nils-herrmann/clean_scirepeval huggingface dataset:
Learning to link with wikipedia

This paper describes how to automatically cross-reference documents with Wikipedia: the largest knowledge base ever known. It explains how machine learning can be used to identify significant terms within unstructured text, and enrich it with links to the appropriate Wikipedia articles. The resulting link detector and disambiguator performs very well, with recall and precision of almost 75%. This performance is constant whether the system is evaluated on Wikipedia articles or "real world" documents.
 This work has implications far beyond enriching documents with explanatory links. It can provide structured knowledge about any unstructured fragment of text. Any task that is currently addressed with bags of words - indexing, clustering, retrieval, and summarization to name a few - could use the techniques described here to draw on a vast network of concepts and semantics.
TernausNetV2: Fully Convolutional Network for Instance Segmentation

The most common approaches to instance segmentation are complex and use two-stage networks with object proposals, conditional random-fields, template matching or recurrent neural networks. In this work we present Ternaus- NetV2 - a simple fully convolutional network that allows extracting objects from a high-resolution satellite imagery on an instance level. The network has popular encoderdecoder type of architecture with skip connections but has a few essential modifications that allows using for semantic as well as for instance segmentation tasks. This approach is universal and allows to extend any network that has been successfully applied for semantic segmentation to perform instance segmentation task. In addition, we generalize network encoder that was pre-trained for RGB images to use additional input channels. It makes possible to use transfer learning from visual to a wider spectral range. For DeepGlobe-CVPR 2018 building detection sub-challenge, based on public leaderboard score, our approach shows superior performance in comparison to other methods.
Speech Acts for Dialogue Agents

A dialogue agent is one that can interact and communicate with other agents in a coherent manner not just with one shot messages but with a sequence of related messages all on the same topic or in service of an overall goal Following the basic insights of speech act theory these communications are seen not just as transmitting information but as actions which change the state of the world Most of these changes will be to the mental states of the agents involved in the conversation as well as the state or context of the dialogue As such speech act theory allows an agent theorist or designer to place agent communication within the same general framework as agent action In general though communicative action requires a more expressive logic of action than is required for something like the single agent blocks world familiar in classical AI planning For one thing there are multiple agents and there is also a possibility of simultaneous and fallible action In studying speech acts the focus is on pragmatics rather than semantics that is how language is used by agents not what the messages themselves mean in terms of truth conditions in a model see for a good introduction to issues in natural language pragmatics As with other aspects of pragmatics such as implicature and presupposition an important concern is what can be inferred perhaps only provisionally or with a certain likelihood as a result of the performance of a speech act While much of speech act work has been analyzing interaction in natural language speech acts are also a convenient level of analysis for arti cial communication languages While the rules for interpreting whether a particular act has been performed will be di erent for an arti cial language presumably simpler with less concern about vague and
What People Do with Consumption Feedback: A Long-Term Living Lab Study of a Home Energy Management System

One of the great societal challenges that we face today concerns the move to more sustainable patterns of energy consumption, reflecting the need to balance both individual consumer choice and societal demands. In order for this ‘energy turnaround’ to take place, however, reducing residential energy consumption must go beyond using energy-efficient devices: More sustainable behaviour and lifestyles are essential parts of future ‘energy aware’ living.Addressing this issue from an HCI perspective, this paper presents the results of a 3-year research project dealing with the co-design and appropriation of a Home Energy Management System (HEMS) that has been rolled out in a living lab setting with seven households for a period of 18 months. Our HEMS is inspired by feedback systems in Sustainable Interaction Design and allows the monitoring of energy consumption in real-time. In contrast to existing research mainly focusing on how technology can persuade people to consume less energy (‘what technology does to people’), our study focuses on the appropriation of energy feedback systems (‘what people do with technology’) and how newly developed practices can become a resource for future technology design. Therefore, we deliberately followed an open research design. In keeping with this approach, our study uncovers various responses, practices and obstacles of HEMS use. We show that HEMS use is characterized by a number of different features. Recognizing the distinctive patterns of technology use in the different households and the evolutionary character of that use within the households, we conclude with a discussion of these patterns in relation to existing research and their meaning for the design of future HEMSs.
BRein Memory: A Single-Chip Binary/Ternary Reconfigurable in-Memory Deep Neural Network Accelerator Achieving 1.4 TOPS at 0.6 W

A versatile reconfigurable accelerator architecture for binary/ternary deep neural networks is presented. In-memory neural network processing without any external data accesses, sustained by the symmetry and simplicity of the computation of the binary/ternaty neural network, improves the energy efficiency dramatically. The prototype chip is fabricated, and it achieves 1.4 TOPS (tera operations per second) peak performance with 0.6-W power consumption at 400-MHz clock. The application examination is also conducted.
Business Process Simulation Survival Guide

Simulation provides a flexible approach to analyzing business processes. Through simulation experiments various “what if” questions can be answered and redesign alternatives can be compared with respect to key performance indicators. This chapter introduces simulation as an analysis tool for business process management. After describing the characteristics of business simulation models, the phases of a simulation project, the generation of random variables, and the analysis of simulation results, we discuss 15 risks, i.e., potential pitfalls jeopardizing the correctness and value of business process simulation. For example, the behavior of resources is often modeled in a rather naı̈ve manner resulting in unreliable simulation models. Whereas traditional simulation approaches rely on hand-made models, we advocate the use of process mining techniques for creating more reliable simulation models based on real event data. Moreover, simulation can be turned into a powerful tool for operational decision making by using real-time process data.
An open software architecture for virtual reality interaction

This article describes OpenTracker, an open software architecture that provides a framework for the different tasks involved in tracking input devices and processing multi-modal input data in virtual environments and augmented reality application. The OpenTracker framework eases the development and maintenance of hardware setups in a more flexible manner than what is typically offered by virtual reality development packages. This goal is achieved by using an object-oriented design based on XML, taking full advantage of this new technology by allowing to use standard XML tools for development, configuration and documentation. The OpenTracker engine is based on a data flow concept for multi-modal events. A multi-threaded execution model takes care of tunable performance. Transparent network access allows easy development of decoupled simulation models. Finally, the application developer's interface features both a time-based and an event based model, that can be used simultaneously, to serve a large range of applications. OpenTracker is a first attempt towards a "'write once, input anywhere"' approach to virtual reality application development. To support these claims, integration into an existing augmented reality system is demonstrated. We also show how a prototype tracking equipment for mobile augmented reality can be assembled from consumer input devices with the aid of OpenTracker. Once development is sufficiently mature, it is planned to make Open-Tracker available to the public under an open source software license.
Emergency Services in Future Intelligent Transportation Systems Based on Vehicular Communication Networks

Over the years, we have harnessed the power of computing to improve the speed of operations and increase in productivity. Also, we have witnessed the merging of computing and telecommunications. This excellent combination of two important fields has propelled our capability even further, allowing us to communicate anytime and anywhere, improving our work flow and increasing our quality of life tremendously. The next wave of evolution we foresee is the convergence of telecommunication, computing, wireless, and transportation technologies. Once this happens, our roads and highways will be both our communications and transportation platforms, which will completely revolutionize when and how we access services and entertainment, how we communicate, commute, navigate, etc., in the coming future. This paper presents an overview of the current state-of-the-art, discusses current projects, their goals, and finally highlights how emergency services and road safety will evolve with the blending of vehicular communication networks with road transportation.
Evaluation of Appearance-Based Methods and Implications for Gaze-Based Applications

Appearance-based gaze estimation methods that only require an off-the-shelf camera have significantly improved but they are still not yet widely used in the human-computer interaction (HCI) community. This is partly because it remains unclear how they perform compared to model-based approaches aswell as dominant, special-purpose eye tracking equipment. To address this limitation, we evaluate the performance of state-of-the-art appearance-based gaze estimation for interaction scenarios with and without personal calibration, indoors and outdoors, for different sensing distances, as well as for users with and without glasses. We discuss the obtained findings and their implications for the most important gaze-based applications, namely explicit eye input, attentive user interfaces, gaze-based user modelling, and passive eye monitoring. To democratise the use of appearance-based gaze estimation and interaction in HCI, we finally present OpenGaze (www.opengaze.org), the first software toolkit for appearance-based gaze estimation and interaction.
TiNb2O7 nanoparticles assembled into hierarchical microspheres as high-rate capability and long-cycle-life anode materials for lithium ion batteries.

As a competitor for Li4Ti5O12 with a higher capacity and extreme safety, monoclinic TiNb2O7 has been considered as a promising anode material for next-generation high power lithium ion batteries. However, TiNb2O7 suffers from low electronic conductivity and ionic conductivity, which restricts the electrochemical kinetics. Herein, a facile and advanced architecture design of hierarchical TiNb2O7 microspheres is successfully developed for large-scale preparation without any surfactant assistance. To the best of our knowledge, this is the first report on the one step solvothermal synthesis of TiNb2O7 microspheres with micro- and nano-scale composite structures. When evaluated as an anode material for lithium ion batteries, the electrode exhibits excellent high rate capacities and ultra-long cyclability, such as 258 mA h g(-1) at 1 C, 175 mA h g(-1) at 5 C, and 138 mA h g(-1) at 10 C, extending to more than 500 cycles.
Learning Deep Representations of Fine-Grained Visual Descriptions

State-of-the-art methods for zero-shot visual recognition formulate learning as a joint embedding problem of images and side information. In these formulations the current best complement to visual features are attributes: manually-encoded vectors describing shared characteristics among categories. Despite good performance, attributes have limitations: (1) finer-grained recognition requires commensurately more attributes, and (2) attributes do not provide a natural language interface. We propose to overcome these limitations by training neural language models from scratch, i.e. without pre-training and only consuming words and characters. Our proposed models train end-to-end to align with the fine-grained and category-specific content of images. Natural language provides a flexible and compact way of encoding only the salient visual aspects for distinguishing categories. By training on raw text, our model can do inference on raw text as well, providing humans a familiar mode both for annotation and retrieval. Our model achieves strong performance on zero-shot text-based image retrieval and significantly outperforms the attribute-based state-of-the-art for zero-shot classification on the Caltech-UCSD Birds 200-2011 dataset.
The state of ransomware. Trends and mitigation techniques

This paper contains an analysis of the payload of the popular ransomware for Windows, Android, Linux, and MacOSX platforms. Namely, VaultCrypt (CrypVault), TeslaCrypt, NanoLocker, Trojan-Ransom.Linux.Cryptor, Android Simplelocker, OSX/KeRanger-A, WannaCry, Petya, NotPetya, Cerber, Spora, Serpent ransomware were put under the microscope. A set of characteristics was proposed to be used for the analysis. The purpose of the analysis is generalization of the collected data that describes behavior and design trends of modern ransomware. The objective is to suggest ransomware threat mitigation techniques based on the obtained information. The novelty of the paper is the analysis methodology based on the chosen set of 13 key characteristics that helps to determine similarities and differences thorough the list of ransomware put under analysis. Most of the ransomware samples presented were manually analyzed by the authors eliminating contradictions in descriptions of ransomware behavior published by different malware research laboratories through verification of the payload of the latest versions of ransomware.
Real-time grasp detection using convolutional neural networks

We present an accurate, real-time approach to robotic grasp detection based on convolutional neural networks. Our network performs single-stage regression to graspable bounding boxes without using standard sliding window or region proposal techniques. The model outperforms state-of-the-art approaches by 14 percentage points and runs at 13 frames per second on a GPU. Our network can simultaneously perform classification so that in a single step it recognizes the object and finds a good grasp rectangle. A modification to this model predicts multiple grasps per object by using a locally constrained prediction mechanism. The locally constrained model performs significantly better, especially on objects that can be grasped in a variety of ways.
Iris: An optimized I/O stack for low-latency storage devices

System software overheads in the I/O path, including VFS and file system code, become more pronounced with emerging low-latency storage devices. Currently, these overheads constitute the main bottleneck in the I/O path and they limit efficiency of modern storage systems. In this paper we present a taxonomy of the current state-of-the-art systems on accelerating accesses to fast storage devices. Furthermore, we present Iris, a new I/O path for applications, that minimizes overheads from system software in the common I/O path. The main idea is the separation of the control and data planes. The control plane consists of an unmodified Linux kernel and is responsible for handling data plane initialization and the normal processing path through the kernel for non-file related operations. The data plane is a lightweight mechanism to provide direct access to storage devices with minimum overheads and without sacrificing strong protection semantics. Iris requires neither hardware support from the storage devices nor changes in user applications. We evaluate our early prototype and we find that it achieves on a single core up to 1:7x and 2:2x better read and write random IOPS, respectively, compared to the XFS and EXT4 file systems. It also scales with the number of cores; using 4 cores Iris achieves 1:84x and 1:96x better read and write random IOPS, respectively. In sequential reads we provide similar performance and in sequential writes we are about 20% better compared to other file systems.
The coming of age of artificial intelligence in medicine

This paper is based on a panel discussion held at the Artificial Intelligence in Medicine Europe (AIME) conference in Amsterdam, The Netherlands, in July 2007. It had been more than 15 years since Edward Shortliffe gave a talk at AIME in which he characterized artificial intelligence (AI) in medicine as being in its "adolescence" (Shortliffe EH. The adolescence of AI in medicine: will the field come of age in the '90s? Artificial Intelligence in Medicine 1993;5:93-106). In this article, the discussants reflect on medical AI research during the subsequent years and characterize the maturity and influence that has been achieved to date. Participants focus on their personal areas of expertise, ranging from clinical decision-making, reasoning under uncertainty, and knowledge representation to systems integration, translational bioinformatics, and cognitive issues in both the modeling of expertise and the creation of acceptable systems.
Joint Extraction of Entities and Relations Based on a Novel Tagging Scheme

Joint extraction of entities and relations is an important task in information extraction. To tackle this problem, we firstly propose a novel tagging scheme that can convert the joint extraction task to a tagging problem. Then, based on our tagging scheme, we study different end-toend models to extract entities and their relations directly, without identifying entities and relations separately. We conduct experiments on a public dataset produced by distant supervision method and the experimental results show that the tagging based methods are better than most of the existing pipelined and joint learning methods. What’s more, the end-to-end model proposed in this paper, achieves the best results on the public dataset.
Aging and wisdom: culture matters.

People from different cultures vary in the ways they approach social conflicts, with Japanese being more motivated to maintain interpersonal harmony and avoid conflicts than Americans are. Such cultural differences have developmental consequences for reasoning about social conflict. In the study reported here, we interviewed random samples of Americans from the Midwest United States and Japanese from the larger Tokyo area about their reactions to stories of intergroup and interpersonal conflicts. Responses showed that wisdom (e.g., recognition of multiple perspectives, the limits of personal knowledge, and the importance of compromise) increased with increasing age among Americans, but older age was not associated with wiser responses among Japanese. Younger and middle-aged Japanese showed greater use of wise-reasoning strategies than younger and middle-aged Americans did. This cultural difference was weaker for older participants' reactions to interpersonal conflicts and was actually reversed for intergroup conflicts. This research has important implications for the study of aging, cultural psychology, and wisdom.
Spark SQL: Relational Data Processing in Spark

Spark SQL is a new module in Apache Spark that integrates relational processing with Spark's functional programming API. Built on our experience with Shark, Spark SQL lets Spark programmers leverage the benefits of relational processing (e.g. declarative queries and optimized storage), and lets SQL users call complex analytics libraries in Spark (e.g. machine learning). Compared to previous systems, Spark SQL makes two main additions. First, it offers much tighter integration between relational and procedural processing, through a declarative DataFrame API that integrates with procedural Spark code. Second, it includes a highly extensible optimizer, Catalyst, built using features of the Scala programming language, that makes it easy to add composable rules, control code generation, and define extension points. Using Catalyst, we have built a variety of features (e.g. schema inference for JSON, machine learning types, and query federation to external databases) tailored for the complex needs of modern data analysis. We see Spark SQL as an evolution of both SQL-on-Spark and of Spark itself, offering richer APIs and optimizations while keeping the benefits of the Spark programming model.
AlphaGo and Monte Carlo tree search: The simulation optimization perspective

In March of 2016, Google DeepMind's AlphaGo, a computer Go-playing program, defeated the reigning human world champion Go player, 4-1, a feat far more impressive than previous victories by computer programs in chess (IBM's Deep Blue) and Jeopardy (IBM's Watson). The main engine behind the program combines machine learning approaches with a technique called Monte Carlo tree search. Current versions of Monte Carlo tree search used in Go-playing algorithms are based on a version developed for games that traces its roots back to the adaptive multi-stage sampling simulation optimization algorithm for estimating value functions in finite-horizon Markov decision processes (MDPs) introduced by Chang et al. (2005), which was the first use of Upper Confidence Bounds (UCBs) for Monte Carlo simulation-based solution of MDPs. We review the main ideas in UCB-based Monte Carlo tree search by connecting it to simulation optimization through the use of two simple examples: decision trees and tic-tac-toe.
Depth Estimation Using Monocular and Stereo Cues

Depth estimation in computer vision and robotics is most commonly done via stereo vision (stereopsis), in which images from two cameras are used to triangulate and estimate distances. However, there are also numerous monocular visual cues— such as texture variations and gradients, defocus, color/haze, etc.—that have heretofore been little exploited in such systems. Some of these cues apply even in regions without texture, where stereo would work poorly. In this paper, we apply a Markov Random Field (MRF) learning algorithm to capture some of these monocular cues, and incorporate them into a stereo system. We show that by adding monocular cues to stereo (triangulation) ones, we obtain significantly more accurate depth estimates than is possible using either monocular or stereo cues alone. This holds true for a large variety of environments, including both indoor environments and unstructured outdoor environments containing trees/forests, buildings, etc. Our approach is general, and applies to incorporating monocular cues together with any off-the-shelf stereo system.