Reviewerly/exHarmony huggingface dataset:
Simulating real profiles for shilling attacks: A generative approach

Collaborative Filtering (CF) approaches are vulnerable to Shilling Attacks, in which malicious users or companies inject a large number of fake profiles in a system in order to manipulate its recommendations. One problem of current Shilling Attack models is that they commonly use straightforward statistical templates, producing profiles with different rating patterns than actual system data, which facilitates its detection, requiring a larger amount of profiles to achieve its goals. To address this problem and create profiles closer to reality, we propose using a generative model, Variational Autoencoder (VAE) to map original data distribution. With VAE, it is possible to generate new profiles based on real data, without explicit copying their actual ratings. Its generated profiles are converted to malicious profiles by adding target item rating value. We test our attack model on MovieLens 100k data set and compare to literature attack models. Our results indicate that our model outperforms all other models in model-based CF system, especially using low attack sizes (from 3% to 5%). Also, analysis comparing profiles generated from it and other approaches shows that our model ratings pattern are very similar to real profiles, which may indicate that attacks mounted using our approach may be less likely to be detected by detection approaches. Thus, we show that our attack model represents an advance on Shilling Attack models, since its superior results in model-based CF and possible indistinction from real profiles may be useful as a baseline to test detection techniques and other tasks among Shilling Attack area.
RIP Emojis and Words to Contextualize Mourning on Twitter

This paper aims to investigate the use of emojis to contextualize mourning on Twitter. Specifically, we seek to determine (i) whether an emoji is sufficient to contextualize expressions of grief; (ii) which emojis most accurately represent mourning; (iii) whether only words are used to contextualize mourning; (iv) which words are used to characterize mourning in tweets; and, (v) if there are differences in the expression of mourning in different languages. For this, we use a multi-stage method to conduct a comprehensive analysis of the manifestations of grieving behavior on Twitter, and created machine learning models to classify expressions of mourning in tweets. The main contributions from this work are (1) a gold standard of manually annotated mourning tweets; (2) classification models produced using machine learning ensemble methods and BERT contextual embeddings; and, (3) an extensive analysis of our findings opening up opportunities for new research. The results of this paper reveal emojis alone are insufficient for identifying expressions of mourning in tweets, and the combination of both emojis and words is the most effective strategy for contextualizing mourning online -- the models achieved the 84.8%-97% F1 score in all datasets. Although words alone are capable of characterizing mourning contexts correctly, the English vocabulary is limited, and the contribution of RIP - the abbreviation for "rest in peace'' - is highly decisive. Our results have also shown that the most relevant emojis for this context were emotional ones, such as \includegraphics[width=1em]twitter_brokenheart.png, and emojis are used in a uniform fashion in both Spanish and English.
Evolving Gaussian process kernels from elementary mathematical expressions for time series extrapolation

Choosing the best kernel is crucial in many Machine Learning applications. Gaussian Processes are a state-of-the-art technique for regression and classification that heavily relies on a kernel function. However, in the Gaussian Processes literature, kernels have usually been either ad hoc designed, selected from a predefined set, or searched for in a space of compositions of kernels which have been defined a priori. In this paper, we propose a Genetic Programming algorithm that represents a kernel function as a tree of elementary mathematical expressions. By means of this representation, a wider set of kernels can be modeled, where potentially better solutions can be found, although new challenges also arise. The proposed algorithm is able to overcome these difficulties and find kernels that accurately model the characteristics of the data. This method has been tested in several real-world time series extrapolation problems, improving the state-of-the-art results while reducing the complexity of the kernels.
An Effective Approach to Classify White Blood Cell Using CNN

The white blood cell plays an important role in the human body, protecting the human body from being invaded by the WBC by removing parasites, fungi, bacteria, and viruses. There are many types of white blood cells, such as eosinophils, lymphocytes, monocytes, and neutrophils. Identification and classification of white blood cells is an important application in clinical trials. The WBC statistics provide information about the state of health of the human body. Many diseases and infections depend on white blood cells in the bloodstream, thus identifying and separating the different WBCs. Nowadays, blood cell diagnostics is performed by laboratory pathologists. Symptomatic testing, however, is a major time-consuming, expensive, and error-free test. An in-depth learning approach, especially the implementation of interactive neural networks, offers additional benefits in the medical field, where a large number of images are analyzed and processed. CNN is expected to acquire a number of semantic features, which will affect the accuracy of the sections to some degree. CNN automatically captures features from a single image and identifies specific types of white cells in different images. In this study, it is proposed to differentiate different types of WBC such as eosinophils, lymphocytes, monocytes, and neutrophils in confusing images of neural network formation. The test will take place on the WBC database with 12,436 images. The improved model was tested to give an accurate result with sectional accuracy of 97% in all classes.
FiLiPo: A Sample Driven Approach for Finding Linkage Points Between RDF Data and APIs

Data integration is an important task in order to create comprehensive RDF knowledge bases. Many data sources are used to extend a given dataset or to correct errors. Since several data providers make their data publicly available only via Web APIs they also must be included in the integration process. However, APIs often come with limitations in terms of access frequencies and speed due to latencies and other constraints. On the other hand, APIs always provide access to the latest data. So far, integrating APIs has been mainly a manual task due to the heterogeneity of API responses. To tackle this problem we present in this paper the FiLiPo (Finding Linkage Points) system which automatically finds connections (i.e., linkage points) between data provided by APIs and local knowledge bases. FiLiPo is an open source sample-driven schema matching system that models API services as parameterized queries. Furthermore, our approach is able to find valid input values for APIs automatically (e.g. IDs) and can determine valid alignments between KBs and APIs. Our results on ten pairs of KBs and APIs show that FiLiPo performs well in terms of precision and recall and outperforms the current state-of-the-art system.
Aspect Level Sentiment Analysis on Zoom Cloud Meetings App Review Using LDA

During the Covid-19 pandemic, almost all community activities are conducted from home. Therefore, video conference technology is needed for people to carry out their normal activities from home. One of the video conference applications is ZOOM Cloud Meetings. Applications certainly have been reviewed given by their users as a reference for new users and companies of the application to know the application’s performance. However, in reviews, some constraints are the number of reviews as well as irregular. Therefore, a solution is needed with sentiment analysis that aims to classify the reviews of the application to be organized by categorizing positive or negative sentiment. In this study, aspect-based sentiment analysis was conducted on ZOOM Cloud Meetings app reviews from Google Play Store. The analysis’s result of the review data obtained three aspects, namely aspects of usability, system, and appearance. The modeling topic used is the Latent Dirichlet Allocation (LDA) method and classification using the Support Vector Machine (SVM). This research resulted in the best performance with the best parameters resulting in the performance accuracy of usability aspect is 88.83%, system aspect with 91.2%, appearance aspect with 94.78%, and performance accuracy of all aspects 91.61%.
Paradigm shift in the field of information special issue editorial

Journal of the Association for Information Science and TechnologyVolume 72, Issue 10 p. 1217-1222 EDITORIAL Paradigm shift in the field of information special issue editorial Rong Tang, Corresponding Author Rong Tang rong.tang@simmons.edu School of Library and Information Science, Simmons University, Boston, Massachusetts, USA Correspondence Rong Tang, School of Library and Information Science, Simmons University, Boston, Massachusetts, USA. Email: rong.tang@simmons.eduSearch for more papers by this authorBharat Mehra, Bharat Mehra School of Library and Information Studies, University of Alabama, Tuscaloosa, Alabama, USASearch for more papers by this authorJia Tina Du, Jia Tina Du UniSA STEM, University of South Australia, Adelaide, South Australia, AustraliaSearch for more papers by this authorYuxiang (Chris) Zhao, Yuxiang (Chris) Zhao School of Economics & Management, Nanjing University of Science & Technology, Nanjing, Jiangsu Province, ChinaSearch for more papers by this author Rong Tang, Corresponding Author Rong Tang rong.tang@simmons.edu School of Library and Information Science, Simmons University, Boston, Massachusetts, USA Correspondence Rong Tang, School of Library and Information Science, Simmons University, Boston, Massachusetts, USA. Email: rong.tang@simmons.eduSearch for more papers by this authorBharat Mehra, Bharat Mehra School of Library and Information Studies, University of Alabama, Tuscaloosa, Alabama, USASearch for more papers by this authorJia Tina Du, Jia Tina Du UniSA STEM, University of South Australia, Adelaide, South Australia, AustraliaSearch for more papers by this authorYuxiang (Chris) Zhao, Yuxiang (Chris) Zhao School of Economics & Management, Nanjing University of Science & Technology, Nanjing, Jiangsu Province, ChinaSearch for more papers by this author First published: 25 August 2021 https://doi.org/10.1002/asi.24566Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat Volume72, Issue10Special Issue: Paradigm Shift in the Field of InformationOctober 2021Pages 1217-1222 RelatedInformation
Knowledge Graph Generation From Text Using Neural Machine Translation Techniques

As the applications of data science become pervasive in daily life, there arises a dire need to represent data in machine-understandable forms like knowledge graphs. Over the years, there have been numerous developments in extracting entities and their relations for augmenting knowledge graphs, but many of them depend on external dependencies like dependency parsers and part-of-speech taggers. These approaches, while indeed accomplishing this task, induce a certain degree of inflexibility in their implementation. Recent explorations in this domain have attempted to utilize Neural Machine Translation techniques to convert natural language to SPARQL queries, with a focus on information retrieval from pre-established Knowledge Graphs. We explore in detail, the variety of approaches followed for SPARQL machine translation, with a keen focus on insertion of extracted knowledge into the graphs.As part of our research, we curated a dataset- Scientists-100, extracted from Dbpedia, for the task of translation of natural language to SPARQL insertion statements. We also propose two models - an Attention RNN and a Transformer for the same. These models achieve an accuracy of 99.27% and a 98.61% respectively on the dataset. In addition to this, we present a metric for examining the syntactic accuracy of the generated SPARQL statements. Our models exhibit 99.25% and 98.71% syntactic accuracy as calculated on the same.
Control-Enabling Adaptive Nonlinear System Identification

In this article, we rely on the theoretical foundations of radial basis function neural networks to form an adaptive parameter estimation problem, which we solve using the recently introduced prescribed performance control methodology. Such combination results in a compact user-configurable adaptive nonlinear system identification methodology that can be used to retrieve the open-loop nonlinear plant dynamics in any compact region of interest.
Convolutional Neural Network Based Approval Prediction of Enhancement Reports

For a given software enhancement report, identifying its possible approval status could help software developers by suggesting feature enhancements to compete in the software industry. An automatic solution for the approval prediction of enhancements could assist all the participants in resolving enhancements. The key challenges are the preprocessing of noisy textual information and the state-of-the-art feature models to combine the syntactical and semantic word information available in the given text. To this end, we propose a deep learning based approach for the approval prediction of enhancement reports that incorporates the users’ sentiments involved in the text. First, we preprocess the textual information of all enhancement reports to avoid noise. Second, we compute the sentiment of each enhancement report using Senti4SD. Third, we combine the bag-of-words (BOW) representation and traditional word2vec based representation to learn the novel deep representation (a recurrent neural network (RNN) with attention based representation) of preprocessed text. Using an attention mechanism enables the model to remember the context over a long sequence of words in an enhancement report. Fourth, based on sentiment and deep representation, we train a deep learning based classifier for the approval prediction of enhancement reports. Finally, we reuse the 40,000 enhancement reports from 10 real software applications to evaluate the proposed approach. The cross-application evaluation suggests that the proposed approach is accurate and outperforms the state-of-the-art. The results of the proposed approach improve the precision from 86.52% to 90.56%, recall from 66.45% to 80.10%, and f-measure from 78.12% to 85.01%.
Hierarchical Conditional Relation Networks for Multimodal Video Question Answering

Video Question Answering (Video QA) challenges modelers in multiple fronts. Modeling video necessitates building not only spatio-temporal models for the dynamic visual channel but also multimodal structures for associated information channels such as subtitles or audio. Video QA adds at least two more layers of complexity – selecting relevant content for each channel in the context of the linguistic query, and composing spatio-temporal concepts and relations hidden in the data in response to the query. To address these requirements, we start with two insights: (a) content selection and relation construction can be jointly encapsulated into a conditional computational structure, and (b) video-length structures can be composed hierarchically. For (a) this paper introduces a general-reusable reusable neural unit dubbed Conditional Relation Network (CRN) taking as input a set of tensorial objects and translating into a new set of objects that encode relations of the inputs. The generic design of CRN helps ease the common complex model building process of Video QA by simple block stacking and rearrangements with flexibility in accommodating diverse input modalities and conditioning features across both visual and linguistic domains. As a result, we realize insight (b) by introducing Hierarchical Conditional Relation Networks (HCRN) for Video QA. The HCRN primarily aims at exploiting intrinsic properties of the visual content of a video as well as its accompanying channels in terms of compositionality, hierarchy, and near-term and far-term relation. HCRN is then applied for Video QA in two forms, short-form where answers are reasoned solely from the visual content of a video, and long-form where an additional associated information channel, such as movie subtitles, presented. Our rigorous evaluations show consistent improvements over state-of-the-art methods on well-studied benchmarks including large-scale real-world datasets such as TGIF-QA and TVQA, demonstrating the strong capabilities of our CRN unit and the HCRN for complex domains such as Video QA. To the best of our knowledge, the HCRN is the very first method attempting to handle long and short-form multimodal Video QA at the same time.
Medical Image Watermarking Scheme in Transform Domain based on Asymmetric crypto-system and Arnold Chaotic Map

Sharing a secret via the internet needs huge protection challenges to keep it in clean hands, and far away from several ways of maleficent manipulations, e.g., hacking and spying. Nowadays, experts proposed many encryption algorithms that work in spatial domain as well as frequency domain, to provide certain amount of privacy for this public network navigators. The proposed algorithm is about embedding an encrypted Electronic Patient Record (EPR) in Discrete Cosine Transform (DCT) resultant of a medical image that is decomposed into third level with Discrete Wavelet Transform (DWT), where the EPR is encrypted using Elgamal encryption, and Arnold map is also exerted in order to increase the randomness and unpredictability levels and to make the algorithm more complex. A variety of tests has been performed to prove the proposed scheme robustness.
Detecting basic human activities and postural transition using robust machine learning techniques by applying dimensionality reduction methods

In the whole world, approximately two billion people are using smartphones for their better life The smartphone’s sensor data consist of high dimensions which increase complexity of training model and degrade overall performance of a classifier. We investigated and analyzed the performance of different machine learning algorithms with various dimensionality reduction techniques on publicly available smartphone-based recognition of human activities and postural transitions dataset. The highest accuracy was obtained at set 1 using support vector machine (SVM) linear kernel (97.40%) followed by set 3 using eXtreme boosting (XGBoost) (96.92%), set 2 using adaptive boosting multi-class (AdaBoost.M1) (95.69%), set 4 using AdaBoost. M1 (82.17%). Using different dimensionality reduction methods for set 1, principal component analysis (PCA) provided the highest accuracy using average neural network (AvNNET) (98.27%), while chi-squared gives the accuracy of 93.72% using the XGBoost classifier. For set 2, PCA gives the highest accuracy of 95.83% using the AvNNET classifier while for set 3 Chi-squared gives an accuracy of 84.46% using average neural network (AvNNET), SVM.L, and stochastic Gradient Boosting Machine (GBM) classifiers. The results reveal that overall dimensionality reduction methods outer performed. The outcomes will be helpful in making wise decisions in detecting various activities in anti-terrorists, surveillance, etc.
Active Sense: Early Staging of Non-Insulin Dependent Diabetes Mellitus (NIDDM) Hinges upon Recognizing Daily Activity Pattern

The Human Activity Recognition (HAR) system allows various accessible entries for the early diagnosis of Diabetes as one of the nescient applications domains for the HAR. Long Short-Term Memory (LSTM) was applied and recognized 13 activities that resemble diabetes symptoms. Afterward, risk factor assessment for an experimental subject identified similar activity pattern attributes between diabetic patients and the experimental subject. Because of this, a trained LSTM model was deployed to monitor the average time length for every activity performed by the experimental subject for 30 consecutive days. Concurrently, the symptomatic diabetes activity patterns of diabetic patients were explored. The cosine similarity of activity patterns of the experimental subject and diabetic patients measured 57.39%, putting the experimental subject into moderate risk factor class. The experimental subject was clinically tested for risk factors using the diabetic clinical diagnosis process, known as the A1C. The A1C level was 6.1%, recognizing the experimental subject as a patient suffering from Diabetes. Thus, the proposed novel approach remarkably classifies the risk factor level based on activity patterns.
Domain-Adaptive Text Classification with Structured Knowledge from Unlabeled Data

Domain adaptive text classification is a challenging problem for the large-scale pretrained language models because they often require expensive additional labeled data to adapt to new domains. Existing works usually fails to leverage the implicit relationships among words across domains. In this paper, we propose a novel method, called Domain Adaptation with Structured Knowledge (DASK), to enhance domain adaptation by exploiting word-level semantic relationships. DASK first builds a knowledge graph to capture the relationship between pivot terms (domain-independent words) and non-pivot terms in the target domain. Then during training, DASK injects pivot-related knowledge graph information into source domain texts. For the downstream task, these knowledge-injected texts are fed into a BERT variant capable of processing knowledge-injected textual data. Thanks to the knowledge injection, our model learns domain-invariant features for non-pivots according to their relationships with pivots. DASK ensures the pivots to have domain-invariant behaviors by dynamically inferring via the polarity scores of candidate pivots during training with pseudo-labels. We validate DASK on a wide range of cross-domain sentiment classification tasks and observe up to 2.9% absolute performance improvement over baselines for 20 different domain pairs. Code is available at https://github.com/hikaru-nara/DASK.
RASAM – A Dataset for the Recognition and Analysis of Scripts in Arabic Maghrebi

The Arabic scripts raise numerous issues in text recognition and layout analysis. To overcome these, several datasets and methods have been proposed in recent years. Although the latter are focused on common scripts and layout, many Arabic writings and written traditions remain under-resourced. We therefore propose a new dataset comprising 300 images representative of the handwritten production of the Arabic Maghrebi scripts. This dataset is the achievement of a collaborative work undertaken in the first quarter of 2021, and it offers several levels of annotation and transcription. The article intends to shed light on the specificities of these writing and manuscripts, as well as highlight the challenges of the recognition. The collaborative tools used for the creation of the dataset are assessed and the dataset itself is evaluated with state of the art methods in layout analysis. The word-based text recognition method used and experimented on for these writings achieves CER of 4.8% on average. The pipeline described constitutes an experience feedback for the quick creation of data and the training of effective HTR systems for Arabic scripts and non-Latin scripts in general.