Reviewerly/exHarmony huggingface dataset:
MRIM: Lightweight saliency-based mixed-resolution imaging for low-power pervasive vision

While many pervasive computing applications increasingly utilize real-time context extracted from a vision sensing infrastructure, the high energy overhead of DNN-based vision sensing pipelines remains a challenge for sustainable in-the-wild deployment. One common approach to reducing such energy overheads is the capture and transmission of lower-resolution images to an edge node (where the DNN inferencing task is executed), but this results in an accuracy-vs-energy tradeoff, as the DNN inference accuracy typically degrades with a drop in resolution. In this work, we introduce MRIM, a simple but effective framework to tackle this tradeoff. Under MRIM, the vision sensor platform first executes a lightweight preprocessing step to determine the saliency of different sub-regions within a single captured image frame, and then performs a saliency-aware non-uniform downscaling of individual sub-regions to produce a “mixed-resolution” image. We describe two novel low-complexity algorithms that the sensor platform can use to quickly compute suitable resolution choices for different regions under different energy/accuracy constraints. Experimental studies, involving object detection tasks evaluated traces from two benchmark urban monitoring datasets as well as a prototype Raspberry Pi-based MRIM implementation, demonstrate MRIM’s efficacy: even with an unoptimized embedded platform, MRIM can provide system energy conservation of 35+% (∼80% in high accuracy regimes) or increase task accuracy by 8+%, over conventional baselines of uniform resolution downscaling or image encoding, while supporting high throughput. On a low power ESP32 vision board, MRIM continues to provide 60+% energy savings over uniform downscaling while maintaining high detection accuracy. We further introduce an automated data-driven technique for determining a close-to-optimal number of MRIM sub-regions (for differential resolution adjustment), across different deployment conditions. We also show the generalized use of MRIM by considering an additional license plate recognition (LPR) task: while alternative approaches suffer 35%–40% loss in accuracy, MRIM suffers only a modest recognition loss of ∼10% even when the transmission data is reduced by over 50%.
Semantic alignment with self-supervision for class incremental learning

Existing class incremental learning methods typically employ knowledge distillation to minimize discrepancies in model outputs. However, these methods are restricted by the mismatch between quondam knowledge and new data. To alleviate these issues, we introduce semantic alignment decouples the classification and distillation in different semantic spaces. The unmatched new data is regarded as out-of-distribution data on the old class distribution, and the corresponding pseudo-labels are attached to the new data using the original network. Intuitively, the pseudo-labels could be consistently preserved in the old semantic space. Moreover, we develop auxiliary self-supervised classifiers to learn more generalized representation, enabling a better stability-plastic trade-off. Furthermore, self-distillation is employed to refine self-supervised knowledge from auxiliary classifiers. Extensive experiments demonstrate that our method achieves the best performance on CIFAR100, ImageNet100, ImageNet, CUB200, and Stanford-Dogs120 datasets. Notably, our method outperforms existing methods by a substantial margin when only one old exemplar is stored per class, i.e., 11.34% and 21.46% improvement on CIFAR100 of 5 phases and 10 phases, respectively.
ANFIS learning using expectation maximization based Gaussian mixture model and multilayer perceptron learning

The Adaptive Neuro-Fuzzy Inference System (ANFIS) is a hybrid learning algorithm that combines the learning ability of neural networks with fuzzy inference systems. While ANFIS has been successfully applied to several real-world problems, effective parameter optimization remains a challenge. This paper presents a novel approach for optimizing parameters of ANFIS in supervised settings. The proposed approach utilizes a combination of probabilistic mixture models and perceptron-based learning to parameterize ANFIS. To learn ANFIS membership functions, it generates a mixture of the finite probability distributions for each input feature. Then the consequent parameters are learned by transforming them into weights of a multi-layer perceptron instance. The effectiveness of the proposed method is evaluated on classification problems of varying complexity, ranging from binary-class to multi-class with variable dimensions. The results show that the proposed algorithm improves ANFIS performance in terms of accuracy rate and speed (train-time) analysis. The effectiveness and efficiency of the proposed approach have been further confirmed using a 5 × 2 cross-validation paired significance t-test. In particular, for binary-class problems, our model outperformed standard methods by up to 10% accuracy improvement. Similarly, for multi-class problems, our model achieved an average increase of 8% in accuracy while reaching up to 14% improvement. On average, the proposed model showed a reduction of about 75% in training time compared to the other models. Overall, the proposed approach offers competitive computational performance and acceptable efficacy for parameter optimization of ANFIS.
Sensitivity of principal components to system changes in the presence of non-stationarity

Abstract Non-stationarity affects the sensitivity of change detection in correlated systems described by sets of measurable variables. We study this by projecting onto different principal components. Non-stationarity is modeled as multiple normal states that exist in the system even before a change occurs. The studied changes occur in mean values, standard deviations or correlations of the variables. Monte Carlo simulations are performed to test the sensitivity for change detection with and without knowledge about non-stationarity for different system dimensions and numbers of normal states. A comparison clearly shows that knowledge about the non-stationarity of the system greatly improves change detection sensitivity for all principal components. This improvement is largest for those components that already provide the greatest possibility for change detection in the stationary case. We illustrate our results with an example using real traffic flow data, in which we detect a weekend and a bank holiday start as anomalies.
Rota’s basis conjecture holds for random bases of vector spaces

In 1989, Rota conjectured that, given n bases B1,…,Bn of the vector space Fn over some field F, one can always decompose the multi-set B1∪⋯∪Bn into transversal bases. This conjecture remains wide open despite of a lot of attention. In this paper, we consider the setting of random bases B1,…,Bn. More specifically, our first result shows that Rota’s basis conjecture holds with probability 1−o(1) as n→∞ if the bases B1,…,Bn are chosen independently uniformly at random among all bases of Fqn for some finite field Fq (the analogous result is trivially true for an infinite field F). In other words, the conjecture is true for almost all choices of bases B1,…,Bn⊆Fqn. Our second, more general, result concerns random bases B1,…,Bn⊆Sn for some given finite subset S⊆F (in other words, bases B1,…,Bn where all vectors have entries in S). We show that when choosing bases B1,…,Bn⊆Sn independently uniformly at random among all bases that are subsets of Sn, then again Rota’s basis conjecture holds with probability 1−o(1) as n→∞.
The Problems of Artificial Intelligence Technology in the Media: Discussions on Watermarks and Identification Measures to Prevent Copyright Infringement and Disinformation by AI Generated Contents

As artificial intelligence technology spreads widely and the level of technology increases, it is causing various social problems. In particular, some artificial intelligence contents are generated so elaborately that it cannot be easily distinguished by general users. If it is not specifically marked as 'content generated by artificial intelligence technology' or 'ai', it may mixed with other contents.&#x0D; We have had high expectations that artificial intelligence technology would bring unprecedented innovation. Due to artificial intelligence technology, however, the contents created by using artificial intelligence technology in all over the world have faced serious social problems, and efforts are being made to find a solution in various places. In particular, there are cases where facts that do not actually exist are fabricated or distributed, posted, or used as if they were human-made works.&#x0D; By technological development continues, there will eventually come a moment when artificial intelligence created content and human-created content will be mixed and it will be difficult to distinguish them without a special method. If it becomes impossible to differentiate the value and characteristics of the production process and results of each content or the boundaries between content will become blurred, content creation activities in which humans have invested a long time and effort will gradually shrink.&#x0D; Korea is pursuing the enactment of the Artificial Intelligence Act to establish policies, including support necessary for the fostering and development of artificial intelligence technology, and to stipulate obligations to protect artificial intelligence service users. Because it takes a considerable amount of time to establish a legal system and apply actual regulations, however, it is moment to discuss identification measures such as watermarks to prevent mis-recognition and confusion regarding artificial intelligence contents that are currently occurring.&#x0D; If standards are established to distinguish contents using artificial intelligence technology, the differentiation between human-created content and artificial intelligence created content will be maintained and the value of each content will not be damaged. It is also expected to help prevent copyright protection protected by law from being infringed and protect users. Above all, in the future, there is a risk that artificial intelligence content and human-created works will not be distinguished, resulting in misunderstanding and confusion, which is reducing the value of existing works and weakening the sense of protection for them. Therefore, there is a need to seriously discuss countermeasures before these problems become bigger.
The Combination of British Parliamentary Debate and Peer Assessment in Teaching Speaking

The main problems of this research were how to improve the speaking ability of EFL students converting grammar, vocabulary, pronunciation, discourse, and strategies. therefore, how to encourage them to use English. This research aimed to see whether the combination of British Parliamentary Debate and Peer Assessment may enhance the speaking ability of EFL students. A quasi-experimental method with non-equivalent group design was carried out in this research. The population was the third semester of English Department students at Universitas Muhammadiyah Parepare. Two classes from the population, representing the experimental class and the control class, were selected using the cluster random selection technique as the study's sample. This research used an interview test with a variety of themes as its instrument. Pre-test, treatment, and post-test were the three steps that made up the data collection processes. The data that obtained by the test were then submitted to a quantitative analysis. The research findings demonstrate that the experimental class students' speaking abilities were better to those of the control class. The data from the experimental class showed that the students' mean score was higher compared to the control class (59,68 &gt; 49,06). In conclusion, the combination of British Parliamentary Debate and Peer Assessment was improved the students’ speaking ability.
Efficient octagonal compression of multimedia data using using LZW-OMCA compressor for secured data transmission

Data compression is the ancestor of image compression, which uses fewer bits to represent the same picture. It is categorised as lossy or lossless depending on the quality required. In a lossless compression situation, no information is lost during the decompression process. Data loss is possible with the lossy technique since it is not reversible. In an effort to boost compression efficiency while maintaining a high xiv reconstruction quality of picture, near lossless approaches have evolved. The medical pictures consist of a large number of items, each of which may be described in detail and utilised for a variety of purposes. The clinically relevant item in 2D medical pictures is referred to as the Region of Interest (ROI), whereas in 3D images, it is referred to as the Volume of Interest (VOI). Saving energy is crucial since it is one of the most limited resources in these networks. However, DTN has an additional difficulty since communication between nodes is maintained so long as they are in physical proximity to one another. However, because to the nodes’ mobility, this may not be long enough to provide the necessary multimedia data transmission. Wireless networks are susceptible to security assaults, and traditional security solutions are computationally demanding, making them unsuitable for networks that constantly need to recharge their batteries. All of these are reasons for tackling the problems of multimedia data processing and transmission via wireless networks in this dissertation. With this in mind, it has been attempted to investigate low-overhead and safe multimedia data compression as a solution to the issue that energy-constrained nodes in these networks limit complex multimedia processing while keeping at least basic security features. LZW-OMCA compression using the Octagonal Multimedia Compression Algorithm is part of the suggested method. The purpose of this is to improve the compression ratio. The proposed approach uses a little bit of crypt to compress data, which makes the data unreadable to anybody except the intended receiver, hence providing network security. The previous proposed works analysed the performance of several compression algorithms applied to multimedia material. Performance assessment utilising MSE, SSIM, and other metrics are used to show the pros and cons of each segment.
Twin extreme learning machine based on heteroskedastic Gaussian noise model and its application in short-term wind-speed forecasting

Extreme learning machine (ELM) has received increasingly more attention because of its high efficiency and ease of implementation. However, the existing ELM algorithms generally suffer from the drawbacks of noise sensitivity and poor robustness. Therefore, we combine the advantages of twin hyperplanes with the fast speed of ELM, and then introduce the characteristics of heteroscedastic Gaussian noise. In this paper, a new regressor is proposed, which is called twin extreme learning machine based on heteroskedastic Gaussian noise (TELM-HGN). In addition, the augmented Lagrange multiplier method is introduced to optimize and solve the presented model. Finally, a significant number of experiments were conducted on different data-sets including real wind-speed data, Boston housing price dataset and stock dataset. Experimental results show that the proposed algorithms not only inherits most of the merits of the original ELM, but also has more stable and reliable generalization performance and more accurate prediction results. These applications demonstrate the correctness and effectiveness of the proposed model.
The Study Of Data Modeling Methodologies For Column-Oriented Databases

Further research regarding databases suggest that applying data modeling in NoSQL databases as in SQL databases could improve its performance. This discovery leads to the birth of many research regarding methodologies for data modeling in NoSQL databases, including ones for column-oriented databases. Two column-oriented databases data modeling methodologies that were discussed in this research that use EER diagrams for their conceptual models are Chebotko methodology which also considers application queries for its database and Poffo methodology which depends only on its EER diagrams. Thus, the purpose of this research is to analyze the difference between both data modeling methodologies in column-wide oriented databases. The testing results show that on the chosen case study, the physical data model made using Chebotko methodology causes queries to retrieve data 2.24 times slower than the ones made using Poffo methodology. In conclusion, modeling data in column-oriented databases using Poffo methodology is generally safer because the size of its corresponding column families could remain small, whereas one would have to exercise caution while modeling data using Chebotko methodology because the column families created could be too big due to inquiring too much information in a query.
Comparison of Support Vector Regression and Artificial Neural Network Algorithm in Predicting the Number of Prospective Student Registrans

The candidate of students have difficulty in determining which college to choose. This difficulty is due to the lack of information about the college to be addressed. One of the actions that can be taken to introduce the campus is bypromoting to high schools. To find out schools that need an in-depth introduction to the X campus, it is necessary to make a prediction system that can show the possibility of the number of registrants from these schools that are still small. In this study, a comparison between the SVR and ANN methods was carried out to determine the best model in predicting the number of prospective applicants from each school. In the process of model formation, the optimal hyperparameter is found in the model using gridsearch. To determine the best model, researchers used the smallest RMSE value obtained from both models. Based on the RMSE results obtained, the ANN model RMSE value is smaller with a value of 0.04929599755078 so this model is the best model. However, after calculating MAAPE, the ANN model has not been able to predict well.
SQL Interface For CRUD Operations On Cloud Firestore

Google developed Cloud Firestore, a document-oriented NoSQL database. Cloud Firestore data may be accessed in two different ways: first, through the Firebase Console, and second, through the Cloud Firestore API. While Firebase Console data management is less efficient because each individual document must be managed individually, Cloud Firestore API has the disadvantages of specific syntax and limitations for complex queries. On the other hand, SQL is a popular query language that can handle complex queries. This paper discusses a library designed to execute SQL queries to Cloud Firestore. The library converts SQL queries into Cloud Firestore API statements, which are subsequently used to run the queries on the database. The library executes INSERT, UPDATE, DELETE, and SELECT SQL-based CRUD (Create, Read, Update, Delete) statements. Additionally, the library includes JOIN, subquery, and aggregate functions, three complex query types that the Cloud Firestore API does not support. We also developed an application as the library's graphical user interface. The results of the library test indicate that SQL queries produced the expected results. Compared to other methods, the usage of SQL queries is easier and more concise to manage data on Cloud Firestore. Application testing results show that all features have run well.
Interaction Design Improvement of Content Preferences, Report, and Homepage Feature of Shopee Video Using User-Centered Design

Shopee is an e-commerce with Shopee Video feature that was launched in the first quarter of 2022 as a response to the short-form video trend and opportunity. So far, Shopee Video is used to help buyers get a good shopping experience and help sellers to expand their business with short-form video promotion. Shopee Video which is merged with Shopee e-commerce leads to interaction design problems related to the merged navigation bar, therefore it becomes less intuitive. As e-commerce, Shopee Video is expected to be more focused to display product-related videos, but there are still many irrelevant videos related to other topics. Users could not choose to only get product-related videos. Users also get unsuitable videos on their Shopee Video or Shopee e-commerce homepage. This research will examine interaction design problems related to the less intuitive Shopee Video and the users' limitations to set up their video preferences. The result of this research is a high fidelity prototype of a mobile application to solve the Shopee Video problem. This research is expected to deliver interaction design to solve Shopee Video users' problems using user-centered design (UCD) approach. This approach is chosen because Shopee Video is an existing Shopee feature, thus will refer to users' satisfaction and feedback. Based on the high fidelity prototype evaluation, the fixed interaction design is effective to use with a 100% completion rate; easy to learn and easy to remember how to use with 90.83 SUS (best imaginable, grade A), 4.81 (easy) SEQ ease of use value, improvement in time on task, and 4.67 (satisfied) satisfaction rating. The interaction design result also achieved its user experience goal, helpful, based on the SUS score and the satisfaction rating.
Detecting Command Injection and Cross-site Scripting Vulnerabilities Using Graph Representations

Web-based applications, such as JavaScript-based applications, have vastly grown in scope and features. As web-based applications grow, the potential of vulnerabilities emerging inside such applications also grows. One of the ways to detect vulnerabilities inside web-based applications is to perform a static code analysis. Several static code analysis tools have been developed and are able to detect vulnerabilities inside JavaScript-based applications. However, these tools use abstract syntax tree representations in their analysis, therefore the analysis can't be performed efficiently. This paper proposes a static code analysis to detect vulnerabilities inside JavaScript-based applications using data-flow graph, control-flow graph, and call graph representations. Using taint analysis, a static code analysis tool is able to detect vulnerabilities in the form of command injection, and cross-site scripting (XSS). Test results showed that the static code analysis tool successfully detected vulnerabilities from four open-source projects.