Reviewerly/exHarmony huggingface dataset:
RF-Keypad: A Battery-Free Keypad Based on COTS RFID Tag Array

With the explosive increase in the Internet of Things (IoT) devices, there is a rising demand for seamless and intuitive interactions between users and smart devices. Existing solutions require either dedicated sensors with microcontroller and battery power or modification of the hardware. This paper presents RF-Keypad to realize a battery-free and wireless touch input interaction solution via commercial off-the-shelf (COTS) RFID devices. RF-Keypad can easily turn an ordinary rigid object into an interaction touch keypad by deploying a tag array on the surface of the object. RF-Keypad extracts the received signal strength (RSS) variation feature of the touch action and builds an RSS-based model to detect the touch events, which is robust to the tag position and orientation. To acquire better performance, best touch area that poses distinct RSS variation feature is studied and optimal placement of the tag array is also investigated to eliminate mutual coupling effect between adjacent tags. Two touch detection algorithms based on RSS variance (RV) and the variance of the RSS variance (VoRV) are proposed, respectively, where RV-based algorithm suits the scenario of fixed antenna-tag distance and VoRV-based algorithm is designed for the scenario of variable antenna-tag distance. We implement a prototype of RF-Keypad with commodity RFID devices. Extensive experiments show that RF-Keypad achieves a touch detection accuracy of higher than 96% under fixed antenna-tag scenario and higher than 91% under variable antenna-tag distance scenario. An intelligent door access application is developed to highlight the practicality and scalability of our solution.
Adaptive Cognitive Agents: Updating Action Descriptions and Plans

In this paper we present an extension of Belief-Desire-Intention agents which can adapt their performance in response to changes in their environment. We consider situations in which the agent's actions no longer perform as anticipated. Our agents maintain explicit descriptions of the expected behaviour of their actions, are able to track action performance, learn new action descriptions and patch affected plans at runtime. Our main contributions are the underlying theoretical mechanisms for data collection about action performance, the synthesis of new action descriptions from this data and the integration with plan reconfiguration. The mechanisms are supported by a practical implementation to validate the approach.
A Heuristic Approach for Scalable Quantum Repeater Deployment Modeling

Long-distance quantum communication presents a significant challenge due to the degrading fidelity of transmitted qubits. Quantum repeaters are proposed to overcome this issue through Bell measurements on entangled qubit pairs. However, despite its necessity to enable wide-area quantum internet, the deployment cost of quantum repeaters can be prohibitively expensive; thus, developing a quantum repeater deployment model that can strike a balance between cost and effectiveness is essential. In this work, we present novel heuristic models to quickly determine a minimum number of quantum repeaters to deploy in large-scale networks to provide end-to-end connectivity between all end hosts. The results show that, compared to the integer linear programming approach, the heuristic methods can find near-optimal solutions while reducing the execution time from days to seconds when evaluated against several synthetic and real-world networks such as SURFnet and ESnet. As reliability is critical for any network, we demonstrate that the heuristic method can estimate deployment models to endure up to two link/node failures.
INFORMATION SYSTEM DESIGN OF TOURIST ATTRACTIONS IN THE WORLD

This research was conducted with the aim of designing and building a tourist information system that can present and provide information on tourist attractions and culture in the world. The background of this research was raised based on the need for an information system that can provide complete information quickly and accurately, because the delivery of information on tourist attractions in the world today still uses brochures, in other words delivery like this. information is not effective enough and has not been implemented. enough to attract tourists to visit tourist attractions in the world. And with this information system it can also make it easier for tourism and local culture to manage information on tourist attractions and culture in that place. This study uses the SDLC (System Development Life Cycle) method which consists of several stages, namely planning, analysis, design, implementation and maintenance of the system. So that it can be used easily by users to get the information services they need.
On the Reduction of Mixed Gaussian and Impulsive Noise in Heavily Corrupted Color Images

Abstract In this paper, a novel approach to the mixed Gaussian and impulsive noise reduction in color images is proposed. The described denoising framework is based on the Non-Local Means (NLM) technique, which proved to efficiently suppress only the Gaussian noise. To circumvent the incapacity of the NLM filter to cope with impulsive distortions, a robust similarity measure between image patches, which is insensitive to the impact of impulsive corruption, was elaborated. To increase the effectiveness of the proposed approach, the blockwise NLM implementation was applied. However, instead of generating a stack of output images that are finally averaged, an aggregation strategy combining all weights assigned to pixels from the processing block was developed and proved to be more efficient. Based on the results of comparisons with the existing denoising schemes, it can be concluded that the novel filter yields satisfactory results when suppressing high-intensity mixed noise in color images. Using the proposed filter the image edges are well preserved and the details are retained while impulsive noise is efficiently removed. Additionally, the computational burden is not significantly increased, compared with the classic NLM, which makes the proposed modification applicative for practical image denoising tasks.
A fast image inpainting algorithm based on an adaptive scanning strategy

OBJECTIVES: In exemplar-based image inpainting algorithms, there are often issues with the calculation of patch similarity for matching, suboptimal strategies for selecting matching patches, and low inpainting speed.METHODS: This paper first uses the variable scale cross-scan block line progressive scan to solve the problem of slow scanning speed and invalid priority formula. Then, an improved weight similarity formula is used for searching to solve the problem of poor computing strategy for similar matching patches. The search range of matching patches gradually increases from small to large until globally searching for similar matching patches to improve the efficiency of inpainting. To further improve the correctness of matching patch selection, this paper uses six levels of priority matching criteria for screening.RESULTS: The experimental results show that the inpainting effect of the proposed method is significantly improved in subjective vision, and the structural similarity (SSIM), peak signal-to-noise ratio (PSNR), and inpainting speed of the inpainting results are all improved.CONCLUSION: For different types of images, the proposed method has a better inpainting effect and higher inpainting speed than the other three advanced methods.
TRADITIONAL AND RECENT TECHNIQUES OF EFFORT ESTIMATION IN SOFTWARE ENGINEERING

In software engineering effort is used to represent measure of use of workforce. It is defined as totaltime that takes members of a software developing team to perform a given task. Software development effort estimating remains a complex problem, and one which continues to attract considerable research attention. The success or failure of projects depends on the accuracy of effort and schedule estimation.The aim of this paper is to present the most relevant methods and models for effort estimation used by software engineers.The primary conclusion is that no single technique is best for all condition because every method or model has an own significance or importance.
Multi-Objective Virtual Machine Placement Algorithm Based on Improved Discrete Differential Evolution

Aiming at the problem of high energy consumption and resource fragmentation caused by unbalanced multidimensional resource usage of servers in current cloud data centers, a virtual machine placement algorithm based on improved discrete differential evolution(IDDE) algorithm was proposed. According to the multi-dimensional resource requirements of virtual machines, the population initialization was used to improve the convergence speed of the algorithm, and the discrete differential mutation and crossover operations were used to ensure the diversity of the population. A multi-group elite selection strategy based on $\varepsilon$ relaxation was proposed to select the optimal virtual machine placement scheme and enhance the global search ability of the algorithm. The simulation results show that compared with the other three algorithms such as the DE algorithm, the IDDE algorithm has a certain improvement effect in reducing energy consumption, improving resource utilization and reducing resource fragmentation.
THE ROLE OF SCIENTIFIC RESEARCH IN THE FORMATION OF COGNITIVE CURIOSITY OF STUDENTS

This article formulates the main conditions for the formation of cognitive activity of students, the organization of research work, the desire for scientific creativity. In addition, the improvement of students' research work is aimed at training highly qualified scientific and experienced specialists in educational institutions. The development of cognitive activity is based on awareness and proficiency in the application of knowledge, providing the skills and abilities necessary for learning and the development of mental abilities. The analysis of the issues of preparing students for scientific and pedagogical activity is focused on cognitive search, self-education, scientific and technical, cultural and informational work, orientation to educational achievements.
Adoption and Implications of Central Bank Digital Currencies (CBDCs) and Cryptocurrencies

Central Bank Digital Currency (CBDC) represents a digitized form of central bank funds that aims to enhance financial services and revolutionize the economy. It is important to note that CBDCs differ from cryptocurrencies as they are centrally regulated by sovereign authorities. When compared to cryptocurrencies. CBDCs prioritize privacy by limiting transaction details disclosure. The value creation approaches also vary - CBDCs adhere more closely to traditional models while cryptocurrencies are considered disruptive innovations. CBDCs provide various benefits including social control mechanisms, innovation stimulation and reduced dependence on banks as intermediaries. However, it is improbable for cryptocurrencies to entirely replace traditional money systems. Instead, CBDCs serve as alternatives to both physical cash and digital currencies by offering a digital medium of exchange coupled with regulatory oversight and respect for user privacy considerations.
Improving Small Footprint Few-shot Keyword Spotting with Supervision on Auxiliary Data

Few-shot keyword spotting (FS-KWS) models usually require large-scale annotated datasets to generalize to unseen target keywords. However, existing KWS datasets are limited in scale and gathering keyword-like labeled data is costly undertaking. To mitigate this issue, we propose a framework that uses easily collectible, unlabeled reading speech data as an auxiliary source. Self-supervised learning has been widely adopted for learning representations from unlabeled data; however, it is known to be suitable for large models with enough capacity and is not practical for training a small footprint FS-KWS model. Instead, we automatically annotate and filter the data to construct a keyword-like dataset, LibriWord, enabling supervision on auxiliary data. We then adopt multi-task learning that helps the model to enhance the representation power from out-of-domain auxiliary data. Our method notably improves the performance over competitive methods in the FS-KWS benchmark.
Zero-Shot Video Moment Retrieval from Frozen Vision-Language Models

Accurate video moment retrieval (VMR) requires universal visual-textual correlations that can handle unknown vocabulary and unseen scenes. However, the learned correlations are likely either biased when derived from a limited amount of moment-text data which is hard to scale up because of the prohibitive annotation cost (fully-supervised), or unreliable when only the video-text pairwise relationships are available without fine-grained temporal annotations (weakly-supervised). Recently, the vision-language models (VLM) demonstrate a new transfer learning paradigm to benefit different vision tasks through the universal visual-textual correlations derived from large-scale vision-language pairwise web data, which has also shown benefits to VMR by fine-tuning in the target domains. In this work, we propose a zero-shot method for adapting generalisable visual-textual priors from arbitrary VLM to facilitate moment-text alignment, without the need for accessing the VMR data. To this end, we devise a conditional feature refinement module to generate boundary-aware visual features conditioned on text queries to enable better moment boundary understanding. Additionally, we design a bottom-up proposal generation strategy that mitigates the impact of domain discrepancies and breaks down complex-query retrieval tasks into individual action retrievals, thereby maximizing the benefits of VLM. Extensive experiments conducted on three VMR benchmark datasets demonstrate the notable performance advantages of our zero-shot algorithm, especially in the novel-word and novel-location out-of-distribution setups.
Polynomial-Model-Based Optimization for Blackbox Objectives

For a wide range of applications the structure of systems like Neural Networks or complex simulations, is unknown and approximation is costly or even impossible. Black-box optimization seeks to find optimal (hyper-) parameters for these systems such that a pre-defined objective function is minimized. Polynomial-Model-Based Optimization (PMBO) is a novel blackbox optimizer that finds the minimum by fitting a polynomial surrogate to the objective function. Motivated by Bayesian optimization the model is iteratively updated according to the acquisition function Expected Improvement, thus balancing the exploitation and exploration rate and providing an uncertainty estimate of the model. PMBO is benchmarked against other state-of-the-art algorithms for a given set of artificial, analytical functions. PMBO competes successfully with those algorithms and even outperforms all of them in some cases. As the results suggest, we believe PMBO is the pivotal choice for solving blackbox optimization tasks occurring in a wide range of disciplines.
Deep learning in medical image registration: introduction and survey

Image registration (IR) is a process that deforms images to align them with respect to a reference space, making it easier for medical practitioners to examine various medical images in a standardized reference frame, such as having the same rotation and scale. This document introduces image registration using a simple numeric example. It provides a definition of image registration along with a space-oriented symbolic representation. This review covers various aspects of image transformations, including affine, deformable, invertible, and bidirectional transformations, as well as medical image registration algorithms such as Voxelmorph, Demons, SyN, Iterative Closest Point, and SynthMorph. It also explores atlas-based registration and multistage image registration techniques, including coarse-fine and pyramid approaches. Furthermore, this survey paper discusses medical image registration taxonomies, datasets, evaluation measures, such as correlation-based metrics, segmentation-based metrics, processing time, and model size. It also explores applications in image-guided surgery, motion tracking, and tumor diagnosis. Finally, the document addresses future research directions, including the further development of transformers.
Absence-Free Vision: An Intelligent Classroom Attendance System with Facial Recognition

Attendance taking is a fundamental process in educational environments, but in Peru, there has been a lack of technological advancements in this area, resulting in disadvantages such as delays and human errors. Therefore, the objective of this research project is to propose a facial recognition system for attendance taking in university classrooms using cameras and artificial intelligence based on machine learning. To achieve this objective, the VDI 2206 design methodology is employed, which allows for establishing project requirements and conducting an initial design. Electronic models will be created and integrated into the proposed system. As a result, a detailed electrical diagram, a flowchart of the facial recognition process, a programming model for accurate student identification, and an implementation model in a real-world environment are expected to be obtained. The proposed system aims to optimize and streamline the attendance taking process in university classrooms, eliminating the need for manual intervention and minimizing errors. It is anticipated that this efficient and accurate solution will enhance academic management and the overall educational experience. In conclusion, this research project focuses on developing a facial recognition system for attendance taking in university classrooms within the Peruvian context. By utilizing advanced technologies and the VDI 2206 design methodology, the aim is to achieve an efficient and reliable system that optimizes the attendance taking process and contributes to an enhanced educational experience.
Prediction Error Estimation in Random Forests

In this paper, error estimates of classification Random Forests are quantitatively assessed. Based on the initial theoretical framework built by Bates et al. (2023), the true error rate and expected error rate are theoretically and empirically investigated in the context of a variety of error estimation methods common to Random Forests. We show that in the classification case, Random Forests' estimates of prediction error is closer on average to the true error rate instead of the average prediction error. This is opposite the findings of Bates et al. (2023) which were given for logistic regression. We further show that this result holds across different error estimation strategies such as cross-validation, bagging, and data splitting.
PPSFL: Privacy-preserving Split Federated Learning via Functional Encryption

&lt;p&gt;In this paper, we propose a novel and efficient privacy-preserving split federated learning (PPSFL) framework, that achieves both privacy protection and model accuracy with reasonable computational and communication cost. We describe the implementations of PPSFL on Multi-layer Perceptron (MLP) and Convolutional Neural Network (CNN) models with distributed clients to evaluate the performance of PPSFL. &lt;/p&gt;
Language-Conditioned Change-point Detection to Identify Sub-Tasks in Robotics Domains

In this work, we present an approach to identify sub-tasks within a demonstrated robot trajectory using language instructions. We identify these sub-tasks using language provided during demonstrations as guidance to identify sub-segments of a longer robot trajectory. Given a sequence of natural language instructions and a long trajectory consisting of image frames and discrete actions, we want to map an instruction to a smaller fragment of the trajectory. Unlike previous instruction following works which directly learn the mapping from language to a policy, we propose a language-conditioned change-point detection method to identify sub-tasks in a problem. Our approach learns the relationship between constituent segments of a long language command and corresponding constituent segments of a trajectory. These constituent trajectory segments can be used to learn subtasks or sub-goals for planning or options as demonstrated by previous related work. Our insight in this work is that the language-conditioned robot change-point detection problem is similar to the existing video moment retrieval works used to identify sub-segments within online videos. Through extensive experimentation, we demonstrate a $1.78_{\pm 0.82}\%$ improvement over a baseline approach in accurately identifying sub-tasks within a trajectory using our proposed method. Moreover, we present a comprehensive study investigating sample complexity requirements on learning this mapping, between language and trajectory sub-segments, to understand if the video retrieval-based methods are realistic in real robot scenarios.
Syntactic Analysis of SMOSS Model Combined with Improved LSTM Model: Taking English Writing Teaching as an Example

Abstract In order to improve learners' syntactic understanding and writing ability, thus effectively improving the quality of English writing teaching, this paper explores the method of combining Sequential Matching on Sliding Window Sequences (SMOSS) model with improved Long Short-Term Memory (LSTM) model in English writing teaching to improve the effect of syntactic analysis. Firstly, this paper analyzes the structure of SMOSS model. Secondly, this paper optimizes the traditional LSTM model by using Connectist Temporal Classification (CTC), and proposes an English text error detection model. Meanwhile, this paper combines the SMOSS model with the optimized LSTM model to form a comprehensive syntactic analysis framework, and designs and implements the structure and code of the framework. Finally, on the one hand, the semantic disambiguation performance of the model is tested by using SemCor data set. On the other hand, taking English writing teaching as an example, the proposed method is further verified by designing a comparative experiment in groups. The results show that: (1) From the experimental data of word sense disambiguation, the accuracy of the SMOSS-LSTM model proposed in this paper is the lowest when the context range is "3 + 3", then it rises in turn at "5 + 5" and "7 + 7", reaches the highest at "7 + 7", and then begins to decrease at "10 + 10"; (2) Compared with the control group, the accuracy of syntactic analysis in the experimental group reached 89.5%, while that in the control group was only 73.2%. (3) In the aspect of English text error detection, the detection accuracy of the proposed model in the experimental group is as high as 94.8%, which is significantly better than the traditional SMOSS-based text error detection method, and its accuracy is only 68.3%. This paper verifies the effectiveness and practicability of applying SMOSS model and improved LSTM model to the syntactic analysis task in English writing teaching, and provides new ideas and methods for the application of syntactic analysis in English teaching.
Let the Models Respond: Interpreting Language Model Detoxification Through the Lens of Prompt Dependence

Due to language models' propensity to generate toxic or hateful responses, several techniques were developed to align model generations with users' preferences. Despite the effectiveness of such methods in improving the safety of model interactions, their impact on models' internal processes is still poorly understood. In this work, we apply popular detoxification approaches to several language models and quantify their impact on the resulting models' prompt dependence using feature attribution methods. We evaluate the effectiveness of counter-narrative fine-tuning and compare it with reinforcement learning-driven detoxification, observing differences in prompt reliance between the two methods despite their similar detoxification performances.