Reviewerly/exHarmony huggingface dataset:
Why is that a Good or Not a Good Frying Pan? – Knowledge Representation for Functions of Objects and Tools for Design Understanding, Improvement, and Generation

The understanding of the functional aspects of objects and tools is of paramount importance in supporting an intelligent system in navigating around in the environment and interacting with various objects, structures, and systems, to help fulfil its goals. A detailed understanding of functionalities can also lead to design improvements and novel designs that would enhance the operations of AI and robotic systems on the one hand, and human lives on the other. This paper demonstrates how a particular object - in this case, a frying pan - and its participation in the processes it is designed to support - in this case, the frying process - can be represented in a general function representational language and framework, that can be used to flesh out the processes and functionalities involved, leading to a deep conceptual understanding with explainability of functionalities that allows the system to answer “why” questions - why is something a good frying pan, say, or why a certain part on the frying pan is designed in a certain way? Or, why is something not a good frying pan? This supports the redesign and improvement on design of objects, artifacts, and tools, as well as the potential for generating novel designs that are functionally accurate, usable, and satisfactory.
Evolutionary Multi-Objective Quantization of Randomization-Based Neural Networks

The deployment of Machine Learning models on hardware devices has motivated a notable research activity around different strategies to alleviate their complexity and size. This is the case of neural architecture search or pruning in Deep Learning. This work places its focus on simplifying randomization-based neural networks by discovering fixed-point quantization policies that optimally balance the trade-off between performance and complexity reduction featured by these models. Specifically, we propose a combinatorial formulation of this problem, which we show to be efficiently solvable by multi-objective evolutionary algorithms. A benchmark for time series forecasting with Echo State Networks over 400 datasets reveals that high compression ratios can be achieved at practically admissible levels of performance degradation, showcasing the utility of the proposed problem formulation to deploy reservoir computing models on resource-constrained hardware devices.
The Automotive BlackBox: Towards a Standardization of Automotive Digital Forensics

There is a trend toward increased cyberattacks on vehicles. Aligned, forensics requirements and standards are emerging. Digital forensics refers to identifying, preserving, verifying, analyzing, documenting, and finally presenting digital evidence with high confidence in its admissibility, thus ensuring forensics soundness. However, current automotive regulations and standards, such as the United Nations Regulation No. 155 and the International Organization for Standardization standard 21434, provide no details or guidelines. Vehicular data is often extracted using tools unsuitable for digital forensics, thus lacking forensics soundness. The data storage is generally not resistant to tampering and often lacks adequate cybersecurity mechanisms. Digital forensics is a relatively new field within the automotive domain, where most of the existing self-monitoring and diagnostic systems only monitor safety-related events. To support a forensic investigation, automotive systems must be extended to securely log and store additional information, especially those related to security events. There is no standardization for automotive digital forensics that defines requirements, needed components, and techniques for the automotive domain. In this paper, we identify and propose requirements for automotive digital forensics and present the Automotive BlackBox, an architecture guiding the design of an automotive diaital forensic-enabled vehicle.
AI Content Self-Detection for Transformer-based Large Language Models

$ $The usage of generative artificial intelligence (AI) tools based on large language models, including ChatGPT, Bard, and Claude, for text generation has many exciting applications with the potential for phenomenal productivity gains. One issue is authorship attribution when using AI tools. This is especially important in an academic setting where the inappropriate use of generative AI tools may hinder student learning or stifle research by creating a large amount of automatically generated derivative work. Existing plagiarism detection systems can trace the source of submitted text but are not yet equipped with methods to accurately detect AI-generated text. This paper introduces the idea of direct origin detection and evaluates whether generative AI systems can recognize their output and distinguish it from human-written texts. We argue why current transformer-based models may be able to self-detect their own generated text and perform a small empirical study using zero-shot learning to investigate if that is the case. Results reveal varying capabilities of AI systems to identify their generated text. Google's Bard model exhibits the largest capability of self-detection with an accuracy of 94\%, followed by OpenAI's ChatGPT with 83\%. On the other hand, Anthropic's Claude model seems to be not able to self-detect.
Nonunitary gates using measurements only

Measurement-based quantum computation (MBQC) is a universal platform to realize unitary gates, only using measurements which act on a pre-prepared entangled resource state. By deforming the measurement bases, as well as the geometry of the resource state, we show that MBQC circuits always transmit and act on the input state but generally realize nonunitary logical gates. In contrast to the stabilizer formalism which is often used for unitary gates, we find that ZX calculus is an ideal computation method of these nonunitary gates. As opposed to unitary gates, nonunitary gates can not be applied with certainty, due to the randomness of quantum measurements. We maximize the success probability of realizing nonunitary gates, and discuss applications including imaginary time evolution, which we demonstrate on a noisy intermediate scale quantum device.
FerKD: Surgical Label Adaptation for Efficient Distillation

We present FerKD, a novel efficient knowledge distillation framework that incorporates partial soft-hard label adaptation coupled with a region-calibration mechanism. Our approach stems from the observation and intuition that standard data augmentations, such as RandomResizedCrop, tend to transform inputs into diverse conditions: easy positives, hard positives, or hard negatives. In traditional distillation frameworks, these transformed samples are utilized equally through their predictive probabilities derived from pretrained teacher models. However, merely relying on prediction values from a pretrained teacher, a common practice in prior studies, neglects the reliability of these soft label predictions. To address this, we propose a new scheme that calibrates the less-confident regions to be the context using softened hard groundtruth labels. Our approach involves the processes of hard regions mining + calibration. We demonstrate empirically that this method can dramatically improve the convergence speed and final accuracy. Additionally, we find that a consistent mixing strategy can stabilize the distributions of soft supervision, taking advantage of the soft labels. As a result, we introduce a stabilized SelfMix augmentation that weakens the variation of the mixed images and corresponding soft labels through mixing similar regions within the same image. FerKD is an intuitive and well-designed learning system that eliminates several heuristics and hyperparameters in former FKD solution. More importantly, it achieves remarkable improvement on ImageNet-1K and downstream tasks. For instance, FerKD achieves 81.2% on ImageNet-1K with ResNet-50, outperforming FKD and FunMatch by remarkable margins. Leveraging better pre-trained weights and larger architectures, our finetuned ViT-G14 even achieves 89.9%. Our code is available at https://github.com/szq0214/FKD/tree/main/FerKD.
MosaicBERT: A Bidirectional Encoder Optimized for Fast Pretraining

Although BERT-style encoder models are heavily used in NLP research, many researchers do not pretrain their own BERTs from scratch due to the high cost of training. In the past half-decade since BERT first rose to prominence, many advances have been made with other transformer architectures and training configurations that have yet to be systematically incorporated into BERT. Here, we introduce MosaicBERT, a BERT-style encoder architecture and training recipe that is empirically optimized for fast pretraining. This efficient architecture incorporates FlashAttention, Attention with Linear Biases (ALiBi), Gated Linear Units (GLU), a module to dynamically remove padded tokens, and low precision LayerNorm into the classic transformer encoder block. The training recipe includes a 30% masking ratio for the Masked Language Modeling (MLM) objective, bfloat16 precision, and vocabulary size optimized for GPU throughput, in addition to best-practices from RoBERTa and other encoder models. When pretrained from scratch on the C4 dataset, this base model achieves a downstream average GLUE (dev) score of 79.6 in 1.13 hours on 8 A100 80 GB GPUs at a cost of roughly $20. We plot extensive accuracy vs. pretraining speed Pareto curves and show that MosaicBERT base and large are consistently Pareto optimal when compared to a competitive BERT base and large. This empirical speed up in pretraining enables researchers and engineers to pretrain custom BERT-style models at low cost instead of finetune on existing generic models. We open source our model weights and code.
Fomento de habilidades lógicas y de desarrollo de software en estudiantes universitarios: aumento de competitividad

The objective of this research is to establish and design the strategies, methodologies, and resources necessary to develop skill in the context of competence of software development among university students, particularly in technology careers. Effectively promoting the level of competitiveness of the graduates and therefore of the organizations where they are integrated. Furthermore, fulfilling the increasing demand for professionals who possess these skills at a local or national level as well as to generate entrepreneurs who form the type of companies required for the development of the country (Mexico-Chile). The research focus will be mixed-longitudinal, the design will be an experimental true experiment type, with post-test design and control groups. There will be groups where the established strategies and methodologies will be applied and analyzed. The independent variables will have two levels: absence in the control group and presence in the experimental group. The application of this study will be carried out in the class of Programming Laboratory (career of LATIC of FCA-UACH), depending on the outcome obtained, the study could be replicated in other institutions. One of the main themes is to offer organizations a sustainable and innovative IT infrastructure offered by the digital skills of the programmer.
Classification of Production Machine Spare Part Stock Data Request Needs Using The K-Nearest Neighbor Method

Spare parts encompass various items that are offered, owned, utilized, or consumed to fulfill consumer desires and requirements. This research implements the K-Nearest Neighbor algorithm on a test dataset consisting of 100 data objects, resulting in a novel classification perspective. The study includes a comprehensive model evaluation process involving Cross Validation on both training and testing datasets, comprising 1000 records with 36 critical and 64 non-critical outcomes. Performance assessment and testing utilizing the RapidMiner Studio application yield optimal results under various modeled scenarios. The accuracy of this algorithm model stands at 98.00%, with a standard deviation of +/- 4.00%.
Comparative Analysis of PHP Frameworks for Development of Academic Information System Using Load and Stress Testing

There are many programming languages available for the developers to pick when they want to make a web services project such as Java JSP, Python, ASP.Net, PHP and many others. The reason that writer chose PHP as its research topic is that PHP is still being one of the most promising languages especially for developing web services. Several PHP Frameworks are now available on the internet promising that their framework can do better than others. Some popular PHP frameworks are Laravel, Symfony, CakePHP, Yii, and CodeIgniter. Performance is the most aspect to be concerned because of the market’s demands entailing them to do so. The next concern that developer would likely need to decide is whether to go with Pure PHP or to go with PHP Framework. To answer those concerns, this research has been conducted in which a module is taken from an academic information system and will be developed into three similar and equivalent web applications by using Laravel, CodeIgniter, and Pure PHP. The author's aim in conducting this research is to obtain more in-depth information regarding the use of the pure PHP programming language and several frameworks such as CodeIgniter and Laravel. There are many statements stating that using the PHP programming language is no longer feasible, even though in reality using PHP for developing web-based applications is still the choice of many programmers based on statistics from w3tech. The results of research conducted by the author show that the PHP programming language is still very reliable in handling load and stress tests. The pure PHP programming language provides slightly better performance results than the CodeIgniter and Laravel frameworks. Hence, the use of pure PHP or frameworks can be determined from the level of needs of the application development to be created.
Design of Continuous Web APP: Guidance and Counseling Management Information System at SMKN 1 Purwakarta using Laravel Framework

An Information System plays a crucial role in addressing the challenges associated with archiving guidance and counseling reports, thereby enhancing the quality of Guidance and Counseling Services at SMKN 1 Purwakarta. This research aims to develop a web application that not only streamlines the archiving process but also provides a foundation for future enhancements. The research process involves a comprehensive literature review, data collection, system analysis, system development, and report generation. Data acquisition encompasses both extensive literature searches and empirical fieldwork, involving interviews and direct observations of relevant stakeholders. The adoption of the Extreme Programming methodology is justified due to its efficiency in rapid software development and adaptability to changing requirements. Laravel, chosen as the framework, offers robust features, an active community, and continuous updates to support ongoing development. The outcome of this research is a Management Information System for guidance and counseling, leveraging Bootstrap for the frontend, Laravel for the backend, and MySQL for the database. The research also provides documentation for future improvements, including the results of black-box testing.
Control of students' knowledge within the scope of teaching environmental disciplines in higher education institutions

Modernization of natural science education and natural sciences in the context of digitalization (December 6–7, 2023. Częstochowa, the Republic of Poland). Riga, Latvia : Baltija Publishing, 2023. 52 pages.
Estimation for Solar Radiation on Different Places from Adjacent Weather Stations Data Through Mathematical Regression Model

In this paper, we show a simple mathematical modeling and its comparison with real measurements for the estimation of solar radiation in places without a measuring instrument. For this prediction we a full year data from three adjacent weather stations located around the point to predict solar radiation. From these real data, we propose a model to estimate radiation and present the results obtained for the model versus the control site for predictions over a full day. The results obtained show a very good approximation to the data obtained by the proposed mathematical model and the instruments to measure radiation for the test point. The mean square error (MSE) was used as a figure of merit to measure the performance of the estimates.
Curriculum-Based Reinforcement Learning for an Effective Multi-Agent Path Planning Algorithm in Warehouse Scenarios

Multi-robot systems can provide substantial increase in efficiency and/or flexibility in different scenarios. Applications in various settings have been studied in the literature, such as disaster management, surveillance, object transportation as well as search-and-rescue. A particular case that can highly benefit from the employment of multiple agents is the logistics in a warehouse scenario. This work proposes an multi-agent Q-learning based algorithm with curriculum learning and transfer learning to perform the path planning process. With progressively more complex stages of training as well as knowledge transfer from one stage to another, the algorithm is capable of achieve high success rates. In order to validate the proposed method, simulations were done to compare it to other combinations of the used techniques, as well as using Q-learning only. Scalability tests were also performed. The proposed method achieved up to 94% success rate after training.
Social Media Marketing &amp; Corporate Sector Customers' Loyalty: The Case Study of the Emirati Telecommunication Sector

Today, social media is a trendsetter of many new and unique aspects, including digital marketing. Consequently, people have more exposure to products and services from all over the world. Similarly, digital marketing is also beneficial to the corporate sector in the UAE as the number of online media users is briskly increasing. As social media usage is widely accompanied by ease of access, ease of use, and useful outcomes, the current study also aims to examine it in the realms of the digital market. We employed an exploratory approach and utilized a self-proposed model in this study. First, we tested the measurement model and then the structural model by following the rules specified in Structural Equation Modelling. Results revealed the relationship between Social Media Marketing and Accessibility <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$(\mathbf{p} &gt;$</tex> 0.000). and Cost-effectiveness remained strongly significant <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$(\mathbf{p} &gt; \mathbf{0.000})$</tex> . However, the relationship between Connectivity and Social Media Marketing is rejected <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$(\mathbf{p} &gt; \mathbf{.065})$</tex> . Moreover, the direct effect of Social Media Marketing on Customers' Loyalty also remained strongly significant <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$(\mathbf{p} &gt; \mathbf{0.000})$</tex> . Finally, the mediating role of Public Relations also remained significant with the significance value at <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{p} &gt; \mathbf{.014}$</tex> . Thus, despite conventional media platforms are still used, social media are mainly preferred, and the approaches regarding Public Relations are the same. What has changed is the method to deliver the marketing message more effectively and the preferences that are more focused on customers to increase their loyalty and long-term relationship.
On Learning Embeddings at the Intersection of Communities and Roles

Graph Neural Networks (GNNs) have established themselves as the state of the art of encoding the nodes of a graph into a low-dimensional space by extracting features from the connectivity structure of the graph as well as the features of the nodes. However, since the embedding of a node is updated according to the information aggregated from the immediate neighborhood, a GNN tends to capture the community memberships of the nodes better than the other side of the coin: the role memberships, which quantify how much nodes carry out specific functions from a structural point of view. In this paper, we present RC-GNNs, a category of GNNs designed to learn embeddings from the community and the role memberships as well as the features of the nodes. RC-GNNs learn from different versions of the same graph, in which the nodes are connected according to either the community or the role memberships. Results show that, compared with models such as k-hop GNNs, k-GNNs, and MixHop, RC-GNNs are up to 4% more accurate in classifying the nodes of CiteSeer, Cora, and PubMed and up to 3% in classifying the graphs of MUTAG, PROTEINS, and Synthie.
A multi-view 3D reconstruction method based on pixel discretization model for robot pose tracking

In the field of visual measurement, camera resolution is a crucial parameter that constrains the improvement of measurement system accuracy. In the simplest binocular system, two pixels sharing corresponding point relationships correspond to a 3D spatial region. However, the measurement accuracy varies for different positions within this region where the target points may reside, allowing for error modeling. In measurement systems composed of three or more cameras, multiple matching pixel points can be obtained for the same target point. Consequently, the process of 3D reconstruction in binocular systems transitions from a definite solution problem to an optimization problem. This paper introduces a 3D reconstruction method based on pixel discretization model. It calculates measurement result weights based on the measurement uncertainties for the same target point by different binocular systems, further enhancing measurement precision. The feasibility of this method is verified through 3D reconstruction experiments using a three-camera system, resulting in improvements of 8.50% and 5.21% when compared to conventional 3D reconstruction methods based on direct centroid and least squares, respectively. In practical applications, this method is suitable for the absolute pose tracking of industrial robots and the construction of global measurement fields.
Improving Object Detection Performance Through Selective Low-Light Enhancement

This paper proposes a selective low-light enhancement algorithm and integrated NMS (Non-Maximum Suppression) operation to improve the accuracy and performance of object detection in low-light environments. The method involves selectively enhancing low-light images by applying the CLHAE (Contrast Limited Adaptive Histogram Equalization) algorithm to generate improved images. The improved and original images are then simultaneously fed into the object detection network, and the NMS is applied to remove redundant detections and obtain the final results. The proposed approach is evaluated on the ExDark dataset, demonstrating superior performance compared to existing methods.