nils-herrmann/clean_scirepeval huggingface dataset:
Document Cards: A Top Trumps Visualization for Documents

Finding suitable, less space consuming views for a document's main content is crucial to provide convenient access to large document collections on display devices of different size. We present a novel compact visualization which represents the document's key semantic as a mixture of images and important key terms, similar to cards in a top trumps game. The key terms are extracted using an advanced text mining approach based on a fully automatic document structure extraction. The images and their captions are extracted using a graphical heuristic and the captions are used for a semi-semantic image weighting. Furthermore, we use the image color histogram for classification and show at least one representative from each non-empty image class. The approach is demonstrated for the IEEE InfoVis publications of a complete year. The method can easily be applied to other publication collections and sets of documents which contain images.
Understanding the search for meaning in life: personality, cognitive style, and the dynamic between seeking and experiencing meaning.

Although several theories assert that understanding the search for meaning in life is important, empirical research on this construct is sparse. Three studies provide the first extensive effort to understand the correlates of the search for meaning in a multistudy research program. Assessed were relations between search for meaning and well-being, cognitive style, and the Big Five, Big Three, Approach/Avoidance, and Interest models of personality, with a particular emphasis on understanding the correlates of search for meaning that are independent of presence of meaning. Conceptual models of the relation between search and presence were tested. Findings suggest that people lacking meaning search for it; the search for meaning did not appear to lead to its presence. Study 3 found that basic motive dispositions moderated relations between search for meaning and its presence. Results highlight the importance of basic personality dispositions in understanding the search for meaning and its correlates.
DILIGENT: Towards a fine-grained methodology for Distributed, Loosely-controlled and evolving Engineering of oNTologies

Ontology engineering processes in truly distributed settings like the Semantic Web or global peer-to-peer systems may not be adequately supported by conventional, centralized ontology engineering methodologies. In this paper, we present our work towards the DILIGENT methodology, which is intended to support domain experts in a distributed setting to engineer and evolve ontologies with the help of a fine-grained methodological approach based on Rhetorical Structure Theory, viz. the DILIGENT model of ontology engineering by argumentation.
CORONET: Fault tolerance for Software Defined Networks

Software Defined Networking, or SDN, based networks are being deployed not only in testbed networks, but also in production networks. Although fault-tolerance is one of the most desirable properties in production networks, there are not much study in providing fault-tolerance to SDN-based networks. The goal of this work is to develop a fault tolerant SDN architecture that can rapidly recover from faults and scale to large network sizes. This paper presents CORONET, a SDN fault-tolerant system that recovers from multiple link failures in the data plane. We describe a prototype implementation based on NOX that demonstrates fault recovery for emulated topologies using Mininet. We also discuss possible extensions to handle control plane and controller faults.
RDF2Rules: Learning Rules from RDF Knowledge Bases by Mining Frequent Predicate Cycles

Recently, several large-scale RDF knowledge bases have bee n built and applied in many knowledge-based applications. To furth er increase the number of facts in RDF knowledge bases, logic rule s can be used to predict new facts based on the existing ones. There for , how to automatically learn reliable rules from large-scale knowledge bases becomes increasingly important. In this paper, w e propose a novel rule learning approach named RDF2Rules for RDF knowledge bases. RDF2Rules first mines frequent predicate c ycl s (FPCs), a kind of interesting frequent patterns in knowledg e bases, and then generates rules from the mined FPCs. Because each FP C can produce multiple rules, and effective pruning strategy is used in the process of mining FPCs, RDF2Rules works very efficient ly. Another advantage of RDF2Rules is that it uses the entity typ e information when generates and evaluates rules, which makes t he learned rules more accurate. Experiments show that our appr o ch outperforms the compared approach in terms of both efficienc y a d accuracy.
Hashtag recommendation for hyperlinked tweets

Presence of hyperlink in a tweet is a strong indication of tweet being more informative. In this paper, we study the problem of hashtag recommendation for hyperlinked tweets (i.e., tweets containing links to Web pages). By recommending hashtags to hyperlinked tweets, we argue that the functions of hashtags such as providing the right context to interpret the tweets, tweet categorization, and tweet promotion, can be extended to the linked documents. The proposed solution for hashtag recommendation consists of two phases. In the first phase, we select candidate hashtags through five schemes by considering the similar tweets, the similar documents, the named entities contained in the document, and the domain of the link. In the second phase, we formulate the hashtag recommendation problem as a learning to rank problem and adopt RankSVM to aggregate and rank the candidate hashtags. Our experiments on a collection of 24 million tweets show that the proposed solution achieves promising results.
Distributed computing in practice: the Condor experience

Since 1984, the Condor project has enabled ordinary users to do extraordinary computing. Today, the project continues to explore the social and technical problems of cooperative computing on scales ranging from the desktop to the world-wide computational Grid. In this paper, we provide the history and philosophy of the Condor project and describe how it has interacted with other projects and evolved along with the field of distributed computing. We outline the core components of the Condor system and describe how the technology of computing must correspond to social structures. Throughout, we reflect on the lessons of experience and chart the course travelled by research ideas as they grow into production systems.
ACIQ: Analytical Clipping for Integer Quantization of neural networks

Unlike traditional approaches that focus on the quantization at the network level, in this work we propose to minimize the quantization effect at the tensor level. We analyze the trade-off between quantization noise and clipping distortion in low precision networks. We identify the statistics of various tensors, and derive exact expressions for the mean-square-error degradation due to clipping. By optimizing these expressions, we show marked improvements over standard quantization schemes that normally avoid clipping. For example, just by choosing the accurate clipping values, more than 40% accuracy improvement is obtained for the quantization of VGG16-BN to 4-bits of precision. Our results have many applications for the quantization of neural networks at both training and inference time. One immediate application is for a rapid deployment of neural networks to low-precision accelerators without time-consuming fine tuning or the availability of the full datasets.
ARQuake: an outdoor/indoor augmented reality first person application

This paper presents an outdoor/indoor augmented reality first person application ARQuake we have developed. ARQuake is an extension of the desktop game Quake, and as such we are investigating how to convert a desktop first person application into an outdoor/indoor mobile augmented reality application. We present an architecture for a low cost, moderately accurate six degrees of freedom tracking system based on GPS, digital compass, and fiducial vision-based tracking. Usability issues such as monster selection, colour, and input devices are discussed. A second application for AR architectural design visualisation is presented.
Enabling Real-Time Context-Aware Collaboration through 5G and Mobile Edge Computing

Creating context-aware ad hoc collaborative systems remains to be one of the primary hurdles hampering the ubiquitous deployment of IT and communication services. Especially under mission-critical scenarios, these services must often adhere to strict timing deadlines. We believe empowering such realtime collaboration systems requires context-aware application platforms working in conjunction with ultra-low latency data transmissions. In this paper, we make a strong case that this could be accomplished by combining the novel communication architectures being proposed for 5G with the principles of Mobile Edge Computing (MEC). We show that combining 5G with MEC would enable inter- and intra-domain use cases that are otherwise not feasible.
A Better Way to Pretrain Deep Boltzmann Machines

We describe how the pretraining algorithm for Deep Boltzmann Machines (DBMs) is related to the pretraining algorithm for Deep Belief Networks and we show that under certain conditions, the pretraining procedure improves the variational lower bound of a two-hidden-layer DBM. Based on this analysis, we develop a different method of pretraining DBMs that distributes the modelling work more evenly over the hidden layers. Our results on the MNIST and NORB datasets demonstrate that the new pretraining algorithm allows us to learn better generative models.
A comparative study between honey and povidone iodine as dressing solution for Wagner type II diabetic foot ulcers.

Honey dressing has been used to promote wound healing for years but scanty scientific studies did not provide enough evidences to justify it benefits in the treatment of diabetic foot ulcers. We conducted a prospective study to compare the effect of honey dressing for Wagner's grade-II diabetic foot ulcers with controlled dressing group (povidone iodine followed by normal saline). Surgical debridement and appropriate antibiotics were prescribed in all patients. There were 30 patients age between 31 to 65-years-old (mean of 52.1 years). The mean healing time in the standard dressing group was 15.4 days (range 9-36 days) compared to 14.4 days (range 7-26 days) in the honey group (p < 0.005). In conclusion, ulcer healing was not significantly different in both study groups. Honey dressing is a safe alternative dressing for Wagner grade-II diabetic foot ulcers.
Anonymous Usage of Location-Based Services Through Spatial and Temporal Cloaking

Advances in sensing and tracking technology enable location-based applications but they also create significant privacy risks. Anonymity can provide a high degree of privacy, save service users from dealing with service providers’ privacy policies, and reduce the service providers’ requirements for safeguarding private information. However, guaranteeing anonymous usage of location-based services requires that the precise location information transmitted by a user cannot be easily used to re-identify the subject. This paper presents a middleware architecture and algorithms that can be used by a centralized location broker service. The adaptive algorithms adjust the resolution of location information along spatial or temporal dimensions to meet specified anonymity constraints based on the entities who may be using location services within a given area. Using a model based on automotive traffic counts and cartographic material, we estimate the realistically expected spatial resolution for different anonymity constraints. The median resolution generated by our algorithms is 125 meters. Thus, anonymous location-based requests for urban areas would have the same accuracy currently needed for E-911 services; this would provide sufficient resolution for wayfinding, automated bus routing services and similar location-dependent services.
Wound microbiology and associated approaches to wound management.

The majority of dermal wounds are colonized with aerobic and anaerobic microorganisms that originate predominantly from mucosal surfaces such as those of the oral cavity and gut. The role and significance of microorganisms in wound healing has been debated for many years. While some experts consider the microbial density to be critical in predicting wound healing and infection, others consider the types of microorganisms to be of greater importance. However, these and other factors such as microbial synergy, the host immune response, and the quality of tissue must be considered collectively in assessing the probability of infection. Debate also exists regarding the value of wound sampling, the types of wounds that should be sampled, and the sampling technique required to generate the most meaningful data. In the laboratory, consideration must be given to the relevance of culturing polymicrobial specimens, the value in identifying one or more microorganisms, and the microorganisms that should be assayed for antibiotic susceptibility. Although appropriate systemic antibiotics are essential for the treatment of deteriorating, clinically infected wounds, debate exists regarding the relevance and use of antibiotics (systemic or topical) and antiseptics (topical) in the treatment of nonhealing wounds that have no clinical signs of infection. In providing a detailed analysis of wound microbiology, together with current opinion and controversies regarding wound assessment and treatment, this review has attempted to capture and address microbiological aspects that are critical to the successful management of microorganisms in wounds.
A Methodology for Realizing High Efficiency Class-J in a Linear and Broadband PA

The design and implementation of a class-J mode RF power amplifier is described. The experimental results indicate the class-J mode's potential in achieving high efficiency across extensive bandwidth, while maintaining predistortable levels of linearity. A commercially available 10 W GaN (gallium nitride) high electron mobility transistor device was used in this investigation, together with a combination of high power waveform measurements, active harmonic load-pull and theoretical analysis of the class-J mode. Targeting a working bandwidth of 1.5-2.5 GHz an initial power amplifier (PA) design was based on basic class-J theory and computer-aided design simulation. This realized a 50% bandwidth with measured drain efficiency of 60%-70%. A second PA design iteration has realized near-rated output power of 39 dBm and improved efficiency beyond the original 2.5 GHz target, hence extending efficient PA operation across a bandwidth of 1.4-2.6 GHz, centered at 2 GHz. This second iteration made extensive use of active harmonic load-pull and waveform measurements, and incorporated a novel design methodology for achieving predistortable linearity. The class-J amplifier has been found to be more realizable than conventional class-AB modes, with a better compromise between power and efficiency tradeoffs over a substantial RF bandwidth.
Commonsense Causal Reasoning Using Millions of Personal Stories

The personal stories that people write in their Internet weblogs include a substantial amount of information about the causal relationships between everyday events. In this paper we describe our efforts to use millions of these stories for automated commonsense causal reasoning. Casting the commonsense causal reasoning problem as a Choice of Plausible Alternatives, we describe four experiments that compare various statistical and information retrieval approaches to exploit causal information in story corpora. The top performing system in these experiments uses a simple co-occurrence statistic between words in the causal antecedent and consequent, calculated as the Pointwise Mutual Information between words in a corpus of millions of personal stories.
Receiver Design for Downlink Non-Orthogonal Multiple Access (NOMA)

Non-orthogonal multiple access (NOMA) is one of the promising radio access techniques for further cellular enhancements toward 5G. Compared to orthogonal multiple access (OMA) such as orthogonal frequency-division multiple access (OFDMA), large performance gains were confirmed via system-level simulations. However, NOMA link-level simulations and the design of the receiver remain of great importance to validate NOMA performance gains. In this paper, we evaluate downlink NOMA link-level performance with multiple receiver designs and propose a novel NOMA transmitter and receiver design, where the signals of multi-users are jointly modulated at transmitter side and detected at receiver side. The predominant advantage of the proposed scheme is that at receiver side interference cancellation to the interference signal is not needed, thus low complexity is achieved. The performances of codeword-level SIC, symbol-level SIC and the proposed receiver are evaluated and compared with ideal SIC. Simulation results show that compared with ideal SIC, downlink NOMA link-level performance depends on actual receiver design and the difference in the power ratio split between the cell edge user and cell center user. In particular, it is shown that codeword-level SIC and the proposed receiver can both provide a good performance even when the power ratio difference between the cell center user and cell edge user is small and with real channel estimation.
PowerPlay: Training an Increasingly General Problem Solver by Continually Searching for the Simplest Still Unsolvable Problem

Most of computer science focuses on automatically solving given computational problems. I focus on automatically inventing or discovering problems in a way inspired by the playful behavior of animals and humans, to train a more and more general problem solver from scratch in an unsupervised fashion. Consider the infinite set of all computable descriptions of tasks with possibly computable solutions. Given a general problem-solving architecture, at any given time, the novel algorithmic framework PowerPlay (Schmidhuber, 2011) searches the space of possible pairs of new tasks and modifications of the current problem solver, until it finds a more powerful problem solver that provably solves all previously learned tasks plus the new one, while the unmodified predecessor does not. Newly invented tasks may require to achieve a wow-effect by making previously learned skills more efficient such that they require less time and space. New skills may (partially) re-use previously learned skills. The greedy search of typical PowerPlay variants uses time-optimal program search to order candidate pairs of tasks and solver modifications by their conditional computational (time and space) complexity, given the stored experience so far. The new task and its corresponding task-solving skill are those first found and validated. This biases the search toward pairs that can be described compactly and validated quickly. The computational costs of validating new tasks need not grow with task repertoire size. Standard problem solver architectures of personal computers or neural networks tend to generalize by solving numerous tasks outside the self-invented training set; PowerPlay's ongoing search for novelty keeps breaking the generalization abilities of its present solver. This is related to Gödel's sequence of increasingly powerful formal theories based on adding formerly unprovable statements to the axioms without affecting previously provable theorems. The continually increasing repertoire of problem-solving procedures can be exploited by a parallel search for solutions to additional externally posed tasks. PowerPlay may be viewed as a greedy but practical implementation of basic principles of creativity (Schmidhuber, 2006a, 2010). A first experimental analysis can be found in separate papers (Srivastava et al., 2012a,b, 2013).
Online variance minimization

We consider the following type of online variance minimization problem: In every trial t our algorithms get a covariance matrix C t and try to select a parameter vector w t−1 such that the total variance over a sequence of trials $\sum_{t=1}^{T} (\boldsymbol {w}^{t-1})^{\top} \boldsymbol {C}^{t}\boldsymbol {w}^{t-1}$ is not much larger than the total variance of the best parameter vector u chosen in hindsight. Two parameter spaces in ℝ n are considered—the probability simplex and the unit sphere. The first space is associated with the problem of minimizing risk in stock portfolios and the second space leads to an online calculation of the eigenvector with minimum eigenvalue of the total covariance matrix $\sum_{t=1}^{T} \boldsymbol {C}^{t}$ . For the first parameter space we apply the Exponentiated Gradient algorithm which is motivated with a relative entropy regularization. In the second case, the algorithm has to maintain uncertainty information over all unit directions u. For this purpose, directions are represented as dyads uu ⊤ and the uncertainty over all directions as a mixture of dyads which is a density matrix. The motivating divergence for density matrices is the quantum version of the relative entropy and the resulting algorithm is a special case of the Matrix Exponentiated Gradient algorithm. In each of the two cases we prove bounds on the additional total variance incurred by the online algorithm over the best offline parameter.
Logarithmic regret algorithms for online convex optimization

In an online convex optimization problem a decision-maker makes a sequence of decisions, i.e., chooses a sequence of points in Euclidean space, from a fixed feasible set. After each point is chosen, it encounters a sequence of (possibly unrelated) convex cost functions. Zinkevich (ICML 2003) introduced this framework, which models many natural repeated decision-making problems and generalizes many existing problems such as Prediction from Expert Advice and Cover’s Universal Portfolios. Zinkevich showed that a simple online gradient descent algorithm achieves additive regret $O(\sqrt{T})$ , for an arbitrary sequence of T convex cost functions (of bounded gradients), with respect to the best single decision in hindsight. In this paper, we give algorithms that achieve regret O(log (T)) for an arbitrary sequence of strictly convex functions (with bounded first and second derivatives). This mirrors what has been done for the special cases of prediction from expert advice by Kivinen and Warmuth (EuroCOLT 1999), and Universal Portfolios by Cover ( 1991). We propose several algorithms achieving logarithmic regret, which besides being more general are also much more efficient to implement. The main new ideas give rise to an efficient algorithm based on the Newton method for optimization, a new tool in the field. Our analysis shows a surprising connection between the natural follow-the-leader approach and the Newton method. We also analyze other algorithms, which tie together several different previous approaches including follow-the-leader, exponential weighting, Cover’s algorithm and gradient descent.