Reviewerly/exHarmony huggingface dataset:
A metastable RNG using interleaved NAND- and NOR-based TEROs

This study introduces a novel approach to address the limitations observed in existing random number generator (RNG) designs, focusing on the resolution for insufficiently covered problem statements and study purposes. In response to these challenges, we propose a high-throughput metastable-based RNG tailored for cryptography applications. Our design is featured with a two-layer transient effect ring oscillator (TERO) architecture, meticulously crafted to mitigate the locking phenomenon and enhance RNG randomness. Leveraging interleaved two-layer TEROs, realised through NAND and NOR SR latches, our RNG achieves unpredictable irregular sampling, crucial for cryptographic applications. To further enhance irregular sampling, the first layer of the RNG employs a multiplexer that selects inputs randomly. This multiplexer is governed by selection signals generated by the second layer of the RNG, ensuring superior randomness. Implemented using TSMC 40-nm CMOS process, our RNG passed the FIPS 140–1 randomness test at a clock frequency of 100 MHz, boasting a remarkable throughput of 50 Mbps. This study represents a significant advancement in RNG technology, addressing previous shortcomings and paving the way for high-speed communication systems' secure cryptographic applications.
Web Application Security: A Pragmatic Exposé

Many individuals, organizations, and industries rely on web applications for the daily operations of their businesses. With the increasing deployment and dependence on these applications, significant attention has been directed towards developing more accurate and secure mechanisms to safeguard them from malicious web-based attacks. The slow adoption of the latest security protocols, coupled with the utilization of inaccurate and inadequately tested security measures, has hindered the establishment of efficient and effective security measures for web apps. This paper reviews recent research and their recommendations for web security over the last four years. It identifies code injection as one of the recent most prevalent web-based attacks. The recommendations presented in this paper offer a practical guide, enabling individuals and security personnel across various industries and organizations to implement tested and proven security measures for web applications. Furthermore, it serves as a roadmap for security developers, aiding them in creating more accurate and quantifiable measures and mechanisms for web security .
DCLGM: Fusion Recommendation Model Based on LightGBM and Deep Learning

Abstract The recommendation system can mine valuable information according to user preferences, so it is widely used in various industries. However, the performance of recommendation systems is generally affected by the problem of data sparsity, and LightGBM can alleviate the impact caused by data sparsity to a certain extent. To this end, this paper proposes a fusion recommendation model based on the LightGBM and deep learning—CLGM model. The model is composed of LighGBM, cross network and deep neural network. First, the features in the dataset are fused and extracted through LightGBM, and the feature with the highest classification accuracy is selected as the input of the neural network layer; Then, using the cross network and the deep neural network, the linear cross combination feature relationship and nonlinear correlation relationship between high-order features are respectively obtained; finally, the results obtained by the pre-order network are linearly weighted and combined to obtain the final recommendation result. In this paper, AUC and Logloss are used as evaluation indicators to verify the model on the public dataset Criteo and dataset Avazu. The simulation experiment results show that, compared with the four typical recommendation models, the recommendation effect of this model is better.
Evaluating the Effectiveness of Bayesian Knowledge Tracing Model-Based Explainable Recommender

Explainable recommendation, which provides an explanation about why a quiz is recommended, helps to improve transparency, persuasiveness, and trustworthiness. However, little research examined the effectiveness of the explainable recommender, especially on academic performance. To survey its effectiveness, the authors evaluate the math academic performance among middle school students (n=115) by giving pre- and post-test questions based evaluation techniques. During the pre- and post-test periods, students were encouraged to use the Bayesian Knowledge Tracing model based explainable recommendation system. To evaluate how well the students were able to do what they could not do, the authors defined growth rate and found recommended quiz clicked counts had a positive effect on the total number of solved quizzes (R=0.343, P=0.005) and growth rate (R=0.297, P=0.017) despite no correlation between the total number of solved quizzes and growth rate. The results suggest that the use of an explainable recommendation system that learns efficiently will enable students to do what they could not do before.
The use of blockchain technology in enterprise financial accounting information sharing

This work intends to comprehensively analyze the application of blockchain technology in enterprise financial accounting information sharing and address prevalent issues such as information opacity, data tampering, and data security in the current practices. Therefore, it writes smart contracts based on the Ethereum platform to achieve the secure sharing of financial accounting information between enterprises. This work employs a randomized experimental design approach, using a computer-generated random number program to divide 100 enterprises into experimental and control groups, each comprising 50 enterprises. Enterprises in the experimental group share financial accounting information using smart contracts on the Ethereum platform during the experiment. The financial personnel of these enterprises upload reconciled data to the corresponding smart contracts using the enterprise’s digital signatures after each month’s accounting process. Enterprises in the control group continue to use traditional methods of financial accounting information sharing (such as email and web platforms) to share financial data files directly. Quantitative analysis is performed to compare the data between the experimental and control groups. Empirical results reveal a notable enhancement in information-sharing efficiency by 25.7%, a 19.8% improvement in data accuracy, and a 13.6% reduction in financial information-sharing costs within the experimental group compared to the control group. This work provides compelling evidence that adopting blockchain-based information-sharing methods can effectively elevate data trustworthiness and security. Supported by systematic empirical findings, this work validates the significant potential of blockchain technology in the realm of enterprise financial accounting information sharing.
A hybrid traffic controller system based on flower pollination algorithm and type-2 fuzzy logic optimized with crow search algorithm for signalized intersections

Abstract In this study, a hybrid traffic signal control (HTSC) system based on phase and time optimization was developed. The Flower Pollination Algorithm (FPA) approach was used for phase optimization, while Type-2 Fuzzy Logic, optimized with the Crow Search Algorithm (CSA), was utilized for time optimization. The hybrid system's performance was investigated using nine different traffic conditions and four different intersection geometries. The hybrid system was compared with three controller systems which are a fixed-time signal controller, a signal controller based on the FPA approach (FPA_TSC), and the optimized Type-1 fuzzy logic signal controller (Type-1 FL-TSC). The HTSC approach achieved the best performance with about 32% improvement over the fixed-time traffic controller and it showed 5% and 6% better performance than the FPA_TSC and Type-1 FL-TSC, respectively. Considering the performance of the new hybrid system, it is effective in minimizing delays and driver dissatisfaction occurring from signalization. It also contributes to the reduction of emissions and fuel consumption. The HTSC approach can be used as an alternative signal control method in the control of intersections with high traffic volume due to its fast and effective performance.
Density estimation based soft actor-critic: deep reinforcement learning for static output feedback control with measurement noise

The state-of-the-art deep reinforcement learning (DRL) methods, including Deep Deterministic Policy Gradient (DDPG), Twin Delayed DDPG (TD3), Proximal Policy Optimization (PPO), Soft Actor-Critic (SAC), among others, demonstrate significant capability in solving the optimal static state feedback control (SSFC) problem. This problem can be modeled as a fully observed Markov decision process (MDP). However, the optimal static output feedback control (SOFC) problem with measurement noise is a typical partially observable MDP (POMDP), which is difficult to solve, especially for the continuous state-action-observation space with high dimensions. This paper proposes a two-stage framework to address this challenge. In the laboratory stage, both the states and the noisy outputs are observable; the SOFC policy is converted to a constrained stochastic SSFC policy, of which the probability density function is generally not analytical. To this end, a density estimation based SAC algorithm is proposed to explore the optimal SOFC policy by learning the optimal constrained stochastic SSFC. Consequently, in the real-world stage, only the noisy outputs and the learned SOFC policy are required to solve the optimal SOFC problem. Numerical simulations and the corresponding experiments with robotic arms are provided to illustrate the effectiveness of our method. The code is available at https://github.com/RanKyoto/DE-SAC.
Ontologies4Cat: investigating the landscape of ontologies for catalysis research data management

Abstract As scientific digitization advances it is imperative ensuring data is Findable, Accessible, Interoperable, and Reusable (FAIR) for machine-processable data. Ontologies play a vital role in enhancing data FAIRness by explicitly representing knowledge in a machine-understandable format. Research data in catalysis research often exhibits complexity and diversity, necessitating a respectively broad collection of ontologies. While ontology portals such as EBI OLS and BioPortal aid in ontology discovery, they lack deep classification, while quality metrics for ontology reusability and domains are absent for the domain of catalysis research. Thus, this work provides an approach for systematic collection of ontology metadata with focus on the catalysis research data value chain. By classifying ontologies by subdomains of catalysis research, the approach is offering efficient comparison across ontologies. Furthermore, a workflow and codebase is presented, facilitating representation of the metadata on GitHub. Finally, a method is presented to automatically map the classes contained in the ontologies of the metadata collection against each other, providing further insights on relatedness of the ontologies listed. The presented methodology is designed for its reusability, enabling its adaptation to other ontology collections or domains of knowledge. The ontology metadata taken up for this work and the code developed and described in this work are available in a GitHub repository at: https://github.com/nfdi4cat/Ontology-Overview-of-NFDI4Cat .
Exploring Automatic Readability Assessment for Science Documents within a Multilingual Educational Context

Abstract Current student-centred, multilingual, active teaching methodologies require that teachers have continuous access to texts that are adequate in terms of topic and language competence. However, the task of finding appropriate materials is arduous and time consuming for teachers. To build on automatic readability assessment research that could help to assist teachers, we explore the performance of natural language processing approaches when dealing with educational science documents for secondary education. Currently, readability assessment is mainly explored in English. In this work we extend our research to Basque and Spanish together with English by compiling context-specific corpora and then testing the performance of feature-based machine-learning and deep learning models. Based on the evaluation of our results, we find that our models do not generalize well although deep learning models obtain better accuracy and F1 in all configurations. Further research in this area is still necessary to determine reliable characteristics of training corpora and model parameters to ensure generalizability.
The art of personalization of education: Artificial Intelligence on the stages of special education

This study investigated the use of artificial intelligence (AI) in special education, with the aim of personalizing teaching and promoting the inclusion of students with disabilities and special needs. The central problem addressed was the need to adapt teaching individually, taking into account the unique characteristics of each student. To achieve this objective, a literature review was conducted, analyzing studies, research and experiences related to AI in special education. The methodology included a literature review of reliable academic sources, such as scientific articles and research reports. The results of the analysis indicated that AI plays a key role in personalizing teaching, adapting content and resources according to the specific needs of students. Furthermore, AI contributes to promoting inclusion by creating accessible and equitable learning environments. However, ethical issues such as student data privacy and transparency in AI algorithms are identified that require careful consideration. Furthermore, practical limitations, such as the availability of technological resources and teacher training, represent challenges to be overcome in the successful implementation of AI in special education. Final considerations highlight the importance of AI as a promising tool for personalizing teaching in special education, as long as its application is conducted ethically and aware of practical limitations.
BIKAGCN: Knowledge-Aware Recommendations Under Bi-layer Graph Convolutional Networks

Abstract Recommender systems are a popular solution for the problem of information overload, offering personalized recommendations to users. Recent years, research has aimed to enhance recommender systems by employing knowledge graphs in conjunction with Graph convolutional network (GCN) to extract user and item features. Although GCN possess a great potential, they are still far from reaching their full capability in recommender systems. This paper introduces a novel approach—knowledge-aware recommendations under bi-layer graph convolutional networks (BIKAGCN) that combines attention and bi-layer GCNs to improve performance. The first layer of the BIKAGCN model trains embedding representations of users and items based on user-item interaction graphs. The second layer introduces a novel knowledge-aware layer of attention and graph convolutional network (KAGCN) layer that leverages both the first layer’s user-item embeddings and item knowledge graph embeddings. Experimental results on three publicly available datasets (MovieLens-20M, Last-FM, and Book-Crossing) demonstrate that BIKAGCN leads to significant performance improvements in recall@20 metric (14.41%, 8.86%, and 20.90%, respectively) compared to currently available state-of-the-art approaches. Moreover, the model maintains satisfactory performance in cold-start cases.The research provides some guidance for the direction of subsequent research on recommender systems.
Utilization of a blockchainized reputation management service for performance enhancement of Smart Grid 2.0 applications

Blockchain has become the technology enabler in delivering modern Smart Grid 2.0 functionalities. Many services including Peer-to-Peer energy trading, distribution network management, financial settlements, and energy data management are catered through blockchain-enabled platforms. However, areas such as service quality-based pricing strategies, supply–demand balancing in distribution system to attain enhanced reliability and consumption-oriented rewarding mechanisms need improving in order to achieve the full benefits of the envisaged grid architecture. In response, this study proposes a novel Blockchain-as-a-Service for Energy Trading (BaaSET) platform, which offers reputation-based services, executed through smart contracts for smart grid applications. Reputation-based grid operations are automatically executed through smart contracts deployed onto a blockchain. The reputation is estimated using power quality and reliability indices, obtained through grid measurements. Further, tests have been conducted to evaluate the associated latency and the implementation cost of the proposed blockchainized service architecture. Test results signify the performance to be comparatively better considering the state-of-the-art. The results further suggest alternatives to improve the scalability of the architecture, to cater the increasing number of stakeholders in the SG 2.0 environment.
Low-cost MEMS accelerometers for earthquake early warning systems: a dataset collected during seismic events in Central Italy

This article describes a dataset of acceleration signals acquired from a low-cost Wireless Sensor Network (WSN) during seismic events that occurred in Central Italy. The WSN consists of 5 low-cost sensor nodes, each embedding an ADXL355 tri-axial MEMS accelerometer with a fixed sampling frequency of 250 Hz. The data was acquired from February 2023 to the end of June 2023. During this period, several earthquake sequences affected the area where the sensor network was installed. Continuous data was acquired from the WSN and then trimmed around the origin time of seismic events that occurred near the installation site, close to the city of Pollenza (MC), Italy. A total of 67 events were selected, whose data is available at the Istituto Nazionale di Geofisica e Vulcanologia (INGV) Seismology data center. The traces acquired from the WSN were then manually annotated by analysts from INGV. Annotations include picking time for P and S phases, when distinguishable from the background noise, alongside an associated uncertainty level for the manual annotations. The resulting dataset consists of 328 3 × 25001 arrays, each associated with its metadata. The metadata includes event data (hypocenter position, origin time, magnitude, magnitude type, etc.), trace-related data (mean, median, maximum, and minimum amplitudes, manual picks, and picks uncertainty), and sensor-specific data (sensor name, sensitivity, and orientation). Furthermore, a small dataset consisting of non-seismic traces is included, with the goal of providing records of noise-only traces, relative to both electronic and environmental/anthropic noise sources. The dataset holds potential for training and developing Machine Learning or signal processing algorithms for seismic data with low signal-to-noise ratios. Additionally, it is valuable for research about earthquakes, structural health monitoring, and MEMS accelerometer performance in civil and seismic engineering applications.
SPEC 2.0 Smart Pill Expert System

The article throws light on the implementation of an enhanced idea of the smart pill expert system. The proposed system is used to automate the capability of dispensing the right dosage of medicines at a given day-time interval. The spec 2.0 can be used at one's home, workplace, at hospitals by a user of any age group, and then possibly expanding the functionalities to a wider audience. It mainly focuses on providing access control and monitoring management functionality through a mobile app on a daily basis with no monthly subscriptions required. There will be alerts and SMSs sent across to the user based on the pill dispensing event's result. The spec 2.0 mainly focuses on providing an “over-dosage” functionality so that the user won't have to consume the medication if he hasn't taken them at the allocated time interval, to prevent over consumption. The system has been tested and the results have been formulated on a daily basis.
Point-level feature learning based on vision transformer for occluded person re-identification

Person re-identification is challenging due to the presence of variations in pose and occlusion, which significantly impact the matching of visual features across different camera views and pose considerable difficulty for accurate person re-identification. This paper proposes a novel method for occluded person re-identification by introducing point-level feature learning based on vision transformers. Our approach utilizes a pose estimator to detect the keypoints of the human body and employs these points to locate intermediate features. These intermediate features of keypoints are input to a pose-based transformer branch to learn point-level features. Then, we design a part-based transformer branch to learn part-level features that capture visual features of different image parts, further enhancing the discriminative power of the learned features. Additionally, we employ a global branch to learn the global-level feature by treating the person's image as a single entity. Finally, we integrate point-level, part-level, and global-level features to represent a person's features. The experimental results on occluded and partial person re-identification datasets demonstrate the effectiveness of our proposed approach in improving re-identification. Our approach shows potential for improving person re-identification in scenarios with occlusion and pose variations.
Iterated Relevance Matrix Analysis (IRMA) for the identification of class-discriminative subspaces

We introduce and investigate the iterated application of Generalized Matrix Learning Vector Quantizaton for the analysis of feature relevances in classification problems, as well as for the construction of class-discriminative subspaces. The suggested Iterated Relevance Matrix Analysis (IRMA) identifies a linear subspace representing the classification specific information of the considered data sets using Generalized Matrix Learning Vector Quantization (GMLVQ). By iteratively determining a new discriminative subspace while projecting out all previously identified ones, a combined subspace carrying all class-specific information can be found. This facilitates a detailed analysis of feature relevances, and enables improved low-dimensional representations and visualizations of labeled data sets. Additionally, the IRMA-based class-discriminative subspace can be used for dimensionality reduction and the training of robust classifiers with potentially improved performance.