Reviewerly/exHarmony huggingface dataset:
Making Architecture Optimization Transparent with Tactic-Based Explanations

Over the past decade, a number of automated techniques and tools have been developed for optimizing architectural designs with respect to quality-attribute goals. In these systems, the optimization process is typically seen as a black box, since it is not possible for a human to have access to the decisions that led to a particular solution generated by an optimization tool. Even when these decisions are available for inspection, the amount of information can be overwhelming for the architect. As a result, humans might not completely understand the rationale behind a given solution or trust that a tool made correct decisions. To mitigate this problem, we propose a semi-automated approach for generating textual explanations for any architectural solution produced by a tool. This kind of explanation provides a summary of the key architectural tactics that were applied to achieve an optimized architecture that satisfies a set of quality-attribute objectives. In this paper, we discuss two procedures for determining the key tactics to be explained. As an initial experiment, we used a popular optimization tool to generate solutions and explanations for a small but non-trivial design space involving performance, reliability, and cost objectives. We also performed an exploratory user study to assess the effectiveness of these explanations.
Support Efficient User Revocation and Identity Privacy in Integrity Auditing of Shared Data

The cloud provides storage for users to share their files in the cloud. Nowadays some shared data auditing schemes are proposed for protecting data integrity. However, preserving the identity privacy of group users and secure user revocation usually result in high computational overhead. Then a shared data auditing scheme supporting identity privacy preserving is proposed that enables users to be effectively revoked. To preserve identity privacy during the audit process, we develop an efficient authenticator generation mechanism that enables public auditing. Our solution supports efficient user revocation, where the authenticator of the revoked user does not need to be regenerated and integrity checking can be performed appropriately. At the same time, the group manager maintains two tables to ensure user traceability. When the user updates data, two tables are modified and updated by the group manager promptly. It shows that our scheme is secure by security analysis. Moreover, concrete experiments prove the performance of the system.
Predicting Velocity and Direction of Ocean Surface Currents using Elman Recurrent Neural Network Method

Background: Ocean surface currents need to be monitored to minimize accidents at ship crossings. One way to predict ocean currents—and estimate the danger level of the sea—is by finding out the currents’ velocity and their future direction. Objective: This study aims to predict the velocity and direction of ocean surface currents. Methods: This research uses the Elman recurrent neural network (ERNN). This study used 3,750 long-term data and 72 short-term data. Results: The evaluation with Mean Absolute Percentage Error (MAPE) achieved the best results in short-term predictions. The best MAPE of the U currents (east to west) was 14.0279% with five inputs; the first and second hidden layers were 50 and 100, and the learning rate was 0.3. While the best MAPE of the V currents (north to south) was 3.1253% with five inputs, the first and second hidden layers were 20 and 50, and the learning rate was 0.1. The ocean surface currents’ prediction indicates that the current state is from east to south with a magnitude of around 169,5773°-175,7127° resulting in a MAPE of 0.0668%. Conclusion: ERNN is more effective than single exponential smoothing and RBFNN in ocean current prediction studies because it produces a smaller error value. In addition, the ERNN method is good for short-term ocean surface currents but is not optimal for long-term current predictions. Keywords: MAPE, ERNN, ocean currents, ocean currents’ velocity, ocean currents’ directions
KAJIAN SASTRA BANDINGAN CERPEN GADIS KOREK API DENGAN CERPEN TERESA : PENDEKATAN PSIKOLOGI SASTRA

This research is important to do in order to determine the psychological condition that occurs in a woman as a follow-up and the violence she experienced. This study aims to describe the differences and similarities in the main female characters in the title Gadis Korek Api by Djenar Maesa Ayu with the poem Teresa by Paulina Flores. The method used in this research is descriptive analysis. The technique used in this research is reading and notetaking technique. The approach taken is using the Psychological Theory of Sigmund Freud (1923) there are three personality elements in psychoanalysis, namely the Id, Ego, and Superego. The results of the study reveal that (1) there are similarities in the two literary works, namely actions against women, (2) differences in the main female characters in the two works, as Nayla has a different ego personality from Id. Meanwhile, Claudia in Teresa's short story is more dominant in personality I than following Ego. Keywords: Comparative Literature, Psychology, Short Stories.
Multi-Stage Fusion Dehazing Network Based on Multi-Resolution

In recent years, the haze phenomenon has become more frequent and significantly impacts high-level vision tasks. Image dehazing has become an increasingly important technique with significant research value. The traditional dehazing algorithms are image enhancement-based dehazing algorithms and image restoration-based dehazing algorithms. However, due to the random distribution of haze, these methods do not recover high-frequency details well. Therefore, to solve the existing problems, in this paper, we have proposed a multi-stage fusion dehazing network based on multi-resolution. Iteratively stacking Residual-based Atrous Spatial Pyramid Convolution (RASPC) and down-sampling convolution layers according to the hazy input image to produce primary feature maps with various resolutions. Then, the adjacent feature maps are fused, and finally, global aggregation is performed to output haze-free images. We compared our method to many popular state-of-the-art approaches on public RESIDE dataset. The experiments demonstrate that our approach can successfully recreate high-frequency details and high-fidelity colors.
Implementation of the demonstration method to improve understanding of using online learning media

Online learning is a method used by schools during the COVID-19 pandemic to anticipate and avoid the spread of disease among students. In practice, there are advantages and disadvantages of online learning that are felt by both students and parents. Students in RT 29 Kelurahan Margasari, Balikpapan City also face difficulties in attending online learning, which impacts the decrease in learning outcomes achievement. Limited access to electronic devices and the lack of understanding to use devices are the basis for issues that affect the online learning process. Training activities on the use of online learning media are carried out to provide an understanding of operating devices, such as laptops and smartphones to access materials through software and collect assignments. An initial survey was conducted as a base for data collection, the team planned several activities according to the conditions of the participants so that the activities can be more precise and can resolve the participants' problems. Through lecture methods, demonstrations, and hands-on practice, this training introduced several applications that can support the online school process. The results of the activities obtained were an increase in knowledge and understanding of elementary school students and parents in recognizing and using several virtual face-to-face support applications and doing homework through the online chatting applications.
McLuhan in Reverse

This text is the transcription of the speech given by the author during the 22nd Annual Convention of the Media Ecology Association. PUC-Rio, July 8-11, 2021.
Structured Kernel Interpolation for Scalable Mixture of Gaussian Processes

Mixture of Gaussian Processes (MoGP) is a promising method that achieves highly accuracy for regression. However, the tremendous training cost of MoGP remains a challenging problem. Although the state-of-the-art sparse MoGP can reduce computational complexity to some extent, the prediction accuracy has been degraded dramatically. Recently, a new structured kernel interpolation (SKI) framework was proposed in the domain of GPs, which generalizes inducing point methods for scalable Gaussian process inference. Based on this amazing idea, we have moved one step further to introduce the SKI framework into MoGP for the sake of addressing the problem mentioned above. In this paper, we propose a scalable mixture of Gaussian processes model (MoKISS-GP), where experts and gating functions are modeled by KISS-GP. Using the Kronecker structure of grid data, the approximate kernel generated by SKI allows us to perform fast calculations while retaining considerable computational accuracy. Finally, experimental results on several well-known datasets demonstrate the effectiveness of our method.
Effective Communication and Organization Culture in Enhancing Employee’s Work Performance During Work from Home (WFH)

The rapid spread of the covid-19 virus has had an impact on the lives and jobs of communities around the world. The existence of new norms has forced the world community to accept and adapt to living with this virus. In Malaysia, work from home is one of the new norms practiced in every sector to curb the spread of covid-19 pandemics. In Government Link Company (GLC), work from home style working is applied to almost all departments and to high risk individuals. The purpose of this research is to explore the effective communication and organizational culture practices required during work from home when there are changes to employee’s behavior and psychological that affects their work performance. Therefore, the use of effective communication is needed to ensure less constraint in conveying information between employees and employers. This research was conducted by using qualitative method. Data was collected through conducting in-depth interviews with different positions level of employees between ages of 25-40. The semi-structured interview questions were designed to elicit the response towards answering the research questions. The findings suggested that during working from home method, behavior and psychological change affected the style of manage between personal matter and work, at the same time there is interference from the environment. Findings also suggested that the appropriate management style is important to create a good organizational culture and foster mutual trust between employers and employees that help them improve working performance at the same time.
Peningkatan Prestasi Belajar Siswa pada Pembelajaran Tematik dengan Metode Demontrasi pada Kelas 1 SD N Tepus 1

The purpose of the research is to improve student achievement in thematic learning with the demonstration method in class 1 SDN Tepus 1. This research is a class action research (classroom action research). The research subjects were the first grade students of SDN Tepus 1 Gunungkidul Regency for the academic year 2021/2022 (22 students), comprising of 14 boys and 8 girls. Data processing techniques are using observation, tests and documentation. The data analysis technique used data triangulation, by comparing the increase in the acquisition of student scores as illustrated by the pre and post test data. The results showed an increase in student learning activity, namely: (1) In cycle 1, 10 students (45.45%) achieved mastery learning; (2) In cycle II, 19 students (86.36%) were able to achieve complete learning; and (3) In cycle III, 22 students (100%) were able to achieve complete learning.
Implementasi Manajemen Perpustakaan di MI Nurul Islam Jombang

This study focuses on the implementation of general management functions in managing libraries in school and how to increase students interest in reading which in closely related to the library management strategy undertaken. In essence, library management is carried out so that students are able to use school facilities to the maximum and help train students to become a generation that cares about literacy. It cannot be denied that education must balance with this increasingly advanced technology, so that to make students able to compete academically through reading media. That is why library management is very important for the sake of achieve the quality of students in both academic and non-academic fields and prepare a generation capable of responding to the challenges of the times.
IMPROVING THE LEARNING OUTCOMES OF CLASS IV STUDENTS IN INDONESIAN LESSON ABOUT FICTION STORIES USING AUDIO VISUAL MEDIA

This study was conducted with the aim of knowing (1) student learning outcomes before using audio visual media in B.Indonesia subjects for grade IV students (2) student learning outcomes after using audio visual media in B.Indonesia subjects for grade IV students. This research is a quantitative descriptive study. The subjects of this study are the fourth grade students of SDN 3 Cimerang, with a total of twenty students. Data collection techniques used in this study were observation and tests. The results of the study concluded that the learning outcomes of B.Indonesia fourth grade students increased. Learning outcomes before using audio-visual media obtained learning outcomes with an average class of 69.25 the number of students who complete is 12 students or 60% and the number of students who have not completed is eight students or 40%. Whereas after using audio-visual media the learning outcomes have increased again, with an average grade of 85.5 the number of students completing is 18 people or 90% and the number of students who have not completed is two people or 10%. Based on the findings and discussion it can be concluded that learning B.Indonesia by using audio visual media can improve the understanding and learning outcomes of fourth grade students.
Application of the Recognition Algorithm of News Sentiment Dissemination Tendency Based on Multi-Mode Information Fusion

Since the popularity of the Internet, it is now customary for people to browse the news on the Internet. In the process of browsing news, there are often comments to express and spread personal emotions. This has become a very popular way of spreading news sentiment. This research is based on information fusion technology to realize the research on the identification algorithm of news sentiment communication tendency. Based on the research results of many scholars, this article first elaborates the definition, method, and framework of information fusion technology. In this paper, the corresponding technology is selected, and then in the research of the tendency recognition algorithm, an emotion tendency recognition algorithm based on the integrated probabilistic reasoning model is proposed. The algorithm proved its effectiveness and robustness through experiments. Finally, the experimental results show that in all the parameter combination conditions, the algorithm can not only reduce the feature dimension but also ensure the accuracy of recognition under the probability of 89%.
Conceptual model visual simulation and the inductive learning of missing domain constraints

Conceptual modeling plays a fundamental role in information systems engineering, and in data and systems interoperability. To play their role as instruments for domain modeling, conceptual models must contain the exact set of constraints that represent the worldview of the relevant domain stakeholders. However, as empirical results show, conceptual modelers are subject to cognitive limitations and biases and, hence, in practice, they systematically produce models that fall short in that respect. Moreover, automating the process of formally assessing conceptual models in this sense (i.e., model validation) is notoriously hard, mainly because the intended worldview at hand lies in the mind of these stakeholders. In this paper, we provide a novel approach to model validation and automated constraint learning that combines, on one hand, Model Finding via the visual simulation of that model’s valid instances and, on the other hand, Inductive Logic Programming techniques. In our approach, we properly channel the results produced by the application of a visual model finding technique as input to a learning process. We then show how the approach is able to support the modeler in identifying missing constraints from the original model. The approach is validated against a catalog of empirically-elicited conceptual modeling anti-patterns. As we show here, the approach is able to support the automated learning of constraints that are needed to rectify a number of relevant anti-patterns in this catalog.
The affinity of information within a multi-agent value of information paradigm

The multi-agent Value of Information (MAVoI) problem is concerned with the capacity of autonomous agents to evaluate the estimated value of available information from and for other agents. Such a paradigm would allow agents to limit information sharing under network constraints, environmental and adversarial, and also to decide when to prioritize inter-agent communications over further information-gathering. In our work, we contribute to the conceptual development of an MAVoI framework by expanding upon a previous paradigm [1] that connected the notion of the Value of Information (VoI), as it originates from decision theory, to the body of work on the Quality of Information (QoI/IQ). Within this paradigm, QoI characterizes the intrinsic attributes of information objects that may be used to judge them, while VoI judges their context- and user-specific utility and fitness-for-use. For teams of multiple, autonomous agents with distributed intelligence, we propose an additional category: the Affinity of Information (AoI). Since classical VoI quantifies the gain in value that comes from more information regarding the expected value of available actions, we propose AoI as those metrics and features that characterize the change in an agent’s state model. We then conceptually illustrate our proposed MAVoI taxonomy in the context of a distributed Simultaneous Localization and Mapping (SLAM) task, in which the mission objective is the localization of multiple objects of interest.
The Construction and Implementation of a Compound Foreign Language Talent Training Model

With increasing global exchanges and cooperation, there is an increasing demand for foreign language interdisciplinary talents. The cultivation of interdisciplinary foreign language talents is a long-term, complicated methodical endeavor, which requires constant exploration by experts in foreign language circles. In order to cultivate compound foreign language talents in colleges and universities, it is important to change the educational concept, enhance the teaching staff, and innovate the talent training mode. In the training of compound foreign language talents in colleges and universities, a compound talent training model of “foreign language + major or direction” should be established, and beginning from the actual situation of the major, the bold exploration and implementation of the transformation of the concept of compound foreign language talent training should be followed through, while strengthening the construction of teaching staff, optimizing the professional curriculum, updating the teaching content, as well as enriching the methods and measures of teaching.
Training in Digital Humanities through Open Data: The Infometrics Chains of Patent2Net (P2N)

humanités numériquesSe former aux humanités numériques par les données ouvertes : les chaînes infométriques de Patent2Net (P2N) Résumés maps, or access conceptual and semantic networks, as well as reading and writing practices, and hermeneutic capabilities.These skills are relatively new, and they are often difficult to apply in front of an audience with varying levels of interest and technical knowledge.In this context, we have developed and tested a set of tools that allow us to develop many of these skills in the treatment of economic, technological, historical or organisational data, and, moreover, multimodal data (text, images, graphs).Through the analysis of a corpus of patents, which we place in the field of humanities, we discuss the operational and conceptual pedagogical contributions of this approach based on real data.sciences de l'information et de la communication, enseignement,
Exploring the Caricature Style Identification and Classification Using Convolutional Neural Network and Human–Machine Interaction Under Artificial Intelligence

The objectives are to explore the application effect of deep learning (DL), neural network (NN) and human–machine interaction (HMI) technology in caricature style recognition and classification in the environment of artificial intelligence (AI). It provides a realistic basis for the integration of caricature and AI technology. The convolutional neural network (CNN) model is optimized, and the ARtoolkit, JAVA-based processing and photoshop are applied, combined with an augmented reality (AR) editor to register the logo of caricature. The results indicate that the output value of the optimized CNN is the largest near the fourth neural node, and the output values of the remaining neural nodes are almost all close to zero. The label value of the cat image is the same as the output value, and the image label range is [0, 10]. The use of AR technology can make caricature images have animation functions, giving people the visual experience that the lotus can move, and the caricature style has changed. When the screen is not touched, the fish under the lotus is in a static state. When the screen is touched with the hand, the fish swims away quickly, indicating that the function of HMI is realized. Therefore, the CNN and HMI technology under AI can successfully perform caricature style recognition and classification, which can provide an experimental reference for the subsequent intelligent development of caricature.
Geometry and scale property of a gold-silver-bearing vein system associated with an oblique-slip fault zone at Gasado Island, Korea

Gasado Island, on the southwestern Korean Peninsula, comprises c. 50 Ma volcanic rocks that are cut by faults and fractures hosting low-sulfidation epithermal Au-Ag mineralization. In this study, we examined the relationship between the fault-fracture networks and auriferous vein systems to better understand the role of brittle structures in controlling hydrothermal fluid flow. Geometrical and fractal analyses were performed to express and quantitatively compare the vein geometries and thickness population. The results were as follows: (1) The geometrical analysis revealed a strong relationship between Au-Ag-bearing quartz veins and NE-SW-striking, oblique sinistral-normal faults, indicating that these structures are a key control on mineralization. (2) Box-counting fractal analysis applied to veins hosted by fault zone demonstrated that the D-values (fractal dimensions) are directly proportional to vein density – indicating that damage zones control vein density. (3) The linear traverse line analysis yielded cumulative frequency plots of vein thicknesses that showed obvious changes in the slope, which are reflected in the variable D-values. The cause of this slope variability is interpreted to be the presence of hybrid veins that fill secondary fault planes, which disturbs the original linear relationship shown by cumulative thickness distribution data. This study shows that vein density and physical properties are strongly controlled by the types, or existence, of fault damage zones. Understanding fault zone architecture may assist with the prediction of locations that concentrate mineralizing fluids and may benefit the initial stage of mineral exploration.