Reviewerly/exHarmony huggingface dataset:
Implementation of input correlation learning with an optoelectronic dendritic unit

The implementation of machine learning concepts using optoelectronic and photonic components is rapidly advancing. Here, we use the recently introduced notion of optical dendritic structures, which aspires to transfer neurobiological principles to photonics computation. In real neurons, plasticity—the modification of the connectivity between neurons due to their activity—plays a fundamental role in learning. In the current work, we investigate theoretically and experimentally an artificial dendritic structure that implements a modified Hebbian learning model, called input correlation (ICO) learning. The presented optical fiber-based dendritic structure employs the summation of the different optical intensities propagating along the optical dendritic branches and uses Gigahertz-bandwidth modulation via semiconductor optical amplifiers to apply the necessary plasticity rules. In its full deployment, this optoelectronic ICO learning analog can be an efficient hardware platform for ultra-fast control.
BUILDING A SCALE OF COMPETENCE TO APPLY INTERDISCIPLINARY INTEGRATED KNOWLEDGE INTO PRACTICE FOR STUDENT

Vietnam began to implement a new general education program to replace the previous one in 2018. According to the new program, the content has many topics that integrate interdisciplinarity. Therefore, teachers need to be able to teach in the direction of interdisciplinary integration to develop students' competence. The current situation in Vietnam shows that teachers are still not confident because they do not have much interdisciplinary integrated knowledge in applying integrated knowledge into practice to the teaching process. This leads to the consequence that when they teach topics related to real life, it will not be feasible, reducing the effectiveness of teaching in schools, and difficult to achieve the goals required by the Ministry of Education and Training. For the above reasons, universities that train pedagogical students also need to make appropriate adjustments to foster and train pedagogical students’ competence in applying interdisciplinary knowledge into practice. The article presents the research results on the process of building and designing the scale, which are specific criteria to measure and evaluate the competence to apply knowledge in the practice of physics pedagogical students. In addition, we also propose an illustrative example to contribute to improving the quality of training for students of Physics pedagogy.
Thermal Pedestrian Detection Based on Different Resolution Visual Image

Abstract Thermal pedestrian detection is a core problem in computer vision. Usually, the corresponding visual image knowledge are used to improve the performance in thermal domain. However, existing methods always assume the same resolution between visible and thermal images. But in reality, there is a problem with this setting. Since thermal imaging acquisition equipment is expensive, the resolution of thermal images is always lower than visible images. To address this issue, we propose a new method, named as Disentanglement Then Restoration (DTR). The key idea is to disentangle the features into content features and modal features, andrestore the complete content features of thermal images by learning the changes of content features caused by different resolutions.Specifically, we first train an object detector such as YOLO to initialize our model. Then, a feature disentanglement network is trained, which can disentangle the features from the backbone as content features and modal features. In the end, the feature disentanglement network is frozen. By forcing the content feature consistency between visual image and upsampled thermal image, the complete content features of low-resolution thermal images are restored. Experiment results on public datasets show that our method performs very well.
Comparative Analysis on the Efficiency of LIS Vis-a-Vis SMIS: A Case Study

Information systems work to make managing data better so that organizational and financial operations can be structured successfully. Schools also spend money on information technologies to enhance their operations, offerings, and outcomes. Information technologies have also grown to a position of considerable relevance in education because of their role in contemporary communities. The department implemented several school-based information systems to assist school administrators and guarantee the caliber of instruction and learning. To provide efficient, open, and effective governance, the school information system must be connected in line with the direction defined by EdTech. With this, the Secondary Schools of Talisay City will integrate the School Information System into its systems to automate grading procedures and information retrieval. Thanks to this shift, teachers and advisors can now simply and quickly obtain the information they need to update.
PENINGKATAN KAPASITAS PUBLIC SPEAKING BAGI APARATUR PEMERINTAH DESA TANJUNG DAYANG SELATAN

Background: The ability to speak in public needs to be mastered by anyone, including village government officials. Not only the apparatus, all elements involved in the village including the Karang Taruna, the Village Representative Body, and the Empowerment and Family Welfare (PKK) of the village need to improve public speaking skills in order to strengthen the village's potential and self-potential. The absence of public speaking learning facilities in the village environment limits the local community's opportunities to develop public speaking skills. Purpose: Therefore, this community service activity aims to improve the public speaking skills of South Tanjung Dayang village apparatus, Indralaya Selatan District, Ogan Ilir Regency, South Sumatra Province. Method: This activity is aimed at village government officials which is carried out using three methods: lectures and questions and answers regarding the basic aspects of public speaking; problem solving by opening discussions about the participants' concrete problems when speaking and solutions to overcome them, as well as demonstrations and simulations. Result: This community service shows that this activity succeeded in increasing participants' understanding of important aspects of public speaking. This can be seen from the average score of the participants during the pre-test which increased from 69.2 with the lowest score of 35 to 85.6 with the lowest score of 65 during the post-test. Statistical analysis using the Wilcoxon test showed that the Sig. (2-tailed) of 0.000 &lt;0.05 which means the alternative hypothesis (Ha) that there is a significant difference between the average pre-test scores and post-test scores has been proven. This shows that public speaking training has a significant influence on increasing the understanding of the participants. Conclusion: The implementation of public speaking training for Tanjung Dayang Selatan village officials as a whole has been successful because there has been a significant increase in understanding.
Preventive remediation methods minimize soil pollution

&lt;span&gt;Soil quality is rich in various substances, as well as a rich variety of uses for life, which leads to the potential for pollution. Once soils are polluted, remediation is mitigative and must be carried out, which has been the focus of many studies so far. However, preventive remediation is the focus of the novelty of this study, which aims to prepare predictive methods. This is a literature review of various studies over the last ten years, which are related to soil quality indicators through the respiration process. Based on the soil microbial respiration process platform, which contains various substances, the results of this study found three preventive remediation methods, namely indicators of substance reactants, gas products, and toxicity to microbes. The three methods simply require measuring the parameters of biochemical oxygen demand, and chemical oxygen demand, in addition to specific measurements of carbon dioxide and microbial enumeration. The advantage of the preventive remediation method is the application of soil response indicators to various types and amounts of contaminants. The implementation of preventive remediation is prior to building infrastructure, which is able to predict changes in soil quality through monitoring, thereby minimizing the potential for mitigative remediation.&lt;/span&gt;
The extent of the application of electronic entrepreneurship in Jordanian universities from the point of view of postgraduate students

The study aimed to identify the extent of the application of electronic entrepreneurship in Jordanian universities from the point of view of graduate students. Results The application of electronic entrepreneurship in Jordanian universities from the point of view of graduate students came to a high degree, and the researcher attributes this to the academic orientation in educational institutions, especially universities, towards keeping pace with the requirements of the modern era, especially the orientation towards electronic entrepreneurship. It recommends increasing the interest of university officials in the subject of electronic entrepreneurship, as it has become an important requirement of the modern era. Keywords: entrepreneurship, electronic entrepreneurship, graduate students
Position and Rotation Invariant Sign Language Recognition from 3D Kinect Data with Recurrent Neural Networks

Sign language is a gesture-based symbolic communication medium among speech and hearing impaired people. It also serves as a communication bridge between non-impaired and impaired populations. Unfortunately, in most situations, a non-impaired person is not well conversant in such symbolic languages restricting the natural information flow between these two categories. Therefore, an automated translation mechanism that seamlessly translates sign language into natural language can be highly advantageous. In this paper, we attempt to perform recognition of 30 basic Indian sign gestures. Gestures are represented as temporal sequences of 3D maps (RGB + depth), each consisting of 3D coordinates of 20 body joints captured by the Kinect sensor. A recurrent neural network (RNN) is employed as the classifier. To improve the classifier's performance, we use geometric transformation for the alignment correction of depth frames. In our experiments, the model achieves 84.81% accuracy.
Mapping the Design Space of Interactions in Human-AI Text Co-creation Tasks

Large Language Models (LLMs) have demonstrated impressive text generation capabilities, prompting us to reconsider the future of human-AI co-creation and how humans interact with LLMs. In this paper, we present a spectrum of content generation tasks and their corresponding human-AI interaction patterns. These tasks include: 1) fixed-scope content curation tasks with minimal human-AI interactions, 2) independent creative tasks with precise human-AI interactions, and 3) complex and interdependent creative tasks with iterative human-AI interactions. We encourage the generative AI and HCI research communities to focus on the more complex and interdependent tasks, which require greater levels of human involvement.
FAIR Begins at home: Implementing FAIR via the Community Data Driven Insights

Arguments for the FAIR principles have mostly been based on appeals to values. However, the work of onboarding diverse researchers to make efficient and effective implementations of FAIR requires different appeals. In our recent effort to transform the institution into a FAIR University by 2025, here we report on the experiences of the Community of Data Driven Insights (CDDI). We describe these experiences from the perspectives of a data steward in social sciences and a data scientist, both of whom have been working in parallel to provide research data management and data science support to different research groups. We initially identified 5 challenges for FAIR implementation. These perspectives show the complex dimensions of FAIR implementation to researchers across disciplines in a single university.
Immense Fidelity Enhancement of Encoded Quantum Bell Pairs at Short and Long-distance Communication along with Generalized Design of Circuit

Quantum entanglement is a unique criterion of the quantum realm and an essential tool to secure quantum communication. Ensuring high-fidelity entanglement has always been a challenging task owing to interaction with the hostile channel environment created due to quantum noise and decoherence. Though several methods have been proposed, achieving almost 100% error correction is still a gigantic task. As one of the main contributions of this work, a new model for large distance communication has been introduced, which can correct all bit flip errors or other errors quite extensively if proper encoding is used. To achieve this purpose, at the very first step, the idea of differentiating the long and short-distance applications has been introduced. Short-distance is determined by the maximum range of applying unitary control gates by the qubit technology. As far as we know, there is no previous work that distinguishes long and short distance applications. At the beginning, we have applied stabilizer formalism and Repetition Code for decoding to distinguish the error correcting ability in long and short distance communication. Particularly for short distance communication, it has been demonstrated that a properly encoded bell state can identify all the bit flip, or phase flip errors with 100% accuracy theoretically. In contrast, if the bell states are used in long distance communication, the error-detecting and correcting ability reduces at huge amounts. To increase the fidelity significantly and correct the errors quite extensively for long-distance communication, a new model based on classical communication protocol has been proposed. All the required circuits in these processes have been generalized during encoding. Proposed analytical results have also been verified with the Simulation results of IBM QISKIT QASM.
Unsupervised Representation Learning in Partially Observable Atari Games

State representation learning aims to capture latent factors of an environment. Contrastive methods have performed better than generative models in previous state representation learning research. Although some researchers realize the connections between masked image modeling and contrastive representation learning, the effort is focused on using masks as an augmentation technique to represent the latent generative factors better. Partially observable environments in reinforcement learning have not yet been carefully studied using unsupervised state representation learning methods. In this article, we create an unsupervised state representation learning scheme for partially observable states. We conducted our experiment on a previous Atari 2600 framework designed to evaluate representation learning models. A contrastive method called Spatiotemporal DeepInfomax (ST-DIM) has shown state-of-the-art performance on this benchmark but remains inferior to its supervised counterpart. Our approach improves ST-DIM when the environment is not fully observable and achieves higher F1 scores and accuracy scores than the supervised learning counterpart. The mean accuracy score averaged over categories of our approach is ~66%, compared to ~38% of supervised learning. The mean F1 score is ~64% to ~33%.
Deep Learning-based RF Fingerprint Authentication with Chaotic Antenna Arrays

Radio frequency (RF) fingerprinting is a tool which allows for authentication by utilizing distinct and random distortions in a received signal based on characteristics of the transmitter. We introduce a deep learning-based authentication method for a novel RF fingerprinting system called Physically Unclonable Wireless Systems (PUWS). An element of PUWS is based on the concept of Chaotic Antenna Arrays (CAAs) that can be cost effectively manufactured by utilizing mask-free laser-enhanced direct print additive manufacturing (LE-DPAM). In our experiments, using simulation data of 300 CAAs each exhibiting 4 antenna elements, we test 3 different convolutional neural network (CNN) architectures under different channel conditions and compare their authentication performance to the current state-of-the-art RF fingerprinting authentication methods.
Audio Visual Language Maps for Robot Navigation

While interacting in the world is a multi-sensory experience, many robots continue to predominantly rely on visual perception to map and navigate in their environments. In this work, we propose Audio-Visual-Language Maps (AVLMaps), a unified 3D spatial map representation for storing cross-modal information from audio, visual, and language cues. AVLMaps integrate the open-vocabulary capabilities of multimodal foundation models pre-trained on Internet-scale data by fusing their features into a centralized 3D voxel grid. In the context of navigation, we show that AVLMaps enable robot systems to index goals in the map based on multimodal queries, e.g., textual descriptions, images, or audio snippets of landmarks. In particular, the addition of audio information enables robots to more reliably disambiguate goal locations. Extensive experiments in simulation show that AVLMaps enable zero-shot multimodal goal navigation from multimodal prompts and provide 50% better recall in ambiguous scenarios. These capabilities extend to mobile robots in the real world - navigating to landmarks referring to visual, audio, and spatial concepts. Videos and code are available at: https://avlmaps.github.io.
Implicit and Explicit Commonsense for Multi-sentence Video Captioning

Existing dense or paragraph video captioning approaches rely on holistic representations of videos, possibly coupled with learned object/action representations, to condition hierarchical language decoders. However, they fundamentally lack the commonsense knowledge of the world required to reason about progression of events, causality, and even the function of certain objects within a scene. To address this limitation we propose a novel video captioning Transformer-based model, that takes into account both implicit (visuo-lingual and purely linguistic) and explicit (knowledge-base) commonsense knowledge. We show that these forms of knowledge, in isolation and in combination, enhance the quality of produced captions. Further, inspired by imitation learning, we propose a new task of instruction generation, where the goal is to produce a set of linguistic instructions from a video demonstration of its performance. We formalize the task using the ALFRED dataset [54] generated using an AI2-THOR environment. While instruction generation is conceptually similar to paragraph captioning, it differs in the fact that it exhibits stronger object persistence, as well as spatially-aware and causal sentence structure. We show that our commonsense knowledge enhanced approach produces significant improvements on this task (up to 57% in METEOR and 8.5% in CIDEr), as well as the state-of-the-art result on more traditional video captioning in the ActivityNet Captions dataset [29].
Note on the Type II codes of length $24$

We express the weight enumerators of self-dual and doubly even (Type II for short) codes of length $24$ with a specified basis. As a consequence, we present some congruence relations among the weight enumerators.
Variation of Gender Biases in Visual Recognition Models Before and After Finetuning

We introduce a framework to measure how biases change before and after fine-tuning a large scale visual recognition model for a downstream task. Deep learning models trained on increasing amounts of data are known to encode societal biases. Many computer vision systems today rely on models typically pretrained on large scale datasets. While bias mitigation techniques have been developed for tuning models for downstream tasks, it is currently unclear what are the effects of biases already encoded in a pretrained model. Our framework incorporates sets of canonical images representing individual and pairs of concepts to highlight changes in biases for an array of off-the-shelf pretrained models across model sizes, dataset sizes, and training objectives. Through our analyses, we find that (1) supervised models trained on datasets such as ImageNet-21k are more likely to retain their pretraining biases regardless of the target dataset compared to self-supervised models. We also find that (2) models finetuned on larger scale datasets are more likely to introduce new biased associations. Our results also suggest that (3) biases can transfer to finetuned models and the finetuning objective and dataset can impact the extent of transferred biases.
A Cyclic Coordinate Descent Method for Convex Optimization on Polytopes

Coordinate descent algorithms are popular for huge-scale optimization problems due to their low cost per-iteration. Coordinate descent methods apply to problems where the constraint set is separable across coordinates. In this paper, we propose a new variant of the cyclic coordinate descent method that can handle polyhedral constraints provided that the polyhedral set does not have too many extreme points such as L1-ball and the standard simplex. Loosely speaking, our proposed algorithm PolyCD, can be viewed as a hybrid of cyclic coordinate descent and the Frank-Wolfe algorithms. We prove that PolyCD has a O(1/k) convergence rate for smooth convex objectives. Inspired by the away-step variant of Frank-Wolfe, we propose PolyCDwA, a variant of PolyCD with away steps which has a linear convergence rate when the loss function is smooth and strongly convex. Empirical studies demonstrate that PolyCDwA achieves strong computational performance for large-scale benchmark problems including L1-constrained linear regression, L1-constrained logistic regression and kernel density estimation.
Analysis of Readiness for Implementation of the Smart City Masterplan in Wonogiri Regency

The development of the world's digital governance era has reached the development of smart city 4.0. Not many cities can make that happen. In Indonesia, the movement towards smart cities is still starting to be echoed. So it is necessary to prepare and have the will of the region to implement a smart city because of the difficulty in preparing to implement a smart city. Wonogiri Regency is one of the districts selected for smart city implementation because the achievement of the Electronic-Based Government System (SPBE) indicator has increased. The purpose of this study was to analyze the readiness of implementing a smart city master plan in the Wonogiri Regency. This research is a qualitative type with data collection techniques through in-depth interviews with 8 purposively selected informants. The researcher found that the readiness of the five components (nature, structure, infrastructure, superstructure, and culture) was 51% in good condition, 32% in moderate condition, and 17% in poor condition. This means that it is necessary to increase the structural and superstructure components where conditions are still bad. Recommendations in increasing readiness for smart city implementation include making regional smart city policies; improving the quality of the relevant apparatus to improve the quality of ICT capabilities; increasing the amount of budget in the smart city program; and preparing supporting institutions