Reviewerly/exHarmony huggingface dataset:
Q-Learning: Solutions for Grid World Problem with Forward and Backward Reward Propagations

In the area of adaptive and responsive problems, Reinforcement Learning algorithms have made significant progress. This paper presents solutions to the grid world problem using the model-free reinforcement learning method known as the Q-Learning algorithm. The solutions are developed with forward and backward reward propagations under the assumption of a Markov decision process for the grid world problem. This study detail the implementation and comparison of these two reward calculation methods. The paper also illustrates how an agent interact with the gird environment during both forward and backward propagations and compare their benefits followed by hyperparameter tuning for better understanding of the model’s convergence.
A Novel State Space Exploration Method for the Sparse-Reward Reinforcement Learning Environment

Sparse-reward reinforcement learning environments pose a particular challenge because the agent receives infrequent rewards, making it difficult to learn an optimal policy. In this paper, we propose NSSE, a novel approach that combines that stratified state space exploration with prioritised sweeping to enhance the informativeness of learning, thus enabling fast learning convergence. We evaluate NSSE on three typical Atari sparse reward environments. The results demonstrate that our state space exploration method exhibits strong performance compared to two baseline algorithms: Deep Q-Network (DQN) and noisy Deep Q-Network (Noisy DQN).
Consumer-Centric Insights into Resilient Small Object Detection: SCIoU Loss and Recursive Transformer Network

As an emerging consumer electronic product, the use of unmanned aerial vehicle(UAV) for a variety of tasks has received growing attention and favor in the enterprise or individual consumer electronics market in recent years. The deep neural network based object detectors are convenient to embed into the UAV product, however, the drone-captured images could bring the potential challenges of object occlusion, large scale difference and complex background to these methods because they are not desinged for the detection of small and tiny objects within the aerial images. To address the problem, we propose an improved YOLO paradigm called SR-YOLO with an Efficient Neck, Shape CIoU and Recursion Bottleneck Transformer for better object detection performance in consumer-level UAV products. Firstly, an efficient neck structure is presented to retain richer features through a small object detection layer and an up-sampling operator suitable for small object detection. Secondly, we design a new prediction box loss function called shape complete-IoU(SCIoU), which utilizes a width (height) limiting factor to alleviate the deficiency that the CIoU only focuses on aspect ratios by taking into account both the aspect ratio and the ratio of the two boxes’ widths. Moreover, combined with recurrent neural network and multi-head self-attention mechanism at the cyclic manner, a recursive bottleneck transformer is constructed to relieve the impact of highly dense scene and occlusion problems exists in UAV images. We conduct the extensive experiments on two public datasets of VisDrone2019 and TinyPerson, where the results show that the proposed model surpasses the compared YOLO by 8.1% and 3.2% in mAP50 respectively. In addition, the analysis and case study also validate our SR-YOLO’s superiority and effectiveness.
Learning Self-Corrective Network via Adaptive Self-Labeling and Dynamic NMS for High-Performance Long-Term Tracking

This article presents a self-corrective network-based long-term tracker (SCLT) including a self-modulated tracking reliability evaluator (STRE) and a self-adjusting proposal postprocessor (SPPP). The targets in the long-term sequences often suffer from severe appearance variations. Existing long-term trackers often online update their models to adapt the variations, but the inaccurate tracking results introduce cumulative error into the updated model that may cause severe drift issue. To this end, a robust long-term tracker should have the self-corrective capability that can judge whether the tracking result is reliable or not, and then it is able to recapture the target when severe drift happens caused by serious challenges (e.g., full occlusion and out-of-view). To address the first issue, the STRE designs an effective tracking reliability classifier that is built on a modulation subnetwork. The classifier is trained using the samples with pseudo labels generated by an adaptive self-labeling strategy. The adaptive self-labeling can automatically label the hard negative samples that are often neglected in existing trackers according to the statistical characteristics of target state, and the network modulation mechanism can guide the backbone network to learn more discriminative features without extra training data. To address the second issue, after the STRE has been triggered, the SPPP follows it with a dynamic NMS to recapture the target in time and accurately. In addition, the STRE and the SPPP demonstrate good transportability ability, and their performance is improved when combined with multiple baselines. Compared to the commonly used greedy NMS, the proposed dynamic NMS leverages an adaptive strategy to effectively handle the different conditions of in view and out of view, thereby being able to select the most probable object box that is essential to accurately online update the basic tracker. Extensive evaluations on four large-scale and challenging benchmark datasets including VOT2021LT, OxUvALT, TLP, and LaSOT demonstrate superiority of the proposed SCLT to a variety of state-of-the-art long-term trackers in terms of all measures. Source codes and demos can be found at https://github.com/TJUT-CV/SCLT.
Celebrating Diversity With Subtask Specialization in Shared Multiagent Reinforcement Learning

Subtask decomposition offers a promising approach for achieving and comprehending complex cooperative behaviors in multiagent systems. Nonetheless, existing methods often depend on intricate high-level strategies, which can hinder interpretability and learning efficiency. To tackle these challenges, we propose a novel approach that specializes subtasks for subgroups by employing diverse observation representation encoders within information bottlenecks. Moreover, to enhance the efficiency of subtask specialization while promoting sophisticated cooperation, we introduce diversity in both optimization and neural network architectures. These advancements enable our method to achieve state-of-the-art performance and offer interpretable subtask factorization across various scenarios in Google Research Football (GRF).
Application Strategies of Hybrid Teaching Model in Physical Education Teaching in Vocational Colleges

To improve physical education in vocational colleges, a hybrid teaching model should be developed, taking into account local conditions, gradual progress, and deep integration. The process includes resetting teaching goals, optimizing teaching content, adjusting teaching segments, and improving teaching evaluation. Teachers can use video resources to interact with students before class, set up different student display projects during the course, encourage group cooperation and inter-group assessment, conduct in-class tests and knowledge competitions to reinforce students’ sports skills, and suggest appropriate after-class activities. An online and offline self-study model can also motivate students to participate in sports.
Force: Highly Efficient Four-Party Privacy-Preserving Machine Learning on GPU

Tremendous efforts have been made to improve the efficiency of secure Multi-Party Computation (MPC), which allows $$n \ge 2$$ parties to jointly evaluate a target function without leaking their own private inputs. It has been confirmed by previous research that Three-Party Computation (3PC) and outsourcing computations to GPUs can lead to huge performance improvement of MPC in computationally intensive tasks such as Privacy-Preserving Machine Learning (PPML). A natural question to ask is whether super-linear performance gain is possible for a linear increase in resources. In this paper, we give an affirmative answer to this question. We propose $$\textsf{Force}$$ , an extremely efficient Four-Party Computation (4PC) system for PPML. To the best of our knowledge, each party in $$\textsf{Force}$$ enjoys the least number of local computations, smallest graphic memory consumption and lowest data exchanges between parties. This is achieved by introducing a new sharing type $$\mathcal {X}\text {-}\textsf{share} $$ along with MPC protocols in privacy-preserving training and inference that are semi-honest secure in the honest-majority setting. By comparing the results with state-of-the-art research, we showcase that $$\textsf{Force}$$ is sound and extremely efficient, as it can improve the PPML performance by a factor of 2 to 38 compared with other latest GPU-based semi-honest secure systems, such as $$\textsf{Piranha}$$ (including $$\textsf{SecureML}$$ , $$\textsf{Falcon}$$ , $$\textsf{FantasticFour}$$ ), $$\textsf{CryptGPU}$$ and $$\textsf{CrypTen}$$ .
A Rule-Based Expert Advisory System for Restaurants Using Machine Learning and Knowledge-Based Systems Techniques

A healthy diet and daily physical activity are a cornerstone in preventing serious diseases and conditions such as heart disease, diabetes, high blood pressure, and hypertension. They also play an important role in the healthy growth and cognitive development for young and old people. Thus, this paper presents a new restaurant advisory system (RAS) using artificial intelligence (AI) techniques such as machine learning, decision tree, and rule-based methods. The proposed system makes a smart decision based on the user's input information to generate a list of appropriate meals that fit his/her health condition. For accuracy and efficiency measurement procedure in the decision-making process, a dataset from 1100 participants suffering from several diseases such as allergy, age, and body has been created and validated. The performance of the RAS was tested using Visual Basic.net Framework and prolog language. The RAS achieves an accuracy of 100% by testing 30 different live cases.
Electronic Currency in Terms of Modern Economic and Legal Challenges

The article provides a brief excursion into the development of cryptocurrencies and presents the theoretical content of a new type of digital money. Today, there is no unified interpretation of cryptocurrency in the world. The history of the origin of crypto-currencies, their distinguishing features from ordinary currency, their legal status, specifics of use, and the attitude of the governments of different countries towards them are characterized. The article highlights the most important features of cryptocurrency, such as decentralization, anonymity, and lack of guarantees. Two possible scenarios for the development of crypto-currency in Georgia are proposed: prohibitive and regulatory. It is noted that cryptocurrency should not be banned, but should be studied first and then regulated.
Characterizing Eye Gaze for Assistive Device Control

Eye gaze tracking is increasingly popular due to improved technology and availability. However, in assistive device control, eye gaze tracking is often limited to discrete control inputs. In this paper, we present a method for collecting both reactionary and control eye gaze signals to build an individualized characterization for eye gaze interface use. Results from a study conducted with motor-impaired participants are presented, offering insights into maximizing the potential of eye gaze for assistive device control. These findings can inform the development of continuous control paradigms using eye gaze.
Z- Laplacian Matrix Factorization: Network Embedding With Interpretable Graph Signals

Network embedding aims to represent nodes with low dimensional vectors while preserving structural information. It has been recently shown that many popular network embedding methods can be transformed into matrix factorization problems. In this paper, we propose the unifying framework “Z-NetMF”, which generalizes random walk samplers to Z- Laplacian graph filters, leading to embedding algorithms with interpretable parameters. In particular, by controlling biases in the time domain, we propose the Z-NetMF-t algorithm, making it possible to scale contributions of random walks of different length. Inspired by node2vec, we design the Z-NetMF-g algorithm, capturing the random walk biases in the graph domain. Moreover, we evaluate the effect of the bias parameters based on node classification and link prediction tasks. The results show that our algorithms, especially the combined model Z-NetMF-gt with biases in both domains, outperform the state-of-art methods while providing interpretable insights at the same time. Finally, we discuss future directions of the Z-NetMF framework.
The Optimization of Digital Currency Electronic Payment in RMB Based on Big Data and Fuzzy Theory

Supply chain management is a key component of the Electronic-China Yuan (e-CNY) infrastructure and is crucial to developing and using e-CNY. This paper first provides an overview of the features of e-CNY and the idea of incorporating supply chain management into the creation of e-CNY software to better meet user payment requirements. This will help to reinforce the CNY's dominant position in the global monetary system. Meanwhile, this paper uses the fuzzy theory evaluation method to assess the software supply chain management informatization level. More broadly, this paper discusses the development level of digital enterprise supply chain management informatization to understand the current situation of e-CNY software supply chain management. The network's energy usage will increase when e-CNY software is developed. This research suggests a routing protocol based on the combination of chaotic particle swarm optimization (CPSO) and ant colony algorithm (ACA). It employs a new CPSO algorithm to optimize the cluster head selection.
A depth map-based collision detection method in VR environment

The characteristics of machinery "three many and three complexes", i.e. many structures, many mechanisms, many parts, and complex types, structures, and movements, make it difficult for students to understand and digest the teachers' exciting lectures. The virtual assembly can help students to learn in a safe environment, but also to be able to get close to and assemble these complex mechanical structures to help them learn better. The collision detection technology between parts in virtual assembly is the main problem. Based on the Yixinghe engine, this paper proposes to use the depth map for subsequent collision judgments for parts that are in the virtual environment, after using the AABB enclosing box to do the initial collision judgments.
Improved and Secure LSB2 of Message Steganography

Data steganography is widely used in message protection from being read, and mostly used methods based on LSB and LSB2 methods are not enough secure. In this research paper a modified LSB2 method will be introduced. This method will use a complex complicated key to enhance the data protection degree. The key will be used to rearrange the message stream of bits before data hiding and before data extraction. The key will be sensitive to any minor changes giving a corrupted unreadable message when any change was done in the data extraction phase o data steganography phases. It will be shown that the proposed method very efficient comparing with other methods base on LSB or LSB2 methods. The proposed method will add an improvements to the process of steganography by reducing both the hiding and extracting times. It will be shown that the proposed modified LSB2 method will be used to treat any message short or long using any color image, the size of the used covering image will not affect the method efficiency, so we can use bigger covering images to enhance the quality of the stego image by optimizing the value of MSE and PSNR. The proposed method will be implemented and various analysis will be introduced to prove the improvements achieved by the proposed method.
The EPOS multi-disciplinary Data Portal for integrated access to solid Earth science datasets

Abstract The European Plate Observing System (EPOS) is a long-term initiative aimed at integrating research infrastructures for solid Earth science in Europe. EPOS provides a sustainable, multidisciplinary user-oriented platform - the EPOS Data Portal - that facilitates data integration, access, use, and re-use, while adhering to the FAIR principles. The paper describes the key governance, community building, and technical aspects for achieving multidisciplinary data integration through the portal. It also outlines the key portal features for aggregating approximately 250 data sources from more than ten different scientific communities. The main architectural concepts underpinning the portal, namely the rich-metadata, the service-driven data provision, and the usage of semantics, are outlined. The paper discusses the challenges encountered during the creation of the portal, describes the community engagement process, and highlights the benefits to the scientific community and society. Future work includes expanding portal functionalities to include data analysis, processing, and visualization and releasing the portal as an open-source software package.
Head Mounted IMU-Based Driver’s Gaze Zone Estimation Using Machine Learning Algorithm

Predicting the driver’s gaze could be important information in preventing accidents while driving. In this study, machine learning models for estimating the driver’s gaze distraction through head movement data were created and their performance was compared and evaluated. Participants wore glasses-type eye trackers and performed the task of selecting the touch screen buttons while driving. The input variable used in the model was data obtained from a 3-axis accelerometer sensor and a 3-axis gyroscope sensor, and the target variable was eye-gaze data. As a result, it was confirmed that the gaze area could be estimated with a precision, sensitivity, specificity, and F1-score of 72.1%, 72.5%, 66.0%, and 69.3%, respectively, only with the head movement sensing data. The model trained using time-series datasets had higher performance than using non-time series datasets. This study presented one alternative that could be used to determine the driver’s status with an inexpensive sensor.
Day of AI: Innovating Pedagogical Practices to Bring AI Literacy to Classrooms at Scale

We present the Day of AI, an innovative program for K-12 educators around the world to bring AI literacy curricula to their local classrooms and communities, all for free. Our open teacher-facing materials, student-facing materials, and supporting technologies were developed to address key challenges in empowering teachers to bring multi-disciplinary, hands-on AI literacy learning opportunities to their students in the face of limited pedagogical practices, curriculum, and resources in their community. We designed a modular, 4-hour format for multiple grade bands, spanning upper elementary through high school, to bring AI literacy to K-12 classrooms at scale. Student learning objectives included: demystifying how AI works, analyzing AI applications, and thinking critically about the ethical use of AI and its societal implications. Our research study utilized a sequential mixed methods approach to design and evaluate the effectiveness of our curriculum and professional development resources to support teachers in bringing the curricula to their classrooms. A total of 108 teachers from over twenty countries participated in our study, who collectively taught the curricula to over 7,000 students. Quantitative and qualitative findings suggest that teachers were well supported by our teacher-facing materials and professional development training. Teachers also positively rated students’ engagement and AI literacy knowledge gains with the curricula.
An Innovative Model for Teacher Presence or Not in Video for Online Instruction Based on Neural Theory

Online learning has become an essential part of all levels of education today. However, whether an instructor should appear or not during online teaching is a dispute. Some empirical studies show that instructor presence enhances learning outcomes and engagement of students, while others reveal that instructor presence or not does not affect learning outcomes. Some studies support the presence of the instructor because the presence of the instructor provides social cues to the learners. On the contrary, others are against instructor presence because instructor presence distracts students from learning content. Therefore, it is necessary to clarify when teachers should or should not appear in videos during online teaching. This article explores the effectiveness of instructor presence in video based on multimedia learning theory. To simplify the problem, we adopt two media channels: audio and video. For a single channel alone, an additional coherent channel will enhance online learning, while a disjointed channel will hinder learning. However, additional sources from the same channel can hinder learning. Time is also a critical factor do decide on the presence of an instructor. If a screen does not appear for enough time, students cannot finish reading and may feel stressed. On the other hand, if the same screen stays on for a long time, students can feel bored. Therefore, the media channels, the media sources, and their duration decide whether an instructor should appear in a video for online learning. This concise principle can explain the dispute about the presence of an instructor during online learning. In addition, it can guide instructors on when they should present in online learning.
The Implementation of Mind Mapping Technique in Teaching Students Speaking Skills at 7th Grade in EFL Classroom in One Junior High School in Pabedilan

This study aims to determine the application of mind mapping techniques in teaching speaking at 7th grade IN EFL CLASSROOM IN ONE JUNIOR HIGH SCHOOL IN PABEDILAN. The object of research is only focused on the learning process, while the subject of this research is the English teacher at the school. During the learning activities carried out in 2 observations and 4 meetings. The first meeting only observed the teacher applying mind mapping in the speaking class. After the second meeting, the researcher prepared an observation checklist to find out how the teacher applied the mind mapping. The third meeting was an interview with the teacher concerned and the last meeting confirmed the results of data processing in several previous meetings while saying goodbye. This study used qualitative research methods. Data collection techniques include observation, interviews and documentation. In the introduction, the researcher observed the teacher teaching process and then proceeded to the next meeting. Based on the data obtained from observations, it can be seen that the application of mind mapping techniques applied by English teacher so far is quite good, because the techniques used by the teacher are in accordance with the theory proposed by Buzan (2004). The results of the interviews showed that the obstacles in the learning process using mind mapping techniques were on the students themselves. Not all students have the same learning style, some are fast, medium and slow. Therefore, they often have to take different ways to understand the same information or course.