nils-herrmann/clean_scirepeval huggingface dataset:
From System Dynamics and Discrete Event to Practical Agent Based Modeling : Reasons , Techniques , Tools

This paper may be considered as a practical reference for those who wish to add (now sufficiently matured) Agent Based modeling to their analysis toolkit and may or may not have some System Dynamics or Discrete Event modeling background. We focus on systems that contain large numbers of active objects (people, business units, animals, vehicles, or even things like projects, stocks, products, etc. that have timing, event ordering or other kind of individual behavior associated with them). We compare the three major paradigms in simulation modeling: System Dynamics, Discrete Event and Agent Based Modeling with respect to how they approach such systems. We show in detail how an Agent Based model can be built from an existing System Dynamics or a Discrete Event model and then show how easily it can be further enhanced to capture much more complicated behavior, dependencies and interactions thus providing for deeper insight in the system being modeled. Commonly understood examples are used throughout the paper; all models are specified in the visual language supported by AnyLogic tool. We view and present Agent Based modeling not as a substitution to older modeling paradigms but as a useful add-on that can be efficiently combined with System Dynamics and Discrete Event modeling. Several multi-paradigm model architectures are suggested.
Loneliness, Social Networks, and Health: A Cross-Sectional Study in Three Countries

It is widely recognized that social networks and loneliness have effects on health. The present study assesses the differential association that the components of the social network and the subjective perception of loneliness have with health, and analyzes whether this association is different across different countries.



A total of 10 800 adults were interviewed in Finland, Poland and Spain. Loneliness was assessed by means of the 3-item UCLA Loneliness Scale. Individuals' social networks were measured by asking about the number of members in the network, how often they had contacts with these members, and whether they had a close relationship. The differential association of loneliness and the components of the social network with health was assessed by means of hierarchical linear regression models, controlling for relevant covariates.



In all three countries, loneliness was the variable most strongly correlated with health after controlling for depression, age, and other covariates. Loneliness contributed more strongly to health than any component of the social network. The relationship between loneliness and health was stronger in Finland (|β| = 0.25) than in Poland (|β| = 0.16) and Spain (|β| = 0.18). Frequency of contact was the only component of the social network that was moderately correlated with health.



Loneliness has a stronger association with health than the components of the social network. This association is similar in three different European countries with different socio-economic and health characteristics and welfare systems. The importance of evaluating and screening feelings of loneliness in individuals with health problems should be taken into account. Further studies are needed in order to be able to confirm the associations found in the present study and infer causality.
Collaborative Filtering for Binary, Positiveonly Data

Traditional collaborative ltering assumes the availability of explicit ratings of users for items. However, in many cases these ratings are not available and only binary, positive-only data is available. Binary, positive-only data is typically associated with implicit feedback such as items bought, videos watched, ads clicked on, etc. However, it can also be the results of explicit feedback such as likes on social networking sites. Because binary, positive-only data contains no negative information, it needs to be treated differently than rating data. As a result of the growing relevance of this problem setting, the number of publications in this field increases rapidly. In this survey, we provide an overview of the existing work from an innovative perspective that allows us to emphasize surprising commonalities and key differences.
The effect of LUT and cluster size on deep-submicron FPGA performance and density

We use a fully timing-driven experimental flow [4]  in which a set of benchmark circuits are synthesized into different cluster-based [2]   logic block architectures, which contain groups of LUTs and flip-flops. We look across all architectures with LUT sizes in the range of 2 inputs to 7 inputs, and cluster size from 1 to 10 LUTs. In order to judge the quality of the architecture we do both detailed circuit level design and measure the demand of routing resources for every circuit in each architecture.
These experiments have resulted in several key contributions. First, we have experimentally determined the relationship between the number of inputs required for a cluster as a function of the LUT size (K) and cluster size (N). Second, contrary to previous results, we have shown that when the cluster size is greater than four, that smaller LUTs (size 2 and 3) are almost as area efficient as 4-input LUTs, as suggested in . However, our results also show that the performance of FPGAs with these small LUT sizes is significantly worse (by almost a factor of 2) than larger LUTs. Hence, as measured by area-delay product, or by performance, these would be a bad choice. Also, we have discovered that LUT sizes of 5 and 6 produce much better area results than were previously believed. Finally, our results show that a LUT size of 4 to 6 and cluster size of between 4 and 10 provides the best area-delay product for an FPGA.
PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space

Few prior works study deep learning on point sets.  is a pioneer in this direction. However, by design PointNet does not capture local structures induced by the metric space points live in, limiting its ability to recognize fine-grained patterns and generalizability to complex scenes. In this work, we introduce a hierarchical neural network that applies PointNet recursively on a nested partitioning of the input point set. By exploiting metric space distances, our network is able to learn local features with increasing contextual scales. With further observation that point sets are usually sampled with varying densities, which results in greatly decreased performance for networks trained on uniform densities, we propose novel set learning layers to adaptively combine features from multiple scales. Experiments show that our network called PointNet++ is able to learn deep point set features efficiently and robustly. In particular, results significantly better than state-of-the-art have been obtained on challenging benchmarks of 3D point clouds.
A molecular phylogeny of marine amphipods in the herbivorous family Ampithoidae

1 December 2015  6 April 2016 doi:10.1111/zsc.12190 Sotka, E.E., Bell, T., Hughes, L.E., Lowry, J.K. & Poore, A.G.B. (2016). A molecular phylogeny of marine amphipods in the herbivorous family Ampithoidae. —Zoologica Scripta, 00, 000–000. Ampithoid amphipods dominate invertebrate assemblages associated with shallow-water macroalgae and seagrasses worldwide and represent the most species-rich family of herbivorous amphipod known. To generate the first molecular phylogeny of this family, we sequenced 35 species from 10 genera at two mitochondrial genes [the cytochrome c oxidase subunit I (COI) and the large subunit of 16 s (LSU)] and two nuclear loci [sodium–potassium ATPase (NAK) and elongation factor 1-alpha (EF1)], for a total of 1453 base pairs. All 10 genera are embedded within an apparently monophyletic Ampithoidae (Amphitholina, Ampithoe, Biancolina, Cymadusa, Exampithoe, Paragrubia, Peramphithoe, Pleonexes, Plumithoe, Pseudoamphithoides and Sunamphitoe). Biancolina was previously placed within its own superfamily in another suborder. Within the family, single-locus trees were generally poor at resolving relationships among genera. Combined-locus trees were better at resolving deeper nodes, but complete resolution will require greater taxon sampling of ampithoids and closely related outgroup species, and more molecular characters. Despite these difficulties, our data generally support the monophyly of Ampithoidae, novel evolutionary relationships among genera, several currently accepted genera that will require revisions via alpha taxonomy and the presence of cryptic species.  Erik Sotka, Department of Biology and the College of Charleston Marine Laboratory, 205 Fort Johnson Road, Charleston, SC 29412, USA. E-mail: SotkaE@cofc.edu Erik E. Sotka, and Tina Bell, Department of Biology and Grice Marine Laboratory, College of Charleston, 205 Fort Johnson Road, Charleston, SC 29412, USA. E-mails: SotkaE@cofc.edu, tinamariebell@gmail.com Lauren E. Hughes, and James K. Lowry, Australian Museum Research Institute, 6 College Street, Sydney, NSW 2010,  megaluropus@gmail.com, stephonyx@gmail.com Alistair G. B. Poore, Evolution & Ecology Research Centre, School of Biological, Earth and Environmental Sciences, University of New South Wales, Sydney, NSW 2052, Australia.
Security for volatile FPGAs

With reconfigurable devices fast becoming complete systems in their own right, interest in their security properties has increased. While research on " FPGA security " has been active since the early 2000s, few have treated the field as a whole, or framed its challenges in the context of the unique FPGA usage model and application space. This dissertation sets out to examine the role of FPGAs within a security system and how solutions to security challenges can be provided. I offer the following contributions. I motivate authenticating configurations as an additional capability to FPGA configuration logic, and then describe a flexible security protocol for remote recon-figuration of FPGA-based systems over insecure networks. Non-volatile memory devices are used for persistent storage when required, and complement the lack of features in some FPGAs with tamper proofing in order to maintain specified security properties. A unique advantage of the protocol is that it can be implemented on some existing FPGAs (i.e., it does not require FPGA vendors to add function-ality to their devices). Also proposed is a solution to the " IP distribution problem " where designs from multiple sources are integrated into a single bitstream, yet must maintain their confidentiality. I discuss the difficulty of reproducing and comparing FPGA implementation results reported in the academic literature. Concentrating on cryptographic implementations , problems are demonstrated through designing three architecture-optimized variants of the AES block cipher and analyzing the results to show that single figures of merit, namely " throughput " or " throughput per slice " , are often meaningless without the context of an application. To set a precedent for reproducibility in our field, the HDL source code, simulation testbenches and compilation instructions are made publicly available for scrutiny and reuse. Finally, I examine payment systems as ubiquitous embedded devices, and evaluate their security vulnerabilities as they interact in a multi-chip environment. Using FPGAs as an adversarial tool, a man-in-the-middle attack against these devices is demonstrated. An FPGA-based defense is also demonstrated: the first secure wired " distance bounding " protocol implementation. This is then put in the context of securing reconfigurable systems. Acknowledgments I dedicate this dissertation to my parents, Mika and Gideon, for their unconditional love and support throughout my life, and to my kind siblings Hadar and Oz, and their families. They have all seen less of me than they deserved in the past twelve years as I was …
Buffer Sharing in Video-On-Demand Servers

This paper describes a buffer sharing technique that strikes a balance between the use of disk bandwidth and memory in order to maximize the performance of a video-on-demand server. We make the key observation that the configuration parameters of the system should be independent of the physical characteristics of the data (e.g., popularity of a clip). Instead, the configuration parameters are fixed and our strategy adjusts itself dynamically at run-time to support a pattern of access to the video clips.
CURRENT KNOWLEDGE OF THE IMPACTS OF GENETICALLY MODIFIED ORGANISMS ON BIODIVERSITY AND HUMAN

2.1 HISTORY OF BIOTECHNOLOGY....................................................................................................6 2.2 MODERN BIOTECHNOLOGY ........................................................................................................6 2.3 THE GM DEBATE........................................................................................................................7 2.4 APPLYING THE PRECAUTIONARY APPROACH TO GMOS .............................................................8 2.5 RISK ASSESSMENT ISSUES ..........................................................................................................9 2.6 LEGAL CONTEXT ......................................................................................................................10 T
Multi-domain Dialog State Tracking using Recurrent Neural Networks

Dialog state tracking is a key component of many modern dialog systems, most of which are designed with a single, welldefined domain in mind. This paper shows that dialog data drawn from different dialog domains can be used to train a general belief tracking model which can operate across all of these domains, exhibiting superior performance to each of the domainspecific models. We propose a training procedure which uses out-of-domain data to initialise belief tracking models for entirely new domains. This procedure leads to improvements in belief tracking performance regardless of the amount of in-domain data available for training the model.
Texture Feature Based Satellite Image Classification Scheme Using SVM

Remote sensing applications have gained considerable research attention now-a-days. Irrespective of the presence of several satellite image applications, there is a constant demand for satellite image classification systems. The satellite image classification system intends to differentiate between the objects being present in the image. It is highly challenging because, the coverage area of the satellite is more, such that the objects appear so small. This makes the process of object differentiation complex. Additionally, the classification accuracy is an important factor, which the classification system must pass through. Taking these challenges into account, this work presents a satellite image classification system, which can classify between the vegetation, soil and water bodies. The objective of this work is met by subdividing the works into three important phases, which are satellite image pre-processing, feature extraction and classification. The image pre-processing phase denoises the image by median filter and the contrast is improved by Contrast Limited Adaptive Histogram Equalization (CLAHE) technique. As the satellite image possess numerous important features, this work extracts multiple features such as first order, GLCM and LWT features. The feature vector is formed out of these features and the classifier SVM is trained. The performance of the proposed approach is observed to be satisfactory in terms of sensitivity, specificity and accuracy.
Training a Binary Weight Object Detector by Knowledge Transfer for Autonomous Driving

Autonomous driving has harsh requirements of small model size and energy efficiency, in order to enable the embedded system to achieve real-time on-board object detection. Recent deep convolutional neural network based object detectors have achieved state-of-the-art accuracy. However, such models are trained with numerous parameters and their high computational costs and large storage prohibit the deployment to memory and computation resource limited systems. Lowprecision neural networks are popular techniques for reducing the computation requirements and memory footprint. Among them, binary weight neural network (BWN) is the extreme case which quantizes the float-point into just 1 bit. BWNs are difficult to train and suffer from accuracy deprecation due to the extreme low-bit representation. To address this problem, we propose a knowledge transfer (KT) method to aid the training of BWN using a full-precision teacher network. We built DarkNetand MobileNet-based binary weight YOLO-v2 detectors and conduct experiments on KITTI benchmark for car, pedestrian and cyclist detection. The experimental results show that the proposed method maintains high detection accuracy while reducing the model size of DarkNet-YOLO from 257 MB to 8.8 MB and MobileNet-YOLO from 193 MB to 7.9 MB.
Word Segmentation of Informal Arabic with Domain Adaptation

Segmentation of clitics has been shown to improve accuracy on a variety of Arabic NLP tasks. However, state-of-the-art Arabic word segmenters are either limited to formal Modern Standard Arabic, performing poorly on Arabic text featuring dialectal vocabulary and grammar, or rely on linguistic knowledge that is hand-tuned for each dialect. We extend an existing MSA segmenter with a simple domain adaptation technique and new features in order to segment informal and dialectal Arabic text. Experiments show that our system outperforms existing systems on broadcast news and Egyptian dialect, improving segmentation F1 score on a recently released Egyptian Arabic corpus to 92.09%, compared to 91.60% for another segmenter designed specifically for Egyptian Arabic.
Multimodal Coreference Resolution for Exploratory Data Visualization Dialogue: Context-Based Annotation and Gesture Identification

The goals of our work are twofold: gain insight into how humans interact with complex data and visualizations thereof in order to make discoveries; and use our findings to develop a dialogue system for exploring data visualizations. Crucial to both goals is understanding and modeling of multimodal referential expressions, in particular those that include deictic gestures. In this paper, we discuss how context information affects the interpretation of requests and their attendant referring expressions in our data. To this end, we have annotated our multimodal dialogue corpus for context and both utterance and gesture information; we have analyzed whether a gesture co-occurs with a specific request or with the context surrounding the request; we have started addressing multimodal co-reference resolution by using Kinect to detect deictic gestures; and we have started identifying themes found in the annotated context, especially in what follows the request.
Person Re-identification by Attributes

Visually identifying a target individual reliably in a crowded environment observed by a distributed camera network is critical to a variety of tasks in managing business information, border control, and crime prevention. Automatic re-identification of a human candidate from public space CCTV video is challenging due to spatiotemporal visual feature variations and strong visual similarity between different people, compounded by low-resolution and poor quality video data. In this work, we propose a novel method for re-identification that learns a selection and weighting of mid-level semantic attributes to describe people. Specifically, the model learns an attribute-centric, parts-based feature representation. This differs from and complements existing low-level features for re-identification that rely purely on bottom-up statistics for feature selection, which are limited in discriminating and identifying reliably visual appearances of target people appearing in different camera views under certain degrees of occlusion due to crowdedness. Our experiments demonstrate the effectiveness of our approach compared to existing feature representations when applied to benchmarking datasets.
Directed test generation using symbolic grammars

We present CESI, an algorithm that combines exhaustive enumeration of test inputs from a structured domain with symbolic execution driven test generation. CESI is a hybrid of two predominant techniques: specification-based enumerative test generation (which exhaustively generates all possible inputs satisfying some constraint) and symbolic directed test generation (which explores program paths based on symbolic path constraint solving). We target programs whose valid inputs are determined by some context free grammar. We introduce symbolic grammars, where the original tokens are replaced with symbolic constants, that link enumerative grammar-based input generation with symbolic directed testing. Symbolic grammars abstract the concrete input syntax, thus reducing the set of input strings that must be enumerated exhaustively. For each enumerated input string, which may contain symbolic constants, symbolic execution based test generation instantiates the constants based on program execution paths. The "template" generated by enumerating valid strings reduces the burden on the symbolic execution to generate syntactically valid inputs and hence exercise interesting code paths. Together, symbolic grammars provide a link between exhaustive enumeration of valid inputs and execution-directed symbolic test generation. In preliminary experiments, CESI is better than if both enumerative and symbolic techniques are used alone.
Authentic Leadership, Trust and Work Engagement

The issue of leadership has been investigated from several perspectives; however, very less from ethical perspective. With the growing number of corporate scandals and unethical roles played by business leaders in several parts of the world, the need to examine leadership from ethical perspective cannot be over emphasized. The importance of leadership credibility has been discussed in the authentic model of leadership. Authentic leaders display high degree of integrity, have deep sense of purpose, and committed to their core values. As a result they promote a more trusting relationship in their work groups that translates into several positive outcomes. The present study examined how authentic leadership contribute to subordinates’ trust in leadership and how this trust, in turn, predicts subordinates’ work engagement. A sample of 395 employees was randomly selected from several local banks operating in Malaysia. Standardized tools such as ALQ, OTI, and EEQ were employed. Results indicated that authentic leadership promoted subordinates’ trust in leader, and contributed to work engagement. Also, interpersonal trust predicted employees’ work engagement as well as mediated the relationship between this style of leadership and employees’ work engagement. Authentic Leadership, Interpersonal Trust, Work Engagement