nils-herrmann/clean_scirepeval huggingface dataset:
Chapter 10 The Enterprise System Experience — From Adoption to Success

173 For some 20 years after John Dearden wrote these words, history proved him right. Today, however, there is a booming market for software packages claiming to provide a total, integrated solution to companies’ information-processing needs. Even companies that choose not to adopt such packages are pursuing aggressive strategies of systems integration by redeveloping custom software and adopting technologies such as data warehousing. Integrated enterprise systems deserve serious research attention because of their great potential for financial, technical, managerial, human, and strategic benefits, costs, and risks. This chapter provides a theoretical framework for analyzing, both retrospectively and prospectively, the business value of enterprise systems. We first describe the historical context in which enterprise systems emerged. Next we identify the key characteristics of enterprise systems, discuss the reasons companies do and do not adopt them, and summarize arguments about why enterprise systems are an important topic for research. We then analyze enterprise systems in terms of the concept of success. We argue that the many facets of success create difficulties for
HOPC: Histogram of Oriented Principal Components of 3D Pointclouds for Action Recognition

Existing techniques for 3D action recognition are sensitive to viewpoint variations because they extract features from depth images which change significantly with viewpoint. In contrast, we directly process the pointclouds and propose a new technique for action recognition which is more robust to noise, action speed and viewpoint variations. Our technique consists of a novel descriptor and keypoint detection algorithm. The proposed descriptor is extracted at a point by encoding the Histogram of Oriented Principal Components (HOPC) within an adaptive spatio-temporal support volume around that point. Based on this descriptor, we present a novel method to detect Spatio-Temporal Key-Points (STKPs) in 3D pointcloud sequences. Experimental results show that the proposed descriptor and STKP detector outperform state-of-the-art algorithms on three benchmark human activity datasets. We also introduce a new multiview public dataset and show the robustness of our proposed method to viewpoint variations.
Information Evolution in Social Networks

Social networks readily transmit information, albeit with less than perfect fidelity. We present a large-scale measurement of this imperfect information copying mechanism by examining the dissemination and evolution of thousands of memes, collectively replicated hundreds of millions of times in the online social network Facebook. The information undergoes an evolutionary process that exhibits several regularities. A meme's mutation rate characterizes the population distribution of its variants, in accordance with the Yule process. Variants further apart in the diffusion cascade have greater edit distance, as would be expected in an iterative, imperfect replication process. Some text sequences can confer a replicative advantage; these sequences are abundant and transfer "laterally" between different memes. Subpopulations of the social network can preferentially transmit a specific variant of a meme if the variant matches their beliefs or culture. Understanding the mechanism driving change in diffusing information has important implications for how we interpret and harness the information that reaches us through our social networks.
Neural Joint Model for Transition-based Chinese Syntactic Analysis

We present neural network-based joint models for Chinese word segmentation, POS tagging and dependency parsing. Our models are the first neural approaches for fully joint Chinese analysis that is known to prevent the error propagation problem of pipeline models. Although word embeddings play a key role in dependency parsing, they cannot be applied directly to the joint task in the previous work. To address this problem, we propose embeddings of character strings, in addition to words. Experiments show that our models outperform existing systems in Chinese word segmentation and POS tagging, and perform preferable accuracies in dependency parsing. We also explore bi-LSTM models with fewer features.
Interactive Graph Cuts for Optimal Boundary and Region Segmentation of Objects in N-D Images

In this paper we describe a new technique for general purpose interactive segmentation of N-dimensional images . The user marks certain pixels as “object” or “background” to provide hard constraints for segmentation. Additional soft constraints incorporate both boundary and region information. Graph cuts are used to find the globally optimal segmentation of the N-dimensional image. The obtained solution gives the best balance of boundary and region properties among all segmentations satisfying the constraints . The topology of our segmentation is unrestricted and both “object” and “background” segments may consist of several isolated parts. Some experimental results are present ed in the context of photo/video editing and medical image segmentation. We also demonstrate an interesting Gestalt example. A fast implementation of our segmentation method is possible via a new max-flow algorithm in .
A data-driven approach to cleaning large face datasets

Large face datasets are important for advancing face recognition research, but they are tedious to build, because a lot of work has to go into cleaning the huge amount of raw data. To facilitate this task, we describe an approach to building face datasets that starts with detecting faces in images returned from searches for public figures on the Internet, followed by discarding those not belonging to each queried person. We formulate the problem of identifying the faces to be removed as a quadratic programming problem, which exploits the observations that faces of the same person should look similar, have the same gender, and normally appear at most once per image. Our results show that this method can reliably clean a large dataset, leading to a considerable reduction in the work needed to build it. Finally, we are releasing the FaceScrub dataset that was created using this approach. It consists of 141,130 faces of 695 public figures and can be obtained from http://vintage.winklerbros.net/facescrub.html.
A new rail-to-rail comparator with adaptive power control for low power SAR ADCs in biomedical application

In this paper, a new IV rail-to-rail comparator is presented with low noise, high speed and low power consumption. We utilize current mirrors to overcome the kickback noise. A new adaptive power control (APC) technique is also proposed to minimize the power dissipation of the comparator. Moreover, it provides an optimized and stable power dissipation irrelative to process and bias variation. A prototype IV rail-to-rail SAR ADC for biomedical application has been implemented in 0.18μm TSMC CMOS technology. It consumes 2.86 μW at 250kS/s and the figure of merit is 85.7 fJ/conversion-step. It shows that this work efficiently reduces 52% to 80% power consumption of the dynamic comparator at 500kS/s to 125kS/s.
A compact planar feed structure for Ka-band satcom-on-the-move tracking antennas

The increasing interest in bi-directional mobile high data rate satellite communications in Ka-band necessitates the development of dedicated antenna tracking systems and feeds. In this paper we describe a compact feed structure based on printed circuit boards for a mobile satellite communications ground terminal with a Cassegrain reflector antenna. The novel structure provides a dual circular polarisation communication mode as well as the TM01 mode for multimode monopulse tracking. This coupler, based on carefully matched transitions from grounded coplanar lines to circular waveguides, is operational at 20GHz and 30 GHz, to cover the downlink and the uplink frequency ranges in Ka-band. This work contributes to the development of a satellite terminal for land-mobile communications in disaster scenarios.
Experimental Missions in W-Band: A Small LEO Satellite Approach

W-band (75-110 GHz) is proposed nowadays as a valuable alternative to intensively-exploited Ku- and Ka-bands for high-speed transmission over satellite networks. In such a framework, some experiments are being carried out, which are targeted to verify the feasibility of exploitation of W-band for broadband service deployment. From a theoretical viewpoint, the large bandwidth availability and the scarce amount of interference typical of W-band should guarantee high capacities. Nevertheless, many crucial aspects are still to be carefully investigated, e.g., signal propagation issues, RF impairments, choice of modulation and coding, efficient antenna design, etc. In this paper, an overview is made on the low Earth orbit (LEO) nano-satellite mission in-orbit key-test and validation of W-band (IKNOW). IKNOW mission is an ongoing advanced feasibility study part of an Italian Space Agency Project, named W-band analysis and verification (WAVE), coordinated by the Department of Electronic Engineering, University of Rome ldquoTor Vergatardquo. The main objective of the IKNOW mission is to tackle some of the unexplored critical aspects concerning W-band satellite transmission. In such a perspective, IKNOW should be regarded as a ldquopilot mission,rdquo whose results will be used for a first uplink-downlink satellite channel characterization, in-orbit validation of W-band technology, and space qualification processes. This paper is focused on the research work carried out in a preliminary phase of the IKNOW study and will also consider a number of elements related to the mission configuration, payload architecture, link analysis, potential RF impairment factors, and atmospheric effects. Proposed analysis and preliminary results shown can provide to interested readers the basic guidelines that will drive the practical implementation of IKNOW mission, as well as the most relevant issues to be faced by future developers of W-band missions using small LEO satellites.
Matching and Retrieval of Distorted and Occluded Shapes Using Dynamic Programming

We propose an approach for matching distorted and possibly occluded shapes using Dynamic Programming (DP). We distinguish among various cases of matching such as cases where the shapes are scaled with respect to each other and cases where an open shape matches the whole or only a part of another open or closed shape. Our algorithm treats noise and shape distortions by allowing matching of merged sequences of consecutive small segments in a shape with larger segments of another shape, while being invariant to translation, scale, orientation and starting point selection. We illustrate the effectiveness of our algorithm in retrieval of shapes on two datasets of two-dimensional open and closed shapes of marine life species. We demonstrate the superiority of our approach over traditional approaches to shape matching and retrieval based on Fourier descriptors and moments. We also compare our method with SQUID, a well known method which is available on the Internet. Our evaluation is based on human relevance judgments following a well-established methodology from the information retrieval field. A preliminary version of this work was presented at the Int. Conf. on Pattern Recognition, Barcelona, Spain, pages 67-71, Vol. 4, Sept. 2000. Corresponding author. Department of Electronic and Computer Engineering, Technical University of Crete, Chania, Crete, GR-73100, Greece, E-mail: petrakis@ced.tuc.gr, URL: http://www.ced.tuc.gr/ ̃petrakis, Intelligent Sensory Information Systems, Faculty of Science, University of Amsterdam, Kruislaan 403, 1098 SJ Amsterdam, The Netherlands. E-mail: diplaros@science.uva.nl. This work is part of the author’s student dissertation at the Department of Electronic and Computer Engineering of the Technical University of Crete. Faculty of Computer Science, Dalhousie University, Halifax, Nova Scotia, Canada B3H 1W5.  eem@cs.dal.ca, URL: http://www.cs.dal.ca/ ̃eem.
Enhancing the quality of life through wearable technology

An overview of the key challenges facing the practice of medicine today is presented along with the need for technological solutions that can "prevent" problems. Then, the development of the Wearable Motherboard/spl trade/ (Smart Shirt) as a platform for sensors and monitoring devices that can unobtrusively monitor the health and well being of individuals (directly and/or remotely) is described. This is followed by a discussion of the applications and impact of this technology in the continuum of life-from preventing SIDS to facilitating independent living for senior citizens. Finally, the future advancements in the area of wearable, yet comfortable, systems that can continue the transformation of healthcare - all aimed at enhancing the quality of life for humans - are presented.
High Performance Packet Processing with FlexNIC

The recent surge of network I/O performance has put enormous pressure on memory and software I/O processing sub systems. We argue that the primary reason for high memory and processing overheads is the inefficient use of these resources by current commodity network interface cards (NICs). We propose FlexNIC, a flexible network DMA interface that can be used by operating systems and applications alike to reduce packet processing overheads. FlexNIC allows services to install packet processing rules into the NIC, which then executes simple operations on packets while exchanging them with host memory. Thus, our proposal moves some of the packet processing traditionally done in software to the NIC, where it can be done flexibly and at high speed.
 We quantify the potential benefits of FlexNIC by emulating the proposed FlexNIC functionality with existing hardware or in software. We show that significant gains in application performance are possible, in terms of both latency and throughput, for several widely used applications, including a key-value store, a stream processing system, and an intrusion detection system.
Towards Zero Unknown Word in Neural Machine Translation

Neural Machine translation has shown promising results in recent years. In order to control the computational complexity, NMT has to employ a small vocabulary, and massive rare words outside the vocabulary are all replaced with a single unk symbol. Besides the inability to translate rare words, this kind of simple approach leads to much increased ambiguity of the sentences since meaningless unks break the structure of sentences, and thus hurts the translation and reordering of the in-vocabulary words. To tackle this problem, we propose a novel substitution-translation-restoration method. In substitution step, the rare words in a testing sentence are replaced with similar in-vocabulary words based on a similarity model learnt from monolingual data. In translation and restoration steps, the sentence will be translated with a model trained on new bilingual data with rare words replaced, and finally the translations of the replaced words will be substituted by that of original ones. Experiments on Chinese-to-English translation demonstrate that our proposed method can achieve more than 4 BLEU points over the attention-based NMT. When compared to the recently proposed method handling rare words in NMT, our method can also obtain an improvement by nearly 3 BLEU points.
Why We Need New Evaluation Metrics for NLG

The majority of NLG evaluation relies on automatic metrics, such as BLEU. In this paper, we motivate the need for novel, systemand data-independent automatic evaluation methods: We investigate a wide range of metrics, including state-of-the-art word-based and novel grammar-based ones, and demonstrate that they only weakly reflect human judgements of system outputs as generated by data-driven, end-to-end NLG. We also show that metric performance is dataand system-specific. Nevertheless, our results also suggest that automatic metrics perform reliably at system-level and can support system development by finding cases where a system performs poorly.
Detection of Duplicate Defect Reports Using Natural Language Processing

Defect reports are generated from various testing and development activities in software engineering. Sometimes two reports are submitted that describe the same problem, leading to duplicate reports. These reports are mostly written in structured natural language, and as such, it is hard to compare two reports for similarity with formal methods. In order to identify duplicates, we investigate using Natural Language Processing (NLP) techniques to support the identification. A prototype tool is developed and evaluated in a case study analyzing defect reports at Sony Ericsson Mobile Communications. The evaluation shows that about 2/3 of the duplicates can possibly be found using the NLP techniques. Different variants of the techniques provide only minor result differences, indicating a robust technology. User testing shows that the overall attitude towards the technique is positive and that it has a growth potential.
Multi-View Dimensionality Reduction via Canonical Correlation Analysis

We analyze the multi-view regression problem where we have two views X = (X, X) of the input data and a target variableY of interest. We provide sufficient conditions under which we can reduce the dimensionality of X (via a projection) without loosing predictive power of Y . Crucially, this projection can be computed via a Canonical Correlation Analysis only on the unlabeled data. The algorithmic template is as f ollows: with unlabeled data, perform CCA and construct a certain projection; with the labeled data, do least squa res regression in this lower dimensional space. We show how, under certain natural assumptions, the number o f labeled samples could be significantly reduced (in comparison to the single view setting) — in particular, we show how this dimen sionality reduction does not loose predictive power ofY (thus it only introduces little bias but could drastically reduce the variance). We explore two separate assumptions under which this is possible and show how, under either assumption alone, dimensionality reduction could reduce the labeled sample complexity. The two assumptions we consider are a conditional independenceassumption and a redundancyassumption. The typical conditional independence assumption is that conditioned onY the viewsX andX are independent — we relax this assumption to: conditioned on some hidden state H the viewsX andX are independent. Under the redundancy assumption, we show that the best predictor from each view is roughly as good as the best predictor u sing both views.
A capacity theory of comprehension: individual differences in working memory.

A theory of the way working memory capacity constrains comprehension is proposed. The theory proposes that both processing and storage are mediated by activation and that the total amount of activation available in working memory varies among individuals. Individual differences in working memory capacity for language can account for qualitative and quantitative differences among college-age adults in several aspects of language comprehension. One aspect is syntactic modularity: The larger capacity of some individuals permits interaction among syntactic and pragmatic information, so that their syntactic processes are not informationally encapsulated. Another aspect is syntactic ambiguity: The larger capacity of some individuals permits them to maintain multiple interpretations. The theory is instantiated as a production system model in which the amount of activation available to the model affects how it adapts to the transient computational and storage demands that occur in comprehension.
The Evolution of Agent-based Simulation Platforms : A Review of NetLogo 5 . 0 and ReLogo

We review and evaluate two recently evolved agent-based simulation platforms: version 5.0 of NetLogo and the ReLogo component of Repast. Subsequent to the similar review we published in 2006, NetLogo has evolved into a powerful platform for scientific modeling while retaining its basic conceptual design, ease of use, and excellent documentation. ReLogo evolved both from NetLogo and Repast; it implements NetLogo’s basic design and its primitives in the Groovy programming language embedded in the Eclipse development environment, and provides access to the Repast library. We implemented the “StupidModel” series of 16 pseudo-models in both platforms; these codes contain many elements of basic agent-based models and can serve as templates for programming real models. ReLogo successfully reimplements much of NetLogo, and its translator was generally successful in converting NetLogo codes into ReLogo. Overall we found ReLogo considerably more challenging to use and a less productive development environment. Using ReLogo requires learning Groovy and Eclipse and becoming familiar with Repast’s complex organization; documentation and learning materials are far less abundant and mature than NetLogo’s. Though we did not investigate thoroughly, it is not clear what kinds of models could readily be implemented in ReLogo but not NetLogo. On average, NetLogo executed our example models approximately 20 times faster than ReLogo.
A statistical MT tutorial work-book

1. The Overall Plan We want to automatically analyze existing human sentence translations, with an eye toward building general translation rules-we will use these rules to translate new texts automatically. I know this looks like a thick workbook, but if you take a day to work through it, you will know almost as much about statistical machine translation as anybody! The basic text that this tutorial relies on is Brown et al, " The Mathematics of Statistical Machine Translation " , Computational Linguistics, 1993. On top of this excellent presentation, I can only add some perspective and perhaps some sympathy for the poor reader, who has (after all) done nothing wrong.2. Basic Probability We're going to consider that an English sentence e may translate into any French sentence f. Some translations are just more likely than others. Here are the basic notations we'll use to formalize " more likely " : P(e)-a priori probability. The chance that e happens. For example, if e is the English string " I like snakes, " then P(e) is the chance that a certain person at a certain time will say " I like snakes " as opposed to saying something else. P(f | e)-conditional probability. The chance of f given e. For example, if e is the English string " I like snakes, " and if f is the French string " maison bleue, " then P(f | e) is the chance that upon seeing e, a translator will produce f. Not bloody likely, in this case. P(e,f)-joint probability. The chance of e and f both happening. If e and f don't influence each other, then we can write P(e,f) = P(e) * P(f). For example, if e stands for " the first roll of the die comes up 5 " and f stands for " the second roll of the die comes up 3, " then P(e,f) = P(e) * P(f) = 1/6 * 1/6 = 1/36. If e and f do influence each other, then we had better write P(e,f) = P(e) * P(f | e). That means: the chance that " e happens " times the chance that " if e happens, then f happens. " If e and f are strings that are mutual translations, then there's definitely some influence.