Reviewerly/exHarmony huggingface dataset:
Unsupervised Judgment of Properties Based on Transformation Recognition

Humans judge equivalence not only by appear-ance, but also by structural similarity, such as through analogy. The relationship between analogy and creativity shows that the identification of structural similarity requires human-level high intelligence. In contrast, machine learning judges equivalence by appearance alone, using local features. Although the identification of structural similarity is important, there is currently no method for judging structural similarity in unsupervised learning. In this paper, we propose a method for judging structural similarity using representation learning, whereby image sequences are represented as a combination of objects and transformations. Conventional representation learning methods generally use vector representations, but this does not permit any judgment of structural similarity. The proposed representation learning method using objects and transformations allows the structure of an equation to reflect the structure of an image sequence, making it possible to assess structural similarity. To represent the input in the form of equations, Lie transformations, which are often used in formulations of the permanence of perception, are adopted for learning the object transformations. Building on the base model, the structure of the equations is then modified by tolerating certain deformations of the objects. To stabilize the learning results, an integration layer is introduced to the base model to simplify the equations. As a result, the proposed method can judge structural similarity even when objects have different appearances.
DyFuzz: Skeleton-based Fuzzing for Python Libraries

Programming libraries are indispensable for programming languages. Programmers can access the pre-written codes in these libraries via the application programmable interfaces (API), optimizing and accelerating their programming tasks. However, defects in these libraries may cause unexpected software behaviors, threatening their robustness and safety. Thus, it is crucial to ensure the quality of the libraries. This paper explores an alternative approach, namely Fuzzing Skeleton API (FSA), for detecting library bugs in Python. For the given API, FSA aims to generate massive inputs, i.e., different argument combinations, and pass them to the API to verify its correctness and reliability. To realize this, FSA first abstracts the API into a skeleton by modeling its usage of parameters as placeholders. Then, it can generate the seed API calls by filling these placeholders with pre-defined arguments. Finally, the approach incorporates four mutation strategies, i.e., bit mutation, literal mutation, element mutation, and attribute mutation, to mutate different arguments and hence generate massive API calls. We have implemented the proposed approach into an automated tool, namely DyFuzz, for testing Python libraries. In less than one month of the fuzzing experiment, DyFuzz detected 14 library bugs, of which nine have been confirmed as unknown bugs.
Blockchain Network Platform for IoT Data Integrity and Scalability

Decentralised technology backed by blockchain has gained a huge popularity in recent years as it secures autonomous ecosystems without needing a central authority. The origin of the blockchain concept began in the financial domain using cryptocurrency, but over the last few years, blockchain has been applied to a variety of industries. In the era of Industry 4.0, most industries are leveraging automation by using Internet of Things (IoT). Despite numerous applications of blockchain in the industries, due to the significant latency in consensus algorithm in blockchain, businesses using IoT technology are facing performance issues in adopting blockchain. A number of studies address the obstacles for transaction processing performance and system scalability, mostly based on a public blockchain. However, they still involve centralised components, and thus fail to fully utilise decentralisation. Therefore, a private blockchain-based IoT data integration platform is proposed to achieve data integrity and system scalability. Along with the lightweight IoT gateway instead of any other additional middleware, the process and the system configuration are streamlined. By using Hyperledger Fabric, the design is validated and it is shown that the proposed architecture outperforms other conventional model in IoT data processing.
From microwave technologies to microwave photonics: R&amp;D Institute of Applied Electrodynamics, Photonics and Living Systems celebrates 35th anniversary

On April 1, 1988, an order of the Kazan Aviation Institute (KAI) was issued on the appointment of the Head of the Branch R&D Laboratory of the USSR Ministry of Radio Industry (ONIL-MRP) and the creation of its scientific and technical council to carry out the scientific program approved by the USSR Council of Ministers, on the basis of a joint order of the Ministry of Radio Industry and Ministry of Higher Education. ONIL-MRP became the first self-supporting subdivision in KAI. In connection with the effective development and significant results achieved in the field of electronic and microwave technologies in technical and living systems, in 1994 ONIL-MRP was renamed the R&D Center for Applied Electrodynamics (NIC-PRE) with the rights of a legal entity. After inclusion in the research program of the center of photonics directions, as interdisciplinary, NIC-PRE was restructured into the R&D Institute of Applied Electrodynamics, Photonics and Living Systems (NII-PREFZHS) using the potentials of photonics, biophotonics and microwave photonics for its further development. Considering the voluminous bibliography devoted to the developments of ONIL-MRP/NIC-PRE/NII-PREFZHS, the authors decided to focus on the “pillar” areas of research, which confirm both the correctness of the initially chosen directions for the development of the NII-PREFZHS, and the significance of its developments from the first years of its creation to today.
On the Performance of Face Recognition Models for Local Faces Dataset

The number of facial recognition models that have been developed makes many users free to choose the desired model. However, the large number of face recognition models will also raise questions about how effectively the model is used, especially when it is used to verify certain types of faces, for example, Indonesian faces. In this paper, the evaluation results of several face recognition models using Indonesian faces are presented. The models evaluated were facenet based on inception resnet v2 from the deepface library, VGGFace from the deepface library, and facenet Google based on inception resnet v1 from the keras-facenet library. The face detection model used for cropping images is SSD. The method used to carry out the evaluation is to compare the embedding results of two Indonesian sample faces and evaluate the actual value and the predicted result value of the calculated distance. This value is then used to calculate the confusion matrix and the accuracy, precision, recall, and f1score parameters will be calculated [1]. The results show that the keras – facenet model gets the best value with an accuracy of 96.51% with a precision of 100% in the dataset using euclidean threshold 0.75. The calculated evaluation results can be used for considerations in using the face recognition models that have been mentioned, especially for Indonesian faces.
Visualizing Student Attention in Smart Classrooms: A Preliminary Study

In recent years, there has been a swift progression in employing novel methods in classrooms to enhance students’ academic achievements, especially in line with the growing digitization of education. Such methods often encompass systems like facial recognition to monitor various aspects of students, including attendance, emotional states, and attention. These tools are capable of evaluating students’ presence and engagement in class, offering quantifiable metrics regarding their concentration and emotions. However, a prominent challenge has been the translation of this data into an accessible form that enables educators to assess and enhance their teaching techniques swiftly. Our suggested solution tackles this issue by offering a real-time visual depiction of students’ classroom status through different visualization techniques. These visual aids allow teachers to promptly recognize trends in student focus, thus aiding in the strategic alteration of teaching styles. Furthermore, these visual representations can be tailored to display various metrics and applied to tasks beyond monitoring attention, like overseeing attendance or assessing student progress. By integrating these advanced visualizations into the educational process, both teaching efficacy and the learning experience for students and teachers alike can be substantially elevated.
Software Code Quality Measurement: Implications from Metric Distributions

Software code quality is a construct with three dimensions: maintainability, reliability, and functionality. Although many firms have incorporated code quality metrics in their operations, evaluating these metrics still lacks consistent standards. We categorized distinct metrics into two types: 1) monotonic metrics that consistently influence code quality; and 2) non-monotonic metrics that lack a consistent relationship with code quality. To consistently evaluate them, we proposed a distribution-based method to get metric scores. Our empirical analysis includes 36,460 high-quality open-source software (OSS) repositories and their raw metrics from SonarQube <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> and CK <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> . The evaluated scores demonstrate great explainability on software adoption. Our work contributes to the multidimensional construct of code quality and its metric measurements, which provides practical implications for consistent measurements on both monotonic and non-monotonic metrics. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> https://www.sonarsource.com <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> https://github.com/mauricioaniche/ck
Empirical Study of Filtered-Based Feature Selection Methods for Arabic Text Classification

Text classification has many applications in various fields; such as news categorization, sentiment analysis, E-mail spam filtering, and others. However, handling textual data is a challenging task owing to the potentially massive number of features (words). The presence of redundant irrelevant features deteriorates the performance of a learning algorithm and makes the process of text classification more complex. This research conducts a comparison study of several filtering-based feature se-lection methods in the context of Arabic text classification. Arabic is a highly complex language syntactically and morphologically which leads to more complicated learning tasks. Proposing a ro-bust classification model is demanding. Remarkably, integrating filtering approaches results in significant improvements in the performance of classification algorithms.
Minimizing Distortion in Secret Image Retrieval: A Novel Solution to Combat Data Loss in Steganography

Steganography, the practice of concealing secret information within innocuous carriers, exploits inherent data redundancies to ensure effective covert communication. However, challenges like lossy compression and data loss can compromise the integrity of the hidden data and complicate accurate recovery. Thus, this paper introduces an new technique in order to retrieve lost data. This approach introduces a novel sender-receiver communication mechanism to extract secret images from steganographic media while compensating for lost portions. The proposed technique enables precise retrieval of confidential images even in scenarios involving significant data loss. Extensive experimental evaluation conducted across diverse steganographic images substantiate the approach’s effectiveness in minimizing distortion during secret image recovery. Notably, the method yields a substantial improvement in the Peak Signal-to-Noise Ratio (PSNR), attesting to enhanced image quality and likeness to the original covert image. Moreover, the collaborative sender-receiver process bolsters recovery accuracy, curbing data loss and preserving the secret image’s integrity.
Efficient Vertical Mining of Frequent Quantitative Patterns

Frequent pattern mining has become popular in big data analytics and knowledge discovery as it discovers sets of items (e.g., merchandise items or events) that co-occur frequently. These frequent patterns are discovered by either horizontally by transaction-centric mining algorithms or vertically by item-centric mining algorithms. Regardless of the mining algorithms used, traditional frequent pattern mining algorithms focus on discovering Boolean frequent patterns, which reveal the presence or absence of specific items within the discovered patterns. However, in many real-life scenarios, the quantities of items within the patterns are crucial. For instance, the quantity of items can significantly impact the profitability of selling the items found in the discovered patterns. An existing quantitative algorithm called Q-VIPER (2022) mined frequent quantitative patterns by representing the big data as a collection of item-centric bitmaps. Each bitmap captures the presence or absence of a transaction containing the item, together with the quantity of that item in each transaction. It then mines quantitative frequent patterns vertically. It works well with small quantity. However, when dealing with large quantity, it generates a large number of sets of candidate quantitative frequent patterns (aka sets of item expressions, or itemexpsets for short). Given that large quantities are not unusual in numerous real-life applications, we design a scalable solution in this paper. The resulting scalable quantitative frequent pattern algorithm called SQ-VIPER significantly reduces the number of candidates to be generated, and thus speeds up the mining process. Evaluation results show that superiority of our SQ-VIPER over the existing Q-VIPER and MQA-M algorithms, which respectively mine quantitative frequent patterns vertically and horizontally.
An Implementation of Recipe Recommendation System Based on Ingredients Availability Using Content-based and Collaborative Filtering

Cooking is an essential part of daily life activity for many people worldwide to fulfill nutritional needs. Basically, it is the process of preparing food by combining different ingredients and applying various cooking techniques. Commonly, most people use the available ingredients they have on hand to prepare meals. The preferred ingredients often depend on what’s in their pantry, refrigerator, or garden, as well as what’s easily accessible from local grocery stores or markets. This paper presents an approach method to providing a list of recommended recipes based on the availability of ingredients and recipe selection behavior of users by implementing a recipe recommendation system, namely EasyCook. Data on recipes is gathered from various websites that provide popular Indonesian food recipes using the web scraping process. Then, content-based filtering is applied as an approach method to identify the similarity of recipe ingredients and generate recipe recommendations. To evaluate the developed system’s effectiveness, aspects of web scraping and the recommendation process based on ingredient availability and user-preferred recipes are investigated. The results show that the developed system effectively provides recommended recipes for users based on ingredient availability and user-preferred recipes.
Sentiment Analysis on Tourism Place using Naive Bayes

The method of extracting information from a text is called text mining. In contrast, sentiment analysis involves examining text data to determine whether a statement expresses good or negative conversations. In this study, a system was developed that categorizes tweets about West Bandung Regency tourism spots that contain both favorable and negative remarks. The Cross Industry Standard Process for Data Mining (CRISP-DM) technique is used to carry out the classification process utilizing the Naive Bayes algorithm. You will learn how locals feel about West Bandung Regency’s tourist attractions through this method. Six scenarios were developed using the categorization findings from 250 pieces of data that included both positive and negative sentiment. The scenario has got the greatest f1-score of 82.8%. These findings demonstrate how effectively the Naive Bayes algorithm performs sentiment analysis.
Federated Learning for Medical Images Analysis: A Meta Survey

Machine learning and deep learning have demonstrated significant promise for many kinds of medical imaging applications, including segmentation, classification, and detection. The quantity of data needed for developing effective models for medical images, however, is substantial and can be restricted by privacy restrictions like HIPAA and GDPR. Federated learning has gained popularity in the field of medical imaging as a privacy-focused approach. This meta-analysis, which focuses on current work, extensively investigates the use of federated learning in medical image processing. On images of the liver, stomach, colon, prostate, breast, and lungs, we examine methods employed throughout the previous five years. The paper explores techniques for classifying medical images, addresses data diversity and privacy concerns, and examines how federated learning is impacted by non-uniform(non-IID) data distribution. The Benefits and challenges of using federated learning for medical imaging are also discussed.
A CNN-MLP Deep Model for Sensor-based Human Activity Recognition

Traditional methods for pattern recognition have demonstrated significant advancements in recent years. However, these techniques have traditionally relied on human intervention to discern crucial insights from data. Deep learning has ushered in a transformative shift by empowering computers to autonomously glean knowledge from data, particularly benefiting our understanding of how individuals interact with mobile and wearable technology. The burgeoning popularity of deep learning stems from its ability to operate effectively with minimal or no human guidance. This research introduces an innovative hybrid deep learning architecture that seamlessly integrates Convolutional Neural Network (CNN) and Multi-Layer Perceptron (MLP) layers. While maintaining precision in activity identification, this CNN-MLP approach excels in localized feature extraction, made possible through the synergy of CNN and MLP layers. Our model delivered outstanding performance on the UCI HAR dataset, achieving a classification accuracy of 97.14%.
Adaptive Sparse Deep Neural Network Inference on Resource-Constrained Cost-Efficient GPUs

Sparse Deep Neural Networks (SpDNNs) has gained great popularity and been widely applied in various machine learning area. Compared to traditional dense DNNs, the unpre-dictable irregularity and sparsity in the sparse weight matrices of SpDNNs make them difficult to be efficiently parallelled. Moreover, most of the recent advanced efforts to optimize SpDNNs are based on high-end GPUs like NVIDIA V100, which may not be affordable to individuals and smaller re-search groups. However, migrating the SpDNNs to those cost-efficient but resource-constrained GPUs confronts enormous challenges, including limitations in both memory and computing resources, as well as the tiresome hyper-parameter tuning in batch parallelism. In this paper, we accelerate SpDNNs on GPUs with more restricted resources through exploiting the memory and computing resources. On one hand, we design the adaptive memory-aware data partition scheme to reduce memory consumption automatically. On the other hand, we propose the Tensor core/CUDA core fusion mechanism to efficiently utilize the hetergeneous computing resources on modern GPU architecture. To the best of our knowledge, we are the first to improve the performance on SpDNNs through adaptive memory tuning and utilizing hetergeneous computing core concurrency. We compare our implementation with the state-of-art previous champions, and the results demonstrate that our work achieves the highest speedup of 1.35 x and 1.39x compared to 2022 champion S&Z on single and multiple NVIDIA Tesla T4 GPUs, respectively. What's more, our work can reach similar or even better throughput compared to 2020 champion H&P on 6 V100 GPUs with only 4 T4 GPUs.
Adversarial Attack Method Based on Dual- frequency Domain Transformation

Deep neural network is vulnerable to adversarial samples, which means altering the image subtly to introduce errors into the neural network model. Adversarial samples can categorize into black-box and white-box attacks, white-box attacks have been able to achieve a high success rate, but the performance of black-box attacks is weak due to the large gap between the substitute model and the victim model. Previously, the way to enhance the substitute model is image transformations on the spatial domain, to simulate different victim models. However, these methods lead to weak transferability, because it can not generate diverse enhanced models. To solve this problem, we propose a dual-frequency-domain transform attack based on DCT(Discrete Cosine Transform) and DWT(Discrete Wavelet Transformation), proposed method optimizes the process of frequency-domain transform, which can greatly enhance the substitute models. Besides, our method can combine with others (gradient attack methods and spatial domain attack methods) to further enhance the attack effect. Experiments show that the attack success rate of our method is good in several mainstream no-defense models.
Enhancing Performance of Multi-Input Neural Networks Using Hadamard Product

Nowadays, neural networks are being used in a lot of different fields. However, using them on mobile devices is tricky as they need a lot of computing power. This can be a problem when fast reaction time is required, especially in an exoskeleton. In this research, we train neural network models to predict ankle movement using sensor data from the calf muscle. From the results, we aim to implement the results in other cases like model optimization or feature selection. So, we used Hadamard products to achieve the goals. Hadamard product is very light so it can achieve model optimization and can measure the value of sensors and each time frame with its' weight values. These weight values can lead to further model optimization and analysis of the data. We tested our system with data from 12 different people and using 64 different input channels. We also tested how well the system could handle changes in data over time, which is important to avoid it getting confused by random noise in the data. In addition, we studied how changing the number of layers in the neural network and the use of the Hadamard product affected the system. We did this by comparing results from a total of 1,200 different models.