Reviewerly/exHarmony huggingface dataset:
Data-Driven Safe Policy Optimization for Black-Box Dynamical Systems With Temporal Logic Specifications

Learning-based policy optimization methods have shown great potential for building general-purpose control systems. However, existing methods still struggle to achieve complex task objectives while ensuring policy safety during learning and execution phases for black-box systems. To address these challenges, we develop data-driven safe policy optimization (D 2 SPO), a novel reinforcement learning (RL)-based policy improvement method that jointly learns a control barrier function (CBF) for system safety and a linear temporal logic (LTL) guided RL algorithm for complex task objectives. Unlike many existing works that assume known system dynamics, by carefully constructing the data sets and redesigning the loss functions of D 2 SPO, a provably safe CBF is learned for black-box dynamical systems, which continuously evolves for improved system safety as RL interacts with the environment. To deal with complex task objectives, we take advantage of the capability of LTL in representing the task progress and develop LTL-guided RL policy for efficient completion of various tasks with LTL objectives. Extensive numerical and experimental studies demonstrate that D 2 SPO outperforms most state-of-the-art (SOTA) baselines and can achieve over 95% safety rate and nearly 100% task completion rates. The experiment video is available at https://youtu.be/2RgaH-zcmkY.
Smooth surface 3D morphology measurement technology based on adaptive light source

The use of surface light sources enables accurate measurement of the morphology of high reflection surfaces. The fringe deflection method is a cost-effective and non-contact technology used to detect defects and measure the morphology of high-reflectivity surfaces. While this method can theoretically achieve global measurement of complex smooth surfaces, it faces practical challenges due to the significant difference in reflectivity across the surface. The high reflectivity area is easily affected by overexposure. Even if the exposure time and aperture can be adjusted, it can lead to significant quantization errors in the low reflectivity area, resulting in regional differences in measurement range errors. To address this issue and achieve simultaneous detection of multi-reflectivity objects without compromising accuracy, this paper proposes a novel detection method that adaptively adjusts the light source. By adjusting the exposure to obtain unsaturated captured images, absolute phase roughness measurement is achieved. Using the relationship between phase and projection fringes, spatial correspondence between the pixel coordinate system and the light source coordinate system is established, and adaptive adjustments are made to the projection light source. This approach enables adaptive intensity adjustments based on the reflectivity of the measured surface, facilitating high-precision measurement of complex surfaces. Overall, this adaptive light source technology enhances the fringe deflection method's accuracy and versatility, making it suitable for measuring complex surfaces with high reflectivity variations.
Implementing Hierarchical Attribute Set Based Encryption Over Key-Policy Attribute Based Encryption

The use of Internet and cloud storage is increasing daily and hence issues related to cloud security are also increasing day-by-day. In this research, we are trying to find a solution that will protect the data integrity and security of cloud data transfers and storage. Here we analyze the Key Policy-Attribute Based Encryption (KP-ABE) and Hierarchical Set Based Attribute based Encryption (HASBE) to find the speed of Execution Time, Data Security Rate, Data Integrity Rate and Space complexity. From the study we concluded that HASBE has a more powerful algorithm with less execution time, more secure and more integrated with less space complexity. HASBE has an ordered hierarchical structure of aligning cloud data. Both KP-ABE and HASBE are based on Attribute encryption where unique attributes like telephone number, email-id, personal id number which can identify a person uniquely will be used. Among the two algorithms it is found that HASBE is more efficient.
A Study on Traceable Group Signature Scheme for Blockchain

A blockchain is a decentralized database system in which a ledger containing transactions is stored and maintained identically by all through consensus among network participants without a central server. Instead of having a central server, network participants make sure that everyone stores and maintains the same ledger containing transactions through consensus. Integrity and reliability of the data stored in the blockchain are guaranteed, but on the other hand, privacy issues may arise. The pseudonymity of the public key reused in transactions causes a privacy invasion of the user’s behavioral pattern, asset size, and offline identity. Therefore, instead of assigning unique public keys to everyone, they are provided through a single signature in a blockchain environment. It can also be described as a method for ensuring anonymity by having members of the same group use the same public key in a transaction while employing the group signature, which has been studied in this paper. This paper defines the key used for group signature is defined as a composition of a “group private key” and a “partial key”. Mainly among them, we consider the leakage of the partial key. In the proposed scheme, it is possible to trace the leaker and the signer from the signature generated by the outsider with the leaked partial key. In this paper, we provide a secure traceable group signature scheme to cope with the leakage of the group signing key.
Visualizing Kubernetes Distributed Systems: An Exploratory Study

Distributed applications running on virtualization- based systems and cloud computing have become popular solutions, allowing developers to focus on application logic rather than dealing with the complexities of distributed systems. However, these applications often become increasingly complex, presenting multiple management challenges. To address this issue, software visualization approaches offer valuable solutions by pro-viding real-time insights into resources and their functionalities, offering a comprehensive overview. This study aims to analyze and evaluate existing software visualization tools for distributed applications on the Kubernetes platform. The objective is to comprehensively examine these tools' features, capabilities, and limitations to understand their effectiveness in visualizing complex distributed systems. Our findings provide valuable insights into the strengths and weaknesses of the available visualization tools, enabling researchers and practitioners to make informed decisions and advancements in software visualization for distributed applications on the Kubernetesplatform. Our research identified eight Kubernetes visualization tools, which were examined and compared based on relevant char-acteristics related to distributed applications and software vi-sualization standards. However, it is worth noting that despite the excellent work done by the community in establishing these first proposals, these tools currently only support, on average, a visualization of 9 % of the total resource types available, as mentioned in the official documentation. Therefore, we propose guidelines followed by a synthesized visualization that can guide further research and development in this area. Our study will assist users in selecting the most suitable Kubernetes visualization tool and encourage researchers and the community to explore new approaches in Kubernetes visualization.
Temporally Distilled High-Dimensional Biphotonic States from Thin Sources

Generation of entangled photons through spontaneous parametric down-conversion (SPDC) from micro- and nanoscale sources offers unprecedented freedom in quantum state engineering, including the ability to generate two-photon states with high-dimensional hyperentanglement. However, as the source of SPDC gets smaller, the role of photoluminescence increases, which leads to the contamination of two-photon states with a thermal background. Here we propose and implement a solution to this problem: by using pulsed SPDC and time distillation, we increase the purity and the heralding efficiency of the photon pairs. In the experiment, we increased the purity of the two-photon states generated in a 7 μm film of lithium niobate from 0.002 to 0.99. With the higher purity we were able to observe and characterize different polarization states of photon pairs generated simultaneously due to relaxed phase matching. In particular, we showed the presence of orthogonally polarized photons that are potentially usable for the generation of polarization entanglement.
Open-Set Face Recognition with Neural Ensemble, Maximal Entropy Loss and Feature Augmentation

Open-set face recognition is a scenario in which biometric systems have incomplete knowledge of all existing subjects. This arduous requirement must dismiss irrelevant faces and focus on subjects of interest only. For this reason, this work introduces a novel method that associates an ensemble of compact neural networks with data augmentation at the feature level and an entropy-based cost function. Deep neural networks pre-trained on large face datasets serve as the preliminary feature extraction module. The neural adapter ensemble consists of binary models trained on original feature representations along with negative synthetic mix-up embeddings, which are adequately handled by the designed open-set loss since they do not belong to any known identity. We carry out experiments on well-known LFW and IJB-C datasets where results show that the approach is capable of boosting closed and open-set identification accuracy.
Digital Competencies in Teaching Performance in an Educational Institution in Peru

The objective of the research is to determine the impact of digital competencies on the performance of teachers at a public educational institution in Peru; where the type of research is basic, the design is non-experimental, the level is descriptive correlational, where the sample is 68 teachers, the technique used is the survey and the instrument is the questionnaire, with a quantitative approach. It was found that the majority of teachers surveyed have considered their digital competencies in the teaching-learning process to be at a regular level, with the most prominent dimension being "Security"; which indicates that teachers are clear about the information they can share with students and about the use of personal data on networks, which were reinforced with the constant use they had in times of pandemic. Also, that the majority of teachers consider their teaching performance in the classroom to be of an adequate level, it is highlighted that the development of professionalism and teaching identity are aspects that are internalized in them and that allow innovation and the development of good practices. It was concluded that there is a positive impact of digital skills on teachers' performance.
THE ROLE OF SCHOOL LITERACY MOVEMENT (SLM) IN INCREASING STUDENT INTEREST IN READING: A LITERATURE REVIEW

This study was prompted by students' lack of interest in reading and literacy abilities in Indonesia. In 2015, Indonesia ranked 62 out of 76 countries in the PISA survey. The government created the School Literacy Movement (SLM) program in 2016, in response to students' low literacy scores and reading interest. The government expects that this program will increase reading enthusiasm in Indonesia. The objective of this study was to investigate the impact of the SLM on students' reading interest. The method utilized is a literature research or review. A literature study is a research design used to collect data sources on a certain topic. As the stage of searching for literary sources, data collecting for the study of literature is carried out using a database search tool. This research is conducted by synthesizing conceptual or empirical articles pertinent to the study to be conducted. It begins with the analysis of articles pertinent to the topic to be reviewed, this entails discussing and reviewing the articles by identifying and classifying them based on the elements to be reviewed from multiple articles that discuss essentially the same subject. According to the papers discovered and evaluated, the school literacy movement has a positive role on students' reading interest, this is proven by the increase of library visits, the increase in problem-solving skills in the learning process, the increase in students' capacity to write Wall Print Magazines, and the increase in the number of students who read before class. As a result, it can be inferred that SLM has a role in enhancing children's interest in reading through various school-based programs and activities.
Application of Texture Analysis Techniques and Image Statistics to Fund us Images for Effective Comparison and Analysis

The qualitative and quantitative image statistics plays a crucial role in medical image analysis. The data can be extracted using the machine learning techniques, containing the maximum information of the image based on their distinctive contrast, texture, or intensity fluctuations, tumors, lesions, or other pathological diseases can be recognised statistically. The term "image statistics" refers to the numerical measurements and traits that are employed to characterize the qualities and arrangement of pixel values inside an image. In this work, different texture analyses are applied and verified for the effective analysis of medical images. By using advanced applications we can even extract the data more efficiently
Synthetic Data Generation Through Image Translation for Improving Out-of-domain MRI Lesion Segmentation

&lt;p&gt;One of the key limitations in machine learning models is poor performance on data that is out of the domain of the training distribution. This is especially true for image analysis in magnetic resonance (MR) imaging, as variations in hardware and software create non-standard intensities, contrasts, and noise distributions across scanners. Recently, image translation models have been proposed to augment data across domains to create synthetic data points. In this thesis, the objective was to investigate the application an unsupervised image translation model to augment MR images from a source dataset to a target dataset to create synthetic data for training segmentation models. Speciﬁcally, the objective was to observe if training on these synthetic data points can approach the performance of a model trained directly on the target distribution. Three conﬁgurations of augmentation between datasets consisting of translation between images, between scanner vendors, and from labels to images. It was found that the segmentation models trained on synthetic data from labels to images conﬁguration yielded the closest performance to the segmentation model trained directly on the target dataset. The Dice coefﬁcient score per each target vendor (GE, Siemens, Philips) for training on synthetic data was 0.63, 0.64, and 0.58, compared to training directly on target dataset was 0.65, 0.72, and 0.61.&lt;/p&gt;
MODERN EDUCATIONAL ORIENTATIONS OF YOUTH: TRENDS AND CHALLENGES

Socio-economic processes in crisis conditions characterized by the transformation of industrial relations and the increase of technological capabilities are discussed. The impact of these processes on the public aura and the structure of society, on the system of vocational education and the industrial sphere is assessed. The modern educational orientations of young people and the problems arising in this case are considered. It is noted that students need to develop a historically grounded mentality and creative skills that take into account the permanent generation of new knowledge, social tensions and the complexity of production processes.
The Interpretation of Wayang Characters in Cyberpunk-Styled Digital Art: A Case Study of Mr. Lakon’s Artworks

Wayang, an Indonesian traditional puppet performance, represents a centuries-old tradition that has been passed down for generations. The extensive journey of wayang has resulted in the continuous transformation of wayang in its artistic aspects, form, and function. In recent years, digital art has emerged as a new form of artistic expression, and artists like Mr.
Predviđanje proizvodnje termoelektrane pomoću neuralnih mreža

With the power systems progress, its complexity grows and there is a greater need for good production organization and the need for information that would help in power system planning. As thermal power plants are responsible for covering the base load, the prediction of its production can be used significantly for good power system organization. This paper represents the use of artificial neural networks for the prediction of thermal plants power production. The data used for neural networks training are measurements of thermal power plant production in power systems of Serbia. Trained neural network can be used for prediction of production on yearly basis or for shorter, hourly or daily prediction.
Voice Cloning Using Artificial Intelligence and Machine Learning: A Review

This paper represents a thorough method for integrating emotions, texttospeech conversion, and state of the art voice cloning. The paper focuses on novel background noise adaptation, emotional voice synthesis, and multi-speaker voice cloning for better speech synthesis. The synthesis of emotive voices, multi-speaker voice cloning, and creative methods for modifying background noise to improve speech synthesis quality are among the topics covered in this study. Additionally, the study explores the domain of emotional artificial intelligence by adding a variety of emotions to artificial voices, improving user engagement through sympathetic reactions. The study also looks at how background noise can be altered to change it from a disturbing to a silent, non-disruptive state. The texttospeech systems usability in noisy conditions is greatly enhanced by this improvement. By integrating these components, the project makes a substantial contribution to text to speech, emotional AI, and voice cloning, creating new avenues for human-computer connection.
Classifying and Predicting The Rating Sentiment of Women's E-commerce Clothing Reviews: A Comparative Study Using SVM, ANN, and BERT Models

Product reviews are crucial for both customers and sellers in determining the quality of certain products. Alongside the review, the rating is also provided to give insight into the overall point of the product. As the review often becomes a consideration for a customer when buying a product online, the review needs to give an objective viewpoint. Therefore, it is important to determine whether the review is reliable. This could be seen both from the rating given by customers and the sentiment of the review. Machine learning and deep learning methods can be applied to classify the review sentiment effectively. The main objective was to check whether the review was considered positive or negative. The review was provided from women’s clothing e-commerce by Nicapotato, which is available on Kaggle. This paper used both SVM and ANN for machine learning methods and BERT for deep learning method to determine the best method for the given dataset by identifying the most reliable and accurate model for classifying reviews. For the text vectorization, SVM and ANN models used TF-IDF technique while BERT model used sentenceBERT. The best method was determined quantitatively by comparing each model's accuracy and F1 Score results. Based on the accuracy and F1 Score result, it turned out that using TF-IDF for text vectorization with ANN gave the best performance compared to other models.
Keynote: In Varietate Concordia: How Software Visualization and Information Visualization Have Evolved From, Around, and Along Each Other

Software visualization (softvis) and information visualization (infovis) have a long, interconnected, and complex joint history. Originally appearing as a subdomain of infovis which focuses on solving problems coming from the software engineering domain, softvis has grown in the last two decades to become a selfstanding field with distinct challenges, key results, events, and community. In the same time, the independent growth of the two fields has made the transfer of ideas, techniques, methods, application cases, and researchers between the two domains increasingly challenges. In this talk, I will present a history of this highly dynamic process and argue about the need for raprochemment of infovis and softvis. This need is supported by two key aspects identified and further discussed: (1) Complementarity of the two fields advocates for more interaction, as shown by success stories from softvis which led to entirely novel branches of development into the infovis field and, conversely, recent key developments in infovis which offer strong potential to be picked up to address existing key challenges in softvis. (2) Commonality, in terms of both fields essentially aiming to solve very similar visualization problems that address very similar data and using related visualization pipelines, advocates on an increasingly joint approach in their further development.
Model-Agnostic Interpretation via Feature Perturbation Visualization

As machine learning algorithms increasingly replace traditional approaches, ensuring their reliability becomes crucial in applications where incorrect decisions can lead to serious consequences. This work proposes a novel model-agnostic in-terpretation approach using feature perturbations, along with a validated visualization tool. The approach enables better understanding of model decisions by domain experts, facilitating effective decision-making in real-world applications.
Would EFL Students learn better through the Implementation of NHT Model?: A CAR Study for Reading Comprehension Improvement of Junior Level

This class action research aims to apply the Numbered Head Together learning model to improve student learning outcomes in English subjects, especially for reading comprehension skills at the junior level, namely in class VII SMP Negeri 1 Merauke in the 2022/2023 academic year. This research is a Classroom Action Research (CAR), where Classroom Action Research (CAR) is research conducted in two stages of the cycle, where each cycle I and cycle II is carried out twice the learning process meeting. Each cycle consists of 4 stages, namely planning, implementation, observation and reflection. The subjects in this study were 32 seventh grade students. The success indicator of this study is if 75% of students can achieve the Minimum Completeness Criteria (KKM) value of 70. The results showed that the application of the Numbered Head Together (NHT) Learning Model in class VII SMP Negeri 1 Merauke showed an increase in the completeness of students' learning achievement in cycle I with an average of 61.65% increasing to 80.28% in cycle II. The percentage of learning completeness also increased from cycle I by 12.5% increasing to 100% in cycle II. In addition, There was also a very significant increase in each teacher and student activity based on the observation sheet. Where in cycle I the teacher's activities were at 89.28% and students at 66.07%, then increased in cycle II, where teacher activities became 96.42% and students at 91.07%.