Reviewerly/exHarmony huggingface dataset:
DPS-Net: Deep Polarimetric Stereo Depth Estimation

Stereo depth estimation usually struggles to deal with textureless scenes for both traditional and learning-based methods due to the inherent dependence on image correspondence matching. In this paper, we propose a novel neural network, i.e., DPS-Net, to exploit both the prior geometric knowledge and polarimetric information for depth estimation with two polarimetric stereo images. Specifically, we construct both RGB and polarization correlation volumes to fully leverage the multi-domain similarity between polarimetric stereo images. Since inherent ambiguities exist in the polarization images, we introduce the iso-depth cost explicitly into the network to solve these ambiguities. Moreover, we design a cascaded dual-GRU architecture to recurrently update the disparity and effectively fuse both the multi-domain correlation features and the iso-depth cost. Besides, we present new synthetic and real polarimetric stereo datasets for evaluation. Experimental results demonstrate that our method outperforms the state-of-the-art stereo depth estimation methods.
Understanding Hessian Alignment for Domain Generalization

Out-of-distribution (OOD) generalization is a critical ability for deep learning models in many real-world scenarios including healthcare and autonomous vehicles. Recently, different techniques have been proposed to improve OOD generalization. Among these methods, gradient-based regularizers have shown promising performance compared with other competitors. Despite this success, our understanding of the role of Hessian and gradient alignment in domain generalization is still limited. To address this shortcoming, we analyze the role of the classifier’s head Hessian matrix and gradient in domain generalization using recent OOD theory of transferability. Theoretically, we show that spectral norm between the classifier’s head Hessian matrices across domains is an upper bound of the transfer measure, a notion of distance between target and source domains. Furthermore, we analyze all the attributes that get aligned when we encourage similarity between Hessians and gradients. Our analysis explains the success of many regularizers like CORAL, IRM, V-REx, Fish, IGA, and Fishr as they regularize part of the classifier’s head Hessian and/or gradient. Finally, we propose two simple yet effective methods to match the classifier’s head Hessians and gradients in an efficient way, based on the Hessian Gradient Product (HGP) and Hutchinson’s method (Hutchinson), and without directly calculating Hessians. We validate the OOD generalization ability of proposed methods in different scenarios, including transferability, severe correlation shift, label shift and diversity shift. Our results show that Hessian alignment methods achieve promising performance on various OOD benchmarks. The code is available here.
End-to-End Question Answering System for Indonesian Documents Using TF-IDF and IndoBERT

In this paper, we propose a Question Answering System (QAS) for Indonesian text using retriever-reader architecture. The retriever is designed to retrieve relevant documents given a question, while the reader goal is to extract answers from those documents. We built the retriever based on TF-Idf model that returns most relevant documents based on cosine similarity score. On the other hand, we built the reader by fine-tuning IndoBERT for question answering task. We performed the evaluation on translated SQuAD dataset, with Exact Match and F1-score as the metrics. The evaluation results show that the proposed system demonstrates good performance, achieving the highest F1-score of 70% and the highest Exact Match score of 53% on the translated SQuAD dataset.
IMPLEMENTATION OF SUPPORT VECTOR MACHINE METHOD IN CLASSIFYING SCHOOL LIBRARY BOOKS WITH COMBINATION OF TF-IDF AND WORD2VEC

The development of technology in education is integral to enhancing its quality, such as implementing information technology in school libraries. Searching for books in school libraries is time-consuming due to conventional book classification, lacking organization based on classifications. Therefore, implementing information technology in school libraries is crucial to improve library management effectiveness. An innovative solution optimizing library management involves leveraging artificial intelligence, particularly machine learning. In applying machine learning to library book classification, Support Vector Machine acts as an algorithm understanding patterns and characteristics of book titles, categorizing them into Dewey Decimal Classification (DDC). The dataset comprises 10 classes aligned with DDC. Random data collection follows an 80:20 scale for training and testing data. Data preprocessing is an initial research stage, addressing imbalanced data through oversampling. Testing the SVM algorithm with a linear kernel and C = 1 parameter is conducted three times using different feature extraction methods: TF-IDF alone, Word2Vec alone, and a combination of TF-IDF and Word2Vec. Model performance evaluation employs K-Fold Cross-Validation. After the three objective tests, the most accurate book classification results were obtained using a combination of TF-IDF and Word2Vec feature extraction. It's concluded that SVM's book classification method can be applied, yielding the highest accuracy of 73% with the TF-IDF and Word2Vec feature extraction combination. This outperforms other feature extraction methods, with precision at 83%, recall at 72%, and an F1-Score of 76%.
Clustering Data Cuaca Ekstrim Indonesia dengan K-Means dan Entropi

As information technology develops in agriculture, more patterns of database systems are manual or computerized. However, the amount of data available does not always match the knowledge that can be generated. The community really needs weather information regardless of the format if it is reliable and valid. One is to determine local climate patterns. The data collection method uses a document study method, then uses data analysis techniques with a scoring system on rainfall data, maximum temperature, minimum temperature, average temperature, humidity, and wind speed. The K-Means Clustering method is a technique for grouping data. From the analysis carried out, there are 3 classes to cluster the weather levels produced by the entropy test to avoid bias towards non-optimal precision and accuracy. The division of the number of clusters can be captured as a type of potential vulnerability, namely high, medium, and mild where the total of all weather data from all BMKG stations throughout Indonesia is 123 data, 52 of which are classified as areas that have the potential to experience extreme high weather, 31 are classified as areas that experience extreme weather, moderate extreme weather potential, and 40 areas classified as areas with mild extreme weather potential.
Gradient-free adversarial attack algorithm based on differential evolution

Deep learning models are susceptible to adversarial examples even in the black-box setting. This means there are security risks in intelligent systems based on deep learning. Research on adversarial attacks is crucial to improving the robustness of deep learning models. Most of the existing algorithms are query-intensive and require models to provide more detailed results. We focus on a restrictive threat model and propose a gradient-free adversarial attack algorithm based on differential evolution. In particular, we design two fitness functions to achieve targeted attacks and non-targeted attacks. And we introduce an elimination mechanism in the selection phase to speed up the convergence of the algorithm. Experiments on MNIST, CIFAR-10, and ImageNet show the effectiveness of the proposed method. The comparison with C&W, ZOO and GenAttack shows our method has better advantages in the attack success rate, the number of queries required for a successful attack, and the information obtained in a single query.
Hybrid Design of Multiplicative Watermarking for Defense Against Malicious Parameter Identification

Multiplicative watermarking (MWM) is an active diagnosis technique for the detection of highly sophisticated attacks, but is vulnerable to malicious agents that use eaves-dropped data to identify and then remove or replicate the watermark. In this work, we propose a scheme to protect the parameters of MWM, by proposing a design strategy based on piecewise affine (PWA) hybrid dynamical systems, called hybrid multiplicative watermarking (HMWM). Due to the design decision to make certain states of the HMWM systems unobservable, we show that parameter reconstruction by an eavesdropper is infeasible, from both a computational and a system-theoretic perspective, while not altering the system's closed-loop performance.
Enhancing Concept Drift Detection in Drifting and Imbalanced Data Streams through Meta-Learning

One of the biggest challenges in learning from data streams is adapting the classification model to new data. Due to the evolving nature of data streams, they are subject to a phenomenon known as concept drift that makes previously learned knowledge and model outdated. Therefore, concept drift must be efficiently detected in order to adapt the classification model. While there exists a plethora of drift detectors, with different mechanisms, selecting the most suitable for a new stream is a difficult task, since apriori knowledge may not be available and changes over time can affect the performance of the detector. This paper proposes a framework that exploits statistical and temporal meta-features from sliding windows to dynamically recommend a suitable drift detector in real-time for unseen chunks of streams according to its properties using Meta-Learning. We performed experiments on 10 real-world data streams and 18 synthetic generated data streams that were subject to concept drift and class imbalance in order to evaluate the performance of the proposed framework. Experiments exposed that the proposed approach was able to enhance the concept drift detection in a variety of scenarios demonstrating robustness to class imbalance and the advantages of dynamically selecting the drift detector.
[Vision Paper] Privacy-Preserving Data Integration

The digital transformation of different processes and the resulting availability of vast amounts of data describing people and their behaviors offer significant promise to advance multiple research areas and enhance both the public and private sectors. Exploiting the full potential of this vision requires a unified representation of different autonomous data sources to facilitate detailed data analysis capacity. Collecting and processing sensitive data about individuals leads to consideration of privacy requirements and confidentiality concerns. This vision paper provides a concise overview of the research field concerning Privacy-Preserving Data Integration (PPDI), the associated challenges, opportunities, and unexplored aspects, with the primary aim of designing a novel and comprehensive PPDI framework based on a Trusted Third-Party microservices architecture.
Efficient Multi-purpose Video Annotation for Fast Labeling

We propose an efficient method to label the frames of a video consistently and quickly. Our method is motivated by welding videos, where molten metal moves continuously, and some frames may be noisy due to the high intensity of arc and spatters that can obscure the desired point to be annotated. The traditional annotation methods which pause the videos and annotate each frame individually are time-consuming and may result in inconsistent labeling between different annotators for noisy frames of a video. Our new proposed method benefits from the fact that video sequences are contiguous. We track the location of the cursor while the video is being played, with the user controlling the playback speed, including the playback direction. We process the recorded cursor locations to convert them to the final annotation. Compared to frame-to-frame annotation methods, we show that our proposed interface speeds up the annotating process by 21 times while maintaining consistency of the labels.
Unsupervised Modulation Recognition Method Based on Multi-Domain Representation Contrastive Learning

Automatic modulation recognition(AMR) is a key technology in the field of electronic reconnaissance. Benefiting from the powerful feature extraction ability of deep neural networks, the AMR algorithm based on supervised deep learning usually achieves pleasant performance. However, in non-cooperative scenarios high-quality and reliable modulation labels are difficult to obtain. Therefore, In this article, a novel unsupervised automatic modulation recognition method based on multi-domain representation contrastive learning (MRC-based UAMR) is proposed. By carefully utilizing unlabeled signal data with a self-supervised contrastive training step across multiple diverse representation domains, which is a more effective way than directly using IQ data as inputs, MRC aims to obtain high-quality signal feature information from unlabelled signals. The conducted experiments clearly showed that MRC remarkably outperforms existing unsupervised algorithms in modulation recognition and exhibits superior generalization ability. Our work significantly reduces the disparity between supervised and unsupervised representation learning in the field of automatic modulation recognition.
THEORETICAL ISSUES OF DIGITALIZATION OF EDUCATION

At present, the role of the issue of digitalization of society has grown. This was facilitated by official documents adopted in the Kyrgyz Republic aimed at the formation of a digital citizen. One of the important parts of the digitalization of society is the digitalization of education. In the implementation of this direction, an important task is to create appropriate conditions, update the content and objectives of education, as well as conduct scientific research. The article discusses the theoretical issues of digitalization of education. The role of the teacher in the formation of a digital citizen is highlighted here. The didactic principles of digitalization of education are analyzed. The learning objectives for the successful implementation of the principles and the learning process are highlighted. The didactic possibilities of digital technology in the formation of digital literacy of students are considered.
Geomorphological Analysis of Chemical Weathering Features in Al-Band Hills Area, Eastern of Misan Governorate, Iraq

A 59 km2 area of soil transect called Al-Band Hills in the eastern part of Misan governorate, south of Iraq was studied. The objective of this study was to understand the behavior of minerals and features of chemical weathering in the transect area soils that developed on fluvial and water-loaded sediments, in order to fulfill this objective, three soil pedons were selected one of them at the top of Hills and second in the middle and third pedon in the bottom of the Hill. The results showed that Al-Band Hills located in semi-arid soils in the east of Misan governorate, south of Iraq are characterized by a low and middle intensity of chemical weathering. In an alkaline environment according to its carbonate minerals richness, with a low amount of precipitation, the intensity, and type of silicate minerals weathering is restricted not only by the climate but by the chemical conditions of the soil solution. According to this study's findings, the intensity of weathering of present soil pedons is mostly inherited from the parent rocks which is transferred by the action of water movement from northern areas from Turkey and Iran which belongs to the Quaternary. This study will give clear sights into the geomorphological and geochemical processes that happen in studied soils in semi-arid regions and show the possibility of using weathering features in carbonate and alkali sediments.
Comparing Between Makassarese and English Adverbs of Time

Contrastive analysis is very important for learning second language acquisition because with knowing the difference and the similarities L1 and L2 that the learner became easy to learn the L2. The research aimed to analyze the simaliries and the differences between Makassarese as L1 and English as L2 in adverbs of time. This study applied a qualitative study to cover the problem. First, the researcher collected the data from books on Makassarese grammar, Buginese Grammar, English grammar and from the researcher as the native speaker of Makassarese. Second, the researcher identified them in every type of adverbs. At last, data were analyzed from the data collection. The finding of the research indicates that; the researcher found all of the adverbs. After analyzing the data, the researcher found the similarities and the differences with comparing both of them. Thus, the researcher can find the Similarities based on functions both Makassares as L1 and English as L2, to explain some wors namely, verbs, adjectives and other adverbs. Based on the position, both can be placed in front, middle and end. Differences based on functions can be found that English has sentence sentences that use many words ending in -ed + -ly. Based on tenses, in English context, the verbs should be changed based on the tenses, for example, go (present V1+es/s), went (Past Verb2), and go (Future will/shal+V1). Besides that, the researcher can know without an adverb of time in the sentence when the situation is happening only see the verb in the sentence. But in Makassares context, the researcher cannot identify the sentence without adverb of time when the action is happening.
Technology of Book Production (from the Experience of A. G. Dostoevskaya)

The article presents an overview of the technical side of the publishing business of A. G. Dostoevskaya based on the materials of her notebooks. Special attention is paid to the period of the release of the Complete Works of F. M. Dostoevsky. The writer’s widow began the production process of publishing books with the purchase of paper from the “Trading House of A. I. Vargunina,” and negotiations with the Panteleev brothers printing house about the appearance of the book, the cost of publication, typing, proofreading, printing and booklets. The next stage was connected with the release of the book from the printing house until the time of its sale: first of all, the issue of storing a huge number of books (several tens of thousands) was solved; Anna Grigoryevna rented storerooms and developed accounting systems for this purpose. After the publication was released from the printing house, it was necessary to check the books for flaws; it was also important to notify readers through ads and booksellers about the appearance of a new edition. The final stage of the publishing process was connected with the sale of editions of the Complete works of Dostoevsky: correspondence with buyers and booksellers, forwarding of works, transfer of books on commission and endless accounting and recording of all volumes — all this required a lot of effort and time and had its own specifics, and if you did not know precisely what to do, you could face a significant loss. At each stage of the publishing process, unforeseen difficulties arose caused by the inexperience of the first Russian individual publisher, the negligence of the persons with whom she had to deal, or a combination of circumstances. It describes not only the problems that Dostoevskaya had to face, but also the ways to solve them, requiring the publisher to be reasonable and firm in spirit. The materials presented for the release of the Complete Works of Dostoevsky are given in the article in the context of the book publishing process and book business of the late 19th — early 20th century, which is a necessary historical commentary on the publishing activities of A. G. Dostoevskaya.
Techniques for Payload Stabilization for Improved Photography During Stratospheric Balloon Flights

Payload-box rotation and swing are perennial challenges to achieving high-quality photography (typically videography) during weather-balloon flights to “near-space” (AKA the stratosphere). Continuous camera motion can lead to blurred still photos, nearly-impossible-to-watch video footage, and precludes time-exposure photography required for most astronomical imaging even though altitudes are reached where the daytime sky appears black. Apparently-random payload rotation, persisting even at altitude, can often exceed servo rotation rates and frustrate attempts to do active camera pointing. Here we discuss mostly-passive payload stabilization strategies we, and our collaborators, have used to mitigate and dampen both swing and rotation of suspended payloads on high-altitude balloon missions, primarily on ascent. In particular, we stress the importance of avoiding single “main” lines and of firmly coupling the payload stack to, as opposed to intentionally trying to decouple (rotationally) from, the neck of the balloon. We discuss consequences these strategies have on stack weight and also on the location of the parachute, sometimes displacing it from its normal location hanging between the neck of the balloon and the payload stack. We expect these payload stabilization techniques will be of particular interest to balloonists planning to photograph the total solar eclipse of August 2017.
Pauline Rowson crime novels on sale on Nook in UK

Barnes and Noble to sell the Nook e reader in the Uk from autumn. Pauline Rowson crime novels and marketing books are available on Nook and Kindle
E-learning: The way to go

Abstract Mahfuzul, an aspiring entrepreneur from Bangladesh, has no doubt that he will be able to turn his dream of running his own business into reality.
Neuroevolution

This chapter covers the third and last part of the book. The book started with the topic of neural networks, which are computational metaphors for the biological brain. Subsequently, the book covered genetic algorithms, computational simulations of species evolution. After these two parts, the question that may naturally be asked is: Is it possible to evolve neural networks in a fashion similar to how our biological brains went through evolution over thousands of years? The answer is yes, and this evolution mechanism is the topic of this third and last part of the book. Neuroevolution is a form of artificial intelligence that combines neural networks and genetic algorithms.
Selecting Machine Learning Models for Metallic Nanoparticles

The outcome of machine learning is influenced by the features used to describe the data, and various metrics are used to measure model performance. In this study we use five different feature sets to describe the same 4000 gold nanoparticles, and 14 different machine learning methods to compare a total of 70 high scoring models. We then use classification and regression to show which meta-features of data sets or machine learning algorithms are important when making a selection. We find that number of features, and those that are strongly correlated, determine the class of model that should be used, but overall quality is almost entirely determined by the cross-validation score, regardless of the sophistication of the algorithm.