Reviewerly/exHarmony huggingface dataset:
Utilizing Deep Learning and Oversampling Methods to Identify Children’s Emotional and Behavioral Risk

Deep neural network (DNN) has been widely used in various artificial intelligence applications and is, unsurprisingly, penetrating the field of school psychology. In the school environment, universal screening is used by teachers to identify children’s emotional and behavioral risk (EBR) within a screener. EBR can be used to predict possible emotional and behavioral disorders, which impact children’s educational and social outcomes. Using the BASC-2 Behavioral and Emotional Screening System Teacher Rating Scale (BASC-2 BESS TRS; Reynolds &amp; Kamphaus (2004). Behavior Assessment System for Children (2nd ed.). Circle Pines, MN: American Guidance Service) norm data, we classified children’s EBR status from normal to at-risk using DNN. Data oversampling was used to overcome the imbalanced sample feature (i.e., few cases with emotional and behavioral disorder). Traditional machine learning methods, such as Naïve Bayes and logistic regression, were included for comparison. The results indicated that the DNN with oversampling achieved the highest performance levels with accuracy (ACC) of .957, precision (PPV) of .545, true positive rate (TPR or sensitivity) of 1.000, and true negative rate (TNR or specificity) of .942 compared with the other methods. This novel method is helpful to provide accurate screening results for early identification of children’s EBR. The current study provides a useful guide for researchers to apply the DNN and oversampling to classification in assessment-related research.
Unsupervised clustering of LA-ICP-MS raster map data for geological interpretation: A case study using epidote from the Yerington district, Nevada

Raster element concentration maps created using laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS) can be used to interpret microscale compositional and textural domains within mineral grains. Raster maps are typically evaluated element by element; however, application of statistical techniques (such as cluster analysis) can enhance the generation of geochemical domains to support interpretation of growth zones, core-rim relationships, sector zones, and compositional-textural associations. Clustered LA-ICP-MS map data can be assessed within individual samples and between multiple samples, and can extend insight from the microscopic scale to the regional scale to better understand geological paragenesis of an area. Our workflow (1) applies a centred log transformation to selected elements in a raster map dataset; (2) uses principal component analysis (PCA) applied to the multi-sample, mono-mineralic dataset to group similar elements in epidote based on geochemical character; (3) applies unsupervised clustering to separate different types and generations of epidote in chemical feature space; (4) presents clustered LA-ICP-MS raster map results for interpretation of inter- and intra-mineral chemical zones; and (5) plots results spatially, across a regional map area, to investigate geological paragenesis. The workflow is illustrated using samples of epidote from the Yerington porphyry-skarn Cu (Mo–Au) district. In the case study area, six clusters are defined by unique mineral compositions: (1) low U; (2) elevated Pb, Mn and low Fe and Sr; (3) elevated Ce, U and low Mn and Pb; (4) elevated U, Ce; low Mn, Pb; (5) elevated Sr, Fe and low Mn, Pb; and (6) elevated Mn, Sr, and Fe and low Ce and U. The regional distribution of these groups is presented as indicating proximity to the porphyry environment (lower concentrations of Ce, U, As and Sb and higher concentrations of Mn, Sr and Fe) versus retrograde skarn (elevated Ce, U, As and Sb).
Fast Streaming k-Means Clustering with Coreset Caching

We present new algorithms for <inline-formula><tex-math notation="LaTeX">$k$</tex-math></inline-formula> -means clustering on a data stream with a focus on providing fast responses to clustering queries. Compared to the state-of-the-art, our algorithms provide substantial improvements in the query time for cluster-center queries while retaining the desirable properties of provably small approximation error and low space usage. Our proposed clustering algorithms systematically reuse the “coresets” (summaries of data) computed for recent queries in answering the current clustering query, a novel technique which we refer to as coreset caching. We also present an algorithm called <monospace>OnlineCC</monospace> that integrates the coreset caching idea with a simple sequential streaming <inline-formula><tex-math notation="LaTeX">$k$</tex-math></inline-formula> -means algorithm. In practice, <monospace>OnlineCC</monospace> algorithm can provide constant query time. We present both theoretical analysis and detailed experiments demonstrating the correctness, accuracy, and efficiency of all our proposed clustering algorithms.
Understanding and Improving Disability Identification in Medical Documents

Disabilities are a problem that affects a large number of people in the world. Gathering information about them is crucial to improve the daily life of the people who suffer from them but, since disabilities are often strongly associated with different types of diseases, the available data are widely dispersed. In this work we review existing proposal for the problem, making an in-depth analysis, and from it we make a proposal that improves the results of previous systems. The analysis focuses on the results of the participants in DIANN shared task was proposed (IberEval 2018), devoted to the detection of named disabilities in electronic documents. In order to evaluate the proposed systems using a common evaluation framework, a corpus of documents, in both English and Spanish, was gathered and annotated. Several teams participated in the task, either using classic methods or proposing specific approaches to deal effectively with the complexities of the task. Our aim is to provide insight for future advances in the field by analyzing the participating systems and identifying the most effective approaches and elements to tackle the problem. We have validated the lessons learned from this analysis through a new proposal that includes the most promising elements used by the participating teams. The proposed system improves, for both languages, the results obtained during the task.
A New Approach for Dynamic and Risk-Based Data Anonymization

Data anonymization is a complex task, as it is dependent on the structure of the dataset, the privacy requirements that we might have and how the anonymized data is going to be processed. Taking just into account these three aspects, it would be possible to set up many anonymization configurations for a single dataset, as each variable that appears on the data could be anonymized using different techniques (generalization, randomization, deletion), and each of them could be configured with a different parameterization. In consequence, the are several alternatives for anonymizing a dataset, especially when it is composed by a high number of variables. For those cases, a manual anonymization process is unfeasible and an automatic approach that allows to determine the best anonymization configuration for the data is essential. Furthermore, it is necessary to determine accurately the risk of each anonymization configuration, in order to verify that the expected privacy requirements are fulfilled. In this paper we present two main contributions: 1) a dynamic risk-based anonymization process that allows to determine the best anonymization configuration for a particular dataset; 2) two new privacy metrics (CAK and R-CAK) that allow to measure the risk of re-identification of the anonymized data, taking into account the knowledge of an adversary that is trying to disclose sensitive attributes from the anonymized dataset.
<scp>Light chain</scp> consensus reinforcement machine learning: An effective blockchain model for Internet of Things using for its advancement and challenges

Abstract Recently, blockchain intersected the Internet of Things (IoT) has come up with an integrated opportunity for different applications such as industries, medical diagnosis, and the education sector. Several conflicts have risen during the intersection, where the purpose of addressing the enormous resource utilization of blockchain, efficiency, and security issues of massive IoT has not been tackled in the present scenario. Presently, Ruff‐chain, blockchain consortium basis, mobile cloud blockchain (MCBC), probed IoT, and proof of work deployed to overcome the drawback of blockchain intersected IoT demands high resource utilization and power consumption. To address this issue, a light chain consensus reinforcement machine learning (LCC‐RML) method has been developed to optimize the blockchain effectively intersected IoT system and it assists in providing a learning methodology from the aspects of resource utilization, data security decentralization, scalability, and latency. In LCC‐RML, without affecting the decentralization system, security, and latency, scalability has been improved with the underlying blockchain approach. Here, a lighter model is designed especially for the blockchain intersected IoT platform, which contains optimized learning procedures, reduced block size, lightweight consensus data structure, and related effective block interval to streamline the data processing. The experimental analysis has been evaluated in the learning framework to improve the performance of the blockchain intersected IoT system with a computational speed of 84.89% and resource utilization reduction of 85.88%. Further in the power consumption has been reduced up to 57.55% with the computation cost of 29.55% with the scalability ratio of 86.88%.
A Game-Theoretic Approach to Sequential Detection in Adversarial Environments

The problem of sequential binary hypothesis testing in an adversarial environment is investigated. Specifically, if there is no adversary, the samples are generated independently by a distribution p; and if the adversary is present, the samples are generated independently by another distribution q. The adversary picks a distribution q ∈ Q with cost c(q). The goal of the defender is to decide whether there is an adversary using samples as few as possible; and the goal of the adversary is to fool the defender. The problem is formulated as a non-zero-sum game between the adversary and the defender. A pair of strategies (attack strategy from the adversary and the sequential hypothesis testing scheme from the detector) is proposed and proved to be a Nash equilibrium pair for the non-zero-sum game asymptotically. Numerical experiments are provided to validate our results.
Learning to Generate Personalized Query Auto-Completions via a Multi-View Multi-Task Attentive Approach

In this paper, we study the task of Query Auto-Completion (QAC), which is a very significant feature of modern search engines. In real industrial application, there always exist two major problems of QAC - weak personalization and unseen queries. To address these problems, we propose M2A, a multi-view multi-task attentive framework to learn personalized query auto-completion models. We propose a new Transformer-based hierarchical encoder to model different kinds of sequential behaviors, which can be seen as multiple distinct views of the user's searching history, and then a prefix-to-history attention mechanism is used to select the most relevant information to compose the final intention representation. To learn more informative representations, we propose to incorporate multi-task learning into the model training. Two different kinds of supervisory information provided by query logs are utilized at the same time by jointly training a CTR prediction model and a query generation model.
Sedentary quantum walks

Let X be a graph with adjacency matrix A. The continuous quantum walk on X is determined by the unitary matrices U(t)=exp⁡(itA) (for t∈R). If X is the complete graph Kn and a∈V(X), then1−|U(t)a,a|≤2/n. Roughly speaking, this means that a quantum walk on a complete graph stays home with high probability. We say that a family of graphs is sedentary if there is a constant c such that 1−|U(t)a,a|≤c/|V(X)| for all t. In this paper we investigate this condition, and produce further examples of sedentary graphs. A cone over a graph X is the graph we get by adjoining a new vertex and making it adjacent to each vertex of X. We prove that if X is the cone over an ℓ-regular graph on n vertices, then |U(t)a,a|≤ℓ2/(ℓ2+4n). It follows that if we choose ℓ and n such that n/ℓ2→0, then a continuous quantum walk starting on the "conical" vertex will remain there with probability close to 1. On the other hand, if ℓ≤2, we show there is a time t such that all entries in the a-column of U(t)ea have absolute value 1/n. We show that there are large classes of strongly regular graphs such that 1−|U(t)a,a|≤c/V(X) for some constant c. On the other hand, for Paley graphs on n vertices, we prove that if t=π/n, then |U(t)a,a|≤1/n.
Towards Tracing Bitcoin Client using Network Traffic Analysis

The lack of supervision is an important reason that hinders the development of cryptocurrencies. In the case of Bitcoin, many lawless elements use Bitcoin for illegal transactions. With the decentralization and anonymity provided by Blockchain, it is difficult for finance institudes to identify them by traditional means of supervision. Based on the existing research on Bitcoin server tracing, this paper proposes an efficient Bitcoin client tracing mechanism to trace from Bitcoin server to the client through traffic analysis. Experiments are carried out and show that under the condition of general network connection, the tracing accuracy is close to no error.
Improving Chinese Clinical Named Entity Recognition Based on BiLSTM-CRF by Cross-Domain Transfer

Named entity recognition (NER) serves as an essential resource in natural language processing (NLP) applications. Most existing named entity recognition models mainly focus on social media, biomedicine and finance. However, the number of researches on Chinese Electronic Medical Records (EMRs) is limited, which fails to model effective medical entities. In this paper, a novel cross-domain transfer model, namely T-BiLSTM-CRF, is proposed for Chinese clinical named entity recognition (e.g., disease, symptom, drug, anatomy). Considering the scarceness of large-scale datasets, we build a hybrid model to fully extract the discriminating information and then integrate it with the target feature to achieve optimal solution. Our proposed method can encode features from different sources to better express various entities and enhance the recognition performance. Extensive experiments were carried out on benchmark CCKS 2018 datasets. Results demonstrate the superiority of the proposed T-BiLSTM-CRF comparing with several representative methods.
A Blockchain-Based Smart Network for IoT-Driven Smart Cities

Internet-of-Things (IoT) networks interoperability is of a crucial role in driving innovation to achieve smart city agenda. However, the potential for a unified and connective digital linkage is undermined by competitive private sector standards and protocols that are dissociated and disunited. In this paper, we propose a blockchain-based standard for IoT integration which is democratized, scalable, and decentralized. As a transactional ledger, the blockchain serves as a central data warehouse capable of monitoring the entrance and exit of user-triggered information. Network interchange and authentication can be facilitated through blockchain-relegation of network communication and security protocol. The outcome is linked with integrated communications, efficiency and a highly functional user experience as the constructs of interoperability develops.
Cantonese Porcelain Image Generation Using User-Guided Generative Adversarial Networks

Automated image style transfer is of great interest given the recent advances in generative adversarial networks (GANs). However, it is challenging to generate synthesized images from abstract masks while preserving detailed patterns for certain kinds of art given small datasets. We propose an intelligent GAN-based system enhanced with user intent and prior knowledge for generating images styled as Cantonese porcelain using user-defined masks. Given a mask with specified objects, our system first generates a synthesized natural image. We then use a novel semantic user intent enhancement module to retrieve semantically relevant images from an image dataset. Objects in the retrieved image are used to refine local patterns in the synthesized image. Finally, the refined image is restyled in the Cantonese porcelain style. The system is trained by 454 pairs of natural images and semantic segmentation of 24 objects from the COCO dataset for synthesized image generation from masks, and 1445 Cantonese porcelain images for style transfer. Experimental results and ablation studies demonstrate that the synthesized and restyled images were improved with local details and enhanced contrast.
Analysis of Deep Neural Networks for Textual Entailment Recognition

Textual entailment methods recognize, pairs of natural language expressions such that a human who reads (and trusts) the first element of a pair would most likely infer that the other element is also true. A lot of research works has already been accomplished in this area. In this paper we analyze the impact of various deep neural network architectures on the performance of recognizing textual entailment pairs. We start with a very simplistic neural network architecture and gradually add elements to the architecture that can possibly improve its performance, thereby analyzing its impact on the performance parameters. We measure the performance based on accuracy, recall, precision, and F1 score of train as well as test data, obtained from SNLI data-set. Here we start by tokenizing and converting the text and the hypothesis into corresponding sequence of word-embedding vectors and comparing these two sequence by passing them through various neural network layers and using a dense layer with softmax activation as the final layer. We construct every new architecture by adding new layers and/or inputs to the previous best architecture. In this paper we try six architectures and analyze their performance in terms of the performance parameters. We analyze the impact of various layers like LSTM, concatenation, cosine similarity function, normalization, dropouts and attention.
An Effective Compressive Sensing based N-gram Approach for plagiarism detection

Plagiarism detection over the document is an important area of research, which directly deals with document and its content copying. Many algorithms for such document processing such as word matching, string matcher, similarity measure and Rabin-Karp etc. were proposed. Previous approaches are limited while dealing with data analysis work with single level of processing. Thus, they either work with document pre-processing execution or further plagiarism detection. In this paper, an advance novel approach which is compressive sensing based on N-gram (CS-RKP) is proposed. This algorithm used sampling module for data processing and further cost function for document redundancy detection, minimization of iteration and further finding similarity over the document. The result observation using computation time, similarity measure shows the efficiency of proposed algorithm.
Free Objects in Constraint-logic Object-oriented Programming

Constraint-logic object-oriented programming facilitates the integrated development of business software that occasionally solves constraint-logic problems or makes other use of structured search. So far, work in constraint-logic object-oriented programming has been limited to considering constraints that only involve logic variables of primitive types; in particular, boolean, integer, and floating-point numbers. However, the availability of object-oriented features calls for the option to use logic variables in lieu of objects as well. Therefore, support for reference-type logic variables (or free objects) is required. With the work at hand, we add support for free objects to a Java-based constraint-logic object-oriented language, Muli. Allowing free objects in statements and expressions results in novel interactions with objects at run time, for instance, non-deterministic execution of polymorphic method invocations (taking arbitrary class hierarchies into account) and non-deterministic type operations. At run time, this is supported by a dynamic type constraint that restricts the possible types of a free object at non-deterministic choices.
Politeness in security directives: Insights in browser compliance for the human element

The technical protection provided by information security technology is necessary as a frontline defense against threats. However, the human element adds great risk to systems and cannot be ignored. This research explores the human elements related to security communications and intention to comply with security directives. Security communications are more-commonly being sent using richer computer-based channels. While the goal of security communications is the gain compliance, there is still much to learn related to what influences a user to comply. This research explores the effects that (im)politeness has on intention to comply with security directives. The research utilized an experiment that focused on secure browser setting directives using several types of online communication. The subjects were randomly assigned to receive communications/directives that were text-based, text/image-based, or video-based. The communications were also grouped into polite and impolite messaging. The results for the overall model indicated perceived richness had a negative influence on perceived mental workload. Additionally, perceived mental workload resulted in a decreased intention to comply with security directives. There were no significant differences between polite and impolite groups for the treatment groups with text-based and text/image-based messaging. However, in the video-based treatment, the relationship between perceived richness and intention to comply with security directives was significantly different between the polite and impolite groups.
Quantum State Redistribution for Ensemble Sources

We consider a generalization of the quantum state redistribution task, where pure multipartite states from an ensemble source are distributed among an encoder, a decoder and a reference system. The encoder, Alice, has access to two quantum systems: system A which she compresses and sends to the decoder, Bob, and the side information system C which she wants to keep at her site. Bob has access to quantum side information in a system B, wants to decode the compressed information in such a way to preserve the correlations with the reference system on average. As figures of merit, we consider both block error (which is the usual one in source coding) and per-copy error (which is more akin to rate-distortion theory), and find the optimal compression rate for the second criterion, and achievable and converse bounds for the first. The latter almost match in general, up to an asymptotic error and an unbounded auxiliary system; for so-called irreducible sources they are provably the same. Full paper forthcoming [1].