Reviewerly/exHarmony huggingface dataset:
TEXT CLASSIFICATION BASED ON SUPPORT VECTOR MACHINE

The development of the Internet has increased the need for daily online information storage. Finding the correct information that we are interested in takes a lot of time, so the use of techniques for organizing and processing text data are needed. These techniques are called text classification or text categorization. There are many methods of text classification, but for this paper we study and apply the Support Vector Machine (SVM) method and compare its effect with the Naïve Bayes probability method. In addition, before implementing text classification, we performed preprocessing steps on the training set by extracting keywords with dimensional reduction techniques to reduce the time needed in the classification process.
The K-Means Clustering Algorithm With Semantic Similarity To Estimate The Cost of Hospitalization

The cost of hospitalization from a patient can be estimated by performing a cluster of patient. One of the algorithms that is widely used for clustering is K-means. K-means algorithm, based on distance still has weaknesses in terms of measuring the proximity of meaning or semantics between data. To overcome this problem, semantic similarity can be used to measure the similarity between objects in clustering, so that, semantic proximity can be calculated. This study aims to conduct clustering of patient data by paying attention to the similarity of the patient’s disease. ICD code is used as a guide in determining a patient’s disease. The K-means method is combined with semantic similarity to measure the proximity of the patient’s ICD code. The method used to measure the semantic similarity between data, in this study, is the semantic similarity of Girardi, Leacock &amp; Chodorow, Rada, and Jaccard Similarity. Cluster quality measurement uses the silhouette coefficient method. Based on the experimental results, the method of measuring semantic similarity data is capable to produce better quality clustering results than without semantic similarity. The best accuracy is 91.78% for the three semantic similarity methods, whereas without semantic similarity the best accuracy is 84.93%.
Through-the-Wall Radar Super-Resolution Imaging Based on Cascade U-Net

High-resolution radar imaging will give us detailed information of target, which becomes basic function of radar systems. Improvement of image resolution of the existing radar system is also important. Based on deep learning, a new method for super-resolution through-the-radar imaging is proposed. A network, called cascade U-net (CU-net), is proposed in this paper. The results of simulation and real data experiments demonstrate the effectiveness of our methods.
Czy memy są tekstami kultury?

Internet memes that are popular on the Web are a kind of commentary on various types of events and occurrences. They perform many functions, from ludic, informational, to persuasive. Their creators always try to surprise the audience with creativity. The aim of this article is to try to answer the question posed in the title: are memes texts of culture? Given the lexicographic data and proposals of researchers on defining the text of culture, memes can be considered as a text of this kind. Memes, just as the texts of culture constitute a significant, coherent and conscious product of the culture, mentality and cultural activity of man. They are an internally organized, ordered according to specific rules, a product of the whole culture. As functioning on the Web, they are a collective good that is able to spread and develop. Thus, one of the most important features is revealed, which is rooted in the memetic theory of Richard Dawkins, namely "contagiousness". Internet memes are a product that implements a certain established cultural pattern. Through their construction, the way of transmission and the power of influence are among media users a carrier of certain ideas, they also influence the imagination and emotions of recipients. They are a new text of (digital) culture.
IMPACT OF EMPLOYEE MOTIVATION ON ORGANIZATIONAL PERFORMANCE.

02Oct 2019 IMPACT OF EMPLOYEE MOTIVATION ON ORGANIZATIONAL PERFORMANCE. Abdi Muse Ali Geelmaale.
MOVIE RECOMMENDATION SYSTEM USING BAG OF WORDS AND SCIKIT-LEARN

Recommendation systems play an important role in our everyday life.Starting from movie/music streaming sites like Netflix or Spotify to the most basic search engines, the core component is comprised of recommendation systems.Even the technology giant Google is known for its search engine above everything else.To emphasize its importance we aim to build a simple recommendation system using the IMDB movie database.Our finished web application can suggest similar movies based on the input provided by the user, considering factors like the plot of the movie, actors present in it, as well as the director.Needless to say, some factors would be common between the input movie and the recommended ones.The web app uses the scikit-learn python library, to match with the input movie with corresponding movies in the dataset with the highest similarity score.Its frontend is designed using Flask and it is deployed on Heroku.We aim to demystify the magic behind a recommendation system and provide an introduction to natural language processing also on the way.
Group Activity Recognition to Support Collaboration in Creative Digital Space

In order to orchestrate collaborative group work, such as a brainstorming workshop or active learning class, a facilitator must monitor all groups simultaneously. In this paper, we propose a method for recognizing group activities, based on machine learning, where the input features are derived from the analysis of raw system events generated by a collaboration system known as "creative digital space" that we developed before. To verify the effectiveness of the proposed method, we also evaluated the results obtained by using the test data collected from active learning classes.
Tkol, Httt, and r/radiohead: High Affinity Terms in Reddit Communities

Language is an important marker of a cultural group, large or small.One aspect of language variation between communities is the employment of highly specialized terms with unique significance to the group.We study these high affinity terms across a wide variety of communities by leveraging the rich diversity of Reddit.com.We provide a systematic exploration of high affinity terms, the often rapid semantic shifts they undergo, and their relationship to subreddit characteristics across 2600 diverse subreddits.Our results show that high affinity terms are effective signals of loyal communities, they undergo more semantic shift than low affinity terms, and that they are partial barrier to entry for new users.We conclude that Reddit is a robust and valuable data source for testing further theories about high affinity terms across communities.
What Do Universities' Virtual Faces Tell at a Glance? A Visual and Textual Analysis of Turkish Universities' Official Homepages

Turkish higher education has been going through a quantitative transformation since 1990s, which has raised a debate on the quality of the HEIs. The quantitative increase is a crucial step for more access to higher education and can be seen as the prerequisite of qualitative growth, yet is is necessary to examine how the newly-established universities in particular function around their vision and mission. Institutional culture and shared values are the ways the universities display their differences; and with communication technologies, their official Internet websites have becomeone of the most effective means of showing their distinct institutional identities. This study examined the representation of all the Turkish universities in their official Internet homepages in regard to research, education, and community service. The homepages of 186 public and foundation universities were analyzed through visual content analysis. It was found that there is a high level of institutional similarity among these universities; research is represented with some specific activities while education and community service are displayed with a very limited number of activities. The visual analysis revealed eight themes and three university functions, and yielded some significant findings on Turkish universities' virtual faces.
Adaptive matched filter for implicit-target recognition: application in three-dimensional reconstruction

The design of matched filters for optical correlators requires explicit knowledge of the shape of the target. This requirement limits its usefulness in applications where the appearance of the target is unspecified or dynamically changing. This research presents the design of an adaptive correlation filter by the optimization of the mean-squared-error criterion when the shape of the target is implicit and embedded on a cluttered background with unknown statistics in the reference image. For this, estimators to obtain the region of support of the target as well as statistical parameters of additive and nonoverlapping noise of the scene are proposed. The performance of the proposed filter is analyzed in terms of detection efficiency and location accuracy of an implicit target in the context of stereo matching and three-dimensional reconstruction.
Beyond word2vec

The \em word2vec methodology such as Skip-gram and CBOW has seen significant interest in recent years because of its ability to model semantic notions of word similarity and distances in sentences. A related methodology, referred to as \em doc2vec is also able to embed sentences and paragraphs. These methodologies, however, lead to different embeddings that cannot be related to one another. In this paper, we present a tensor factorization methodology, which simultaneously embeds words and sentences into latent representations in one shot. Furthermore, these latent representations are concretely related to one another via tensor factorization. Whereas \em word2vec and \em doc2vec are dependent on the use of contextual windows in order to create the projections, our approach treats each document as a structural graph on words. Therefore, all the documents in the corpus are jointly factorized in order to simultaneously create an embedding for the individual documents and the words. Since the graphical representation of a document is much richer than a contextual window, the approach is capable of designing more powerful representations than those using the \em word2vec family of methods. We use a carefully designed negative sampling methodology to provide an efficient implementation of the approach. We relate the approach to factorization machines, which provides an efficient alternative for its implementation. We present experimental results illustrating the effectiveness of the approach for document classification, information retrieval and visualization.
Estimation of Neural Sources from EEG Measurements Using Sequential Monte Carlo Method

The localization of neural sources is a crucial issue in medical, scientific and technical applications.However, the dipole sources in the brain may vary in nature and change constantly with time, adding to the difficulty of source localization.In this paper, the dynamic neural sources in the brain are simulated by sequential Monte-Carlo (MC) method, based on electroencephalography (EEG) data.Firstly, the EEG data were considered as a state space model.Considering the nonlinearity of the EEG data, the sequential M-C method was introduced as a particle filter, and the Metropolis-Hastings (M-H) resampling was employed to alleviate the particle impoverishment of general particle filter.The accuracy of our method in source localization was verified through two experiments, using both synthetic and real data.The research results shed important new light on the research of brain neurology.
Improving Students’ English Speaking Skill Through the Implementation of Talking Stick Method to the Fifth Grade Students of State Primary School 028226 Binjai

This research was primarily carried out to prove that the implementation of Talking Stick method could improve students’ English speaking skill and to describe the students’ responses to the implementation of Talking Stick method in the learning teaching processes of English speaking skill. This research is Classroom Action Research (henceforth, CAR) which was carried out in two cycles. Each cycle consisted of four meetings. The research subject of this CAR are the fifth grade students of State Primary School 028226 Binjai Class V-1 in the Academic Year of 2019/2020. There are 33 students as the research subject. The analysis of quantitative data showed that the students’ mean score increased continually from 55,63 in the pretest, 66,36 in the posttest of cycle 1, to 76,81 in the posttest of cycle 2. The analysis of qualitative data showed that the learning teaching processes of English speaking skill through the implementation of Talking Stick method ran very well. The students were more active, and motivated to learn English speaking skill. The class atmosphere was more alive and enjoyable after Talking Stick method was implemented in the English speaking classes. It is advisable that English teachers of primary school students apply Talking Stick method as one of alternatives to improve their students’ English speaking skill.
Spatio-Temporal Representation Matching-Based Open-Set Action Recognition by Joint Learning of Motion and Appearance

In this paper, we propose the spatio-temporal representation matching (STRM) for video-based action recognition under the open-set condition.Open-set action recognition is a more challenging problem than closed-set action recognition since samples of the untrained action class need to be recognized and most of the conventional frameworks are likely to give a false prediction.To handle the untrained action classes, we propose STRM, which involves jointly learning both motion and appearance.STRM extracts spatio-temporal representations from video clips through a joint learning pipeline with both motion and appearance information.Then, STRM computes the similarities between the ST-representations to find the one with highest similarity.We set the experimental protocol for open-set action recognition and carried out experiments on UCF101 and HMDB51 to evaluate STRM.We first investigated the effects of different hyperparameter settings on STRM, and then compared its performance with existing state-of-the-art methods.The experimental results showed that the proposed method not only outperformed existing methods under the open-set condition, but also provided comparable performance to the state-of-the-art methods under the closed-set condition.
PEVR: Pose Estimation for Vehicle Re-Identification

Re-identification is a challenging task because the available information is partial. This paper presents an approach to tackle vehicle re-identification (Re-id) problem. We focus on pose estimation for vehicles, which is an important module of vehicle Re-id. Person Re-id received huge attention, while vehicle re-id was ignored, but recently the computer vision community have started focusing on this topic and have tried to solve this problem by only using spatiotemporal information while neglecting the driving direction. The proposed technique is using visual features to find poses of the vehicle which helps to find driving directions. Experiments are conducted on publicly available datasets VeRi and CompCars, the proposed approach got excellent results.
DeepGeneMD: A Joint Deep Learning Model for Extracting Gene Mutation-Disease Knowledge from PubMed Literature

Understanding the pathogenesis of genetic diseases through different gene activities and their relations to relevant diseases is important for new drug discovery and drug repositioning. In this paper, we present a joint deep learning model in a multi-task learning paradigm for gene mutation-disease knowledge extraction, DeepGeneMD, which adapts the state-of-the-art hierarchical multi-task learning framework for joint inference on named entity recognition (NER) and relation extraction (RE) in the context of the AGAC (Active Gene Annotation Corpus) track at 2019 BioNLP Open Shared Tasks (BioNLP-OST). It simultaneously extracts gene mutation related activities, diseases, and their relations from the published scientific literature. In DeepGeneMD, we explore the task decomposition to create auxiliary subtasks so that more interactions between different learning subtasks can be leveraged in model training. Our model achieves the average F1 score of 0.45 on recognizing gene activities and disease entities, ranking 2nd in the AGAC NER task; and the average F1 score of 0.35 on extracting relations, ranking 1st in the AGAC RE task.
On the Well-Posedness of Uncalibrated Photometric Stereo Under General Lighting

Uncalibrated photometric stereo aims at estimating the 3D-shape of a surface, given a set of images captured from the same viewing angle, but under unknown, varying illumination. While the theoretical foundations of this inverse problem under directional lighting are well-established, there is a lack of mathematical evidence for the uniqueness of a solution under general lighting. On the other hand, stable and accurate heuristical solutions of uncalibrated photometric stereo under such general lighting have recently been proposed. The quality of the results demonstrated therein tends to indicate that the problem may actually be well-posed, but this still has to be established. The present paper addresses this theoretical issue, considering first-order spherical harmonics approximation of general lighting. Two important theoretical results are established. First, the orthographic integrability constraint ensures uniqueness of a solution up to a global concave–convex ambiguity, which had already been conjectured, yet not proven. Second, the perspective integrability constraint makes the problem well-posed, which generalizes a previous result limited to directional lighting. Eventually, a closed-form expression for the unique least-squares solution of the problem under perspective projection is provided, allowing numerical simulations on synthetic data to empirically validate our findings.
Real-Time Visual Tracking with Variational Structure Attention Network

Online training framework based on discriminative correlation filters for visual tracking has recently shown significant improvement in both accuracy and speed. However, correlation filter-base discriminative approaches have a common problem of tracking performance degradation when the local structure of a target is distorted by the boundary effect problem. The shape distortion of the target is mainly caused by the circulant structure in the Fourier domain processing, and it makes the correlation filter learn distorted training samples. In this paper, we present a structure–attention network to preserve the target structure from the structure distortion caused by the boundary effect. More specifically, we adopt a variational auto-encoder as a structure–attention network to make various and representative target structures. We also proposed two denoising criteria using a novel reconstruction loss for variational auto-encoding framework to capture more robust structures even under the boundary condition. Through the proposed structure–attention framework, discriminative correlation filters can learn robust structure information of targets during online training with an enhanced discriminating performance and adaptability. Experimental results on major visual tracking benchmark datasets show that the proposed method produces a better or comparable performance compared with the state-of-the-art tracking methods with a real-time processing speed of more than 80 frames per second.
Latent-Variable Generative Models for Data-Efficient Text Classification

Generative classifiers offer potential advantages over their discriminative counterparts, namely in the areas of data efficiency, robustness to data shift and adversarial examples, and zero-shot learning (Ng and Jordan, 2002;Yogatama et al., 2017;Lewis and Fan, 2019).In this paper, we improve generative text classifiers by introducing discrete latent variables into the generative story, and explore several graphical model configurations.We parameterize the distributions using standard neural architectures used in conditional language modeling and perform learning by directly maximizing the log marginal likelihood via gradient-based optimization, which avoids the need to do expectation-maximization.We empirically characterize the performance of our models on six text classification datasets.The choice of where to include the latent variable has a significant impact on performance, with the strongest results obtained when using the latent variable as an auxiliary conditioning variable in the generation of the textual input.This model consistently outperforms both the generative and discriminative classifiers in small-data settings.We analyze our model by using it for controlled generation, finding that the latent variable captures interpretable properties of the data, even with very small training sets.
Context-Aware Personalized POI Sequence Recommendation

The Point Of Interest (POI) sequence recommendation applies to scenarios like itinerary and travel route planning which belongs to the class of NP-hard problem. What’s more, the external environment like the weather, time can affect the user’s check-in behavior such as people prefer to check-in in ice cream shop when the temperature is higher. We propose an algorithm to solve these problems that called Context-Aware Personalized POI sequence Recommendation based on reinforcement learning (CAPR for short). First, we model the users dynamic preferences that incorporate contextual information associated with the users’ sequence of check-ins. Then we use the Monte Carlo Tree Search algorithm to select the user-satisfied POI in different environments. What’s more, we can get the context-aware personalized POI sequence under the specified time limit. Finally, we test the proposed algorithm using an open source dataset. The experimental results show that the weather and time context can improve the accuracy of the recommendation. Our algorithm can improve the effectiveness of travel recommendations.
Understanding the Effect of Textual Adversaries in Multimodal Machine Translation

It is assumed that multimodal machine translation systems are better than text-only systems at translating phrases that have a direct correspondence in the image.This assumption has been challenged in experiments demonstrating that state-of-the-art multimodal systems perform equally well in the presence of randomly selected images, but, more recently, it has been shown that masking entities from the source language sentence during training can help to overcome this problem.In this paper, we conduct experiments with both visual and textual adversaries in order to understand the role of incorrect textual inputs to such systems.Our results show that when the source language sentence contains mistakes, multimodal translation systems do not leverage the additional visual signal to produce the correct translation.We also find that the degradation of translation performance caused by textual adversaries is significantly higher than by visual adversaries.