nils-herrmann/clean_scirepeval huggingface dataset:
Behavior planning for character animation

This paper explores a behavior planning approach to automatically generate realistic motions for animated characters. Motion clips are abstracted as high-level behaviors and associated with a behavior finite-state machine (FSM) that defines the movement capabilities of a virtual character. During runtime, motion is generated automatically by a planning algorithm that performs a global search of the FSM and computes a sequence of behaviors for the character to reach a user-designated goal position. Our technique can generate interesting animations using a relatively small amount of data, making it attractive for resource-limited game platforms. It also scales efficiently to large motion databases, because the search performance is primarily dependent on the complexity of the behavior FSM rather than on the amount of data. Heuristic cost functions that the planner uses to evaluate candidate motions provide a flexible framework from which an animator can control character preferences for certain types of behavior. We show results of synthesized animations involving up to one hundred human and animal characters planning simultaneously in both static and dynamic environments.
An embodied cognitive science?

The last ten years have seen an increasing interest, within cognitive science, in issues concerning the physical body, the local environment, and the complex interplay between neural systems and the wider world in which they function. Yet many unanswered questions remain, and the shape of a genuinely physically embodied, environmentally embedded science of the mind is still unclear. In this article I will raise a number of critical questions concerning the nature and scope of this approach, drawing a distinction between two kinds of appeal to embodiment: (1) 'Simple' cases, in which bodily and environmental properties merely constrain accounts that retain the focus on inner organization and processing; and (2) More radical appeals, in which attention to bodily and environmental features is meant to transform both the subject matter and the theoretical framework of cognitive science.
Multi-Scale Recurrent Tracking via Pyramid Recurrent Network and Optical Flow

The target in a tracking sequence can be considered as a set of spatiotemporal data with various locations in different frames, and the problem how to extract spatiotemporal information of the target effectively has drawn increasing interest recently. In this paper, we exploit spatiotemporal information by different-scale-context aggregation through the proposed pyramid multi-directional recurrent network (PRNet) together with the FlowNet. The PRNet is proposed to memorize the multi-scale spatiotemporal information of self-structure of the target. The FlowNet is employed to capture motion information for discriminating targets from the background. And the two networks form the FPRNet, being trained jointly to learn more useful spatiotemporal representations for visual tracking. The proposed tracker is evaluated on OTB50, OTB100 and TC128 benchmarks, and the experimental results show that the proposed FPRNet can effectively address different challenging cases and achieve better performance than the state-of-theart trackers.
Systems AI: A Declarative Learning Based Programming Perspective

Data-driven approaches are becoming dominant problem-solving techniques in many areas of research and industry. Unfortunately, current technologies do not make such techniques easy to use for application experts who are not fluent in machine learning nor for machine learning experts who aim at testing ideas on real-world data and need to evaluate those as a part of an end-to-end system. We review key efforts made by various AI communities to provide languages for highlevel abstractions over learning and reasoning techniques needed for designing complex AI systems. We classify the existing frameworks based on the type of techniques as well as the data and knowledge representations they use, provide a comparative study of the way they address the challenges of programming real-world applications, and highlight some shortcomings and future directions.
Understanding and Improving Interpolation in Autoencoders via an Adversarial Regularizer

Autoencoders provide a powerful framework for learning compressed representations by encoding all of the information needed to reconstruct a data point in a latent code. In some cases, autoencoders can “interpolate”: By decoding the convex combination of the latent codes for two datapoints, the autoencoder can produce an output which semantically mixes characteristics from the datapoints. In this paper, we propose a regularization procedure which encourages interpolated outputs to appear more realistic by fooling a critic network which has been trained to recover the mixing coefficient from interpolated data. We then develop a simple benchmark task where we can quantitatively measure the extent to which various autoencoders can interpolate and show that our regularizer dramatically improves interpolation in this setting. We also demonstrate empirically that our regularizer produces latent codes which are more effective on downstream tasks, suggesting a possible link between interpolation abilities and learning useful representations.
Generative Image Modeling using Style and Structure Adversarial Networks

Current generative frameworks use end-to-end learning and generate images by sampling from uniform noise distribution. However, these approaches ignore the most basic principle of image formation: images are product of: (a) Structure: the underlying 3D model; (b) Style: the texture mapped onto structure. In this paper, we factorize the image generation process and propose Style and Structure Generative Adversarial Network (S-GAN). Our S-GAN has two components: the StructureGAN generates a surface normal map; the Style-GAN takes the surface normal map as input and generates the 2D image. Apart from a real vs. generated loss function, we use an additional loss with computed surface normals from generated images. The two GANs are first trained independently, and then merged together via joint learning. We show our S-GAN model is interpretable, generates more realistic images and can be used to learn unsupervised RGBD representations.
Demystifying Fog Computing: Characterizing Architectures, Applications and Abstractions

Internet of Things (IoT) has accelerated the deployment of millions of sensors at the edge of the network, through Smart City infrastructure and lifestyle devices. Cloud computing platforms are often tasked with handling these large volumes and fast streams of data from the edge. Recently, Fog computing has emerged as a concept for low-latency and resource-rich processing of these observation streams, to complement Edge and Cloud computing. In this paper, we review various dimensions of system architecture, application characteristics and platform abstractions that are manifest in this Edge, Fog and Cloud eco-system. We highlight novel capabilities of the Edge and Fog layers, such as physical and application mobility, privacy sensitivity, and a nascent runtime environment. IoT application case studies based on first-hand experiences across diverse domains drive this categorization. We also highlight the gap between the potential and the reality of Fog computing, and identify challenges that need to be overcome for the solution to be sustainable. Taken together, our article can help platform and application developers bridge the gap that remains in making Fog computing viable.
Differential Privacy: A Survey of Results

Over the past five years a new approach to privacy-preserving data analysis has born fruit [13, 18, 7, 19, 5, 37, 35, 8, 32]. This approach differs from much (but not all!) of the related literature in the statistics, databases, theory, and cryptography communities, in that a formal and ad omnia privacy guarantee is defined, and the data analysis techniques presented are rigorously proved to satisfy the guarantee. The key privacy guarantee that has emerged is differential privacy. Roughly speaking, this ensures that (almost, and quantifiably) no risk is incurred by joining a statistical database. In this survey, we recall the definition of differential privacy and two basic techniques for achieving it. We then show some interesting applications of these techniques, presenting algorithms for three specific tasks and three general results on differentially private learning.
SemEval 2017 Task 10: ScienceIE - Extracting Keyphrases and Relations from Scientific Publications

We describe the SemEval task of extracting keyphrases and relations between them from scientific documents, which is crucial for understanding which publications describe which processes, tasks and materials. Although this was a new task, we had a total of 26 submissions across 3 evaluation scenarios. We expect the task and the findings reported in this paper to be relevant for researchers working on understanding scientific content, as well as the broader knowledge base population and information extraction communities.
A 10 GHz low-power multi-modulus frequency divider using Extended True Single-Phase Clock (E-TSPC) Logic

This paper presents a multi-modulus frequency divider (MMD) based on the Extended True Single-Phase Clock (E-TSPC) Logic. The MMD consists of four cascaded divide-by-2/3 E-TSPC cells. The basic functionality of the MMD and the E-TSPC 2/3 divider are explained. The whole design was implemented in an [0.13] m CMOS process from IBM. Simulation and measurement results of the MMD are shown. Measurement results indicates a maximum operating frequency of [10] GHz and a power consumption of [4] mW for each stage. These results are compared to other state of the art dual modulus E-TSPC dividers, showing the good position of this design relating to operating frequency and power consumption.
Tricaine methane‐sulfonate (MS‐222) application in fish anaesthesia

Tricaine methane-sulfonate (MS-222) application in fish anaesthesia By N. Topic Popovic, I. Strunjak-Perovic, R. Coz-Rakovac, J. Barisic, M. Jadan, A. Persin Berakovic and R. Sauerborn Klobucar Laboratory of Ichthyopathology – Biological Materials,  Boskovic Institute, Zagreb, Croatia; Department of Anaesthesiology, University Hospital Clinic, Zagreb, Croatia
1 Using OWL and SWRL to Represent and Reason with Situation-based Access Control Policies

Access control is a central problem in confidential ity management, in particular in the healthcare dom ain, where many stakeholders require access to patients' health records.  is a conceptual model that allows for modeling hea lt c re scenarios of data-access requests; thus it can be used to formulate data-access polici es, where health organizations can specify their regulations involving access to patients’ data acco rding to the context of the request. The model's ce ntral concept is the Situation, a formal representation of a patient's data-acces s scenario. In this paper, we present the SitBAC Knowledge Framework, a formal healthcare-oriented, contextbased access-control framework that makes it possible to represent and implement SitBAC as a knowledge model along with an associated inference method, using OWL and SWRL. Within the SitBAC knowledge framework, scenarios of data access are r epresented as formal Web Ontology language (OWL)-based Situation classes, formulating data-acc ess rule classes. A set of data-access rule classes makes up the organization's data-access policy. An incoming data-access request, represented as an individual of an OWL-based Situation class, is eval uated by the inference method against the data-acce ss policy to produce an ‘approved/denied’ response. Th e method uses a Description Logics (DL)-reasoner and a Semantic Web Rule Language (SWRL) engine during the inference process. The DL reasoner is used for knowledge classification and for real-time realization of the incoming data-access request as a member of an existing Situation class to infer the appropriate response. The SWRL engine is used to in fer new knowledge regarding the incoming data-access re quests, which are required for the realization process. We evaluated the ability of the SitBAC knowledge fr amework to provide correct responses by representing and reasoning with real-life healthcar e scenarios.
A financial Ratio Analysis of Commercial Bank Performance in South Africa

This paper investigates the performance of South Africa’s commercial banking sector for the period 20052009. Financial ratios are employed to measure the profitability, liquidity and credit quality performance of five large South African based commercial banks. The study found that overall bank performance increased considerably in the first two years of the analysis. A significant change in trend is noticed at the onset of the global financial crisis in 2007, reaching its peak during 2008-2009. This resulted in falling profitability, low liquidity and deteriorating credit quality in the South African Banking sector.
Exploring the Website Features that can Support Online Collaborative Shopping?

Shopping is a social activity that people enjoy doing with friends and close ones. In this paper we name such joint shopping activity performed by two or more people together as “collaborative shopping”, and investigate the possible means of carrying it out online. We start with identifying the objectives of collaborative shopping (i.e., socializing and purchasing) and propose website features (communication support and decision support) to support these objectives. Our study posits that a better fit between the shopping objective and the website feature provided results in higher process satisfaction and outcome satisfaction. This research advocates further research in online collaborative shopping and hopes to provide guidelines for website designers. We believe online collaborative shopping deserves more research attention, not only from a purely research-oriented perspective, but also for its practical potential for making online shopping a success.
Business process variability modeling

A submitted manuscript is the author's version of the article upon submission and before peer-review. There can be important differences between the submitted version and the official published version of record. People interested in the research are advised to contact the author for the final version of the publication, or visit the DOI to the publisher's website. • The final author version and the galley proof are versions of the publication after peer review. • The final published version features the final layout of the paper including the volume, issue and page numbers.
North-Holland Combining forecasts : A review and annotated

Considerable literature has accumulated over the years regarding the combination of forecasts. The primary conclusion of this line of research is that forecast accuracy can be substantially improved through the combination of multiple individual forecasts. Furthermore, simple combination methods often work reasonably well relative to more complex combinations. This paper provides a review and annotated bibliography of that literature, including contributions from the forecasting, psychology, statistics, and management science literatures. The objectives are to provide a guide to the literature for students and researchers and to help researchers locate contributions in specific areas, both theoretical and applied. Suggestions for future research directions include (1) examination of simple combining approaches to determine reasons for their robustness, (2) development of alternative uses of multiple forecasts in order to make better use of the information they contain, (3) use of combined forecasts as benchmarks for forecast evaluation, and (4) study of subjective combination procedures. Finally, combining forecasts should become part of the mainstream of forecasting practice. In order to achieve this, practitioners should be encouraged to combine forecasts, and software to produce combined forecasts easily should be made available.
Nutritional screening and early treatment of malnutrition in cancer patients

Malnutrition is a frequent complication in patients with cancer and can negatively affect the outcome of treatments. On the other hand, side effects of anticancer therapies can also lead to inadequate nutrient intake and subsequent malnutrition. The nutritional screening aims to identify patients at risk of malnutrition for prompt treatment and/or careful follow-up.  This manuscript highlights the need of an interdisciplinary approach (oncologist, nutritionist, dietitian, psychologist, etc.) to empower patients who are experiencing loss of physiological and biological function, fatigue, malnutrition, psychological distress, etc., as a result of cancer disease or its treatment, and maintain an acceptable quality of life.  It is necessary to make all healthcare professionals aware of the opportunity to identify cancer patients at risk of malnutrition early in order to plan the best possible intervention and follow-up during cancer treatment and progression.
A procedural texture generation framework based on semantic descriptions

Procedural textures are normally generated from mathematical models with parameters carefully selected by experienced users. However, for naive users, the intuitive way to obtain a desired texture is to provide semantic descriptions such as ”regular,” ”lacelike,” and ”repetitive” and then a procedural model with proper parameters will be automatically suggested to generate the corresponding textures. By contrast, it is less practical for users to learn mathematical models and tune parameters based on multiple examinations of large numbers of generated textures. In this study, we propose a novel framework that generates procedural textures according to user-defined semantic descriptions, and we establish a mapping between procedural models and semantic texture descriptions. First, based on a vocabulary of semantic attributes collected from psychophysical experiments, a multi-label learning method is employed to annotate a large number of textures with semantic attributes to form a semantic procedural texture dataset. Then, we derive a low dimensional semantic space in which the semantic descriptions can be separated from one other. Finally, given a set of semantic descriptions, the diverse properties of the samples in the semantic space can lead the framework to find an appropriate generation model that uses appropriate parameters to produce a desired texture. The experimental results show that the proposed framework is effective and that the generated textures closely correlate with the input semantic descriptions.
Co2 Emissions, Electricity Consumption and Output in Asean

This study examines the causal relationship between carbon dioxide emissions, electricity consumption and economic growth within a panel vector error correction model for five ASEAN countries over the period 1980 to 2006. The long-run estimates indicate that there is a statistically significant positive association between electricity consumption and emissions and a non-linear relationship between emissions and real output, consistent with the Environmental Kuznets Curve. The long-run estimates, however, do not indicate the direction of causality between the variables. The results from the Granger causality tests suggest that in the long-run there is unidirectional Granger causality running from electricity consumption and emissions to economic growth. The results also point to unidirectional Granger causality running from emissions to electricity consumption in the short-run.
Determinants of post-exercise glycogen synthesis during short-term recovery.

The pattern of muscle glycogen synthesis following glycogen-depleting exercise occurs in two phases. Initially, there is a period of rapid synthesis of muscle glycogen that does not require the presence of insulin and lasts about 30-60 minutes. This rapid phase of muscle glycogen synthesis is characterised by an exercise-induced translocation of glucose transporter carrier protein-4 to the cell surface, leading to an increased permeability of the muscle membrane to glucose. Following this rapid phase of glycogen synthesis, muscle glycogen synthesis occurs at a much slower rate and this phase can last for several hours. Both muscle contraction and insulin have been shown to increase the activity of glycogen synthase, the rate-limiting enzyme in glycogen synthesis. Furthermore, it has been shown that muscle glycogen concentration is a potent regulator of glycogen synthase. Low muscle glycogen concentrations following exercise are associated with an increased rate of glucose transport and an increased capacity to convert glucose into glycogen. The highest muscle glycogen synthesis rates have been reported when large amounts of carbohydrate (1.0-1.85 g/kg/h) are consumed immediately post-exercise and at 15-60 minute intervals thereafter, for up to 5 hours post-exercise. When carbohydrate ingestion is delayed by several hours, this may lead to ~50% lower rates of muscle glycogen synthesis. The addition of certain amino acids and/or proteins to a carbohydrate supplement can increase muscle glycogen synthesis rates, most probably because of an enhanced insulin response. However, when carbohydrate intake is high (> or =1.2 g/kg/h) and provided at regular intervals, a further increase in insulin concentrations by additional supplementation of protein and/or amino acids does not further increase the rate of muscle glycogen synthesis. Thus, when carbohydrate intake is insufficient (<1.2 g/kg/h), the addition of certain amino acids and/or proteins may be beneficial for muscle glycogen synthesis. Furthermore, ingestion of insulinotropic protein and/or amino acid mixtures might stimulate post-exercise net muscle protein anabolism. Suggestions have been made that carbohydrate availability is the main limiting factor for glycogen synthesis. A large part of the ingested glucose that enters the bloodstream appears to be extracted by tissues other than the exercise muscle (i.e. liver, other muscle groups or fat tissue) and may therefore limit the amount of glucose available to maximise muscle glycogen synthesis rates. Furthermore, intestinal glucose absorption may also be a rate-limiting factor for muscle glycogen synthesis when large quantities (>1 g/min) of glucose are ingested following exercise.
Frictional Resistance Calculations on a Ship using CFD

In the present study, we conducted propeller open water test, resistance test, and propeller, ship hull interaction for a ship’s resistance and propulsion performance, using computational fluid dynamics techniques, where a K-epsilon, K-omega turbulence viscous models were employed. For convenience of mesh generation, unstructured meshes were used in the propeller region of a ship, where the hull shape is formed of delicate curved surfaces. On the other hand, structured meshes were generated for the remaining part of the hull and its domain, i.e., the region of relatively simple geometry. To facilitate the rotating propeller for propeller a moving reference frame motion type technique was adopted. The computational results were validated by comparing with the existing experimental data. In this work we are interested in predicting the frictional resistance offered to a ship during is motion. To this effect we start off with a consideration of the resistance offered to the bare hull in the absence of the