Reviewerly/exHarmony huggingface dataset:
Deep learning-based video analysis pipeline for person detection and re-identification in aerial imagery

In addition to conventional camera networks, the deployment of drones allows for increased flexibility in surveillance tasks. Key components in modern analysis systems required to quickly assess a large amount of recorded aerial video data are the detection, tracking, and re-identification of persons. Each of the components is influenced by the characteristics of the aerial data and must be robust against challenges such as different flight altitudes and varying acquisition views and angles. In this work, we introduce a fast and efficient framework for person search which is specifically tailored to the characteristics of aerial data recorded by drones. In contrast to most of the works on person search and re-identification, we incorporate a tracking technique to add relevant context information about persons' movements to the retrieval results. In general, we focus on the three pipeline stages person detection, tracking, and re-identification as itself as well as the interplay between the components. For this, we adapt current state-of-the-art approaches for detection to the specific characteristics of aerial data and speed up the inference time by several modifications. Next, we apply a deep learning-based tracking approach, namely Deep SORT, to generate person tracks based on the detections. For the re-identification stage, we employ a lightweight re-identification model which is applied to generate features for both tracking and re-identification. To demonstrate the suitability of our proposed video analysis pipeline, we evaluate each component as well as their interplay on the P-DESTRE dataset.
Improving readability by modifying graphic QR code microstructure

White modules of quick response codes (QR codes) are degraded by dot gain, especially when printed at smaller sizes, which results in more false blacks than false whites in error analysis, and also severely affects decoding ability. To reduce the false blacks generated from printing and to improve code readability, modifying the microstructure of graphic QR codes by reducing the size of the information dots is proposed. Experimental results show that the resultant QR code reduces the number of false blacks and thus the error rate. This method applies to the physical output of both conventional and graphic QR codes.
Data Augmentation for Writer Identification Using a Cognitive Inspired Model

Assuming that two people do not have the same handwriting and do not write twice identically, handwriting can be considered a biometric characteristic of a person, frequently used in forensic document analysis. In the writer identification and verification scenario, the number of samples available for training is not always sufficient. Thus, in this work, we investigate the impact of the increase in the number of manuscript samples on the writer identification task, using synthetic samples generation inspired on a cognitive model. To better compare the proposed approach, we also performed experiments using a Gaussian Filter to generate new samples. Experiments were accomplished on the IAM and CVL databases, containing samples collected from 301 and 310 volunteer writers, respectively. From genuine samples of these databases, we generate new samples of images using the Duplicator approach. Feature vectors obtained using some well-known texture operators were used to feed an SVM classifier. Experiments showed that increasing the number of synthetic samples in the training set can be beneficial for the writer identification task, particularly when there are very few manuscript samples available for some writers. In the best scenario, the gain increased 31.7% in the identification rate using three genuine and fifteen duplicated samples at line-level.
Imbalanced Learning in Assessing the Risk of Corruption in Public Administration

This research aims to identify the corruption of the civil servants in the Federal District, Brazilian Public Administration. For this purpose, a predictive model was created integrating data from eight different systems and applying logistic regression to real datasets that, by their nature, present a low percentage of examples of interest in identifying patterns for machine learning, a situation defined as a class imbalance. In this study, the imbalance of classes was considered extreme at a ratio of 1:707 or, in percentage terms, 0.14% of the interest class to the population. Two possible approaches were used, balancing with resampling techniques using synthetic minority oversampling technique SMOTE and applying algorithms with specific parameterization to obtain the desired standards of the minority class without generating bias from the dominant class. The best modeling result was obtained by applying it to the second approach, generating an area value on the ROC curve of around 0.69. Based on sixty-eight features, the respective coefficients that correspond to the risk factors for corruption were found. A subset of twenty features is discussed in order to find practical utility after the discovery process.
Image Target Detection Method Using the Yolov5 Algorithm

Human fall detection is a problem to be solved in video processing, and it is applied to the intelligent transportation and the smart medical care. A new method that combines the yolov5 algorithm and the ResNet-50 network is proposed in this paper to realize real-time human fall detection in video images. First, the yolov5 algorithm is used to detect moving targets in video images, and then the detected images with moving human targets are preprocessed. Furthermore, the processed images are sent to the convolutional neural network-ResNet 50 to perform the classification of determining whether the human body falls. In this paper, ResNet-50 network uses UR Fall Datasets—an online open dataset—to process video frames into images, as sample data for training. Results show that the accuracy of the algorithm proposed in this paper can reach 93.9%, which has a good application effect in the field of fall detection.
Aircraft segmentation algorithm based on Unet and improved Yolov4

In order to overcome the problems of poor effect and low accuracy of semantic segmentation of aircraft target, a small target segmentation method based on improved Yolov4 and Unet is proposed. This method combines Yolov4, Unet and Mobilenet. In order to reduce the parameters of the model, Mobilenet is used to improve the Yolov4 backbone network. The improved Yolov4 is used to extract the ROI in the image, then the image is sent to VGG16-Unet for segmentation, finally, the segmented sub objects are fused with the original image to obtain a complete segmentation image. The RSOD-Dataset is used as the training dataset, and UCAS-AOD dataset is used to verify the generalization ability of the model. The experimental results show that the model has a good segmentation effect on the aircraft target in the image.
GraphCP: An I/O-Efficient Concurrent Graph Processing Framework

Big data applications increasingly rely on the analysis of large graphs. In order to analyze and process the large graphs with high cost efficiency, researchers have developed a number of out-of-core graph processing systems in recent years based on just one commodity computer. On the other hand, with the rapidly growing need of analyzing graphs in the real-world, graph processing systems have to efficiently handle massive concurrent graph processing (CGP) jobs. Unfortunately, due to the inherent design for single graph processing job, existing out-of-core graph processing systems usually incur redundant data accesses and storage and severe competition of I/O bandwidth when handling the CGP jobs, thus leading to very long waiting time experienced by users for the computing results. In this paper, we propose an I/O-efficient out-of-core graph processing system, GraphCP, to support the processing of CGP jobs. GraphCP proposes a benefit-aware sharing execution model that shares the I/O access and processing of graph data among the CGP jobs and adaptively schedules the loading of graph data, which efficiently overcomes above challenges faced by existing out-of-core graph processing systems. In addition, GraphCP organizes the graph data with a Source-Sorted Sub-Block graph representation for better processing capacity and I/O access locality. Extensive evaluation results show that GraphCP is 10.3x and 4.6x faster than two state-of-the-art out-of-core graph processing systems GridGraph and GraphZ respectively, and 2.1x faster than a CGP-oriented graph processing system Seraph.
EMRM: Enhanced Multi-source Review-Based Model for Rating Prediction

Rating prediction, whose goal is to predict user preference for unconsumed items, has become one of the core tasks in recommendation systems. Recently, many deep learning-based methods have been applied to the field of recommendation systems and have achieved great performance, especially when user reviews are available. User reviews usually contain rich semantic information and can reflect the preferences of users. However, user reviews are usually sparse. To alleviate this problem, we propose a method called EMRM, which stands for Enhanced Multi-source Review-based Model for rating prediction, to collect multi-source auxiliary reviews for each user. EMRM not only collects multi-source auxiliary reviews from nearest neighbors but also from farthest neighbors who have dissimilar consuming behaviors and historical rating records, so it can improve both the accuracy and diversity of recommendations. Our method extracts useful semantic information from user reviews and multi-source auxiliary reviews by applying Ordered-Neurons Long Short-Term Memory (ON-LSTM). Experimental results demonstrate that EMRM achieves better rating prediction accuracy than other baselines on three real-world datasets.
Continuous Entailment Patterns for Lexical Inference in Context

Combining a pretrained language model (PLM) with textual patterns has been shown to help in both zero-and few-shot settings.For zero-shot performance, it makes sense to design patterns that closely resemble the text seen during selfsupervised pretraining because the model has never seen anything else.Supervised training allows for more flexibility.If we allow for tokens outside the PLM's vocabulary, patterns can be adapted more flexibly to a PLM's idiosyncrasies.Contrasting patterns where a "token" can be any continuous vector vs. those where a discrete choice between vocabulary elements has to be made, we call our method CONtinuous pAtterNs (CONAN).We evaluate CONAN on two established benchmarks for lexical inference in context (LIiC) a.k.a.predicate entailment, a challenging natural language understanding task with relatively small training sets.In a direct comparison with discrete patterns, CONAN consistently leads to improved performance, setting a new state of the art.Our experiments give valuable insights into the kind of pattern that enhances a PLM's performance on LIiC and raise important questions regarding our understanding of PLMs using text patterns. 1
The Impact of the ‘Right to Be Forgotten’ on Algorithmic Fairness

Enterprises may often deal with situations in which they cannot use a part of their data for machine learning: (1) Privacy laws grant users to determine that their data should not be used any longer by the data holder (i.e., a data record must be removed). (2) Privacy laws may require anonymization, which leads to the removal and masking of specific attributes from a dataset. (3) To avoid ethical issues, it may be necessary to prevent machine learning algorithms from using attributes in their decision-making that may be viewed as discriminatory (i.e., a feature may not be used because it is unethical). Given these challenges, we analyze how removing data from an information system in the cases mentioned above affects its predictive performance and the fairness of its decisions.
Coseparable Nonnegative Matrix Factorization

.Nonnegative matrix factorization (NMF) is a popular model in the field of pattern recognition. The aim is to find a low rank approximation for nonnegative matrix \(M\) by a product of two nonnegative matrices \(W\) and \(H\) . In general, NMF is NP-hard to solve while it can be solved efficiently under a separability assumption, which requires that the columns of the factor matrix are some columns of the input matrix \(M\) . In this paper, we generalize the separability assumption based on 3-factor NMF ( \(M=P_1SP_2\) ), and require that \(S\) is a submatrix of the input matrix \(M\) . We refer to this NMF as a Coseparable NMF (CoS-NMF). In the paper, we discuss and study mathematical properties of CoS-NMF, and present its relationships with other matrix factorizations such as generalized separable NMF, tri-symNMF, biorthogonal trifactorization and CUR decomposition. An optimization method for CoS-NMF is proposed, and an alternating fast gradient method is employed to determine the rows and the columns of \(M\) for the submatrix \(S\) . Numerical experiments on synthetic data sets, document data sets, and facial data sets are conducted to verify the effectiveness of the proposed CoS-NMF model. By comparison with state-of-the-art methods, the CoS-NMF model performs very well in a coclustering task by finding useful features, and keeps a good approximation to the input data matrix as well.Keywordsnonnegative matrix factorizationseparabilitycoseparablecoclusteringMSC codes68T1065F30
Inventory and Content Separation in Grammatical Descriptions of Languages of the World

Grammatical descriptions of languages of the world form a sub-genre of scholarly documents in the field of linguistics. A document of this genre may be modeled as a concatenation of table of contents, sociolinguistic description, phonological description, morphosyntactic description, comparative remarks, lexicon, text, bibliography and index (where morphosyntactic description is the only mandatory section). Separation of these parts is useful for information extraction, bibliometrics and information content analysis. Using a collection of over 10 000 digitized grammatical descriptions and an associated bibliography with document-level categorizations, we show that standard techniques from text classification can be adapted to classify individual pages. Assuming that the divisions of interest form continuous page ranges, we can achieve the sought after division in a transparent way. In contrast to previous work on similar tasks in other domains, no use is made of formatting cues, no additional annotated data is needed, high-quality OCR is not required, and the document collection is highly multilingual.
Efficient Private Cloud Resources Platform

Cloud-computing is becoming an increasingly popular model for accessing computing resources. The demand for advanced computing capabilities is increasing; however, conventional resource management systems have many drawbacks such as being costly, limited resources, and inefficiency. Therefore, an advanced management is needed. In this paper, we propose an innovative private cloud platform structure designed to secure sensitive company information, which other cloud offerings may not always protect effectively. The aim of this platform is to boost the efficiency in determining an enterprise's ability, custom priorities, and security threats. As a result, the proposed private cloud system provides account login with a username and password for each client. Then after login, each client can access to a proposed private cloud system and using different services privately such as file sharing, word document, slideshows, spreadsheets, local emails, photo sharing and display, online video playing, and also there is a specific storage area for each user after he login to his account. This storage area will enable the user for storing his files and documents on the cloud and using, retrieve or even editing them from any end device connecting to this private cloud inside organization, universities, Enterprise Company etc. without required to an internet connection for any reason with take in consideration the high privacy and security.
C2F-3DToothSeg: Coarse-to-fine 3D tooth segmentation via intuitive single clicks

3D tooth segmentation is a challenging fundamental problem in computer-aided design (CAD) systems for orthodontic treatment planning. Existing methods of tooth segmentation cannot meet the requirements for both efficiency and accuracy. This paper presents a coarse-to-fine 3D tooth segmentation algorithm, called C2F-3DToothSeg, to improve tooth segmentation. The algorithm consists of two steps: extraction of the coarse gum line and optimization of this line. A concavity-sensitive harmonic field is searched in a local region of entire dental meshes to produce a series of isolines. The best isoline is selected as the coarse gum line, which is then optimized by a heuristic feature line extraction scheme to yield an accurate gum line. Extensive visual and numerical experiments demonstrate that our C2F-3DToothSeg has better overall performance in accuracy and efficiency compared to other existing methods directly applicable in orthodontic CAD systems. A point on the tooth surface is loosely found to locate the center zone of each individual tooth via a simple intuitive single click of the operation. The experiments also show that our method is robust to different complex dental models; for example, it copes with bulge bubbles, decay and cavity issues, and dental accessories.
Product Aspect Ranking Using Sentimental Analysis

E-commerce is a digital-platform where people can buy and sell things online. E-commerce allows the customers to purchase products anytime and from anywhere. It also gives the consumers the privilege to review positively or negatively on any product over the platform. Since people acknowledge the online reviews as an important information on product, it helps the customer in making decision. User reviews are their feedback on a particular product or service. In this paper, the aspect-based sentiment analysis is performed. The reviews are pre-processed and then polarity is determined using sentiment analysis. Sentiment classification is done using naïve bayes (NB) and support vector machine (SVM) algorithm classifies the reviews into positive or negative ones. Then the ranking is done by the ranking algorithm based on their importance.
Automated Quality of Service Monitoring for 5G and Beyond Using Distributed Ledgers

The viability of new mission-critical networked applications such as connected cars or remote surgery is heavily dependent on the availability of truly customized network services at a Quality of Service (QoS) level that both the network operator and the customer can agree on. This is difficult to achieve in today’s mainly "best effort" Internet. Even if a level of service were to be agreed upon between a consumer and an operator, it is important for both parties to be able to scalably and impartially monitor the quality of service delivered in order to enforce the service level agreement (SLA). Building upon a recently proposed architecture for automated negotiation of SLAs using smart contracts, we develop a low overhead solution for monitoring these SLAs and arranging automated payments based on the smart contracts. Our solution uses cryptographically secure bloom filters to create succinct summaries of the data exchanged over fine-grained epochs. We then use a state channel-based design for both parties to quickly and scalably agree and sign off on the data that was delivered in each epoch, making it possible to monitor and enforce at run time the agreed upon QoS levels.
Text Classification Study Based on Graph Convolutional Neural Networks

Because the research of machine learning and deep learning so far has presented good results in the field of text classification, but it ignores the influence of words and words, and the relationship to a certain extent between words and documents on text classification in an overall perspective. Existing text classification researches are usually based on graph convolutional neural networks, in this paper, by using Mish() activation function to deal with the problem of experimental data in the hard zero boundary and by modifying the momentum of one-step moving average in the optimizer, a certain degree of improvement is proposed to the short-term memory problem after the optimization of experimental parameters, which is called LTM-TEXT-GCN. By comparison with Text-GCN, the experiment results demonstrate the effect of LTM-TEXT-GCN, and the improvement was 0.14%, 0.64%, 0.8% and 0.13% on the four data sets, respectively.
Neural Text Categorization with Transformers for Learning Portuguese as a Second Language

We report on the application of a neural network based approach to the problem of automatically categorizing texts according to their proficiency levels and suitability for learners of Portuguese as a second language. We resort to a particular deep learning architecture, namely Transformers, as we fine-tune GPT-2 and RoBERTa on data sets labeled with respect to the standard CEFR proficiency levels, that were provided by Camões IC, the Portuguese official language institute. Despite the reduced size of the data sets available, we found that the resulting models overperform previous carefully crafted feature based counterparts in most evaluation scenarios, thus offering a new state-of-the-art for this task in what concerns the Portuguese language.
Evaluation of the Effect of Art Content on Mental States Using Mirror Display with AR Function

As there are many opportunities to look in a mirror in daily life, an interesting theme is to investigate what kind of effect it has when art content is displayed together at that time. A mirror display that has a display function, as well as a mirror function, has been put into practical use. Using the AR function of the mirror display, we carried out psychological experiments in which art content is displayed along with one's face and full-body images and it was evaluated how it affects mental state. By preliminary experiments, ten video artworks that are considered appropriate were selected as art content. Next, 35 subjects were asked to read aloud the scenarios prepared in advance to induct the stressed state and the depressed state. Then they watched the mirror display where the art content is displayed together with their face and whole-body images. The difference in the mental state between the art-display and the non-art-display conditions was compared and evaluated. As a result, it was revealed that without art content there was not much change in mental state, but when art content was displayed, the depressed or stressed state was improved to a normal state.