Reviewerly/exHarmony huggingface dataset:
Statistical artifacts: Critical approaches to the analysis of obsidian artifacts by portable X-ray fluorescence

In the past decade, chemical assay by portable X-ray fluorescence (pXRF) has become a common method of determining the geological origin of obsidian artifacts. We present source assignments made for 355 artifacts from the Late Postclassic site of Guadalupe, Honduras. Statistical studies of concentration data for six individual elements imply that obsidian from at least three sources (Güinope, La Esperanza, and Ixtepeque), and possibly two more (El Chayal and Otumba), was exploited in ancient times, but yield conflicting and ambiguous results for 5% of the collection. We argue that pXRF studies need to be conducted more critically and demonstrate how different statistical tools can yield contradictory source assignments for a significant portion of an obsidian collection. Visual sourcing supports discriminant function analysis over Mahalanobis distance assignments. Furthermore, element ratios imply that Otumba is not actually present. We are concerned that in obsidian studies, pXRF data are not consistently interpreted in ways that yield reproducible results. We propose an integrated methodology that employs pXRF, statistical analysis, visual sourcing, and—when needed and appropriate—destructive chemical assay to determine the source of “high-stake” artifacts.
Male vs. female EFL students: Who is better in speaking skill?

This research is aimed to investigate whether or not female EFL students' ability in speaking skill is better than male students. It deals with the hypothesis that mentions female students are better than male students in speaking skill. The method applied is the descriptive quantitative approach in which ten male and ten female students of the first grade of one of the high schools in Banda Aceh, Indonesia, become randomly the samples. The speaking test is done in order to collect the data and it is conducted to identify the male and female ability in English speaking skill, especially in the five aspects of speaking skill: pronunciation, grammar, fluency, comprehension, and vocabulary. The results show that the females actually dominate the higher scores in all aspects of speaking, however, the mean scores of male and female students are relatively similar, female students reach 68.5 while male students reach 63.3. The t-test is lower than t-table (-1.671.72), which means that the alternative hypothesis (Ha) of this study is rejected and the null hypothesis (Ho) is accepted. Therefore, it can be inferred that the first-grade female students of the high school under study have better performance in speaking skill than the male students, despite the difference is trivial.
An Interactive Image Retrieval Approach to Searching for Images on Social Media

Searching for images posted within social media services such as Twitter relies on matching textual queries to the contents of the posts that include the images. Unfortunately, social media posts may not always provide accurate or meaningful descriptions of the contents of the embedded images, making searching for images a challenging task. In this research, we augment the textual contents of the posts with new information extracted from the images using image processing and deep learning methods, and provide a visual interface to enable interactive image retrieval. A user study was conducted with 28 participants to collect evidence on how our approach was used in relation to Vakkari's three-stage model of information seeking. We also analyzed participants' perceptions of usefulness, ease of use, and satisfaction in comparison to a common grid-based image search interface. The results from this study highlight the value of providing visual and interactive features to enable searchers to discover images from social media sources.
A Real-Time Video Streaming System over IPv6+MPTCP Technology

Today, a steadily increasing number of users are not just passively consuming Internet content, but also share and publish content. Users publish text, photos and videos. With the availability of 5G high-speed, low-latency mobile broadband networks, real-time video streaming will also be possible. We believe this will become a very popular application in the coming years. But the more popular a service is, the higher the need for resilience. In this paper, we introduce our work-in-progress live video streaming platform for future mobile edge computing scenarios, which makes use of MPTCP+IPv6 to support multi-homing for resilience and multi-path transport for load balancing. As a proof of concept, we will show that the platform is (1) compatible with IPv6, (2) utilizes load balancing when possible and (3) provides robustness by network redundancy.
Event Notification in CAN-Based Sensor Networks

Preventive and reactive maintenance require the collection of an ever-increasing amount of information from industrial plants and other complex systems such as those based on robotized cells, a need that can be fulfilled by means of a suitable event notification mechanism. At the same time, timing and delivery reliability requirements in those scenarios are typically less demanding than those in other cases, thus enabling the adoption of best-effort notification approaches. This paper presents, evaluates, and compares some of those approaches, based on either standard Controller Area Network (CAN) messaging or a recently proposed protocol extension called CAN with eXtensible in-frame Reply (CAN XR). In the second case, the combined use of Bloom filters is also envisaged to increase flexibility. Results show that the latter approaches are advantageous in a range of event generation rates and network topologies of practical relevance.
Lessons Learned from Users Reading Highlighted Abstracts in a Digital Library

Finding relevant documents is essential for researchers of all disciplines. We investigated an approach for supporting searchers in their relevance decision in a digital library by automatically highlighting the most important keywords in abstracts. We conducted an eye-tracking study with 25 subjects and observed very different search and reading behavior which lead to diverse results. Some of the participants liked that highlighted abstracts accelerate their relevance decision, while others found that they disturb the reading flow. What many agree on is that the quality of highlighting is crucial for trust and system credibility.
Hierarchical Expert Neural Network System for Speech Recognition

This work proposes a hierarchical architecture composed of a expert neural network set based on the ensemble method with dynamic selection of classifiers for application in speech recognition systems. Therefore, 30 commands in the Brazilian Portuguese language were coded by a two-dimensional time matrix, resulting from the application of the discrete cosine transformation in the mel-cepstral coefficients. These patterns were modified by means of a nonlinear transformation to a high-dimensionality space through a set of Gaussian radial basis functions (GRBFs) parameterized with the centroid and covariance characteristics of the classes. The classification was made through the dynamic classifier selection approach, in which multilayer perceptron and learning vector quantization configurations were analyzed to constitute the multiple classifiers specialized in the subdivisions made in the total of classes to be recognized. Then, given a new test pattern, the GRBF that presents the highest value of the receptive field in relation to the input feature vector indicates the class to which the pattern is nearer, thus directing to the expert neural network that provides the final result of classification based on the local accuracy.
The impact of Chinese library and information science on outside disciplines: A citation analysis

This paper uses citation analysis to measure the intellectual impact of Chinese library and information science on outside disciplines. It analyses 469 journals in disciplines outside the library and information science field containing citations to 20 Chinese library and information science journals from 1996 to 2015. It shows that Chinese library and information science mainly receives citations from related disciplines, such as business and management, communication, and computer science, and that the majority of library and information science articles are rarely cited. The library and information science subjects of bibliometrics, information technology and knowledge management are most likely to be cited.
Bayesian Evaluation and Comparison of Ontology Alignment Systems

Ontology alignment systems are evaluated by various performance scores, which are usually computed by a ratio related directly to the frequency of the true positives. However, such ratios provide little information regarding the uncertainty of the overall performance of the corresponding systems. The comparison is also drawn merely by the juxtaposition of computed scores, and specify that one system is superior to one another provided that its score is higher. Nonetheless, the comparison based solely on two figures would not quantify the significance of difference and would not determine the extent to which one system is better. The problem compounds for comparison over multiple benchmarks since averages and micro-averages of performance scores are considered. In this paper, the evaluation of alignment systems is translated into a statistical inference problem by introducing the notion of risk for alignment systems. The risk with respect to a performance score is shown to follow a binomial distribution and is equivalent to the complement of the score, e.g., precision risk = 1 - precision. It is also demonstrated that the maximum likelihood estimation (MLE) is precisely equivalent to the conventional evaluation by using ratios. Instead of using the MLE, the Bayesian model is developed to estimate the risk with respect to a score (or equivalently, the score itself) as a probability distribution from the performance of the systems over single or multiple benchmarks. As a result, the evaluation outcome is a distribution instead of a figure, which provides a broader view of the overall system performance. A Bayesian test is also devised to compare various systems based on their estimated risks, which can compute the confidence that one system is superior to one another. We report the result of applying the proposed methodology to multiple tracks from the ontology alignment evaluation initiative (OAEI).
Image aesthetic assessment using a saliency symbiosis network

As a result of people taking more and more pictures in their lives, image assessment technology, which can automatically help people choose high quality pictures quickly, has become particularly important. Most algorithms use peak signal-to-noise ratio (PSNR) to assess image quality. However, images with high scores on PSNR are not as beautiful as individuals think. Image aesthetic assessment technology can come closer to human aesthetic standards. We report on a method named saliency symbiosis network for image aesthetic assessment. This is significant because we improved the conventional convolutional neural networks (CNN) method, which gets very close to the human visual mechanism after adding saliency features in CNN. Owing to considering limitations of CNN input size, we also proposed a pooling strategy to improve the ability of the model to accept arbitrary input sizes. Afterward, we propose an effective mean Huber loss function, which becomes less sensitive to outliers and can quickly train the model to being optimal. The experiment results proved that the proposed method, by using very small training data, performed the highest accuracy in image aesthetic assessment and classification.
Managing uncertainty in imputing missing symptom value for healthcare of rural India

In India, 67% of the total population live in remote area, where providing primary healthcare is a real challenge due to the scarcity of doctors. Health kiosks are deployed in remote villages and basic health data like blood pressure, pulse rate, height-weight, BMI, Oxygen saturation level (SpO2) etc. are collected. The acquired data is often imprecise due to measurement error and contains missing value. The paper proposes a comprehensive framework to impute missing symptom values by managing uncertainty present in the data set.The data sets are fuzzified to manage uncertainty and fuzzy c-means clustering algorithm has been applied to group the symptom feature vectors into different disease classes. The missing symptom values corresponding to each disease are imputed using multiple fuzzy based regression model. Relations between different symptoms are framed with the help of experts and medical literature. Blood pressure symptom has been dealt with using a novel approach due to its characteristics and different from other symptoms. Patients' records obtained from the kiosks are not adequate, so relevant data are simulated by the Monte Carlo method to avoid over-fitting problem while imputing missing values of the symptoms. The generated datasets are verified using Kulberk-Leiber (K-L) distance and distance correlation (dCor) techniques, showing that the simulated data sets are well correlated with the real data set.Using the data sets, the proposed model is built and new patients are provisionally diagnosed using Softmax cost function. Multiple class labels as diseases are determined by achieving about 98% accuracy and verified with the ground truth provided by the experts.It is worth to mention that the system is for primary healthcare and in emergency cases, patients are referred to the experts.
Comparison of filter banks of DWT in recovery of image using one dimensional signal vector

Digital filter bank of DWT (discrete wavelet transform) provides the approximate and detail components of a signal, whereas the perfection of the filter bank depends on recovery of the signal. In this paper, we convert an image into a one dimensional signal vector then the perfection of the filter bank is observed on recovery of signal vector hence the image. Here orthogonal FIR (finite impulse response), FIR under window method, IIR (infinite impulse response) Butterworth, Chebyshev type-I, type-II and synchrosqueezed transform are compared in context of image recovery under statistical parameters: mean error, variance of error, difference of skewness and kurtosis, cross-correlation coefficient, PSNR (peak signal to noise ratio) and histogram. The orthogonal FIR combination gives the best results and synchrosqueezed transform is the worst case for reconstruction of image.
Filter-based feature selection in the context of evolutionary neural networks in supervised machine learning

This paper presents a workbench to get simple neural classification models based on product evolutionary networks via a prior data preparation at attribute level by means of filter-based feature selection. Therefore, the computation to build the classifier is shorter, compared to a full model without data pre-processing, which is of utmost importance since the evolutionary neural models are stochastic and different classifiers with different seeds are required to get reliable results. Feature selection is one of the most common techniques for pre-processing the data within any kind of learning task. Six filters have been tested to assess the proposal. Fourteen (binary and multi-class) difficult classification data sets from the University of California repository at Irvine have been established as the test bed. An empirical study between the evolutionary neural network models obtained with and without feature selection has been included. The results have been contrasted with nonparametric statistical tests and show that the current proposal improves the test accuracy of the previous models significantly. Moreover, the current proposal is much more efficient than the previous methodology; the time reduction percentage is above 40%, on average. Our approach has also been compared with several classifiers both with and without feature selection in order to illustrate the performance of the different filters considered. Lastly, a statistical analysis for each feature selector has been performed providing a pairwise comparison between machine learning algorithms.
Intelligent travelling visitor estimation model with big data mining

Smart estimation modelling of tourism tourists has become aresearch challenge due to increment of tourists today. Since existing models have many limitations, asmart estimation model of traveler quantity is proposed in this paper. First, data nodes of visitors are classified by using Bayesian decision tree (BDT). Then, an analysismodel of tourism visitors is constructed. Last, asmart estimate model of tourist traveler is constructed by the implemented differential operator according to Autoregressive Integrated Moving Average model. Experimental results show that the accuracy of the proposed model is higher than traditional models.
Research on Data Mining Technology Based on Machine Learning Algorithm

In recent years, the domestic society has made great progress, and various advanced technologies and concepts have been applied and developed continuously. Machine learning algorithm is a new algorithm which plays a great role in all walks of life. Machine learning is a process of self-improvement using the system itself, and computer programs can automatically improve performance with the accumulation of experience. It is proposed for many specific learning tasks, so that computers can extract features from many data and discover hidden rules, so machine learning has been widely used in data mining. In this paper, the application of machine learning algorithm in data mining is studied in detail. With the help of mobile terminal data, the outdoor terminal of GSM network is positioned effectively, and a three-stage positioning algorithm is proposed, which greatly improves the positioning speed and accuracy.
Hooze

Recent innovations in fashion and smart textiles have contributed new visions of wearable computing, addressing the body through the cultural and social self. In this work, we draw on speculative design, maker technologies, and zoomorphism to explore how wearables might support sociability, and present Hooze, a fashion accessory that entices touch through its zoomorphic qualities and visual appearance. We describe our design and prototyping process, and reflect on how Hooze inspires transformative designs of wearables.
Characterizing Same Work Relationships in Large-Scale Digital Libraries

As digital libraries grow, they are prompting new consideration into same-work relationships. They provide unique opportunities for resource discovery, but their scale and aggregated models lead to challenges presented by duplicates and variants. Addressing this problem is complicated by metadata inconsistencies as well as structural/content differences. Following from work in algorithmically identifying duplicate works in the HathiTrust Digital Library, we present some cases that complicate our existing language for work entity relationships. These serve to contextualize the complexities of same-work alignment in digital libraries, ground future discussion around content similarity, and inform methods to better identify duplicates in large-scale digital libraries.
Four computational approaches for solving a class of boundary value problems arising in chemical reactor industry

In this work the performance of four new and different approaches are presented for numerical solution of some nonlinear boundary value problems which arise in modelling a tabular adiabatic chemical reactor. Quasi-linearization and converting differential equations to integral equation techniques and derivative and integration operational matrices of Cubic B-spline wavelets via some projection methods are applied to reducing the nonlinear problem to some algebraic system. For study of effect of involved parameters in main problem and for showing the accuracy and efficiency of the introduced methods some cases of main problem are given and findings are compared with the results of alternative methods for numerical solving of this class of equations.
Research on Multi-Source Data Integration Based on Ontology and Karma Modeling

The purpose of data integration is that integrates multi-source heterogeneous data. Ontology solves semantic describing of multi-source heterogeneous data. The authors propose a practical approach based on ontology modeling and an information toolkit named Karma modeling for fast data integration, and demonstrate an application example in detail. Armed Conflict Location &amp; Event Data Project (ACLED) is a publicly available conflict event dataset designed for disaggregated conflict analysis and crisis mapping. The authors analyzed the ACLED dataset and domain knowledge to build an Armed Conflict Event ontology, then constructed Karma models to integrate ACLED datasets and publish RDF data. Through SPARQL query to check the correctness of published RDF data. Authors design and developed an ACLED Query System based on Jena API, Canvas JS, and Baidu API, etc. technologies, which provides convenience for governments and researches to analyze regional conflict events and crisis early warning, and it verifies the validity of constructed ontology and the correctness of Karma modeling.
LsSQLIDP : Literature survey on SQL injection detection and prevention techniques

Every smallest service on the internet is made available through web applications. Services like online shopping, online banking system, e-booking system for railways or airlines and many more are all available at your doorstep with the help of internet. With these numerous applications comes the bulk amount of data they store on daily basis in their backend databases. The issue is how to store the data in an efficient and secure manner so that it should not be misused. Nowadays most of the applications use Cloud storage for this purpose. But is the data really secured on Cloud? With the increasing cases of cyber attacks one can figure out the answer to this question. The attacks like code injection attacks, denial of service attack, spoofing, phishing attack, http flood attack et al. are some of the major attacks challenging the protection of these applications. One of the major harmful attacks is SQL injection attacks (SQLinAs). There are several detection and prevention techniques for the same yet the applications are highly vulnerable to SQLinAs. To better understand the attack, all the existing types, detection and prevention techniques for SQLinAs are analyzed and showcased in this paper.