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Background In Software Engineering (SE), conference publications have high importance both in effective communication and in academic careers. Researchers actively discuss how a paper should be organized to be accepted in mainstream conferences. Aiming This work tackles the problem of generalizing and characterizing the type of papers accepted at SE conferences. Method The paper offers a new perspective in the analysis of SE literature: a categorization scheme for SE papers is obtained by merging, extending and revising related proposals from a few existing studies. The categorization scheme is used to classify the papers accepted at three top-tier SE conferences during five years (2012–2016). Results While a broader experience is certainly needed for validation and fine-tuning, preliminary outcomes can be observed relative to what problems and topics are addressed, what types of contributions are presented and how they are validated. Conclusions The results provide insights to paper writers, paper reviewers and conference organizers in focusing their future efforts, without any intent to provide judgments or authoritative guidelines.

Critical flaws continue to exist at the level of domain, requirement, and/or design specification, and specification verification (i.e. to check whether a specification has desirable properties) is still one of the most important challenges in software/system engineering. CafeOBJ is an executable algebraic specification language system and domain/requirement/design engineers can write proof scores in CafeOBJ for improving quality of specifications by the specification verification. This paper describes advances of the proof scores for the specification verification in CafeOBJ from the author’s point of view.

Comparative analysis of packaging print quality was carried out on the printed output with reference to using Computer to Film (CtF) and Computer to Conventional Plate (CtCP). The comparison of the print results from the two systems is analyzed from cost, durability, and dot gain. In this analysis, 400 gram duplex paper was used and two types of print references were used, namely CtF and CtCP. The pre-printing equipment used is the manufacture of conventional printing plates and plates. What is achieved when observing is comparing two print references with the help of a quality control (QC) tool. Before comparing the two print references, direct printing is done with the two print references, using an offset machine. The differences that exist in CtF and CtCP are caused by different irradiation processes. CtF goes through two irradiation processes while CtCP is in the irradiation process using only lasers. The irradiation process using a laser is uneven and makes the plate not get a raster (neat) result. This process is very influential with the result of the trigger. CtF goes through an iterative process but the results appear better than CtCP.Keywords— Print packaging, Duplex, CtF, CtCp

In this paper, we propose a data augmentation framework for Optical Character Recognition (OCR). The proposed framework is able to synthesize new viewing angles and illumination scenarios, effectively enriching any available OCR dataset. Its modular structure allows to be modified to match individual user requirements. The framework enables to comfortably scale the enlargement factor of the available dataset. Furthermore, the proposed method is not restricted to single frame OCR but can also be applied to video OCR. We demonstrate the performance of our framework by augmenting a 15% subset of the common Brno Mobile OCR dataset. Our proposed framework is capable of leveraging the performance of OCR applications especially for small datasets. Applying the proposed method, improvements of up to 2.79 percentage points in terms of Character Error Rate (CER), and up to 7.88 percentage points in terms of Word Error Rate (WER) are achieved on the subset. Especially the recognition of challenging text lines can be improved. The CER may be decreased by up to 14.92 percentage points and the WER by up to 18.19 percentage points for this class. Moreover, we are able to achieve smaller error rates when training on the 15% subset augmented with the proposed method than on the original nonaugmented full dataset.

Automatically detecting the positions of key-points (e.g., facial key-points or finger key-points) in an image is an essential problem in many applications, such as driver's gaze detection and drowsiness detection in automated driving systems. With the recent advances of Deep Neural Networks (DNNs), Key-Points detection DNNs (KP-DNNs) have been increasingly employed for that purpose. Nevertheless, KP-DNN testing and validation have remained a challenging problem because KP-DNNs predict many independent key-points at the same time -- where each individual key-point may be critical in the targeted application -- and images can vary a great deal according to many factors. In this paper, we present an approach to automatically generate test data for KP-DNNs using many-objective search. In our experiments, focused on facial key-points detection DNNs developed for an industrial automotive application, we show that our approach can generate test suites to severely mispredict, on average, more than 93% of all key-points. In comparison, random search-based test data generation can only severely mispredict 41% of them. Many of these mispredictions, however, are not avoidable and should not therefore be considered failures. We also empirically compare state-of-the-art, many-objective search algorithms and their variants, tailored for test suite generation. Furthermore, we investigate and demonstrate how to learn specific conditions, based on image characteristics (e.g., head posture and skin color), that lead to severe mispredictions. Such conditions serve as a basis for risk analysis or DNN retraining., to appear in ISSTA 2021

The emerging of information technology (IT)and operational technology (OT) convergence has driven the smart grid technology adoption in the European (EU) energy system for better visibility and automated controllability. On the other hand, the energy system infrastructures can be threaten by the cyber attacks due to the increasing of information and communication technology integration. The cyber vulnerabilities are caused by the increasing of internet connection and the application complexities. It is crucial to identify essential skills in the field of cyber security protection and defense for the students. This paper presents the outcome of a literature review and a workshop with stakeholders from industry and academia about the state of the art and trends in the education of cyber security in smart grids.

Coarse-grained reconfigurable arrays (CGRAs) are programmable hardware platforms that can be used to realize application-specific accelerators for higher performance and energy efficiency. A CGRA is a 2D array of configurable logic blocks & interconnect, where the logic blocks are typically large & ALU-like, and the interconnect is word-wide. CGRA-ME is a software framework that enables the modelling and exploration of CGRA architectures, as well as research on CGRA CAD algorithms. With CGRA-ME, an architect can specify a CGRA architecture at a high level of abstraction. A set of applications can be mapped onto the architecture to assess the mappability, power, performance and cost. CGRA-ME also allows one to generate synthesizable Verilog RTL for the modelled CGRA, permitting its implementation as an ASIC or FPGA overlay. In this paper, we describe the CGRA-ME framework [5] and overview its capabilities and current limitations. We discuss ongoing and prior research conducted with the framework, as well as outline future plans. We believe CGRA-ME will be a valuable contribution to the community, enabling new research on CGRA CAD & architectures.

Due to the increasingly growing demand for user identification on cell phones, PCs, laptops, and so on, face anti-spoofing has risen to significance and is an active research area in academia and industry. The detection of the real face then recognize it present an important challenge regarding the techniques that can be used to spoof any recognition system like masks, printed photos. This paper we present an anti-spoofing face method to solve the real-world scenario that learns the target domain classifier based on samples used for training in a particular source domain. Specifically, with the conventional regression CNN, the Spatial/Channel-wise Attention Modules were introduced. Two modules, namely the Spatial-wise Attention Module and the Channel-wise Attention Module, were used at spatial and channel levels to improve local features and ignore the irrelevant features. Extensive experiments on current collections with benchmarks datasets verifies that the recommended solution will significantly benefit from the two modules and better generalization capability by providing significantly improved results in anti-spoofing.

Open-source hardware such as RISC-V has been gaining substantial momentum. Recently, they have begun to embrace Google's Android operating system to leverage its software ecosystem. Despite the encouraging progress, a challenging issue arises: a majority of Android applications are written in native languages and need to be recompiled to target new hardware platforms. Unfortunately, this recompilation process is not scalable because of the explosion of new hardware platforms. To address this issue, we present WasmAndroid, a high-performance cross-platform runtime for native programming languages on Android. WasmAndroid only requires developers to compile their source code to WebAssembly, an efficient and portable bytecode format that can be executed everywhere without additional reconfiguration. WasmAndroid can also trans-pile existing application binaries to WebAssembly when source code is not available. WebAssembly's language model is very different from C/C++ and this mismatch leads to many unique implementation challenges. In this paper, we provide workable solutions and conduct a preliminary system evaluation. We show that WasmAndroid provides acceptable performance to execute native applications in a cross-platform manner.

Drowsiness can be dangerous when performing tasks that require constant attention, such as driving a vehicle. Sleepiness is correlated with a variety of physiological variables, such as eye closing, head movements, pulse rate, eye twitch rate, etc. Also, the yawn can be considered as an accurate indicator of drowsiness and fatigue. Yawning detection is very important for the safety purpose of drivers as it will let the driver know if he/she is getting drowsy. Driving at that moment may not be safe. Several automatic yawning detection techniques have been developed for driver's drowsiness monitoring system. Nevertheless, correctly detecting the yawning of the driver and predicting exhaustion in real-time situations is still a crucial challenge. In this paper, we will be reviewing various existing machine learning approaches for driver's yawning detection. In previous approaches, various classical machine learning algorithms such as viola-Jones, contour activation algorithm and SVM have been used for yawning detection, but these approaches failed to predict yawning in realtime situations. Using Deep learning techniques, we can make a real-time yawn detection system with high accuracy. We find that some precious Deep learning algorithms like CNN, RNN, LSTM, Bi-LSTM can detect the patterns with high accuracy. After the comparison of various algorithms and techniques, we find that with the help of Deep learning algorithms the yawning can be detected in real time with high accuracy.

Human recogntion technologies are gaining significant research attention, where the model can be trained to be more precise to recognize the poses performed by objects. Activity identification is a kind of problem, which needs more research consideration and improvement. There is also an increasing need to detect different poses of objects. To tackle the issue, different sensors like Gyroscope and Accelerometer are required to classify the data in the form of images using machine learning algorithms like SVM and CNN. These approaches help us in implementing real-time applicaions such as health monitoring, tracking sports activity and security. The paper also discusses about its benefits, limitations and prominent approach for human activity recognition.

Machine learning (ML) has increasingly been adopted in safety-critical systems such as autonomous vehicles (AVs) and industrial robotics. In these domains, reliability and safety are important considerations, and hence it is critical to ensure the resilience of ML systems to faults and errors. On the other hand, soft errors are becoming more frequent in commodity computer systems due to the effects of technology scaling and reduced supply voltages. Further, traditional solutions for masking hardware faults such as Triple-Modular Redundancy (TMR) are prohibitively expensive in terms of their energy and performance overheads. Therefore, there is a compelling need to ensure the resilience of ML applications to soft errors on commodity hardware platforms.We first experimentally assess the resilience of safety-critical ML applications to soft errors. We demonstrate through fault injection experiments that even a single bit flip due to a soft error can lead to misclassification in Deep Neural Network (DNN) applications deployed in AVs, leading to safety violations. However, not all the errors in an DNN will result in serve consequences such as safety violations, and hence it is sufficient to protect the DNN from the ones that do. Unfortunately, finding all possible errors that result in safety violations is a very compute intensive task. We propose BinFI, a fault injection approach that efficiently injects critical faults that are highly likely to result in safety violations, based on the unique properties of DNNs. Finally, we propose Ranger, an approach to protect DNNs from critical faults with minimal performance overheads and no accuracy loss. We will conclude by presenting some of our ongoing work, and the future challenges in this area.

The Electric Field and Voltage Distribution (EFVD) are an important parameter for assessing high voltage bushing transformer performance. However, conducting laboratories experiment is dangerous, difficult and expensive due to several aspects. Therefore, Finite Element Method (FEM) software is the best option used as a tool for the assessment of bushing transformer's performance in terms of EFVD. But, before an assessment of analysis could be carried out, an accurate model of bushing transformer must first to be designed. In this research, Computer Aided Design (CAD) software has been employed to design the 145kV bushing transformer based on actual dimension. Upon completion, the design has been exported to FEM software for further analysis. In FEM software, measurement and analysis of electric field and voltage distribution (EFVD) have been carried out. The measurements are performed at various locations of bushing transformer such as of the porcelain surface (both air and oil side), along with aluminum foils, and at oil-impregnated paper (OIP). The results obtained have been compared with other researchers and it is found very satisfactory.

The aim of this study was to examine the effects that reading on screens (using digital devices with different screen sizes) and on paper have on reading time and comprehension. The study group was...

This article reviews research on informal digital English (IDLE) learning that has increased in the field of English language teaching to other language speakers and computer-assisted language learning written by Ju Seong Lee (2019), entitled Quantity and Diversity of informal digital learning of English published by Language Learning & Technology. This present paper uses descriptive qualitative analysis in an attempt to understand how the quantity and diversity of IDLE can make a unique contribution to the English language outcomes of EFL learners from the researcher's perspective. Lee uses hierarchical linear regression analysis to show that IDLE Quantity, Age, and Major are significant predictors of two affective variables (Confidence and Pleasure), while IDLE Diversity and Major significantly predict productive language outcomes (Speaking and Productive Vocabulary Knowledge), score in the standard English Test (TOEIC), and one affective variable (Lack of Anxiety). This present article aims to review and discuss the findings on the strengths and the weaknesses found in Lee’s 2019 article. The article Lee made seems to possess a clear flow on how to explain these two types of education and make this article easy to understand. Therefore, the replication of Lee’s research is easy enough for similar research purposes.

With new technology, writing became a skill that is being developed year after year. The present study questions whether there is a difference between paper-based and computer-based writing in terms of errors and lexico-grammar. It aims at exploring sentence-level errors and lexico-grammatical competence in two writing genres in a collaborative writing environment within paper-based and computer-based writing. A sample of 73 female intermediate level learners participated in the study at the University of Business and Technology (UBT), in Saudi Arabia. This mixed-methods research is significant in the literature of second language writing since it highlights genre awareness, lexico-grammatical competence, analyzing errors, and collaboration in two styles of writing. The reading-based writing tasks acted as a reflection of the learners' lexico-grammatical competence on paper and via Web 2.0 tool (Padlet). Statistically, the Mann-Whitney U-tests showed that there was no significant difference between paper-based and computer-based groups in the sentence-level errors in narrative genre, whereas there was a significant difference between the two different tools of writing groups in the sentence-level errors in opinion genre. However, there was no significant difference between paper-based and computer-based groups in the clauses (lexico-grammar) of the two groups. Immediate semi-structured interviews were conducted and analyzed through NVIVO to get more insights from the learners to explain the comparison between the paper-based and the computer-based writing. In light of the significant findings, implications are sought to create an equillibrium between paper-based and computer-based writing, along with enhancing collaboration in second language writing.

Background The traditional informed consent (IC) process rarely emphasizes research participants’ comprehension of medical information, leaving them vulnerable to unknown risks and consequences associated with procedures or studies. Objective This paper explores how we evaluated the feasibility of a digital health tool called Virtual Multimedia Interactive Informed Consent (VIC) for advancing the IC process and compared the results with traditional paper-based methods of IC. Methods Using digital health and web-based coaching, we developed the VIC tool that uses multimedia and other digital features to improve the current IC process. The tool was developed on the basis of the user-centered design process and Mayer’s cognitive theory of multimedia learning. This study is a randomized controlled trial that compares the feasibility of VIC with standard paper consent to understand the impact of interactive digital consent. Participants were recruited from the Winchester Chest Clinic at Yale New Haven Hospital in New Haven, Connecticut, and healthy individuals were recruited from the community using fliers. In this coordinator-assisted trial, participants were randomized to complete the IC process using VIC on the iPad or with traditional paper consent. The study was conducted at the Winchester Chest Clinic, and the outcomes were self-assessed through coordinator-administered questionnaires. Results A total of 50 participants were recruited in the study (VIC, n=25; paper, n=25). The participants in both groups had high comprehension. VIC participants reported higher satisfaction, higher perceived ease of use, higher ability to complete the consent independently, and shorter perceived time to complete the consent process. Conclusions The use of dynamic, interactive audiovisual elements in VIC may improve participants’ satisfaction and facilitate the IC process. We believe that using VIC in an ongoing, real-world study rather than a hypothetical study improved the reliability of our findings, which demonstrates VIC’s potential to improve research participants’ comprehension and the overall process of IC. Trial Registration ClinicalTrials.gov NCT02537886; https://clinicaltrials.gov/ct2/show/NCT02537886

Electronic paper (i.e., e-paper) is a display technology that aims to imitate and substitute the conventional paper. Previous studies of e-paper mainly focus on evaluating or making practical use of its readability. However, there is little research to explore the potential of e-paper on input-oriented applications. In this paper, we introduce a document composition system named SwiftPad for e-paper. Specifically, SwiftPad renovates the famous TEX typesetting system, enabling users to compose high typographic quality documents on e-paper in a WYSIWYG (what you see is what you get), offline-first, and collaborative fashion. Building such a system on resource-constrained e-paper with low screen refresh rate creates unique challenges. In this paper, we identity these challenges and provides workable solutions. We also provide a preliminary evaluation of the new system.

We randomly selected 100 journal articles published in five proteomics journals in 2019 and manually examined each of them against a set of 13 criteria concerning the statistical analyses used, all of which were based on items mentioned in the journals' instructions to authors. This included questions such as whether a pilot study was conducted and whether false discovery rate calculation was employed at either the quantitation or identification stage. These data were then transformed to binary inputs, analyzed via machine learning algorithms, and classified accordingly, with the aim of determining if clusters of data existed for specific journals or if certain statistical measures correlated with each other. We applied a variety of classification methods including principal component analysis decomposition, agglomerative clustering, and multinomial and Bernoulli naïve Bayes classification and found that none of these could readily determine journal identity given extracted statistical features. Logistic regression was useful in determining high correlative potential between statistical features such as false discovery rate criteria and multiple testing corrections methods, but was similarly ineffective at determining correlations between statistical features and specific journals. This meta-analysis highlights that there is a very wide variety of approaches being used in statistical analysis of proteomics data, many of which do not conform to published journal guidelines, and that contrary to implicit assumptions in the field there are no clear correlations between statistical methods and specific journals.

2010년대 한국사회에서 혐오표현이 본격적으로 부상하며 언론학 또한 혐오 문제에 각별한 관심을 기울여 왔다. 본 논문은 언론학 분과에서 연구된 지난 10년의 혐오 관련 논문을 메타 분석하여 한국 사회에서 어떻게 혐오가 문제시되었고 현재에 이르렀는지를 입체적으로 살펴볼 목적으로 기술되었다. 분석논문은 한국연구재단에 등재된 사회과학 신문방송학 분야 학술지로 한정되었고 정량적 분석과 정성적 분석을 시행하였다. 정량적 분석을 통해 혐오표현, 표현의 자유, 여성혐오가 주된 연구키워드였으며 여성 연구자의 학술 개입이 두드려졌음을 확인하였다. 정성적 분석은 지난 10년 동안 여성혐오뿐만 아니라 레드콤플렉스, 지역, 이주 노동자와 외국인, 정치혐오와 같이 사실상 전방위적으로 한국 사회의 혐오 대상이 폭증하였음을 보여준다. 이는 신자유주의가 초래한 사회 갈등 심화와 더불어 모바일 미디어로의 매체 전경 변화, 언론과 미디어 플랫폼의 취약한 자율규제가 복합적으로 영향을 미친 탓이다. 이와 같은 상황 속에서 언론학은 표현의 자유를 적극적으로 해석하여 2010년대 후반에는 사회적 소수자의 표현의 자유를 보호하기 위한 혐오표현 규제 논의를 펼쳐내기 시작하였다.

Marked mix-nets were introduced by Pereira and Rivest as a mechanism to allow very efficient mixing that ensures privacy but at the cost of not guaranteeing integrity. This is useful in a number of e-voting schemes such as STAR-Vote and Selene. However, the proposed marked mix-net construction comes with no proof of security and, as we show in this paper, does not provide privacy even in the presence of a single corrupt authority. Fortunately, the attack that we present is easy to prevent and we show several possible ways to address it. Finally while the original marked mix-net paper worked with ElGamal, we identify conditions that the adopted encryption scheme should satisfy in order to be appropriate for a marked mix-net. This opens the possibility of building marked mix-nets based on intractability assumptions which are believed to hold in the presence of a quantum computer.

The JEL classification system is a standard way of assigning key topics to economic articles to make them more easily retrievable in the bulk of nowadays massive literature. Usually the JEL (Journal of Economic Literature) is picked by the author(s) bearing the risk of suboptimal assignment. Using the database of the Collaborative Research Center from Humboldt-Universität zu Berlin we employ a new adaptive clustering technique to identify interpretable JEL (sub)clusters. The proposed Adaptive Weights Clustering (AWC) is available on http://www.quantlet.de/ and is based on the idea of locally weighting each point (document, abstract) in terms of cluster membership. Comparison with $$k$$ k -means or CLUTO reveals excellent performance of AWC.

The task of predicting the interactions between drugs and targets plays a key role in the process of drug discovery. There is a need to develop novel and efficient prediction approaches in order to avoid costly and laborious yet not-always-deterministic experiments to determine drug–target interactions (DTIs) by experiments alone. These approaches should be capable of identifying the potential DTIs in a timely manner. In this article, we describe the data required for the task of DTI prediction followed by a comprehensive catalog consisting of machine learning methods and databases, which have been proposed and utilized to predict DTIs. The advantages and disadvantages of each set of methods are also briefly discussed. Lastly, the challenges one may face in prediction of DTI using machine learning approaches are highlighted and we conclude by shedding some lights on important future research directions.

How best to estimate the accuracy of a predictive rule has been a longstanding question in statistics. Approaches to this task range from simple methods like Mallow’s Cp to algorithmic techniques l...

Die Biointelligenz zählt derzeit zu den am meisten beachteten Innovationspfaden in Deutschland und Europa. Maßgeblich für die Entwicklung der Vision der Biointelligenz sind die jüngsten Erfolge der Bio- und Informationstechnologie. So ist es nicht verwunderlich, dass die wissenschaftliche Gemeinschaft, politische Entscheidungsträger wie auch Vertreter der Industrie dem Thema Biointelligenz geradezu enthusiastisch gegenüberstehen. In der Region Stuttgart-Tübingen haben sich beispielsweise über 40 Wissenschaftler im Kompetenzzentrum Biointelligenz zusammengeschlossen, um das Thema gemeinsam, interdisziplinär voranzubringen. Nicht nur die Bundesregierung, sondern auch verschiedene Landesregierungen, planen umfangreiche Fördermaßnahmen. Auf Ebene der EU-Kommission wird hinter vorgehaltener Hand teilweise sogar der Begriff Industrie 5.0 verwendet. Fakt ist, dass die Biointelligenz enormes Potenzial für Sprunginnovationen und Standortvorteile in nahezu allen industriellen Branchen eröffnet. Vielfach wird der Begriff jedoch missverstanden, was in letzter Konsequenz dazu führt, dass die Bedeutung ausgehöhlt und damit das Potenzial für den Wirtschaftsstandort Deutschland unterminiert wird. Für Wissenschaft, Industrie und Politik ist es entscheidend einen Konsens zu finden. Dazu soll dieser Artikel einen Beitrag leisten, indem er die Begriffsherkunft zusammenfasst, einen detaillierten Definitionsvorschlag präsentiert und eine Kategorisierungssystematik erarbeitet. Biointelligence is currently one of the most noted innovation paths in Germany and Europe. Crucial to the development of the Biointelligence vision are recent biotech and information technology breakthroughs. It is therefore not surprising that the scientific community, political decision-makers as well as industry representatives are enthusiastic about the subject of biointelligence. In the Stuttgart-Tübingen region, for example, more than 40 scientists have joined forces in the Competence Center Biointelligence to promote the topic in an interdisciplinary fashion. Not only the federal government, but also various state governments are planning extensive support measures. At the level of the European Commission, the term Industry 5.0 is sometimes even used behind closed doors. The fact is that biointelligence opens up enormous potential for break through innovations, leap frogging and competitive advantages in almost all industrial sectors. In many cases, however, the term is misunderstood, which ultimately leads to undermining the meaning and thus undermining the potential for Germany as a business location. For science, industry and politics it is crucial to find a consensus. This article thus aims to contribute by summarizing the term background, presenting a detailed definition and developing a categorization system.

The aim of the present study was to explore the relationship between testing mode (taking a test on computer versus paper) and two other factors: (1) cognitive load and (2) scratch paper use in an undergraduate general chemistry class setting. Cognitive load was measured with two self-report questions (perceived mental effort and level of difficulty) and scratch paper use was analyzed manually. All 221 students completed three assessments administered either on computer or paper. The assessments included a variety of chemistry topics with an equal number of three question types (algorithmic, conceptual, and definition). There was no significant difference in the cognitive load imposed by computer or paper-based tests at the overall test level or by question type. Students utilized scratch paper more on paper-based than online tests, especially when the questions were conceptual, and they used scratch paper the most for algorithmic questions. Altogether, these results provide further support that online testing can be implemented in educational settings without imposing additional cognitive load on students.

Furniture design depends on different materials. Under the social background of building an ecological and civilized society, furniture design based on paper materials makes people have a new understanding and change of paper products. Home design works exist depending on different materials. With many materials, designers can use their imagination and hands to give it different shapes and colors, and combine the author's personal consciousness and emotions to create. In various industries, traditional design ideas, design methods and design means lag behind the development of the times due to their inherent shortcomings, thus restricting the improvement of production and production efficiency. Future furniture must be inseparable from the concept of green environmental protection and recycling. This paper will take furniture products as the breakthrough point, explore the new structure and new technology of furniture product design based on computer aided design, look for the future design trend of furniture products based on the social form of energy saving and environmental protection, and analyze and explain the circulation mode of paper materials themselves.

The author claimed for the artifact associated with his paper the following ACM Reproducibility badges:(1) Artifact Available,(2) Artifact Evaluated-Functional,(3) Results Reproduced. After an in-depth review process, we agree to assign all the requested badges as we found it to meet the following requirements:i) it is uploaded on a persistent repository, accessible via a DOI; ii) it is well documented, consistent with the presented data, complete of all the necessary software sources and packages, and exercisable; iii) it is exhaustive in the reproduction of all the relevant data of the paper. Some curves in some reproduced plots are truncated, due to the computational limits imposed by the short-term deadline of the review process. Nevertheless, the overall trends are respected, and the curves are supporting the paper's claims.

This is like an abstract to a paper, but it is more abstract. In fact, it is the introduction to something which is a not paper. The global Covid-19 pandemic of 2020 represented an inflection point for our post-post-modern world, a moment where our old normal was dramatically arrested. We are now in a state of comprehensive flux as ‘new normals’ emerge, begin to solidify, and may evolve into an—as yet undetermined—futures. This not paper is a facet and exploration of that flux as it relates to publication and conference culture, video conferencing systems, and how we both conduct, and share, research. You should read the whole of this abstract, but then you should take a step inside the not paper, it lives on the web over here https://designresearch.works/thisisnotapaper/

In modern academic social network, it is very difficult for scholars to find academic papers consistent with their research direction. Time is a critical factor in paper recommendation. As time goes on, the impact of an academic paper would gradually fade. Likewise, the research interests of users may also change. Therefore, we propose a temporal perceptual neural collaborative filtering model that integrates the multi-dimensional features of papers. We conducted our experiments on the dataset from CiteULike, comparing the recommended results by using four time-decay functions and evaluating our model with multiple evaluation indicators. The satisfactory results show that our model is effective in filtering out the expired papers by considering the characteristics of papers and the changes of scholars' interests.

Summary models, whether extractive or abstractive, have achieved great success recently. For long academic papers, the abstractive model with the encoder-decoder architecture mainly only relies on the attentional context vector for generation, unlike humans who have already mastered the salient information of the source text to have full control over what to write. While the extracted sentences always contain the correct and salient information which can be used to control the abstraction process. Therefore, based on a hierarchical encoder-decoder architecture specifically for academic papers, we proposed a summary model with an Instructor, an encoder in essence by taking the guiding sentences as the input to further control the generating process. In the encoder part, the final hidden state from Instructor is directly added to the basic hierarchical hidden state from the encoder. Experimental results on arXiv/PubMed show that the only encoder-improved model can generate better abstract. In the decoder part, the context vector from Instructor is integrated with the original discourse-aware context vector for the generation. The results show that Instructor is effective for control and our model can generate a more accurate and fluent abstract with significantly higher ROUGE values.

Measuring the quality of research work is an essential component of the scientific process. With the ever-growing rates of articles being submitted to top-tier conferences, and the potential consistency and bias issues in the peer review process identified by scientific community, it is thus of great necessary and challenge to automatically evaluate submissions. Existing works mainly focus on exploring relevant factors and applying machine learning models to simply be accurate at predicting the acceptance of a given academic paper, while ignoring the interpretability power which is required by a wide range of applications. In this paper, we propose a framework to construct decision sets that consist of unordered if-then rules for predicting paper acceptance. We formalize decision set learning problem via a joint objective function that simultaneously optimize accuracy and interpretability of the rules, rather than organizing them in a hierarchy. We evaluate the effectiveness of the proposed framework by applying it on a public scientific peer reviews dataset. Experimental results demonstrate that the learned interpretable decision sets by our framework performs on par with state-of-the-art classification algorithms which optimize exclusively for predictive accuracy and much more interpretable than rule-based methods.

New discoveries in science are often built upon previous knowledge. Ideally, such dependency information should be made explicit in a scientific knowledge graph. The Keystone Framework was proposed for tracking the validity dependency among papers. A keystone citation indicates that the validity of a given paper depends on a previously published paper it cites. In this paper, we propose and evaluate a strategy that repurposes rhetorical category classifiers for the novel application of extracting keystone citations that relate to research methods. Five binary rhetorical category classifiers were constructed to identify Background, Objective, Methods, Results, and Conclusions sentences in biomedical papers. The resulting classifiers were used to test the strategy against two datasets. The initial strategy assumed that only citations contained in Methods sentences were methods keystone citations, but our analysis revealed that citations contained in sentences classified as either Methods or Results had a high likelihood to be methods keystone citations. Future work will focus on fine tuning the rhetorical category classifiers, experimenting with multiclass classifiers, evaluating the revised strategy with more data, and constructing a larger gold standard citation context sentence dataset for model training.