Reviewerly/exHarmony huggingface dataset:
Normalized Stochastic Heavy Ball with Adaptive Momentum1

The heavy ball momentum technique is widely used in accelerating the machine learning training process, which has demonstrated significant practical success in optimization tasks. However, most heavy ball methods require a preset hyperparameter that will result in excessive tuning, and a calibrated fixed hyperparameter may not lead to optimal performance. In this paper, we propose an adaptive criterion for the choice of the normalized momentum-related hyperparameter, motivated by the quadratic optimization training problem, to eliminate the adverse for tuning the hyperparameter and thus allow for a computationally efficient optimizer. We theoretically prove that our proposed adaptive method promises convergence for L-Lipschitz functions. In addition, we verify its practical efficiency on existing extensive machine learning benchmarks for image classification tasks. The numerical results show that besides the speed improvement, our proposed methods enjoy advantages, including more robust to large learning rates and better generalization.
FATRER: Full-Attention Topic Regularizer for Accurate and Robust Conversational Emotion Recognition

This paper concentrates on the understanding of interlocutors’ emotions evoked in conversational utterances. Previous studies in this literature mainly focus on more accurate emotional predictions, while ignoring model robustness when the local context is corrupted by adversarial attacks. To maintain robustness while ensuring accuracy, we propose an emotion recognizer augmented by a full-attention topic regularizer, which enables an emotion-related global view when modeling the local context in a conversation. A joint topic modeling strategy is introduced to implement regularization from both representation and loss perspectives. To avoid over-regularization, we drop the constraints on prior distributions that exist in traditional topic modeling and perform probabilistic approximations based entirely on attention alignment. Experiments show that our models obtain more favorable results than state-of-the-art models, and gain convincing robustness under three types of adversarial attacks. Code: https://github.com/ludybupt/FATRER.
What PISA Set Aside? The Ignored Role of Writing Skills and Reading Fluency in OECD PISA Reading Literacy Assessments

The OECD/PISA Reading Literacy Assessments (RLA) are the most accepted but controversial large-scale measurements, which generate significant debates about their validity. The consequences drawn from the survey reports of 2000-2022 seem to be misleading in several cases, especially how they discuss/ignore writing skills and reading fluency. In my lecture, I claim that (1) the PISA
Environmental Conservation in non-Hazardous and Toxic Waste Management Program at Company Unit of PT Pertamina Patra Niaga Region Sumbagut

This study aims to introduce the initiatives undertaken by various divisions within PT Pertamina Patra Niaga Region Sumbagut in effectively managing non-B3 waste with a focus on environmental preservation. Qualitative methods and participatory action research techniques were employed to achieve this objective. The findings of this research demonstrate that non-hazardous solid waste is mitigated through digitization and repurposing. Digitization is applied to paper waste to prevent accumulation, while repurposing involves converting organic, plastic, and rubber waste into reusable objects. Both waste management models prioritize environmental conservation and lead to cost savings. The efficient utilization of non-B3 solid waste serves as an exemplary approach to minimizing waste impacts throughout various activities, underscoring a strong commitment to environmental preservation.
Dimensions of Similarity: Towards Interpretable Dimension-Based Text Similarity

This paper paves the way for interpretable and configurable semantic similarity search, by training state-of-the-art models for identifying textual similarity guided by a set of aspects or dimensions. The similarity models are analyzed as to which interpretable dimensions of similarity they place the most emphasis on. We conceptually introduce configurable similarity search for finding documents similar in specific aspects but dissimilar in others. To evaluate the interpretability of these dimensions, we experiment with downstream retrieval tasks using weighted combinations of these dimensions. Configurable similarity search is an invaluable tool for exploring datasets and will certainly be helpful in many applied natural language processing research applications.
FedCoop: Cooperative Federated Learning for Noisy Labels

Federated Learning coordinates multiple clients to collaboratively train a shared model while preserving data privacy. However, the training data with noisy labels located on the participating clients severely harm the model performance. In this paper, we propose FedCoop, a cooperative Federated Learning framework for noisy labels. FedCoop mainly contains three components and conducts robust training in two phases, data selection and model training. In the data selection phase, in order to mitigate the confirmation bias caused by a single client, the Loss Transformer intelligently estimates the probability of each sample’s label to be clean through cooperating with the helper clients, which have high data trustability and similarity. After that, the Feature Comparator evaluates the label quality for each sample in terms of latent feature space in order to further improve the robustness of noisy label detection. In the model training phase, the Feature Matcher trains the model on both the noisy and clean data in a semi-supervised manner to fully utilize the training data and exploits the feature of global class to increase the consistency of pseudo labeling across the clients. The experimental results show FedCoop outperforms the baselines on various datasets with different noise settings. It effectively improves the model accuracy up to 62% and 27% on average compared with the baselines.
Take Expert Advice Judiciously: Combining Groupwise Calibrated Model Probabilities with Expert Predictions

Training the machine learning (ML) models require a large amount of data, still the capacity of these models is limited. To enhance model performance, recent literature focuses on combining ML models’ predictions with that of human experts, a setting popularly known as the human-in-the-loop or human-AI teams. Human experts can complement the ML models as they are well-equipped with vast real-world experience and sometimes have access to private information that may not be accessible while training the ML model. Existing approaches for combining an expert and ML model either require end-to-end training of the combined model or require expert annotations for every task. End-to-end training further needs a custom loss function and human annotations, which is cumbersome, results in slower convergence, and may adversely impact the ML model’s accuracy. On the other hand, using expert annotations for every task is also cost-ineffective. We propose a novel technique that optimizes the cost of seeking the expert’s advice while utilizing the ML model’s predictions to improve accuracy. Our model considers two intrinsic parameters: the expert’s cost for each prediction and the misclassification cost of the combined human-AI model. Further, we present the impact of group-wise calibration on the combined model that improves the overall model’s performance. Experimental results on our combined model with group-wise calibration show a significant increase in accuracy with limited expert advice against different established ML models for the image classification task. In addition, the combined model’s accuracy is always greater than that of the ML model, irrespective of the expert’s accuracy, the expert’s cost, and the misclassification cost.
FedPerturb: Covert Poisoning Attack on Federated Learning via Partial Perturbation

Federated learning breaks through the barrier of data owners by allowing them to collaboratively train a federated machine learning model without compromising the privacy of their own data. However, Federation Learning also faces the threat of poisoning attacks, especially from the client model updates, which may impair the accuracy of the global model. To defend against the poisoning attacks, previous work aims to identify the malicious updates in high dimensional spaces. However, we find that the distances in high dimensional spaces cannot identify the changes in a small subset of dimensions, and the small changes may affect the global models severely. Based on this finding, we propose an untargeted poisoning attack under the federated learning setting via the partial perturbations on a small subset of the carefully selected model parameters, and present two attack object selection strategies. We experimentally demonstrate that the proposed attack scheme achieves high attack success rate on five state-of-the-art defense schemes. Furthermore, the proposed attack scheme remains effective at low malicious client ratios and still circumvents three defense schemes with a malicious client ratio as low as 2%.
Active Finetuning Protein Language Model: A Budget-Friendly Method for Directed Evolution

Directed evolution is a widely-used strategy of protein engineering to improve protein function via mimicking natural mutation and selection. Machine learning-assisted directed evolution (MLDE) approaches aim to learn a fitness predictor, thereby efficiently searching for optimal mutants within the vast combinatorial mutation space. Since annotating mutants is both costly and labor-intensive, how to efficiently sample and utilize informative protein mutants to train the predictor is a critical problem in MLDE. Previous MLDE works just simply utilized pre-trained protein language models (PPLMs) for sampling without tailoring to the specific target protein of interest, which has not fully exploited the potential of PPLMs. In this work, we propose a novel method, the Actively-Finetuned Protein language model for Directed Evolution(AFP-DE), which leverages PPLMs to actively sample and fine-tune themselves, continuously improving the model’s sampling and overall performance through iterations, to achieve efficient directed protein evolution. Extensive experiments have shown the effectiveness of our method in generating optimal mutants with minimal annotation effort, outperforming previous works even with fewer annotated mutants, making it budget-friendly for biological experiments.
Enhancing OOD Generalization in Offline Reinforcement Learning with Energy-Based Policy Optimization

Offline Reinforcement Learning (RL) is an important research domain for real-world applications because it can avert expensive and dangerous online exploration. Offline RL is prone to extrapolation errors caused by the distribution shift between offline datasets and states visited by behavior policy. Existing offline RL methods constrain the policy to offline behavior to prevent extrapolation errors. But these methods limit the generalization potential of agents in Out-Of-Distribution (OOD) regions and cannot effectively evaluate OOD generalization behavior. To improve the generalization of the policy in OOD regions while avoiding extrapolation errors, we propose an Energy-Based Policy Optimization (EBPO) method for OOD generalization. An energy function based on the distribution of offline data is proposed for the evaluation of OOD generalization behavior, instead of relying on model discrepancies to constrain the policy. The way of quantifying exploration behavior in terms of energy values can balance the return and risk. To improve the stability of generalization and solve the problem of sparse reward in complex environment, episodic memory is applied to store successful experiences that can improve sample efficiency. Extensive experiments on the D4RL datasets demonstrate that EBPO outperforms the state-of-the-art methods and achieves robust performance on challenging tasks that require OOD generalization.
Create and Find Flatness: Building Flat Training Spaces in Advance for Continual Learning

Catastrophic forgetting remains a critical challenge in the field of continual learning, where neural networks struggle to retain prior knowledge while assimilating new information. Most existing studies emphasize mitigating this issue only when encountering new tasks, overlooking the significance of the pre-task phase. Therefore, we shift the attention to the current task learning stage, presenting a novel framework, C&amp;F (Create and Find Flatness), which builds a flat training space for each task in advance. Specifically, during the learning of the current task, our framework adaptively creates a flat region around the minimum in the the loss landscape. Subsequently, it finds the parameters’ importance to the current task based on their flatness degrees. When adapting the model to a new task, constraints are applied according to the flatness and a flat space is simultaneously prepared for the impending task. We theoretically demonstrate the consistency between the created and found flatness. In this manner, our framework not only accommodates ample parameter space for learning new tasks but also preserves the preceding knowledge of earlier tasks. Experimental results exhibit C&amp;F’s state-of-the-art performance as a standalone continual learning approach and its efficacy as a framework incorporating other methods. Our work is available at https://github.com/Eric8932/Create-and-Find-Flatness.
Special Issue: Graph Algorithms: Theory and Applications - A Special Issue Dedicated to the Memory of Professor Takao Nishizeki — Preface

International Journal of Foundations of Computer ScienceVol. 34, No. 07, pp. 691-692 (2023) No AccessSpecial Issue: Graph Algorithms: Theory and Applications - A Special Issue Dedicated to the Memory of Professor Takao Nishizeki — PrefaceMd. Saidur Rahman and Hsu-Chun YenMd. Saidur RahmanDepartment of Computer Science and Engineering, Bangladesh University of Engineering and Technology (BUET), Dhaka-1205, Bangladesh and Hsu-Chun YenDepartment of Electrical Engineering, National Taiwan University, Taipei, Taiwan 106, R.O.C.https://doi.org/10.1142/S0129054123020045Cited by:0 (Source: Crossref) Next This article is part of the issue: Special Issue: Graph Algorithms: Theory and Applications — A Special Issue Dedicated to the Memory of Professor Takao NishizekiGuest Editors: Md. Saidur Rahman (Bangladesh University of Engineering and Technology, Bangladesh) and Hsu-Chun Yen (National Taiwan University, Taiwan) AboutSectionsPDF/EPUB ToolsAdd to favoritesDownload CitationsTrack CitationsRecommend to Library ShareShare onFacebookTwitterLinked InRedditEmail Remember to check out the Most Cited Articles! Check out these Handbooks in Computer Science FiguresReferencesRelatedDetails Recommended Vol. 34, No. 07 Metrics History Published: 29 September 2023 PDF download
POTENSI RAINWATER HARVESTING DALAM MENGURANGI RISIKO KEKERINGAN DI KABUPATEN KARAWANG

&lt;p class="Abstract"&gt;&lt;em&gt;&lt;span lang="EN-US"&gt;Drought occurs quite often in Indonesia, closely related to water availability for community needs. One of the districts in West Java Province experiencing drought problems is Karawang district, where the RWH (Rainwater Harvesting) concept will be applied. This study aimed to analyze the risk of drought-related to applying the potential RWH in reducing drought risk. This study only focuses on meteorological drought, determined by SPI (Standardized Precipitation Index). Identification of potential areas for the application of RWH is carried out using the Weighted Method Overlay approach in the Geographic Information System (GIS), which was modified by adding a parameter of potential drought hazard. The potential reliability of RWH, as seen from Volumetric Reliability (VR) for supplying the average water requirement of MCK (Bath Washing Toilet) per year, indicates that most of the Karawang district area is potential for applying RWH. The results of adding the potential drought hazard parameters on applying the RWH concept show the decreasing area of very potential and potential category areas into 5,38%, the increase of drought resistance capacity is 17.5%, and the risk-reducing to 8.3%. Those results are due to the changes in the area of drought in the risk analysis. The assumptions of 60 rainy days on the average rainy day in a year and all households making RWH storage systems were used in the analysis. The results of this study can be used as a first step to see the potential of RWH in Karawang Regency both in terms of area and reliability.&lt;/span&gt;&lt;/em&gt;&lt;/p&gt;
MITIGASI BANJIR BERDASARKAN HUJAN 3 HARIAN MAKSIMUM TAHUNAN DENGAN SIMULASI PEMASANGAN PARAPET DI KOTA SURAKARTA

&lt;em&gt;Surakarta City often floods when there is high rainfall, according to BNPB 2016 three consecutive days of rain can cause flood disasters. Hydrological analysis is needed to determine the flood discharge due to the maximum three-day rain. With the development of TRMM satellite rain data technology can be used in hydrological analysis to overcome the problem of limited data availability at rainfall gauges. The purpose of this research is to determine the value of discharge, inundation area caused by a maximum of three days of rain. In addition, this research also evaluates flood events and determines appropriate efforts in overcoming floodwater runoff by installing parapets. The use of HEC-RAS software in this research is for flood analysis and simulation of parapet installation in areas experiencing river water runoff. The calculated flood discharge is the annual flood discharge from 2000 - 2019. The method of calculating the discharge uses HSS Gama 1. Then flood tracing is carried out to determine the area of inundation that occurs. The process of mapping and determining the extent of flooding that occurred in each sub-district was carried out with the help of software GIS. The highest flood area due to river runoff occurs in Laweyan Subdistrict, which is 45,80 ha. From this location, a parapet installation simulation was carried out as one of the flood mitigation efforts. Simulation of parapet installation was carried out in Pajang Village and Laweyan Village, after simulating parapet installation, the flood area for Laweyan Subdistrict was 2,21 ha. &lt;/em&gt;
Building Disaster Resilient Tourism through Strengthening Disaster Management Strategies in Batu City

Indonesia has a high intensity of disaster events. The high incidence of disasters in Indonesia has had a detrimental impact on several sectors, one of which is the tourism sector. This study was conducted to find out actors who play a role in disaster management strategy and their relation to national defense. This study used QGIS software, MICMAC, and MACTOR for analysis. MICMAC analysis emphasizes factors or variables, and then MACTOR emphasizes actors. This research found that the institutional capacity between institutions in Batu City was good, but still needed to be improved. The role of actors in improving disaster management in tourism areas in Batu City is very crucial, especially BPBD and DLH as actors who influence other actors on strategy and disaster management in Batu City. The role of actors in disaster management through risk mapping, preparing emergency response plans, and involving the community in prevention efforts are important actions in disaster management. The success of these efforts will contribute to overall national security and stability. This condition led to the involvement of the Indonesian National Armed Forces (TNI) in emergency disaster response operations.
Mobile application-oriented interface hijacking security defense method

The traditional PC security protection measures cannot effectively guarantee the security of the mobile end, so APP security incidents, as an important carrier of the mobile end, occur frequently. Frequent interface hijacking attacks on mobile terminals seriously threaten the information and property security of every user. The proliferation of interface hijacking attacks poses a serious threat to the privacy and property security of the people, and has a negative impact on China's economic development and social stability. Especially with the rapid popularization of mobile payment, internet finance and mobile banking, the security of APP as one of the carriers has become a crucial part of Internet security. The interface hijacking attack is an Android malicious attack aimed at stealing the privacy information entered by the user. The process is generally as follows: the attacker will continuously scan the running information of the application on the current device in the background. Once the target application is starting, the attacker will immediately start the fake interface to cover the target application, to trick the user into entering sensitive information and transmit it to the outside.
Daytime to Night Image Translation Based on Semantic Segmentation

Image-to-image translation is the process of converting images from one domain to another. The goal of image-to-image translation is to learn the mapping between input and output images. In the face of complex scenes, for example, when the field changes greatly from day to night, the daytime image scenes have complex semantic information, which makes it difficult for convolutional neural networks to learn specific texture details, resulting in ambiguities and other phenomena after image translation. Semantic segmentation is a visual scene understanding task. It understands and recognizes the image content from the pixel level, and then performs image segmentation according to the semantic information. The purpose is to predict the category label of each pixel in the input picture, so as to provide clearer and structured semantic information. In view of the mature development of semantic segmentation technology, we propose a day-to-night image translation method based on semantic segmentation. Through unsupervised learning, semantic information is provided by images and semantic segmentation graphs to generate more reasonable nighttime domain images.
Research on Network Information Security Detection System Based on Computer Virtual Reality Technology

A network intrusion data detection system based on big data analysis is proposed. Complete the sensor, packet sniffer, anomaly analysis, alarm and other functions. In terms of software, it is mainly composed of data acquisition module and anomaly detection module. A large number of experiments have proved that the method of using big data to carry out network attack can solve the current problem. It's safer and more efficient.
Eye-tracking Control Wheelchair Method Based on Landmarks of Face

At the moment, the issue of fatigue and freedom restriction affects the intelligent wheelchair collection's control source. We looked into ways to enable quadriplegic people use wheelchairs by eye guidance. A manually annotated data set is used to address the issue that the face landmarks are overly numerous and the pupil landmarks are calculated. And the main components of heat map processing, which make it simple to increase the detection window. The lightweight convolution attention mechanism is integrated with the two-stage down and up sampling Unet network to reinforce the constraint between crucial locations and enhance the network's performance. In order to capture the most important details of the user's face in real time, we mounted the Raspberry Pi camera right in front of the wheelchair. We generate the appropriate command information for the wheelchair's motor drive by analyzing various photos. In order to ensure that users have a comfortable experience, additional security features include a specific face signal, speed restriction, and automatic obstacle avoidance. It can achieve 95% accurate real-time landmark detection through experimental verification. Additionally, it can control and track the position of wheelchairs for various users.