nils-herrmann/clean_scirepeval huggingface dataset:
Modeling customer satisfaction for new product development using a PSO-based ANFIS approach

When developing new products, it is important to understand customer perception towards consumer products. It is because the success of new products is heavily dependent on the associated customer satisfaction level. If customers are satisfied with a new product, the chance of the product being successful in marketplaces would be higher. Various approaches have been attempted to model the relationship between customer satisfaction and design attributes of products. In this paper, a particle swarm optimization (PSO) based ANFIS approach to modeling customer satisfaction is proposed for improving the modeling accuracy. In the approach, PSO is employed to determine the parameters of an ANFIS from which better customer satisfaction models in terms of modeling accuracy can be generated. A notebook article swarm optimization NFIS computer design is used as an example to illustrate the approach. To evaluate the effectiveness of the proposed approach, modeling results based on the proposed approach are compared with those based on the fuzzy regression (FR), ANFIS and genetic algorithm (GA)-based ANFIS approaches. The comparisons indicate that the proposed approach can effectively generate customer satisfaction models and that their modeling results outperform those based on the other three methods in terms of mean absolute errors and variance of errors.
Ubiquitous sensing for smart and aware environments

s computing technology increasingly becomes part of our daily activities, we are required to consider what is the future of computing and how will it change our lives? To address this question, we are interested in developing technologies that would allow for ubiquitous sensing and recognition of daily activities in an environment. Such environments will be aware of the activities performed within it and will be capable of supporting these activities without increasing the cognitive load on the users in the space. Toward this end, we are prototyping different types of smart and aware spaces, each supporting a different part of our daily life and each varying in function and detail. Our most significant effort in this direction is the building of the " Aware Home " at Georgia Tech. In this article, we outline the research issues we are pursuing toward the building of such smart and aware environments , and especially the Aware Home. We are interested in developing an infrastructure for ubiquitous sensing and recognition of activities in environments. Such sensing will be transparent to everyday activities, while providing the embedded computing infrastructure with an awareness of what is happening in a space. We expect such a ubiquitous sensing infrastructure to support different environments, with varying needs and complexities. These sensors can be mobile or static, configuring their sensing to suit the task at hand while sharing relevant information with other available sensors. This config-urable sensor-net will provide high-end sensory data about the status of the environment, its inhabitants, and the ongoing activities in the environment. To achieve this contextual knowledge of the space that is being sensed and to model the environment and the people within it requires methods for both low-level and high-level signal processing and interpretation. We are also building such signal-understanding methods to process the sensory data captured from these sensors and to model and recognize the space and activities in them. A significant aspect of building an aware environment is to explore easily accessible and more pervasive computing services than are available via traditional desktop computing. Computing and sensing in such environments must be reliable , persistent (always remains on), easy to interact with, and transparent (the user does not know it is there and does not need to search for it). The environment must be aware of the users it is interacting with and be capable of unencumbered and intelligent interaction. …
Bayesian-Based Iterative Method of Image Restoration *

It was assumed that the degraded image H was of the form H= W*S, where W is the original image, S is the point spread function, and * denotes the operation of convolution. It was also assumed that W, S, and H are discrete probability-frequency functions, not necessarily normalized. That is, the numerical value of a point of W, S, or H is considered as a measure of the frequency of the occurrence of an event at that point. S is usually in normalized form. Units of energy (which may be considered unique events) originating at a point in W are distributed at points in H according to the frequencies indicated by S. H then represents the resulting sums of the effects of the units of energy originating at all points of W. In what follows, each of the three letters has two uses when subscripted. For example, Wi indicates either the ith location in the array W or the value associated with the ith location. The unsubscripted letter refers to the entire array or the value associated with the array as in W = E i Wi. The doublesubscripted Wi j in two dimensions is interpreted similarly to Wi in one dimension. In the approximation formulas, a subscript r appears, which is the number of the iteration.
Unsupervised neuron selection for mitigating catastrophic forgetting in neural networks

Catastrophic forgetting is a well studied problem in artificial neural networks in which past representations are rapidly lost as new representations are constructed. We hypothesize that such forgetting occurs due to overlap in the hidden layers, as well as the global nature in which neurons encode information. We introduce a novel technique to mitigate forgetting which effectively minimizes activation overlapping by using online clustering to effectively select neurons in the feedforward and back-propagation phases. We demonstrate the memory retention properties of the proposed scheme using the MNIST digit recognition data set.
Compact Feedforward Sequential Memory Networks for Large Vocabulary Continuous Speech Recognition

In acoustic modeling for large vocabulary continuous speech recognition, it is essential to model long term dependency within speech signals. Usually, recurrent neural network (RNN) architectures, especially the long short term memory (LSTM) models, are the most popular choice. Recently, a novel architecture, namely feedforward sequential memory networks (FSMN), provides a non-recurrent architecture to model long term dependency in sequential data and has achieved better performance over RNNs on acoustic modeling and language modeling tasks. In this work, we propose a compact feedforward sequential memory networks (cFSMN) by combining FSMN with low-rank matrix factorization. We also make a slight modification to the encoding method used in FSMNs in order to further simplify the network architecture. On the Switchboard task, the proposed new cFSMN structures can reduce the model size by 60% and speed up the learning by more than 7 times while the models still significantly outperform the popular bidirection LSTMs for both frame-level cross-entropy (CE) criterion based training and MMI based sequence training.
Fast(er) Exact Decoding and Global Training for Transition-Based Dependency Parsing via a Minimal Feature Set

We first present a minimal feature set for transition-based dependency parsing, continuing a recent trend started by Kiperwasser and Goldberg (2016a) and Cross and Huang (2016a) of using bi-directional LSTM features. We plug our minimal feature set into the dynamic-programming framework of Huang and Sagae (2010) and Kuhlmann et al. (2011) to produce the first implementation of worst-case Opn3q exact decoders for arc-hybrid and arceager transition systems. With our minimal features, we also present Opn3q global training methods. Finally, using ensembles including our new parsers, we achieve the best unlabeled attachment score reported (to our knowledge) on the Chinese Treebank and the “second-best-in-class” result on the English Penn Treebank. Publication venue: EMNLP 2017
Local graph sparsification for scalable clustering

In this paper we look at how to sparsify a graph i.e. how to reduce the edgeset while keeping the nodes intact, so as to enable faster graph clustering without sacrificing quality. The main idea behind our approach is to preferentially retain the edges that are likely to be part of the same cluster. We propose to rank edges using a simple similarity-based heuristic that we efficiently compute by comparing the minhash signatures of the nodes incident to the edge. For each node, we select the top few edges to be retained in the sparsified graph. Extensive empirical results on several real networks and using four state-of-the-art graph clustering and community discovery algorithms reveal that our proposed approach realizes excellent speedups (often in the range 10-50), with little or no deterioration in the quality of the resulting clusters. In fact, for at least two of the four clustering algorithms, our sparsification consistently enables higher clustering accuracies.
CLARE: A Joint Approach to Label Classification and Tag Recommendation

Data classification and tag recommendation are both important and challenging tasks in social media. These two tasks are often considered independently and most efforts have been made to tackle them separately. However, labels in data classification and tags in tag recommendation are inherently related. For example, a Youtube video annotated with NCAA, stadium, pac12 is likely to be labeled as football, while a video/image with the class label of coast is likely to be tagged with beach, sea, water and sand. The existence of relations between labels and tags motivates us to jointly perform classification and tag recommendation for social media data in this paper. In particular, we provide a principled way to capture the relations between labels and tags, and propose a novel framework CLARE, which fuses data CLAssification and tag REcommendation into a coherent model. With experiments on three social media datasets, we demonstrate that the proposed framework CLARE achieves superior performance on both tasks compared to the state-of-the-art methods.
Narrow-Band Microwave Filter Using High-Q Groove Gap Waveguide Resonators With Manufacturing Flexibility and No Sidewalls

This paper presents a new type of narrow band filter with good electrical performance and manufacturing flexibility, based on the newly introduced groove gap waveguide technology. The designed third and fifth-order filters work at Ku band with 1% fractional bandwidth. These filter structures are manufactured with an allowable gap between two metal blocks, in such a way that there is no requirement for electrical contact and alignment between the blocks. This is a major manufacturing advantage compared to normal rectangular waveguide filters. The measured results of the manufactured filters show reasonably good agreement with the full-wave simulated results, without any tuning or adjustments.
CORPORATE REPUTATION AND SUSTAINED SUPERIOR FINANCIAL PERFORMANCE

Good corporate reputations are critical because of their potential for value creation, but also because their intangible character makes replication by competing firms considerably more difficult. Existing empirical research confirms that there is a positive relationship between reputation and financial performance. This paper complements these findings by showing that firms with relatively good reputations are better able to sustain superior profit outcomes over time. In particular, we undertake an analysis of the relationship between corporate reputation and the dynamics of financial performance using two complementary dynamic models. We also decompose overall reputation into a component that is predicted by previous financial performance, and that which is ‘left over’, and find that each (orthogonal) element supports the persistence of above-average profits over time.
Predicting User Replying Behavior on a Large Online Dating Site

Online dating sites have become popular platforms for people to look for potential romantic partners. Many online dating sites provide recommendations on compatible partners based on their proprietary matching algorithms. It is important that not only the recommended dates match the user’s preference or criteria, but also the recommended users are interested in the user and likely to reciprocate when contacted. The goal of this paper is to predict whether an initial contact message from a user will be replied to by the receiver. The study is based on a large scale real-world dataset obtained from a major dating site in China with more than sixty million registered users. We formulate our reply prediction as a link prediction problem of social networks and approach it using a machine learning framework. The availability of a large amount of user profile information and the bipartite nature of the dating network present unique opportunities and challenges to the reply prediction problem. We extract user-based features from user profiles and graph-based features from the bipartite dating network, apply them in a variety of classification algorithms, and compare the utility of the features and performance of the classifiers. Our results show that the user-based and graph-based features result in similar performance, and can be used to effectively predict the reciprocal links. Only a small performance gain is achieved when both feature sets are used. Among the five classifiers we considered, random forests method outperforms the other four algorithms (naive Bayes, logistic regression, KNN, and SVM). Our methods and results can provide valuable guidelines to the design and performance of recommendation engine for online dating sites.
Learning by imitation: a hierarchical approach.

To explain social learning without invoking the cognitively complex concept of imitation, many learning mechanisms have been proposed. Borrowing an idea used routinely in cognitive psychology, we argue that most of these alternatives can be subsumed under a single process, priming, in which input increases the activation of stored internal representations. Imitation itself has generally been seen as a "special faculty." This has diverted much research towards the all-or-none question of whether an animal can imitate, with disappointingly inconclusive results. In the great apes, however, voluntary, learned behaviour is organized hierarchically. This means that imitation can occur at various levels, of which we single out two clearly distinct ones: the "action level," a rather detailed and linear specification of sequential acts, and the "program level," a broader description of subroutine structure and the hierarchical layout of a behavioural "program." Program level imitation is a high-level, constructive mechanism, adapted for the efficient learning of complex skills and thus not evident in the simple manipulations used to test for imitation in the laboratory. As examples, we describe the food-preparation techniques of wild mountain gorillas and the imitative behaviour of orangutans undergoing "rehabilitation" to the wild. Representing and manipulating relations between objects seems to be one basic building block in their hierarchical programs. There is evidence that great apes suffer from a stricter capacity limit than humans in the hierarchical depth of planning. We re-interpret some chimpanzee behaviour previously described as "emulation" and suggest that all great apes may be able to imitate at the program level. Action level imitation is seldom observed in great ape skill learning, and may have a largely social role, even in humans.
Analog Layout Generator for CMOS Circuits

In this paper, we present a new layout level automation tool for analog CMOS circuits, namely, analog layout generator (ALG). ALG is capable of generating individual or matched components as well as placement and routing. ALG takes performance considerations into account, optimizing the layout in each step. A distinguishing feature of the tool is primarily providing spectra of generation possibilities ranging from full custom to automatic generation. ALG is not only designed to work as a standalone tool but also implemented to be the final step of an analog automation flow. The flow supports circuit level specification in addition to layout level user specifications, so that it can be integrated into an analog automation system. Another feature of ALG is its interaction with a layout adviser tool, namely, YASA. YASA performs sensitivity simulations using a spicelike simulator providing sensitivities of performance parameters with respect to circuit parameters.
A Chinese wall security model for decentralized workflow systems

Workflow systems are gaining importance as an infrastructure for automating inter-organizational interactions, such as those in Electronic Commerce. Execution of inter-organiz-ational workflows may raise a number of security issues including those related to conflict-of-interest among competing organizations. Moreover, in such an environment, a centralized Workflow Management System is not desirable because: (i) it can be a performance bottleneck, and (ii) the systems are inherently distributed, heterogeneous and autonomous in nature. In this paper, we propose an approach to realize decentralized workflow execution, in which the workflow is divided into partitions called self-describing workflows, and handled by a light weight workflow management component, called workflow stub, located at each organizational agent. We argue that placing the task execution agents that belong to the same conflict-of-interest class in one self-describing workflow may lead to unfair, and in some cases, undesirable results, akin to being on the wrong side of the Chinese wall. We propose a Chinese wall security model for the decentralized workflow environment to resolve such problems, and a restrictive partitioning solution to enforce the proposed model.
An on-chip wideband and low-loss duplexer for 3G/4G CMOS radios

A wideband integrated RF duplexer supports 3G/4G bands I, II, III, IV, and IX, and achieves a TX-to-RX isolation of more than 55dB in the transmit-band, and greater than 45dB in the corresponding receive-band across 200MHz of bandwidth. A 65nm CMOS duplexer/LNA achieves a transmit insertion loss of 2.5dB, and a cascaded receiver noise figure of 5dB with more than 27dB of gain, exceeding the commercial external duplexers performance at considerably lower cost and area.
BlueSniff: Eve Meets Alice and Bluetooth

Much of Bluetooth’s data remains confidential in practice due to the difficulty of eavesdropping it. We present mechanisms for doing so, therefore eliminating the data confidentiality properties of the protocol. As an additional security measure, devices often operate in “undiscoverable mode” in order to hide their identity and provide access control. We show how the full MAC address of such master devices can be obtained, therefore bypassing the access control of this feature. Our work results in the first open-source Bluetooth sniffer.
Similarities and differences between TQM , six sigma and lean

During the last decades, different quality management concepts, including total quality management (TQM), six sigma and lean, have been applied by many different organisations. Although much important work has been documented regarding TQM, six sigma and lean, a number of questions remain concerning the applicability of these concepts in various organisations and contexts. Hence, the purpose of this paper is to describe the similarities and differences between the concepts, including an evaluation and criticism of each concept.  Within a case study, a literature review and face-to-face interviews in typical TQM, six sigma and lean organisations have been carried out.  While TQM, six sigma and lean have many similarities, especially concerning origin, methodologies, tools and effects, they differ in some areas, in particular concerning the main theory, approach and the main criticism. The lean concept is slightly different from TQM and six sigma. However, there is a lot to gain if organisations are able to combine these three concepts, as they are complementary. Six sigma and lean are excellent road-maps, which could be used one by one or combined, together with the values in TQM.  The paper provides guidance to organisations regarding the applicability and properties of quality concepts. Organisations need to work continuously with customer-orientated activities in order to survive; irrespective of how these activities are labelled. The paper will also serve as a basis for further research in this area, focusing on practical experience of these concepts.
ONCOMINE: a cancer microarray database and integrated data-mining platform.

DNA microarray technology has led to an explosion of oncogenomic analyses, generating a wealth of data and uncovering the complex gene expression patterns of cancer. Unfortunately, due to the lack of a unifying bioinformatic resource, the majority of these data sit stagnant and disjointed following publication, massively underutilized by the cancer research community. Here, we present ONCOMINE, a cancer microarray database and web-based data-mining platform aimed at facilitating discovery from genome-wide expression analyses. To date, ONCOMINE contains 65 gene expression datasets comprising nearly 48 million gene expression measurements form over 4700 microarray experiments. Differential expression analyses comparing most major types of cancer with respective normal tissues as well as a variety of cancer subtypes and clinical-based and pathology-based analyses are available for exploration. Data can be queried and visualized for a selected gene across all analyses or for multiple genes in a selected analysis. Furthermore, gene sets can be limited to clinically important annotations including secreted, kinase, membrane, and known gene-drug target pairs to facilitate the discovery of novel biomarkers and therapeutic targets.
Big Data׳s Impact on Privacy, Security and Consumer Welfare By: Nir Kshetri

The purpose of this paper is to highlight the costs, benefits, and externalities associated with organizations׳ use of big data. Specifically, it investigates how various inherent characteristics of big data are related to privacy, security and consumer welfare. The relation between characteristics of big data and privacy, security and consumer welfare issues are examined from the standpoints of data collection, storing, sharing and accessibility. The paper also discusses how privacy, security and welfare effects of big data are likely to vary across consumers of different levels of sophistication, vulnerability and technological savviness. Big data | Externalities | Privacy | Security | Personally identifiable information |
Fast and Accurate Entity Recognition with Iterated Dilated Convolutions

Today when many practitioners run basic NLP on the entire web and large-volume traffic, faster methods are paramount to saving time and energy costs. Recent advances in GPU hardware have led to the emergence of bi-directional LSTMs as a standard method for obtaining pertoken vector representations serving as input to labeling tasks such as NER (often followed by prediction in a linear-chain CRF). Though expressive and accurate, these models fail to fully exploit GPU parallelism, limiting their computational efficiency. This paper proposes a faster alternative to Bi-LSTMs for NER: Iterated Dilated Convolutional Neural Networks (ID-CNNs), which have better capacity than traditional CNNs for large context and structured prediction. Unlike LSTMs whose sequential processing on sentences of length N requires O(N) time even in the face of parallelism, ID-CNNs permit fixed-depth convolutions to run in parallel across entire documents. We describe a distinct combination of network structure, parameter sharing and training procedures that enable dramatic 14-20x testtime speedups while retaining accuracy comparable to the Bi-LSTM-CRF. Moreover, ID-CNNs trained to aggregate context from the entire document are even more accurate while maintaining 8x faster test time speeds.