False Connection Research Articles

A Novel Method of Using Machine Learning Techniques to Protect Clouds Against Distributed Denial of Service (DDoS) Attacks.

The term "cloud computing" describes a method of providing hardware- and software-based services over the internet. This allows users to access their data and apps from any device. The benefits of cloud computing include scalability, virtualization, access to user assets, lower infrastructure costs, and flexibility. However, one drawback is that it is susceptible to distributed denial of service attacks, which occur when multiple computer systems collaborate to target a particular resource, website, or server. Distributed denial of service (DDoS) attacks present a serious risk to computer networks and constitute a major cyber security challenge. This results in a denial of service for end users, as a result of false connection requests, a flood of messages, and twisted packets causing the system to slow down or even crash. Real people and services cannot access cloud computing. The issue of machine learning algorithms for identifying distributed denial of service (DDoS) attacks is explored in this article. In order to identify and defend cloud systems from harmful assaults, we developed a new machine learning approach in this work that is based on transfer learning. This study offers NSL-KDD datasets and two methodologies. There are two types of filters available: the Learning Vector Quantization (LVQ) Filters and the Principal Component Analysis (PCA) method, which reduces dimensionality. The features selected from each method were pooled using Decision Tree (DT), Naïve Bayes (NB), and Support Vector Machine (SVM) to detect distributed denial of service attacks (DDoS). We contrasted the results of several classifications. In terms of attack detection, LVQ-based DT performed better results as compared to other methods.

False Boss Connection for Precision Machining of Composites with Soft and Brittle Characteristics

False Boss Connection for Precision Machining of Composites with Soft and Brittle Characteristics

Research on multi-physical field analysis and assessment technique of virtual connection defects in cluster cable joints based on digital twin technology

As the “blood vessels” and “nerves” of the power grid, cables are important carriers for transmitting electric energy, and their failures will affect the safe and stable operation of the power grid. In this paper, a digital twin model of thermal characteristics is constructed for 10 kV YJV×400 cluster cable and its joint model. Based on the multi-physical field coupling analysis method, the fluid field, temperature field and current-carrying capacity of the cluster cable are analyzed. The temperature rise and airflow distribution characteristics of the joint and its external insulation when the defect of joint false connection occurs are analyzed. Based on the fuzzy analytic hierarchy process (FAHP), weights are assigned to the locations of multiple measurement points of the cable external insulation, and the temperature distribution of each measurement point is combined with the digital twin technology to calculate the hot spot temperature of the cable, so as to assess the defects of the joints of the cables. The digital twin platform is used for real-time monitoring of cable status and assessment of defects. The temperature rise experiments of cables under different carrying capacity and joint defects show that this method can accurately and efficiently calculate hot spot temperatures and assess defects of joints in cluster cables.

IDENTIFYING MISLEADING INFORMATION AND TYPES OF FAKES

The aim of this paper is to prove that people in an age of new media formats and media technologies should be media literate persons. The authors give the definitions of “media and information literacy” (knowledge, skills and abilities to effectively interact with the media and other information services and develop critical thinking) and “digital literacy” (the ability to use digital technologies to discover, evaluate, use, and create information). Different types of misleading information, such as: misinformation, disinformation and malinformation are analyzed in this paper. We want our students to be aware of such misleading information, to have the ability to use information from a variety of sources and effectively solve problems in an electronic environment. It is rather important to understand the varied kinds of the misleading information: satire or parody, false connection, misleading content, false context, imposter content, manipulated content and fabricated content which can be less or more harmful. The authors give examples of five fact-checking rules which are not no universal but can be basic principles.

The Old Testament Biblical Context of Jesus as Ebed Yahweh

Judaism's ideas of Ebed Yahweh and the Messiah are tasked with mending the false connection between Yahweh and his people and guiding them toward the disposition God has given them via his appointment. It's not implausible to think that the Ebed figure Deutero-Isaiah refers to lived up to his ideals and eventually went back to Jerusalem to continue his prophetic work. The qualities of Yahweh, the issue of evil, and theodicy are just a few of the topics covered in the writings of the Old Testament philosopher viewpoints on the entire ancient Israelite religion. To determine if Jesus serves as the Servant of God referred to in Deutero-Isaiah, the paper contextualizes Isaiah chapters 53 and 61. The evolution of Heilsgeschichte alludes to Ebed Yahweh’s figure. The researcher concluded that even in Palestine and mainstream crucial element thought could show that such a concept existed, it was, at best, improbable and flimsy. However, this does not change the reality that the Suffering Servant of God and the Suffering Prophet are linked. The Synoptic did apply ideas, death and Ebed's restoration covenant between God and his people to himself.

Interdisciplinary Approach to Identify and Characterize COVID-19 Misinformation on Twitter: Mixed Methods Study.

Studying COVID-19 misinformation on Twitter presents methodological challenges. A computational approach can analyze large data sets, but it is limited when interpreting context. A qualitative approach allows for a deeper analysis of content, but it is labor-intensive and feasible only for smaller data sets. We aimed to identify and characterize tweets containing COVID-19 misinformation. Tweets geolocated to the Philippines (January 1 to March 21, 2020) containing the words coronavirus, covid, and ncov were mined using the GetOldTweets3 Python library. This primary corpus (N=12,631) was subjected to biterm topic modeling. Key informant interviews were conducted to elicit examples of COVID-19 misinformation and determine keywords. Using NVivo (QSR International) and a combination of word frequency and text search using key informant interview keywords, subcorpus A (n=5881) was constituted and manually coded to identify misinformation. Constant comparative, iterative, and consensual analyses were used to further characterize these tweets. Tweets containing key informant interview keywords were extracted from the primary corpus and processed to constitute subcorpus B (n=4634), of which 506 tweets were manually labeled as misinformation. This training set was subjected to natural language processing to identify tweets with misinformation in the primary corpus. These tweets were further manually coded to confirm labeling. Biterm topic modeling of the primary corpus revealed the following topics: uncertainty, lawmaker's response, safety measures, testing, loved ones, health standards, panic buying, tragedies other than COVID-19, economy, COVID-19 statistics, precautions, health measures, international issues, adherence to guidelines, and frontliners. These were categorized into 4 major topics: nature of COVID-19, contexts and consequences, people and agents of COVID-19, and COVID-19 prevention and management. Manual coding of subcorpus A identified 398 tweets with misinformation in the following formats: misleading content (n=179), satire and/or parody (n=77), false connection (n=53), conspiracy (n=47), and false context (n=42). The discursive strategies identified were humor (n=109), fear mongering (n=67), anger and disgust (n=59), political commentary (n=59), performing credibility (n=45), overpositivity (n=32), and marketing (n=27). Natural language processing identified 165 tweets with misinformation. However, a manual review showed that 69.7% (115/165) of tweets did not contain misinformation. An interdisciplinary approach was used to identify tweets with COVID-19 misinformation. Natural language processing mislabeled tweets, likely due to tweets written in Filipino or a combination of the Filipino and English languages. Identifying the formats and discursive strategies of tweets with misinformation required iterative, manual, and emergent coding by human coders with experiential and cultural knowledge of Twitter. An interdisciplinary team composed of experts in health, health informatics, social science, and computer science combined computational and qualitative methods to gain a better understanding of COVID-19 misinformation on Twitter.

Sparsity-guided multiple functional connectivity patterns for classification of schizophrenia via convolutional network.

The explorations of brain functional connectivity network (FCN) using resting-state functional magnetic resonance imaging can provide crucial insights into discriminative analysis of neuropsychiatric disorders, such as schizophrenia (SZ). Pearson's correlation (PC) is widely used to construct a densely connected FCN which may overlook some complex interactions of paired regions of interest (ROIs) under confounding effect of other ROIs. Although the method of sparse representation takes into account this issue, it penalizes each edge equally, which often makes the FCN look like a random network. In this paper, we establish a new framework, called convolutional neural network with sparsity-guided multiple functional connectivity, for SZ classification. The framework consists of two components. (1) The first component constructs a sparse FCN by integrating PC and weighted sparse representation (WSR). The FCN retains the intrinsic correlation between paired ROIs, and eliminates false connection simultaneously, resulting in sparse interactions among multiple ROIs with the confounding effect regressed out. (2) In the second component, we develop a functional connectivity convolution to learn discriminative features for SZ classification from multiple FCNs by mining the joint spatial mapping of FCNs. Finally, an occlusion strategy is employed to explore the contributive regions and connections, to derive the potential biomarkers in identifying associated aberrant connectivity of SZ. The experiments on SZ identification verify the rationality and advantages of our proposed method. This framework also can be used as a diagnostic tool for other neuropsychiatric disorders.

MEDYA OKURYAZARLIĞI, DOĞRULUK KONTROLÜ VE SİBER ZORBALIK: BİLGİ DOĞRULAMA YÖNTEMLERİ

The acceleration of digitalization and post-truth debates due to the pandemic process focuses on technology and its effects. As the order evolves from the known universe to the Metaverse, individuals are also in the middle of a centralization where one single instrument controls everything with technological proximity. Centralization forces everyone into a more accessible and efficient communication process by consolidating many possibilities into a single device, but it also makes the spread of information faster and more uncontrolled than ever, diminishing the importance of truth. This post-truth world creates individuals who create their reality, impacting the growth of individuals the most. Cyberbullying emerges at this point, endangering children and teenagers' development and mental health. From a media perspective, technological advancements do not guarantee correct information dissemination. As the spread rate and opportunities increase, so do false information and news. It triggers the spread of false information, fake news, and cyberbullying when the truth is irrelevant. Therefore, the need for verifying information arises. Fact-checking methods are directly related to digital literacy and media literacy problems. Understanding how to verify the information and protect against false, deceptive, and fake news is crucial. The study examines two information verification platforms. Research findings reveal that news is verified by various methods and techniques using numerous tools by platforms, primarily by photo or video content, resulting in the detection of information distortions such as false connection and fabricated-manipulated content. The study also found that various photo/video verification tools and anonymous websites were frequently used.

K-Test: An Analytical Model for Measurement-Based Server Selection Based on Response Time

Different paradigms employ server selection to establish a fast connection to end users, thereby reducing the delay associated with exchanging data between mobile users and servers. To efficiently infer the statistics of the server response, we propose an analytical model for server selection based on <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$K$</tex-math></inline-formula> measured response times, namely, the K-Test method. First, the probability that the user device correctly selects the best server and the corresponding time taken in K-Test method are investigated. Then, we study the server switch when connection failure is encountered. The proposed analytical model for K-Test method considers the distribution of the response time of candidate servers and derives three metrics, namely the probability of selecting the best server, the expected latency of the server selection process, and the false connection failure detection probability. The analytical model is validated by the simulation. Numerical results show that both the value of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$K$</tex-math></inline-formula> and the number of candidate servers significantly affect the selection probability. Also, it needs more test rounds to correctly find the best server when users experience a variation in the network response times.

RRL-GAT: Graph Attention Network-Driven Multilabel Image Robust Representation Learning

Exploring the characterization laws of image data and improving the efficiency of image data characterization knowledge is essential to promote the development of the Internet of Things technology. Considering that images in the real world usually contain multiple objects, and the objects are closely dependent. For these reasons, it brings great challenges to the robust representation learning of multilabel images. In general, researchers model the relationship between objects based on a class activation map and use graph convolution to mine the dependencies between objects. However, graph structure data often contain noise, which means that the edges between nodes are sometimes not so reliable, and the relative importance of neighbors is also different. Based on this, our goal is to reduce noisy connections and false connections between objects, eliminate multilabel image representation bias, and learn robust representations. Therefore, we propose a robust representation learning method for multilabel images driven by graph attention network (RRL-GAT). Specifically, to reduce the accidental false connection of objects in the image, we propose the class attention graph convolution module (C-GAT) to mine the strong association structure between categories. Besides, for the dynamic correlation between objects in the image, we propose an adaptive graph attention convolution module (A-GAT) to capture the subtle dynamic dependencies in the image. The results on two authoritative data sets show that our method is significantly better than all current state-of-the-art methods. Besides, the visualization results show that RRL-GAT can capture the semantic relationship of a specific input image and has sufficient recognizability.

Multimodal Fake News Detection

Over the last few years, there has been an unprecedented proliferation of fake news. As a consequence, we are more susceptible to the pernicious impact that misinformation and disinformation spreading can have on different segments of our society. Thus, the development of tools for the automatic detection of fake news plays an important role in the prevention of its negative effects. Most attempts to detect and classify false content focus only on using textual information. Multimodal approaches are less frequent and they typically classify news either as true or fake. In this work, we perform a fine-grained classification of fake news on the Fakeddit dataset, using both unimodal and multimodal approaches. Our experiments show that the multimodal approach based on a Convolutional Neural Network (CNN) architecture combining text and image data achieves the best results, with an accuracy of 87%. Some fake news categories, such as Manipulated content, Satire, or False connection, strongly benefit from the use of images. Using images also improves the results of the other categories but with less impact. Regarding the unimodal approaches using only text, Bidirectional Encoder Representations from Transformers (BERT) is the best model, with an accuracy of 78%. Exploiting both text and image data significantly improves the performance of fake news detection.

Perbandingan Performansi Model pada Algoritma K-NN terhadap Klasifikasi Berita Fakta Hoaks Tentang Covid-19

During Covid-19 pandemic, there was various hoax news about Covid-19. There are truth-clarification platforms for hoax news about Covid-19 such as Jala Hoax and Saber Hoax which categorize into misinformation and disinformation. Classification of supervised learning methods is applied to carry out learning from fact labels. Dataset is taken from Jala Hoax and Saber Hoax as many as 559 data which are made into Class 1 (Misleading Content, Satire/Parody, False Connection), Class 2 (False Context, Imposter Content), Class 3 (Fabricated and Manipulated Content). K-Nearest Neighbor (K-NN) is used to classify categories of misinformation and disinformation. Dissimilarity measure Jaccard Distance is compared with Euclidean, Manhattan, and Minkowski and uses k-value variance in the K-NN to determine the performance comparison results for each test. Results of Jaccard Distance at the value of k = 4 get a higher value than other model with an accuracy 0.696, precision 0.710, recall 0.572, and F1-Score. Maximum results tend to be on label of the most data class in Class 1 (Misleading Content, Satire or Parody, False Connection) with a total of 58 correct data from 61 test data.

Extensible image object co-segmentation with sparse cooperative relations

In the co-segmentation problems of sparse related image group, how to mine correlations between common objects has been one of the most significant steps. In this paper, we first prioritize the processing efficiency and construct sparse cooperative graph for related images in feature space, instead of examining the full correlations of common foregrounds which lead to high computation cost and great risk of false connection. And more importantly, we design a flexible and efficient extending strategy to reuse the cooperative graph for incremental image data. Then, with the sparse correlations of the image group, we propose a unilateral proposal co-selection method for pair-wise region level co-segmentation. Finally, we have achieved the segmentation results with further pixel level refinement. Our experimental results on publicly available datasets show that, compared with the approaches using dense cooperative constraints, the proposed method can achieve more competitive results with extremely sparse correlative constraints, which shows its bright application prospects for sparse correlated image groups with incremental data due to its high efficiency and flexible extensibility.

MANIPULATION TECHNIQUES OF DISSEMINATING MISINFORMATION AS A MEANS OF GENERATING ANTI-RUSSIAN PROPAGANDA IN THE ENGLISH LANGUAGE MEDIA HEADLINES: A CASE STUDY OF THE 2018 SALISBURY ATTACK AS DEPICTED IN THE ENGLISH-LANGUAGE NEWS STORIES

The paper attempts to describe a number of linguistic and pragmatic aspects of modeling the anti-Russian discourse in the English language media headlines. The authors focus on the coverage of the Skripal poisoning case and the specific language means employed by a range of English-language news sources, such as The Guardian, BBC, CNN, Politico, The Mirror, The Daily Mail, The New Zealand Herald, The Herald. The results of the study indicate that one of the most effective and widespread media manipulation techniques is misinformation accompanied by a discrepancy between the headline and the content of the article. The research seeks to classify manipulation techniques according to the way of their actualization in the language and the degree of misinformation: full fabrication, partial fabrication, manipulated content, selective quoting, false connection, emphasizing communication relevant elements by means of the actual division of the sentence. The implementation of such manipulation techniques is aimed at shaping public opinion on the incident at issue in order to promote a negative image of Russia and its leader in terms of their alleged involvement in the Skripal attack.

The False Connection Between Mass Violence and Mental Illness Redux

The False Connection Between Mass Violence and Mental Illness Redux

Adaptive Backstepping Control of a Pneumatic System With Unknown Model Parameters and Control Direction

The performance of the pneumatic elements and the micro-controller steadily increases as the price of them decreases. This trend promotes high performance tracking control research on pneumatic servo systems. However, it is very difficult to obtain exact model parameters, which is one of the main obstacles to design a high performance controller. Moreover, in some application cases, the control direction is undetermined because of the possible false or incorrect valve outlets connection. At the same time, the inaccuracy of the proportional valve zero points also degrades the tracking performance of pneumatic servo systems. In this paper, an adaptive backstepping control approach is proposed for a pneumatic position servo system considering unknown model parameters, unknown control direction, and inaccurate valve zero points. The proposed method combines the Nussbaum function and backstepping technique to design a position tracking controller of a pneumatic servo system with the aforementioned uncertainties. By using the Lyapunov method, the designed controller is proved to be stable, and the tracking error asymptotically converges to zero. The experimental results demonstrate the effectiveness and the superiority of the proposed approach as compared with some existing methods, even for negative direction case for which the other methods fail. The performance of the proposed controller is further improved by considering the proportional valve zero points.

Automatic Removal of False Connections in Diffusion MRI Tractography Using Topology-Informed Pruning (TIP).

Diffusion MRI fiber tracking provides a non-invasive method for mapping the trajectories of human brain connections, but its false connection problem has been a major challenge. This study introduces topology-informed pruning (TIP), a method that automatically identifies singular tracts and eliminates them to improve the tracking accuracy. The accuracy of the tractography with and without TIP was evaluated by a team of 6 neuroanatomists in a blinded setting to examine whether TIP could improve the accuracy. The results showed that TIP improved the tracking accuracy by 11.93% in the single-shell scheme and by 3.47% in the grid scheme. The improvement is significantly different from a random pruning (p value < 0.001). The diagnostic agreement between TIP and neuroanatomists was comparable to the agreement between neuroanatomists. The proposed TIP algorithm can be used to automatically clean-up noisy fibers in deterministic tractography, with a potential to confirm the existence of a fiber connection in basic neuroanatomical studies or clinical neurosurgical planning.

Captivation, false connection and secret societies in Singapore

Interminable ritual repetition of set movements (taolu) has resulted in Chinese martial arts facing trenchant criticism as being useless in fight sports, mixed martial arts, and actual combat. In Singapore, the neglect of body-callousing or conditioning methods in Chinese martial arts may render them unfit for unarmed combat. This led me to ask whether the entire edifice of set practice in the martial arts is based upon a false connection. Researching Hong Shen Choy Li Fut, a Chinese fighting style notoriously infested with gangsters in the red-light district of Singapore, I was informed that all Chinese martial arts and lion dance associations are triads. Nevertheless, even here I was shown curious dancelike interpretations for martial arts moves taught. Does the endless repetition of sets captivate the performer into a delusional belief that they are becoming a better fighter? Are the audiences of such sets, performed in dramatic rendition, similarly held captive in a false connection?

Book Review: Bonhoeffer's Black Jesus: Harlem Renaissance Theology and an Ethic of Resistance

s Black Jesus: Harlem Renaissance Theology and an Ethic of Resistance. By Reggie L. Williams. Waco, Tex.: Baylor University Press, 2014. xii + 184 pp. $39.95 (paper).Bonhoeffer s Black Jesus is, currently, the only book-length study of and race available today, now that Josiah Youngs No Difference in the Fare: Dietrich and the Problem of Racism (1998) is no longer in print. Bonhoeffer's formative years in Harlem have been little explored, but scholars are beginning to understand that without this period in s life, his later theology is unintelligible. Reggie Williams focuses on s time in Harlem in order to build a constructive theological ethic based on solidarity.Williams makes the argument that Bonhoeffer had formative experiences in New York in a key historical moment that inspired his efforts in Germany to uncouple the false connection between white imperialist identity and Jesus and its tragic imprint for Christianity (p. 3). He interprets s time in Harlem and his later theology through the lens of what he calls empathie resistance, a resistance born from empathy and solidarity with the oppressed. This empathy allowed to overcome the imperialist and colonialist understandings of Christian faith that he harbored from being raised in Germany in order to stand with those who were despised, that is, with Blacks while in America and with Jews once he moved back to Germany.After a brief biographical sketch of leading up to his time in Harlem, Williams moves on to an introduction to colonialism and postcolonial attempts at doing theology, with an extended discussion of the theology of Willie James Jennings. This chapter demonstrates that the Christian imagination was bred during colonialism to resist empathy through the brutal practices of slavery and oppression of non-white, non-European peoples. This diseased colonialist imagination is evident in some of s own early writing and sermons, wherein he strongly aligned himself with the white-supremacist Volk theology that ran rampant in post-World War I Germany. It was this diseased imagination that the Harlem Renaissance helped to overcome.Williams provides not only an introduction to s theology, but also an introduction to the literature and art of the Harlem Renaissance and a brief history of Abyssinian Baptist Church in Harlem. He takes readers through the relevant literature of the events of the Harlem Renaissance in order to contextualize Bonhoeffers own time in Harlem. He provides a special focus on the figure of the Black Christ in the works of various Harlem Renaissance authors, giving particular attention to Countee Cullen s poem The Black Christ. …

GAM: Genomic Assemblies Merger

Motivations. In the last 3 years more than 20 assemblers have been proposed to tackle the hard task of assembling. Recent evaluation efforts (Assemblathon 1 and GAGE) demonstrated that none of these tools clearly outperforms the others. However, results clearly show that some assemblers performs better than others on specific regions and statistics while poorly performing on other regions and evaluation measures. With this picture in mind we developed GAM (Genomic Assemblies Merger) whose primary goal is to merge two or more assemblies in order to obtain a more contiguous one. Moreover, as a by-product of the merging step, GAM is able to correct mis-assemblies. GAM does not need global alignment between contigs, making it unique among others Assembly Reconciliation tools. In this way a computationally expensive alignment is avoided, and paralog sequences (likely to create false connection among contigs) do not represent a problem. GAM procedure is based only on the information coming from reads used in the assembling phases, and it can be used even on assemblies obtained with different datasets. Methods. Let us concentrate on the the merging of two assemblies, dubbed M and S. As a preprocessing step, that is an almost mandatory analysis, reads (or a subset of them) used in the assembling phase are aligned against M and S using a SAM-compatible aligner (e.g., BWA, rNA). GAM takes as input M, S and the two SAM files produced in the preprocessing step. The main idea is to identify fragments belonging to M and S having high similarity. For this purpose, GAM identifies regions, named blocks, belonging to M and S that share an high enough amount of reads (i.e. regions sharing the same aligned reads). After all blocks are identified the Assembly Graph (AG) is built: each node corresponds to a block and a directed edge connects block A to block B if the first precedes the second in either M or S (see Fig.1). Once AG is available, the merging phase can start. As a first step GAM identifies genomic regions in which assemblies contradict each other (loops, bifurcations, etc.). These areas represent potential inconsistencies between the two sequences. We chose to be as much conservative as possible electing (for example) M to be the Master assembly: all its contigs are supposed to be correct and cannot be contradicted. S becomes the Slave and everywhere an inconsistency is found, M is preferred to S. After the identification and the resolution of problematic regions, GAM visits the simplified graph, merges contigs accordingly to blocks and edges in AG (each merging phase is performed using a Smith-Waterman algorithm variant) and finally outputs the new improved assembly. GAM is not only limited to contigs, it can also work with scaffolds, filling the N's inserted by an assembler and not by the other. Results. GAM has been tested on several real datasets, in particular on Olea's chloroplast (241X Illumina paired reads and 21X 454 paired reads), Populus trichocarpa (82X Illumina paired reads), boa constrictor (40X Illumina paired reads). Illumina reads have average length of 100 bp and insert size of 500 bp. All tests have been performed on a computer equipped with 8 cores and 32GB RAM. ABySS and CLC were selected as assemblers. Results are summarized in Fig. 1. Olea's chloroplast has been used as a proof of concept experiment. The presence of a reference sequence allowed GAM's output validation (using dnadiff). Two assemblies were obtained with CLC using Illumina and 454 data. GAM was used to merge them. Figure 1 shows how GAM assembly is not only more contiguous but also more correct: while Master (CLC-Illumina) and Slave (CLC-454) have 58 and 39 suspicious regions respectively, GAM has only 14 of those. On Populus trichocarpa and Boa constrictor, CLC assemblies were used as master due to their better contiguity. In both cases assemblies returned by GAM were more contiguous (see Fig. 1).