palanca Memorial Award-winning short story, “Kaming Mga Seroks”, in Seroks Iteration 1: Mirror Man, which is set in a dystopic future where cloning is a booming industry, and genetic templates are pirated to create seroks or clones of clones. Mirror Man employs a fragmented style of storytelling that crafts a long-form narrative that is neither plot nor character-driven but world-driven. Through a mix of interviews, messages, recordings, and more, Mirror Man delves deeper into the world’s history and current events than it does into the lives of its recurring characters. In this way, the world of Seroks is not so much a backdrop against which the story takes place but the story’s main draw as it utilizes its dystopic setting to critique Philippine society, thus prompting the reader to reconsider the trajectory of the Philippines and reimagine its future.

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.

Paper (or plastic) certificates are considered as a means against different forgery of product quality and brand falsification. It is commonly to use barcodes or data matrices to solve this problem. However such approach does not work usually against such sophisticated attacks as cloning of certificate copies. In the current paper we propose to use a digital watermarking and estimation of inner noises of scanners and printers in order to detect cloning attack effectively. Algorithm of cloning attack detecting is presented. The probabilities of attack missing and false alarm are proved.

There is currently a huge interest around autonomous vehicles from both industry and academia. This is mainly due to recent advances in machine learning and deep learning, allowing the development of promising methods for autonomous driving. The gap toward full autonomy is incrementally being reduced with essentially three main existing approaches. First, Modular systems that combine a pipeline of methods with each solving one specific sub-task of driving. Second, Direct Perception techniques that directly estimate affordances (car orientation, distances between lane borders, etc) used to compute control commands through a simple logic. Finally, end-to-end frameworks that automatically map raw sensor data to actuation values. The objective of this paper is to review some recent works focusing on end-to-end deep learning models for lane stable driving, as well as some publicly available real world datasets and open-source simulators that enable the development and evaluation of such methods.

Disaster response operations typically involve multiple decision-makers, and each decision-maker needs to make its decisions given only incomplete information on the current situation. To account for these characteristics – decision making by multiple decision-makers with partial observations to achieve a shared objective –, we formulate the decision problem as a decentralized-partially observable Markov decision process (dec-POMDP) model. To tackle a well-known difficulty of optimally solving a dec-POMDP model, multi-agent reinforcement learning (MARL) has been used as a solution technique. However, typical MARL algorithms are not always effective to solve dec-POMDP models. Motivated by evidence in single-agent RL cases, we propose a MARL algorithm augmented by pretraining. Specifically, we use behavioral cloning (BC) as a means to pretrain a neural network. We verify the effectiveness of the proposed method by solving a dec-POMDP model for a decentralized selective patient admission problem. Experimental results of three disaster scenarios show that the proposed method is a viable solution approach to solving dec-POMDP problems and that augmenting MARL with BC for its pretraining seems to offer advantages over plain MARL in terms of solution quality and computation time.

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.

Graphical abstract, Highlights • AGDE is utilized to optimize quantum cloning circuit parameters. • The experimental results reveal that AGDE is outperformed the other well-known metaheuristics algorithms. • AGDE is minimized the parameter values of cloning difference error value down to 10−8. • The qualitative and quantitative measurements proved the superiority of AGDE., Introduction Quantum cloning operation, started with no-go theorem which proved that there is no capability to perform a cloning operation on an unknown quantum state, however, a number of trials proved that we can make approximate quantum state cloning that is still with some errors. Objectives To the best of our knowledge, this paper is the first of its kind to attempt using meta-heuristic algorithm such as Adaptive Guided Differential Evolution (AGDE), to tackle the problem of quantum cloning circuit parameters to enhance the cloning fidelity. Methods To investigate the effectiveness of the AGDE, the extensive experiments have demonstrated that the AGDE can achieve outstanding performance compared to other well-known meta-heuristics including; Enhanced LSHADE-SPACMA Algorithm (ELSHADE-SPACMA), Enhanced Differential Evolution algorithm with novel control parameter adaptation (PaDE), Improved Multi-operator Differential Evolution Algorithm (IMODE), Parameters with adaptive learning mechanism (PALM), QUasi-Affine TRansformation Evolutionary algorithm (QUATRE), Particle Swarm Optimization (PSO), Gravitational Search Algorithm (GSA), Cuckoo Search (CS), Bat-inspired Algorithm (BA), Grey Wolf Optimizer (GWO), and Whale Optimization Algorithm (WOA). Results In the present study, AGDE is applied to improve the fidelity of quantum cloning problem and the obtained parameter values minimize the cloning difference error value down to 10-8. Conclusion Accordingly, the qualitative and quantitative measurements including average, standard deviation, convergence curves of the competitive algorithms over 30 independent runs, proved the superiority of AGDE to enhance the cloning fidelity.

Code cloning, which reuses a fragment of source code via copy-and-paste with or without modifications, is a common way for code reuse and software prototyping. However, the duplicated code fragments often affect software quality, resulting in high maintenance cost. The existing clone detectors using shallow textual or syntactical features to identify code similarity are still ineffective in accurately finding sophisticated functional code clones in real-world code bases. This article proposes functional code clone detector using attention ( FCCA ), a deep-learning-based code clone detection approach on top of a hybrid code representation by preserving multiple code features, including unstructured (code in the form of sequential tokens) and structured (code in the form of abstract syntax trees and control-flow graphs) information. Multiple code features are fused into a hybrid representation, which is equipped with an attention mechanism that pays attention to important code parts and features that contribute to the final detection accuracy. We have implemented and evaluated FCCA using 275 777 real-world code clone pairs written in Java. The experimental results show that FCCA outperforms several state-of-the-art approaches for detecting functional code clones in terms of accuracy, recall, and F1 score.

Industry 4.0 and highly automated critical infrastructure can be seen as cyber‐physical‐social systems controlled by the Collective Intelligence. Such systems are essential for the functioning of the society and economy. On one hand, they have flexible infrastructure of heterogeneous systems and assets. On the other hand, they are social systems, which include collaborating humans and artificial decision makers. Such (human plus machine) resources must be pre‐trained to perform their mission with high efficiency. Both human and machine learning approaches must be bridged to enable such training. The importance of these systems requires the anticipation of the potential and previously unknown worst‐case scenarios during training. In this paper, we provide an adversarial training framework for the collective intelligence. We show how cognitive capabilities can be copied (“cloned”) from humans and trained as a (responsible) collective intelligence. We made some modifications to the Generative Adversarial Networks architectures and adapted them for the cloning and training tasks. We modified the Discriminator component to a so‐called “Turing Discriminator”, which includes one or several human and artificial discriminators working together. We also discussed the concept of cellular intelligence, where a person can act and collaborate in a group together with their own cognitive clones. peerReviewed

Online code clones are code fragments that are copied from software projects or online sources to Stack Overflow as examples. Due to an absence of a checking mechanism after the code has been copied to Stack Overflow, they can become toxic code snippets, e.g., they suffer from being outdated or violating the original software license. We present a study of online code clones on Stack Overflow and their toxicity by incorporating two developer surveys and a large-scale code clone detection. A survey of 201 high-reputation Stack Overflow answerers (33% response rate) showed that 131 participants (65%) have ever been notified of outdated code and 26 of them (20%) rarely or never fix the code. 138 answerers (69%) never check for licensing conflicts between their copied code snippets and Stack Overflow's CC BY-SA 3.0. A survey of 87 Stack Overflow visitors shows that they experienced several issues from Stack Overflow answers: mismatched solutions, outdated solutions, incorrect solutions, and buggy code. 85% of them are not aware of CC BY-SA 3.0 license enforced by Stack Overflow, and 66% never check for license conflicts when reusing code snippets. Our clone detection found online clone pairs between 72,365 Java code snippets on Stack Overflow and 111 open source projects in the curated Qualitas corpus. We analysed 2,289 non-trivial online clone candidates. Our investigation revealed strong evidence that 153 clones have been copied from a Qualitas project to Stack Overflow. We found 100 of them (66%) to be outdated, of which 10 were buggy and harmful for reuse. Furthermore, we found 214 code snippets that could potentially violate the license of their original software and appear 7,112 times in 2,427 GitHub projects., IEEE Transactions on Software Engineering (Early Access)

Making logical copies, or clones, of files and directories is critical to many real-world applications and workflows, including backups, virtual machines, and containers. An ideal clone implementation meets the following performance goals: (1) creating the clone has low latency; (2) reads are fast in all versions (i.e., spatial locality is always maintained, even after modifications); (3) writes are fast in all versions; (4) the overall system is space efficient. Implementing a clone operation that realizes all four properties, which we call a nimble clone , is a long-standing open problem. This article describes nimble clones in B-ϵ-tree File System (BetrFS), an open-source, full-path-indexed, and write-optimized file system. The key observation behind our work is that standard copy-on-write heuristics can be too coarse to be space efficient, or too fine-grained to preserve locality. On the other hand, a write-optimized key-value store, such as a Bε-tree or an log-structured merge-tree (LSM)-tree, can decouple the logical application of updates from the granularity at which data is physically copied. In our write-optimized clone implementation, data sharing among clones is only broken when a clone has changed enough to warrant making a copy, a policy we call copy-on-abundant-write . We demonstrate that the algorithmic work needed to batch and amortize the cost of BetrFS clone operations does not erode the performance advantages of baseline BetrFS; BetrFS performance even improves in a few cases. BetrFS cloning is efficient; for example, when using the clone operation for container creation, BetrFS outperforms a simple recursive copy by up to two orders-of-magnitude and outperforms file systems that have specialized Linux Containers (LXC) backends by 3--4×.

Everyone today actively uses online social networks to get in touch with their friends, for career opportunities, and business also. Some of the most popular social networks are Facebook, Instagram, LinkedIn, and Twitter. But the question is how much their authentication systems are built in a secured way. The authentication systems mostly depend on the user's general details such as name, photo, and location. In such systems, mischievous persons can easily create fake profiles by cloning the user’s identity to abuse the user’s information. Using fake profiles they can misuse original user’s photos, contacts, and videos. This kind of mischievous person can be identified by the use of privacy detection mechanisms. So, this research emphasizes some data mining techniques for protecting the original user’s information and to identify the malicious accounts in social network sites. By the assessment of 3PS (Publically Privacy Protection System), this work employs the malicious account detection method in OSN depending upon the mischievous person’s uncountable shared posts in a day and latest activity and behaviors. Examining the network similarity and comparison of attributes threshold values referred to the original user’s profile can be used to identify the malicious accounts. For this E_SVM-NN classifier is used based on the feature reduction techniques. This work involves in creating OSN accounts for experiments and investigates the latest updates, posts, comments, photos, and performing online search etc. which are used to evaluate the effectiveness and significance of the proposed work in contrast to the previous works.

CRISPR-based technologies are paramount in genome engineering and synthetic biology. Prime editing (PE) is a technology capable of installing genomic edits without double-stranded DNA breaks (DSBs) or donor DNA. Prime editing guide RNAs (pegRNAs) simultaneously encode both guide and edit template sequences. They are more design intensive than CRISPR single guide RNAs (sgRNAs). As such, application of PE technology is hindered by the limited throughput of manual pegRNA design. To that end, we designed a software tool, Prime Induced Nucleotide Engineering Creator of New Edits (PINE-CONE), that enables high-throughput automated design of pegRNAs and prime editing strategies. PINE-CONE translates edit coordinates and sequences into pegRNA designs, accessory guides, and oligonucleotides for facile cloning workflows. To demonstrate PINE-CONE’s utility in studying disease-relevant genotypes, we rapidly design a library of pegRNAs targeting Alzheimer’s Disease single nucleotide polymorphisms (SNPs). Overall, PINE-CONE will accelerate the application of PEs in synthetic biology and biomedical research.

Islam as religion that applies universally and etemally encourage its people to have a positive and constructive attitude towords the development of science and technology (Science and technology),as long as science and technology is in line with Islamic law. The issue of genetic engineering the most recent isuue of cloning has become a hot ethical polemic in the west. This paper explores the Islamic view around the issue of cloning. According to Islamic law, it tums out that is no clear information that regulates this isuue.Among the mujtahids there is no question cloning animals, but if this is applied to human it will cause problems. Because cloning without the need for male sperma/husband. Without going through marriage, marriage guardian issues and other. This discourse is still developimg, but to arrive at a fatwa that allows it is still waiting for the continuation of the human cloning process in the future.

There can be many reasons for anyone to make a digital copy (clone) of own decision-making behavior. This enables virtual presence of a professional decision-maker simultaneously in many places and processes of Industry 4.0. Such clone can be used as one’s responsible representative when the human is not available. Pi-Mind (“Patented Intelligence”) is a technology, which enables “cloning” cognitive skills of humans using adversarial machine learning. In this paper, we present a cyber-physical environment as an adversarial learning ecosystem for cloning image classification skills. The physical component of the environment is provided by the logistic laboratory with camera-surveillance over the conveyors. The digital component of the environment contains special modifications of Generative Adversarial Networks, which include a human-operator as a trainer, an autonomous Pi-Mind clone as a trainee (a discriminator) and a smart digital adversary as a challenger (generator of sophisticated decision situations, emergencies and attacks, which supposedly catalyzes the cloning process).

The phenomenon of chimera state cloning in a two-layer network of bistable time-delayed feedback oscillators has been experimentally studied for the first time in cases of unidirectional and mutual coupling between the network layers. It is established that the cloning of chimera states in the case of mutual coupling is only possible provided a proper choice of the initial conditions for oscillators.

The paper is an attempt at geopolitical contextualization and realpolitik reading of comic works by Alejandro Jodorowsky. The focus of the analysis is on the so-called ?Jodoverse? - a segment of Jodorowsky?s opus which includes three great science-fiction sagas - ?The Incal?, ?The Saga of the Metabarons? and ?Technopriests?. These works, which can be defined as ?space operas? in terms of genre, vividly evoke a futuristic vision of one of the possible cosmic futures of humanity. This paper aims to map those motives in this fictional universe that draw inspiration from the tradition of classical geopolitics, i.e., the practice of political realism. The author?s initial assumption is that the Jodoverse is designed to function as a (popular-cultural) reflection of earthly geopolitical principles in the mirror of outer space and that, therefore, the depiction of astropolitics in the works of Jodorowsky is nothing but cloning of realpolitik in infinite space above the earth?s orbit. In order to confirm this assumption, the author will use the geopolitical and astropolitical concepts of Karl Schmidt and Everett Dolman as a key to unravelling the secrets of the Jodoverse. For that purpose, Schmidt?s concept of the nomos of the earth will be used, as well as the teaching on technological determinism which is present in the works of both theorists. By applying these concepts to Jodorowsky?s comics, the author will try to prove how the ideas of classical geopolitics have their counterparts in the cosmic phantasms of this genius of the ninth art.

For software engineering, if two code fragments are closely similar with minor modifications or even identical due to a copy-paste behavior, that is called software/code clone. Code clones can cause trouble in software maintenance and debugging process because identifying all copied compromised code fragments in other locations is time-consuming. Researchers have been working on code clone detection issues for a long time, and the discussion mainly focuses on software engineering management and system maintenance. Another considerable issue is that code cloning provides an easy way to attackers for malicious code injection. A thorough survey work of code clone identification/detection from the security perspective is indispensable for providing a comprehensive review of existing related works and proposing future potential research directions. This paper can satisfy above requirements. We review and introduce existing security-related works following three different classifications and various comparison criteria. We then discuss three further research directions, (i) deep learning-based code clone vulnerability detection, (ii) vulnerable code clone detection for 5G-Internet of Things devices, and (iii) real-time detection methods for more efficiently detecting clone attacks. These methods are more advanced and adaptive to technological development than current technologies, and still have enough research space for future studies.

Cloning has become a hot topic since technology activists in the field of genetics explained the results of their research that the reproductive process can occur with the help of technology. People have taken advantage of the results of this technology, although the debate about its use in humans is still not a comprehensive concept. This study aims to assess the extent to which the human cloning program is viewed from natural law, considering that cloning is an activity to bring back humans who have died. The method used in this research is literature review with a holistic analysis of the natural law view of human cloning. The results of this research indicate that the human cloning program is returned to the beneficiaries of this program and is fully responsible for the decisions taken

The objective of this project is to automate the process of driving a car. The result of this project will surely reduce the number of hazards happening everyday. Our world is in progress and self driving car is on its way to reach consumer‟s door-step but the big question still lies that will people accept such a car which is fully automated and driverless. The idea is to create an autonomous Vehicle that uses only some sensors (collision detectors, temperature detectors etc.) and camera module to travel between destinations with minimal/no human intervention. The car will be using a trained Convolutional Neural Network (CNN) which would control the parameters that are required for smoothly driving a car. They are directly connected to the main steering mechanism and the output of the deep learning model will control the steering angle of the vehicle. Many algorithms like Lane Detection, Object Detection are used in tandem to provide the necessary functionalities in the car.

The relevance of the research is due to the invariance of approaches to the legal regulation of public relations in the field of human cloning, the need for semantic differentiation of the concept of cloning, which ensures formation of an adequate attitude of society and the state to human cloning and development of an optimal model of legal regulation of public relations in this area. The purpose of the research is to select and justify a promising model of legal regulation of public relations in the field of human cloning in modern Russia. Achieving this goal calls for analysis of international legal acts and the system of legislation of foreign countries, as well as approaches to regulating public relations that have been formed in legal doctrine and medical practice. Based on the obtained empirical data, the author conducts a General theoretical characterization of the type of legal regulation, suggest recommendations for improving the legal regime, and substantiates the prospects of the proposed model of legal regulation of public relations in the field of human cloning. The level of philosophical methodology in the study is represented by the General principles of knowledge and categorical structure of science as a whole. The level of General scientific principles and forms of research is characterized by applying the systemic analysis techniques, the method of ascent from the abstract to the concrete, as well as General logical methods: analysis, synthesis, abstraction, and analogy. At the level of concrete scientific methodology, this study employs the comparative method and the special legal method.

The billions of users all over the world are spending online social network, such as Twitter, Facebook, Tumbler and LinkedIn. The flaws in this social media may indications to abuse user’s information and execute an attack of identity cloning. In this work mainly focuses, a new method for hiding data to hide precise details in profile pictures to learn botnets and fake profiles. In this paper, presents an ordering and investigation of recognition mechanisms of genetic copy attacks on online network of social, purely established on attribute likeness, friend network likeness, and profile inquiry for a time interval and record of Internet Protocol sequences. This work, proposals an algorithm for transform discrete wavelet for hiding the data. So, the system can prevent the replica attacks and offer the whole user data privacy protective. Similarly, when users upload the profile image or photos it would be first watermarked and then updated. Java static watermarking systems and algorithms is been used for watermarking procedure. Here tracking any fake users updating the same profile picture given easier and tracking their IP address also became easier. Also, our system raises certain features which can be investigated to the users through process of registration. Thus, providing secure authentication. Hence avoiding clone attacks in social media became easier.

Smart home systems are developed to interconnect and automate household appliances and create ubiquitous home services. Such a system is mainly driven by the communications among Internet-of-Things (IoT) objects along with Radio Frequency IDentification (RFID) technologies, where the RFID techniques in the IoT network are commonly prone to malicious attacks due to the inherent weaknesses of underlying wireless radio communications. Thus, it causes the smart home systems vulnerable to some active attacks such as the jamming and cloning attacks, which in turn threaten to home breach and personal information disclosure. This paper therefore proposes a new trust-based authentication scheme to effectively address two typical attacks, jamming and cloning attacks, in smart home environment. The evaluation shows that our solution can significantly reduce the authentication failure in jamming attacks, increase the detection probability of cloning attacks, and improve the authentication efficiency to manage the authentication delay in a reasonable time.

Prime editing enables diverse genomic alterations to be written into target sites without requiring double-strand breaks or donor templates. The design of prime-editing guide RNAs (pegRNAs), which must be customized for each edit, can however be complex and time consuming. Compared with single guide RNAs (sgRNAs), pegRNAs have an additional 3' extension composed of a primer binding site and a reverse-transcription template. Here we report a web tool, which we named pegFinder ( http://pegfinder.sidichenlab.org ), for the rapid design of pegRNAs from reference and edited DNA sequences. pegFinder can incorporate sgRNA on-target and off-target scoring predictions into its ranking system, and nominates secondary nicking sgRNAs for increasing editing efficiency. CRISPR-associated protein 9 variants with expanded targeting ranges are also supported. To facilitate downstream experimentation, pegFinder produces a comprehensive table of candidate pegRNAs, along with oligonucleotide sequences for cloning.

The Structural Classification of Proteins—extended (SCOPe, https://scop.berkeley.edu) knowledgebase aims to provide an accurate, detailed, and comprehensive description of the structural and evolutionary relationships amongst the majority of proteins of known structure, along with resources for analyzing the protein structures and their sequences. Structures from the PDB are divided into domains and classified using a combination of manual curation and highly precise automated methods. In the current release of SCOPe, 2.08, we have developed search and display tools for analysis of genetic variants we mapped to structures classified in SCOPe. In order to improve the utility of SCOPe to automated methods such as deep learning classifiers that rely on multiple alignment of sequences of homologous proteins, we have introduced new machine-parseable annotations that indicate aberrant structures as well as domains that are distinguished by a smaller repeat unit. We also classified structures from 74 of the largest Pfam families not previously classified in SCOPe, and we improved our algorithm to remove N- and C-terminal cloning, expression and purification sequences from SCOPe domains. SCOPe 2.08-stable classifies 106 976 PDB entries (about 60% of PDB entries).

This chapter builds upon your current understanding of object-oriented development by examining the topic of interface-based programming. Here you’ll learn how to define and implement interfaces and come to understand the benefits of building types that support multiple behaviors. Along the way, you will also examine a number of related topics, such as obtaining interface references, explicit interface implementation, and the construction of interface hierarchies. You’ll also examine a number of standard interfaces defined within the .NET base class libraries. As you will see, your custom classes and structures are free to implement these predefined interfaces to support a number of useful behaviors, such as object cloning, object enumeration, and object sorting.

Summary Gateway cloning employs the use of the ccdb toxin and has low colony numbers, making it difficult to apply at scale to clone libraries of cDNA vectors. In this protocol, we describe MegaGate, a toxin-less Gateway technology capable of robust cDNA library cloning that is efficient, cheap, and scalable. MegaGate eliminates the ccdb toxin used in Gateway recombinase cloning and instead utilizes meganuclease-mediated digestion to eliminate background vectors during cloning and is 99.8% efficient with high colony numbers. For complete details on the use and execution of this protocol, please refer to Kramme et al. (2021)., Graphical abstract, Highlights • MegaGate is a toxin-less Gateway molecular cloning tool • MegaGate shows 99.8% cloning efficiency and high colony numbers compared with Gateway • MegaGate can clone 300+ genes into barcoded destination vectors in a single pool • MegaGate is universally compatible with existing pENTR libraries and ORF cloning, Gateway cloning employs the use of the ccdb toxin and has low colony numbers, making it difficult to apply at scale to clone libraries of cDNA vectors. In this protocol, we describe MegaGate, a toxin-less Gateway technology capable of robust cDNA library cloning that is efficient, cheap, and scalable. MegaGate eliminates the ccdb toxin used in Gateway recombinase cloning and instead utilizes meganuclease-mediated digestion to eliminate background vectors during cloning and is 99.8% efficient with high colony numbers.

Preselection digests are a common strategy to reduce the background in the ligation step of molecular cloning. However, choosing fitting restriction enzymes by hand is not trivial and may lead to errors, potentially costing a lot of time and work. We therefore created preselector.uni-jena.de (https://preselector.uni-jena.de/), a free online tool to find such restriction enzymes. The tool uses regular expressions to find recognition sites of restriction enzymes in the DNA sequences provided by the user. This new tool compares the sets of restriction sites and reports the enzymes that cut one sequence but not the other sequences to the user. These enzymes are then the ones suitable for a preselection digest. Thus, preselector.uni-jena.de is a fast, reliable, and free-to-use tool to help researchers designing preselection digestion strategies for their cloning., Graphical Abstract Graphical AbstractFind your preselection enzymes with preselector.uni-jena.de.