Reviewerly/exHarmony huggingface dataset:
HHT: An Ontology to Represent Territorial Dynamics for Digital Humanities

The notion of territory plays a major role in human and social sciences as it allows to anchor in a spatio-temporal context the facts studied in humanities. Representation of territories as spatio-temporal entities has been tackled in various ways. However, approaches for an historical context are scarce, as most approaches are designed for either a contemporaneous or case specific use. Notably, most ontologies used to represent territories are focused on spatial representation and do not intend to ecompass the impact of actors over said space, which happens to be a defining dimension of territories in humanities. In order to represent historical territories, we proposed a new version of the previously conceived HHT ontology (Hierarchical Historical Territory) to represent hierarchical historical territories and include actors representation. The resulting ontology encompasses the description of evolving territories, territorial divisions, explicit change representation, the will of actors to change established characteristics of territories and allows to represent territories without having to know their geometry by relying on a notion of building blocks to replace polygonal geometry.
Federated Learning: Centralized and P2P for a Siamese Deep Learning Model for Diabetes Foot Ulcer Classification

It is a known fact that AI models need massive amounts of data for training. In the medical field, the data are not necessarily available at a single site but are distributed over several sites. In the field of medical data sharing, particularly among healthcare institutions, the need to maintain the confidentiality of sensitive information often restricts the comprehensive utilization of real-world data in machine learning. To address this challenge, our study experiments with an innovative approach using federated learning to enable collaborative model training without compromising data confidentiality and privacy. We present an adaptation of the federated averaging algorithm, a predominant centralized learning algorithm, to a peer-to-peer federated learning environment. This adaptation led to the development of two extended algorithms: Federated Averaging Peer-to-Peer and Federated Stochastic Gradient Descent Peer-to-Peer. These algorithms were applied to train deep neural network models for the detection and monitoring of diabetic foot ulcers, a critical health condition among diabetic patients. This study compares the performance of Federated Averaging Peer-to-Peer and Federated Stochastic Gradient Descent Peer-to-Peer with their centralized counterparts in terms of model convergence and communication costs. Additionally, we explore enhancements to these algorithms using targeted heuristics based on client identities and f1-scores for each class. The results indicate that models utilizing peer-to-peer federated averaging achieve a level of convergence that is comparable to that of models trained via conventional centralized federated learning approaches. This represents a notable progression in the field of ensuring the confidentiality and privacy of medical data for training machine learning models.
Towards lane detection using a generative adversarial network

Traffic accidents are one of the main causes of death in Mexico, the collisions are caused mostly due to human error, therefore attempts have been made to reduce these shortcomings with driver assistance systems. This paper presents a study conducted to explore the capabilities of a Generative Adversarial Network in terms of application in lane detection on a highway, it is proposed to use a metric known as Dice index which measures the similarity between images and a pre-processing method based on color spaces, as well as a technique called Superpixels which is based on clustering. Finally, the results are compared with a neural network called LaneNet developed for the TuSimple database. The results obtained from this methodology needs to be optimized with future work, however, it opens the door to possible research with this type of network.
Joint Deep Learning Optical Band Selection and Classification Method for Spectral Data

This paper proposes a joint deep learning-based optical band selection and classification method by acquiring only the relevant data. The proposed method despite using fewer bands provides better performance than state-of-the-art.
Advancing Road Scene Semantic Segmentation with UNet-EfficientNetb7

Semantic segmentation is an essential tool for autonomous vehicles to comprehend their surroundings. Due to the need for both effectiveness and efficiency, semantic segmentation for autonomous driving is a difficult task. Present-day models’ appealing performances typically come at the cost of extensive computations, which are unacceptable for self-driving vehicles. Deep learning has recently demonstrated significant performance improvements in terms of accuracy. Hence, this work compares U-Net architectures such as UNet-VGG19, UNet-ResNet101, and UNet-EfficientNetb7, combining the effectiveness of compound-scaled VGG19, ResNet101, and EfficientNetb7 as the encoders for feature extraction. And, U-Net decoder is used for regenerating the fine-grained segmentation map. Combining both low-level spatial information and high-level feature information allows for precise segmentation. Our research involves extensive experimentation on diverse datasets, including the CamVid (Cambridge-driving Labeled Video Database) and Cityscapes (a comprehensive road scene understanding dataset). By implementing the UNet-EfficientNetb7 architecture, we achieved notable mean Intersection over Union (mIoU) values of 0.8128 and 0.8659 for the CamVid and Cityscapes datasets, respectively. These results outshine alternative contemporary techniques, underscoring the superior precision and effectiveness of the UNet-EfficientNetb7 model. This study contributes to the field by addressing the crucial challenge of efficient yet accurate semantic segmentation for autonomous driving, offering insights into a model that effectively balances performance and computational demands.
AN INTELLIGENT SYSTEM FOR TEMPERATURE FORECASTING BASED ON A MODIFIED FUZZY NEURAL NETWORK

In the setting of ambiguous weather forecasting, neural networks outperform traditional ap-proaches in identifying nonlinear temperature dynamics with uncertainty. However, in order to improve neural network accuracy, an intelligent adaptation for a specific location that is implemented through an intelligent forecasting system with a modified fuzzy neural network with an attention mechanism adapted for conditions of weather forecasting ambiguity registered on various meteorological websites is required. The study describes the design and test simulation of an intelligent system for a temperature forecast based on a modified fuzzy neural network with an adaptive attention mechanism, highlighting significant forecasting aspects such as nonlinear temperature dynamics based on repository data, using modified authors’ software. The findings of the system developed demonstrate its robustness and a decrease in the root mean square error of its forecast by three on average compared to the recurrent neural networks in the setting of undefined and ambiguous weather forecast.
Recurrent Update Representation Based on Multi-head Attention Mechanism for Joint Entity and Relation Extraction

Joint extraction of entities and relations from unstructured text is an important task in information extraction and knowledge graph construction. However, most of the existing work only considers the information of the context in the sentence and the information of the entities, with little attention to the information of the possible relations between the entities, which may lead to the failure to extract valid triplets. In this paper, we propose a recurrent update representational method based on multi-head attention mechanism for relation extraction. We use a multi-head attention mechanism to interact the information between the relational representation and the sentence context representation, and make the feature information of both fully integrated by cyclically updating the representation. The model performs relation extraction after the representation is updated. Using this approach we are able to leverage the relationship information between entities for relational triple extraction. Our experimental results on four public datasets show that our approach is effective and the model outperforms all baseline models.
A Novel Human Interaction Framework Using Quadratic Discriminant Analysis with HMM

Human-human interaction recognition is crucial in computer vision fields like surveillance, human-computer interaction, and social robotics. It enhances systems’ ability to interpret and respond to human behavior precisely. This research focuses on recognizing human interaction behaviors using a static image, which is challenging due to the complexity of diverse actions. The overall purpose of this study is to develop a robust and accurate system for human interaction recognition. This research presents a novel image-based human interaction recognition method using a Hidden Markov Model (HMM). The technique employs hue, saturation, and intensity (HSI) color transformation to enhance colors in video frames, making them more vibrant and visually appealing, especially in low-contrast or washed-out scenes. Gaussian filters reduce noise and smooth imperfections followed by silhouette extraction using a statistical method. Feature extraction uses the features from Accelerated Segment Test (FAST), Oriented FAST, and Rotated BRIEF (ORB) techniques. The application of Quadratic Discriminant Analysis (QDA) for feature fusion and discrimination enables high-dimensional data to be effectively analyzed, thus further enhancing the classification process. It ensures that the final features loaded into the HMM classifier accurately represent the relevant human activities. The impressive accuracy rates of 93% and 94.6% achieved in the BIT-Interaction and UT-Interaction datasets respectively, highlight the success and reliability of the proposed technique. The proposed approach addresses challenges in various domains by focusing on frame improvement, silhouette and feature extraction, feature fusion, and HMM classification. This enhances data quality, accuracy, adaptability, reliability, and reduction of errors.
Pengembangan Kompetensi Bahasa Inggris Guru di SD Muhammadiyah 5 Porong

Community service activities aimed at teachers in the Muhammadiyah 5 Porong Elementary School in the form of training and mentoring of English language materials are based on the need for the application of the use of English in the school environment, especially since English has become a local content lesson. This service activity is carried out for 6 months. In this activity, training and mentoring is(Permatasari, Ressa Dwi Citra; Taufiq, 2021) centered on three main materials, namely the use of English in the classroom, namely the use of English that can be inserted or included in the material taught by the teacher. The second material is English for activities outside the classroom, namely interaction activities between teachers and students outside of class hours and the third is English for daily communication, which can be practiced between teachers on various occasions within the school. The teachers are very enthusiastic about this activity, and it is hoped that there will be follow-up activities related to the material taught later. Furthermore, it is hoped that after this activity, teachers will be able and brave to teach in English, using it in school activities and in daily activities to support the use of English in schools.
The influence of second-order effect on circuit design

The purpose of this paper is to deeply study and understand the influence of second-order effects (channel length modulation effect, bulk effect, subthreshold conductivity short channel effect) on the performance of electronic devices and circuits, and to propose effective methods and techniques to reduce their negative effects. The author will pay attention to the mechanism and characteristics of channel length modulation effect, bulk effect, subthreshold conductivity and short channel effect, and the key factors leading to their changes. Through theoretical analysis, process inversion and simulation, various effective suppression or compensation strategies are explored to improve the performance of electronic devices and circuits and enhance their reliability and stability. Through the in-depth study of the second-order effect, readers can better understand and solve the influence brought by the second-order effect.
Perkembangan Berbagi Pengetahuan Pada Pendidikan Jarak Jauh

The development of knowledge sharing in distance education has become a significant topic in the era of globalization, supported by innovation and technological advancements. Open University (UT) is an educational institution that adopts a distance education model and utilizes information technology as a learning medium. UT employs various forms of e-learning, such as online tutorials, television programs, web resources, digital libraries, and more, to facilitate interaction among students, course materials, tutors, and fellow students. This article delves into how knowledge sharing management is crucial in the context of distance education, drawing insights from UT's instructional model and knowledge management theory. Knowledge management is considered essential for enhancing student performance during the learning process and in their future careers. The article also outlines how information technology serves as a tool to facilitate knowledge sharing in distance education. This research offers valuable insights into how knowledge management and knowledge sharing can be applied within the realm of distance education, particularly at institutions like Open University. It serves as a guide for other educational organizations seeking to enhance the effectiveness of their distance learning programs by implementing principles of knowledge management and information technology.
Measurement of surface form of cylindrical optics using non-contact multiwavelength interferometry

This paper investigates the feasibility of measuring surface form of cylindrical optics using non-contact multiwavelength interferometry. Two plano-convex lenses, one with an acylindrical prescription and the other with a cylindrical prescription were measured on a non-contact multiwavelength interferometer and their surface form error was evaluated. To corroborate the results obtained from this, the two lenses were measured on a contact type PGI (phase grating interferometer) gauge, and the peak-to-valley (PV), root mean square (RMS), and radius of curvature (RoC) parameters were quantified to compare the 3D height maps obtained from these two different measurement methods. It is shown that there is a good agreement between the multiwavelength interferometer and PGI gauge and the key differences including measurement setup and cycle time between the two methods are discussed.
Synthesis of Approximate Parametric Circuits for Variational Quantum Algorithms

This work develops a novel approach to exploit synthesized, approximate circuits for the ansatz of variational quantum algorithms (VQA) and demonstrates its effectiveness for NchooseK, a domain-specific language supporting quantum-based solving of constraint-based problems. Synthesis is generalized to produce parametric circuits of short depth in close approximation of the original circuit offline. This removes syn-thesis from the critical path (online) between repeated quantum circuit executions of VQA while reducing circuit depth, thereby resulting in higher fidelity results than the baseline without synthesis. Simulation experiments indicate improvements of 98% on average. Further, experiments indicate that this approach can obtain viable solutions when the baseline could not. All of this is achieved with an average variation in circuit depth of less than 10%.
MRA-Depth: Multi-Resolution Attention based Monocular Depth Estimation

Delicate feature would become weaker or even disappear as going through deeper network layer, which results in jagged edges of objects in the depth estimation. To address this problem, we propose a multi-resolution attention based monocular depth estimation by capturing and recalibrating the dependencies of channels between different resolutions, including: we develop a multi-resolution attention encoder by embedding resolution and spatial attention, so as to extract the higher quality features in encoder, and we design a reusable multi-resolution fusion model in decoder, in which different scale features are integrated and then extract multi-resolution attention maps, so as to enhance detail information locally and perspective structure features globally. Experimental results illustrate the proposed network is capable to predict more precise depth and sharper contour, and to outperforms related methods on the KITTI benchmark.
Natural Language to Code: How Far Are We?

A longstanding dream in software engineering research is to devise effective approaches for automating development tasks based on developers' informally-specified intentions. Such intentions are generally in the form of natural language descriptions. In recent literature, a number of approaches have been proposed to automate tasks such as code search and even code generation based on natural language inputs. While these approaches vary in terms of technical designs, their objective is the same: transforming a developer's intention into source code. The literature, however, lacks a comprehensive understanding towards the effectiveness of existing techniques as well as their complementarity to each other. We propose to fill this gap through a large-scale empirical study where we systematically evaluate natural language to code techniques. Specifically, we consider six state-of-the-art techniques targeting code search, and four targeting code generation. Through extensive evaluations on a dataset of 22K+ natural language queries, our study reveals the following major findings: (1) code search techniques based on model pre-training are so far the most effective while code generation techniques can also provide promising results; (2) complementarity widely exists among the existing techniques; and (3) combining the ten techniques together can enhance the performance for 35% compared with the most effective standalone technique. Finally, we propose a post-processing strategy to automatically integrate different techniques based on their generated code. Experimental results show that our devised strategy is both effective and extensible.
DISQ: Dynamic Iteration Skipping for Variational Quantum Algorithms

In the noisy intermediate scale quantum (NISQ) era, the Variational Quantum Algorithm (VQA) has emerged as one of the most promising approaches to harness the power of quantum computers. In VQA, a classical optimizer iteratively updates the parameters of a variational quantum circuit to minimize a cost objective obtained by executing the quantum circuit on real quantum hardware. However, the deployment of VQA applications on NISQ devices encounters substantial noise, which degrades training stability. Moreover, the drift of noise is particularly intractable due to its dynamic nature in duration and magnitude. Noise drift leads to significant deviations in VQA iteration's objective function estimation and shapes a dynamic noisy landscape, which poses a considerable challenge for stable VQA parameter training, thereby hampering the accurate convergence of VQA optimizations. This paper proposes DISQ to craft a stable landscape for VQA training and tackle the noise drift challenge. DISQ adopts a “drift detector” with a reference circuit to identify and skip iterations that are severely affected by noise drift errors. Specifically, the circuits from the previous training iteration are re-executed as a reference circuit in the current iteration to estimate noise drift impacts. The iteration is deemed compromised by noise drift errors and thus skipped if noise drift flips the direction of the ideal optimization gradient. To enhance noise drift detection reliability, we further propose to leverage multiple reference circuits from previous iterations to provide a well-founded judge of current noise drift. Nevertheless, multiple reference circuits also introduce considerable execution overhead. To mitigate extra overhead, we propose Pauli-term subsetting (prime and minor subsets) to execute only observable circuits with large coefficient magnitudes (prime subset) during drift detection. Only this minor subset is executed when the current iteration is drift-free. Evaluations across various applications and QPUs demonstrate that DISQ can mitigate a significant portion of the noise drift impact on VQAs and achieve 1.51-2.24× fidelity improvement over the traditional baseline. DISQ's benefit is 1.1-1.9× over the best alternative approach while boosting average noise detection speed by 2.07×.
QisDAX: An Open Source Bridge from Qiskit to Trapped-Ion Quantum Devices

Quantum computing has become widely available to researchers via cloud-hosted devices with different technologies using a multitude of software development frameworks. The vertical stack behind such solutions typically features quantum language abstraction and high-level translation frameworks that tend to be open source, down to pulse-level programming. However, the lower-level mapping to the control electronics, such as controls for laser and microwave pulse generators, remains closed source for contemporary commercial cloud-hosted quantum devices. One exception is the ARTIQ (Advanced Real-Time Infrastructure for Quantum physics) open-source library for trapped-ion control electronics. This stack has been complemented by the Duke ARTIQ Extensions (DAX) to provide modularity and better abstraction. It, however, remains disconnected from the wealth of features provided by popular quantum computing languages. This paper contributes QisDAX, a bridge between Qiskit and DAX that fills this gap. QisDAX provides interfaces for Python programs written using IBM's Qiskit and transpiles them to the DAX abstraction. This allows users to generically interface to the ARTIQ control systems accessing trapped-ion quantum devices. Consequently, the algorithms expressed in Qiskit become available to an open-source quantum software stack. This provides the first open-source, end-to-end, full-stack pipeline for remote submission of quantum programs for trapped-ion quantum systems in a non-commercial setting.
Accel-GCN: High-Performance GPU Accelerator Design for Graph Convolution Networks

Graph Convolutional Networks (GCNs) are pivotal in extracting latent information from graph data across various domains, yet their acceleration on mainstream GPUs is challenged by workload imbalance and memory access irregularity. To address these challenges, we present Accel-GCN, a GPU accelerator architecture for GCNs. The design of Accel-GCN encompasses: (i) a lightweight degree sorting stage to group nodes with similar degree; (ii) a block-level partition strategy that dynamically adjusts warp workload sizes, enhancing shared memory locality and workload balance, and reducing metadata overhead compared to designs like GNNAdvisor; (iii) a combined warp strategy that improves memory coalescing and computational parallelism in the column dimension of dense matrices. Utilizing these principles, we formulate a kernel for SpMM in GCNs that employs block-level partitioning and combined warp strategy. This approach augments performance and multi-level memory efficiency and optimizes memory bandwidth by exploiting memory coalescing and alignment. Evaluation of Accel-GCN across 18 benchmark graphs reveals that it outperforms cuSPARSE, GNNAdvisor, and graph-BLAST by factors of 1.17×, 1.86×, and 2.94× respectively. The results underscore Accel-GCN as an effective solution for enhancing GCN computational efficiency. The implementation can be found on Github*.