Reviewerly/exHarmony huggingface dataset:
Short-Text Conceptualization Based on a Co-ranking Framework via Lexical Knowledge Base

The problem of short-text conceptualization is important, and has attracted increasing attention. Recent probabilistic algorithms have demonstrated remarkable successes. However most of them are limited to the assumption that all the observed terms in given short-text are conditionally independent, ignoring the interaction among terms (and concepts), as well as the beneficial reactions from concepts to terms. To overcome these problems, recently some co-rank paradigms are proposed, unfortunately neither they fails to integrate the co-occurrence feature nor they fails to utilize the semantic similarity implicit in the lexical knowledge base. Therefore, previous works could not release robust concept representation. Faced with this problem, this paper proposes a novel framework based on both statistic information (e.g., co-occurrence feature in large-scale corpus) and semantic information (e.g., semantic similarity in lexical knowledge base), for co-ranking terms and their corresponding concepts simultaneously, This co-ranking framework utilizes several graphs: the concept graph, the term graph and the subordination graph. The experimental results show that our method achieves higher accuracy and efficiency in short-text conceptualization than the state-of-the-art algorithms.
Finite point sets in recognizing location and orientation of machine parts of complex shapes

Abstract In the article, a method of recognizing the position, location and orientation of irregular machine parts with a complex outline of the external contour is suggested. Recognition is performed on the basis of a raster image that has undergone preliminary processing in a vision system. The developed method is based on matching the finite set of points retrieved from the external contour of the classified object. The originality of the approach consists of the form of the object pattern taken into account in determining the degree of similarity to reference objects. The similarity index was defined in the category of fuzzy sets. A cellular automaton has been proposed to generate the outline of the perimeter quickly while simultaneously smoothing it out. The process chain necessary for the recognition function to be implemented has been presented. The developed method has been illustrated with an example related to recognizing the position, location and orientation of a rear wheel pin. The results were compared to the classic Blair-Bliss, Danielsson and Haralick shape coefficients. The sensitivity of the developed method to a change in the scale and rotating the object as well as errors in outlining its edge has been tested. The obtained results confirmed the advantageous features of the developed method, both in the aspect of the recognition quality and the practically viable time of waiting for the results of processing.
No More than What I Post: Preventing Linkage Attacks on Check-in Services

With the flourishing of location based social networks, posting check-ins has become a common practice to document one's daily life. Users usually do not consider check-in records as violations of their privacy. However, through analyzing two real-world check-in datasets, our study shows that check-in records are vulnerable to linkage attacks. Specifically, adversary is able to uniquely re-identify over 52~66 percent users in other anonymous mobility datasets and 60~80 percent users have more than 60 percent probability leaking unreported mobility records. In addition, we further demonstrate that the privacy sensitivity of check-in records can be more accurately measured by including the information of additional mobility data compared with only looking at check-ins. Based on this observation, we design a partition-and-group framework to integrate the information of check-ins and additional mobility data to attain a novel privacy criterion-k <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">τ,l</sup> -anonymity. It ensures adversaries with arbitrary background knowledge cannot use check-ins to re-identify users in other anonymous datasets or learning unreported mobility records. The proposed framework achieves favorable performance against state-of-art baseline in terms of improving check-in utility by 24~57 percent while providing stronger privacy guarantee at the same time. We believe this study will open a new angle in attaining both privacy-preserving and useful check-in services.services.
Secure and Efficient Outsourcing of Large Scale Linear Fractional Programming

Mohammed, Nedal M. Lomte, Santosh S.One of the powerful applications in the age of cloud computing is the outsourcing of scientific computations to cloud computing which makes cloud computing a very powerful computing paradigm, where the customers with limited computing resources and storage devices can outsource the sophisticated computation workloads into powerful service providers. This paper investigates the secure outsourcing of linear fractional programming (LFP). We propose a secure, efficient, and verifiable scheme to offload large-scale (LFP) computation in the cloud side. We implement the proposed schema on the customer side laptop and use AWS compute domain elastic compute cloud (EC2) for the cloud side.
SentiLangN: A Language-Neutral Graph-Based Approach for Sentiment Analysis in Microblogging Data

In this paper, we present a language-neutral graph-based sentiment analysis approach, SentiLangN, which uses character n-gram graph for modelling textual data to handle language-neutral unstructured expressions and noisy data. Since ordering and positioning of characters and words in a document plays a vital role in content analysis, the SentiLangN employs the longest common subsequence and degree similarity to capture inherent semantics of the textual data.
PPCU Sam: Open-source face recognition framework

In recent years by the popularization of AI, an increasing number of enterprises deployed machine learning algorithms in real life settings. This trend shed light on leaking spots of the Deep Learning bubble, namely the catastrophic decrease in quality when the distribution of the test data shifts from the training data. It is of utmost importance that we treat the promises of novel algorithms with caution and discourage reporting near perfect experimental results by fine-tuning on fixed test sets and finding metrics that hide weak points of the proposed methods. To support the wider acceptance of computer vision solutions we share our findings through a case-study in which we built a face-recognition system from scratch using consumer grade devices only, collected a database of 100k images from 150 subjects and carried out extensive validation of the most prominent approaches in single-frame face recognition literature. We show that the reported worst-case score, 74.3% true-positive ratio drops below 46.8% on real data. To overcome this barrier, after careful error analysis of the single-frame baselines we propose a low complexity solution to cover the failure cases of the single-frame recognition methods which yields an increased stability in multi-frame recognition during test time. We validate the effectiveness of the proposal by an extensive survey among our users which evaluates to 89.5% true-positive ratio.
Detecting Anomalous Behaviour from Textual Content in Financial Records

Most financial institutions mainly use numerical statistics to detect anomalous (malpractice) activity. The textual content in financial records however contains precious information which to date has not been effectively used for detection of anomalous behaviors by users because these are often unintelligible, cluttered with abbreviations, numbers and symbols, which makes it difficult to build a framework system that can coherently understand and draw conclusions. Rule-based techniques have been proposed but such systems are easy to elude, as they are difficult to generalize and do not scale up. The work presented in this paper differs from previous work in that we exclusively base anomalous activities on text (excluding numerical values) in financial records and treat this as a classification problem for a deep learning network. We propose four solutions using deep learning techniques on textual data to distinguish between normal with anomalous behaviors of the users. The results of our experiments convincingly show that use of the textual content in financial records yields greater accuracy in anomalous behavior detection. They also suggest that deep learning is a viable and effective solution for real time anomaly detection by financial institutions.
Monitoring of Volume of Air in Inhalation From Triflo Using Video Processing

Breathing exercise training with a flow-oriented incentive spirometer (or Triflo) is important in lung expansion rehabilitation for patients with lung surgery or pulmonary injury. In this article, we propose a novel technique to estimate the volume of air inhaled and the sustained inhalation time based on video processing. The proposed technique combines a randomized circle detection algorithm to detect the three balls in Triflo tubes and a normalized cross-correlation algorithm to track these three balls that rise due to inhalation. The values of inhalation and the sustained inhalation time intervals are automatically recorded as screenshots on a personal computer for later analysis by a doctor. Three major experiments were conducted to verify the advantages of this proposed system. The first experiment evaluated the performances of both the ball detection and the tracking algorithms showing 3.68% error. The second experiment applied the proposed method implemented, so that it displays in plots the changes in the inhalation rate, as the training of a postoperative volunteer progressed. In the last experiment, a healthy subject employs Triflo to test the inhalation training format for lung volume regeneration with three levels of difficulty, to record the number of successful breathing maneuvers. When the data are displayed in rate versus time plots, a graph can help a doctor to appropriately plan the training program for the benefit of patients, because a more adaptable therapy can be realized and varying degrees of difficulty can be implemented. In addition, the patients can comfortably practice in home-based rehabilitation, seeing the improvements in the inspiratory muscle performance in computer screenshots.
Color Image Quality Assessment with Multi Deep Convolutional Networks

The existing objective natural Image Quality Assessment (IQA) methods are mostly designed for grayscale images. These methods ignore the correlations between color information and quality distortions, and lack of investigating the feature representations of color information. Consequently, the prediction accuracy and generalization performance on color images are not satisfactory. In view of this, a multi deep Convolutional Neural Network (CNN) architecture is designed for color image quality assessment, which deeply explores the color information in different color spaces. Firstly, a group set of component maps are generated with multi-scale transformation and multi-color-space transformation on the color image. Secondly, the general CNN structures are adopted and improved to learn effective feature representations related to image quality. Each component map is fed to a single CNN, thus forming a multi-CNNs model. Thirdly, each CNN is trained using transfer learning method with pretrained models on large scale datasets. Finally, multiple output feature eigenvectors are fused and mapped to the subjective quality scores to construct the quality prediction model. The systematic experimental results on multiple artificial databases and the real-world database showed that the proposed prediction model outperforms existing IQA methods.
Improving a Syntactic Graph Convolution Network for Sentence Compression

Sentence compression is a task of compressing sentences containing redundant information into short semantic expressions, simplifying the text structure and retaining important meanings and information. Neural network-based models are limited by the size of the window and do not perform well when using long-distance dependent information. To solve this problem, we introduce a version of the graph convolutional network (GCNs) to utilize the syntactic dependency relations, and explore a new way to combine GCNs with the Sequence-to-Sequence model (Seq2Seq) to complete the task. The model combines the advantages of both and achieves complementary effects. In addition, in order to reduce the error propagation of the parse tree, we dynamically adjust the dependency arc to optimize the construction process of GCNs. Experiments show that the model combined with the graph convolution network is better than the original model, and the performance in the Google sentence compression dataset has been effectively improved.
Comparison of Predictions between Artificial Neural Networks and Gaussian Processes in EMC Investigations

Due to the increasing size and complexity of investigations in the field of electromagnetic compatibility, it is a desirable objective to reduce the calculation effort of such studies. In some of them, a large number of similarly constructed DUTs are investigated. For these cases, similar results are expected under equal conditions. Studies have shown, that artificial neural networks (ANNs) can reduce the effort of such investigations. This paper presents a further reduction method, the Gaussian processes. In addition, ANNs and Gaussian processes are compared and the results predictability regarding to EMC are evaluated.
Harmonization and Categorization of Metrics and Criteria for Evaluation of Recommender Systems in Healthcare From Dual Perspectives

Researchers' choice of metrics and criteria in evaluating recommender systems depends on what the researcher feels is popular among other researchers, or sometimes based on the objective of the research. There is no harmonized set of criteria and metrics that can be referenced when evaluating recommender systems in healthcare. In this article, a set of metrics and criteria are harmonized and categorized as a guide for evaluating recommender systems. By means of an online survey, the opinions of forty-four experienced researchers and other stakeholders from eight countries and four continents were sought on the relevance of identified metrics and criteria. Analysis of the results show speed and timeliness are at the top. Topping the list of criteria is the provision of information that will guide users to useful decisions. The result is presented from two logical perspectives. Four categories are then identified as a useful guide for evaluating recommender systems.
Solar Powered Soil Condition Activated Irrigation System with Automated Bird Repellant

Abstract Irrigation plays a very important role in agriculture. Not all geographical regions utilize irrigation which is an important activity that is expensive. For the regions where irrigation is used, it commonly amounts to significant energy consumption. Traditional methods of farming are time-consuming and can only be efficient on a small scale. This project was implemented to make irrigation progressively inventive, easy to use, more efficient and cheaper than the current irrigation system and to solve traditional farming problems like an attack on crops by bird thereby increasing agricultural productivity. This system incorporates a soil moisture sensor to guarantee the application of water to soil adequately. It uses a PIR sensor and a buzzer to solve the problem of attack on crops by birds. This project is powered by a solar panel which is one of the cheapest form of renewable energy and environmentally friendly. The main objective of this project is to upturn the agricultural productivity of the nation by automating the irrigation process. Bird attacks on farmlands are also responsible for destroying a vast amount of crops. One of the traditional problems of agriculture is that farmers frequently neglect to water their plants; it can be as a result of inattentiveness or because of the fact that the farmers are far from home. The utilization of the soil moisture sensor limits the human presence. The microcontroller utilizes the real-time readings of the sensors and automates the irrigation and bird repellent process. From the results, it was observed that the irrigation time was higher in totally dry clay compared to partially wet clay and wet clay respectively. For clay, the irrigation time is in order of magnitude dry clay &gt; partial wet clay &gt; wet clay of the irrigation other soil samples. For the loamy soil, the dry soil was higher. It follows the same trend as the clay in magnitude. It can be found in sandy soil that the wet sample was higher than the totally dry and partially wet sandy soil in terms irrigation time
Multi‐scale object detection by bottom‐up feature pyramid network

The deep neural networks has been developed fast and shown great successes in many significant fields, such as smart surveillance, self-driving and face recognition. The detection of the object with multi-scale and multi-aspect-ratio is still the key problem. In this study, the authors propose a bottom-up feature pyramid network, coordinating with multi-scale feature representation and multi-aspect-ratio anchor generation. Firstly, the multi-scale feature representation is formed by a set of fully convolutional layers which is catenated after the backbone network. Secondly, in order to link the multi-scale feature, the deconvolutional layer is involving after each multi-scale feature map. Thirdly, to tackle the problem of adopting object with different aspect ratios, the anchors on each multi-scale feature map are generated by six shapes. The proposed method is evaluated on PASCAL visual object detection dataset and reach the accuracy of 80.5%.
The landscape of archived studies in a social science data infrastructure: Investigating the ICPSR metadata records

ABSTRACT Data sharing and reuse practices in social sciences have changed in the past few decades with the continuing development of cyber‐infrastructures. However, little is known about how such practices chronically change over time. Via the lens of 35 years' worth of archived data on the world's largest social science data infrastructure, ICPSR, this study aims to reveal data characteristics (e.g., archived data themes and temporal distribution) that shape and represent the trend of social science data sharing and reuse. After a preliminary analysis on metadata of 10,362 studies and 159,616 associated citation records, an upward trend is found regarding the number of studies archived on ICPSR and their citation counts. We also report problems with metadata standards and the challenges of reusing data derived from these studies. Our results can be a cornerstone to investigate the roles of ICPSR as a research data infrastructure within the context of open science.
A Study on the Intention of Using Online E-Learning Platform to Assist in Teaching of Junior High School Teachers

The conceptual research framework in this study is based on the Decomposed Theory of Planned Behavior and cooperates with empirical investigations and research. It adopts snowball sampling to send online questionnaires to the junior high school teachers who have used online e-learning platform teaching. A total of 108 valid questionnaires were collected in order to study on factors affecting the junior high school teachers' intention of using online learning platforms to assist teaching. All data were hypothesized to be verified by confirmatory factor analysis, and structural equation models. The research found (1) There are eight key factors affecting the teaching intention of junior high school teachers using the online learning platforms: perceived usefulness, compatibility, peer influence, self-efficacy, resource facilitating condition, attitude, subjective norm, and perceived behavioral control; (2) perceived usefulness and compatibility have a significant positive effect on attitude; peer influence has a significant positive effect on subjective norm; self-efficacy and resource facilitating conditions have a significant positive effect on perceived behavioral control; user attitudes has a significant positive effect on behavioral intentions.
Node Recovery in Wireless Sensor Networks via Blockchain

Wireless Sensor Network (WSN) is a network of nodes connected through a wireless channel. The sensor nodes in the network are resource constrained in terms of energy, storage and computational power. Node failure is a common phenomenon, which occurs due to environmental factors, adversary attacks, draining of battery power, etc. After node failure, recovery is challenging that needs a strong mechanism. In this paper, Blockchain-based Node Recovery (BNR) scheme for WSNs is proposed. In BNR scheme, recovery of failed nodes is on the basis of node degree. The working mechanism of the scheme is that firstly, the failed nodes are detected using the state (active or inactive) of Cluster Heads (CHs). In the second phase, the recovery process is initiated for inactive nodes. The main purpose of this step is to recover the failed CH, which ultimately results in restoring the active states of its member nodes. NodeRecovery Smart Contract (SC) is written for the purpose. The cost analysis for NodeRecovery is also performed in the proposed work. Moreover, the security analysis is performed to ensure the security of the proposed scheme. Effectiveness of the proposed model is shown by the simulation results.
Dropped Pronoun Recovery in Chinese Conversations with Knowledge-Enriched Neural Network

Dropped pronoun recovery, which aims to detect the type of pronoun dropped before each token, plays a vital role in many applications such as Machine Translation and Information Extraction. Recently, deep neural networks have been applied to this task. Though promising improvements have been observed, these methods recover dropped pronouns from the limited context in a small-size window and lack common sense to connect the referred entity to a proper pronoun. In this paper, we propose a knowledge-enriched neural attention framework for Chinese dropped pronoun (DP) recovery. A structured attention mechanism is used to capture the semantics of DP referents from the wider context. External knowledge, which consists of a knowledge base and a hierarchical pronoun-category assumption, is also incorporated in our model to provide pronoun classification information of referred entity and contextual dependency degree. Results on three different conversational genres show that our approach achieves a convincing improvement over the current state of the art.
CVAE-Attention

Text sentiment classification is an important domain in NLP, and the related technical research has been mature. The sentiment classification of text with the "but" contrastive marker is a challenging problem. In this paper, a semi-supervised framework based on conditional variational autoencoder using attention, called CVAE-Attention, is proposed for sentiment classification. In the CVAE-Attention framework, the attention mechanism is introduced to cope with the contrastive structure. The latent semantic information of the clause after "but" (but-clause) is extracted through the attention model, and is incorporated into the generative model to enlarge the effect of the but-clause. Experiments show that the proposed method is effective compared with other state-of-the-art semi-supervised methods.