Reviewerly/exHarmony huggingface dataset:
Pareto Assumption for Constrained PSO-Based Locomotive Resistance Minimization

The mechanical resistance of a locomotive is crucial for power consumption. It is desirable to maintain this resistance at a minimum value for energy efficiency under optimal operation conditions. The optimal conditions can be found by particle swarm optimization with constraints. The particle swarm optimization method is a highly preferred type of heuristic algorithm because of its advantages, such as fewer parameters, faster speed, and a simpler flow diagram. However, fast convergence can be misleading in finding the optimum solution in some cases. Pareto analysis is used in this proposed study to prevent missing the target. When the literature is searched, it is seen that there are various studies using this method. However, in all of these studies, the results of the particle swarm method have been interpreted as whether or not they complied with Pareto’s 80/20 rule. The validity of the Pareto analysis is taken as an assumption, and with the help of this assumption, the coefficients of a locomotive’s mathematical equation were changed, and finally the results were found by applying the particle herd optimization method. Finally, a novel hybrid method has been created by including the Pareto optimality condition to particle swarm optimization. The results are compared with this innovative hybrid method of Pareto and particle swarm and the results found using only the particle swarm method.
Adaptive Gradient Methods for Constrained Convex Optimization.

We provide new adaptive first-order methods for constrained convex optimization. Our main algorithms AdaACSA and AdaAGD+ are accelerated methods, which are universal in the sense that they achieve nearly-optimal convergence rates for both smooth and non-smooth functions, even when they only have access to stochastic gradients. In addition, they do not require any prior knowledge on how the objective function is parametrized, since they automatically adjust their per-coordinate learning rate. These can be seen as truly accelerated Adagrad methods for constrained optimization. We complement them with a simpler algorithm AdaGrad+ which enjoys the same features, and achieves the standard non-accelerated convergence rate. We also present a set of new results involving adaptive methods for unconstrained optimization and monotone operators.
Progressive Multi-Scale Residual Network for Single Image Super-Resolution.

Multi-scale convolutional neural networks (CNNs) achieve significant success in single image super-resolution (SISR), which considers the comprehensive information from different receptive fields. However, recent multi-scale networks usually aim to build the hierarchical exploration with different sizes of filters, which lead to high computation complexity costs, and seldom focus on the inherent correlations among different scales. This paper converts the multi-scale exploration into a sequential manner, and proposes a progressive multi-scale residual network (PMRN) for SISR problem. Specifically, we devise a progressive multi-scale residual block (PMRB) to substitute the larger filters with small filter combinations, and gradually explore the hierarchical information. Furthermore, channel- and pixel-wise attention mechanism (CPA) is designed for finding the inherent correlations among image features with weighting and bias factors, which concentrates more on high-frequency information. Experimental results show that the proposed PMRN recovers structural textures more effectively with superior PSNR/SSIM results than other small networks. The extension model PMRN$^+$ with self-ensemble achieves competitive or better results than large networks with much fewer parameters and lower computation complexity.
Semi-supervised Collaborative Filtering by Text-enhanced Domain Adaptation

Data sparsity is an inherent challenge in the recommender systems, where most of the data is collected from the implicit feedbacks of users. This causes two difficulties in designing effective algorithms: first, the majority of users only have a few interactions with the system and there is no enough data for learning; second, there are no negative samples in the implicit feedbacks and it is a common practice to perform negative sampling to generate negative samples. However, this leads to a consequence that many potential positive samples are mislabeled as negative ones and data sparsity would exacerbate the mislabeling problem. To solve these difficulties, we regard the problem of recommendation on sparse implicit feedbacks as a semi-supervised learning task, and explore domain adaption to solve it. We transfer the knowledge learned from dense data to sparse data and we focus on the most challenging case -- there is no user or item overlap. In this extreme case, aligning embeddings of two datasets directly is rather sub-optimal since the two latent spaces encode very different information. As such, we adopt domain-invariant textual features as the anchor points to align the latent spaces. To align the embeddings, we extract the textual features for each user and item and feed them into a domain classifier with the embeddings of users and items. The embeddings are trained to puzzle the classifier and textual features are fixed as anchor points. By domain adaptation, the distribution pattern in the source domain is transferred to the target domain. As the target part can be supervised by domain adaptation, we abandon negative sampling in target dataset to avoid label noise. We adopt three pairs of real-world datasets to validate the effectiveness of our transfer strategy. Results show that our models outperform existing models significantly.
Research on the Development of SLA Indicators for Personal Information Protection of Public IT Maintenance Business

In the field of public informatization maintenance business, the attacks of external illegal users such as unauthorized leakage, destruction, and alteration due to intentional or inadequate management of personal information are increasing. In order to prevent such security incidents in advance, it is necessary to develop and quantitatively manage SLA indicators. This study presents the privacy SLA indicators and suggests specific methods such as information collection method and timing of the privacy SLA indicators. In order to confirm the validity and reliability of the proposed SLA indicators, an online survey was conducted with a group of experts. As a result, it was evaluated that compliance rate of personal information destruction and compliance rate of personal information protection system would be effective when applied to new and revised SLA indicators in terms of importance and validity. In the future, using SLA indicators for personal information protection as a standard for public information maintenance will contribute to improving SW quality and securing safety.
Learning to Generate Customized Dynamic 3D Facial Expressions

Recent advances in deep learning have significantly pushed the state-of-the-art in photorealistic video animation given a single image. In this paper, we extrapolate those advances to the 3D domain, by studying 3D image-to-video translation with a particular focus on 4D facial expressions. Although 3D facial generative models have been widely explored during the past years, 4D animation remains relatively unexplored. To this end, in this study we employ a deep mesh encoder-decoder like architecture to synthesize realistic high resolution facial expressions by using a single neutral frame along with an expression identification. In addition, processing 3D meshes remains a non-trivial task compared to data that live on grid-like structures, such as images. Given the recent progress in mesh processing with graph convolutions, we make use of a recently introduced learnable operator which acts directly on the mesh structure by taking advantage of local vertex orderings. In order to generalize to 4D facial expressions across subjects, we trained our model using a high resolution dataset with 4D scans of six facial expressions from 180 subjects. Experimental results demonstrate that our approach preserves the subject's identity information even for unseen subjects and generates high quality expressions. To the best of our knowledge, this is the first study tackling the problem of 4D facial expression synthesis.
Performing Image Compression and Decompression Using Matrix Substitution Technique

Now a days, large amount of information storage and transmission is common in public sectors as well as private sectors like Governments, military, and so on. With increase of demand in digital network more and more accurate images are transmitted. Whenever we are transmitting an image with larger size it occupies more space. In order to reduce the size, Image compression an efficient and advantageous technique is used to remove the redundant data from the image. Thereby the storage space in memory is reduced and also it takes less time to transmit the image. Therefore, development of efficient image compression techniques has become necessary. In this paper, a new algorithm matrix substitution technique is proposed in order to reduce the occupied space by replacing group of bits with a single bit.
Obituary of Great Teacher, Prof. Kazuhiko Atsumi

was 91 years of Age.This is a big lost to the Society, and we all feel very sad to the family of Prof.
Web indexing and its usefulness in information management and retrieval process: A discussion

Web indexing or web marketing is the concept of two decades back in the fields of service operations, communications, academics, and research. It is also increasing importance in the LIS field comparing to the traditional marketing concepts as the library services are not confined within the four walls. Users access information breaking geographical restrictions from the information technology-based automated and digitized library. Indexing and the web indexing play important roles in the information management and retrieval process. It helps in making process of the documents more accessible and transparent. The importance of web indexing is increasing because of the gather of information. Considering greater importance to this topic, web marketing and its applicability in managing and disseminating digital information, this study has been undertaken for proper establishment. Â
Misclassification cost-sensitive ensemble learning: A unifying framework

Over the years, a plethora of cost-sensitive methods have been proposed for learning on data when different types of misclassification errors incur different costs. Our contribution is a unifying framework that provides a comprehensive and insightful overview on cost-sensitive ensemble methods, pinpointing their differences and similarities via a fine-grained categorization. Our framework contains natural extensions and generalisations of ideas across methods, be it AdaBoost, Bagging or Random Forest, and as a result not only yields all methods known to date but also some not previously considered.
A Study on the Effect of Supply Chain Integration on Logistic Flexibility and Supply Chain Risk Management Performance of Chinese Automobile Industry

중국에서 경제 및 규모의 발전과 함께 급속도로 성장하고 있는 자동차 산업은 그 중요도가 점차 높아지고 있으며, 다양한 부품 과 최신 기술을 함께 필요로 한다는 점에서 공급체인통합과 물류 관리에 있어서의 유연성 그리고 위험관리가 필수적인 분야로 인 식되고 있다. 본 연구에서는 공급체인통합 요인들을 살펴보고 이러한 요인들이 물류 유연성과 위험관리 성과에 미치는 영향관계 를 파악하였다. 본 연구의 실증분석 대상은 중국 내 6대 자동차 클러스터에 위치한 중국 현지 자동차 기업들을 대상으로 2019년 8월부터 2019년 12월까지 해당 지역들에서 추출된 207부를 대상으로 실증 분석을 하였다. 그 결과, 조직내부통합 요인은 외부통합 요인인 공급자 통합과 고객통합에 양(+)의 영향을 주는 것으로 나타났으며, 공급자통합요인과 고객통합요인은 물류 유연성에 긍정 적인 영향력을 행사하는 것으로 나타났다. 또한, 물류 유연성과 공급체인위험관리성과는 유의한 양(+)의 관계에 있었다. 이를 통하 여, 공급체인통합전략은 조직내부통합이 우선적으로 이루어져야 하며, 외부 통합의 수준을 높이는 것이 물류 유연성의 향상을 가 져오며, 높은 물류 유연성이 공급체인위험관리성과를 향상시킬 수 있다는 시사점을 도출하였다.In China, the rapidly growing automotive industry with the development of economy and scale is becoming increasingly important, and it requires flexibility and risk management in supply chain integration and logistics management in that it requires various parts and the latest technology together. In this study, we examine the factors of supply chain integration and examine the relationship between these factors on logistics flexibility and risk management performance. We conducted an empirical analysis of 207 samples from 6 chinese automobile clusters from August to December in 2019. As a result, it was found that internal integration factor has a positive effect on external integration factors, both supplier integration and customer integration, and supplier integration factor and customer integration factor exert a positive influence on logistics flexibility. In addition, there was a significant positive relationship between logistics flexibility and supply chain risk management performance. Through this, the supply chain integration strategy should first be done inside the organization, and raising the level of external integration brings about the improvement of logistics flexibility, and draws the implication that high logistics flexibility can improve the supply chain risk management performance.
Analysis of the Viability of the LexDoBusiness Collaborative Platform

The objective of this work is to validate the viability of the LexDoBusiness collaborative platform project with the main stakeholders.Considering that the LexDoBusiness project should address several issues, including digital transformation, that brings together several services essential to its success, such as legal, accounting, tax and technological services, this work, regarding the research methodology, uses Design Science Research because it is considered the most appropriate for this project, which had the external collection of secondary data present in the platforms of INE, RACIUS and PORDATA, as well as the collection of primary data through a semi-structured survey addressed to clients of a network of lawyers and potential clients.The LexDoBusiness value proposition is the personalization of its services and in the relationship with its customers, as the permanent support, offering in a single platform, the qualification of entrepreneurs, support in different areas such as legal and conflict mediation, marketing, accounting and tax, as well as the IT area with the development and hosting of the company's website.Clients will also be able to check anywhere and at any time the status of the resolution of their case, the procedural costs, fees, documents and invoices.The results of the questionnaire, confirmed the research problem initially formulated that most clients prefer to hire lawyers with physical offices, which led the business plan to take this preference into consideration rather than the provision of legal services exclusively online without neglecting the economic and financial viability of the project.
An Intelligent Model for Text Forensics from Arabic Twitter Posts

The number of twitter users in Arab countries at a rapid pace of growth. As for the social networking platforms, they are a suitable environment for luring innocent users and commit different forms of crimes against them by cyber predators through claiming something they are not such as fake ID, age or gender targeting certain segments of the society. This have resulted in a huge number of cybercrimes including Phishing, harassment, cyber terrorism and many other forms. In this context, a number of different research efforts in the literature are available for English but very rare for Arabic texts. This article proposes an intelligent technique for enhancing text preprocessing that impacting the accuracy and performance of the classification task. The proposed model was evaluated against recently collected dataset from Arabic twitter users using different classifiers including naïve bays and neural networks to predict age attribute.
Dual Graph Embedding for Object-Tag LinkPrediction on the Knowledge Graph

Knowledge graphs (KGs) composed of users, objects, and tags are widely used in web applications ranging from E-commerce, social media sites to news portals. This paper concentrates on an attractive application which aims to predict the object-tag links in the KG for better tag recommendation and object explanation. When predicting the object-tag links, both the first-order and high-order proximities between entities in the KG propagate essential similarity information for better prediction. Most existing methods focus on preserving the first-order proximity between entities in the KG. However, they cannot capture the high-order proximities in an explicit way, and the adopted margin-based criterion cannot measure the first-order proximity on the global structure accurately. In this paper, we propose a novel approach named Dual Graph Embedding (DGE) that models both the first-order and high-order proximities in the KG via an auto-encoding architecture to facilitate better object-tag relation inference. Here the dual graphs contain an object graph and a tag graph that explicitly depict the high-order object-object and tag-tag proximities in the KG. The dual graph encoder in DGE then encodes these high-order proximities in the dual graphs into entity embeddings. The decoder formulates a skip-gram objective that maximizes the first-order proximity between observed object-tag pairs over the global proximity structure. With the supervision of the decoder, the embeddings derived by the encoder will be refined to capture both the first-order and high-order proximities in the KG for better link prediction. Extensive experiments on three real-world datasets demonstrate that DGE outperforms the state-of-the-art methods.
The Effectiveness of Using Guided Reading Strategy to Improve Students’ Reading Comprehension

This research belongs to experimental quantitative research and was carried out at SMPN 16 Banda Aceh. The sample was eight grade students which consists of 30 students. In collecting the data, the researcher used pre-test, treatment and post-test with 27 multiple choices as scoring. Then data was analysed using t-test formula. The researcher used recount text as the topic of the reading passage when the students were given pre-test, treatment, and post-test. The aim of this research was to investigate whether or not guided reading strategy can  improve students’ score in reading comprehension at SMPN 16 Banda Aceh. Therefore, the result had positively answered the research question stated in the beginning of the study. The result showed students’ mean of pre-test was 32, while the mean of post-test was 78. The hypothesis result by using t-test showed that the obtained t-score was higher than t-table (8.17 > 2.052) , the difference is statistically significant. Therefore, based on the complication, there is significant difference in reading comprehension score of students before given treatment using guided reading strategy and after treatment. Thus, it can be said that guided reading strategy improved students’ reading comprehension and hence Ha is accepted because after given treatment by guided reading strategy, the students had better achievement in reading comprehension.
Cost-sensitive business failure prediction when misclassification costs are uncertain: A heterogeneous ensemble selection approach

Abstract In order to assess risks associated with establishing relationships with corporate partners such as clients, suppliers, debtors or contractors, decision makers often turn to business failure prediction models. While a large body of literature has focused on optimizing and evaluating novel methods in terms of classification accuracy, recent research has acknowledged the existence of asymmetric misclassification costs associated with prediction errors and thus, advocates the usage of alternative evaluation metrics. However, these papers often assume a misclassification cost matrix to be known and fixed for both the training and the evaluation of models, whereas in reality these costs are often uncertain. This paper presents a methodological framework based upon heterogeneous ensemble selection and multi-objective optimization for cost-sensitive business failure prediction that accommodates uncertainty at the level of misclassification costs. The framework assumes unknown costs during model training and accommodates varying degrees of uncertainty during model deployment. Specifically, NSGA-II is deployed to optimize cost space resulting in a set of pareto-optimal ensemble classifiers where every learner minimizes expected misclassification cost for a specific range of cost ratios. An extensive set of experiments evaluates the method on multiple data sets and for different scenarios that reflect the extent to which cost ratios are known during model deployment. Results clearly demonstrate the ability of our method to minimize cost under the absence of exact knowledge of misclassification costs.
CLEFIA- A Encryption Algorithm using Novel S-Box Architecture

Lightweight cryptography is a class in cryptography that is employed in resource constrained devices like embedded systems to provide security.CLEFIA is one of the light weight cryptography algorithms which suits the resource constrained environments in providing security thus enhancing the privacy.
Language Classification Using Neural Networks

In this project a model has been created that with an audio sequence as input can classify the language being spoken to be either english or french. The focus of the project has been to experiment ...
Humanidades digitais no arquivo universitário: o caso do Arquivo Central da Universidade Federal de Santa Catarina

This research analyzed the role of Digital Humanities in the University Archive. The Digital Humanities present a reflection on the practices recommended by the introduction of Information and Communication Technology in this context. Information units that preserve the memory of humanity must adapt the techniques of organization and availability of cultural heritage to the new reality: inclusion of ICTs and the needs of information users in the digital world. The university archive must have and support pedagogical, cultural, teaching, research and extension activities, with the intention of meeting research requests and the teaching, student and functional communities of the university. The general objective is to characterize the role of Digital Humanities in the Central Archive of the Federal University of Santa Catarina. Methodologically, the research was characterized as exploratory with a qualitative approach, carried out through a case study. Data analysis followed the coding and categorization procedures. As a result, it was identified that the university archive studied realizes that the application of the concept of Digital Humanities promotes the increase of research strategies and the generation of knowledge.
GMNet: Graph Matching Network for Large Scale Part Semantic Segmentation in the Wild

The semantic segmentation of parts of objects in the wild is a challenging task in which multiple instances of objects and multiple parts within those objects must be detected in the scene. This problem remains nowadays very marginally explored, despite its fundamental importance towards detailed object understanding. In this work, we propose a novel framework combining higher object-level context conditioning and part-level spatial relationships to address the task. To tackle object-level ambiguity, a class-conditioning module is introduced to retain class-level semantics when learning parts-level semantics. In this way, mid-level features carry also this information prior to the decoding stage. To tackle part-level ambiguity and localization we propose a novel adjacency graph-based module that aims at matching the relative spatial relationships between ground truth and predicted parts. The experimental evaluation on the Pascal-Part dataset shows that we achieve state-of-the-art results on this task.