nils-herrmann/clean_scirepeval huggingface dataset:
A framework to improve communication during the requirements elicitation process in GSD projects

Achieving a shared understanding of requirements is difficult in any situation, even more so in global software development projects. In such environments, people must deal not only with the lack of face to face communication, but also with other issues such as time difference, cultural diversity and a large amount of information originating from different sources throughout the world. Obtaining the right requirements therefore implies extra effort. In order to minimize such problems, we propose a framework that focuses on analyzing the factors that may be problematic in global software development and which suggests a set of strategies to improve the requirements elicitation process in such environments. In this paper, we describe the different phases of our framework and present the results of an experiment that test part of this framework. The results indicate that applying some of the strategies proposed in the framework seems to positively affect the stakeholders’ satisfaction with regard to communication. Moreover, the quality of the written software requirements specifications seems to be better as well when using those strategies.
Comparing Convolutional Neural Networks to Traditional Models for Slot Filling

We address relation classification in the context of slot filling, the task of finding and evaluating fillers like “Steve Jobs” for the slot X in “X founded Apple”. We propose a convolutional neural network which splits the input sentence into three parts according to the relation arguments and compare it to state-ofthe-art and traditional approaches of relation classification. Finally, we combine different methods and show that the combination is better than individual approaches. We also analyze the effect of genre differences on performance.
Analog in-memory subthreshold deep neural network accelerator

Low duty-cycle mobile systems can benefit from ultra-low power deep neural network (DNN) accelerators. Analog in-memory computational units are used to store synaptic weights in on-chip non-volatile arrays and perform current-based calculations. In-memory computation entirely eliminates off-chip weight accesses, parallelizes operation, and amortizes readout power costs by reusing currents. The proposed system achieves 900nW measured power, with an estimated energy efficiency of 0.012pJ/MAC in a 130nm SONOS process.
Towards reliable interactive data cleaning: a user survey and recommendations

Data cleaning is frequently an iterative process tailored to the requirements of a specific analysis task. The design and implementation of iterative data cleaning tools presents novel challenges, both technical and organizational, to the community. In this paper, we present results from a user survey (N = 29) of data analysts and infrastructure engineers from industry and academia. We highlight three important themes: (1) the iterative nature of data cleaning, (2) the lack of rigor in evaluating the correctness of data cleaning, and (3) the disconnect between the analysts who query the data and the infrastructure engineers who design the cleaning pipelines. We conclude by presenting a number of recommendations for future work in which we envision an interactive data cleaning system that accounts for the observed challenges.
Attitudes towards user experience (UX) measurement

User experience (UX), as an immature research area, is still haunted by the challenges of defining the scope of UX in general and operationalising experiential qualities in particular. To explore the basic question whether UX constructs are measurable, we conducted semi-structured interviews with eleven UX professionals where a set of questions in relation to UX measurement were explored. The interviewees expressed scepticism as well as ambivalence towards UX measures and shared anecdotes related to such measures in different contexts. Besides, the data suggested that design-oriented UX professionals tended to be sceptical about UX measurement. To examine whether such an attitude prevailed in the HCI community, we conducted a survey with essentially the same set of questions used in the interviews. Altogether 367 responses were received; 170 of them were valid and analysed. The survey provided empirical evidence on this issue as a baseline for progress in UX measurement. Overall, results indicated that attitude was favourable and there were nuanced views on details of UX measurement, implying good prospects for its acceptance, given further progress in research and education in UX measurement where UX modelling grounded in theories can play a crucial role. Mutual recognition of the value of objective measures and subjective accounts of user experience can enhance the maturity of this area.
IMG2DSM: Height Simulation From Single Imagery Using Conditional Generative Adversarial Net

This letter proposes a groundbreaking approach in the remote-sensing community to simulating the digital surface model (DSM) from a single optical image. This novel technique uses conditional generative adversarial networks whose architecture is based on an encoder–decoder network with skip connections (generator) and penalizing structures at the scale of image patches (discriminator). The network is trained on scenes where both the DSM and optical data are available to establish an image-to-DSM translation rule. The trained network is then utilized to simulate elevation information on target scenes where no corresponding elevation information exists. The capability of the approach is evaluated both visually (in terms of photographic interpretation) and quantitatively (in terms of reconstruction errors and classification accuracies) on subdecimeter spatial resolution data sets captured over Vaihingen, Potsdam, and Stockholm. The results confirm the promising performance of the proposed framework.
A review of methods for spike sorting: the detection and classification of neural action potentials.

The detection of neural spike activity is a technical challenge that is a prerequisite for studying many types of brain function. Measuring the activity of individual neurons accurately can be difficult due to large amounts of background noise and the difficulty in distinguishing the action potentials of one neuron from those of others in the local area. This article reviews algorithms and methods for detecting and classifying action potentials, a problem commonly referred to as spike sorting. The article first discusses the challenges of measuring neural activity and the basic issues of signal detection and classification. It reviews and illustrates algorithms and techniques that have been applied to many of the problems in spike sorting and discusses the advantages and limitations of each and the applicability of these methods for different types of experimental demands. The article is written both for the physiologist wanting to use simple methods that will improve experimental yield and minimize the selection biases of traditional techniques and for those who want to apply or extend more sophisticated algorithms to meet new experimental challenges.
Range-free localization schemes for large scale sensor networks

Wireless Sensor Networks have been proposed for a multitude of location-dependent applications. For such systems, the cost and limitations of the hardware on sensing nodes prevent the use of range-based localization schemes that depend on absolute point-to-point distance estimates. Because coarse accuracy is sufficient for most sensor network applications, solutions in range-free localization are being pursued as a cost-effective alternative to more expensive range-based approaches. In this paper, we present APIT, a novel localization algorithm that is range-free. We show that our APIT scheme performs best when an irregular radio pattern and random node placement are considered, and low communication overhead is desired. We compare our work via extensive simulation, with three state-of-the-art range-free localization schemes to identify the preferable system configurations of each. In addition, we study the effect of location error on routing and tracking performance. We show that routing performance and tracking accuracy are not significantly affected by localization error when the error is less than 0.4 times the communication radio radius.
An inverse problem approach for automatically adjusting the parameters for rendering clouds using photographs

Clouds play an important role in creating realistic images of outdoor scenes. Many methods have therefore been proposed for displaying realistic clouds. However, the realism of the resulting images depends on many parameters used to render them and it is often difficult to adjust those parameters manually. This paper proposes a method for addressing this problem by solving an inverse rendering problem: given a non-uniform synthetic cloud density distribution, the parameters for rendering the synthetic clouds are estimated using photographs of real clouds. The objective function is defined as the difference between the color histograms of the photograph and the synthetic image. Our method searches for the optimal parameters using genetic algorithms. During the search process, we take into account the multiple scattering of light inside the clouds. The search process is accelerated by precomputing a set of intermediate images. After ten to twenty minutes of precomputation, our method estimates the optimal parameters within a minute.
A Survey of Classification Methods in Data Streams

With the advance in both hardware and software technologies, automated data generation and storage has become faster than ever. Such data is referred to as data streams. Streaming data is ubiquitous today and it is often a challenging task to store, analyze and visualize such rapid large volumes of data. Most conventional data mining techniques have to be adapted to run in a streaming environment, because of the underlying resource constraints in terms of memory and running time. Furthermore, the data stream may often show concept drift, because of which adaptation of conventional algorithms becomes more challenging. One such important conventional data mining problem is that of classification. In the classification problem, we attempt to model the class variable on the basis of one or more feature variables. While this problem has been extensively studied from a conventional mining perspective, it is a much more challenging problem in the data stream domain. In this chapter, we will re-visit the problem of classification from the data stream perspective. The techniques for this problem need to be thoroughly re-designed to address the issue of resource constraints and concept drift. This chapter reviews the state-of-the-art techniques in the literature along with their corresponding advantages and disadvantages.
Radical atoms: beyond tangible bits, toward transformable materials

From GuI to TuI Humans have evolved a heightened ability to sense and manipulate the physical world, yet the digital world takes little advantage of our capacity for hand-eye coordination. A tangible user interface (TUI) builds upon our dexterity by embodying digital information in physical space. Tangible design expands the affordances of physical objects so they can Graphical user interfaces (GUIs) let users see digital information only through a screen, as if looking into a pool of water, as depicted in Figure 1 on page 40. We interact with the forms below through remote controls, such as a mouse, a keyboard, or a touchscreen (Figure 1a). Now imagine an iceberg, a mass of ice that penetrates the surface of the water and provides a handle for the mass beneath. This metaphor describes tangible user interfaces: They act as physical manifestations of computation, allowing us to interact directly with the portion that is made tangible—the “tip of the iceberg” . Radical Atoms takes a leap beyond tangible interfaces by assuming a hypothetical generation of materials that can change form and CoVer STorY
AND/OR Multi-Valued Decision Diagrams (AOMDDs) for Graphical Models

Inspired by AND/OR search spaces for graphical models recen tly introduced, we propose to augment Multi-Valued Decision Diagrams (MDD) with AND node s, in order to capture function decomposition structure and to extend these compiled data s tructures to general weighted graphical models (e.g., probabilistic models). We present the AND/OR Multi-Valued Decision Diagram (AOMDD) which compiles a graphical model into a canonical fo rm that supports polynomial (e.g., solution counting, belief updating) or constant time (e.g. quivalence of graphical models) queries. We provide two algorithms for compiling the AOMDD of a graphi cal model. The first is search based, and works by applying reduction rules to the trace of t he memory intensive AND/OR search algorithm. The second algorithm is based on a Bucket Elimina tio schedule for assembling the AOMDD for a graphical model starting from the AOMDDs for its f unctions, and combining them via theAPPLY operator. For both algorithms, the compilation time and the siz of the AOMDD are, in the worst case, exponential in the tr ewidthof the graphical model, rather than pathwidthas is known for ordered binary decision diagrams (OBDDs). We also intr duce the concept of semantic treewidth, which helps explain why the size of a decision diagram is oft en much smaller than the worst case bound.
DeepDefense: Identifying DDoS Attack via Deep Learning

Distributed Denial of Service (DDoS) attacks grow rapidly and become one of the fatal threats to the Internet. Automatically detecting DDoS attack packets is one of the main defense mechanisms. Conventional solutions monitor network traffic and identify attack activities from legitimate network traffic based on statistical divergence. Machine learning is another method to improve identifying performance based on statistical features. However, conventional machine learning techniques are limited by the shallow representation models. In this paper, we propose a deep learning based DDoS attack detection approach (DeepDefense). Deep learning approach can automatically extract high-level features from low-level ones and gain powerful representation and inference. We design a recurrent deep neural network to learn patterns from sequences of network traffic and trace network attack activities. The experimental results demonstrate a better performance of our model compared with conventional machine learning models. We reduce the error rate from 7.517% to 2.103% compared with conventional machine learning method in the larger data set.
The Spatial Node Distribution of the Random Waypoint Mobility Model

The random waypoint model is a frequently used mobility model for simulation–based studies of wireless ad hoc networks. This paper investigates the spatial node distribution that results from using this model. We show and interpret simulation results on a square and circular system area, derive an analytical expression of the expected node distribution in one dimension, and give an approximation for the two–dimensional case. Finally, the concept of attraction areas and a modified random waypoint model, the random borderpoint model, is analyzed by simulation.
A Load-Power Adaptive Dual Pulse Modulated Current Phasor-Controlled ZVS High-Frequency Resonant Inverter for Induction Heating Applications

A new prototype of an efficiency-improved zero voltage soft-switching (ZVS) high-frequency resonant (HF-R) inverter for induction heating (IH) applications is presented in this paper. By adopting the dual pulse modulation mode (DPMM) that incorporates a submode power regulation scheme such as pulse density modulation, pulse frequency modulation, and asymmetrical pulse width modulation into main one of the resonant current phase angle difference ( θ) control, the IH load power can be widely regulated under the condition of ZVS, while significantly improving the efficiency in the low output power setting. The essential performances on the output power regulations and ZVS operations with the DPMM schemes are demonstrated in an experiment based on a 1 kW-60 kHz laboratory prototype of the ZVS HF-R inverter. The validity of each DPMM scheme is originally compared and evaluated from a practical point of view.
ROUGE: A Package For Automatic Evaluation Of Summaries

ROUGE stands for Recall-Oriented Understudy for Gisting Evaluation. It includes measures to automatically determine the quality of a summary by comparing it to other (ideal) summaries created by humans. The measures count the number of overlapping units such as n-gram, word sequences, and word pairs between the computer-generated summary to be evaluated and the ideal summaries created by humans. This paper introduces four different ROUGE measures: ROUGE-N, ROUGE-L, ROUGE-W, and ROUGE-S included in the ROUGE summarization evaluation package and their evaluations. Three of them have been used in the Document Understanding Conference (DUC) 2004, a large-scale summarization evaluation sponsored by NIST.
A research framework for analysing eBusiness models

28 June 2004  29 August 2004  24 September 2004 Online publication date: 9 November 2004  As eBusiness is moving towards maturity, research interests shift to the investigation of opportunities for market exploitation of eBusiness technologies. As a result, the debate around business models naturally becomes more topical. However, while many researchers and practitioners are contemplating business models, there is an alarming lack of theoretical tools in the literature to structure and codify knowledge in the area. This paper draws on an extensive review of the literature to propose an analytic framework that decomposes the area of business models into eight research sub-domains. The proposed framework is then applied to organize and critically review existing research under each sub-domain as well as to define an agenda of future challenges on business model research. The framework can benefit future research by allowing researchers to better concentrate their efforts and place their contributions in an overall context, thus assisting in building a coherent body of knowledge in the challenging research domain of business models. European Journal of Information Systems (2004) 13, 302–314. doi:10.1057/palgrave.ejis.3000513
A Fine-Grained Performance Model of Cloud Computing Centers

Accurate performance evaluation of cloud computing resources is a necessary prerequisite for ensuring that quality of service parameters remain within agreed limits. In this paper, we employ both the analytical and simulation modeling to addresses the complexity of cloud computing systems. Analytical model is comprised of distinct functional submodels, the results of which are combined in an iterative manner to obtain the solution with required accuracy. Our models incorporate the important features of cloud centers such as batch arrival of user requests, resource virtualization, and realistic servicing steps, to obtain important performance metrics such as task blocking probability and total waiting time incurred on user requests. Also, our results reveal important insights for capacity planning to control delay of servicing users requests.
A signal analysis of network traffic anomalies

Identifying anomalies rapidly and accurately is critical to the efficient operation of large computer networks. Accurately characterizing important classes of anomalies greatly facilitates their identification; however, the subtleties and complexities of anomalous traffic can easily confound this process. In this paper we report results of signal analysis of four classes of network traffic anomalies: outages, flash crowds, attacks and measurement failures. Data for this study consists of IP flow and SNMP measurements collected over a six month period at the border router of a large university. Our results show that wavelet filters are quite effective at exposing the details of both ambient and anomalous traffic. Specifically, we show that a pseudo-spline filter tuned at specific aggregation levels will expose distinct characteristics of each class of anomaly. We show that an effective way of exposing anomalies is via the detection of a sharp increase in the local variance of the filtered data. We evaluate traffic anomaly signals at different points within a network based on topological distance from the anomaly source or destination. We show that anomalies can be exposed effectively even when aggregated with a large amount of additional traffic. We also compare the difference between the same traffic anomaly signals as seen in SNMP and IP flow data, and show that the more coarse-grained SNMP data can also be used to expose anomalies effectively.
Looking for ELMo's friends: Sentence-Level Pretraining Beyond Language Modeling

Work on the problem of contextualized word representation—the development of reusable neural network components for sentence understanding—has recently seen a surge of progress centered on the unsupervised pretraining task of language modeling with methods like ELMo (Peters et al., 2018). This paper contributes the first large-scale systematic study comparing different pretraining tasks in this context, both as complements to language modeling and as potential alternatives. The primary results of the study support the use of language modeling as a pretraining task and set a new state of the art among comparable models using multitask learning with language models. However, a closer look at these results reveals worryingly strong baselines and strikingly varied results across target tasks, suggesting that the widely-used paradigm of pretraining and freezing sentence encoders may not be an ideal platform for further work.
Cross lingual text classification by mining multilingual topics from wikipedia

This paper investigates how to effectively do cross lingual text classification by leveraging a large scale and multilingual knowledge base, Wikipedia. Based on the observation that each Wikipedia concept is described by documents of different languages, we adapt existing topic modeling algorithms for mining multilingual topics from this knowledge base. The extracted topics have multiple types of representations, with each type corresponding to one language. In this work, we regard such topics extracted from Wikipedia documents as universal-topics, since each topic corresponds with same semantic information of different languages. Thus new documents of different languages can be represented in a space using a group of universal-topics. We use these universal-topics to do cross lingual text classification. Given the training data labeled for one language, we can train a text classifier to classify the documents of another language by mapping all documents of both languages into the universal-topic space. This approach does not require any additional linguistic resources, like bilingual dictionaries, machine translation tools, or labeling data for the target language. The evaluation results indicate that our topic modeling approach is effective for building cross lingual text classifier.
Building Textual Entailment Specialized Data Sets: a Methodology for Isolating Linguistic Phenomena Relevant to Inference

This paper proposes a methodology for the creation of specialized data sets for Textual Entailment, made of monothematic Text-Hypothesis pairs (i.e. pairs in which only one linguistic phenomenon relevant to the entailment relation is highlighted and isolated). The annotation procedure assumes that humans have knowledge about the linguistic phenomena relevant to inference, and a classification of such phenomena both into fine grained and macro categories is suggested. We experimented with the proposed methodology over a sample of pairs taken from the RTE-5 data set, and investigated critical issues arising when entailment, contradiction or unknown pairs are considered. The result is a new resource, which can be profitably used both to advance the comprehension of the linguistic phenomena relevant to entailment judgments and to make a first step towards the creation of large-scale specialized data sets.