Miracle-dz/UniFAR-FTUs huggingface dataset:
An Evaluation Framework for Publications on Artificial Neural networks in Sales Forecasting

Although artificial neural networks (ANN) promise superior performance in forecasting theory, they are not yet an established method in business practice. The vast degrees of freedom to parameterize ANNs have lead to countless heuristic approaches to simplify modeling, training, network selection and evaluation implemented with varying success. Consequently, a systematic evaluation is required in order to identify successful heuristics and derive sound guidelines to ANN modeling from publications. As each forecasting domain imposes different heuristics for classification or point prediction on specific datasets, a literature review is conducted, identifying 2538 publications within the domain of ANN forecasting but only 32 of them applicable to the domain of sales forecasting. The identified publications are evaluated through a framework regarding their validity and reliability in experiment design and documentation, in order to promote superior publications, derive recommendations for future experiments and possibly identify gaps in current research and practice.
University of Florida And Katara Starkey University of Florida May 2000 Preliminary — Not for Quotation

This paper concerns the Industry-University Cooperative Research Centers (IUCRCs) and their influence on a sample of industrial R&D laboratories in the chemicals, machinery, electrical equipment, and transportation equipment industries. IUCRCs are centers of applied engineering within universities that are expressly designed to be dependent on industrial support, and thus to be more sensitive to the research needs of firms. Along these lines we find that membership in the IUCRCs is associated with the importance to R&D laboratories of hiring graduate students, faculty consultants and co-authoring with university faculty, just as one would expect from the stated mission of the IUCRCs. In additional research we explore the implications of IUCRC membership and the stock of R&D (in a sub-sample of cited IUCRCs) for patenting and research expenditure by the R&D laboratories. We find a positive relationship between IUCRC membership, particularly the NSF centers, and laboratory patenting and research expenditure, even while holding constant several measures of size and specialization. But IUCRC membership is concentrated on the larger and more science-oriented laboratories, again especially for the NSF centers. This suggests that the apparent IUCRC effects partly show the dependence of membership on laboratory size and quality. This suspicion is confirmed when we undertake a study of laboratory membership. We find that this is more likely the larger is the laboratory and the relevant pool of university research that the laboratory draws upon.
A Cost-Value Approach for Prioritizing Requirements

Developing software systems that meet stakeholders' needs and expectations is the ultimate goal of any software provider seeking a competitive edge. To achieve this, you must effectively and accurately manage your stakeholders' system requirements: the features, functions, and attributes they need in their software system. Once you agree on these requirements, you can use them as a focal point for the development process and produce a software system that meets the expectations of both customers and users. However, in real world software development, there are usually more requirements than you can implement given stakeholders' time and resource constraints. Thus, project managers face a dilemma: how do you select a subset of the customers' requirements and still produce a system that meets their needs? The authors developed a cost-value approach for prioritizing requirements and applied it to two commercial projects.
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Resolving Complement Anaphora

Proceedings of the Second Workshop on Anaphora Resolution (WAR II). Editor: Christer Johansson. NEALT Proceedings Series, Vol. 2 (2008), 41-53. © 2008 The editors and contributors. Published by Northern European Association for Language Technology (NEALT) http://omilia.uio. no/nealt . Electronically published at Tartu University Library (Estonia) http://hdl.handle. net/10062/7129 .
InterMR : Interoperable MANET Routing across Coalitions

Tactical military networks require efficient interoperation. This is critical in multiple coalition deployments where mission coordination relies on intercommunications. This is also a major challenge since different coalitions may operate with heterogeneous protocols. The existing interconnection protoco ls designed for the Internet (e.g., BGP) are not adequate for mob ile MANETs. In this paper, we present a novel Inter-MANET Routing solution called InterMR which is designed to withstand coalition deployment challenges. The first contribution offered by InterMR is an Inter-MANET address scheme based on a generalized node naming scheme (e.g., symbolic name, property, etc.) rather than strictly the traditional IP address; this allows dynamic merge/split of MANETs and also removes the need for a centralized Name Server. Our second contribution is to provide a seamless routing mechanism across heterogeneous MANETs without modifying the internal routing mechanisms in each MANET. The third contribution is efficient, transparent adaptation to MANET topology changes by dynamically assigning the gateway functionalities. We show by packet-level simulation, that the performance of InterMR can be improved by as much as 195% using adaptive gateway assignment. We show by analysis that the control O/H is modest, making InterMR quite scalable to large tactical interconnects.
Support vector machines with indefinite kernels

Training support vector machines (SVM) with indefinite kernels has recently attracted attention in the machine learning community. This is partly due to the fact that many similarity functions that arise in practice are not symmetric positive semidefinite, i.e. the Mercer condition is not satisfied, or the Mercer condition is difficult to verify. Previous work on training SVM with indefinite kernels has generally fallen into three categories: (1) positive semidefinite kernel approximation, (2) non-convex optimization, and (3) learning in Krein spaces. All approaches are not fully satisfactory. They have either introduced sources of inconsistency in handling training and test examples using kernel approximation, settled for approximate local minimum solutions using non-convex optimization, or produced nonsparse solutions. In this paper, we establish both theoretically and experimentally that the 1-norm SVM, proposed more than 10 years ago for embedded feature selection, is a better solution for extending SVM to indefinite kernels. More specifically, 1-norm SVM can be interpreted as a structural risk minimization method that seeks a decision boundary with large similarity margin in the original space. It uses a linear programming formulation that remains convex even if the kernel matrix is indefinite, and hence can always be solved quite efficiently. Also, it uses the indefinite similarity function (or distance) directly without any transformation, and, hence, it always treats both training and test examples consistently. Finally, it achieves the highest accuracy among all methods that train SVM with indefinite kernels with a statistically significant evidence while also retaining sparsity of the support vector set.
The WALKTHRU editor : towards realistic and effective interaction with virtual building environments

This thesis describes the development of WALKEDIT, an object placement editor for the Berkeley architectural WALKTHRU system. In addition to incorporating editing operations commonly found in 2D and 3D model editors, two new major results were achieved. First, a system for simple and natural direct manipulation of 3D objects was created. This system, which we call Object Associations, is a software framework that provides a uni ed method for designing and implementing convenient direct manipulation behaviors for objects in a 3D virtual environment. A combination of nearly realistic pseudophysical behavior and idealized goal-oriented properties is used to disambiguate 2D mouse actions on the display screen into appropriate and natural object motion in the 3D virtual world, and to determine valid and desirable nal locations for objects being manipulated. Objects selected for relocation actively look for nearby objects or structures to associate and align themselves with. An automated implicit grouping mechanism falls out of this process. Concept, structure, and our implementation of this framework are presented. Second, the realism of the WALKTHRU real-time rendering system was enhanced by the addition of physical simulation software. A rst set of routines provides the virtual user with an adjustable eye height, which is dynamically maintained through a combination of feedback control techniques and discrete time physical simulation. This gives a much more natural feeling to moving through the building, allowing the user to look up and down while walking, and permitting realistic use of stairs and elevators. In a separate experiment, the Lin-Canny closest features algorithm and a fast contact force computation algorithm was integrated with the object associations system, allowing true collision detection and pseudo-static simulation of moving objects. The implementation, tradeo s, and success of this experiment are presented and discussed.
Creating a Unique Transnational Place: Deterritorialized Discourse and the Blending of Time and Space in Online Social Media

This study describes how members of a transnational social network of Mexican bilinguals living in Chicago manipulate their language on online social media to facilitate and maintain close connections across borders. Using a discourse-centered online ethnographic approach, I examine conversations posted on members’ Facebook walls and the contexts in which the discourses are formed. I argue that members of this transnational social network engage in the use of deterritorialized discourse to create chronotopes; that is, through discourse, members connect temporal and spatial relationships and form them into a single constructed context. These chronotopes help members recontextualize Facebook as a unique transnational social place that connects families and allows for the continuation of cultural practices that maintain their transnationalism. This study sheds light on the use of linguistic resources and modes of communication to examine how individuals construct imagined experiences within a real intimate community in the deterritorialized space of online social media.
A G ] 1 1 D ec 2 00 1 AN ADDITIVE VERSION OF HIGHER CHOW GROUPS

The cosimplicial scheme ∆ • = ∆ was used in [2] to define higher Chow groups. In this note, we let t tend to 0 and replace ∆ • by a degenerate version Q • = Q to define an additive version of the higher Chow groups. For a field k, we show the Chow group of 0-cycles on Q n in this theory is isomorphic to the absolute (n − 1)-Kähler forms Ω n−1 k. An analogous degeneration on the level of de Rham cohomol-ogy associated to " constant modulus " degenerations of varieties in various contexts is discussed.
Ground Truth , Reference Truth & “ Omniscient Truth ”-- Parallel Phrases in Parallel Texts for MT Evaluation

Recently introduced automated methods of evaluating machine translation (MT) systems require the construction of parallel corpora of source language (SL) texts with human reference translations in the target language (TL). We present a novel method of exploiting and augmenting these resources for task-based MT evaluation, assessing how accurately people can extract Who, When, and Where elements of information from TL output texts of different MT engines. This paper reports on the first phase of our research establishing a baseline MT evaluation process with (i) the construction and (ii) the annotation and inter-annotator rates of an annotated extraction corpus, and (iii) our results applying the corpus in the evaluation of three Arabic-to-English MT engines. In this corpus, the elements of interest are identified as parallel phrases across the parallel texts of the SL, the reference translations, and the MT engine outputs, where they are annotated and called, respectively the Ground Truth (GT), Reference Truth (RT), and Omniscient Truth (OT) items in the parallel texts. Our evaluation of three MT engines with the corpus yields precision and recall accuracy measures that, together with a loss measure, clearly rank the engines and, unlike other evaluation metrics, indicate diagnostically where output improvements will assist on extraction.
Optimising injection moulding parameters that satisfies part qualities by using Taguchi method

The main purpose of this research is to identify the most influenced injection moulding parameter to satisfy polycarbonate part qualities by using Design of Experiment (DOE) of Taguchi method. The part qualities selected in this research were warpage, shrinkage and ultimate tensile strength. Through this experiment, four most significant process parameters were chosen: melt temperature, injection pressure, packing pressure and injection time, base on previous research findings. By using Taguchi orthogonal array L9 (34), the signal to noise (S/N) ratio and analysis of variance (ANOVA) had been done. It was found that the most significant process parameter which posses the lowest value of defects (warpage and shrinkage) and optimum value of mechanical property (ultimate tensile strength) was injection time. In order to validate the experimental result, confirmation test has been done based on the set of best combination process parameters determined from S/N ratio. As the result, warpage and ultimate tensile strength were improved about 36.73% and 2.71% respectively, while for minimum shrinkage produce by the best combination parameters was 0.006598. As a conclusion, Taguchi method was recognized as the effective methods to be used to optimise the parameter setting and to predict and solve the quality problems that occur in injection moulded parts.
A reputation bootstrapping approach for computer network collaborative defense

Computer network collaborative defense is used to protect networks from collaborative attacks. To ensure the credible of defense entities, trust relationships are necessary to be established. When there are not enough history records for reputation evaluation, the reputation bootstrapping technique to establish an initial reputation value for newcomers based on limited record about their past behaviors becomes important and is short of a proper solution currently. We present a reputation bootstrapping approach. Our approach considers about different node types and gives each node a reputation different from each other, reflecting the specific status of the new nodes. Experimental results show that the approach minimizes the future failure rate of coordination.
Two Properties of Stochastic KPP Equations: Ergodicity and Pathwise Property

In this paper we study the random approximate travelling wave solutions of the stochastic KPP equations. Two new properties of the stochastic KPP equations are obtained. We prove the ergodicity that for almost all sample paths, behind the wavefront x = γt , the lower limit of 1 t t 0 u(s, x) ds as t →∞ is positive, and ahead of the wavefront, the limit is zero. In some cases, behind the wavefront, the limit of 1 t 0 u(s, x) ds as t →∞ exists and is positive almost surely. We also prove that behind the wavefront, for almost every ω, the solution of some special stochastic KPP equations converges to a stationary trajectory of the corresponding stochastic differential equation. In front of the wavefront, the solution converges to 0, which is another stationary trajectory of the corresponding SDE. We also study the space derivative of the solution for large times. We show that away from the wavefront, for almost all large t the solution is flat in the x-direction for almost all sample paths.
The Case for Fast and Invariant-Preserving Geo-Replication

Cloud storage systems showcase a range of consistency models, from weak to strong consistency. Weakly consistent systems enable better performance, but cannot maintain strong application invariants, which strong consistency trivially supports. This paper takes the position that it is possible to both achieve fast operation and maintain application invariants. To that end, we propose the novel abstraction of invariant-preserving CRDTs, which are replicated objects that provide invariant-safe automatic merging of concurrent updates. The key technique behind the implementation of these CRDTs is to move replica coordination outside the critical path of operations execution, to enable low normal case latency while retaining the coordination necessary to enforce invariants. In this paper we present ongoing work, where we show different invariant-preserving CRDTs designs and evaluate the latency of operations using a counter that never goes negative.
Engineering Process Transformation to Manage (In)consistency

Inconsistencies pose a severe issue to overcome in collaborative modeling scenarios, especially in settings with different domains involved. This is due to the significantly different formalisms employed that have overlapping semantic domains. A pertinent example are today’s mechatronic and Cyber-Physical Systems. In this paper, we propose an approach for managing inconsistencies based on explicitly modeled linguistic and ontological properties. We argue that to fully understand the reasons of their occurrence and impact on the overall design, inconsistencies should be investigated in the context of the process they emerge in. For this purpose, we propose a language for modeling processes in conjunction with the properties of the engineered system. Characteristics of inconsistencies are identified in terms of process models and properties. A method for optimal selection of management techniques is provided. We demonstrate our ideas on a case study of a real mechatronic system.
A stochastic geometry model for fMRI data

Functional magnetic resonance imaging (fMRI) is a principal method for mapping the human brain. fMRI data consist of a sequence of MR scans of the brain acquired during stimulation of speciic cortical areas, and the purpose of analysing the data is to detect activated areas, i.e. areas where the intensity changes according to the stimulation paradigm. A common analysis procedure is to estimate the activity pattern non-parametricly by smoothing the data spatially. The focus is then on assessing signiicance of peaks or clusters in the smoothed activation surface by means of multiple hypothesis testing, rather than assessing the uncertainty of the estimated pattern itself. In this paper we formulate a more structured model for the spatial activation pattern. We achieve this by considering a stochastic geometry model where the activation surface is given by a sum of Gaussian functions, which to some extent can be thought of as individual centres of activation in the brain. The model is formulated in a Bayesian framework, where the prior distribution of the centres is given by a marked point process density. An advantage of this approach is that inference can be carried out by simulation techniques, and hence it is easy, though time consuming, to evaluate the uncertainty of the estimate or to test hypotheses of interest regarding the activation. Furthermore in this framework, we are able to model the temporal pattern of the activation with fewer assumptions than usually imposed. This reveals signii-cant non-stationarities in the analysed data, which violate the common assumption of stationarity of the haemodynamic response.
Voice over Multi-Channel Multi-Radio WLANs with Power Capture and Imperfect Channel

VoIP offers cheap voice communication which instigated its quick market growth. IEEE 802.11 WLANs offer cheap wireless coverage which can be used to provide VoIP service in mobile devices. But call capacity of such networks is very low compared to wired networks. To increase voice capacity in WLANs incorporation of power capture, increased data rate, and use of multiple channels are of great importance. Moreover, these networks should be carefully designed considering voice quality requirements to avoid call jitter and call drops. We propose an analytical model to estimate VoIP call capacity for multichannel WLANs with consideration for power capture, imperfect channel, and multiple network interfaces. We employed ITU-T E-model to assess voice quality which is used as the limiting factor to ensure that voice quality does not degrade in such networks. The model will be extremely helpful to network designers in planning single channel or multi-channel WLANs.
The Distribution of Mood An Exploration of Distributional Compositions in Sentiment Classification

Distributional semantics is a research area investigating unsupervised datadriven models for quantifying semantic relatedness. This thesis investigates the possibilities of using distributional semantic models for sentiment classification of utterances, by composing distributional vectors of words in utterances. For evaluation I use a set of manually classified movie reviews. While the purpose of this study has been to test compositions in distributional semantic model, the work has mainly been focused on finding a useful model configuration for the DSM. The thesis concludes that more associative window sizes performed better than less associative ones. Weighting the DSM by PPMI gave the most stable performance improvements as well. Context selection is essential for achieving higher scores. While DSM does not reach beyond baseline results in its evaluation, there are still unexplored areas in which potential improvements may lie.