Miracle-dz/UniFAR-FTUs huggingface dataset:
Adaptive Questionnaires for Direct Identification of Optimal Product Design

We consider the problem of identifying the most profitable product design from a finite set of candidates under unknown consumer preference. A standard approach to this problem follows a two-step strategy: First, estimate the preference of the consumer population, represented as a point in part-worth space, using an adaptive discrete-choice questionnaire. Second, integrate the estimated part-worth vector with engineering feasibility and cost models to determine the optimal design. In this work, we (1) demonstrate that accurate preference estimation is neither necessary nor sufficient for identifying the optimal design, (2) introduce a novel adaptive questionnaire that leverages knowledge about engineering feasibility and manufacturing costs to directly determine the optimal design, and (3) interpret product design in terms of a nonlinear segmentation of part-worth space, and use this interpretation to illuminate the intrinsic difficulty of optimal design in the presence of noisy questionnaire responses. We establish the superiority of the proposed approach using a well-documented optimal product design task. This study demonstrates how the identification of optimal product design can be accelerated by integrating marketing and manufacturing knowledge into the adaptive questionnaire.
Music genre recognition based on visual features with dynamic ensemble of classifiers selection

This paper introduces the use of a dynamic ensemble of classifiers selection scheme with a pool of classifiers created to perform automatic music genre classification. The classifiers are based on support vector machine trained with textural features extracted from spectrogram images using Local Binary Patterns. The results obtained on the Latin Music Database showed that local feature extraction and the k-nearest oracle (KNORA) for dynamic ensemble of classifiers selection can reach a recognition rate of 83%, which is a little better than the best result ever reported on this dataset using the restrictions imposed by “artist filter”. In addition, the results are compared with those obtained from traditional approaches using acoustic features.
The Effects of Self-Efficacy on Motivation of Reading English Academic Text

The motivation of reading the academic text, especially in the English language, has been a problem for students of higher education. Whereas, reading ability is one of the important factors of students’ successful learning. This research aims to analyze the role of student’s self-efficacy in the level of motivation to read the textbooks in the English Language. This research applied quantitative approach. The population of this research is the whole students majoring in Management, Faculty of Economics, Universitas Islam Indonesia, that take Basic Academic Reading course in the first semester. The research’s primary data was collected by closed questionnaire, and supported by structured interviews (by 90% response rate). Meanwhile, the sampling technique used is purposive random sampling, and data analysis method used is Pearson Product Moment’s correlative coefficient. The result of this research revealed that students’ Self-Efficacy (SE) has a significant positive influence on their Motivation in Interest Aspect (MI), Motivation in Dedication Aspect (MD), and Motivation in Self-confidence Aspect (MC).
The Lack of A Priori Distinctions Between Learning Algorithms

Plutowski, M. , et al. (1994). Cross-validation estimates integrated mean squared error. In Advances in neural information processing systems 6, Cowan et al. some constant set by k and m. It's also true that E(C OTS | φ, d X) is not drastically different if one considers d X 's with a different m'. Accordingly, our summand doesn't vary drastically between d X 's of one m' and d X 's of another. Since n >> m and π(x) is uniform though, almost all of the terms in the sum have m' = m.
PD58-09 EXTRACTING STRUCTURED INFORMATION FROM PATHOLOGY REPORTS USING NATURAL LANGUAGE PROCESSING AND MACHINE LEARNING

INTRODUCTION AND OBJECTIVES: Detailed pathologic information for the estimated 1.74 million Americans diagnosed with cancer every year is locked away as unstructured free text, unavailable for use without manual abstraction. Our objective was to optimize NLP algorithms to extract detailed pathologic details from cancer pathology reports. Building on current pipelines, we developed feature specific optimizations for information extraction from prostate cancer pathology reports and evaluate if high quality information extraction was possible with a minimal training data. METHODS: We used a corpus of 3,232 free text pathology reports from radical prostatectomy specimens at UCSF, each with detailed manual annotations for 20 data elements, such as gleason scores, margin status, extracapsular extension, seminal vesicle invasion, tumor volume, and numbers of lymph nodes (positive and dissected). The full corpus was divided up so that the training, validation, development test, and true test sets contained 65%, 15%, 10%, and 10% reports each, respectively. We then created an NLP pipeline using NLTK and investigated the performance of multiple machine learning methods using scikit-learn and pytorch. We applied random forests, support vector machines, boosting, logistic regression and convolutional networks to the full training dataset as well randomly selected subsets of 8, 16, 32, 64, and 128 reports. RESULTS: We calculated the F1 evaluation metric weighted by support (number of true instances for each label) for each data field using the development test set. When working with the full training corpus (n=2066), convolutional networks perform the best (mean weighted F1 0.968 across all 12 clinical data elements). However, under smaller data conditions with less annotated data for training, boosting typically performs best. Moreover, with only 32 labeled reports we are able to achieve a mean weighted F1 score of 0.91 across fields. CONCLUSIONS: An NLP pipeline using both traditional statistical and machine-learning based methods can extract detailed prostate cancer pathology data from unstructured free text pathology reports with high accuracy, even with small sets of training data. Table. No title available. Source of Funding: None
Divisive Normalization, Line Attractor Networks and Ideal Observers

Gain control by divisive inhibition, a.k. a. divisive normalization, has been proposed to be a general mechanism throughout the visual cortex. We explore in this study the statistical properties of this normalization in the presence of noise. Using simulations, we show that divisive normalization is a close approximation to a maximum likelihood estimator, which, in the context of population coding, is the same as an ideal observer. We also demonstrate analytically that this is a general property of a large class of nonlinear recurrent networks with line attractors. Our work suggests that divisive normalization plays a critical role in noise filtering, and that every cortical layer may be an ideal observer of the activity in the preceding layer.
Novel framework for proactive handover with seamless multimedia over WLANs

Supporting multimedia applications over 802.11 wireless LANs requires low latency and seamless handover between multiple access points. However, the existing handover process in 802.11 products suffers from very high delay and frequent service disruption, which are not acceptable for streaming multimedia applications. In order to reduce this high delay and service disruption, the authors have designed and implemented a new proactive handover strategy over 802.11. The strategy intelligently issues proactive scan and handover triggers to reduce the effective channel scanning delay. Subsequently, it reserves resources in advance, to reduce the handoff reconnection delay and provide necessary QoS guarantee. Using actual implementation and simulation study, the authors demonstrate that their proposed strategy is capable of achieving magnitudes of latency, jitter and throughput improvements during the 802.11 handover operations, thereby providing seamless multimedia transmission.
Artificial curiosity based on discovering novel algorithmic predictability through coevolution

One explores a spatio-temporal domain by predicting and learning from success/failure what's predictable and what's not. The author studies a "curious" embedded agent that differs from previous explorers in the sense that it can limit its predictions to fairly arbitrary, computable aspects of event sequences and thus can explicitly ignore almost arbitrary unpredictable, random aspects. It constructs initially random algorithms mapping event sequences to abstract internal representations (IRs). It also constructs algorithms predicting IRs from IRs computed earlier. It wants to learn novel algorithms creating IRs useful for correct IR predictions, without wasting time on those learned before. This is achieved by a co-evolutionary scheme involving two competing modules co-evolutionary designing single algorithms to be executed. The modules can bet on the outcome of IR predictions computed by the algorithms they have agreed upon. If their opinions differ then the system checks who's right, punishes the loser (the surprised one), and rewards the winner. A reinforcement learning algorithm forces each module to maximise reward. This motivates both modules to lure the other into agreeing upon algorithms involving predictions that surprise it. Since each module essentially can put in its veto against algorithms it does not consider profitable, the system is motivated to focus on those computable aspects of the environment where both modules still have confident but different opinions. Once both share the same opinion on a particular issue, the winner loses a source of reward-an incentive to shift the focus of interest onto novel, yet unknown algorithms.
Polyelectrolyte persistence length: Attractive effect of counterion correlations and fluctuations

The persistence length of a single, strongly charged, stiff polyelectrolyte chain is investigated theoretically. Path integral formulation is used to obtain the effective electrostatic interaction between the monomers. We find significant deviations from the classical Odijk, Skolnick and Fixman (OSF) result. An induced attraction between monomers is due to thermal fluctuations and correlations between bound counterions. The electrostatic persistence length is found to be smaller than the OSF value and indicates a possible mechanical instability (collapse) for highly charged polyelectrolytes with multivalent counterions. In addition, we calculate the amount of condensed counterions on a slightly bent polyelectrolyte. More counterions are found to be adsorbed as compared to the Manning condensation on a cylinder.
Efficiency Optimizations on Yao's Garbled Circuits and Their Practical Applications

The advance of cloud computing and big data technologies brings out major changes in the ways that people make use of information systems. While those technologies extremely ease our lives, they impose the danger of compromising privacy and security of data due to performing the computation on an untrusted remote server. Moreover, there are also many other real-world scenarios requiring two or more (possibly distrustful) parties to securely compute a function without leaking their respective inputs to each other. In this respect, various secure computation mechanisms have been proposed in order to protect users' data privacy. Yao's garbled circuit protocol is one of the most powerful solutions for this problem. In this thesis, we first describe the Yao's protocol in detail, and include the complete list of optimizations over the Yao's protocol. We also compare their advantages in terms of communication and computation complexities, and analyse their compatibility with each other. We also look into generic Yao implementations (including garbled RAM) to demonstrate the use of this powerful tool in practice. We compare those generic implementations in terms of their use of garbled circuit optimizations. We also cover the specific real-world applications for further illustration. Moreover, in some scenarios, the functionality itself may also need to be kept private which leads to an ideal solution of secure computation problem. In this direction, we finally cover the problem of Private Function Evaluation, in particular for the 2-party case where garbled circuits have an important role. We finally analyse the generic mechanism of Mohassel et al. and contribute to it by proposing a new technique for the computation of the number of possible circuit mappings.
Numeracy and Adults' Learning Readiness and Commitment: Results from a Large National Random Sample of Participants.

The rapid changes in our society have amplified the need for adult learning opportunities. However, adults often make decisions not to persist in formal learning experiences in a smooth, linear fashion. The decision to pause or terminate formal learning is a complex behavioral decision that includes knowledge, the cognitive process, personal belief, and environmental context. Because the construct of numeracy also necessitates the use of content, cognitive processes, dispositions, and context, this study examined the link between adults’ numeracy abilities and learning readiness and commitment. This study analyzed the program for the International Assessment of Adult Competencies (PIAAC) Survey Adult Skills which was collected via a representative national random sample. The findings suggest numeracy abilities have a small, positive relationship to readiness to learn and learning persistence.
Central Command Architecture for High-Order Autonomous Unmanned Aerial Systems

This paper is the first in a two-part series that introduces an easy-to-implement central command architecture for high-order autonomous unmanned aerial systems. This paper discusses the development and the second paper presents the flight test results. As shown in this paper, the central command architecture consists of a central command block, an autonomous planning block, and an autonomous flight controls block. The central command block includes a staging process that converts an objective into tasks independent of the vehicle (agent). The autonomous planning block contains a non-iterative sequence of algorithms that govern routing, vehicle assignment, and deconfliction. The autonomous flight controls block employs modern controls principles, dividing the control input into a guidance part and a regulation part. A novel feature of high-order central command, as this paper shows, is the elimination of operator-directed vehicle tasking and the manner in which deconfliction is treated. A detailed example illustrates different features of the architecture.
Extraplanar Emission-Line Gas in Edge-On Spiral Galaxies. I. Deep Emission-Line Imaging

The extraplanar diffuse ionized gas (eDIG) in 17 nearby, edge-on disk galaxies is studied using deep Taurus Tunable Filter Hα and [N II] λ6583 images and conventional interference filter Hα+[N II] λλ6548, 6583 images that reach flux levels generally below ∼1 × 10-17 ergs s-1 cm-2 arcsec-2. [N II] λ6583/Hα excitation maps are available for 10 of these objects. All but one galaxy in the sample exhibit eDIG. The contribution of the eDIG to the total Hα luminosity is relatively constant, on the order of 12% ± 4%. The Hα scale height of the eDIG derived from a two-exponential fit to the vertical emission profile ranges from 0.4 to 17.9 kpc, with an average of 4.3 kpc. This average value is noticeably larger than the eDIG scale height measured in our Galaxy and other galaxies. This difference in scale height is probably due in part to the lower flux limits of our observations. The ionized mass of the extraplanar component inferred by assuming a constant filling factor of 0.2 and a constant path length through the disk of 5 kpc ranges from 1.4 × 107 to 2.4 × 108 M⊙, with an average value of 1.2 × 108 M⊙. Under these same assumptions, the recombination rate required to keep the eDIG ionized ranges from 0.44 × 106 to 13 × 106 s-1 cm-2 of the disk, or about 10%-325% of the Galactic value. A quantitative analysis of the topology of the eDIG confirms that several galaxies in the sample have a highly structured eDIG morphology. The distribution of the eDIG emission is often correlated with the locations of the H II regions in the disk, supporting the hypothesis that the predominant source of ionization of the eDIG is photoionization from OB stars located in the H II regions. A strong correlation is found between the IR (or far-IR) luminosities per unit disk area (basically a measure of the star formation rate per unit disk area) and the extraplanar ionized mass, further providing support for a strong connection between the disk and eDIG components in these galaxies. The excitation maps confirm that the [N II]/Hα ratios are systematically higher in the eDIG than in the disk. Although photoionization by disk OB stars is generally able to explain these elevated [N II]/Hα ratios, a secondary source of ionization appears to be needed when one also takes into account other line ratios; more detail is given in a companion paper (our Paper II).
Splitting the atom: A new approach to Neighbourhood Interchangeability in Constraint Satisfaction Problems

Gaussian Processes (GPs) are promising Bayesian methods for classification and regression problems. They have also been used for semi-supervised learning tasks. In this paper, we propose a new algorithm for solving semi-supervised binary classification problem using sparse GP regression (GPR) models. It is closely related to semi-supervised learning based on support vector regression (SVR) and maximum margin clustering. The proposed algorithm is simple and easy to implement. It gives a sparse solution directly unlike the SVR based algorithm. Also, the hyperparameters are estimated easily without resorting to expensive cross-validation technique. Use of sparse GPR model helps in making the proposed algorithm scalable. Preliminary results on synthetic and real-world data sets demonstrate the efficacy of the new algorithm.
Efficient Prediction of Cross-Site Scripting Web Pages using Extreme Learning Machine

— Malicious code is a way of attempting to acquire sensitive information by sending malicious code to the trustworthy entity in an electronic communication. JavaScript is the most frequently used command language in the web page environment. If the hackers misuse the JavaScript code there is a possibility of stealing the authentication and confidential information about an organization and user. The attack is based on the malicious JavaScript code inserted into pages by intruders or hackers. Various attacks like redirect, script injection and XSS which usually include to transmitting private data to attacker or redirecting the victim to web content controlled by hacker. A cross-site scripting vulnerability allows the introduction of malicious content on a web site that is then served to users. Therefore filtering malicious JavaScript code is necessary for any web application. The aim of this work is to analyze different malicious code attacks phenomenon, various types of malicious code attacks. The experimental results obtained on XSS classification in web pages using Extreme Learning Machine techniques. ELM approach can be found in its high sparseness, it can also be seen that ELM accomplishes better and more balanced classification for individual categories as well in very less training time comparative to other classification algorithms. The data are collected from the real web pages and various features are extracted to classify the malicious web page using supervised learning algorithms and the results demonstrate that the proposed features lead to highly accurate classification of malicious page.
Ligation of Fc gamma receptor IIB inhibits antibody-dependent enhancement of dengue virus infection

The interaction of antibodies, dengue virus (DENV), and monocytes can result in either immunity or enhanced virus infection. These opposing outcomes of dengue antibodies have hampered dengue vaccine development. Recent studies have shown that antibodies neutralize DENV by either preventing virus attachment to cellular receptors or inhibiting viral fusion intracellularly. However, whether the antibody blocks attachment or fusion, the resulting immune complexes are expected to be phagocytosed by Fc gamma receptor (FcγR)-bearing cells and cleared from circulation. This suggests that only antibodies that are able to block fusion intracellularly would be able to neutralize DENV upon FcγR-mediated uptake by monocytes whereas other antibodies would have resulted in enhancement of DENV replication. Using convalescent sera from dengue patients, we observed that neutralization of the homologous serotypes occurred despite FcγR-mediated uptake. However, FcγR-mediated uptake appeared to be inhibited when neutralized heterologous DENV serotypes were used instead. We demonstrate that this inhibition occurred through the formation of viral aggregates by antibodies in a concentration-dependent manner. Aggregation of viruses enabled antibodies to cross-link the inhibitory FcγRIIB, which is expressed at low levels but which inhibits FcγR-mediated phagocytosis and hence prevents antibody-dependent enhancement of DENV infection in monocytes.
An ID-based Broadcast Encryption Scheme for Collaborative Design

Collaborative design is one of core processes for networked manufacturing, so how to safely transfer design information in the network has important significance. In order to provide the confidentiality of transmission information on the level of design team in collaborative design system, a new broadcast encryption scheme based on designer’s identity is presented. The new scheme is based on broadcast encryption theory. By introducing master key of design teams and distributing the public and secret parameters reasonably, every designer can encrypt design documents and transmit them expediently. The encrypted document can be decrypted only by the members of the designated design team. In the new scheme the private key escrow problem of ID-based public key system is solved. Analysis shows that the new scheme is secure under the discrete logarithm assumption and users out of the designated design team can’t decrypt the documents. The new scheme can be used in the collaborative design system to protect the security of design information.
Adaptive ARMA filtering and energy normalization for robust speech recognition

In this paper we evaluate the effect of applying a previously proposed energy normalization scheme on a simple noise robust feature post processing scheme, MVA, in automatic speech recognition (ASR). A Noise energy-adaptive approach has also been proposed and successfully implemented. Here, the integration of non-logarithmic raw energy normalization in cepstral mean and variance normalization and applying an auto-regression moving-average (ARMA) filtering has led to improvements of up to 58% compared to the Aurora 2 clean-trained baseline system and up to 24% compared to MVA alone. Further improvements are also obtained by applying the adaptive approach. Our method is also very simple to implement and hence can easily be integrated with features other than the standard MFCC such as LPC, PLP etc.