Reviewerly/exHarmony huggingface dataset:
A NEW MOBILE RF DRIVE TEST APPLICATION

&lt;p&gt;To enhance the quality of service (QoS) provided to users, network operators are compelled to continually refine their network infrastructure through the iterative process of drive testing. Drive testing is a method used to measure and assess the coverage, capacity, and QoS in a mobile radio network. However, the conventional paradigm governing drive testing, which entails the deployment of either radio frequency equipment or software solutions such as TEMS Investigation accompanied by GPS and an MS (Mobile Station), is marred by inherent limitations that hinder precision in data collection spatiality and necessitate a cumbersome use of multiple tools. In our work, we embark on a novel trajectory, anchoring our non-invasive drive test methodology on the potency of a wearable device such as smart phones. Inspired by this, we design an Android-based drive test platform that stands as a fulcrum of innovation. This platform consists primarily of a mobile application that acts as a measuring instrument, and an API to relay the data collected by the mobile application. This Android-based approach provides operators with heightened precision in both location determination and parameter measurements. Moreover, it streamlines service provisioning, eliminating the need for a range of complex tools, and instead offering a simple application-based solution. By adopting our mobile application, the drive testing process becomes adaptable to a variety of scenarios, including roads with high traffic density or narrow pathways. Our methodology signifies a significant step forward in improving resources-efficient drive testing process for operators.&lt;/p&gt;
CLTTS: A Cooperative Learning Strategy for Non-Autoregressive Text-to-Speech

&lt;p&gt;Non-autoregressive text-to-speech (TTS) has recently received a lot of atten-tion due to its reliability and fast reasoning. Despite its outstanding achieve-ment, non-autoregressive speech synthesis still faces some critical challenges. A major issue is that non-autoregressive methods necessitate an external toolkit to align the speech with the transcript, thus substantially complicat-ing the process of building the model. Besides, non-autoregressive methods suffer from the one-to-many mapping issue, where the same transcript may correspond to speech in numerous styles. This problem may harm the ex-pressiveness of the generated speech because the model tends to provide output with an average style. To address the above issues, this paper pro-poses a cooperative learning strategy for non-autoregressive speech synthe-sis. Specifically, the suggested method employs both an autoregressive and a non-autoregressive TTS model during the training procedure. The autore-gressive model is trained as a partner at each iteration, providing essential alignment information and also the prosody embedding of the speech to the non-autoregressive model. After receiving the above useful knowledge, the non-autoregressive model can be further trained without relying on external alignment tools. Meanwhile, the prosody embedding from the autoregressive model and the pitch information from the raw audio can be utilised together to alleviate the one-to-many mapping problem. Experimental results demon-strate that our approach can produce comparable speech to the most popular FastSpeech 2 model while drastically reducing the complexity of constructing a non-autoregressive TTS model.&lt;/p&gt;
Subjective Bayesian Network-based Interdependent Mission Impact Assessment with Game-Theoretic Attack-Defense Interactions

&lt;p&gt;An accurate assessment of a mission system's performance, called Mission Impact Assessment (MIA), can effectively identify and counteract any issues in the current system to avoid potential risks or vulnerabilities that may cause mission failure. Although the importance of MIA research has been recognized to mitigate the system's potential risk for more than a decade, little research has shown a comprehensive MIA framework with experimental validation showing the inference performance of the MIA tools under realistic attack-defense interactions. To fill this gap, we propose an interdependent mission impact assessment (MIA), called iMIA, that can adequately capture the inter-relationships of the key components in a mission system and environment and accurately infer a mission outcome (i.e., success or failure) under uncertainty. In the proposed iMIA, we first consider Subjective Bayesian Networks to evaluate how successfully a given system introduces impacts on mission success under uncertainty due to a lack of evidence. In addition, we consider strategic attack-defense interactions based on a hypergame theory in which the attacker and defender can take actions under their perceived uncertainty. Our extensive experiments showed the outperformance of our proposed MIA up to 70% and 25% over the performance of the baseline and the state-of-the-art Bayesian Network-based counterparts, respectively, in terms of inference accuracy in mission performance (e.g., mission outcomes, such as mission success or failure). We also provide in-depth sensitivity analyses to identify the key system components that should be considered more carefully to avoid mission failure.&lt;/p&gt;
PECULIARITIES OF DESIGNING GROUP WORK ON THE DEVELOPMENT OF SUBJECTNESS IN THE VIRTUAL LEARNING SPACE

Group work designing on the development of subjectivity in a virtual educational space is an important and relevant task for both psychology and pedagogy. Its solution and practical implementation have a substantial impact on the effectiveness of distance learning and psychological development, in particular, on the development of adult subjectivity. Subjectivity is defined as the competence of being a subject of activity, a source of influence on reality, and an intention of its transformation; accordingly, the subject of activity is characterized by such qualities as purposefulness, activity, and initiative. The degree of development of these qualities characterizes the degree of subjectivity development. Subjectivity is a targeting and competence to change reality in the broadest sense. Virtual educational spaces are learning tools as well as tools for personality and intelligence development. The design of an educational (or educational and developmental) course in a virtual educational space for the development of subjectivity, which includes group educational and psychological work (and, accordingly, group dynamics), includes such components as defining the goal of education, formulating tasks (volumes of work), training program, psychological-pedagogical scenario, student model, teacher (leader) model, capabilities of the technological educational platform, etc. Further research into the possibilities and features of designing distance educational and development courses, studying the specifics of their design on different electronic educational platforms, with different communication tools, with different content, with the presence and absence of video communication, multimedia component, "mixed" format of work, etc., is necessary.
Satellite image super-resolution reconstruction based on ACGAN and dual-channel dense residual network

Abstract For solving the problem of simple information image super-resolution reconstruction, this paper proposes a method based on ACGAN and dual-channel dense residual networks. Firstly, the different information represented by the images is classified to form different types of datasets. Focusing on image feature information, ACGAN is used to learn image feature information to generate images with this kind of feature information in this type of dataset so as to achieve image enhancement. Secondly, the study designed and proposed a dual-channel dense residual network to train the enhanced image dataset and achieve image super-resolution reconstruction. Experiments show that the method proposed in this paper not only can obtain higher reconstruction image quality than other methods but also obtain higher PSNR and SSIM than others. From this point of view, the application of this technology will have a far-reaching impact on the research of image super-resolution reconstruction with simple and sparse feature information.
Understanding Machine Learning Explainability Models in the context of Pancreatic Cancer Treatment

The increasing adoption of artificial intelligent systems at sensitive domains where humans are particularly, such as medicine, has provided the context to deeply explore ways of making machine learning models (ML) understandable for their final users. The success of such systems require the trust of their users, and thus there is a need to design and provide methods to understand the decisions made by such systems. We start from a public Pancreatic Cancer dataset and experiment with different ML models on a diagnosis scenario with the goal to decide whether a patient should be prescribed with a chemotherapy treatment. To validate the diagnosis results we explore different explainability approaches: Decision Tree, Random Forest, and model agnostic ad-hoc models, and compare them against a standard Pancreatic Cancer treatment set of rules. The increasing adoption of artificial intelligent systems at sensitive domains where humans are particularly, such as medicine, has provided the context to deeply explore ways of making machine learning models (ML) understandable for their final users. The success of such systems require the trust of their users, and thus there is a need to design and provide methods to understand the decisions made by such systems. We start from a public Pancreatic Cancer dataset and experiment with different ML models. To validate the diagnostic results we explore different explainability approaches: Decision Tree based approach, Random Forest based approach, and different model agnostic ad-hoc approaches, and we compare them against a standard Pancreatic Cancer treatment set of rules
Introducing a Human Activity Recognition Dataset Gathered on Real-Life Conditions

Human activity recognition (HAR) has garnered significant scientific interest in recent years. The widespread use of smartphones enabled convenient and cost-effective data collection, eliminating the need for additional wearables. Given that, this paper introduces a novel HAR dataset in which participants had freedom in choosing smartphone orientation and placement during activities, ensuring data variability. It also includes contributions from diverse individuals, reflecting unique smartphone usage habits. Moreover, it comprises measurements from accelerometer, gyroscope, magnetometer, and GPS, corresponding to one of four activities: inactive, active, walking, or driving. Unlike other datasets, the collected data in this study were obtained from smartphones used in real-life scenarios
Homography Estimation Using Marker Projection Control: A Case of Calibration-Free Projection Mapping

Homography transformation, or planar projective transformation, between camera and projector images, which can be estimated by observing four or more pairs of corresponding points between two images, plays a crucial role in dynamic projection mapping with an uncalibrated projector-camera pair. This study discusses a case of dynamic projection mapping onto a moving textured surface where the point correspondence is provided through an actively controlled (not static) marker pattern projection by the projector along with the movement of the surface. Qualitative advantages are discussed in terms of system implementation, the image processing algorithm, and artifact perception by human observers. In addition, a quantitative analysis of alignment errors in the projection mapping is presented to show the effectiveness of the active control of marker patterns.
Implementation of Parallel Navigation and PID Controller for Drone Swarm Pursuit

The usage of a drone in pursuit has been researched quite extensively, but with only one drone, the pursuit suffers from sensory limitations where sensors are either expensive or have limited capabilities. In this paper, a drone swarm controller system for pursuit is developed to solve the problem based on Parallel Navigation (PN) and Proportional-Integral-Derivative (PID). The controller consists of two main parts with different functions, each in charge of navigation and control modulation. Navigation is performed by a High-Level Controller built on PN, which allows navigation toward an object while avoiding obstacles simultaneously. Control modulation is performed by a Low-Level Controller based on a PID controller. The implementation study is then performed in a simulated environment with varying swarm members. Results show that the controller system successfully coordinated the drone swarm to surround the target after it is found, and pursuit with more drones consistently locates the target faster.
Toward training NLP models to take into account privacy leakages

With the rise of machine learning and data-driven models especially in the field of Natural Language Processing (NLP), a strong demand for sharing data between organisations has emerged. However datasets are usually composed of personal data and thus subject to numerous regulations which require anonymization before disseminating the data. In the medical domain for instance, patient records are extremely sensitive and private, but the de-identification of medical documents is a complex task. Recent advances in NLP models have shown encouraging results in this field, but the question of whether deploying such models is safe remains.In this paper, we evaluate three privacy risks on NLP models trained on sensitive data. Specifically, we evaluate counterfactual memorization, which corresponds to rare and sensitive information which has too much influence on the model. We also evaluate membership inference as well as the ability to extract verbatim training data from the model. With this evaluation, we can cure data at risk from the training data and calibrate hyper parameters to provide a supplementary utility and privacy trade-off to the usual mitigation strategies such as using differential privacy. We exhaustively illustrate the privacy leakage of NLP models through a use-case using medical texts and discuss the impact of both the proposed methodology and mitigation schemes.
Improving Student Learning Outcomes Through the TaRL Learning Model on Discussion

The primary aim of this research was to enhance students' learning outcomes by implementing the Teaching at The Right Level (TaRL) learning model in the context of Discussion Text Material. The TaRL model is an innovative approach to improving learning outcomes for students, with a particular emphasis on foundational skills like literacy and numeracy. This model encompasses three key stages: Assessment, Grouping, and Remediation. The research employed Classroom Action Research (PTK) methodology and consisted of two cycles conducted in March and May 2023. The participants in this research were 36 students from Class X.4 at SMA Negeri 1 Palembang. Data collection involved the use of observation techniques and written tests. Descriptive analysis, employing percentage techniques, was utilized to analyze the collected data. The research findings clearly demonstrated the positive impact of applying the Teaching at The Right Level (TaRL) learning model on students' learning outcomes. The test results revealed a significant improvement, with scores increasing from the initial stage (pre-cycle) of 39% to 72% in 1st Cycle (representing a 33% increase), and further rising to 97% in 2nd Cycle (a 25% increase compared to 1st Cycle). These results strongly indicate that the implementation of the Teaching at The Right Level (TaRL) learning model effectively enhances students' ability to comprehend English discussion texts Class X.4 students at SMA Negeri 1 Palembang during the 2022/2023 Academic Year.
DEVELOPING ANIMATION VIDEOS AS LEARNING MEDIA FOR STUDENTS SPEAKING ABILITY IN DESCRIPTION TEXT ON JUNIOR HIGH SCHOOL 2 METRO

Animation has many benefits such as clearer and attractivelearning, the learning process is more efficient, effective because students canrepeat it as needed, can improve the quality of learning outcomes, and can overcomethe limitations of space and time between teachers and student.This study of learningmedia in the form of animated videos will be very helpful for teachers and students asan attractive learning media and make it easier for students to learn and understandEnglish subjects, especially in speaking skill on descriptive text material. The type ofthis research are research and development (R&amp;D). The models of this research areADDIE model. There are five steps in ADDIE model; analysis, design, development,implementation, and evaluation. Assessment on video animation by media expertsand material experts gets a percentage 79% and 82%. Students and teachers gave apositive assessment that is 82% and 96%, each assessment was in the very goodcategory.
Privacy through Diffusion: A White-listing Approach to Sensor Data Anonymization

Generative models have shown great promise in synthesizing high-quality time-series data that resemble the sensor data generated by mobile and IoT devices, but do not reveal the user's private attributes. These synthesized data can be treated as the obfuscated version of the sensor data and sent to downstream applications. However, existing obfuscation techniques that rely on generative models require the user to enumerate all inferences they deem intrusive. This black-listing approach would inevitably result in privacy loss if the definition of intrusive inferences changes after releasing the obfuscated data. In this work, we propose a white-listed approach to sensor data obfuscation based on a guided denoising diffusion model and a surrogate model for the desired inference. We evaluate this obfuscation model on a human activity recognition dataset and show that the proposed obfuscation model provides an acceptable privacy-utility trade-off, without assuming knowledge of the private attributes.
Fully Homomorphic Privacy-Preserving Naive Bayes Machine Learning and Classification

Despite the revolutionary advancement of homomorphic encryption (HE) technology, no efficient fully homomorphic Naive Bayes (NB) classifier that can perform training with HE-encrypted data has been developed without using a decryption function. In this study, we propose an approximate homomorphic logarithm calculation method with a relative error of less than 0.01% on average. Using the SIMD function of the underlying HE scheme, the logarithm values for thousands of encrypted probability values can be calculated in approximately 2.5s with the help of a GPU. Based on this, we propose an efficient fully homomorphic NB method. The proposed NB classifier could complete the training on the breast cancer dataset considered within approximately 14.3s, and perform inference for a query in 0.84s. This is estimated around 28 times faster compared to the recent privacy-preserving NB classifier supporting an analogous level of security by Liu et al. in 2017 on the same computational environment and the same CKKS HE operations performance.
Does complimentary information from multispectral imaging improve face presentation attack detection?

Presentation Attack Detection (PAD) has been extensively studied, particularly in the visible spectrum. With the advancement of sensing technology beyond the visible range, multispectral imaging has gained significant attention in this direction. We present PAD based on multispectral images constructed for eight different presentation artifacts resulted from three different artifact species. In this work, we introduce Face Presentation Attack Multispectral (FPAMS) database to demonstrate the significance of employing multispectral imaging. The goal of this work is to study complementary information that can be combined in two different ways (image fusion and score fusion) from multispectral imaging to improve the face PAD. The experimental evaluation results present an extensive qualitative analysis of 61650 sample multispectral images collected for bonafide and artifacts. The PAD based on the score fusion and image fusion method presents superior performance, demonstrating the significance of employing multispectral imaging to detect presentation artifacts.
Reti-Diff: Illumination Degradation Image Restoration with Retinex-based Latent Diffusion Model

Illumination degradation image restoration (IDIR) techniques aim to improve the visibility of degraded images and mitigate the adverse effects of deteriorated illumination. Among these algorithms, diffusion model (DM)-based methods have shown promising performance but are often burdened by heavy computational demands and pixel misalignment issues when predicting the image-level distribution. To tackle these problems, we propose to leverage DM within a compact latent space to generate concise guidance priors and introduce a novel solution called Reti-Diff for the IDIR task. Reti-Diff comprises two key components: the Retinex-based latent DM (RLDM) and the Retinex-guided transformer (RGformer). To ensure detailed reconstruction and illumination correction, RLDM is empowered to acquire Retinex knowledge and extract reflectance and illumination priors. These priors are subsequently utilized by RGformer to guide the decomposition of image features into their respective reflectance and illumination components. Following this, RGformer further enhances and consolidates the decomposed features, resulting in the production of refined images with consistent content and robustness to handle complex degradation scenarios. Extensive experiments show that Reti-Diff outperforms existing methods on three IDIR tasks, as well as downstream applications. Code will be available at \url{https://github.com/ChunmingHe/Reti-Diff}.
Unveiling the Unseen Potential of Graph Learning through MLPs: Effective Graph Learners Using Propagation-Embracing MLPs

Recent studies attempted to utilize multilayer perceptrons (MLPs) to solve semi-supervised node classification on graphs, by training a student MLP by knowledge distillation (KD) from a teacher graph neural network (GNN). While previous studies have focused mostly on training the student MLP by matching the output probability distributions between the teacher and student models during KD, it has not been systematically studied how to inject the structural information in an explicit and interpretable manner. Inspired by GNNs that separate feature transformation $T$ and propagation $\Pi$, we re-frame the KD process as enabling the student MLP to explicitly learn both $T$ and $\Pi$. Although this can be achieved by applying the inverse propagation $\Pi^{-1}$ before distillation from the teacher GNN, it still comes with a high computational cost from large matrix multiplications during training. To solve this problem, we propose Propagate & Distill (P&D), which propagates the output of the teacher GNN before KD and can be interpreted as an approximate process of the inverse propagation $\Pi^{-1}$. Through comprehensive evaluations using real-world benchmark datasets, we demonstrate the effectiveness of P&D by showing further performance boost of the student MLP.