nils-herrmann/clean_scirepeval huggingface dataset:
Improving flight performance of the flapping wing MAV DelFly II

Recent years have seen an increasing interest in micro aerial vehicles (MAVs) and flapping flight in connection to that. The Delft University of Technology has developed a flapping wing MAV, “DelFly II”, which relies on a flapping bi-plane wing configuration for thrust and lift. The ultimate aim of the present research is to improve the flight performance of the DelFly II from both an aerodynamic and constructional perspective. This is pursued by a parametric wing geometry study in combination with a detailed aerodynamic and aeroelastic investigation. In the geometry study an improved wing geometry was found, where stiffeners are placed more outboard for a more rigid in-flight wing shape. The improved wing shows a 10% increase in the thrust-to-power ratio. Investigations into the swirling strength around the DelFly wing in hovering flight show a leading edge vortex (LEV) during the inand out-stroke. The LEV appears to be less stable than in insect flight, since some shedding of LEV is present. Nomenclature Symbol Description Unit f Wing flapping frequency Hz P Power W R DelFly wing length (semi-span) mm T Thrust N λci Positive imaginary part of eigenvalue τ Dimensionless time Abbreviations LEV Leading Edge Vortex MAV Micro Aerial Vehicle UAV Unmanned Aerial Vehicle
Learning Map-Independent Evaluation Functions for Real-Time Strategy Games

Real-time strategy (RTS) games have drawn great attention in the AI research community, for they offer a challenging and rich testbed for both machine learning and AI techniques. Due to their enormous state spaces and possible map configurations, learning good and generalizable representations for machine learning is crucial to build agents that can perform well in complex RTS games. In this paper we present a convolutional neural network approach to learn an evaluation function that focuses on learning general features that are independent of the map configuration or size. We first train and evaluate the network on a winner prediction task on a dataset collected with a small set of maps with a fixed size. Then we evaluate the network’s generalizability to three set of larger maps. by using it as an evaluation function in the context of Monte Carlo Tree Search. Our results show that the presented architecture can successfully capture general and map-independent features applicable to more complex RTS situations.
ESTIMATING WITH BIM : A SURVEY OF US CONSTRUCTION COMPANIES

The use of Building Information Modeling (BIM) in the construction industry is on the rise. It is widely acknowledged that adoption of BIM would cause a seismic shift in the business processes within the construction industry and related fields. Cost estimation is a key aspect in the workflow of a construction project. Processes within estimating, such as quantity survey and pricing, may be automated by using existing BIM software in combination with existing estimating software. The adoption of this combination of technologies is not as widely seen as might be expected. Researchers conducted a survey of construction practitioners to determine the extent to which estimating processes were automated in the conjunction industry, with the data from a BIM model. Survey participants were asked questions about how BIM was used within their organization and how it was used in the various tasks involved in construction cost estimating. The results of the survey data revealed that while most contractors were using BIM, only a small minority were using it to automate estimating processes. Most organizations reported that employees skilled in BIM did not have the estimating experience to produce working estimates from BIM models and vice-versa. The results of the survey are presented and analyzed to determine conditions that would improve the adoption of these new business processes in the construction estimating field.
A Protection Motivation Theory Approach to Home Wireless Security

Research in socio-technical factors in computer security has traditionally focused on employees and their work practice within the premises of the organization. However, with universal access to computing and the diverse means of connecting such devices to each another and to the global Internet, work carried out has shifted outside one central physical location to encompass a variety of possible points-of-access that include homes, modern cafés, and public libraries. Given the ubiquity of wireless devices used for last-hop access to the Internet from home, we look at the security of home wireless networks. In this work, we identify the variables that affect the decision of home wireless network users to implement security features on their networks. Our study is based on the protection motivation theory. A survey was conducted on 189 home users to identify and characterize predictors that differentiate between users who secure their home wireless networks and those who don’t. Results of the analysis identified the following variables as significant: perceived severity, response efficacy, self efficacy, and response cost.
A Closer Look at Deep Learning Heuristics: Learning rate restarts, Warmup and Distillation

The convergence rate and final performance of common deep learning models have significantly benefited from heuristics such as learning rate schedules, knowledge distillation, skip connections, and normalization layers. In the absence of theoretical underpinnings, controlled experiments aimed at explaining these strategies can aid our understanding of deep learning landscapes and the training dynamics. Existing approaches for empirical analysis rely on tools of linear interpolation and visualizations with dimensionality reduction, each with their limitations. Instead, we revisit such analysis of heuristics through the lens of recently proposed methods for loss surface and representation analysis, viz., mode connectivity and canonical correlation analysis (CCA), and hypothesize reasons for the success of the heuristics. In particular, we explore knowledge distillation and learning rate heuristics of (cosine) restarts and warmup using mode connectivity and CCA. Our empirical analysis suggests that: (a) the reasons often quoted for the success of cosine annealing are not evidenced in practice; (b) that the effect of learning rate warmup is to prevent the deeper layers from creating training instability; and (c) that the latent knowledge shared by the teacher is primarily disbursed to the deeper layers.
Representation, Reasoning, and Relational Structures: a Hybrid Logic Manifesto

This paper is about the good side of modal logic, the bad side of modal logic, and how hybrid logic takes the good and fixes the bad. In essence, modal logic is a simple formalism for working with relational structures (or multigraphs). But modal logic has no mechanism for referring to or reasoning about the individual nodes in such structures, and this lessens its effectiveness as a representation formalism. In their simplest form, hybrid logics are upgraded modal logics in which reference to individual nodes is possible. But hybrid logic is a rather unusual modal upgrade. It pushes one simple idea as far as it will go: represent all information as formulas. This turns out to be the key needed to draw together a surprisingly diverse range of work (for example, feature logic, description logic and labelled deduction). Moreover, it displays a number of knowledge representation issues in a new light, notably the importance of sorting.
Improving Post-Click User Engagement on Native Ads via Survival Analysis

In this paper we focus on estimating the post-click engagement on native ads by predicting the dwell time on the corresponding ad landing pages. To infer relationships between features of the ads and dwell time we resort to the application of survival analysis techniques, which allow us to estimate the distribution of the length of time that the user will spend on the ad. This information is then integrated into the ad ranking function with the goal of promoting the rank of ads that are likely to be clicked and consumed by users (dwell time greater than a given threshold). The online evaluation over live tra c shows that considering post-click engagement has a consistent positive e↵ect on both CTR, decreases the number of bounces and increases the average dwell time, hence leading to a better user post-click experience.
Direct slicing of STEP based NURBS models for layered manufacturing

Direct slicing of CAD models to generate process planning instructions for solid freeform fabrication may overcome inherent disadvantages of using stereolithography format in terms of the process accuracy, ease of file management, and incorporation of multiple materials. This paper will present the results of our development of a direct slicing algorithm for layered freeform fabrication. The direct slicing algorithm was based on a neutral, international standard (ISO 10303) STEP-formatted non-uniform rational B-spline (NURBS) geometric representation and is intended to be independent of any commercial CAD software. The following aspects of the development effort will be presented: (1) determination of optimal build direction based upon STEP-based NURBS models; (2) adaptive subdivision of NURBS data for geometric refinement; and (3) ray-casting slice generation into sets of raster patterns. The development also provides for multi-material slicing and will provide an effective tool in heterogeneous slicing processes.
TransNet: Translation-Based Network Representation Learning for Social Relation Extraction

Conventional network representation learning (NRL) models learn low-dimensional vertex representations by simply regarding each edge as a binary or continuous value. However, there exists rich semantic information on edges and the interactions between vertices usually preserve distinct meanings, which are largely neglected by most existing NRL models. In this work, we present a novel Translation-based NRL model, TransNet, by regarding the interactions between vertices as a translation operation. Moreover, we formalize the task of Social Relation Extraction (SRE) to evaluate the capability of NRL methods on modeling the relations between vertices. Experimental results on SRE demonstrate that TransNet significantly outperforms other baseline methods by 10% to 20% on hits@1. The source code and datasets can be obtained from https: //github.com/thunlp/TransNet.
Adversarial Link Prediction in Social Networks

Link prediction is one of the fundamental tools in social network analysis, used to identify relationships that are not otherwise observed. Commonly, link prediction is performed by means of a similarity metric, with the idea that a pair of similar nodes are likely to be connected. However, traditional link prediction based on similarity metrics assumes that available network data is accurate. We study the problem of adversarial link prediction, where an adversary aims to hide a target link by removing a limited subset of edges from the observed subgraph. We show that optimal attacks on local similarity metrics—that is, metrics which use only the information about the node pair and their network neighbors—can be found in linear time. In contrast, attacking Katz and ACT metrics which use global information about network topology is NP-Hard. We present an approximation algorithm for optimal attacks on Katz similarity, and a principled heuristic for ACT attacks. Extensive experiments demonstrate the efficacy of our methods.
Media Literacy and the Challenge of New Information and Communication Technologies

Within both academic and policy discourses, the concept of media literacy is being extended from its traditional focus on print and audiovisual media to encompass the internet and other new media. The present article addresses three central questions currently facing the public, policy-makers and academy: What is media literacy? How is it changing? And what are the uses of literacy? The article begins with a definition: media literacy is the ability to access, analyse, evaluate and create messages across a variety of contexts. This four-component model is then examined for its applicability to the internet. Having advocated this skills-based approach to media literacy in relation to the internet, the article identifies some outstanding issues for new media literacy crucial to any policy of promoting media literacy among the population. The outcome is to extend our understanding of media literacy so as to encompass the historically and culturally conditioned relationship among three processes: (i) the symbolic and material representation of knowledge, culture and values; (ii) the diffusion of interpretative skills and abilities across a (stratified) population; and (iii) the institutional, especially, the state management of the power that access to and skilled use of knowledge brings to those who are ‘literate’.
JDoc60 series: information research over six decades

The aim of this work is to explore, and characterise the transition types used to tell stories through information visualization. This was done by qualitatively coding the dataset of VAST challenge videos over the last four years based on the McCloud taxonomy of transition types in comics [1, 2] after refining the model and redefining its components. 1. MOMENT-TO-MOMENT 2. ACTION-TO-ACTION 3. SUBJECT-TO-SUBJECT 4. SCENE-TO-SCENE 5. ASPECT-TO-ASPECT 6. NON-SEQUITUR McCloud Taxonomy of Panel-to-Panel Transitions in Comics Refinement of McCloud  A subject is an entity/actor that does something in the story. For example, an employee, or an IP address. An action could be related to how the analyst(s) interacted with the data/tool (Action Type 1), or actions that represent part of the scenario (Action Type 2). If a video was divided into sub-topics, each sub-topic was considered a a scene. A group of story units that lead to a finding is also considered a scene. An Aspect-toAspect transition occurs when the visual representations, or applications changed from a story unit to another to look at the data from different angles. For example, between map view and timeline. Finally, a moment is a point of time in the story. (a) A transition diagram showing nested grouping of aspects in the first part. . (VAST 2011MC2University of Konstanz). (c) A transition diagram showing the occurrence of a Scene-to-Scene transition between a group of story units and the finding/conclusion drawn from this group. (VAST 2010MC1Georgia Institute of Technology) (d) A transition diagram showing a delayed transition and emerging of finding at the end of a long series of actions. (VAST 2010MC1giCentre, City University London) Sample Transition Diagrams and Observations. Future Work The next step after coding the videos is to look at all generated diagrams and analyse them by investigating different factors, such as the nature of the task/data, award winners, etc.
Design , Analysis and Optimization of Leaf Spring

In New Light commercial vehicle development, Engine is mounted at rear to have low Engine Noise and Vibration inside cabin. At the same time there is a need of high load carrying rear suspension to suit market requirement. In this paper complete design of leaf spring rear suspension for rear engine is discussed.This is nontraditional type of suspension with leaf spring application for rear engine vehicle. Traditionally, for light commercial vehicles, Engine is placed at front/middle giving huge space for traditional rear axle with differential inside. Design of rear suspension is verified and validated successfully for durability and handling by doing finite element analysis and testing.
PUSHOVER ANALYSIS OF UNREINFORCED MASONRY STRUCTURES BY FIBER FINITE ELEMENT METHOD

Finite element analysis A 2D finite element analysis for the numerical prediction of capacity curve of unreinforced masonry (URM) walls is conducted. The studied model is based on the fiber finite element approach. The emphasis of this paper will be on the errors obtained from fiber finite element analysis of URM structures under pushover analysis. The masonry material is modeled by different constitutive stress-strain model in compression and tension. OpenSees software is employed to analysis the URM walls. Comparison of numerical predictions with experimental data, it is shown that the fiber model employed in OpenSees cannot properly predict the behavior of URM walls with balance between accuracy and low computational efforts. Additionally, the finite element analyses results show appropriate predictions of some experimental data when the real tensile strength of masonry material is changed. Hence, from the viewpoint of this result, it is concluded that obtained results from fiber finite element analyses employed in OpenSees are unreliable because the exact behavior of masonry material is different from the adopted masonry material models used in modeling process.
Automated Testing of Android Apps: A Systematic Literature Review

Automated testing of Android apps is essential for app users, app developers, and market maintainer communities alike. Given the widespread adoption of Android and the specificities of its development model, the literature has proposed various testing approaches for ensuring that not only functional requirements but also nonfunctional requirements are satisfied. In this paper, we aim at providing a clear overview of the state-of-the-art works around the topic of Android app testing, in an attempt to highlight the main trends, pinpoint the main methodologies applied, and enumerate the challenges faced by the Android testing approaches as well as the directions where the community effort is still needed. To this end, we conduct a systematic literature review during which we eventually identified 103 relevant research papers published in leading conferences and journals until 2016. Our thorough examination of the relevant literature has led to several findings and highlighted the challenges that Android testing researchers should strive to address in the future. After that, we further propose a few concrete research directions where testing approaches are needed to solve recurrent issues in app updates, continuous increases of app sizes, as well as the Android ecosystem fragmentation.
Gender and the Use of Exclamation Points in Computer-Mediated Communication: An Analysis of Exclamations Posted to Two Electronic Discussion Lists

Past research has reported that females use exclamation points more frequently than do males. Such research often characterizes exclamation points as ‘‘markers of excitability,’’ a term that suggests instability and emotional randomness, yet it has not necessarily examined the contexts in which exclamation points appeared for evidence of ‘‘excitability.’’ The present study uses a 16-category coding frame in a content analysis of 200 exclamations posted to two electronic discussion groups serving the library and information science profession. The results indicate that exclamation points rarely function as markers of excitability in these professional forums, but may function as markers of friendly interaction, a finding with implications for understanding gender styles in email and other forms of computer-mediated communication.
The Adoption of Electronic Banking in Tunisia : An Exploratory Study

This paper aims to check if the current and prompt technological revolution altering the whole world has crucial impacts on the Tunisian banking sector. Particularly, this study seeks some clues on which we can rely in order to understand the customers’ behavior regarding the adoption of electronic banking. To achieve this purpose, an empirical research is carried out in Tunisia and it reveals that a panoply of factors is affecting the customers-attitude toward e-banking. For instance; age, gender and educational qualifications seem to be important and they split up the group into electronic banking adopters and traditional banking defenders and so, they have significant influence on the customers’ adoption of e-banking. Furthermore, this study shows that despite the presidential incentives and in spite of being fully aware of the e-banking’s benefits, numerous respondents are still using the conventional banking. It is worthy to mention that the fear of loss because of transactions errors or hackers plays a significant role in alienating Tunisian customers from online banking. Finally, a number of this study’s limitations are highlighted and some research perspectives are suggested.
Understanding user behavior in e-commerce with Long Short-term Memory ( LSTM ) and Autoencoders

Over the past few years, it has been proved that a fast website has a positive influence on the conversion rate of e-commerce websites. However, it is not yet known what other reasons related to performance explain the fact that some users convert and others do not. In this thesis, we analyze the behavior of users in three e-commerce websites, in order to analyze the path of the user in the site, and estimate a series of features that impact the behavior of the user. Two approaches were proposed: Sequence Modeling and Anomaly Detection. The former is an application of Long Short-term Memory (LSTM) in the path of the user as sequential data, aiming to predict whether the user purchases an item on the site. The latter is an alternative use of Autoencoders to detect anomalies, where the buyers are considered anomalies, this approach allows to analyze the influence of other features related to performance in the final decision of the user. We found that the path of the user does not influence the decision of purchasing a product. In contrast, we show that the user becomes more likely to purchase after a certain number of steps on the website. In addition, we are capable of showing the influence of the performance on each e-commerce website for specific page groups. Based on the results a decision-making tool was developed to estimate the possible impact caused by a positive increment of speed.
Optimization of Webpage Downloading Performance with Content-aware Mobile Edge Computing

With increased complexity of webpages nowadays, computation latency incurred by webpage processing during downloading operations has become a newly identified factor that may substantially affect user experiences in a mobile network. In order to tackle this issue, we propose a simple but effective transport-layer optimization technique which requires necessary context information dissemination from the mobile edge computing (MEC) server to user devices where such an algorithm is actually executed. The key novelty in this case is the mobile edge's knowledge about webpage content characteristics which is able to increase downloading throughput for user QoE enhancement. Our experiment results based on a real LTE-A test-bed show that, when the proportion of computation latency varies between 20% and 50% (which is typical for today's webpages), the downloading throughput can be improved up to 34.5%, with reduced downloading time by up to 25.1%
Types of Machine Learning Algorithms

Supervised learning --where the algorithm generates a function that maps inputs to desired outputs. One standard formulation of the supervised learning task is the classification problem: the learner is required to learn (to approximate the behavior of) a function which maps a vector into one of several classes by looking at several input-output examples of the function. • Unsupervised learning --which models a set of inputs: labeled examples are not available. • Semi-supervised learning --which combines both labeled and unlabeled examples to generate an appropriate function or classifier. • Reinforcement learning --where the algorithm learns a policy of how to act given an observation of the world. Every action has some impact in the environment, and the environment provides feedback that guides the learning algorithm. • Transduction --similar to supervised learning, but does not explicitly construct a function: instead, tries to predict new outputs based on training inputs, training outputs, and new inputs. • Learning to learn --where the algorithm learns its own inductive bias based on previous experience.