nils-herrmann/clean_scirepeval huggingface dataset:
Applying the roofline model

The recently introduced roofline model plots the performance of executed code against its operational intensity (operations count divided by memory traffic). It also includes two platform-specific performance ceilings: the processor's peak performance and a ceiling derived from the memory bandwidth, which is relevant for code with low operational intensity. The model thus makes more precise the notions of memory- and compute-bound and, despite its simplicity, can provide an insightful visualization of bottlenecks. As such it can be valuable to guide manual code optimization as well as in education. Unfortunately, to date the model has been used almost exclusively with back-of-the-envelope calculations and not with measured data. In this paper we show how to produce roofline plots with measured data on recent generations of Intel platforms. We show how to accurately measure the necessary quantities for a given program using performance counters, including threaded and vectorized code, and for warm and cold cache scenarios. We explain the measurement approach, its validation, and discuss limitations. Finally, we show, to this extent for the first time, a set of roofline plots with measured data for common numerical functions on a variety of platforms and discuss their possible uses.
Invisible antenna combination embedded in the roof of a car with high efficiency for reception of SDARS - and GPS - signals

For the reception of Satellite Digital Audio Radio Services (SDARS) and Global Positioning Systems (GPS) transmitted via satellite an invisible antenna combination embedded in the roof of a car is presented. Without changing the surface of the vehicle the antenna combination can be completely embedded in a metal cavity and covered by a thick dielectric part of the roof. The measurement results show a high efficiency and a large bandwidth which exceeds the necessary bandwidth significantly for both services. The antenna combination offers a radiation pattern which is tailored to the reception of SDARS signals transmitted via highly-elliptical-orbit (HEO) satellites, geostationary earth orbit (GEO) satellites and terrestrial repeaters and for GPS signals transmitted via medium earth orbit (MEO) satellites. Although the antennas are mounted in such a small mounting volume, the antennas are decoupled optimally.
Modeling Syntactic Context Improves Morphological Segmentation

The connection between part-of-speech (POS) categories and morphological properties is well-documented in linguistics but underutilized in text processing systems. This paper proposes a novel model for morphological segmentation that is driven by this connection. Our model learns that words with common affixes are likely to be in the same syntactic category and uses learned syntactic categories to refine the segmentation boundaries of words. Our results demonstrate that incorporating POS categorization yields substantial performance gains on morphological segmentation of Arabic.
MoonGen: A Scriptable High-Speed Packet Generator

We present MoonGen, a flexible high-speed packet generator. It can saturate 10 GbE links with minimum-sized packets while using only a single CPU core by running on top of the packet processing framework DPDK. Linear multi-core scaling allows for even higher rates: We have tested MoonGen with up to 178.5 Mpps at 120 Gbit/s. Moving the whole packet generation logic into user-controlled Lua scripts allows us to achieve the highest possible flexibility. In addition, we utilize hardware features of commodity NICs that have not been used for packet generators previously. A key feature is the measurement of latency with sub-microsecond precision and accuracy by using hardware timestamping capabilities of modern commodity NICs. We address timing issues with software-based packet generators and apply methods to mitigate them with both hardware support and with a novel method to control the inter-packet gap in software. Features that were previously only possible with hardware-based solutions are now provided by MoonGen on commodity hardware. MoonGen is available as free software under the MIT license in our git repository at
Connections between the lines: augmenting social networks with text

Network data is ubiquitous, encoding collections of relationships between entities such as people, places, genes, or corporations. While many resources for networks of interesting entities are emerging, most of these can only annotate connections in a limited fashion. Although relationships between entities are rich, it is impractical to manually devise complete characterizations of these relationships for every pair of entities on large, real-world corpora.
 In this paper we present a novel probabilistic topic model to analyze text corpora and infer descriptions of its entities and of relationships between those entities. We develop variational methods for performing approximate inference on our model and demonstrate that our model can be practically deployed on large corpora such as Wikipedia. We show qualitatively and quantitatively that our model can construct and annotate graphs of relationships and make useful predictions.
Extending SME to Handle Large-Scale Cognitive Modeling

Analogy and similarity are central phenomena in human cognition, involved in processes ranging from visual perception to conceptual change. To capture this centrality requires that a model of comparison must be able to integrate with other processes and handle the size and complexity of the representations required by the tasks being modeled. This paper describes extensions to Structure-Mapping Engine (SME) since its inception in 1986 that have increased its scope of operation. We first review the basic SME algorithm, describe psychological evidence for SME as a process model, and summarize its role in simulating similarity-based retrieval and generalization. Then we describe five techniques now incorporated into the SME that have enabled it to tackle large-scale modeling tasks: (a) Greedy merging rapidly constructs one or more best interpretations of a match in polynomial time: O(n2 log(n)); (b) Incremental operation enables mappings to be extended as new information is retrieved or derived about the base or target, to model situations where information in a task is updated over time; (c) Ubiquitous predicates model the varying degrees to which items may suggest alignment; (d) Structural evaluation of analogical inferences models aspects of plausibility judgments; (e) Match filters enable large-scale task models to communicate constraints to SME to influence the mapping process. We illustrate via examples from published studies how these enable it to capture a broader range of psychological phenomena than before.
A snapshot of public web services

Web Service Technology has been developing rapidly as it provides a flexible application-to-application interaction mechanism. Several ongoing research efforts focus on various aspects of web service technology, including the modeling, specification, discovery, composition and verification of web services. The approaches advocated are often conflicting---based as they are on differing expectations on the current status of web services as well as differing models of their future evolution. One way of deciding the relative relevance of the various research directions is to look at their applicability to the currently available web services. To this end, we took a snapshot of the currently publicly available web services. Our aim is to get an idea of the number, type, complexity and composability of these web services and see if this analysis provides useful information about the near-term fruitful research directions.
Functional neuroanatomy of remote episodic, semantic and spatial memory: a unified account based on multiple trace theory.

We review lesion and neuroimaging evidence on the role of the hippocampus, and other structures, in retention and retrieval of recent and remote memories. We examine episodic, semantic and spatial memory, and show that important distinctions exist among different types of these memories and the structures that mediate them. We argue that retention and retrieval of detailed, vivid autobiographical memories depend on the hippocampal system no matter how long ago they were acquired. Semantic memories, on the other hand, benefit from hippocampal contribution for some time before they can be retrieved independently of the hippocampus. Even semantic memories, however, can have episodic elements associated with them that continue to depend on the hippocampus. Likewise, we distinguish between experientially detailed spatial memories (akin to episodic memory) and more schematic memories (akin to semantic memory) that are sufficient for navigation but not for re-experiencing the environment in which they were acquired. Like their episodic and semantic counterparts, the former type of spatial memory is dependent on the hippocampus no matter how long ago it was acquired, whereas the latter can survive independently of the hippocampus and is represented in extra-hippocampal structures. In short, the evidence reviewed suggests strongly that the function of the hippocampus (and possibly that of related limbic structures) is to help encode, retain, and retrieve experiences, no matter how long ago the events comprising the experience occurred, and no matter whether the memories are episodic or spatial. We conclude that the evidence favours a multiple trace theory (MTT) of memory over two other models: (1) traditional consolidation models which posit that the hippocampus is a time-limited memory structure for all forms of memory; and (2) versions of cognitive map theory which posit that the hippocampus is needed for representing all forms of allocentric space in memory.
PinADX: an interface for customizable debugging with dynamic instrumentation

Dynamic binary instrumentation systems have become popular frameworks for building custom program analysis tools. For example, Pin [8], Valgrind , and DynamoRIO  have been used to build a variety of memory checking, thread checking, cache simulation, and profiling tools. However, there has been little emphasis on building custom debugging tools with these frameworks. We introduce PinADX, a set of debugging extensions to the Pin instrumentation framework that allow easy development of custom debugger features on both Linux and Windows. This paper presents the PinADX API, demonstrates its use in a simple debugger extension, discusses the implementation details in the Pin instrumentation system, and shows that the performance is better than a native debugger for some typical debugging tasks. We believe that dynamic instrumentation technology is a powerful tool for building advanced debugging features and that an easy to use API will greatly facilitate the development of such features.
Does Gamification Work? -- A Literature Review of Empirical Studies on Gamification

This paper reviews peer-reviewed empirical studies on gamification. We create a framework for examining the effects of gamification by drawing from the definitions of gamification and the discussion on motivational affordances. The literature review covers results, independent variables (examined motivational affordances), dependent variables (examined psychological/behavioral outcomes from gamification), the contexts of gamification, and types of studies performed on the gamified systems. The paper examines the state of current research on the topic and points out gaps in existing literature. The review indicates that gamification provides positive effects, however, the effects are greatly dependent on the context in which the gamification is being implemented, as well as on the users using it. The findings of the review provide insight for further studies as well as for the design of gamified systems.
Web User Interaction Mining from Touch-Enabled Mobile Devices

Web services that thrive on mining user interaction data such as search engines can currently track clicks and mouse cursor activity on their Web pages. Cursor interaction mining has been shown to assist in user modeling and search result relevance, and is becoming another source of rich information that data scientists and search engineers can tap into. Due to the growing popularity of touch-enabled mobile devices, search systems may turn to tracking touch interactions in place of cursor interactions. However, unlike cursor interactions, touch interactions are difficult to record reliably and their coordinates have not been shown to relate to regions of user interest. A better approach may be to track the viewport coordinates instead, which the user must manipulate to view the content on a mobile device. These recorded viewport coordinates can potentially reveal what regions of the page interest users and to what degree. Using this information, search system can then improve the design of their pages or use this information in click models or learning to rank systems. In this position paper, we discuss some of the challenges faced in mining interaction data for new modes of interaction, and future research directions in this field.
Issues in evaluating semantic spaces using word analogies

The offset method for solving word analogies has become a standard evaluation tool for vector-space semantic models: it is considered desirable for a space to represent semantic relations as consistent vector offsets. We show that the method’s reliance on cosine similarity conflates offset consistency with largely irrelevant neighborhood structure, and propose simple baselines that should be used to improve the utility of the method in vector space evaluation. Vector space models of semantics (VSMs) represent words as points in a high-dimensional space (Turney and Pantel, 2010). There is considerable interest in evaluating VSMs without needing to embed them in a complete NLP system. One such intrinsic evaluation strategy that has gained in popularity in recent years uses the offset approach to solving word analogy problems (Levy and Goldberg, 2014; Mikolov et al., 2013c; Mikolov et al., 2013a; Turney, 2012). This method assesses whether a linguistic relation — for example, between the base and gerund form of a verb (debug and debugging) — is consistently encoded as a particular linear offset in the space. If that is the case, estimating the offset using one pair of words related in a particular way should enable us to go back and forth between other pairs of words that are related in the same way, e.g., scream and screaming in the base-to-gerund case . Since VSMs are typically continuous spaces, adding the offset between debug and debugging to scream is unlikely to land us exactly on any particular word. The solution to the analogy problem is therefore taken to be the word closest in debug debugging
Data Glove Embedded with 6-DOF Inertial Sensors for Hand Rehabilitation

A hand injury can have great impact on a person's daily life. However, the current manual evaluations of hand functions are imprecise and inconvenient. In this research, a data glove embedded with 6-axis inertial sensors is proposed. With the proposed angle calculating algorithm, accurate bending angles are measured to estimate the real-time movements of hands. This proposed system can provide physicians with an efficient tool to evaluate the recovery of patients and improve the quality of hand rehabilitation.
Stacked Hierarchical Labeling

In this work we propose a hierarchical approach for labeling semantic objects and regions in scenes. Our approach is reminiscent of early vision literature in that we use a decomposition of the image in order to encode relational and spatial information. In contrast to much existing work on structured prediction for scene understanding, we bypass a global probabilistic model and instead directly train a hierarchical inference procedure inspired by the message passing mechanics of some approximate inference procedures in graphical models. This approach mitigates both the theoretical and empirical difficulties of learning probabilistic models when exact inference is intractable. In particular, we draw from recent work in machine learning and break the complex inference process into a hierarchical series of simple machine learning subproblems. Each subproblem in the hierarchy is designed to capture the image and contextual statistics in the scene. This hierarchy spans coarse-to-fine regions and explicitly models the mixtures of semantic labels that may be present due to imperfect segmentation. To avoid cascading of errors and overfitting, we train the learning problems in sequence to ensure robustness to likely errors earlier in the inference sequence and leverage the stacking approach developed by Cohen et al.
Emerging Technologies and Applications on Interactive Entertainments

With the substantial development of digital technologies in multimedia, network communication and user interfaces, we are seeing an increasing number of applications of these technologies, in particular in the entertainment domain. They include computer gaming, elearning, high-definition and interactive TVs, and virtual environments. The development of these applications typically involves the integration of existing technologies as well as the development of new technologies. This Introduction summarizes latest interactive entertainment technologies and applications, and briefly highlights some potential research directions. It also introduces the seven papers that are accepted to the special issue. Hopefully, this will provide the readers some insights into future research topics in interactive entertainment technologies and applications.
Simple vs. compound mark hierarchical marking menus

We present a variant of hierarchical marking menus where items are selected using a series of inflection-free simple marks, rather than the single "zig-zag" compound mark used in the traditional design. Theoretical analysis indicates that this simple mark approach has the potential to significantly increase the number of items in a marking menu that can be selected efficiently and accurately. A user experiment is presented that compares the simple and compound mark techniques. Results show that the simple mark technique allows for significantly more accurate and faster menu selections overall, but most importantly also in menus with a large number of items where performance of the compound mark technique is particularly poor. The simple mark technique also requires significantly less physical input space to perform the selections, making it particularly suitable for small footprint pen-based input devices. Visual design alternatives are also discussed.
Emerging photoluminescence in monolayer MoS2.

Novel physical phenomena can emerge in low-dimensional nanomaterials. Bulk MoS(2), a prototypical metal dichalcogenide, is an indirect bandgap semiconductor with negligible photoluminescence. When the MoS(2) crystal is thinned to monolayer, however, a strong photoluminescence emerges, indicating an indirect to direct bandgap transition in this d-electron system. This observation shows that quantum confinement in layered d-electron materials like MoS(2) provides new opportunities for engineering the electronic structure of matter at the nanoscale.
Detecting algorithmically generated malicious domain names

Recent Botnets such as Conficker, Kraken and Torpig have used DNS based "domain fluxing" for command-and-control, where each Bot queries for existence of a series of domain names and the owner has to register only one such domain name. In this paper, we develop a methodology to detect such "domain fluxes" in DNS traffic by looking for patterns inherent to domain names that are generated algorithmically, in contrast to those generated by humans. In particular, we look at distribution of alphanumeric characters as well as bigrams in all domains that are mapped to the same set of IP-addresses. We present and compare the performance of several distance metrics, including KL-distance, Edit distance and Jaccard measure. We train by using a good data set of domains obtained via a crawl of domains mapped to all IPv4 address space and modeling bad data sets based on behaviors seen so far and expected. We also apply our methodology to packet traces collected at a Tier-1 ISP and show we can automatically detect domain fluxing as used by Conficker botnet with minimal false positives.
Towards a Blockchain Ontology

Blockchain technology is regarded as highly disruptive, but there is a lack of formalization and standardization of terminology. Not only because there are several (sometimes propriety) implementation platforms, but also because the academic literature so far is predominantly written from either a purely technical or an economic application perspective. The result of the confusion is an offspring of blockchain solutions, types, roadmaps and interpretations. For blockchain to be accepted as a technology standard in established industries, it is pivotal that ordinary internet users and business executives have a basic yet fundamental understanding of the workings and impact of blockchain. This conceptual paper provides a theoretical contribution and guidance on what blockchain actually is by taking an ontological approach. Enterprise Ontology is used to make a clear distinction between the datalogical, infological and essential level of blockchain transactions and smart contracts.
Graph-Sparse LDA: A Topic Model with Structured Sparsity

Topic modeling is a powerful tool for uncovering latent structure in many domains, including medicine, finance, and vision. The goals for the model vary depending on the application: sometimes the discovered topics are used for prediction or another downstream task. In other cases, the content of the topic may be of intrinsic scientific interest. Unfortunately, even when one uses modern sparse techniques, discovered topics are often difficult to interpret due to the high dimensionality of the underlying space. To improve topic interpretability, we introduce Graph-Sparse LDA, a hierarchical topic model that uses knowledge of relationships between words (e.g., as encoded by an ontology). In our model, topics are summarized by a few latent concept-words from the underlying graph that explain the observed words. GraphSparse LDA recovers sparse, interpretable summaries on two real-world biomedical datasets while matching state-of-the-art prediction performance.
User profiling and classification for fraud detection in mobile communications networks

The topic of this thesis is fraud detection in mobile communications networks by means of user profiling and classification techniques. The goal is to first identify relevant user groups based on call data and then to assign a user to a relevant group. Fraud may be defined as a dishonest or illegal use of services, with the intention to avoid service charges. Fraud detection is an important application, since network operators lose a relevant portion of their revenue to fraud. Whereas the intentions of the mobile phone users cannot be observed, it is assumed that the intentions are reflected in the call data. The call data is subsequently used in describing behavioral patterns of users. Neural networks and probabilistic models are employed in learning these usage patterns from call data. These models are used either to detect abrupt changes in established usage patterns or to recognize typical usage patterns of fraud. The methods are shown to be effective in detecting fraudulent behavior by empirically testing the methods with data from real mobile communications networks.  No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, electronic, mechanical, photocopying, recording, or otherwise, without the prior permission of the author.