Related Work Section Research Articles

What can rhetoric bring us? Incorporating rhetorical structure into neural related work generation

The ever-increasing volume of research literature poses challenges for researchers in keeping up with related works in their fields. Automating the generation of related work section holds promise for saving time and effort. However, current models often fall short of producing coherent and logically correct related work with multiple sentences, a phenomenon we refer to as rhetorical structure chaos. Rhetorical structure describes how adjacent spans of units are connected to each other, and logically correct rhetorical structure is essential for a well-structured related work. Hence, to tackle the rhetorical structure chaos issue, this paper explicitly incorporates rhetorical structure information into related work generation. Firstly, we conduct the first rhetorical structure analysis for related work section, which provides insights into understanding the organization and arrangement of contents within related work. Then, based on two preliminary studies on rhetorical structure, we present a novel related work generation model called RSGen, which incorporates rhetorical structure at both the encoding and decoding stages. The encoding stage is facilitated with a rhetorical structure-based graph encoder, while the decoding process is guided by a rhetorical plan — ordered sequence of rhetorical functions of the related work. We conduct extensive experiments on three related work generation datasets to evaluate the performance of our model. The results show that our approach achieves state-of-the-art performance on ROUGE metrics. An ablation study and more analyses further highlight the remarkable efficacy of introducing rhetorical structure into both the encoding and decoding stages.

Advancements in Glaucoma Diagnosis: The Role of AI in Medical Imaging.

The progress of artificial intelligence algorithms in digital image processing and automatic diagnosis studies of the eye disease glaucoma has been growing and presenting essential advances to guarantee better clinical care for the population. Given the context, this article describes the main types of glaucoma, traditional forms of diagnosis, and presents the global epidemiology of the disease. Furthermore, it explores how studies using artificial intelligence algorithms have been investigated as possible tools to aid in the early diagnosis of this pathology through population screening. Therefore, the related work section presents the main studies and methodologies used in the automatic classification of glaucoma from digital fundus images and artificial intelligence algorithms, as well as the main databases containing images labeled for glaucoma and publicly available for the training of machine learning algorithms.

CloudIoT paradigm acceptance for e-learning: analysis and future challenges

E-learning is the theme interrelated to the virtualized distance learning with the help of electronic communication machines, certainly with the help of Internet. CloudIoT paradigm is the combination of cloud resource and internet of thing which become prevalent now days due to the flexibility and fast access for those reason different countries used CloudIoT paradigm different purposes. E-learning is one of the best examples where virtual environment provides cost-effective alternative to physical labs as well as to run scientific applications. The world order change in education sector due to Covid-19 all activity shift in to e-learning system. In this paper we present the review about CloudIoT paradigm and it usage in e-learning system as well as we extant taxonomy of CloudIoT paradigm for e-leaning purpose. In the related work section we present the existing contribution in the field of e-learning using CloudIoT paradigm are highlighted. We also contemporaneous the most standard framework which carried out for e-leaning using CloudIoT paradigm is discuss. The contribution section of the paper present the issue being faced by in adopting CloudIoT paradigm for e-learning are discussed along with recommendation and future work.

Generating a related work section for scientific papers: an optimized approach with adopting problem and method information

Generating a related work section for scientific papers: an optimized approach with adopting problem and method information

Automatic related work section generation: experiments in scientific document abstracting

Related work sections or literature reviews are an essential part of every scientific article being crucial for paper reviewing and assessment. However, writing a good related work section is an activity which requires considerable expertise to identify, condense/summarize, and combine relevant information from different sources. In this work we compare different automatic methods to produce “descriptive” related work sections given as input the set of papers which need to be described. The main contribution of our work is a neural sequence learning process which produces citation sentences to be included in a related work section of an article. We train the neural architecture using an available scientific data set of citation sentences and we test over a data set of related work sections; we also compare the performance to a set of baseline extractive summarizers, an abstractive summarizer and a state of the art CNNs approach. Our results indicate that our approach outperforms the simple as well as the informed baselines.

GEP‐based classifiers with drift‐detection

AbstractIn the paper, we propose two gene expression programming (GEP)‐based ensemble classifiers with different drift detection mechanisms. In the related work section, we briefly review GEP as a classification tool, incremental classifiers, and concept drift detectors. Next, the structure of our two‐level GEP ensemble with metagenes is described. Further on, two integrated classifiers with drift detection algorithm and Wilcoxon rank sum test drift detector are proposed. The approach is validated in the computational experiment in which several real‐life and artificial datasets with concept drift have been used. Experiment confirmed that the proposed approach can be competitive to existing solutions. In the conclusion section, we briefly outline directions for future research.

ToC-RWG: Explore the Combination of Topic Model and Citation Information for Automatic Related Work Generation

Automatic related work generation is a new challenge in multi-document scientific summarization focusing on refining a related work section for a given scientific paper. In this paper, we propose a brand new framework ToC-RWG for related work generation by incorporating topic model and citation information. We present an unsupervised generative probabilistic model, called QueryTopicSum, which utilizes a LDA-style model to characterize the generative process of both the scientific paper and its reference papers. We also take advantage of citations of reference papers to identify Cited Text Spans (CTS) from reference papers. This approach provides us with a perspective of annotating the importance of the reference papers from the academic community. With QueryTopicSum and the identified CTS as candidate sentences, an optimization framework based on minimizing KL divergence is exerted to select the most representative sentences for related work generation. Our evaluation results on a set of 50 scientific papers along with their corresponding reference papers show that ToC-RWG achieves a considerable improvement over generic multi-document summarization and scientific summarization baselines.

Stable Confident Rating Prediction in Collaborative Filtering

Stability of matrix factorization is estimated in terms of gradient descent estimation at each iteration which ultimately defines the stability of recommendations in collaborative filtering techniques. Also, stability is inversely proportional to total number of iterations used for estimating ratings. This gives rise to the need of the method which possesses better rating predictability within less number of iterations. The accuracy of rating prediction is found to be better when user to user trust score is estimated using similarity of individual rated items. The trust estimation is also prone to sparsity due to irregularity of ratings in large volume of data sets (big data). Based on the experimentation strategy and platform requirements, the method of trust evaluation is proposed in this positional paper. The Lyapunov stability solver functions can be used directly in obtaining solution for the trust score amongst users which can bring sufficient stability in learning stages of matrix factorization process and hence better performance in predicting the ratings of non-rated items. Here, the results obtained possess the sufficient gravity for consideration of predicted ratings which also keeps errors in prediction at lowest level for rated items. The papers are addressed from similar domain in related work section to compare proposed work in terms of novelty and performance. The results obtained are satisfactory, which are assessed in terms of mean absolute error (MAE).

Three-step coreference-based summarizer for Polish news texts

Abstract This article addresses the problem of automatic summarization of press articles in Polish. The main novelty of this research lays in the proposal of a three-step summarization algorithm which benefits from using coreference information. In related work section, all coreference-based approaches to summarization are presented. Then we describe in detail all publicly available summarization tools developed for Polish language. We state the problem of single-document press article summarization for Polish, describing the training and evaluation dataset: the POLISH SUMMARIES CORPUS. Next, a new coreference-based extractive summarization system NICOLAS is introduced. Its algorithm utilises advanced third-party preprocessing tools to extract the coreference information from the text to be summarized. This information is transformed into a complex set of features related to coreference concepts (mentions and coreference clusters) that are used for training the summarization system (on the basis of a manually prepared gold summaries corpus). The proposed solution is compared to the best publicly available summarization systems for Polish language and two state-of-the-art tools, developed for English language, but adapted to Polish for this article. NICOLAS summarization system obtains best scores, for selected metrics outperforming other systems in a statistically significant way. The evaluation also contains calculation of interesting upper-bounds: human performance and theoretical upper-bound.

A Hybrid Approach Toward Research Paper Recommendation Using Centrality Measures and Author Ranking

The volume of research articles in digital repositories is increasing. This spectacular growth of repositories makes it rather difficult for researchers to obtain related research papers in response to their queries. The problem becomes worse when a researcher with insufficient knowledge of searching research articles uses these repositories. In the traditional recommendation approaches, the results of the query miss many high-quality papers, in the related work section, which are either published recently or have low citation count. To overcome this problem, there needs to be a solution which considers not only structural relationships between the papers but also inspects the quality of authors publishing those articles. Many research paper recommendation approaches have been implemented which includes collaborative filtering-based, content-based, and citation analysis-based techniques. The collaborative filtering-based approaches primarily use paper-citation matrix for recommendations, whereas the content-based approaches only consider the content of the paper. The citation analysis considers the structure of the network and focuses on papers citing or cited by the paper of interest. It is therefore very difficult for a recommender system to recommend high-quality papers without a hybrid approach that incorporates multiple features, such as citation information and author information. The proposed method creates a multilevel citation and relationship network of authors in which the citation network uses the structural relationship between the papers to extract significant papers, and authors' collaboration network finds key authors from those papers. The papers selected by this hybrid approach are then recommended to the user. The results have shown that our proposed method performs exceedingly well as compared with the state-of-the-art existing systems, such as Google scholar and multilevel simultaneous citation network.

GB-PANDAS:

Dynamic affinity scheduling has been an open problem for nearly three decades. The problem is to dynamically schedule multi-type tasks to multi-skilled servers such that the resulting queueing system is both stable in the capacity region (throughput optimality) and the mean delay of tasks is minimized at high loads near the boundary of the capacity region (heavy-traffic optimality). As for applications, dataintensive analytics like MapReduce, Hadoop, and Dryad fit into this setting, where the set of servers is heterogeneous for different task types, so the pair of task type and server determines the processing rate of the task. The load balancing algorithm used in such frameworks is an example of affinity scheduling which is desired to be both robust and delay optimal at high loads when hot-spots occur. Fluid model planning, the MaxWeight algorithm, and the generalized c?-rule are among the first algorithms proposed for affinity scheduling that have theoretical guarantees on being optimal in different senses, which will be discussed in the related work section. All these algorithms are not practical for use in data center applications because of their non-realistic assumptions. The join-the-shortest-queue-MaxWeight (JSQMaxWeight), JSQ-Priority, and weighted-workload algorithms are examples of load balancing policies for systems with two and three levels of data locality with a rack structure. In this work, we propose the Generalized-Balanced-Pandas algorithm (GB-PANDAS) for a system with multiple levels of data locality and prove its throughput optimality. We prove this result under an arbitrary distribution for service times, whereas most previous theoretical work assumes geometric distribution for service times. The extensive simulation results show that the GB-PANDAS algorithm alleviates the mean delay and has a better performance than the JSQMaxWeight algorithm by up to twofold at high loads. We believe that the GB-PANDAS algorithm is heavy-traffic optimal in a larger region than JSQ-MaxWeight, which is an interesting problem for future work.

Automatic generation of related work through summarizing citations

SummaryRelated work is a component of a scientific paper, which introduces other researchers' relevant works and makes comparisons with the current author's work. Automatically generating the related work section of a writing paper provides a tool for researchers to accomplish the related work section efficiently without missing related works. This paper proposes an approach to automatically generating a related work section by comparing the main text of the paper being written with the citations of other papers that cite the same references. Our approach first collects the papers that cite the reference papers of the paper being written and extracts the corresponding citation sentences to form a citation document. It then extracts keywords from the citation document and the paper being written and constructs a graph of the keywords. Once the keywords that discriminate the two documents are determined, the minimum Steiner tree that covers the discriminative keywords and the topic keywords is generated. The summary is generated by extracting the sentences covering the Steiner tree. According to ROUGE evaluations, the experiments show that the citations are suitable for related work generation and our approach outperforms the three baseline methods of MEAD, LexRank, and ReWoS. This work verifies the general summarization method based on connotation and extension through citation.

Semantically linking events for massive scientific literature research

PurposeThis paper aims to semantically linking scientific research events implied by scientific and technical literature to support information analysis and information service applications. Literature research is an important method to acquire scientific and technical information which is important for research, development and innovation of science and technology. It is difficult but urgently required to acquire accurate, timely, rapid, short and comprehensive information from the large-scale and fast-growing literature, especially in the big data era. Existing literature-based information retrieval systems focus on basic data organization, and they are far from meeting the needs of information analytics. It becomes urgent to organize and analyze scientific research events related to scientific and technical literature for forecasting development trend of science and technology.Design/methodology/approachScientific literature such as a paper or a patent is represented as a scientific research event, which contains elements including when, where, who, what, how and why. Metadata of literature is used to formulate scientific research events that are implied in introduction and related work sections of literature. Named entities and research objects such as methods, materials and algorithms can be extracted from texts of literature by using text analysis. The authors semantically link scientific research events, entities and objects, and then, they construct the event space for supporting scientific and technical information analysis.FindingsThis paper represents scientific literature as events, which are coarse-grained units comparing with entities and relations in current information organizations. Events and semantic relations among them together formulate a semantic link network, which could support event-centric information browsing, search and recommendation.Research limitations/implicationsThe proposed model is a theoretical model, and it needs to verify the efficiency in further experimental application research. The evaluation and applications of semantic link network of scientific research events are further research issues.Originality/valueThis paper regards scientific literature as scientific research events and proposes an approach to semantically link events into a network with multiple-typed entities and relations. According to the needs of scientific and technical information analysis, scientific research events are organized into event cubes which are distributed in a three-dimensioned space for easy-to-understand and information visualization.

Balancing Between Exploration and Exploitation in ACO

In order to balance the preference of the artificial entities towards exploration or exploitation (in their transition rule), a novel technique is proposed for replacing the random function used by the classical Ant Colony Optimization (ACO) algorithms for solving the Traveling Salesman Problem (TSP). The proposed Beta Distribution function (B), or random:betavariate(a; b) has the proven capability (depicted through test-runs) of influencing the algorithm’s solution quality and convergence speed. Consequently, this paper will introduce in the related work section the classical ACO algorithm, with a focus on the transition rule used for choosing the next node in the problem’s associated graph, followed by the related work on this topic, and it will continue with the introduction of the B function which will be presented both from a theoretical and practical perspective in relation with the scope: balancing between exploration and exploitation in order to improve the performance of the ACO algorithm for the TSP. The paper concludes that the B-EAS has the ability to find better solution than EAS for a set of benchmarks from the TSPLib library.

Editorial: How to extend a conference paper to a journal paper

This issue contains two highly technical papers on fault prediction and model checking. Non-negative sparse-based SemiBoost for software defect prediction, by Tiejian Wang, Zhiwu Zhang, Xiaoyuan Jing, and Yanli Liu, presents a novel semi-supervised learning technique to predict where faults could be in software. (Recommended by Giuliano Antoniol.) Past-Free[ze] reachability analysis: Reaching further with DAG-directed exhaustive state-space analysis, by Ciprian Teodorov, Luka Le Roux, Zoé Drey, and Philippe Dhaussy, presents a new algorithm to perform reachability analysis in model checkers that significantly reduces the state-space explosion problem. (Recommended by Ronald Olsson.) In a previous editorial, I set out STVR's policy on extending conference papers to journal submissions [May 2016]. The major requirements are that the journal version must have at least 30% new material, the journal version must include a citation to the conference paper, and the journal version must discuss the conference paper and summarize the new material. Here I suggest some practical guidelines for how to do the extension. If the same title is used, then you would have two papers with the same title. That is confusing to everybody who reads or references either paper. If the same abstract is used, then no reader (reviewer or otherwise) will believe the journal submission is different. Don't just add a few sentences, rewrite the whole thing! It might be possible to just have one of (3) or (4), not both, but from my experience, it would be very hard to get that through the reviewers. Many times I have seen papers extended with only a new idea, and the reviewers almost invariably ask for a major revision, with an empirical evaluation required. Likewise, if a paper extends the experiment without adding a new theory or idea, the reviewers are very likely to respond by saying “nothing new, reject.” That is, a stronger experiment (more subjects, more rigorous analysis, etc.) may increase our confidence in the conclusions, but if the conclusions are unchanged, it's very hard to convince the reviewers to publish the new version. Several other things should be changed as well. I suggest throwing out the old introduction, outlining a new introduction, and then writing it completely from a blank page. Again, if the reviewers have read the introduction before, you are almost asking them to reject the paper on the basis of not enough new material. Add additional related work. Add a lot of additional related work! Conference papers are almost always limited to 8 to 10 pages, and we often make our related work section as short as possible to leave room for the “good stuff” (our ideas). With a journal version, you have much more space to write, so use it. Journal reviewers are much more likely to criticize not having enough references than having too many references. Add more examples and explanations. Add more data in the form of tables and figures. Reflect on all the hard decisions you made when forcing the conference paper into a small box, and put some of that material back in. The editor or reviewers will do a side-by-side comparison of the conference and journal papers. You want it to look as if the conference paper was the primitive or immature version, and the journal paper is all grown up. You emphatically do not want the journal version to look like the same paper with a few sentences added here and there. Finally, rewrite everything. Clean up the writing, polish the sentence structure, check the grammar and punctuation again, and edit, edit, edit. My favorite writing teacher told me there are no great writers, just great editors. I want to thank Rob Hierons for discussions leading to this editorial, and many students for helping me refine these suggestions.

Erratum to: Conflict-Free Chromatic Art Gallery Coverage

The authors wish to acknowledge a mistake in the Related Work Section of the paper “Conflict-Free Chromatic Art Gallery Coverage” [1], where we mentioned conflictfree colorings of hypergraphs. In the conflict-free coloring of a hypergraph H , every edge e (a subset of vertices) must have a vertex that is uniquely colored among the vertices in e. For instance, consider the geometric hypergraph induced by axis-aligned rectangles: Its vertices correspond to a finite set of axis-aligned rectangles, and each maximal subset of rectangles with a common intersection forms a hyperedge. We erroneously wrote that for these hypergraphs, the conflict-free chromatic number has a tight Θ(logn) bound, but in fact only an upper bound of O(log2 n) (shown by Smorodinsky in [3]), and a lower bound of Ω(logn) (given by Pach and Tardos in [2]) is known.

Implications of peer selection strategies by publishers on the performance of P2P swarming systems

In peer-to-peer swarming systems, as peers join a swarm to download a content they bring resources such as bandwidth and memory to the system. That way, the capacity of the system increases with the arrival rate of peers. Furthermore, if publishers are intermittent, increasing the arrival rate of peers can increase content availability [7]. In the presence of stable publishers that have enough service capacity for peers to smoothly complete their download [6], increasing the arrival rate of peers decreases the probability that a piece will be unavailable among peers. However, if the capacity of the stable publisher, U pieces/second, is not large enough, it has been shown that the system might be unstable [3, 5, 14]. Hajek and Zhou [3, 14], following up work by Mathieu and Reynier [5], have shown that if the arrival rate of peers, λ, is greater than U , the number of peers increases unboundedly with time. It has also been shown that simple strategies can alleviate, and in some cases resolve, the instability problem. For instance, if peers reside in the system after completing their downloads, on average, the same time that they take to download a piece, then the system is always stable [14]. Nevertheless, as peers have no incentive to stay in the system after completing their downloads, it is important to investigate whether other simple strategies that do not depend on providing incentives for peers to remain online after the download completion can improve system performance and stability. In a peer to peer system, each peer has to make two decisions before transmitting each piece: 1) which piece to transmit and 2) to whom to transmit it. Although the former question has received some attention in previous works (for instance, it has been shown that rarest-first piece selection and random useful piece selection yield the same stability region [3]), to the best of our knowledge the implications of the peer selection strategy have not been discussed yet (previous works assumed random peer selection [3, 9, 14], a notable exception being [5] – see related work section). Let the throughput be the rate at which peers leave the system. The goal of this paper is to evaluate the impact of different peer selection strategies on the throughput (hence, stability) of the system. We pose the following questions: a) how to increase the throughput of the system by letting peers strategically select their neighbors? b) how does throughput scale with the number of peers in a closed peer-to-peer swarming system? We provide the following answers to the above questions. First, we derive an upper bound on the throughput when the stable publisher adopts the most deprived peer selection [1] and rarest-first piece selection, while peers adopt random peer selection and random useful piece selection. The bound is significantly larger than the maximum attainable throughput when both peers and publishers adopt random peer and random useful piece selection. Then, we consider a closed system and we use a simple Markov chain model to study how the throughput of the system scales with the number of peers.

Reputation-based decisions for logic-based cognitive agents

Computational trust and reputation models have been recognized as one of the key technologies required to design and implement agent systems. These models manage and aggregate the information needed by agents to efficiently perform partner selection in uncertain situations. For simple applications, a game theoretical approach similar to that used in most models can suffice. However, if we want to undertake problems found in socially complex virtual societies, we need more sophisticated trust and reputation systems. In this context, reputation-based decisions that agents make take on special relevance and can be as important as the reputation model itself. In this paper, we propose a possible integration of a cognitive reputation model, Repage, into a cognitive BDI agent. First, we specify a belief logic capable to capture the semantics of Repage information, which encodes probabilities. This logic is defined by means of a two first-order languages hierarchy, allowing the specification of axioms as first-order theories. The belief logic integrates the information coming from Repage in terms if image and reputation, and combines them, defining a typology of agents depending of such combination. We use this logic to build a complete graded BDI model specified as a multi-context system where beliefs, desires, intentions and plans interact among each other to perform a BDI reasoning. We conclude the paper with an example and a related work section that compares our approach with current state-of-the-art models.

MPICH-V Project: A Multiprotocol Automatic Fault-Tolerant MPI

High performance computing platforms such as Clusters, Grid and Desktop Grids are becoming larger and subject to more frequent failures. MPI is one of the most used message passing libraries in HPC applications. These two trends raise the need for fault-tolerant MPI. The MPICH-V project focuses on designing, implementing and comparing several automatic fault-tolerant protocols for MPI applications. We present an extensive related work section highlighting the originality of our approach and the proposed protocols. We then present four fault-tolerant protocols implemented in a new generic framework for fault-tolerant protocol comparison, covering a large spectrum of known approaches from coordinated checkpoint, to uncoordinated checkpoint associated with causal message logging. We measure the performance of these protocols on a micro-benchmark and compare them with the NAS benchmark, using an original fault tolerance test. Finally, we outline the lessons learned from this in depth fault-tolerant protocol comparison of MPI applications.

Sensors for Detection of Misbehaving Nodes in MANETs

The fact that security is a critical problem when implementing mobile ad hoc networks (MANETs) is widely acknowledged. One of the different kinds of misbehaviour a node may exhibit is selfishness. A selfish node wants to preserve its resources while using the services of others and consuming their resources. One way of preventing selfishness in a MANET is a detection and exclusion mechanism. In this paper, we focus on the detection and present different kinds of sensors that will find selfish nodes. First we present simulations that show the negative effects which selfish nodes cause in MANET. In the related work section we will analyze the detection mechanisms proposed by others. Our new detection mechanisms that we describe in this paper are called activity-based overhearing, iterative probing, and unambiguous probing. Simulation-based analyses of these mechanisms show that they are highly effective and can reliably detect a multitude of selfish behaviours.