Reviewerly/exHarmony huggingface dataset:
Representing the Chinese Seal Stamping Catalogs Using IIIF &amp; Serverless

Fudan University Library's digital collection Seal Stamping Catalogs Virtual Library follows the IIIF (International Image Interoperability Framework) standard and adopts the Serverless architecture based on AWS. Therefore, it implements the advanced interactive functionality of image-based resources to support academic research and development. At the same time, it provides a service of high-precision image resources with a good interactive experience yet being cost-effective.
Detection of Counterfeit Cosmetic Products Using Image Processing and Text Extraction

The growing popularity of the cosmetics industry has made it vulnerable to duplication and counterfeiting. One in every five individuals purchases counterfeits cosmetics. Consumption of such cosmetics can be harmful to health. They might sometimes contain chemicals such as arsenic or other carcinogenic chemicals. The packagings of the counterfeit products are designed in such a manner that someone can barely differentiate it from the original one. The packaging of the duplicate products differs from that of the original in terms of certain features such as dimension, font style, font size, color, and ingredients. This paper reports the development of a system that uses the application of image processing and text extraction techniques that will enable a user to determine the authenticity of the test product. The system uses the features of the authentic product and compares them with the features of the test product to determine its authenticity. The system has been tested with some of the counterfeit and original cosmetic products.
Forecasting and Early Warning Technology of Roof Disaster in Coal Mining Face

Based on the classic mine pressure theory and a large number of on-site data, in order to realize the forecast and early warning of the roof disaster at the working face, the joint analysis technology of the regional support and the self-correcting technology of the predicted waveform are proposed. Based on the analysis of the working state of the hydraulic support, this technology uses the computer's massive data rapid processing function to pass the pressure criterion and the periodical pressure during the production process., to analyze, reason, and modify the pressure criterion and periodic pressure in the production process, thereby predicting the periodic pressure waveform and finally completing the roof accurate analysis and prediction of movement laws, and alarms when dangers may occur, reducing the occurrence of roof disasters.
ExaEvo

As the availability of high-performance computing systems rises to unprecedented levels in the exascale era, vastly scalable parallelism is now an accessible, viable option for a wide range of scientific fields and algorithmic implementations. This abundance of increasingly complex parallel and distributed systems necessitates new approaches to software parallelism and scalability for finding high-quality solutions to complex problems at previously unattainable rates. This research investigates the implicit and explicit use of MPI topologies applied to parallel evolutionary algorithms (EAs) in order to dynamically arrange processes and communication channels during runtime for optimal performance, adapting to underlying system architecture and state. An island model EA employing numerical optimization has been chosen as the benchmark for evaluating these techniques. Initial results using a random search of the topology space offer promising results, with some cases resulting in both faster performance and improved fitness, indicating that a multi-objective evolutionary search approach could provide near optimal performance in regard to both communication speed and solution quality, thus resulting in a novel, performance enhancing meta-EA approach, where the top-level EA evolves the MPI topology on which the lower-level parallel EA is executing.
Video Lifelog Retrieval System for Ambiguous Search Queries

The fifth-generation mobile network (5G) will soon become popular, and data utilization will increase. Although there is a large volume of video data, making it difficult to manage and use such data in our daily lives, there is huge potential with such data. We now often take photos using smartphones, but in future recording our lives by wearing video cameras would become popular. Since such data have a large amount of information, a video lifelog retrieval system is necessary. Although many studies on video retrieval have been conducted on some domains, e.g. TV programs, movies and so on, there have been few conducted on lifelogs. One of the main differences between them is regarding user queries and the fact that our memories are often ambiguous. We propose a video lifelog retrieval system that takes advantage of ambiguous search queries. We discuss the effectiveness of our system from our evaluation involving our own lifelog data we collected. We also discuss the characteristics of lifelog data and problems of our system.
Development of biography information system based on semantic web using biography ontology: Requirement phase

Abstract The development of the research world is increasingly proven by the integration of supporting technologies, including applications for referral processes, data processing, online journals, journal indexes, and many other applications. Some of these applications, of course, apply web technologies including the semantic web. The purpose of this study is to know which areas of interest and research are being or have been done, provide explanations related to the fields of research, education, personal, awards and a number of other things. This study develops biographical ontologies that are applied to biographical information systems using qualitative methods of analyzing the requirement of biographical ontologies and developing biographical ontology models. The results of this study are the initial phases in the form of the requirement for biographical ontologies that are implemented in biographical ontology models and represent representations of biographical information system designs.
Cooperative coevolutionary genetic algorithm using hierarchical clustering of linkage tree

Genetic algorithms (GAs) are stochastic optimization methods that mimic the evolution of living organisms and have been applied in various fields. Cooperative Coevolution (CC) is one of the methods to make GAs more efficient. CC divides a decision variable into several sub-problems and evolves the individuals independently in each of the sub-problems. Variables with dependencies need to be optimized mutually. In this paper, we focus on Linkage Tree as a method of analyzing the dependency of decision variables. Linkage Tree calculates the dependency between decision variables from the distribution of individuals as the distance between the decision variables, and builds a tree by using hierarchical clustering. We propose an algorithm that can efficiently search for problems with dependencies between variables by introducing Linkage Tree into CC methods. We evaluated the search performance of the proposed method using benchmark functions and confirmed the performance improvement by comparing it with the conventional methods.
Estimation Fusion Algorithm

Multiple source information-based autonomous navigation is essentially an estimation fusion problem. This chapter will go deep into the estimation fusion algorithm. Section 3.1 presents linear models and algorithms, mainly including linear unified model and its fusion algorithm as well as covariance intersection algorithm in the distributed fusion, which can be used to estimate the parameters of a static system and states of a dynamic system. Sections 3.2 and 3.3 describe the centralized Kalman filter and distributed Kalman filter of a dynamic system, respectively, and discuss several implementation models of centralized filtering and typical models of distributed filtering.
Fused Geometry Augmented Images for Analyzing Textured Mesh

In this paper, we propose a novel multi-modal mesh surface representation fusing texture and geometric data. Our approach defines an inverse mapping between different geometric descriptors computed on the mesh surface or its down-sampled version, and the corresponding 2D texture image of the mesh, allowing the construction of fused geometrically augmented images. This new fused modality enables us to learn feature representations from 3D data in a highly efficient manner by simply employing standard convolutional neural networks in a transfer-learning mode. In contrast to existing methods, the proposed approach is both computationally and memory efficient, preserves intrinsic geometric information and learns highly discriminative feature representation by effectively fusing shape and texture information at data level. The efficacy of our approach is demonstrated for the tasks of facial action unit detection, expression classification, and skin lesion classification, showing competitive performance with state of the art methods.
Implementation and experiments for interactive lyrics transcreation system

The evolution of the Internet has enabled us to enjoy the music created in various countries. But still, it is often difficult to understand the lyrics written in foreign languages. Professional translators have published many international songs with lyrics that fit the melody so that the ordinary people can enjoy the lyrics of such international songs. This paper discusses lyrics transcreation into the Japanese language. Also, the paper presents an interactive visual lyrics transcreation system and describes the details of its implementation. This system allows users to select temporary lyrics from a set of tentative translations and then freely modify the lyrics with a real-time visualization mechanism. We also propose a lyrics translation algorithm that solves an essential problem of lyrics translation into Japanese. In this study, we interviewed two experts regarding problems with lyrics translation and received reviews of our presented system. We also conducted preliminary experiments with 19 participants to determine the best combination of user interface components for our system. We performed additional user experiments inviting 12 participants to compare lyrics transcreation using the presented system to manual lyrics transcreation. Lyrics transcreation by the presented system brought better results against those of manual transcreation.
Reinforcement Learning based Strategy Selection in StarCraft

Artificial intelligence (AI) applied on real-time strategy (RTS) games, such as StarCraft has become a hot topic in recent years. Currently, many researches mainly focused on the aspects of applying reinforcement learning (RL) on the aspects, such as the building order, map analysis, macro- and micro-management, etc. This paper innovatively proposes a mechanism applying reinforcement learning on pre-designed game strategies, which can be considered as human experience and combinations of both the macro- and micro-management. In our work, game strategies are formed by many sub-strategy modules that consist a series of orderly actions corresponding to certain components and game stages. Further, by applying RL on the strategy modules, the proposed mechanism can effectively improve selection of the game strategies, and balance the AI decision and human experience. Our bot was tested against some bots selected from AIIDE 2017-2018. The results show that with the RL framework, our bot can improve the overall winning rate from 42.79% to 59.36%. Particularly, the improvements on opponent 32.06%, 25.18%, and 30.32%, against the Zerg bot, Arrakhammer, the Protoss bot, Xelnaga, the Terran bot, UAlbertaBot.
Augmented Reality Environment for the trajectory tracking of mobile robot

In this paper, we present an Augmented Reality Environment (RAE) to guide a mobile robot. The objective of this work is to allow a user to control and follow a desired trajectory. In this context, we propose a control design based on a Augmented Reality technology to facilitate the piloting of the robot. This work proposes a hardware and software architecture based on Graphic User Interface (GUI)for tracking trajectory using a virtual objects. The results obtained is show the good performances of the proposed AR environment for the simplicity of using the AR system for planning and execution of the robot's trajectories.
Enhancing the Utilization of Dot-Product Engines in Deep Learning Accelerators

In the last couple of years, the goal of deploying Deep Neural Networks (DNNs) in the embedded domain has led to the development of several dedicated DNN Hardware Accelerators (HWAs), many of which rely on a Dot-Product Engine (DPE)-based architecture. A DPE is a hardware block that receives two input vectors of the same size and produces one scalar value. Nevertheless, when the actual input vector does not match the DPEs native input-size, the DPE becomes underutilized. This is particularly observed for Convolutional Neural Networks (CNNs). In this article, we introduce kernel linearization, a technique to mitigate the underutilization of DPE-based HWA for DNNs. To demonstrate the benefits of this method, we implemented AEXelerator, a DPE-based HWA prototyped on FPGA and validated using automotive relevant DNNs. Our evaluation shows that our architecture achieves a high DPE utilization even in the presence of large vector-size mismatches.
Towards Word Sense Disambiguation by Reasoning

In this paper, we describe a practical application of Vampire for Word Sense Disam- biguation (WSD), which is an important research area in the field of Natural Language Processing (NLP). Its objective is choosing the intended sense of a word in a given context. In particular, we propose a method for the automatic disambiguation of the semantic rela- tions in BLESS, which is a dataset designed to evaluate models of distributional semantics, by choosing the WordNet synset it belongs to. For this purpose, we use the knowledge in Adimen-SUMO, which is obtained by means of a suitable transformation of the knowledge in the core of SUMO1 into first-order logic (FOL) and enables its use by FOL automated theorem provers such as Vampire. By exploiting the semantic mapping between WordNet and SUMO, we apply a black-box testing method that enables the automatic creation a set of conjectures for each word pair by considering the semantic relations provided by BLESS. Then, these conjectures are evaluated using Vampire and, according to the outcomes, each word is disambiguated to a single synset. Finally, we compare the results provided by our proposal and different disambiguation systems that can be found in the literature.
Predicting Group Size for Software Issues in an Open-Source Software Development Environment

In an open-source software development environment, it is difficult to determine an ideal group size for handling software issues because developers may respond to issues based on their domain knowledge and interest, and there are no predetermined teams. This paper proposes using Classification and Regression Trees (CaRT) model for predicting group size in open-source software issues. The proposed CaRT model is tested on data mined from Sequelize repository on GitHub. Social information foraging theory is also adapted to predict group size for software issues and is compared with the proposed CaRT model.
Software Fault Prediction Using Cross-Validation

Suresh, YeresimeSoftware faults are dangerous. Software systems are often essential to a business operation or organization, and failures in such systems cause disruption of some goal-directed activity (mission critical). Faults in safety-critical systems may result in death, loss of property, or environmental harm. Run-time faults are the most damaging as they are not always detectable during the testing process. Detecting faults before they occur gives the designers a brief inner view of the possible failure and their frequency of appearance. This helps in focused testing and saves time during the software development. Prediction models have the ability to differentiate between various patterns. This article showcases the effectiveness of cross-validation in design and development of a neural network for software fault detection.
A Linear Metric for Multivariate Discrete Finite Data Based on Data-derived Analytical Meshes

In this article, we show how to define a metric on a fixed interval I ⊆ R. This metric measures the degree of overlap of two groups of linear data. Then we extend this metric to multivariate discrete data via linearization of the data via the data-based analytical meshes, which is uniquely determined by the given data. We also demonstrate how to apply this metric. This metric is novel and could be applied further in dealing with high-dimension data and could also be used in the real world problems.
Analisis Pemenuhan Standar Sarana dan Prasarana Sekolah

The purpose of this study was to determine the fulfillment of facilities and infrastructure of general learning spaces, supporting spaces and special learning spaces at SMK Almamater Telaga. This type of research is quantitative with the type of descriptive research. Data collection techniques using interviews, observation, data collection instruments and documentation. The results of the study are 1) the facilities and infrastructure of the general learning space are met according to the standards, and not according to the standards namely Biology Lab, Physics Lab, Chemistry Lab, Natural Sciences, Language Lab and technical drawing practice room. Whereas the facilities for 2 classrooms are in accordance with the standard and 13 classrooms, the library, and the computer lab are not yet in accordance with the standard, 2) the supporting facilities and infrastructure are met according to the standard, toilets, counseling rooms, UKS, and circulation rooms not according to standards, 3) facilities and special learning space infrastructure are not yet up to standard. Suggestions in this research are to pay more attention to the development of LAB infrastructure in Chemistry, Biology, Physics, Science, Technical Drawing Practice Room, Counseling Room, UKS Room and circulation room. In addition, the area of special learning space for all majors is adjusted according to the work area of each department.
Low-resource automatic cartoon image creation from limited samples

In this work, a framework that can automatically create cartoon images with low computation resources and small training datasets is proposed. The proposed system performs region segmentation and learns a region relationship tree from each learning image. The segmented regions are clustered automatically with an enhanced clustering mechanism with no prior knowledge of number of clusters. According to the topology represented by region relationship tree and clustering results, the regions are reassembled to create new images. A swarm intelligence optimization procedure is designed to coordinate the regions to the optimized sizes and positions in the created image. Rigid deformation using moving least squares is performed on the regions to generate more variety for created images. Compared with methods based on Generative Adversarial Networks, the proposed framework can create better images with limited computation resources and a very small amount of training samples.
Search Topic Analysis of ACM Digital Library

As currently the ACM Digital Library (ACM DL) is developing its new version, this study aimed to understand the usages of ACM DL. Based on the log data of ACM DL, we investigated the characteristics of the queries submitted by users, the distribution features of search topics in ACM DL and the relationship between query reformulation and search topic. In the future, we will study the complex relation between search sessions in-depth and how to classify the users' search topics better. It will help digital library serve users better.
Informatization of the process of independent preparation for the State Final Examination (SFE)

This article is devoted to Information Technologies designed to help students during their preparation for the State final certification.
Interacting with Mobile Music Applications

Music information encountering within the mobile Internet environment mainly occurs in hedonic-oriented situations, which is quite different from the information encountering in utilitarian-oriented situations in prior work. To explore the main influencing factors of users' music information encountering experiences, we recruited 30 participants and conducted semi-structured interviews on their information encountering interacting with mobile music applications. The qualitative data were analyzed by three rounds of coding, and a model was proposed to identify the relevant influencing factors. The findings show that five main categories of factors can trigger users' music information encountering experience from both the user- and the application-related perspective.
Medical Image Recognition: An Explanation and Hands-On Example of Convolutional Networks

This chapter consists of two sections. The first part covers a brief explanation of convolutional neural networks. We discuss the motivation behind using convolution in a neural network and present some of the common operations used in practice alongside with convolution. Then, we list some variations of the convolution layer and we set the guidelines as to when the types of CNN layer are used to manage certain tasks. In the latter section, we will demonstrate the application of a CNN on skin melanoma segmentation with the written approaches and steps to train our model. We provide succinct explanations and hopefully, this will give a better understanding of CNNs in the context of medical imaging. We encourage readers to follow along on their own and try the actual code available from the GitHub repository provided in the second section.
Facial motion analysis using template matching

Motion is a real issue in video sequence analysis since it describes an object in three dimensions whereas the images are defined by a 3D scene projection in a 2D plane. An important problem in tracking of a point between two successive images is that only one pixel can't be tracked since the intensity pixel value can both change due to noise and can be confused with adjacent pixels. As a result, it's often impossible to determine the pixel location in the next frame, based only on the local information. In this article, we are interested in facial motion tracking for talking faces. When talking, points on the face move and their surround intensity change in a complex way. Therefore, we propose a comparison study of three methods for facial motion tracking to estimate the trajectory of each facial point on a talking face by analyzing templates intensities levels belonging two successive images. The first method is based on block matching: Normalized Sum of Squared Differences (NSSD), the second on normalized cross correlation (NCC), whereas the third method concerns the Kanade-Lucas-Tomasi Tracking (KLT tracker). Results obtained are compared, based on tracked error on the trajectory of a video by measuring the root-mean-squared intensity difference between the current and the last template.