Reviewerly/exHarmony huggingface dataset:
UnseenCode: Invisible On-screen Barcode with Image-based Extraction

Screen-camera communication techniques achieve one-way communication with widely-used screen and camera devices. Popular barcode methods use visible spatial patterns to represent data, which has been considered obtrusive to human observers. Recent works borrow ideas from visual light communication (VLC), and use inter-frame pixel change to modulate data. To recover pixel change, the receiver has to record and process video frames. Such video-based extraction has high hardware requirements and lacks reliability. Inspired by VLC-based methods, we propose UnseenCode, an invisible on-screen barcode scheme. It leverages inter-frame embedment from VLC-based methods to embed invisible barcodes into arbitrary on-screen contents. Unlike VLC-based methods, UnseenCode does not require video-based extraction. We propose an image-based extraction method based on cross-component correlation of color images. Any off-the-shelf smartphones with camera capability can be used to read UnseenCode by capturing on-screen contents. We propose the full implementation of UnseenCode for evaluation. Experimental results show that UnseenCode decoding algorithm is reliable and applicable under various screen and camera settings. UnseenCode provides up to 2.5 kbit capacity with less than 5% error rate.
Concentrations, variations and distribution of molybdenum (Mo) in catchment outlet sediments of China: Conclusions from the China geochemical baselines project

The concentrations, variations and distribution of molybdenum (Mo) in catchment outlet sediments from China were studied in this contribution based on the results of recently-finished, continental-scale, China Geochemical Baselines (CGB) project. The data from CGB were combined with other published data-sets with the purpose of understanding the spatial distribution of Mo in China and, in addition, relating the observed geochemical features to natural and/or anthropogenic processes. This geochemical mapping with a density of ca. 3000 km2 per site involves catchment sediments in two horizons at each site: a top sample (TOS) at a depth of 0–25 cm and a bottom sample (BOS) at a depth greater than 100 cm. In total, 3382 TOS and 3380 BOS samples were collected. The analytical results show that the concentrations of Mo in the catchment outlet sediments of China range from 0.07 to 57.86 μg/g for the TOS and 0.09–32.43 μg/g for the BOS. The median and mean values of the TOS data are 0.73 and 1.01 μg/g respectively, and those of the BOS data are 0.71 and 0.94 μg/g. The variations of both the TOS and BOS are high at this continental scale. The Mo concentrations vary greatly from one geological and/or geographic unit to another. For instance, from the Cathaysia Block with the TOS and BOS median values of 1.22 and 1.46 μg/g to the North China craton with those of approximately 0.61 and 0.58 μg/g. Various processes govern the spatial distribution of Mo in the catchment sediments of China but regional-scale natural sources (e.g. lithologies) predominate. Sedimentary sandstones and carbonate rocks, alluvium in plain terrain and aeolian materials (e.g. loess) account for the extensive low concentrations (<0.5 μg/g) at such regions as the Songpan-Ganzi orogen and Songliao Basin; whereas shales, basalts and granitoids like granodiorite, plagiogranite and granite porphyry are taken to be responsible for the high concentrations (>1.2 μg/g) in the Yangtze and Cathaysia blocks. Simultaneously, some local mineralized areas can be also identified at this scale because the sampled sites just happened to fall on sediments derived from mineralized rocks or anthropogenic addition by mining activities. Furthermore, the TOS share the similar patterns with the BOS indicating relatively stable inheritance and no apparent extensive secondary enrichment or depletion in the catchment sediments of China. The CGB demonstrates that a series of baseline levels of Mo are needed at different regions and scales and ultra-low density mapping can still deliver robust geochemical patterns.
Automating the process of identifying the preferred representational system in Neuro Linguistic Programming using Natural Language Processing

Neuro Linguistic Programming (NLP) is a methodology used for recognition of human behavioral patterns and modification of the behavior. A significant part of this process is influenced by the theory of representational systems which equates to the five main senses. The preferred representational system of an individual can explain a large part of exhibited behaviors and characteristics. There are different methods to recognize the representational systems, one of which is to investigate the sensory-based words in the used language during the conversation. However, there are difficulties during this process since there is not a single reference method used for identification of representational systems and existing ones are subject to human interpretations. Some human errors like lack of experience, personal judgment, different levels of skill and personal mistakes may also affect the accuracy and reliability of the existing methods. This research aims to apply a new approach that is to automate the identification process in order to remove human errors, thereby increasing the accuracy and precision. Natural Language Processing has been used for automating this process, and an intelligent software has been developed to identify the preferred representational system with increased accuracy and reliability. This software has been tested and compared to human identification of representational systems. The results of the software are similar to a NLP practitioner, and the software responds more accurately than a human practitioner in various parts of the process. This novel methodology will assist the NLP practitioners to obtain an improved understanding of their clients' behavioral patterns and the associated cognitive and emotional processes.
A critical discussion into the core of swarm intelligence algorithms

The literature is now filled with swarm intelligence algorithms developed by taking inspiration from a number of insects and other animals and phenomena, such as ants, termites, bees, fishes and cockroaches, to name just a few. Many, if not most, of these bioinspirations carry with them some common issues and features which happen at the individual level, promoting very similar collective emergent phenomena. Thus, despite using different biological metaphors as inspiration, most algorithms present a similar structure and it is possible to identify common macro-processes among them. In this context, this paper identifies a set of common features among some well-known swarm-based algorithms and how each of these approaches implement them. By doing this, we provide the community with the core features of swarm-intelligence algorithms. This diagnostic is crucial and timely to the field, because once we are able to list and explain these commonalities, we are also able to better analyze and design swarm intelligence algorithms.
Using automatic image processing to analyze visual artifacts created by students in scientific argumentation

Abstract Science classes should support students' development of scientific argumentation. While previous studies have analyzed argumentative texts, they have overlooked the ways in which other types of representations, including images, affect the production of such texts. In addition, studies into the use of visual images in science education have offered mostly qualitative analyses. To fill these gaps in the research, this study used techniques of automated image processing to extract relevant information from student‐generated visual artifacts. Specifically, it used a series of image‐processing algorithms to automatically extract and quantify features of images created by students to serve as evidence in support of scientific arguments. Using various statistical analyses, we identified the relationships between the extracted features and the students' performance levels in constructing scientific arguments. The results revealed that the presence of water in a student's image correlated significantly with that student's claim and explanation scores and that the amount of water present in a student's image correlated significantly with that student's claim score, but not with their explanation score. These results indicate that automatic image processing can successfully identify image features that affect students' performance in scientific argumentation. Using this analysis as an example, we discuss implications for incorporating automated image processing into further research into scientific argumentation and the development of automated feedback.
An unsupervised Bayesian hierarchical method for medical fraud assessment

The increasing size and complexity of health care industry makes it attractive for fraudsters, therefore medical fraud assessment has gained more importance. Statistical methods can help medical auditors reveal fraud patterns within medical claims data. This paper proposes an unsupervised Bayesian hierarchical method as a prescreening tool to aid in medical fraud assessment. The proposed hierarchical model helps the investigators group medical procedures and identifies the hidden patterns among providers and medical procedures. Outlier detection and similarity assessment are conducted to analyze the billing differences among providers. We illustrate the utilization of the proposed method using U.S. Medicare Part B data and discuss the potential insights for medical audit decision‐making.
Fast distributed video deduplication via locality-sensitive hashing with similarity ranking

The exponentially growing amount of video data being produced has led to tremendous challenges for video deduplication technology. Nowadays, many different deduplication approaches are being rapidly developed, but they are generally slow and their identification processes are somewhat inaccurate. Till now, there is rare work that studies the generic hash-based distributed framework and the efficient similarity ranking strategy for video deduplication. This paper proposes a flexible and fast distributed video deduplication framework based on hash codes. It is able to support the hash table indexing using any existing hashing algorithm in a distributed environment and can efficiently rank the candidate videos by exploring the similarities among the key frames over multiple tables using MapReduce strategy. Our experiments with a popular large-scale dataset demonstrate that the proposed framework can achieve satisfactory video deduplication performance.
Reduction of Training Data Using Parallel Hyperplane for Support Vector Machine

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Modeling of Multivariable Fuzzy Systems by Semitensor Product

A new fuzzy formulation technique based on semitensor product of matrices is proposed in this paper to construct multivariable fuzzy logic systems. A fuzzy relation matrix (FRM) model is suggested for direct modeling and indirect identification of the fuzzy matrix expression using the observed input-output data of fuzzy dynamical process. The center points of fuzzy sets for every output variable are optimized by using least squares estimation. The effectiveness of the proposed fuzzy formulation technology is verified by simulations. Test results show that the proposed formulation technique is an effective design method to construct and optimize fuzzy relation structure matrices for multivariable fuzzy systems. It can be used to design FRM models for multi-input multi-output systems.
Semi-wrapper feature subset selector for feed-forward neural networks: Applications to binary and multi-class classification problems

This paper explores widely the data preparation stage within the process of knowledge discovery and data mining via feature subset selection in the context of two very well-known neural models: radial basis function neural networks and multi-layer perceptron. It is known the best performance of wrapper attribute selection methods based on the evaluation measure provided by a classifier, although the temporal complexity of learning neural networks practically precludes the use of wrapper techniques, especially in complex databases with high dimensionality and a large number of labels. In this paper, we propose the use of the Naïve Bayes classifier as a fitness function within a semi-wrapper feature selection approach. The Naïve Bayes classifier is a good fast approach to a neural network and utilising it as a measure of goodness in a backward search on a ranking provides a specific attribute selection method for neural networks in complex data. The test-bed consists of 34 binary and multi-class classification problems and 7 feature selectors. Of these, there are 6 data sets with upwards of 5 classes. According to the reported accuracy results that have been supported by non-parametric statistical tests in different scenarios, our method has been shown to be very suitable for both kinds of neural networks. Moreover, the reduced feature-space is around 20% of the full attribute space. The speedup with the aforementioned semi-wrapper is very outstanding and its value fluctuates, on average, from about 1.5 with radial basis function neural networks to around 30 with multi-layer perceptron.
Towards Next Generation Knowledge Graphs for Disaster Management

Timely access to information of critical resources is the fundamental requirement for the disaster management domain. Existing information systems to support the disaster management services lack interoperability and have several challenges to efficiently access heterogeneous information, especially open data. Such challenges include heterogeneity of data sources and formats (e.g., a hospital resource entity in Data. Gov versus OpenStreetMap), inconsistency of vocabularies (e.g., an entity type in Data.Gov schema versus OpenStreetMap schema), and incompleteness of information in a single source (e.g., lack of all relevant metadata for a hospital resource entity in Data. Gov). Knowledge graphs facilitate in addressing these challenges. They enable semantic computing applications to timely access relevant information about a critical resource across diverse data sources with both human and machine-understandable interpretations. In this paper, we will illustrate the design of a Disaster Knowledge Graph (DisasterKG) based system for querying critical resources and showcase how such interoperable information access could improve decision making throughout disaster management for preparedness, response, and recovery. On the flip side, our experience shows that such an approach may not be efficient by itself and cost-effective as still the considerable human effort is required for semantic information integration. Towards addressing this, we consider the Deep Learning based KG embedding approach as a potential alternative and argue for combining this approach with the traditional KG construction approach in complementary ways to get the best of both worlds.
Semi-supervised image registration using deep learning

Deep metrics have been shown effective as similarity measures in multi-modal image registration; however, the metrics are currently constructed from aligned image pairs in the training data. In this paper, we propose a strategy for learning such metrics from roughly aligned training data. Symmetrizing the data corrects bias in the metric that results from misalignment in the data (at the expense of increased variance), while random perturbations to the data, i.e. dithering, ensures that the metric has a single mode, and is amenable to registration by optimization. Evaluation is performed on the task of registration on separate unseen test image pairs. The results demonstrate the feasibility of learning a useful deep metric from substantially misaligned training data, in some cases, the results are significantly better than from Mutual Information. Data augmentation via dithering is, therefore, an effective strategy for discharging the need for well-aligned training data; this brings deep learning based registration from the realm of supervised to semi-supervised machine learning.
DeepTagRec: A Content-cum-User Based Tag Recommendation Framework for Stack Overflow

In this paper, we develop a content-cum-user based deep learning framework DeepTagRec to recommend appropriate question tags on Stack Overflow. The proposed system learns the content representation from question title and body. Subsequently, the learnt representation from heterogeneous relationship between user and tags is fused with the content representation for the final tag prediction. On a very large-scale dataset comprising half a million question posts, DeepTagRec beats all the baselines; in particular, it significantly outperforms the best performing baseline TagCombine achieving an overall gain of 60.8% and 36.8% in precision@3 and recall@10 respectively. DeepTagRec also achieves 63% and 33.14% maximum improvement in exact-k accuracy and top-k accuracy respectively over TagCombine.
Comparison of Machine Learning Techniques for Prediction Problems

Last three decades, artificial intelligence have become mandatory for human lives since the revolution of computers. As a part of it, Machine Learning and the proposed techniques are used to solve the real life problems instead of humans by using their extended capabilities for classification, optimization, and as well as prediction. However, there is a big mystery for the optimum technique for any kind of data and problem domain. In this paper, preliminary experiments are performed using recent and challenging nine datasets and four popular machine learning techniques in order to determine optimum technique for prediction domain. Obtained results are analyzed using three evaluation methods; MSE value, EV and R2 scores. Obtained results showed that, increment or degradation of instances in datasets do not affect the performance of the techniques directly and neural network algorithms produce higher and more steady prediction rates.
Communication of partial ignorance with qubits

We introduce a class of communication tests where the task is to communicate partial ignorance by means of a physical system. We present a full characterization of the implementations of these tests in the qubit case and partial results for qudits. A peculiar observation is that two physical systems with the same operational dimensions may differ with respect to implementations of these tasks, as is shown to be the case for the qubit and rebit. Finally, we consider the natural question whether some of the communication tests are more difficult than others. A new preordering that we call the ultraweak matrix majorization is presented to answer this question in a theory-independent way.
Predicting Web Vulnerabilities in Web Applications Based on Machine Learning

Building a secure website is time-consuming, expensive and challenging task for web developers. Researchers to identify webpage sinks to address security efforts, as it helps to reduce time and money to secure web application, are introducing different web vulnerabilities prediction models. Some of the well-known web vulnerabilities are SQL Injection, Cross Site Scripting (XSS) and Cross Site Request Forgery (CSRF). Different machine learning methods are being employed by the existing vulnerability prediction models to prevent vulnerable components in web applications. However, majority of these methods cannot challenge all web vulnerabilities. Therefore, this paper proposed a method namely NMPREDICTOR to predict vulnerable files in website for vulnerability prediction as a classification problem by predicting legitimate or vulnerable code. In addition, it is an effort to employ the classification on different classifier of machine learning algorithms to judge elimination of vulnerable components. Numerous experiments have been conducted in our study to evaluate the performance of our proposed model. Through our proposed method, we have builds 6 classifiers on a training set of labeled files represented by their software metrics and text features. Additionally, we builds a Meta classifier, which combines the six underlying classifiers i.e. J48, Naive Bayes and Random forest. NMPREDICTOR is evaluated on datasets of three web applications, which offers 223 superior quality vulnerabilities found in PHPMyAdmin, Moodle and Drupal. Our proposed method shows a clearly has an advantage over results of existing studies in case of Drupal, PhpMyAdmin and Moodle.
On Precision of Code Clone Detection Tools

Precision and recall are the main metrics used to measure the correctness of clone detectors. These metrics require the existence of labeled datasets containing the ground truth - samples of clone and non-clone pairs. For source code clone detectors, in particular, there are some techniques, as well as a concrete framework, for automatically evaluating recall, down to different types of clones. However, evaluating precision is still challenging, because of the intensive and specialized manual effort required to accomplish the task. Moreover, when precision is reported, it is typically done over all types of clones, making it hard to assess the strengths and weaknesses of the corresponding clone detectors. This paper presents systematic experiments to evaluate precision of eight code clone detection tools. Three judges independently reviewed 12,800 clone pairs to compute the undifferentiated and type-based precision of these tools. Besides providing a useful baseline for future research in code clone detection, another contribution of our work is to unveil important considerations to take into account when doing precision measurements and reporting the results. Specifically, our work shows that the reported precision of these tools leads to significantly different conclusions and insights about the tools when different types of clones are taken into account. It also stresses, once again, the importance of reporting inter-rater agreement.
Optical Character Recognition Based Intelligent Database Management System for Examination Process Control

Computer Vision techniques have recently opened several new avenues of research owing to advancement in hardware and software technologies. This technique relies on extracting relevant information from images to take intelligent decisions. In this project, we are using two subclasses of computer vision techniques namely optical character recognition (OCR) and object recognition to develop an intelligent database management system for post examination process control. Using MNIST database, in which we have 10,000 samples of test set and 60,000 samples of training set, we have applied a deep learning algorithm for handwritten digits recognition on an examination sheet. Images are adjusted using pre-processing techniques. After extraction of information from the first sheet of examination copy, we have classified the signatures from invigilator and examiner using feature extraction algorithm where signatures are treated as objects. The extracted information is stored in database and individual and collective scores are then computed. Our model also generates graphs of result for better understanding. This initial work reduces the workload of post examination data entry process and makes it time efficient. In future, it is expected to be integrated in an android based application for automatic post examination control expected to be used at author's university.