nils-herrmann/clean_scirepeval huggingface dataset:
Lexical tone perception in musicians and non-musicians

It has been suggested that music and speech maintain entirely dissociable mental processing systems. The current study, however, provides evidence that there is an overlap in the processing of certain shared aspects of the two. This study focuses on fundamental frequency (pitch), which is an essential component of melodic units in music and lexical and/or intonational units in speech. We hypothesize that extensive experience with the processing of musical pitch can transfer to a lexical pitch-processing domain. To that end, we asked nine English-speaking musicians and nine Englishspeaking non-musicians to identify and discriminate the four lexical tones of Mandarin Chinese. The subjects performed significantly differently on both tasks; the musicians identified the tones with 89% accuracy and discriminated them with 87% accuracy, while the non-musicians identified them with only 69% accuracy and discriminated them with 71% accuracy. These results provide counter-evidence to the theory of dissociation between music and speech processing.
Building a Dynamic Reputation System for DNS

The Domain Name System (DNS) is an essential protocol used by both legitimate Internet applications and cyber attacks. For example, botnets rely on DNS to support agile command and control infrastructures. An effective way to disrupt these attacks is to place malicious domains on a “blocklist” (or “blacklist”) or to add a filtering rule in a firewall or network intrusion detection system. To evade such security countermeasures, attackers have used DNS agility, e.g., by using new domains daily to evade static blacklists and firewalls. In this paper we propose Notos, a dynamic reputation system for DNS. The premise of this system is that malicious, agile use of DNS has unique characteristics and can be distinguished from legitimate, professionally provisioned DNS services. Notos uses passive DNS query data and analyzes the network and zone features of domains. It builds models of known legitimate domains and malicious domains, and uses these models to compute a reputation score for a new domain indicative of whether the domain is malicious or legitimate. We have evaluated Notos in a large ISP’s network with DNS traffic from 1.4 million users. Our results show that Notos can identify malicious domains with high accuracy (true positive rate of 96.8%) and low false positive rate (0.38%), and can identify these domains weeks or even months before they appear in
MicroProteins: small size-big impact.

MicroProteins (miPs) are short, usually single-domain proteins that, in analogy to miRNAs, heterodimerize with their targets and exert a dominant-negative effect. Recent bioinformatic attempts to identify miPs have resulted in a list of potential miPs, many of which lack the defining characteristics of a miP. In this opinion article, we clearly state the characteristics of a miP as evidenced by known proteins that fit the definition; we explain why modulatory proteins misrepresented as miPs do not qualify as true miPs. We also discuss the evolutionary history of miPs, and how the miP concept can extend beyond transcription factors (TFs) to encompass different non-TF proteins that require dimerization for full function.
Optimization of agitation speed in spinner flask for microcarrier structural integrity and expansion of induced pluripotent stem cells

In recent times, the study and use of induced pluripotent stem cells (iPSC) have become important in order to avoid the ethical issues surrounding the use of embryonic stem cells. Therapeutic, industrial and research based use of iPSC requires large quantities of cells generated in vitro. Mammalian cells, including pluripotent stem cells, have been expanded using 3D culture, however current limitations have not been overcome to allow a uniform, optimized platform for dynamic culture of pluripotent stem cells to be achieved. In the current work, we have expanded mouse iPSC in a spinner flask using Cytodex 3 microcarriers. We have looked at the effect of agitation on the microcarrier survival and optimized an agitation speed that supports bead suspension and iPS cell expansion without any bead breakage. Under the optimized conditions, the mouse iPSC were able to maintain their growth, pluripotency and differentiation capability. We demonstrate that microcarrier survival and iPS cell expansion in a spinner flask are reliant on a very narrow range of spin rates, highlighting the need for precise control of such set ups and the need for improved design of more robust systems.
A Supervised Fine-Grained Sentiment Analysis System for Online Reviews

Sentiment analysis, as a heatedly-discussed research topic in the area of information extraction, has attracted more attention from the beginning of this century. With the rapid development of the Internet, especially the rising popularity of Web2.0 technology, network user has become not only the content maker, but also the receiver of information. Meanwhile, benefiting from the development and maturity of the technology in natural language processing and machine learning, we can widely employ sentiment analysis on subjective texts. In this paper, we propose a supervised learning method on fine-grained sentiment analysis to meet the new challenges by exploring new research ideas and methods to further improve the accuracy and practicability of sentiment analysis. First, this paper presents an improved strength computation method of sentiment word. Second, this paper introduces a sentiment information joint recognition model based on Conditional Random Fields and analyzes the related knowledge of the basic and semantic features. Finally, the experimental results show that our approach and a demo system are feasible and effective.
Musculoskeletal findings of Loeys-Dietz syndrome.

Loeys-Dietz syndrome is a recently recognized multisystemic disorder caused by mutations in the genes encoding the transforming growth factor-beta receptor. It is characterized by aggressive aneurysm formation and vascular tortuosity. We report the musculoskeletal demographic, clinical, and imaging findings of this syndrome to aid in its diagnosis and treatment.



We retrospectively analyzed the demographic, clinical, and imaging data of sixty-five patients with Loeys-Dietz syndrome seen at one institution from May 2007 through December 2008.



The patients had a mean age of twenty-one years, and thirty-six of the sixty-five patients were less than eighteen years old. Previous diagnoses for these patients included Marfan syndrome (sixteen patients) and Ehlers-Danlos syndrome (two patients). Spinal and foot abnormalities were the most clinically important skeletal findings. Eleven patients had talipes equinovarus, and nineteen patients had cervical anomalies and instability. Thirty patients had scoliosis (mean Cobb angle [and standard deviation], 30 degrees +/- 18 degrees ). Two patients had spondylolisthesis, and twenty-two of thirty-three who had computed tomography scans had dural ectasia. Thirty-five patients had pectus excavatum, and eight had pectus carinatum. Combined thumb and wrist signs were present in approximately one-fourth of the patients. Acetabular protrusion was present in approximately one-third of the patients and was usually mild. Fourteen patients had previous orthopaedic procedures, including scoliosis surgery, cervical stabilization, clubfoot correction, and hip arthroplasty. Features of Loeys-Dietz syndrome that are important clues to aid in making this diagnosis include bifid broad uvulas, hypertelorism, substantial joint laxity, and translucent skin.



Patients with Loeys-Dietz syndrome commonly present to the orthopaedic surgeon with cervical malformations, spinal and foot deformities, and findings in the craniofacial and cutaneous systems.
Flavor network and the principles of food pairing

The cultural diversity of culinary practice, as illustrated by the variety of regional cuisines, raises the question of whether there are any general patterns that determine the ingredient combinations used in food today or principles that transcend individual tastes and recipes. We introduce a flavor network that captures the flavor compounds shared by culinary ingredients. Western cuisines show a tendency to use ingredient pairs that share many flavor compounds, supporting the so-called food pairing hypothesis. By contrast, East Asian cuisines tend to avoid compound sharing ingredients. Given the increasing availability of information on food preparation, our data-driven investigation opens new avenues towards a systematic understanding of culinary practice.
Mining evolutionary multi-branch trees from text streams

Understanding topic hierarchies in text streams and their evolution patterns over time is very important in many applications. In this paper, we propose an evolutionary multi-branch tree clustering method for streaming text data. We build evolutionary trees in a Bayesian online filtering framework. The tree construction is formulated as an online posterior estimation problem, which considers both the likelihood of the current tree and conditional prior given the previous tree. We also introduce a constraint model to compute the conditional prior of a tree in the multi-branch setting. Experiments on real world news data demonstrate that our algorithm can better incorporate historical tree information and is more efficient and effective than the traditional evolutionary hierarchical clustering algorithm.
Algorithm Visualization: The State of the Field

We present findings regarding the state of the field of Algorithm Visualization (AV) based on our analysis of a collection of over 500 AVs. We examine how AVs are distributed among topics, who created them and when, their overall quality, and how they are disseminated. There does exist a cadre of good AVs and active developers. Unfortunately, we found that many AVs are of low quality, and coverage is skewed toward a few easier topics. This can make it hard for instructors to locate what they need. There are no effective repositories of AVs currently available, which puts many AVs at risk for being lost to the community over time. Thus, the field appears in need of improvement in disseminating materials, propagating known best practices, and informing developers about topic coverage. These concerns could be mitigated by building community and improving communication among AV users and developers.
Inkjet printed System-in-Package design and manufacturing

Additive manufacturing technology using inkjet offers several improvements to electronics manufacturing compared to current nonadditive masking technologies. Manufacturing processes can be made more efficient, straightforward and flexible compared to subtractive masking processes, several time-consuming and expensive steps can be omitted. Due to the additive process, material loss is minimal, because material is never removed as with etching processes. The amounts of used material and waste are smaller, which is advantageous in both productivity and environmental means. Furthermore, the additive inkjet manufacturing process is flexible allowing fast prototyping, easy design changes and personalization of products. Additive inkjet processing offers new possibilities to electronics integration, by enabling direct writing on various surfaces, and component interconnection without a specific substrate. The design and manufacturing of inkjet printed modules differs notably from the traditional way to manufacture electronics. In this study a multilayer inkjet interconnection process to integrate functional systems was demonstrated, and the issues regarding the design and manufacturing were considered.
Semi-supervised learning of dialogue acts using sentence similarity based on word embeddings

This paper describes a methodology for semi-supervised learning of dialogue acts using the similarity between sentences. We suppose that the dialogue sentences with the same dialogue act are more similar in terms of semantic and syntactic information. However, previous work on sentence similarity mainly modeled a sentence as bag-of-words and then compared different groups of words using corpus-based or knowledge-based measurements of word semantic similarity. Novelly, we present a vector-space sentence representation, composed of word embeddings, that is, the related word distributed representations, and these word embeddings are organised in a sentence syntactic structure. Given the vectors of the dialogue sentences, a distance measurement can be well-defined to compute the similarity between them. Finally, a seeded k-means clustering algorithm is implemented to classify the dialogue sentences into several categories corresponding to particular dialogue acts. This constitutes the semi-supervised nature of the approach, which aims to ameliorate the reliance of the availability of annotated corpora. Experiments with Switchboard Dialog Act corpus show that classification accuracy is improved by 14%, compared to the state-of-art methods based on Support Vector Machine.
Which Factors Influence ERP Implementation Projects in Small and Medium-Sized Enterprises?

The aim of our study was to provide a contribution to the research field of the critical success factors (CSFs) of ERP projects, with specific focus on smaller enterprises (SMEs). Therefore, we conducted a systematic literature review in order to update the existing reviews of CSFs. On the basis of that review, we led several interviews within German SMEs and with ERP consultants experienced with ERP implementations in SMEs. As a result, we showed that all factors found in the literature also affected the success of ERP projects in SMEs. However, within those projects, technological factors gained much more importance compared to the factors which influence the success from larger ERP projects the most. For SMEs, factors like Organizational fit of the ERP system as well as ERP system tests are even more important than Top management support or Project management, which were the most important factors for large-scaled companies.
An Introduction to Software Architecture

As the size of software systems increases, the algorithms and data structures of the computation no longer constitute the major design problems. When systems are constructed from many components, the organization of the overall system—the software architecture—presents a new set of design problems. This level of design has been addressed in a number of ways including informal diagrams and descriptive terms, module interconnection languages, templates and frameworks for systems that serve the needs of specific domains, and formal models of component integration mechanisms. In this paper we provide an introduction to the emerging field of software architecture. We begin by considering a number of common architectural styles upon which many systems are currently based and show how different styles can be combined in a single design. Then we present six case studies to illustrate how architectural representations can improve our understanding of complex software systems. Finally, we survey some of the outstanding problems in the field, and consider a few of the promising research directions.
Evaluating bag-of-visual-words representations in scene classification

Based on keypoints extracted as salient image patches, an image can be described as a "bag of visual words" and this representation has been used in scene classification. The choice of dimension, selection, and weighting of visual words in this representation is crucial to the classification performance but has not been thoroughly studied in previous work. Given the analogy between this representation and the bag-of-words representation of text documents, we apply techniques used in text categorization, including term weighting, stop word removal, feature selection, to generate image representations that differ in the dimension, selection, and weighting of visual words. The impact of these representation choices to scene classification is studied through extensive experiments on the TRECVID and PASCAL collection. This study provides an empirical basis for designing visual-word representations that are likely to produce superior classification performance.
A General Model and Analysis of Physical Layer Capture in 802.11 Networks

While packet capture has been observed in real implementations of 802.11 devices, there is a lack of accurate models that describe the phenomenon. We present a general analytical model and an iterative method that predicts error probabilities and throughputs of packet transmissions with multiple senderreceiver pairs. Our model offers a more accurate prediction than previous work by taking into account the cumulative strength of interference signals and using the BER model to convert a signal to interference and noise ratio value to a bit error probability. This permits the analysis of packet reception at any transmission rate with interference from neighbors at any set of locations. We also prove that our iterative method converges, and we verify the accuracy of our model through simulations in Qualnet. Last, we present a rate assignment algorithm to reduce the average delay as an application of our analysis.
Tailoring entity resolution for matching product offers

Product matching is a challenging variation of entity resolution to identify representations and offers referring to the same product. Product matching is highly difficult due to the broad spectrum of products, many similar but different products, frequently missing or wrong values, and the textual nature of product titles and descriptions. We propose the use of tailored approaches for product matching based on a preprocessing of product offers to extract and clean new attributes usable for matching. In particular, we propose a new approach to extract and use so-called product codes to identify products and distinguish them from similar product variations. We evaluate the effectiveness of the proposed approaches with challenging real-life datasets with product offers from online shops. We also show that the UPC information in product offers is often error-prone and can lead to insufficient match decisions.
Learning without Forgetting

When building a unified vision system or gradually adding new apabilities to a system, the usual assumption is that training data for all tasks is always available. However, as the number of tasks grows, storing and retraining on such data becomes infeasible. A new problem arises where we add new capabilities to a Convolutional Neural Network (CNN), but the training data for its existing capabilities are unavailable. We propose our Learning without Forgetting method, which uses only new task data to train the network while preserving the original capabilities. Our method performs favorably compared to commonly used feature extraction and fine-tuning adaption techniques and performs similarly to multitask learning that uses original task data we assume unavailable. A more surprising observation is that Learning without Forgetting may be able to replace fine-tuning with similar old and new task datasets for improved new task performance.
Signal/Collect: Graph Algorithms for the (Semantic) Web

The Semantic Web graph is growing at an incredible pace, enabling opportunities to discover new knowledge by interlinking and analyzing previously unconnected data sets. This confronts researchers with a conundrum: Whilst the data is available the programming models that facilitate scalability and the infrastructure to run various algorithms on the graph are missing. Some use MapReduce – a good solution for many problems. However, even some simple iterative graph algorithms do not map nicely to that programming model requiring programmers to shoehorn their problem to the MapReduce model. This paper presents the Signal/Collect programming model for synchronous and asynchronous graph algorithms. We demonstrate that this abstraction can capture the essence of many algorithms on graphs in a concise and elegant way by giving Signal/Collect adaptations of various relevant algorithms. Furthermore, we built and evaluated a prototype Signal/Collect framework that executes algorithms in our programming model. We empirically show that this prototype transparently scales and that guiding computations by scoring as well as asynchronicity can greatly improve the convergence of some example algorithms. We released the framework under the Apache License 2.0 (at http://www.ifi.uzh.ch/ddis/research/sc).
A Layered Model of Human Body and Garment Deformation

In this paper we present a framework for learning a three layered model of human shape, pose and garment deformation. The proposed deformation model provides intuitive control over the three parameters independently, while producing aesthetically pleasing deformations of both the garment and the human body. The shape and pose deformation layers of the model are trained on a rich dataset of full body 3D scans of human subjects in a variety of poses. The garment deformation layer is trained on animated mesh sequences of dressed actors and relies on a novel technique for human shape and posture estimation under clothing. The key contribution of this paper is that we consider garment deformations as the residual transformations between a naked mesh and the dressed mesh of the same subject.
Protocol Implementation Using Integrated Layer Processing

Integrated Layer Processing (ILP) is an implementation concept which "permit[s] the implementor the option of performing all the [data] manipulation steps in one or two integrated processing loops" . To estimate the achievable benefits of ILP a file transfer application with an encryption function on top of a user-level TCP has been implemented and the performance of the application in terms of throughput and packet processing times has been measured. The results show that it is possible to obtain performance benefits by integrating marshalling, encryption and TCP checksum calculation. They also show that the benefits are smaller than in simple experiments, where ILP effects have not been evaluated in a complete protocol environment. Simulations of memory access and cache hit rate show that the main benefit of ILP is reduced memory accesses rather than an improved cache hit rate. The results further show that data manipulation characteristics may significantly influence the cache behavior and the achievable performance gain of ILP.