nils-herrmann/clean_scirepeval huggingface dataset:
PDDL2.1: An Extension to PDDL for Expressing Temporal Planning Domains

In recent years research in the planning community has moved increasingly towards application of planners to realistic problems involving both time and many types of resources. For example, interest in planning demonstrated by the space research community has inspired work in observation scheduling, planetary rover exploration and spacecraft control domains. Other temporal and resource-intensive domains including logistics planning, plant control and manufacturing have also helped to focus the community on the modelling and reasoning issues that must be confronted to make planning technology meet the challenges of application. The International Planning Competitions have acted as an important motivating force behind the progress that has been made in planning since 1998. The third competition (held in 2002) set the planning community the challenge of handling time and numeric resources. This necessitated the development of a modelling language capable of expressing temporal and numeric properties of planning domains. In this paper we describe the language, pddl2.1, that was used in the competition. We describe the syntax of the language, its formal semantics and the validation of concurrent plans. We observe that pddl2.1 has considerable modelling power — exceeding the capabilities of current planning technology — and presents a number of important challenges to the research community.
Kalman filters improve LSTM network performance in problems unsolvable by traditional recurrent nets

The long short-term memory (LSTM) network trained by gradient descent solves difficult problems which traditional recurrent neural networks in general cannot. We have recently observed that the decoupled extended Kalman filter training algorithm allows for even better performance, reducing significantly the number of training steps when compared to the original gradient descent training algorithm. In this paper we present a set of experiments which are unsolvable by classical recurrent networks but which are solved elegantly and robustly and quickly by LSTM combined with Kalman filters.
N ov 2 01 8 Adaptive Task Allocation for Mobile Edge Learning

This paper aims to establish a new optimization paradigm for implementing realistic distributed learning algorithms, with performance guarantees, on wireless edge nodes with heterogeneous computing and communication capacities. We will refer to this new paradigm as “Mobile Edge Learning (MEL)”. The problem of dynamic task allocation for MEL is considered in this paper with the aim to maximize the learning accuracy, while guaranteeing that the total times of data distribution/aggregation over heterogeneous channels, and local computing iterations at the heterogeneous nodes, are bounded by a preset duration. The problem is first formulated as a quadratically-constrained integer linear problem. Being an NP-hard problem, the paper relaxes it into a non-convex problem over real variables. We thus proposed two solutions based on deriving analytical upper bounds of the optimal solution of this relaxed problem using Lagrangian analysis and KKT conditions, and the use of suggest-and-improve starting from equal batch allocation, respectively. The merits of these proposed solutions are exhibited by comparing their performances to both numerical approaches and the equal task allocation approach.
Detecting sources of computer viruses in networks: theory and experiment

We provide a systematic study of the problem of finding the source of a computer virus in a network. We model virus spreading in a network with a variant of the popular SIR model and then construct an estimator for the virus source. This estimator is based upon a novel combinatorial quantity which we term rumor centrality. We establish that this is an ML estimator for a class of graphs. We find the following surprising threshold phenomenon: on trees which grow faster than a line, the estimator always has non-trivial detection probability, whereas on trees that grow like a line, the detection probability will go to 0 as the network grows. Simulations performed on synthetic networks such as the popular small-world and scale-free networks, and on real networks such as an internet AS network and the U.S. electric power grid network, show that the estimator either finds the source exactly or within a few hops in different network topologies. We compare rumor centrality to another common network centrality notion known as distance centrality. We prove that on trees, the rumor center and distance center are equivalent, but on general networks, they may differ. Indeed, simulations show that rumor centrality outperforms distance centrality in finding virus sources in networks which are not tree-like.
UNDERSTANDING THE EFFECT OF INTERDEPENDENCY AND VULNERABILITY ON THE PERFORMANCE OF CIVIL INFRASTRUCTURE

Vulnerability is a measure ofthe extent to which a community, structure, service s or geographic area is likely to be damaged or disrupted by the im pact of particular hazard. Current asset management practices focuses on studying factors that affect p rformance of isolated infrastructure networks and model a set of actions to control the expected performanc e of these networks. This approach ignores the underlying spatial and functional interdependencies among these infrastructure networks and their vulnerability. The purpose of this paper is to intr oduce a new method that recognizes the effect of sp atial and functional interdependencies on vulnerability r ating of water, sewer and road networks. The propos ed method consists of: 1) risk assessment model, 2) in terdependency assessment model and 3) vulnerability assessment model. The risk model is composed of two modules: 1) water and sewer risk module and 2) road infrastructure module. The water and sewer ris k module will cluster these assets into three risk categories based on environmental, social, operatio nal and economical factors. The road infrastructure module will cluster road assets into three risk cat egories by using rational factorial technique based on road type, serviceability index, traffic load, and freez and thaw index. The interdependency model will de ploy the risk ranking to perform geospatial analysis in ArcGIS which results in determining the interdepend t layers of waters, sewers and roads. The vulnerabili ty model will deploy fuzzy neural networks techniqu e to determine the vulnerability rating based on spatial and functional interdependencies. The fuzzy neural networks are utilized to overcome the lack of histo r cal data and incorporate experts’ preferences for establishing the knowledge base for vulnerability a ssessment. The expected contribution of this framew ork is to aid decision makers in understanding the inte rdependencies between civil infrastructure assets a nd to which extent such interdependencies can compromise as ts performance.
A Formal Study of Slicing for Multi-threaded Programs with JVM Concurrency Primitives

Previous work has shown that program slicing can be a useful step in model-checking software systems. We are interested in applying these techniques to construct models of multi-threaded Java programs. Past work does not address the concurrency primitives found in Java, nor does it provide the rigorous notions of slice correctness that are necessary for reasoning about programs with non-deterministic behaviour and potentially innnite computation traces. In this paper, we deene the semantics of a simple multi-threaded language with concurrency primitives matching those found in the Java Virtual Machine, we propose a bisimulation-based notion of correctness for slicing in this setting, we identify notions of dependency that are relevant for slicing multi-threaded Java programs, and we use these dependencies to specify a program slicer for the language presented in the paper. Finally, we discuss how these dependencies can be reened to take into account common programming idioms of concurrent Java software.
Data Mining Approaches for Intrusion Detection

In this paper we discuss our research in developing general and systematic methods for intrusion detection. The key ideas are to use data mining techniques to discover consistent and useful patterns of system features that describe program and user behavior, and use the set of relevant system features to compute (inductively learned) classifiers that can recognize anomalies and known intrusions. Using experiments on the s ndmailsystem call data and the network tcpdumpdata, we demonstrate that we can construct concise and accurate classifiers to detect anomalies. We provide an overview on two general data mining algorithms that we have implemented: the association rules algorithm and the frequent episodes algorithm. These algorithms can be used to compute the intraand interaudit record patterns, which are essential in describing program or user behavior. The discovered patterns can guide the audit data gathering process and facilitate feature selection. To meet the challenges of both efficient learning (mining) and real-time detection, we propose an agent-based architecture for intrusion detection systems where the learning agents continuously compute and provide the updated (detection) models to the detection agents.
A penalty method for American options with jump diffusion processes

The fair price for an American option where the underlying asset follows a jump diffusion process can be formulated as a partial integral differential linear complementarity problem. We develop an implicit discretization method for pricing such American options. The jump diffusion correlation integral term is computed using an iterative method coupled with an FFT while the American constraint is imposed by using a penalty method. We derive sufficient conditions for global convergence of the discrete penalized equations at each timestep. Finally, we present numerical tests which illustrate such convergence.
Approximate logic synthesis for error tolerant applications

Error tolerance formally captures the notion that -- for a wide variety of applications including audio, video, graphics, and wireless communications -- a defective chip that produces erroneous values at its outputs may be acceptable, provided the errors are of certain types and their severities are within application-specified thresholds. All previous research on error tolerance has focused on identifying such defective but acceptable chips during post-fabrication testing to improve yield. In this paper, we explore a completely new approach to exploit error tolerance based on the following observation: If certain deviations from the nominal output values are acceptable, then we can exploit this flexibility during circuit design to reduce circuit area and delay as well as to increase yield. The specific metric of error tolerance we focus on is error rate, i.e., how often the circuit produces erroneous outputs. We propose a new logic synthesis approach for the new problem of identifying how to exploit a given error rate threshold to maximally reduce the area of the synthesized circuit. Experiment results show that for an error rate threshold within 1%, our approach provides 9.43% literal reductions on average for all the benchmarks that we target.
Antimicrobial profile of moringa oleifera lam. Extracts against some food – borne microorganisms

The chloroform and ethanol extracts of seeds and leaf of Moringa oleifera were investigated for antimicrobial activity against some selected food – borne microorganisms as a first step in the screening of the extracts for preliminary sanitizing/preservative properties on foods. The preliminary phytochemical screening and antimicrobial assay were carried out using standard procedures. The results of the phytochemical analysis revealed differences in the presence of the phytochemicals among the extracts. Saponins were detected in all the extracts while tannins were only detected in Moringa oleifera leaf chloroform extract. The antibacterial assay results show that M. oleifera leaf ethanol extract exhibited broad spectrum activity against the test organisms with Escherichia coli, Pseudomonas aeruginosa, Staphylococcus aureus and Enterobacter aerogenes susceptible. The MIC values ranged between 2.0 and >4.0mg/ml for all the organisms. M. oleifera seed chloroform extract was only active against E. coli and Salmonella typhimurium. The MIC values ranged between 1.0 and >4.0mg/ml for the tested organisms respectively. Antifungal activity result revealed 100% inhibition in growth of Mucor and Rhizopus species by M. oleifera seed chloroform extract at concentration of 1mg/ml. Standard Ketoconazole (control) inhibited the test organisms by 100% at 0.5mg/ml concertration. The result of this study have shown the potentials of M. oleifera extracts as sanitizers/preservatives by inhibiting the growth of the test organisms, which range from food – borne pathogens to spoilage causing organisms in foods.
Humanoid robot Lola: Design and walking control

In this paper we present the humanoid robot LOLA, its mechatronic hardware design, simulation and real-time walking control. The goal of the LOLA-project is to build a machine capable of stable, autonomous, fast and human-like walking. LOLA is characterized by a redundant kinematic configuration with 7-DoF legs, an extremely lightweight design, joint actuators with brushless motors and an electronics architecture using decentralized joint control. Special emphasis was put on an improved mass distribution of the legs to achieve good dynamic performance. Trajectory generation and control aim at faster, more flexible and robust walking. Center of mass trajectories are calculated in real-time from footstep locations using quadratic programming and spline collocation methods. Stabilizing control uses hybrid position/force control in task space with an inner joint position control loop. Inertial stabilization is achieved by modifying the contact force trajectories.
Functional reactive programming from first principles

Functional Reactive Programming, or FRP, is a general framework for programming hybrid systems in a high-level, declarative manner. The key ideas in FRP are its notions of behaviors and events. Behaviors are time-varying, reactive values, while events are time-ordered sequences of discrete-time event occurrences. FRP is the essence of Fran, a domain-specific language embedded in Haskell for programming reactive animations, but FRP is now also being used in vision, robotics and other control systems applications. 
In this paper we explore the formal semantics of FRP and how itrelates to an implementation based on streams that represent (and therefore only approximate) continuous behaviors. We show that, in the limit as the sampling interval goes to zero, the implementation is faithful to the formal, continuous semantics, but only when certain constraints on behaviors are observed. We explore the nature of these constraints, which vary amongst the FRP primitives. Our results show both the power and limitations of this approach to language design and implementation. As an example of a limitation, we show that streams are incapable of representing instantaneous predicate events over behaviors.
Sign Language Recognition using Sequential Pattern Trees

This paper presents a novel, discriminative, multi-class classifier based on Sequential Pattern Trees. It is efficient to learn, compared to other Sequential Pattern methods, and scalable for use with large classifier banks. For these reasons it is well suited to Sign Language Recognition. Using deterministic robust features based on hand trajectories, sign level classifiers are built from sub-units. Results are presented both on a large lexicon single signer data set and a multi-signer Kinect™ data set. In both cases it is shown to out perform the non-discriminative Markov model approach and be equivalent to previous, more costly, Sequential Pattern (SP) techniques.
Ranking with local regression and global alignment for cross media retrieval

Rich multimedia content including images, audio and text are frequently used to describe the same semantics in E-Learning and Ebusiness web pages, instructive slides, multimedia cyclopedias, and so on. In this paper, we present a framework for cross-media retrieval, where the query example and the retrieved result(s) can be of different media types. We first construct Multimedia Correlation Space (MMCS) by exploring the semantic correlation of different multimedia modalities, during which multimedia content and co-occurrence information is utilized. We propose a novel ranking algorithm, namely ranking with Local Regression and Global Alignment (LRGA), which learns a robust Laplacian matrix for data ranking. In LRGA, for each data point, a local linear regression model is used to predict the ranking values of its neighboring points. We propose a unified objective function to globally align the local models from all the data points so that an optimal ranking value can be assigned to each data point. LRGA is insensitive to parameters, making it particularly suitable for data ranking. A relevance feedback algorithm is proposed to improve the retrieval performance. Comprehensive experiments have demonstrated the effectiveness of our methods.
BabyTalk: Understanding and Generating Simple Image Descriptions

We present a system to automatically generate natural language descriptions from images. This system consists of two parts. The first part, content planning, smooths the output of computer vision-based detection and recognition algorithms with statistics mined from large pools of visually descriptive text to determine the best content words to use to describe an image. The second step, surface realization, chooses words to construct natural language sentences based on the predicted content and general statistics from natural language. We present multiple approaches for the surface realization step and evaluate each using automatic measures of similarity to human generated reference descriptions. We also collect forced choice human evaluations between descriptions from the proposed generation system and descriptions from competing approaches. The proposed system is very effective at producing relevant sentences for images. It also generates descriptions that are notably more true to the specific image content than previous work.
AmphiBot I: an amphibious snake-like robot

This article presents a project that aims at constructing a biologically inspired amphibious snake-like robot. The robot is designed to be capable of anguilliform swimming like sea-snakes and lampreys in water and lateral undulatory locomotion like a snake on ground. Both the structure and the controller of the robot are inspired by elongate vertebrates. In particular, the locomotion of the robot is controlled by a central pattern generator (a system of coupled oscillators) that produces travelling waves of oscillations as limit cycle behavior. We present the design considerations behind the robot and its controller. Experiments are carried out to identify the types of travelling waves that optimize speed during lateral undulatory locomotion on ground. In particular, the optimal frequency, amplitude and wavelength are thus identified when the robot is crawling on a particular surface.
Deep Cross-Modal Projection Learning for Image-Text Matching

The key point of image-text matching is how to accurately measure the similarity between visual and textual inputs. Despite the great progress of associating the deep cross-modal embeddings with the bi-directional ranking loss, developing the strategies for mining useful triplets and selecting appropriate margins remains a challenge in real applications. In this paper, we propose a cross-modal projection matching (CMPM) loss and a cross-modal projection classification (CMPC) loss for learning discriminative image-text embeddings. The CMPM loss minimizes the KL divergence between the projection compatibility distributions and the normalized matching distributions defined with all the positive and negative samples in a mini-batch. The CMPC loss attempts to categorize the vector projection of representations from one modality onto another with the improved norm-softmax loss, for further enhancing the feature compactness of each class. Extensive analysis and experiments on multiple datasets demonstrate the superiority of the proposed approach.
Deep neural networks for automatic detection of screams and shouted speech in subway trains

Deep Neural Networks (DNNs) have recently become a popular technique for regression and classification problems. Their capacity to learn high-order correlations between input and output data proves to be very powerful for automatic speech recognition. In this paper we investigate the use of DNNs for automatic scream and shouted speech detection, within the framework of surveillance systems in public transportation. We recorded a database of sounds occurring in subway trains in real conditions of exploitation and used DNNs to classify the sounds into screams, shouts and other categories. We report encouraging results, given the difficulty of the task, especially when a high level of surrounding noise is present.
A3: a coding guideline for HCI+autism research using video annotation

Due to the profile of strengths and weaknesses indicative of autism spectrum disorders (ASD), technology may play a key role in ameliorating communication difficulties with this population. This paper documents coding guidelines established through cross-disciplinary work focused on facilitating communication development in children with ASD using computerized feedback. The guidelines, referred to as A3 (pronounced A-Cubed) or Annotation for ASD Analysis, define and operationalize a set of dependent variables coded via video annotation. Inter-rater reliability data are also presented from a study currently in-progress, as well as related discussion to help guide future work in this area. The design of the A3 methodology is well-suited for the examination and evaluation of the behavior of low-functioning subjects with ASD who interact with technology.
Topic Models for Mortality Modeling in Intensive Care Units

Mortality prediction is an important problem in the intensive care unit (ICU) because it is helpful for understanding patients’ evolving severity, quality of care, and comparing treatments. Most ICU mortality models primarily consider structured data and physiological waveforms (Le Gall et al., 1993). An important limitation of these structured data approaches is that they miss a lot of vital information captured in providers’ free text notes and reports. In this paper, we propose an approach to mortality prediction that incorporates the information from free text notes using topic modeling.
Understanding the Container Ecosystem : A Taxonomy of Building Blocks for Container Lifecycle and Cluster Management

Container technologies, skyrocketing in popularity, are diverse and difficult to compare. In particular, components which are involved in running containers in dedicated and managed environments are hard to grasp but essential for a practical adoption. By providing an analysis of a large number of state-ofthe-art software projects we create a taxonomy of building blocks, which constitute the cornerstones of the container ecosystem. The taxonomy is shown to be applicable to systems in the context of container technology, but requires an understanding of their inside functionality.