Reviewerly/exHarmony huggingface dataset:
Enhancing energy efficiency in cloud scaling: A DRL-based approach incorporating cooling power

The rapid growth of cloud computing significantly boosts energy usage, driven mainly by CPU operations and cooling. While cloud scaling efficiently allocates resources for changing workloads, current energy-driven methods often prioritize energy metrics combined with throughput, execution time, or SLA compliance, neglecting cooling power's influence on energy consumption. To bridge this gap, we propose a deep reinforcement learning (DRL)-based autoscaler that considers cooling power as a critical factor for decision-making. Our approach employs DRL to dynamically adjust cloud resources, aiming to maximize energy efficiency and meet performance objectives. DRL, unlike RL, uses neural networks to handle the extensive state–action space in cloud scaling, overcoming the challenge of limited memory capacity for storing Q-values. In this study, we evaluate the performance of our proposed solution through a simulation-based experiment. We compare the performance of the proposed DRL-based autoscalers against an RL-based autoscaler. Our findings indicate that the DDQN-based autoscaler consistently outperforms other algorithms by maintaining optimal Power Usage Effectiveness (PUE) levels and improving task execution speed during high workloads. In contrast, the DQN-based autoscaler excels at sustaining optimal PUE levels during lower task loads, with a faster convergence rate at a scaling factor of 2 compared to scaling factor 1.
A Visual Real-Time Mobile E-Logbook System to Capture Design Activities, Decisions, and Ideas for Engineers

Logbook is one of the important tools to engineers, which is used to capture engineers’ design activities, decisions, and ideas. It is useful to engineers in accomplishing project designs, organising, tracking, and providing post-design evaluation. However, the lack of digitalization of logbooks had made the engineers’ design activities heavily dependent on human intervention. Therefore, this research work explores the potential of digitalization of logbooks by redesigning and developing the paper-based logbook into a visual real-time mobile application. The focus of the developed visual e-logbook is to move the old-fashioned way of capturing design activities and decisions into a more systematic and effective way which allows engineers to capture and access logs at any point of time. The visual e-logbook application uses Swift programming language, SwiftUI framework and XCode as the main Integrated Development Environment (IDE). The implemented visual e-logbook is then tested with case studies and results show that the visual e-logbook has good potential for engineering design projects by replacing the paper logbook. Feedback is thus collected from the case studies for future implementation such as search and filtering function, enable collaboration, and reminder function.
Prototype Selection for Multilabel Instance-Based Learning

Reducing the size of the training set, which involves replacing it with a condensed set, is a widely adopted practice to enhance the efficiency of instance-based classifiers while trying to maintain high classification accuracy. This objective can be achieved through the use of data reduction techniques, also known as prototype selection or generation algorithms. Although there are numerous algorithms available in the literature that effectively address single-label classification problems, most of them are not applicable to multilabel data, where an instance can belong to multiple classes. Well-known transformation methods cannot be combined with a data reduction technique due to different reasons. The Condensed Nearest Neighbor rule is a popular parameter-free single-label prototype selection algorithm. The IB2 algorithm is the one-pass variation of the Condensed Nearest Neighbor rule. This paper proposes variations of these algorithms for multilabel data. Through an experimental study conducted on nine distinct datasets as well as statistical tests, we demonstrate that the eight proposed approaches (four for each algorithm) offer significant reduction rates without compromising the classification accuracy.
Improving Autonomous Robot Gripper Position on Lifting Trash Objects based on Object Geometry Parameters and Centroid Modification

Waste management in the modern urban era requires a more advanced and efficient approach. Autonomous robots have emerged as an innovative solution to address this challenge. The robot’s success in collecting trash cannot be separated from the ability of the robot’s gripper to pick up the trash. This study aims to improve the positioning accuracy of the gripper robot in lifting trash objects. If the geometrical parameters of the objects are different, there is a possibility that the centroid points are in the 2D area so that the gripper robot cannot grip and lift the trash objects. Likewise, if there is a difference in weight, the robot gripper will have difficulty lifting the object because the object is one-sided. For this reason, removing trash objects needs to be improved by considering several parameters, not only the centroid parameter. A method for lifting trash objects is proposed based on several parameters: geometry, centroid and trash object type. The proposed method is Object Geometry Parameters and Centroid Modification (OGP-CM). The test results show that the OGP-CM method can set the centroid position based on the geometric parameters and the type of trash. On the same object geometry, the improvement in accuracy is relatively low, ranging from 0.46% to 1.72%. A relatively great improvement in accuracy occurs for different object geometries, ranging from 11.54% to 13.09%. Thus, improving the position of the autonomous robot gripper in lifting objects using OGP-CM has been successfully carried out.
Solar Radiation Prediction Based on Conformer-GLaplace-SDAR Model

Solar energy, as a clean energy source, has tremendous potential for utilization. The advancement of solar energy utilization technology has led to an increasing demand for solar energy, resulting in a growing need for the accurate prediction of solar radiation. The main objective of this study is to develop a novel model for predicting solar radiation intervals, in order to obtain accurate and high-quality predictions. In this study, the daily sunshine duration (SD), average relative humidity (RHU), and daily average temperature (AT) were selected as the indicators affecting the daily global solar radiation (DGSR). The empirical study conducted in this research utilized daily solar radiation data and daily meteorological data collected at the Hami station in Xinjiang from January 2009 to December 2016. In this study, a novel solar radiation interval prediction model was developed based on the concept of “point prediction + interval prediction”. The Conformer model was employed for the point prediction of solar radiation, while the Generalized Laplace (GLaplace) distribution was chosen as the prior distribution to account for the prediction error. Furthermore, the Solar DeepAR Forecasting (SDAR) model was utilized to estimate parameters of the fitted residual distribution and achieve the interval prediction of solar radiation. The results showed that both models performed well, with the Conformer model achieving a Mean Squared Error (MSE) of 0.8645, a Mean Absolute Error (MAE) of 0.7033 and the fitting coefficient R2 of 0.7751, while the SDAR model demonstrated a Coverage Width-based Criterion (CWC) value of 0.068. Compared to other conventional interval prediction methods, our study’s model exhibited superior accuracy and provided a more reliable solar radiation prediction interval, offering valuable information for ensuring power system safety and stability.
Reading Habits in ICT age among the P.G. Students of T. M. Bhagalpur University, Bhagalpur, Bihar: A Survey

The present study was conducted on 120 P.G. students of T. M. Bhagalpur the University of Bihar State. The main objective of the research is to examine the reading behavior of postgraduate students, with a particular focus on the types of reading materials they engage with, The study aims to analyze the duration of time postgraduate students devote to reading and evaluate how their reading habits are influenced by internet and other ICT media. The study is based on the survey method and a structured Questionnaire was distributed among 120 students of the PG Department of various faculties of T. M. Bhagalpur, in which a filled questionnaire was received from 105 students. The findings indicate a noticeable variation in the reading patterns of postgraduate students with regards to the amount of time they spend on reading, The research focuses on exploring various aspects of postgraduate students' reading behavior, including their utilization of different reading materials, the influence of internet and ICT on their reading habits, and their reliance on mobile internet to search for study materials.
A Visual-Based Energy Efficient Chatbot: Relationship between Sentiment Analysis and Customer Satisfaction

The evolution of Chatbots today has been seen to be popular in various service sectors such as education, business, as well as banking. The assistance provided by the system has been able to ease the tasks that otherwise need to be done by human agents. This paper highlights the development of the visual-based energy efficient Chatbot (VBE2Chatbot), testing, and explores the changes of sentiment projected by the end user during the pre-survey stage and post-survey stage. The visual-based and energy efficient Chatbot system refers to one that is designed using visual elements (such as images, videos, or graphical interfaces) to enhance user experience whilst being optimized for energy efficient (optimising code, server-side processing, smart wake-up and sleep modes and caching). This paper also highlights the findings of sentiment analysis based on customer satisfaction. On the whole, findings of the study showed positive results on its satisfaction based on the design and development of a visual-based energy efficient Chatbot (VBE2Chatbot). More work needs to be conducted to integrate AI, visual elements, and energy efficient elements into the VBE2Chatbot.
The Use of Canva and Powtoon Learning Media in Learning Poetry Materials to Improve Student Learning Outcomes for High School

This study aims to test the effectiveness of Canva and Powtoon learning media in learning poetry material to improve student learning outcomes in class X IPA 1 SMAN 1 Kelapa Kampit. The research subjects were class X IPA 1 students at Kelapa Kampit State Senior High School. The method used in this research is research and development. The application of information technology-based learning materials is expected to improve teaching standards in the classroom. The study results show that the Canva and Powtoon learning media are appropriate for learning Indonesian for class X IPA 1 SMA Negeri 1 Kelapa Kampit. This is because it meets the requirements for media validation, material validation, and evaluation validation. The pretest and posttest results found that Canva and Powtoon media helped class X IPA 1 SMA Negeri 1 Kelapa Kampit understand the material needed to recognize the atmosphere, themes, and meaning in the poetry anthology. Based on this, the Canva and Powtoon learning tools can be developed in teaching Indonesian, especially when teaching students how to recognize the atmosphere, themes, and meaning of poetry anthologies.
Tool for snapshotting of aggregated data from streaming data

In the modern world, streaming data is widespread in a significant number of subject areas. At the same time, there is often a need for stream processing of data in real time. In stream processing, approximate algorithms, which have higher efficiency than exact algorithms, are in high demand, as well as stream state forecasting. In databases, materialized views are used to store query results, but most implementations do not have the ability to update them incrementally. Thus, there is a need in the market for a tool that builds incrementally updated materialized views of streaming data, and also makes it possible to forecast the state of a stream and use approximate algorithms for processing streaming data. In addition, due to the high diversity of streaming data, their sources and algorithms for their processing and forecasting, such a tool should be extensible. The author of the article has developed such a tool. In the article, the architecture and mechanism of functioning of the tool are reviewed. The prospects for its further development are also studied in the article.
Link Prediction-Based Multi-Identity Recognition of Darknet Vendors

The darknet has been a notorious hub for illegal and criminal activities. One of the major challenges in identifying criminals involved in darknet market transactions is the presence of multi-identity vendors, who operate under different accounts to evade detection. Existing methods have limitations such as incomplete feature characterization, imprecise data annotation, and an inability to analyze across markets. In this paper, we propose a new approach to address these issues. Rich information of traded goods is collected from 21 currently existing English darknet markets as experimental data. Our method entails extracting text and image features and computing the writing style of suppliers. Subsequently, the feature dimension is reduced and pseudo labeling is applied to improve label accuracy. Further, four types of data about vendors are mapped onto a heterogeneous information network to characterize potential relationships among darknet vendors. By leveraging the graph neural network algorithm and Siamese neural networks, the link relationship between different accounts is predicted to determine whether they belong to the same supplier. The experimental results demonstrate the effectiveness of our proposed method in identifying different accounts belonging to the same supplier with a maximum accuracy of 99.75% and a recall rate of 99.09%.
Improvements of Homomorphic Secure Evaluation of Inverse Square Root

Secure machine learning has attracted much attention recently. The celebrated CKKS homomorphic encryption scheme has played a key role in such an application. Inverse square root is widely used in machine learning, such as vector normalization, clustering, etc., but it is not a function that can be easily processed by CKKS. In 2022, Panda proposed a Newton iterative algorithm for homomorphic evaluation of inverse square root using CKKS scheme. The initial value of the iteration is selected as two straight lines intersecting at one point, which involves a very expensive homomorphic comparison operation. In this paper, we propose two novel methods for selecting the initial value of the inverse square root Newton iterative algorithm. Specifically, Taylor expansion and rational function are used as an initial value to avoid the homomorphic comparison operation and achieve a significant improvement of efficiency. The Taylor expansion method greatly reduces the initial value calculation consumption, but appropriately increases the number of Newton iterations. Compared with the Taylor expansion method, the rational function method is more costly in the initial value calculation stage but reduces the number of Newton iterations. Experiments are conducted on the SEAL open source library and we find that, while reaching the same accuracy, the total number of homomorphic levels consumed by the Taylor expansion method is about $$83.3\%$$ of the best known results, and the rational function method is about $$56.9\%$$ .
Block Ciphers Classification Based on Randomness Test Statistic Value via LightGBM

Cryptographic algorithms classification, which can detect the underlying encryption algorithm on sufficient large ciphertexts, is essential to encrypted traffic analysis and protocol compliance detection. Previous studies have typically employed various feature quantities and models for feature learning in analyzing encryption algorithms. Unlike these, this work performs a broader feature selection and extracts features from the P-values of the randomness test and their data distributions for different block cipher algorithms. This work utilizes the LightGBM framework to focus on block cipher algorithms classification in ECB mode. It takes six algorithms to test the classification scheme, including AES-128, AES-192, AES-256, DES, 3DES and SM4, with an average accuracy of 82%. To compare the accuracy, this work analyzes the influence weights of random features and experiments with the classification accuracy of different schemes on the same ciphertext blocks. The experiment results show that our scheme is effective in classifying block ciphers.
Forward Secure Lattice-Based Ring Signature Scheme in the Standard Model

A ring signature scheme allows a group member to generate a signature on behalf of the whole group, while the verifier can not tell who computed this signature. However, most predecessors do not guarantee security from the secret key leakage of signers. In 2002, Anderson proposed forward security mechanism to reduce the effect of such leakage. In this paper, we construct the first lattice-based ring signature scheme with forward security. Our scheme combines the binary tree and lattice basis delegation technique to realize a key evolution mechanism, where secret keys are ephemeral and updated with generating nodes in the binary tree. Thus, adversaries cannot forge the past signature even if the users’ present secret keys are revealed. Moreover, our scheme can offer unforgeability under the standard model. Furthermore, our proposed scheme is expected to realize post-quantum security due to the underlying Short Integer Solution (SIS) problem in lattice-based cryptography.
AFLOW: Developing Adversarial Examples Under Extremely Noise-Limited Settings

Extensive studies have demonstrated that deep neural networks (DNNs) are vulnerable to adversarial attacks. Despite the significant progress in the attack success rate that has been made recently, the adversarial noise generated by most of the existing attack methods is still too conspicuous to the human eyes and proved to be easily detected by defense mechanisms. Resulting that these malicious examples cannot contribute to exploring the vulnerabilities of existing DNNs sufficiently. Thus, to better reveal the defects of DNNs and further help enhance their robustness under noise-limited situations, a new inconspicuous adversarial examples generation method is exactly needed to be proposed. To bridge this gap, we propose a novel Normalize Flow-based end-to-end attack framework, called AFLOW, to synthesize imperceptible adversarial examples under strict constraints. Specifically, rather than the noise-adding manner, AFLOW directly perturbs the hidden representation of the corresponding image to craft the desired adversarial examples. Compared with existing methods, extensive experiments on three benchmark datasets show that the adversarial examples built by AFLOW exhibit superiority in imperceptibility, image quality and attack capability. Even on robust models, AFLOW can still achieve higher attack results than previous methods.
SCOPE: A Cross-Chain Supervision Scheme for Consortium Blockchains

The consortium chain is widely used in multi-organization collaboration and data sharing in various industries due to its decentralization, non-tampering, and traceability. With its popularity, supervising these distributed systems has become a challenge for governments. The centralized supervision model destroys the distributed nature of blockchains and cannot provide open and transparent supervision services. Therefore, researchers propose to build a blockchain to supervise multiple consortium chains in one industry. Under this idea, the cross-chain supervision scheme becomes the focus of research. However, most existing cross-chain schemes are designed for digital currency transfer. If applied to a supervision scenario, they suffer from two shortcomings. First, the over-coupled interchain relationship cannot meet flexible supervisory requirements. Second, they cannot guarantee data authenticity during off-chain transmission. Aiming at the shortcomings, we design SCOPE, a cross-chain supervision scheme for consortium chains. The contribution of our work lies in three aspects. Firstly, we deploy a relay chain to implement automatic supervision based on the publish-subscribe model, reducing cross-chain overhead. Secondly, we propose a verification method for the authenticity of cross-chain data by calculating the reputation value of oracle nodes and performing threshold signatures based on reputation weights. Finally, we implement a prototype system and test it. The results show that SCOPE provides good scalability and achieves low latency. Compared with the verification method based on BLS threshold signatures, SCOPE obtains a higher success rate in verifying the authenticity of cross-chain data.
A Novel Deep Ensemble Framework for Online Signature Verification Using Temporal and Spatial Representation

Although considerable improvements have been made in online signature verification (OSV) over the last decade, none of them take both temporal and spatial information into consideration, and thus there is still a room for boosting the performance. In this paper, we propose a novel ensemble based deep learning framework, which consists of a convolutional neural network model and our recently designed convolutional gated recurrent network (CGRN) for extracting spatial feature and temporal feature, respectively. However, it is not easy to combine these two types of features since temporal feature is two-dimensional with various length while the other is a fixed-length vector. In order to incorporate both types of representation, we firstly introduce cosine similarity for spatial feature to calculate the shape similarity and use dynamic time warping (DTW) for temporal feature alignment. Thereafter, the distance between reference signature and given signature is obtained by multiplying DTW distance and similarity score. In addition, we design a novel approach for DTW distance normalization, which significantly enhances the verification accuracy. Our method achieves new state-of-the-art result on DeepSignDB, and outperforms other existing OSV methods with at least 16.2% relative improvement in finger scenario.
BDTS: Blockchain-Based Data Trading System

Trading data through blockchain platforms is hard to achieve fair exchange. Reasons come from two folds: Firstly, guaranteeing fairness between sellers and consumers is a challenging task as the deception of any participating parties is risk-free. This leads to the second issue where judging the behavior of data executors (such as cloud service providers) among distrustful parties is impractical in the context of traditional trading protocols. To fill the gaps, in this paper, we present a blockchain-based data trading system, named BDTS. BDTS implements a fair-exchange protocol in which benign behaviors can get rewarded while dishonest behaviors will be punished. Our scheme requires the seller to provide consumers with the correct encryption keys for proper execution and encourage a rational data executor to behave faithfully for maximum benefits from rewards. We analyze the strategies of consumers, sellers, and dealers in the trading game and point out that everyone should be honest about their interests so that the game will reach Nash equilibrium. Evaluations prove efficiency and practicability.