Reviewerly/exHarmony huggingface dataset:
Future Speech Interfaces with Sensors and Machine Intelligence

Speech is the most spontaneous and natural means of communication. Speech is also becoming the preferred modality for interacting with mobile or fixed electronic devices. However, speech interfaces have drawbacks, including a lack of user privacy; non-inclusivity for certain users; poor robustness in noisy conditions; and the difficulty of creating complex man–machine interfaces. To help address these problems, the Special Issue “Future Speech Interfaces with Sensors and Machine Intelligence” assembles eleven contributions covering multimodal and silent speech interfaces; lip reading applications; novel sensors for speech interfaces; and enhanced speech inclusivity tools for future speech interfaces. Short summaries of the articles are presented, followed by an overall evaluation. The success of this Special Issue has led to its being re-issued as “Future Speech Interfaces with Sensors and Machine Intelligence-II” with a deadline in March of 2023.
Test and Training Data Generation for Object Recognition in the Railway Domain

AI development and test is a data driven endeavor. To date it is magnitudes more laborious to collect and annotate training and test data, than to provide a problem matching architecture and train it. In the KI-LOK project, a case study seeks to validate an object recognition system to prevent potentially fatal behavior of autonomous train operations. To accommodate for the vast amount of possible scenarios a train could encounter during operation, we propose a tool chain to automatically generate labeled synthetic images and videos. We start from an ontology of elements such as: Tracks, houses, vehicles or signals, these elements are then sampled and modeled in 3D to represent a scenario. Since the objects and locations of the elements in a scenario are known, no manual annotation or labeling of the data is required. By sampling from an ontology it will be possible to build comprehensive and balanced datasets of scenarios to train and test AI, while adding the benefit of corner case generation by reducing to certain elements in the ontology. This article reports on the current status of the project and the goals it tries to achieve.
FD-Leaks: Membership Inference Attacks Against Federated Distillation Learning

With the enhanced sensing and computing power of mobile devices, emerging technologies and applications generate massive amounts of data at the edge network, assigning new requirements for improving the privacy and security. Federated learning signifies the advancement in protecting the privacy of intelligent IoT applications. However, recent researches reveal the inherent vulnerabilities of the federated learning that the adversary could obtain the private training data from the publicly shared gradients. Although model distillation is considered a state-of-the-art technique to solve the gradient leakage by hiding gradients, our experiments show that it still has the risk of membership information leakage at the client level. In this paper, we propose a novel client-based membership inference attack in federated distillation learning. Specifically, we first comprehensively analyze model distillation in defensive capabilities for deep gradient leakage. Then, by exploiting that the adversary can learn other participants’ model structure and behavior during federated distillation, we design membership inference attacks against other participants based on private models of malicious clients. Experimental results demonstrate the superiority of our proposed method in terms of efficiency in resolving deep leakage of gradients and high accuracy of membership inference attack in federated distillation learning.
The Technology of Forming the Professional Culture of Future Teachers: from Reflection to Creativity

The authors devoted their article to the theoretical study of the formation of children's personal and collective culture in the training of future teachers. In particular, we studied the combination of neuropsychological and cultural-educational aspects in the formation of personal and collective culture as an acquired psychological structure based on the nature-defined deep functions. The aim of the article was a theoretical analysis of related neuroscientific and educational approaches, formulation of components of professional culture of future educators on the basis of this analysis and development of the author's comprehensive program "The wheel of professionalism of a future teacher". To achieve this goal, the authors used theoretical methods of systematic analysis of the literature, comparison, extrapolation of pedagogical provisions to the neuroscientific plane; structuring and systematization of methodological provisions (components of professional culture of future educators). Among the empirical methods - explication and creative development of didactic material (integrated program) and elements of sociological research (survey) to find out the students' satisfaction with the implemented program. The relevance of the article lies in the significant discrepancy between the content of higher pedagogical education of future educators (particularly in Ukraine) and the reality of pedagogical activity, which does not take into account the general cognitive imitation and imitative mechanisms of learning culture. As a result, the neurocognitive and pedagogical reasoning for the development of the program was carried out. We also identified the main criteria for the formation of professional culture (spiritual-creative, cognitive, motivational-value, reflexive-perceptive, instrumental) and modeled a comprehensive program of formation of the basics of professional culture of future educators of educational institutions, which consists of training work on each of the components. The main result is the materialization of the program itself and the determination of a high degree of satisfaction with the students among whom it was implemented. Significance of the article. The formulated patterns of formation of professional culture and development of the culture of students on the principle of imitation are universal, and each of the above components is illustrated by specific practical and activity material, which may serve as recommendations for improving both the formation of future educators, and educational work in a preschool institution.
Research on key technologies of intelligent operation and maintenance based on log center

The complexity of the power operation and maintenance management system is increasing. The traditional fragmented operation and maintenance management mode will reduce the operation and maintenance efficiency. The complex multi system environment makes it more and more difficult to accurately locate and solve problems. It is necessary to research a new centralized and intelligent log data analysis and operation and maintenance management platform, so as to improve the operation and maintenance management efficiency and achieve effective support for the development of power business. This paper focuses on the research of key technologies for log center, through the research of centralized processing and management of massive log data in power system, the analysis of cross system operation and maintenance data and the method of problem location, the intelligent analysis of multi-dimensional data in power system and the method of prediction and alarm, the flexible customized global large screen display and trend analysis, Build the theoretical model, technical framework and solution of the centralized log operation and maintenance integration platform to support the implementation of the log center platform.
Effect of camera distance and angle on color of diverse skin tone‐based standards in smartphone photos

Accurate and reproducible color capture is vital in medical photography. Camera distance and angle are particularly important as they are highly variable in a clinical setting. To account for variability in illumination, camera technology, and geometric effects, color standards are often used for color correction. To explore how geometry affects color, we quantified the change in CIELAB color value of a color standard for diverse skin tones at varying smartphone camera distances and angles. Whereas both chromaticity (a* and b*) and lightness (L*) were affected by angle, distance only affected L* (standard error of measurement, SEM > 1 CIELAB unit). Flash usage did not generally reduce distance and angle associated variability. Compared to compressed (JPG) format, raw (DNG) images had decreased median variability across different distances and angles. These findings suggest that in medical photography, inconsistent camera distance and angle can increase variability in photographed skin appearance over time.
Effect Of MVVM Architecture Pattern on Android Based Application Performance

The performance of android-based mobile applications is very important to pay attention to because performance will be related to how users will feel the experience of the application. This relates to memory usage and CPU usage on android devices when using applications. There are several ways used to improve the performance of android-based mobile applications, one of which is by applying an architecture pattern. The use of the right architecture is expected to produce good performance for android devices when running an application. Therefore, the author conducted a study on whether the application of the MVVM architecture pattern by changing the program code with the refactoring method will affect memory usage, CPU, and code execution time in Android-based applications. This is implemented using the Android Studio application and using android profiler tools to measure the performance of the application.
Visualisasi Data Tweet di Sektor Pendidikan Tinggi Pada Saat Masa Pandemi

The Covid-19 pandemic has had an impact on many sectors, including higher education. Various public opinions about higher education have emerged on social media during the pandemic, with varying foci of discussion. The purpose of this study is twofold. The first is to compare the results of semantic data analysis with and without stemming. Second, to determine the topic of discussion among Twitter users about higher education. This study was carried out by collecting tweet data for a year during the pandemic, from March 2020 to March 2021. The data then pre-processed using a combination of methods to ensure that there was minimal noise data. The analysis is performed using information theory, bigram phrases, semantic relationship analysis between words, and visualized using gephi in the final stage. The findings of this study explain in detail why the stemming method cannot be used directly for preprocessing data that requires semantic understanding. Data visualization also reveals that Twitter users who talk about higher education are more concerned with campus life and new student admissions. This research evaluates the stemming process because the results show that using stemming eliminates semantic meaning between words. This research also visualizes semantic relationships in tweets about higher education
Discovering Top-k Periodic-Frequent Patterns in Very Large Temporal Databases

Discovering periodic-frequent patterns in temporal databases is a challenging data mining problem with abundant applications. It involves discovering all patterns in a database that satisfy the user-specified minimum support (minSup) and maximum periodicity (maxPer) constraints. MinSup controls the minimum number of transactions in which a pattern must appear in a database. MaxPer controls the maximum time interval within which a pattern must reappear in the database. Setting an appropriate minSup and maxPer values for any given database is an open research problem. This paper addresses this open problem by proposing a solution to discover top-k periodic-frequent patterns in a temporal database. Top-k periodic-frequent patterns represent a total of k periodic-frequent patterns with the lowest periodicity value in a database. An efficient depth-first search algorithm, called Top-k Periodic-Frequent Pattern Miner (k-PFPMiner), which takes only k threshold as an input was presented to find all desired patterns in a database. Experimental results on synthetic and real-world databases demonstrate that our algorithm is memory and runtime efficient and highly scalable.
Puncturable Key Wrapping and Its Applications

We introduce puncturable key wrapping (PKW), a new cryptographic primitive that supports fine-grained forward security properties in symmetric key hierarchies. We develop syntax and security definitions, along with provably secure constructions for PKW from simpler components (AEAD schemes and puncturable PRFs). We show how PKW can be applied in two distinct scenarios. First, we show how to use PKW to achieve forward security for TLS 1.3 0-RTT session resumption, even when the server’s long-term key for generating session tickets gets compromised. This extends and corrects a recent work of Aviram, Gellert, and Jager (Journal of Cryptology, 2021). Second, we show how to use PKW to build a protected file storage system with file shredding, wherein a client can outsource encrypted files to a potentially malicious or corrupted cloud server whilst achieving strong forward-security guarantees, relying only on local key updates.
Jammin’ on the Deck

Currently, a vast majority of symmetric-key cryptographic schemes are built as block cipher modes. The block cipher is designed to be hard to distinguish from a random permutation and this is supported by cryptanalysis, while (good) modes can be proven secure if a random permutation takes the place of the block cipher. As such, block ciphers form an abstraction level that marks the border between cryptanalysis and security proofs. In this paper, we investigate a re-factored version of symmetric-key cryptography built not around the block ciphers but rather the deck function: a keyed function with arbitrary input and output length and incrementality properties. This allows for modes of use that are simpler to analyze and still very efficient thanks to the excellent performance of currently proposed deck functions. We focus on authenticated encryption (AE) modes with varying levels of robustness. Our modes have built-in support for sessions, but are also efficient without them. As a by-product, we define a new ideal model for AE dubbed the jammin cipher. Unlike the OAE2 security models, the jammin cipher is both a operational ideal scheme and a security reference, and addresses real-world use cases such as bi-directional communication and multi-key security.
A Relevance of Learning a Foreign Language for an Early Age | Relevansi Pembelajaran Bahasa Asing untuk Anak Usia Dini

The process of learning a foreign language in early childhood greatly influences their psychological development. Because of that, teachers are expected to be able to provide variations in each foreign language learning. Interesting learning methods will make children feel happy and not pressured in the process of learning a foreign language.The background of this research is the suitability of foreign language learning in early childhood, due to the lack of adjustment between the roles of parents and teachers in the foreign language learning process.Therefore, researchers are interested in conducting research related to the relevance of foreign language learning. This study aims to determine the suitability of foreign language learning for early childhood.The type of research used is literature study, data collection techniques by examining books, journals, and articles related to the research topic.The result of this literature study is that learning foreign languages for early childhood is very relevant by using a variety of learning methods.In addition to learning methods, the role of teachers and parents also influences the process of learning foreign languages for children. Creating a happy learning atmosphere can also accelerate children's comprehension in learning foreign languages.In psychology, the human brain has been genetically prepared for language, its connection with the existence of foreign language learning for early childhood is to improve human language power as early as possible.
SAPMS: A Semantic-Aware Privacy-Preserving Multi-keyword Search Scheme in Cloud

Most of the traditional privacy-preserving search schemes adopt TF-IDF model which is on the basis of keyword frequency statistics. The embedding semantic association between keywords and documents are not considered. To solve this problem, we propose an efficient semantic-aware privacy-preserving multi-keyword search scheme over encrypted cloud data. The LDA topic model is adopted to generate the topic information-embedded vectors for documents and queried keywords. The homomorphic encryption on vectors is used to perform privacy-preserving semantic relevance score computation between queried keywords and documents. To achieve efficient search processing, a novel tree-based index is designed, which is constructed following the divisive hierarchical clustering algorithm. By using the tree-based index, a depth-first privacy-preserving multi-keyword search algorithm is proposed. The experimental results show that the proposed scheme outperforms the existing schemes in terms of the semantic precision of search results and the search time cost.
Hybrid Kernel Extreme Learning Machine-Based Empirical Wavelet Transform for Water Quality Prediction Using Only River Flow as Predictor

During the last few years, monitoring and controlling water quality in freshwater ecosystems was strongly facilitated by the increasing number of in situ stations, certainly in combination with the high number of developed models. Several water quality variables have received a great deal of attention regarding their environmental importance, while other variables have rarely been studied in detail using modeling strategies. Generally speaking, water variables were linked to building robust models and rarely are the models using fewer variables. Machine learning algorithm aiming to accurately build relationships between water quality variables are widely used and acknowledged. In the present investigation, we tried to introduce a new modeling strategy for predicting two water quality variables: water pH and specific conductance (SC) using kernel extreme learning machine models (KELM). The major contribution of our study is that we used only the river flow as relevant predictor and a single-input and single-output (SISO) model was proposed for predicting water pH and SC. Two scenarios were analyzed and compared. First, SISO models were developed and compared. Second, to greatly increase the performances of the KELM models, we have used the empirical wavelet transform (EWT) algorithm for decomposing the river flow time series into several multiresolution analysis components (MRA), which were used as new input variables. Data collected at the USG websites were used to test the proposed algorithms, and we find that the EWT clearly exhibited high accuracies compared with the SISO models, and it provides a very robust estimate of the water pH and SC. For water pH, it was found that the KELM models based on EWT were more accurate compared with the models without EWT, exhibiting R, NSE, RMSE, and MAE values ranging from 0.888 to 0.981, from 0.767 to 0.961, from 0.038 to 0.074, and from 0.027 to 0.058, respectively. In addition, for the SC, it was found that KELM models based on EWT were more accurate exhibiting R, NSE, RMSE, and MAE values ranging from 0.897 to 0.974, from 0.804 to 0.947, from 2.352 to 5.374, and from 1.528 to 4.152, respectively.
ChatGPT and Big Data: Enhancing Text-to-Speech Conversion

Text-to-speech (TTS) conversion is a crucial technology for various applications, including accessibility, education, and entertainment. With the rapid growth of big data, TTS conversion systems face new challenges in terms of data size and diversity. In this paper, we propose to use the state-of-the-art language model ChatGPT to enhance TTS conversion for big data. We first introduce the background of TTS conversion and big data, and then review the existing TTS conversion systems and their limitations. Next, we describe the architecture and training of ChatGPT, and how it can be applied to TTS conversion. Finally, we evaluate the performance of the ChatGPT-based TTS conversion system on a large-scale real-world big data dataset, and compare it with the existing TTS systems. Our experimental results demonstrate that ChatGPT can significantly improve the quality and efficiency of TTS conversion for big data.
Finite-angle Magnetosphere Boundary CT Reconstruction Technique Based on Generative Adversarial Networks

对地球磁层进行软X射线成像探测是近年来磁层研究的前沿方向.由二维X射线成像图重构三维磁层边界的方法研究是与成像探测相关的重要研究课题.传统的计算机断层成像技术（CT）重构方法在图像数据很少时无法得到较好的重构结果，甚至无法重构三维结构.考虑到地球磁层空间分布范围方面的约束，当前卫星任务的轨道设计很难满足扫描角度的全方位覆盖，只能从有限角度观测磁层，这给磁层的CT重构带来困难.作为三维重构研究的基础，本文考查简化的二维磁层结构重构方法，采用基于对抗神经网络的CT技术对简化的地球磁层边界结构进行重构.首先使用改进的生成式对抗网络（GAN）对有限角度的卫星扫描图像进行图像补全，进而使用代数迭代重建方法（ART）重构磁层.实验表明，当扫描角度大于90&#176;时，生成式对抗网络能有效准确地补全缺失图像，重构效果较好.
Local sampling of the SU(1,1) Wigner function

Despite its indisputable merits, the Wigner phase-space formulation has not been widely explored for systems with SU(1,1) symmetry, as a simple operational definition of the Wigner function has proved elusive in this case. We capitalize on unique properties of the parity operator, to derive in a consistent way a bona fide SU(1,1) Wigner function that faithfully parallels the structure of its continuous-variable counterpart. We propose an optical scheme, involving a squeezer and photon-number-resolving detectors, that allows for direct point-by-point sampling of that Wigner function. This provides an adequate framework to represent SU(1,1) states satisfactorily.
Gram-GAN: Image Super-Resolution Based on Gram Matrix and Discriminator Perceptual Loss

The solution of a high-resolution (HR) image corresponding to a low-resolution (LR) image is not unique in most cases. However, single-LR-single-HR supervision is widely adopted in single-image super-resolution (SISR) tasks, which leads to inflexible inference logic of the model and poor generalization ability. To improve the flexibility of model inference, we constructed a novel form of supervision, except for the ground truth (GT). Specifically, considering the structural properties of natural images, we propose using extra supervision to focus on the textural similarity of the images. As textural similarity does not account for the position information of images, a Gram matrix was constructed to break the limitations of spatial position and focus on the textural information. Besides the use of traditional perceptual loss, we propose a discriminator perceptual loss based on the two-network architecture of generative adversarial networks (GAN). The difference between the discriminator features used in this loss and the traditional visual geometry group (VGG) features is that the discriminator features can describe the relevant information from the perspective of super-resolution. Quantitative and qualitative experiments were performed to demonstrate the effectiveness of the proposed method.