Reviewerly/exHarmony huggingface dataset:
Increasing Accuracy of Support Vector Machine (SVM) By Applying N-Gram and Chi-Square Feature Selection for Text Classification

Text mining is a technique that can be used for data processing. News classification is one of the text mining applications. Support Vector Machine is an algorithm that can be used for news classification. However, SVM performance is less than optimal when applied to large datasets. The number of attributes used is also a problem in classification. The number of these attributes will affect the performance of the classifier. This research aims to increase the Accuracy of SVM by applying N-gram and Chi-square feature selection. SVM accuracy without addition N-gram and feature selection have an accuracy of 96.40&#x0025;. SVM accuracy by applying bigram and Chi-square feature selection with 70&#x0025; feature reduction increased 0.95&#x0025; has an accuracy of 97.35&#x0025;. SVM accuracy by applying unigram and Chi-square feature selection with 90&#x0025; reduction features increased by 1.58&#x0025; with the highest accuracy value 97.98&#x0025;. With this best pattern, the testing data is tested, and the results show improvement. SVM accuracy without applying N-gram and without feature selection has an accuracy of 76.80&#x0025;. SVM accuracy by applying bigram and Chi-square with 70&#x0025; feature reduction has an accuracy of 82&#x0025;. SVM accuracy by applying unigram and Chi-square with a 90&#x0025; reduction feature obtains the highest accuracy of 82.40&#x0025;. From these studies, SVM performance is influenced by applying N-gram and Chi-square, which affect the number of features. The best text classification performance can be obtained maximally if the N-gram value and the feature amount are determined precisely.
User encoding for clustering in very sparse recommender systems tasks

Abstract Recommender Systems are a very useful tool which let companies and service providers focus in the preferences of their customers, helping them to avoid an overwhelming variety of choices. In this context, clustering tools can play an important role to detect groups of customers with similar tastes. Thus, companies can make personalized marketing campaigns, offering to their users new products which have been consumed by other users with comparable preferences. In this paper we present a general framework to cluster users with respect to their tastes when the registers stored about the interactions between users and products are extremely scarce. Commonly, clustering methods employ the values of features describing the samples to be clustered (users in our case), but such features are not always available. We propose some alternative representations for users, in which their tastes are gathered to some extent, so that clustering algorithms can take advantage and make more homogeneous groups in this regard. To illustrate the performance of the whole framework, we tested it on six popular datasets commonly used as a benchmark for recommender systems, as well as on an extremely sparse real-world dataset that records the preferences of readers to click promoted links in digital publications. In the experimental section we compare our proposed representations to other common user encodings. We show that clustering users attending only to their feature values or to the items they have evaluated gives rise to the worst scores in terms of taste homogeneity.
Manifestations of professional maladaptation and emotional burnout syndrome among internship doctors

The relevance of this work is in the fact that when identifying professional maladaptation and the systemic nature of the “emotional burnout” syndrome manifestations, actions must be developed for their prevention. Otherwise, there will be manifestations of young doctors' professional deformation, depersonalization phenomena, and a deterioration in the quality of life. All this can lead to the risk of outflow of young personnel from the profession. The reform of the medical personnel training system in Ukraine is underway: the time of internship training in a clinic is increasing, and the time for department training is decreasing. The first encounter of the internship doctor with practical activities leads to a number of stresses for them. Signs of professional maladaptation appear. This leads to the emergence of the “burnout” syndrome. All these factors may result in the outflow of young personnel from the profession. A study of the first-year interns' group (n=181) was conducted. A significant degree of emotional burnout symptoms development and a significant level of professional maladaptation manifestations were revealed. It is proposed to integrate mandatory courses for professional development of the personality with the formation of competencies for adaptation, prevention of emotional burnout, self-regulation of psycho-emotional states into the system of an internship doctors training.
Pre-training for Ad-hoc Retrieval

Designing pre-training objectives that more closely resemble the downstream tasks for pre-trained language models can lead to better performance at the fine-tuning stage, especially in the ad-hoc retrieval area. Existing pre-training approaches tailored for IR tried to incorporate weak supervised signals, such as query-likelihood based sampling, to construct pseudo query-document pairs from the raw textual corpus. However, these signals rely heavily on the sampling method. For example, the query likelihood model may lead to much noise in the constructed pre-training data. \blfootnote{$\dagger$ This work was done during an internship at Huawei.} In this paper, we propose to leverage the large-scale hyperlinks and anchor texts to pre-train the language model for ad-hoc retrieval. Since the anchor texts are created by webmasters and can usually summarize the target document, it can help to build more accurate and reliable pre-training samples than a specific algorithm. Considering different views of the downstream ad-hoc retrieval, we devise four pre-training tasks based on the hyperlinks. We then pre-train the Transformer model to predict the pair-wise preference, jointly with the Masked Language Model objective. Experimental results on two large-scale ad-hoc retrieval datasets show the significant improvement of our model compared with the existing methods.
Contextual Recommendations Using NLP in Digital Marketing

Digital marketing deals with the component of marketing which utilizes online platforms and digital technologies for the marketing of products and services. Even with substantial advances in digital marketing, major challenges which still exists for brands and advertisers in this domain can be broadly classified into two categories: Optimal way/cost-effective strategies to reach the right audience and Adherence to strict privacy laws and reduce dependency on PII (Personally Identifiable Information) data. This paper proposes a unique approach using NLP and Embeddings to provide recommendations in the form of similar keywords and similar site domains to reach the most relevant audiences in a cookieless world. The developed framework dynamically learns and provides new recommendations and bids for targeting users based on variables namely: similarity score, average bid price and scale from past data. The major benefit of this approach is that it utilizes a neural network model to learn associations between keywords and site domains thereby eliminating the requirement of any user-level data. We tested our approach against traditional user-based overlap models and observed a 3x increase in CVR 3.6x decrease in CPA for final conversion point.Bahirat, Aishwary
A New Approach for End to End Automation Testing Platform with Cloud Computing for 5G Product

Fast development, fast integration, fast delivery is extremely critical for any organizations. It is essential to develop end to end automation platform for quick delivery of software features in the entire software lifecycle management. Developing one platform can help to reduce the separation between developer, tester and operators which including continuous integration, continuous testing and continuous delivery. In addition, it will reduce the software development lifecycle, delivery frequent and high quality software versions in automated way. Meanwhile, moving the platform to cloud will speed up the platform deployment and enhance the portability. Besides, it promote the process more and more agile at software development and operations.This paper presents an entire automated pipeline, trigger from source code change by developers, create resource from data center to deploy the containerized testing framework. It follows the best practice of DevOps and base on Jenkins for Cl stage. The innovation here is we use the resource from data center which is a private cloud resource, thus it fully support the scalability and overall ease of use.
HyperGraph Convolution Based Attributed HyperGraph Clustering

Attributed Graph Clustering (AGC) and Attributed Hypergraph Clustering (AHC) are important topics in graph mining with many applications. For AGC, amongst the unsupervised methods that combine the graph structure with node attributes, graph convolution has been shown to achieve impressive results. However, the effects of graph convolution on AGC have not yet been adequately studied. In this paper, we show that graph convolution attempts to find the best trade-off between node attribute distance and the number of inter-cluster edges. On the one hand, we show that compared to clustering node attributes directly, graph convolution produces a greater distance between node attributes in the same cluster and a smaller distance between node attributes in different clusters (which is detrimental for clustering). On the other hand, we show that graph convolution benefits clustering by considerably reducing the number of edges among different clusters. We then extend our result on AGC to AHC and leverage the hypergraph convolution to propose an unsupervised, fast, and memory-efficient algorithm (GRAC) for AHC, which achieves excellent performance on popular supervised clustering measures.
Cooperative Distributed Navigation, Guidance, and Control Systems for Autonomous Surface Vehicles with Different Physical Connections

In the paper, we describe our studies to realize a control algorithm for distributed marine robots with rigid/elastic mechanical constraints between them. The work is carried out in the framework of the EUMR TNA project DINGOS. Our strategy follows the line to adapt conventional cooperative path-following algorithms from literature to the described problem in order to start to extend the concept of DP3, currently related to Dynamic Positioning, to the phase of motion along a path of a vessel. The usability of different concepts is evaluated following some well-defined quality criteria in a kinematic simulation.
Scale-Aware Distillation Network for Lightweight Image Super-Resolution

Many lightweight models have achieved great progress in single image super-resolution. However, their parameters are still too many to be applied in practical applications, and it still has space for parameter reduction. Meanwhile, multi-scale features are usually underutilized by researchers, which are better for multi-scale regions’ reconstruction. With the renaissance of deep learning, convolution neural network based methods has prompted many computer vision tasks (e.g., video object segmentation [, , ], human parsing [], human-object interaction detection []) to achieve significant progresses. To solve this limitation, in this paper, we propose a lightweight super-resolution network named scale-aware distillation network (SDNet). SDNet is built on many stacked scale-aware distillation blocks (SDB), which contain a scale-aware distillation unit (SDU) and a context enhancement (CE) layer. Specifically, SDU enriches the hierarchical features at a granular level via grouped convolution. Meanwhile, the CE layer further enhances the multi-scale feature representation from SDU by context learning to extract more discriminative information. Extensive experiments are performed on commonly-used super-resolution datasets, and our method achieves promising results against other state-of-the-art methods with fewer parameters.
Group Re-Identification Based on Single Feature Attention Learning Network (SFALN)

People often go together in groups, and group re-identification (G-ReID) is an important but less researched topic. The goal of G-ReID is to find a group of people under different surveillance camera perspectives. It not only faces the same challenge with traditional ReID, but also involves the changes in group layout and membership. To solve these problems, we propose a Single Feature Attention Learning Network (SFALN). The proposed network makes use of the abundant ReID datasets by transfer learning, and extracts effective feature information of the groups through attention mechanism. Experimental results on the public dataset demonstrate the state-of-the-art effectiveness of our approach.
Hildegard of Bingen and Her Scribes

This chapter demonstrates several ways in which scribes and the scriptorium were central to life in Hildegard of Bingen’s community, perhaps even before the women departed from the Disibodenberg. Under the probable supervision of Hildegard’s provost the monk Volmar, nuns in Hildegard’s scriptorium were responsible for the copying, and hence the preservation of Hildegard’s writings, from the letter collections of the earlier attested periods of scribal activity to the feverish activity of the final decade or so of Hildegard’s life, to the compilation and preparation of the Riesencodex, Wiesbaden, Hochschul- und Landesbibliothek RheinMein, MS 2, which forms a kind of critical edition of her writings. Many scribal hands were involved in the work, suggesting that copying was an important part of monastic discipline on the Rupertsberg. This essay introduces the major features of the house style and some of the problems of studying scribal practices, focusing on the habits of one scribe who worked on two copies of Scivias. A complete list of the manuscripts of Hildegard’s trilogy surviving in Rupertsberg copies is provided as well.
Exploring Cybercrime Disruption through Laboratory Experiments

This position paper proposes a previously neglected method for investigating cybercrime disruption: laboratory experiments. As a case study for the exploration of this method, it examines slander and Sybil operations against cybercriminal marketplaces. While there has been some scholarly discussion of these interventions, carrying out empirical work on the effectiveness of these tactics is challenging. This position paper reports on a feasibility pilot study, and the results should be viewed as suggestive, rather than conclusive. The paper’s core value is in providing proof of concept for employing this methodology within cybercrime research, and offering insights for future studies of these, or other, disruption tactics.
Road scenes segmentation across different domains by disentangling latent representations

Abstract Deep learning models obtain impressive accuracy in road scene understanding; however, they need a large number of labeled samples for their training. Additionally, such models do not generalize well to environments where the statistical properties of data do not perfectly match those of training scenes, and this can be a significant problem for intelligent vehicles. Hence, domain adaptation approaches have been introduced to transfer knowledge acquired on a label-abundant source domain to a related label-scarce target domain. In this work, we design and carefully analyze multiple latent space-shaping regularization strategies that work together to reduce the domain shift. More in detail, we devise a feature clustering strategy to increase domain alignment, a feature perpendicularity constraint to space apart features belonging to different semantic classes, including those not present in the current batch, and a feature norm alignment strategy to separate active and inactive channels. In addition, we propose a novel evaluation metric to capture the relative performance of an adapted model with respect to supervised training. We validate our framework in driving scenarios, considering both synthetic-to-real and real-to-real adaptation, outperforming previous feature-level state-of-the-art methods on multiple road scenes benchmarks.
Video Summarization Using Fully Convolutional Residual Dense Network

Video summarization is a keenly intellective video compression technique to select a subset of keyframes or keyshots which are combined to represent shorter and compendious summary of the original input video without losing the contextual semantics of the same. Previous work has shown that extracting rich contextual information from the input video frames is imperative for generating summary that is closer to human interpretation of the original input video. However, recent convolutional architectures were unable to account for the same. In this paper, we institute a novel relation between image super resolution and video summarization and introduce a novel architecture by adapting residual dense network (RDN), which fully exploits both local and global structural context. Experimental results indicate that introducing a modified RDN (SUM-RDN) unit significantly improves the performance over standard convolutional networks.Parihar, Anil Singh Mittal, Ritvik Himanshu Jain, Prashuk
Design and Implementation of Learning System Based on T-LSTM

One-to-one teaching is an ideal way to realize personalized and adaptive learning, but limited to teachers, it can’t be applied on a large scale. Now artificial intelligence has been widely used, so we can design a learning system to assist teaching, so as to realize part of the personalized and adaptive learning. In this paper, a learning system based on T-LSTM’s recurrent neural network is proposed to help achieve this goal. On the one hand, it can balance subjects, on the other hand, it can avoid the exercise too difficult to affect learning confidence or too simple to affect learning efficiency.
Memory Augmented Variational Auto-Encoder for Anomaly Detection

Anomaly detection is of great importance due to its wide application in industrial areas. Deep auto-encoders have enabled significant advances in anomaly detection over the past decade. However, it has been observed that sometimes the auto-encoder are likely to reconstruct anomalies due to the &#x201C;excellent&#x201D; generalization ability, resulting in the miss detection of anomalies. To mitigate this deficiency, scholars have proposed the memory-augmented deep auto-encoder method recently. However, more recent work on memory-augmented uses continuous memory representations or key-values pairs in memory for read/write without forgetting mechanism, and it is assumed that data such as early access to the network are normal data. To deal with those imperfectness, we present a VAE-based memory-augmented network with a trained memory network: MEMVAE, addressing scheme to provide a strong guarantee to detect anomalous. In particular, Gated Recurrent Unit (GRU) cells are employed as the encoder and decoder to capture latent temporal structure. We then train an external memory module that records common patterns of the prototype. In addition, we present a weighted regular score that simply updates our memory entries. Our detector reports a reconstruction probability as the anomaly score. Experimental results on tabular data and time series data show that MEMVAE demonstrates significant improvements and better performance.
Efficient light transport acquisition by coded illumination and robust photometric stereo by dual photography using deep neural network

We present an efficient and robust photometric stereo (PS) measurement by a setup with an optical diffuser. The setup which includes a single projector that places point light sources on the diffuser, extends the possibility of flexible measurement without the limitation from employing physical light sources, i.e. a number and illumination shape. By taking advantage of the setup, we design an illumination for effective and robust surface normal estimation. Unlike the previous techniques, we utilize deep neural network consists of a renderer and a PS module to find multiplexed illumination patterns, which are suitable for PS measurement. Another challenging problem is to measure objects with micro-structures which reflect the light randomly according to lighting and viewing directions. To overcome the problem, we propose a novel PS measurement using a dual-photography setup, which allows us to analyze the angular distribution of reflection by capturing reflection pattern on the diffuser. We show a smooth surface normal can be estimated by simply applying a low-pass filter on the captured images. Moreover, we also propose an effective sampling to deal with time-consuming measurement of dual photography setup. We show that by utilization of the trained sampling codes by DNN considering light transport in the setup, the number of the measurement is drastically reduced.
Multi-objective Release Plan Rescheduling in Agile Software Development

Scrum is an agile software development framework followed nowadays by many software companies worldwide. Since it is an iterative and incremental methodology, the software is developed in releases. For each release, the software development team and the customer agree upon a development plan. However, the context of the software project may change due to unpredicted circumstances that generally arise, for example, new software requirements or changes in the development team. Consequently, these factors force the release plan to be adjusted. When the release plan is modified, it is necessary to consider at least four criteria to minimize the economic and operational impact of these changes. Therefore, this activity can be analyzed as a multi-objective problem. In the last three decades, multi-objective evolutionary algorithms have become an effective and efficient tool to solve multi-objective problems. In this paper, we evaluate three multi-objective optimization approaches when solving the release plan rescheduling problem. Mainly, we focus our investigation on analyzing the conflict between the considered objectives and on the performance of the Pareto-based, the indicator-based, and the decomposition-based multi-objective optimization approaches.
Teaching in the Era of COVID-19: From Narratives of a 25 Years of Experienced In-Service Korean Elementary School Teacher

This study will examine a teacher's educational belief change in the situation of COVID-19. The participant of this study is a Korean teacher who hold 25 years of teaching experience. For the data collection, 5 times of interviews, 1 diary entry, 1 letter to her students were collected. In addition to this, researchers observed the participant's daily routine in the online teaching context, more than 4 times. With these data, the teacher's narrative has been studied. The Korean teacher experienced many difficulties. Though she was veteran teacher, it was something difficult to cope with. However, she tried to find the best for her students. There were colleagues and teacher communities, and she interacted with them. Her journey in the COVID-19 situation represented that there were no changes in her teacher belief. Preferably, it was the chance to achieve teacher agency and reconstructing and firming her teacher beliefs.
The technology of using fuzzy cognitive maps from a mathematical point of view

A broader view of the technology of fuzzy cognitive maps is described, in which the cognitive map is considered as a carrier of computational procedures. This approach can be described as a generalized system dynamics. This interpretation makes it easier to obtain theoretical results that can characterize the behavior of complex systems. In particular, in the case of simple computational procedures, the relationship between the degree of influence of factors and the structure of the system, namely, the presence of connecting paths and cycles in the corresponding digraph, is clarified.