Reviewerly/exHarmony huggingface dataset:
Logarithmic Continual Learning

We introduce a neural network architecture that logarithmically reduces the number of selfrehearsal steps in the generative rehearsal of continually learned models.In continual learning (CL), training samples come in subsequent tasks, and the trained model can access only a current task.Contemporary CL methods employ generative models to replay previous samples and train them recursively with a combination of current and regenerated past data.This recurrence leads to superfluous computations as the same past samples are regenerated after each task, and the reconstruction quality successively degrades.In this work, we address these limitations and propose a new generative rehearsal architecture that requires, at most, a logarithmic number of retraining sessions for each sample.Our approach leverages the allocation of past data in a set of generative models such that most of them do not require retraining after a task.The experimental evaluation of our logarithmic continual learning approach shows the superiority of our method with respect to the state-of-the-art generative rehearsal methods.
Cross-Domain Object Detection with Missing Classes in Target Domain

Many existing methods focus on detecting either objects from different domains or those of rare classes, but it's difficult for them to tackle the two issues together. However, in the real world, due to the difficulty of collecting samples of special classes, deep learning practitioners have to use simulated images to substitute for them. To deal with this scenario, in this paper, we research a new task: cross-domain object detection with missing classes in target domain, where there are only partial classes have images and annotations in the target domain. We devise a simple but effective play-and-plug method to address this new task, named the three-stage learning approach with domain and class information preservation. In addition, extensive experiments demonstrate our method is effective and can boost the performance when added to existing unsupervised domain adaptation object detectors.
Escaping Filter-based Adversarial Example Defense: A Reinforcement Learning Approach

An adversarial example is a specially-crafted example with subtle and intentional perturbations that causes a machine learning model to make a false classification. A plethora of papers have proposed to use filters to effectively defend against adversarial example attacks. However, we demonstrate that the filter-based defenses may not be reliable in this paper. We develop AEDescaptor, a scheme to escape the filter-based defenses. AEDescaptor uses a specially -crafted policy gradient reinforcement learning algorithm to generate adversarial exam-ples even if the filters are used to interrupt the backpropagation channel (that is used in traditional adversarial example attack algorithms). Furthermore, we design a customized algorithm to reduce the possible action space in policy gradient reinforcement learning to accelerate AEDescaptor training while still ensuring that AEDescaptor generates successful adversarial examples. The intensive experiments demonstrate that AEDescaptor-generated adversarial examples have good performance (in terms of success rate and transferability) to escape the filter-based defenses.
Corpus Based Self-Assessment Platform for Latvian Language Learners

This paper presents a self-assessment platform for Latvian language learners in the breakthrough (A1) and Waystage (A2) levels.The self-assessment platform contains three types of exercises (typing, inflection and gap filling) based on error analysis of the Latvian Language Learner corpus (LaVA).All exercises are automatically generated based on data from multiple corpora.The automatically generated exercises are useful not only for learners outside of classroom or even outside of any formal education setting, but also for educators and authors of learning aids.Currently the self-assessment platform is tailored for language learners at the beginner level, but it can be easily extended for more advanced levels.The self-assessment platform is freely available online (http://uzdevumi.riks.korpuss.lv/en/)and the interface is translated in two language -Latvian and English.
Augmentation-induced Consistency Regularization for Classification

Deep neural networks have become popular in many supervised learning tasks, but they may suffer from overfitting when the training dataset is limited. To mitigate this, many researchers use data augmentation, which is a widely used and effective method for increasing the variety of datasets. However, the randomness introduced by data augmentation causes inevitable inconsistency between training and inference, which leads to poor improvement. In this paper, we propose a consistency regularization framework based on data augmentation, called CR-Aug, which forces the output distributions of different sub models generated by data augmentation to be consistent with each other. Specifically, CR-Aug evaluates the discrepancy between the output distributions of two augmented versions of each sample, and it utilizes a stop-gradient operation to minimize the consistency loss. We implement CR-Aug to image and audio classification tasks and conduct extensive experiments to verify its effectiveness in improving the generalization ability of classifiers. Our CR-Aug framework is ready-to-use, it can be easily adapted to many state-of-the-art network architectures. Our empirical results show that CR-Aug outperforms baseline methods by a significant margin.
A Study on the Restrictions of Deep Learning

As circumstances, in a self-governing steerage circumstance, the inputs are the sensor analyses along with likewise the outputs are truly the needs of the auto. In a picture localization issue, the inputs are in truth photographs in addition to the outcomes are bounding compartments around things of interest. Having said that, a job like "Determine regulative approach." remains not clear, as the region of functional plannings to pass is each hazy and also most definitely big
The Challenges of Online Dating and Digital Relationships during COVID-19

The COVID-19 pandemic has affected almost every aspect of human lives across the globe.Digital technology plays a critical role in our renewed essence to relationships -romantic and otherwise because of accessibility, anonymity and less emotional burden due to perceived control on relationships in the digital space.Online dating platforms have changed the way we build and sustain relationships.Online dating received an upsurge following the COVID-19 pandemic as people squared in quarantined spaces.While online dating can be one way to maintain social connection, finding potential partners, and / or romantic or sexual interaction to cope with the stress imposed by the pandemic, several consequences of online dating need to understand and promote sexual and mental health.This paper explores challenges and consequences of online dating and its public health significance.Online dating has evolved digital relationships as potential coping mechanisms to deal with a sense of insecurities and threat on safety and belongingness.It has changed the dynamics of relationship, emotional expression, trust and fidelity.It has created negative consequences
VideoPipe 2022 Challenge: Real-World Video Understanding for Urban Pipe Inspection

Video understanding is an important problem in computer vision. Currently, the well-studied task in this research is human action recognition, where the clips are manually trimmed from the long videos, and a single class of human action is assumed for each clip. However, we may face more complicated scenarios in the industrial applications. For example, in the real-world urban pipe system, anomaly defects are fine-grained, multi-labeled, domain-relevant. To recognize them correctly, we need to understand the detailed video content. For this reason, we propose to advance research areas of video understanding, with a shift from traditional action recognition to industrial anomaly analysis. In particular, we introduce two high-quality video benchmarks, namely QV-Pipe and CCTV-Pipe, for anomaly inspection in the real-world urban pipe systems. Based on these new datasets, we will host two competitions including (1) Video Defect Classification on QV-Pipe and (2) Temporal Defect Localization on CCTV-Pipe. In this report, we describe the details of these benchmarks, the problem definitions of competition tracks, the evaluation metric, and the result summary. We expect that, this competition would bring new opportunities and challenges for video understanding in smart city and beyond. The details of our VideoPipe challenge can be found in https://videopipe.github.io.
Penerapan Teknologi Metaverse pada Bank Syari’ah

Computer Science innovation plays a major role in everyday life-changing and enriching human interaction, communication, and social transactions. Currently, the fourth wave of computational innovation is underway relating to spatial (mapping), immersive technologies such as Virtual Reality (VR), and Augmented Reality (AR). Metaverse is a new interface that is expected to be used to perform all kinds of human-computer interactions. Bank BRI and Bank Mandiri are conventional banks that are starting to enter the metaverse ecosystem. Of the features that have been provided by BSI, there are no features related to the metaverse. From the search that the author did from various sources,untill now there has been no Islamic bank in Indonesia that has begun to penetrate the world of the metaverse. In this article, the focus of the discussion is on the metaverse application that can be used for virtual branch offices of Islamic banks. With the virtual branch in Metaverse, of course, it is hoped that it can be a new, fun journey for customers while at the same time being able to reach a wider community to carry out various digital transaction services as well as better customer service in the future. Spatial is one of the most worthy metaverse applications for the banking world, including Islamic banks. Spatial application provides features that can serve customers in the metaverse realm at a low cost. Spatial empowers users to take advantage of beautiful privacy spaces to share engaging content, build close-knit communities, and drive meaningful sales of creative works and products. Keywords: Augmented Reality, Metaverse, Islamic banking, Virtual Branch, Spatial.
TECHNOLOGICAL CHALLENGES OF OUR TIME IN THE DIGITALIZATION OF THE EDUCATION OF THE FUTURE

The purpose of this article is to analyze the features and prospects for the development of the process of using modern technologies in the educational process in the context of the study of the global trend of digitalization, which determines the digital transformation of education in the future. The scientific novelty of the article lies in the determination that digital education is not a substitute for traditional education, but an effective addition to the quality provision of educational services thanks to the introduction of advanced technologies into the educational process in accordance with the technological challenges of the present and future. The practical significance of the obtained results is manifested in the possibility of using them to develop a strategy for solving existing problems of educational digitalization, finding tools for optimizing the process of obtaining online education. Deepening the process of digital transformation of education will make it possible to expand the possibilities of comprehensive use of modern innovative technologies to increase the level of efficiency and accessibility of education, which in turn contributes to the formation of competitive specialists who possess digital competence skills - the key to building a successful career in the future.
Constraints on Hyper-parameters in Deep Learning Convolutional Neural Networks

Convolutional Neural Network (CNN), a type of Deep Learning, has a very large number of hyper-meters in contrast to the Artificial Neural Network (ANN) which makes the task of CNN training more demanding.The reason why the task of tuning parameters optimization is difficult in the CNN is the existence of a huge optimization space comprising a large number of hyper-parameters such as the number of layers, number of neurons, number of kernels, stride, padding, rows or columns truncation, parameters of the backpropagation algorithm, etc.Moreover, most of the existing techniques in the literature for the selection of these parameters are based on random practice which is developed for some specific datasets.In this work, we empirically investigated and proved that CNN performance is linked not only to choosing the right hyperparameters but also to its implementation.More specifically, it is found that the performance is also depending on how it deals when the CNN operations require setting of hyper-parameters that do not symmetrically fit the input volume.We demonstrated two different implementations, crop or pad the input volume to make it fit.Our analysis shows that padding performs better than cropping in terms of prediction accuracy (85.58% in contrast to 82.62%) while takes lesser training time (8 minutes lesser).
Embedding Contrastive Unsupervised Features to Cluster In- And Out-of-Distribution Noise in Corrupted Image Datasets

Using search engines for web image retrieval is a tempting alternative to manual curation when creating an image dataset, but their main drawback remains the proportion of incorrect (noisy) samples retrieved. These noisy samples have been evidenced by previous works to be a mixture of in-distribution (ID) samples, assigned to the incorrect category but presenting similar visual semantics to other classes in the dataset, and out-of-distribution (OOD) images, which share no semantic correlation with any category from the dataset. The latter are, in practice, the dominant type of noisy images retrieved. To tackle this noise duality, we propose a two stage algorithm starting with a detection step where we use unsupervised contrastive feature learning to represent images in a feature space. We find that the alignment and uniformity principles of contrastive learning allow OOD samples to be linearly separated from ID samples on the unit hypersphere. We then spectrally embed the unsupervised representations using a fixed neighborhood size and apply an outlier sensitive clustering at the class level to detect the clean and OOD clusters as well as ID noisy outliers. We finally train a noise robust neural network that corrects ID noise to the correct category and utilizes OOD samples in a guided contrastive objective, clustering them to improve low-level features. Our algorithm improves the state-of-the-art results on synthetic noise image datasets as well as real-world web-crawled data. Our work is fully reproducible github.com/PaulAlbert31/SNCF.
Adversarial Attacks in DNN-based Modulation Recognition: A Preliminary Study

With the development of information technology and 5G communication, the number of wireless devices has increased significantly, and the complexity of the communication system has also been rapidly upgraded. Due to the complexity and the high dimensionality of signals, many researchers have applied deep learning to modulation recognition tasks. However, although deep learning exhibits strong capabilities for modulation recognition, its vulnerability to adversarial examples should also be addressed. In this paper, we conduct a preliminary study on the security of DNN-based modulation recognition models. We build multiple high-accuracy DNN-based models on existing research and test their robustness by implementing multiple adversarial attacks on the well-trained models. The results show that adversarial examples pose a very serious threat to DNN-based modulation recognition models.
OPTIMIZATION OF THE TRAJECTORY OF SENSORS MOTION TAKING INTO ACCOUNT THE IMPORTANCE OF THE AREAS OF THE MONITORING AREA SEGMENTS AND THE PROBABILITY OF DETECTION OF OBJECTS

Due to the widespread use of sensors in data collection and processing, one of the key criteria is the amount of information accumulated and energy efficiency. While monitoring the territory, the movement of research objects is common. As a result there is a change in the probability of their detection in the segment of the territory. Also, segments may be of varying importance. Taking these factors into account will significantly increase the amount of information accumulated. The article presents a method of constructing the optimal trajectory of sensors motion taking into account the importance of territory segments and the probability of detection of objects. The method is based on the representation of distribution of the probability of detection of objects and the importance of territory segments in the form of layers and their integration into a layer of the probable value of detected objects. Seven classes of the probable value of detected objects with corresponding numerical and graphical equivalents are considered. As optimal trajectory of sensors motion the trajectory which provides minimum energy expenditure is meant. Energy efficiency is achieved by constructing a trajectory of minimum length as a solution to the salesman’s problem. The set of points at which the trajectory is built is formed on the basis of the layer of the probable value of the detected objects after the procedure of replacing the nodes. A separate node replacement class, or superposition of node replacement classes, is proposed for each class of probable value of detected objects. Replacement of five, three and two nodes is described. A genetic algorithm with modification of crossing and selection rules was used to find a solution to this problem. A set of trajectories is constructed using the proposed algorithm. The analysis of the obtained results confirmed the efficiency of the developed method and allowed to increase the energy efficiency when covering a given area by 76 %.
Fake Satellite Image Detection via Parallel Subspace Learning (PSL)

A new method, called PSL-DefakeHop, is proposed to detect fake satellite images based on the parallel subspace learning (PSL) framework in this work. The DefakeHop method was developed previously for detection of deepfake generated faces under the successive subspace learning (SSL) framework. PSL is proposed to extract features from responses of multiple single-stage filter banks (or called PixelHops), which operate in parallel, and it improves SSL that extracts features from multi-stage cascaded filter banks. PSL has two advantages. First, PSL preserves discriminant features often lie in high-frequency channels, which are however ignored by SSL. Second, decisions from multiple filter banks can be ensembled to further improve detection accuracy. To demonstrate the effectiveness of the proposed PSL-DefakeHop method, we evaluate it on the UW Fake Satellite Image dataset and observe perfect classification performance (i.e., 100% F1 score, precision and recall).
Construction of Fresh Agricultural Product Supply Chain Traceability Platform Based on Alliance Blockchain

Aiming at the problems of scattered data, information asymmetry, multi-source heterogeneity, and low security in the traceability system of agricultural product supply chain, this paper designs a fresh agricultural product data traceability platform based on the consortium blockchain, realizes the registration of multiple users and the storage of internal sensitive information through the blockchain smart contract technology, This platform designs a traceability structure based on the actual needs of the traceability system of the fresh agricultural product supply chain, and further ensures the safety and reliability of the transactions of all parties in the fresh agricultural product supply chain.
Signal Instant Messenger Forensics

This chapter focuses on the examination of the Signal instant message application. It presents an integrated framework that covers the trigger of investigation to the final report. The framework is an iterative, real-time data flow centred framework. The framework outlines the options to obtaining, monitoring and capturing Signal data flow in real-time mode, and how to conduct OSINT investigation with Signal built-in functionalities. The framework also introduces how the forensic data extraction of physical devices and the forensic findings from forensic images can be used as a data source to support the real-time investigation.
Maxine’s Turing Test – A Player-Program as Co-Ethnographer of Socio-Aesthetic Interaction in Improvised Music

Beyond the goal of refining system design to the needs and tastes of users, user evaluation of interactive music systems offers a method of examining the nature of musical creativity as understood by its human practitioners. In the case of improvising music systems, user study and evaluation of a system’s ability to improvise may be useful in the ethnomusicological study of musical interaction in contemporary improvised music. A survey of preliminary findings based on the interactions of an improvising system, Maxine, with several improvisers is discussed, with results suggesting methodological reconfigurations of the purpose and goals of evaluating of interactive musical metacreations.
Effects of Emotional Intelligence to Learning Achievement in Elementary School

The influence of emotional intelligence on learning achievement in high-grade students of an elementary school in Wakatobi Regency, Indonesia. Emotional intelligence is one of the essential factors that should be possessed by students who need to achieve better learning achievement in school because, according to existing theories, emotional intelligence dramatically affects student learning achievement. The reality of this sat is quite interesting to study. The individual has a high chance of achieving high learning achievement. On the other hand, if a person has relatively low emotional intelligence, then that person certainly has the opportunity to get pretty low learning achievement. Whereas in reality, individuals lack emotional intelligence. High can achieve learning achievements that are biased to be proud of. This study aims to determine the effect of emotional intelligence on learning achievement in high-grade students. This study is quantitative. Variable covered emotional intelligence and learning achievement. The subjects in this study were students of the 4th, 5th, and 6th grades. The data collection method used is an emotional intelligence scale and documentation in the form of an average student report card score. The data analysis technique in this study uses simple linear regression analysis. R Square (coefficient of determination) was obtained of 0.103. This means that the effective contribution that emotional intelligence makes to learning achievement is 10.3%, while the remaining 89.6% is influenced by other factors. Such factors can be internal factors or external factors. This suggests that emotional intelligence has no effect on student learning achievement. Y=123.13+(16,555X), which means that each addition of one emotional intelligence value will reduce the learning achievement value by 16,555. These results show that emotional intelligence has no influence on learning achievement.