Reviewerly/exHarmony huggingface dataset:
StyleDisentangle: Disentangled Image Editing Based on StyleGAN2

Thanks to the development of Generative Adversarial Networks (GANs), StyleGAN2 can generate highly realistic images by inputting a latent code and then editing them in the latent space. Disentangled image editing is crucial, where the goal is to change the desired attributes of an image while keeping the other attributes intact. As a solution, we introduce the StyleDisentangle framework for image editing. The fundamental concept of StyleDisentangle is to define attributes through two distinct sets of information: semantic segmentation coordinates - identifying the region in the image related to the attribute, and latent code coordinates - identifying the dimensions related to attributes in latent code. By utilizing these two distinct sets of coordinates, we can precisely determine the position of each attribute within the attribute editing space, resulting in disentangled image editing. We conducted extensive experiments to demonstrate the effectiveness of our method on multiple datasets and additionally compared our results with state-of-the-art methods.
COVER: A Heuristic Greedy Adversarial Attack on Prompt-Based Learning in Language Models

Prompt-based learning has been proved to be an effective way in pre-trained language models (PLMs), especially in low-resource scenarios like few-shot settings. However, the trustworthiness of PLMs is of paramount significance and potential vulnerabilities have been shown in prompt-based templates that could mislead the predictions of language models, causing serious security concerns. In this paper, we will shed light on some vulnerabilities of PLMs, by proposing a prompt-based adversarial attack on manual templates in black box scenarios. First of all, we design character-level and word-level heuristic approaches to break manual templates separately. Then we present a greedy algorithm for the attack based on the above heuristic destructive approaches. Finally, we evaluate our approach with the classification tasks on three variants of BERT series models and eight datasets. And comprehensive experimental results justify the effectiveness of our approach in terms of attack success rate and attack speed.
DAGE: Dropout with Action Gradient Estimator for Continuous Control

For actor-critic algorithms in reinforcement learning, the policy update is guided by a Q function, so the quality of the policy is largely affected by the quality of the Q function. Most work has focused on how to estimate a more accurate Q function, such as using a dropout operator. However, we show that for continuous control scenarios using deterministic policy gradient algorithms, the quality of the Q function does not depend on the accuracy of the Q function itself, but on the accuracy of the action gradient of the Q function. Motivated by this observation, we propose Dropout with Action Gradient Estimator (DAGE), which aims at estimating the action gradient of the Q function accurately instead of the Q function itself. We conduct sufficient experiments on PyBullet Control Suite, and empirically show that DAGE can estimate a more accurate action gradient and achieve better performance than baselines.
DeepLRA: An Efficient Long Running Application Scheduling Framework with Deep Reinforcement Learning in the Cloud

With the growth of cloud computing, an increasing number of long-running applications (LRAs) are running in the cloud, providing scalability, cost-effectiveness, and flexibility. Considering LRA interactions and resource interferences, scheduling LRAs in the cloud poses significant challenges regarding runtime performance maximization and efficient resource utilization. However, existing schedulers are usually constraint-based methods requiring priori knowledge and hard to balance LRA performance and efficient resource utilization. To address this problem, we propose DeepLRA, a novel and efficient LRA scheduling framework in the cloud. Specifically, we introduce Deep Reinforcement Learning (DRL) in LRA scheduling, where the agent learn the scheduling policy without human intervention. Furthermore, a multi-objective LRA scheduling is designed with multi-agent training. Extensive simulation experiments conducted with real-world workloads indicate that DeepLRA outperforms the state-of-the-art in the multi-objective LRA scheduling. DeepLRA shows $$26.1\%$$ and $$36.9\%$$ average improvement in throughput and efficient resource utilization over Kubernetes, respectively.
A Dynamic-aware Heterogeneous Graph Neural Network for Next POI Recommendation

Next point-of-interest (POI) recommendation is of great importance for both location-based service providers and users. Current state-of-the-art methods view users and POIs as unified latent representations, and model users’ transition patterns from global and local views. However, most of them still have following limitations: 1) Ignoring user’s dynamic behavioral intention, which is significantly influenced by current temporal and spatial factors. 2) Insufficiently considering different activity connotations of POIs in various temporal contexts. To tackle these challenges, we propose a novel method Dynamic-aware Heterogeneous Graph Neural Network (DyHGN) for next POI recommendation, which jointly learns fine-grained representations from global and local views. In the global view, we first construct a series of dynamic-aware heterogeneous graphs, and design a fine-grained temporal enhanced graph neural network to learn users’ dynamic behavioral intentions and POIs’ dynamic activity connotations. In the local view, we propose a dynamic information aggregation module that employs a well-designed information enhancement layer to enhance robustness of the model. Furthermore, we improve the attention mechanism to learn important spatio-temporal factors in users’ behavior. Extensive experimental results on two real-world public datasets demonstrate the effectiveness of our proposed method.
Be Informed of the Known to Catch the Unknown

Many real-world applications are perturbed by the misprediction of the unknown instances into the known or seen domain. The issue is more compounded when we have to recognize the unknowns as well as correctly classify the knowns in a mixed bag of known and unknown instances. In this article, we present a scheme that can efficiently classify instances from the seen classes and can also detect instances coming from unseen (unknown) classes. We have integrated the principles of reverse nearest neighborhood and the principles of intuitionistic fuzzy sets for this purpose. Reverse nearest neighborhood provides a natural and elegant way of tackling the issue of unknown class without incommoding the known class classifications. Further, we incorporate intuitionistic fuzzy sets to infer the unknown class memberships of the instances from the reverse nearest neighbor information of the known classes. Empirical evidence on five real-world datasets indicates the improved efficaciousness of the proposed method over six state-of-the-art competing methods.
Non-revisiting Stochastic Search for Automatic Graph Learning

In recent years, automatic graph learning (AutoGL) has been widely concerned by academia and industry because it can significantly reduce the threshold and labor cost of graph learning. It has shown powerful functions in hyper-parameter optimization, model selection, graph neural architecture search, and feature engineering. With the development of the network structure, the time and computing resources consumed by the AutoGL process are increasing. AutoGL can be viewed as a bilevel optimization problem encompassing inner and outer optimization. The inner optimization focuses on optimizing the model parameters through techniques like stochastic gradient descent, aiming to minimize the loss function and enhance model performance. On the other hand, the outer optimization aims to identify the best configuration settings for hyperparameters and neural network structures. To address the computational cost associated with heuristic algorithms in bilevel optimization for AutoGL, the non-revisiting idea is proposed to keep a record of all previously evaluated individuals to avoid redundant search. Experiments on multiple search algorithms demonstrate that non-revisiting can improve the time performance of AutoGL. The time performance of the search algorithm with non-revisiting is improved by 40 $$\%$$ to 80 $$\%$$ compared with that without non-revisiting under the condition that the experimental accuracy remains unchanged.
Generalized Knowledge Distillation for Topic Models

Topic modeling is used in the analysis of textual data to estimate the underlying topics within the dataset. Knowledge distillation has been attracting attention as a means of transferring knowledge from a large teacher model to a small student model in the field of deep learning. Knowledge distillation can be categorized into three types depending on the type of knowledge to be distilled: response-based, feature-based, and relation-based. To the best of our knowledge, previous studies on knowledge distillation used in topic models have all focused on response and/or feature knowledge, but these methods cannot transfer the structural knowledge of the teacher model to the student model. To solve this problem, we propose a generalized knowledge-distillation method that combines all three types of knowledge distillation, including the relation-based knowledge distillation with contrastive learning, which had not been used for neural topic models. Our experiments show that our neural topic model, trained with the proposed method, improves topic coherence compared to baseline models without knowledge distillation.
Few-Shot Table-to-Text Generation with Structural Bias Attention

Table-to-text generation task refers to converting tabular data into language text to facilitate easier understanding and analysis of the table. Recently, pre-trained models have made significant advancements in this kind of tasks. However, the inherent structural differences between tabular data and text, and the lack of domain-specific knowledge in few-shot datasets, make it challenging for pre-trained models to generate faithful text. To solve these problems, we proposed a framework that encodes tables by obtaining structural bias attention through pruning full self-attention, distinguishing the importance of cells from a structural perspective. We use the pre-trained model with the structural bias framework to the generation component of Prototype-to-Generation. To encourage prototype memory to adhere to the table content and generate more accurate and aligned sentences, we employ Reinforcement Learning. We conducted extensive experiments on three few-shot table datasets. Compared to previous advanced methods, our model achieved superior performance across multiple evaluation metrics.
ASSESSMENT OF HEAVY METAL POLLUTION IN SOIL AT A REFUSE DUMPSITE IN UNIVERSITY OF CROSS RIVER STATE (UNICROSS), CALABAR, SOUTH-SOUTH, NIGERIA

Total concentration and pollution indices of mercury (Hg), lead (Pb), chromium (Cr), cadmium (Cd), arsenic (As), nickel (Ni), and cobalt (Co) in dumpsite soil located in University of Cross River State, Calabar were evaluated in this work. Soil samples were collected at 0 - 15cm depth from two sampling stations at the dumpsite once in the months of April, August and December, 2021 and analyzed to the closest mg/kg using Atomic Absorption Spectrophotometer (AAS). The concentrations of the heavy metals showed that Cd&gt;Pb&gt;Ni&gt;Cr&gt;As&gt;Co. All the concentrations of the heavy metals examined when compared with those of average value of heavy metals in shale showed that all the metals were lower than their corresponding world average value except Cd. Geoaccumulation index indicated unpolluted to moderately polluted for all metals studied except Cd which indicated strong to very strongly polluted. Contamination factor for Cd indicated very high contamination. Contamination degree for site 1 and 2 indicated considerable degree of contamination and moderate degree of contamination respectively. Pollution load index values investigated were within the baseline level for all the stations. The heavy metal pollution is attributable to the waste deposited at the dumpsite located in the institution. It is recommended that the dumpsite be relocated to another location that is far away from the residential area and from drinking water sources like boreholes.
Metrics for Evaluating Actionability in Explainable AI

To enable recourse, explanations provided to people should be actionable, that is, explain what a person should do to change the model’s decision. However, what actionability means in the context of explainable AI is unclear. In this paper, we explore existing tools that others developed to evaluate actionability in their respective domains. To our knowledge, no prior work in the XAI field has developed such a tool to evaluate the actionability of explanation. We conducted an experimental study to validate two existing actionability tools for discriminating the actionability of two types of explanations. Our results indicate that the two existing actionability tools reveal metrics relevant for conceptualising actionability for the XAI community.
Data Interoperability Model in Integrated Public Service Applications Based on Government Service Bus (Case Study: Tangerang Regency Communication and Information Office)

The Tangerang Regency Government, as part of its digital transformation efforts, has been working to enhance public services through the development and implementation of e-Government initiatives. This research aims to conduct an in-depth analysis of data interoperability service modeling in e-Government applications based on Web Services using the Government Service Bus (GSB). The study will address the advantages and challenges associated with using the Government Service Bus, and provide practical recommendations for implementing the Government Service Bus as an effective application integration solution. COBIT 2019 is used as an assessment tool for the current condition. The research results reveal that a total of 138 applications have been identified, with 74 of them being public service applications. While 80% of the public service sector has been incorporated into integrated public service applications, their utilization remains limited. The current capability level of APO11 is at level 1, with an expected level of 2. Similarly, APO14 is at level 1, expected to reach level 2, and MEA01 is at level 0, with an expected level of 2. The author has proposed a data interoperability model that can be implemented within the Tangerang Regency Communication and Information Office, utilizing GSB as the middleware for interoperability processes, particularly for public service applications. Based on the proposed data interoperability model, the author also concludes that with its implementation, the Communication and Information Office will be able to address a significant portion of the technical recommendations derived from the gap analysis, although not necessarily all of them.
EVALUASI HAPUSAN DARAH TEPI DARAH K3EDTA SEGERA DI PERIKSA DAN DI TUNDA 24 JAM DALAM SUHU 2-8C

Since specimen collection, preanalytical factors such as time and temperature could affect the peripheral blood smear morphology of erythrocytes, leukocytes, and thrombocytes. Delays are often caused by the limited number of medical personnel so work is not properly organized. This research is an observational qualitative descriptive study to determine the morphology of blood smears immediately checked and delay 24 hours at 2-8 ?. This study aims to determine the evaluation of K3EDTA venous peripheral blood smears immediately checked and delayed 24 hours at 2-8?. From 25 K3EDTA blood samples of Maarif Hasyim Latif Sidoarjo University students, it was found that there were no changes in the morphology of erythrocytes and platelets. Meanwhile, in leukocyte cells, changes occur in the neutrophil segment and stab granules, which is the granules fade, and the leukocyte nuclei can still be identified properly, so that do not affect the examination results. In conclusion, the Evaluation of the K3EDTA Peripheral Blood Smear was immediately checked and delay 24 Hours at the temperature of 2-8 ? has no change, which is still within normal limits. Keywords : Blood Smear, Delayed, K3EDTA, Immediately checked
IMPLEMENTASI PENGGUNAAN APLIKASI SIMPEL DALAM PEMBELAJARAN DARING DI SD KARANGJATI 1 TAHUN AJARAN 2021/2022

The spread of Covid-19 is very influential in the field of education. In the learning process during a pandemic like this, the Indonesian government recommends conducting online learning. The Ngawi district government is innovating to create a learning application SIMPEL (Learning Management Information System) which is expected to help the online learning process in Ngawi District.This study aims to determine the implementation of the use of the SIMPEL application, the constraints when used and find out the shortcomings and advantages of the SIMPEL application during the online learning process at Karangjati 1 Elementary School. This research is a qualitative descriptive study. The research was conducted with the subject of teachers and students of class V SD Karangjati 1. Data collection techniques were carried out by observation, interviews and documentation.The results of the study show that the implementation of the SIMPEL application in online learning at Karangjati 1 Elementary School is considered effective and easy to apply. Complete SIMPEL application learning media helps teachers convey learning and makes it easier for students to accept learning. Some of the obstacles to the SIMPEL application when used are internet network connection and SIMPEL application errors, while the disadvantages of the SIMPEL application are short duration videos, require a large quota, do not form children's characters, there is no direct interaction between teachers and students and learning media cannot be downloaded
Implementasi Standar Nasional Perpustakaan Perguruan Tinggi (SNP 010: 2011) di UIN Alauddin Makassar

This study aims to examine the implementation of the National Library Standards for Higher Education (SNP 010:2011) at UIN Alauddin Makassar library and to analyze the obstacles in the implementation of such National Library Standards. This is a descriptive research with qualitative approach. Data was collected through observation, interview and documentation techniques. The data collected was then analyzed qualitatively in three stages; data reduction, data presentation and conclusion drawing. The results showed that the seven standard indicators of SNP 010: 2011; collection standards, facilities and infrastructure, services, library staff, management, implementation, as well as information and communication technology at UIN Alauddin Makassar library have met the needs of libraries and users as national standards required. However, some obstacles found in the implementation of such SNP 010: 2011, thoser are: the limited number of library staff with a librarian education background, limited funds allocated to assist the implementation of human resource development activities, managerial continuity of UIN Alauddin Makassar Library was interrupted by the shift of senior librarians to functional lecturers, and the lack of computer facilities for users to browse library materials.
COMPARISON OF SOCIAL SCIENCE STUDY OUTCOMES USING A MAKE A MATCH MODEL WITH HANDOUT-ASSISTED SNOWBALL THROWING

Various learning models can eliminate students' boredom while learning. The make a match and snowball throwing learning models are learning models that use a game system. The problem in this study is whether the social studies integrated learning outcomes using the make a match model are better than the snowball throwing learning model assisted by handout media for class VIII students of SMP Negeri 18 Banda Aceh. This study aims to find out whether the learning outcomes of integrated social studies using the make a match learning model are better than the snowball throwing learning model assisted by media handouts for class VIII students of SMP Negeri 18 Banda Aceh. This study uses a quantitative approach, with the type of experimental research. Sampling was taken using purposive sampling technique, two classes were taken from class VIII-4 and VIII-5, totaling 60 participants. Data collection techniques using pre-test data and post-test data. Data analysis techniques using the t test. The results of data processing obtained tcount &gt; ttable or 3.13 &gt; 1.67 so that Ha is accepted. It can be concluded that the integrated social studies learning outcomes taught using the Make A Match learning model are better. DOI: http://dx.doi.org/10.23960/jlg.v4.i1.27398
THE RELATIONSHIP BETWEEN STUDENT PERCEPTIONS OF E-LEARNING AND THE LEARNING MOTIVATION LEVEL OF STUDENTS OF THE SOCIAL SCIENCE EDUCATION DEPARTMENT, UNIVERSITY OF LAMPUNG

This study aims to determine the relationship between perceptions of the e-learning method and the level of learning motivation of students of the Social Science Education Department, University of Lampung. This study used a quantitative approach using a quantitative survey research design and data analysis using the Stata program version 14.2.The results of this study idicate (53%) students of the Department of Social Sciences, University of Lampung have a curently perception of the e-learning method and (52%) low learning motivation. Based on the Simultaneous Test (F test) it was found that there was a relationship between students' perceptions of the e-learning method and the level of learning motivation of students in the Department of Social Science Education, University of Lampung with a Prob&gt;F value of 0.0097. Because of that there is a significant relationship between students' perceptions of the e-learning method with the level of student motivation in the Department of Social Science Education, University of Lampung.
Entropy Based Feature Fusion Using Deep Learning for Waste Object Detection and Classification Model

Object Detection is the task of localization and classification of objects in a video or image. In recent times, because of its widespread applications, it has obtained more importance. In the modern world, waste pollution is one significant environmental problem. The prominence of recycling is known very well for both ecological and economic reasons, and the industry needs higher efficiency. Waste object detection utilizing deep learning (DL) involves training a machine-learning method to classify and detect various types of waste in videos or images. This technology is utilized for several purposes recycling and sorting waste, enhancing waste management and reducing environmental pollution. Recent studies of automatic waste detection are difficult to compare because of the need for benchmarks and broadly accepted standards concerning the employed data and metrics. Therefore, this study designs an Entropy-based Feature Fusion using Deep Learning for Waste Object Detection and Classification (EFFDL-WODC) algorithm. The presented EFFDL-WODC system inherits the concepts of feature fusion and DL techniques for the effectual recognition and classification of various kinds of waste objects. In the presented EFFDL-WODC system, two major procedures can be contained, such as waste object detection and waste object classification. For object detection, the EFFDL-WODC technique uses a YOLOv7 object detector with a fusion-based backbone network. In addition, entropy feature fusion-based models such as VGG-16, SqueezeNet, and NASNet models are used. Finally, the EFFDL-WODC technique uses a graph convolutional network (GCN) model performed for the classification of detected waste objects. The performance validation of the EFFDL-WODC approach was validated on the benchmark database. The comprehensive comparative results demonstrated the improved performance of the EFFDL-WODC technique over recent approaches.