Reviewerly/exHarmony huggingface dataset:
Design of incomplete 3D information image recognition system based on SIFT algorithm and wireless network

Abstract The traditional image recognition technology can transform some expression form of image into the data which can be processed by computer, and recognize the image with decision function. However, in actual applications, incomplete 3D images will be encountered. In order to screen the required image information from a large amount of images, it is necessary to recognize and match the image, and so the research has long-term application value. In this paper, SIFT algorithm was used to extract the feature vectors for incomplete 3D information recognition. Then, the modeling method of circular matching pattern was proposed, and the pattern of mathematical recognition was adopted in the process of image recognition. After that, on the basis of a large number of domestic and foreign research literatures, the process of “image hypothesis” converted to “image recognition” was completed. Finally, the system simulation was carried out by using Microsoft Visual Studio. For the incomplete 3D information image, the image feature recognition system was completed.
Automated Censoring of Cigarettes in Videos Using Deep Learning Techniques

Studies have shown that exposure to videos containing instances of cigarette smoking is more likely to induce subsequent smoking behaviour in people, especially adolescents and young adults. Thus, censoring cigarettes in videos has become an urgent task. Automation will make the otherwise tedious process less cumbersome and more efficient. In this paper, we propose an approach for the same using concepts of Deep Learning and Computer Vision. We have manually developed a dataset by acquiring images from multiple online sources and then segmenting, cleaning and augmenting them. Features, extracted using the Convolutional layers of the Inception V3 model from these images, are used to train a neural network. This model detects cigarettes with an accuracy of 89.99%. Finally, censoring is carried out by generating predictions on video frames using the aforementioned model.
Oblique Decision Trees from Derivatives of ReLU Networks.

We show how neural models can be used to realize piece-wise constant functions such as decision trees. The proposed architecture, which we call locally constant networks, builds on ReLU networks that are piece-wise linear and hence their associated gradients with respect to the inputs are locally constant. We formally establish the equivalence between the classes of locally constant networks and decision trees. Moreover, we highlight several advantageous properties of locally constant networks, including how they realize decision trees with parameter sharing across branching / leaves. Indeed, only $M$ neurons suffice to implicitly model an oblique decision tree with $2^M$ leaf nodes. The neural representation also enables us to adopt many tools developed for deep networks (e.g., DropConnect (Wan et al., 2013)) while implicitly training decision trees. We demonstrate that our method outperforms alternative techniques for training oblique decision trees in the context of molecular property classification and regression tasks.
A new technique based on 3D convolutional neural networks and filtering optical flow maps for action classification in infrared video

Human action in video sequences provides three-dimensional spatio-temporal signals that characterize both visual appearance and motion dynamics. The aim of this work is to recognize human action in infrared video by focusing mainly on dynamic information. We developed a new technique based on deep 3D convolutional neural networks (3D CNNs) that take optical flow maps as input. Our approach consists mainly of three parts: 1) computation of optical flow maps; 2) filtering of these maps, using an entropy measurement in order to increase the classification rate and reduce the run time by eliminating sequences that do not contain human action; and 3) classification using 3D CNN. The experimental results obtained by our approach on the InfAR dataset show considerable improvement in comparison with results obtained by existing models.
Miscellaneous Graph Preliminaries

Summary This article contains many auxiliary theorems which were missing in the Mizar Mathematical Library [2] to the best of the author’s knowledge. Most of them regard graph theory as formalized in the GLIB series (cf. [8]) and most of them are preliminaries needed in [7] or other forthcoming articles.
RETRACTED: Service complaint identification in hotel social media: A two-step classification approach

Hotel social media provides access to dissatisfied customers and their experiences with services. However, due to massive topics and posts in social media, and the sparse distribution of complaint-related posts and, manually identifying complaints is inefficient and time-consuming. In this study, we propose a supervised learning method including training samples enlargement and classifier construction. We first identified reliable complaint and noncomplaint samples from the unlabeled dataset by using small labeled samples as training samples. Combining the labeled samples and enlarged samples, classification algorithms support vector machine and k-nearest neighbor were then adopted to build binary classifiers during the classifier construction process. Experimental results indicate the proposed method can identify complaints from social media efficiently, especially when the amount of labeled training samples is small. This study provides an efficient approach for hotel companies to distinguish a certain kind of consumer complaint information from large number of unrelated information in hotel social media.
Improving the Professional Methodical Preparation of Future History Teachers through Didactic Games

This article focuses on the scientific and pedagogical bases of the system of improvement of professional methodological preparation of future history teachers through didactic games and didactic technologies.
Alexander Burganov: a New Code of Time and Space

Summary: The article is devoted to the analysis of the work of Alexander Burganov, an outstanding contemporary sculptor and graphic artist. The study of the categories of Space and Time on the example of his works is the main theme of the article. The author believes that Time and Space in Burganov’s work are unique categories and correspond to reality least of all; the author gives examples of specific works and convinces that Burganov has modeled a bright, recognizable, unique personal code with the help of sculptures, graphic works, texts, installations, and artistic gestures. Today, this is especially noticeable as the culture of the 21st century has sharpened and almost brought to a logical conclusion the awareness of artistic being, formed in the second half of the 20th century, and is now on the verge of new creative realities leading to conceptual discoveries in art. The author believes that the artist keenly captures the invisible waves emitted by the developing cultures and subcultures of the 21st century, in which a body devoid of naturalistic sensuality is coldly and symbolically akin to antique statues. The author pays great attention to the analysis of new materials in Burganov’s creative work, believing that the 21st century has brought great changes not only to the form but also to the substance of the bodies of statues and sculptural compositions. Marble is replaced with plastic, bronze coexists with gypsum, metal meshes surround voids and follow the movements of the wind. Paper on an iron frame is a rightful hero of this “Olympus”. It should be noted that there is no place for imitation. New material dictates its conditions for shaping and participates in the interpretation of a new image.Analyzing the contemporary space of culture, the author states that the experiences of European crises have brought into art restraint and minimalism, democracy and the idea of some kind of universal permissiveness in creating and judging art. However, the predominance of mass culture, which received a completely new face and opportunity owing to the virtual space spilled over reality, was the main marker of the culture of the early 21st century. Today, a mixture of styles and cultures, a combination of incongruous is a new reality introduced into our lives by virtual culture. The author emphasizes that it was this invisible space that Alexander Burganov could feel and has reflected in his work. This has made his works relevant and understandable to a new generation which almost does not feel the classics. The author concludes that Alexander Burganov’s work has become a kind of bridge connecting the worlds of classics and modernity, high and grassroots culture, myths and reality.
Edge Minimization in de Bruijn Graphs

This paper introduces the de Bruijn graph edge minimization problem, which is related to the compression of de Bruijn graphs: find the order-k de Bruijn graph with minimum edge count among all orders. We describe an efficient algorithm that solves this problem. Since the edge minimization problem is connected to the BWT compression technique called "tunneling", the paper also describes a way to minimize the length of a tunneled BWT in such a way that useful properties for sequence analysis are preserved. Although being a restriction, this is significant progress towards a solution to the open problem of finding optimal disjoint blocks that minimize space, as stated in Alanko et al. (DCC 2019).
Multi-Agent Routing Value Iteration Network

In this paper we tackle the problem of routing multiple agents in a coordinated manner. This is a complex problem that has a wide range of applications in fleet management to achieve a common goal, such as mapping from a swarm of robots and ride sharing. Traditional methods are typically not designed for realistic environments hich contain sparsely connected graphs and unknown traffic, and are often too slow in runtime to be practical. In contrast, we propose a graph neural network based model that is able to perform multi-agent routing based on learned value iteration in a sparsely connected graph with dynamically changing traffic conditions. Moreover, our learned communication module enables the agents to coordinate online and adapt to changes more effectively. We created a simulated environment to mimic realistic mapping performed by autonomous vehicles with unknown minimum edge coverage and traffic conditions; our approach significantly outperforms traditional solvers both in terms of total cost and runtime. We also show that our model trained with only two agents on graphs with a maximum of 25 nodes can easily generalize to situations with more agents and/or nodes.
Simplifying the Process of Creating Augmented Outdoor Scenes

Creating augmented reality content has experienced increasing interest. However, existing tools for the broad audience are often limited in the capabilities they provide – applying filters and placing predefined assets. In this paper, we propose an intuitive framework to generate large scale augmented overlays for outdoor scenes supporting the creative process and allowing simple sharing of the location-based experience. We introduce a novel concept of using virtual reality for content creation. This includes using a simple 3D city model. Based on this model we are also able to improve the resulting AR visualization by handling occlusions as well as a content-specific improved alignment.
Enhancing Simple Models by Exploiting What They Already Know

There has been recent interest in improving performance of simple models for multiple reasons such as interpretability, robust learning from small data, deployment in memory constrained settings as well as environmental considerations. In this paper, we propose a novel method SRatio that can utilize information from high performing complex models (viz. deep neural networks, boosted trees, random forests) to reweight a training dataset for a potentially low performing simple model of much lower complexity such as a decision tree or a shallow network enhancing its performance. Our method also leverages the per sample hardness estimate of the simple model which is not the case with the prior works which primarily consider the complex model's confidences/predictions and is thus conceptually novel. Moreover, we generalize and formalize the concept of attaching probes to intermediate layers of a neural network to other commonly used classifiers and incorporate this into our method. The benefit of these contributions is witnessed in the experiments where on 6 UCI datasets and CIFAR-10 we outperform competitors in a majority (16 out of 27) of the cases and tie for best performance in the remaining cases. In fact, in a couple of cases, we even approach the complex model's performance. We also conduct further experiments to validate assertions and intuitively understand why our method works. Theoretically, we motivate our approach by showing that the weighted loss minimized by simple models using our weighting upper bounds the loss of the complex model.
Social networks and academic performance self-perception in business sciences students

Considering that individuals and organizations for different activities increasingly use social networks (SN), this paper seeks to describe the level of usefulness that business sciences university students find in them, and the impact thereof on their Academic Performance self-perception (APS). The results show that in both Mexico and Spain, the target audience considers that SN are useful for academic topics, this being more noticeable in the first country; Likewise, APS is better in Mexican students. On the other hand, using linear regressions as a statistical technique, in both nations was found that the more usefulness obtained from the SN, the higher students’ APS, which turned out more noticeable in the Spanish case. In today’s world, where thanks to information and communication technologies (ICTs), there are high possibilities for interconnection between people and organizations, these findings are of interest to universities, government bodies and companies because they all offer products and/or services to a public who needs to feel that the former know more and learn about them better, for which SN become a current and versatile tool for dissemination.
Why We Go Where We Go: Profiling User Decisions on Choosing POIs

While Point-of-Interest (POI) recommendation has been a popular topic of study for some time, little progress has been made for understanding why and how people make their decisions for the selection of POIs. To this end, in this paper, we propose a user decision profiling framework, named PROUD, which can identify the key factors in people's decisions on choosing POIs. Specifically, we treat each user decision as a set of factors and provide a method for learning factor embeddings. A unique perspective of our approach is to identify key factors, while preserving decision structures seamlessly, via a novel scalar projection maximization objective. Exactly solving the objective is non-trivial due to a sparsity constraint. To address this, our PROUD adopts a self projection attention and an L2 regularized sparse activation to directly estimate the likelihood of each factor to be a key factor. Finally, extensive experiments on real-world data validate the advantage of PROUD in preserving user decision structures. Also, our case study indicates that the identified key decision factors can help us to provide more interpretable recommendations and analyses.
Why Ensemble Techniques Are Needed

According to the Cambridge dictionary, an ensemble is defined as a group of things or people acting or taken together as a whole. The word ensemble was first used in the context of musicians who regularly play together. An ensemble of musicians is the sum of individual compositions by multiple musicians. Similarly, in machine learning, ensemble learning is a combination of multiple machine learning techniques performed together.
Cops and Robbers on Graphs of Bounded Diameter

The game of Cops and Robbers is a well-known game played on graphs. In this paper, we consider the class of graphs of bounded diameter. We improve the strategy of cops and the previously used probabilistic method, which results in an improved upper bound for the cop number of graphs of bounded diameter. In particular, for graphs of diameter 4, we improve the upper bound from $n^{\frac{2}{3}+o(1)}$ to $n^{\frac{3}{5}+o(1)}$ and for diameter 3 from $n^{\frac{2}{3}+o(1)}$ to $n^{\frac{4}{7}+o(1)}$.
Exploring Workout Repetition Counting and Validation Through Deep Learning

Studying human motion from images and videos has turned into an interesting topic of research given the recent advances in computer vision and deep learning algorithms. When focusing on the automatic procedure of tracking physical exercises, cameras can be used for full human pose estimation in relation to worn sensors. In this work, we propose a method for workout repetition counting and validation based on a set of skeleton-based and deep semantic features that are obtained from a 2D human pose estimation network. Given that some of the individuals’ body parts might be occluded throughout physical exercises, we also perform a multi-view analysis on supporting cameras to improve our recognition rates. Nevertheless, the obtained results for a single-view approach show that we are able to count valid repetitions with over $$90\%$$ precision scores for 4 out of 5 considered exercises, while recognizing more than $$50\%$$ of the invalid ones.
Estimation and inference for mixture of partially linear additive models

In this paper, a semiparametric mixture of regression models is proposed, where the regression functions are partially linear additive while the mixing proportions and variances are unknown constant. The asymptotic normality of the SBK estimators and the model selection consistency of the proposed BIC based on B-spline estimators are established. Simulations and applications are presented to illustrate the performance of the proposed.
Comparative Sentiment Analysis using Difference Types of Machine Learning Algorithm

In today's world business are becoming online based. Companies sell their products and seek for consumer's feedback. When all the consumer writes their review about that's a product, It's becomes difficult to say that product is good or not based on their review. That's where Deep learning come. By using this, we can extract opinion or sentiment from the text which is written by the consumer. This is sentiment analysis. It can classify the emotional status of that review. Our project detects opinion from consumer's review whether it is good or bad. We use SVM, Naive Bayes algorithm and some methods. We use the Naive Bayes algorithm because we want to know how often words occur in the document. And then we use SVM for classifying whether words are positive or negative. For our researching purpose, we use the Amazon consumer review data set, which was available online. Some methods that we are using for preprocessing and cleaned the document where just words are left. We trained our model so well with twenty-four thousand data. So, it will give us the best accuracy and we make this model with the best algorithm and after that, it gives the accuracy of 98.39%. This project will help us in real life when we are having trouble with product reviews. Our machine will help us to determine which review is good and which review is bad and make a category of a positive and negative review and saves our time.
Correspondence, Canonicity, and Model Theory for Monotonic Modal Logics

We investigate the role of coalgebraic predicate logic, a logic for neighborhood frames first proposed by Chang, in the study of monotonic modal logics. We prove analogues of the Goldblatt–Thomason theorem and Fine’s canonicity theorem for classes of monotonic neighborhood frames closed under elementary equivalence in coalgebraic predicate logic. The elementary equivalence here can be relativized to the classes of monotonic, quasi-filter, augmented quasi-filter, filter, or augmented filter neighborhood frames, respectively. The original, Kripke-semantic versions of the theorems follow as a special case concerning the classes of augmented filter neighborhood frames.
Auditing ML Models for Individual Bias and Unfairness

We consider the task of auditing ML models for individual bias/unfairness. We formalize the task in an optimization problem and develop a suite of inferential tools for the optimal value. Our tools permit us to obtain asymptotic confidence intervals and hypothesis tests that cover the target/control the Type I error rate exactly. To demonstrate the utility of our tools, we use them to reveal the gender and racial biases in Northpointe's COMPAS recidivism prediction instrument.
Entity Type Enhanced Neural Model for Distantly Supervised Relation Extraction (Student Abstract)

Distantly Supervised Relation Extraction (DSRE) has been widely studied, since it can automatically extract relations from very large corpora. However, existing DSRE methods only use little semantic information about entities, such as the information of entity type. Thus, in this paper, we propose a method for integrating entity type information into a neural network based DSRE model. It also adopts two attention mechanisms, namely, sentence attention and type attention. The former selects the representative sentences for a sentence bag, while the latter selects appropriate type information for entities. Experimental comparison with existing methods on a benchmark dataset demonstrates its merits.
Real-time Feedback Method of Ship Sensor Network Learning Monitoring

Liu, X. and Ma, L., 2020. Real-time feedback method of ship sensor network learning monitoring. In: Yang, Y.; Mi, C.; Zhao, L., and Lam, S. (eds.), Global Topics and New Trends in Coastal Research: Port, Coastal and Ocean Engineering. Journal of Coastal Research, Special Issue No. 103, pp. 934–938. Coconut Creek (Florida), ISSN 0749-0208.In view of the problem that the traditional real-time feedback method of ship sensor network learning monitoring has poor real-time effect, a new real-time feedback method of ship sensor network learning monitoring is designed. The monitoring real-time feedback data is divided into explicit feedback data and implicit feedback data. The random sampling method is used to select the data set, and SPSS Clementine tool is used to mine the explicit feedback data and implicit feedback data respectively. The data to be processed is introduced to realize preprocessing, including processing missing values, data exception detection and data conversion. The real-time feedback model of ship sensor network learning monitoring is designed to realize the real-time feedback of network learning monitoring. The real-time feedback model of ship sensor network learning monitoring is composed of learning control module, learning achievement evaluation module and learning real-time feedback module. In order to prove the real-time effect of the designed feedback method, the traditional ship sensor network learning monitoring real-time feedback method is compared with that of the designed method. Experimental results show that the real-time performance of this method is better than that of the traditional method.