Reviewerly/exHarmony huggingface dataset:
On Depth of Immersion in Forecasting Task

The forecast of dynamic processes is based on mathematical models which usually describe determinate and stochastic characteristics of processes adequately. Parameter values of considered models are estimated by observation data over the processes in the past. It often occurs that there is data excess, which means that an additional number of data does not contribute to the precision of forecasting but makes it more expensive. This work is devoted to the estimation of forecasting depth at the fixed horizon. It was assumed that time series, of which forecast is considered, possess some informative features that allow establishing the balance between the horizon and the depth of the forecast. The algorithm of depth estimation has been offered and real non-stationary time series has been analyzed. This analysis demonstrated that there exists quasi-optimal depth for a fixed horizon of forecasting.
Analysis of mixed learning mode of distance education based on MOOC

In order to improve the evaluation ability of mixed learning in distance education, a mixed learning mode of distance education based on massive open online course (MOOC) is proposed. By building the distance education based on MOOC mixed study effect evaluation of heterogeneous storage structure of data model, remote education characteristics of distribution of blended learning model, remote education multi-parameter fusion model of blended learning and remote education blended learning process and effect evaluation of multi-parameter fusion model, multidimensional characteristics evaluation method is applied to the remote education blended learning model optimisation. The simulation results show that this method has better process control ability and higher reliability in learning effect evaluation, which effectively improves the mixed learning effect of MOOC-based distance education.
RegNeRF: Regularizing Neural Radiance Fields for View Synthesis from  Sparse Inputs

Neural Radiance Fields (NeRF) have emerged as a powerful representation for the task of novel view synthesis due to their simplicity and state-of-the-art performance. Though NeRF can produce photorealistic renderings of unseen viewpoints when many input views are available, its performance drops significantly when this number is reduced. We observe that the majority of artifacts in sparse input scenarios are caused by errors in the estimated scene geometry, and by divergent behavior at the start of training. We address this by regularizing the geometry and appearance of patches rendered from unobserved viewpoints, and annealing the ray sampling space during training. We additionally use a normalizing flow model to regularize the color of unobserved viewpoints. Our model outperforms not only other methods that optimize over a single scene, but in many cases also conditional models that are extensively pre-trained on large multi-view datasets.
Provable Continual Learning via Sketched Jacobian Approximations

An important problem in machine learning is the ability to learn tasks in a sequential manner. If trained with standard first-order methods most models forget previously learned tasks when trained on a new task, which is often referred to as catastrophic forgetting. A popular approach to overcome forgetting is to regularize the loss function by penalizing models that perform poorly on previous tasks. For example, elastic weight consolidation (EWC) regularizes with a quadratic form involving a diagonal matrix build based on past data. While EWC works very well for some setups, we show that, even under otherwise ideal conditions, it can provably suffer catastrophic forgetting if the diagonal matrix is a poor approximation of the Hessian matrix of previous tasks. We propose a simple approach to overcome this: Regularizing training of a new task with sketches of the Jacobian matrix of past data. This provably enables overcoming catastrophic forgetting for linear models and for wide neural networks, at the cost of memory. The overarching goal of this paper is to provided insights on when regularization-based continual learning algorithms work and under what memory costs.
On divisors of sums of polynomials

Let $\mathcal{A}$ and $\mathcal{B}$ be finite sets of polynomials of degree $n$ over a finite field. We show, that if $\mathcal{A}$ and $\mathcal{B}$ are large enough, then $A+B$ has an irreducible divisor of large degree for some $A\in\mathcal{A}$ and $B\in \mathcal{B}$.
Medical Entity Linking in Laypersons’ Language

Due to the vast amount of health-related data on the Internet, a trend toward digital health literacy is emerging among laypersons. We hypothesize that providing trustworthy explanations of informal medical terms in social media can improve information quality. Entity linking (EL) is the task of associating terms with concepts (entities) in the knowledge base. The challenge with EL in lay medical texts is that the source texts are often written in loose and informal language. We propose an end-to-end entity linking approach that involves identifying informal medical terms, normalizing medical concepts according to SNOMED-CT, and linking entities to Wikipedia to provide explanations for laypersons.
Discriminator Synthesis: On reusing the other half of Generative  Adversarial Networks

Generative Adversarial Networks have long since revolutionized the world of computer vision and, tied to it, the world of art. Arduous efforts have gone into fully utilizing and stabilizing training so that outputs of the Generator network have the highest possible fidelity, but little has gone into using the Discriminator after training is complete. In this work, we propose to use the latter and show a way to use the features it has learned from the training dataset to both alter an image and generate one from scratch. We name this method Discriminator Dreaming, and the full code can be found at https://github.com/PDillis/stylegan3-fun.
Efficient joint noise removal and multi exposure fusion

Multi-exposure fusion (MEF) is a technique for combining different images of the same scene acquired with different exposure settings into a single image. All the proposed MEF algorithms combine the set of images, somehow choosing from each one the part with better exposure. We propose a novel multi-exposure image fusion chain taking into account noise removal. The novel method takes advantage of DCT processing and the multi-image nature of the MEF problem. We propose a joint fusion and denoising strategy taking advantage of spatio-temporal patch selection and collaborative 3D thresholding. The overall strategy permits to denoise and fuse the set of images without the need of recovering each denoised exposure image, leading to a very efficient procedure.
Multi-modal application: Image Memes Generation

Meme is an interesting word. Internet memes offer unique insights into the changes in our perception of the world, the media and our own lives. If you surf the Internet for long enough, you will see it somewhere on the Internet. With the rise of social media platforms and convenient image dissemination, Image Meme has gained fame. Image memes have become a kind of pop culture and they play an important role in communication over social media, blogs, and open messages. With the development of artificial intelligence and the widespread use of deep learning, Natural Language Processing (NLP) and Computer Vision (CV) can also be used to solve more problems in life, including meme generation. An Internet meme commonly takes the form of an image and is created by combining a meme template (image) and a caption (natural language sentence). In our project, we propose an end-to-end encoder-decoder architecture meme generator. For a given input sentence, we use the Meme template selection model to determine the emotion it expresses and select the image template. Then generate captions and memes through to the meme caption generator. Code and models are available at github
A Quantum Natural Language Processing Approach to Musical Intelligence

There has been tremendous progress in Artificial Intelligence (AI) for music, in particular for musical composition and access to large databases for commercialisation through the Internet. We are interested in further advancing this field, focusing on composition. In contrast to current black-box AI methods, we are championing an interpretable compositional outlook on generative music systems. In particular, we are importing methods from the Distributional Compositional Categorical (DisCoCat) modelling framework for Natural Language Processing (NLP), motivated by musical grammars. Quantum computing is a nascent technology, which is very likely to impact the music industry in time to come. Thus, we are pioneering a Quantum Natural Language Processing (QNLP) approach to develop a new generation of intelligent musical systems. This work follows from previous experimental implementations of DisCoCat linguistic models on quantum hardware. In this chapter, we present Quanthoven, the first proof-of-concept ever built, which (a) demonstrates that it is possible to program a quantum computer to learn to classify music that conveys different meanings and (b) illustrates how such a capability might be leveraged to develop a system to compose meaningful pieces of music. After a discussion about our current understanding of music as a communication medium and its relationship to natural language, the chapter focuses on the techniques developed to (a) encode musical compositions as quantum circuits, and (b) design a quantum classifier. The chapter ends with demonstrations of compositions created with the system.
A New Blended Approach of Data Hiding Using XNOR Operation

This paper proposed a new data hiding technique which uses a blended approach of security. It uses a blend of cryptography and steganography at the same time to provide two layers of security. At first, data is encrypted using Rail Fence cryptography algorithm and at the second level the encrypted data is inserted into an image by using insertion algorithm based on XNOR operation. Encrypted bits are put into the pixels of the image which becomes a difficult task for the attackers to find the hidden data. In this approach, the concept of spatial domain is used for hiding the message and retrieval of message. The preliminary experimental results based on PSNR, MSE, Histogram and Coefficient of Correlation show that the proposed method provides better imperceptibility and hiding capacity. It was also compared with some trade off methods on the basis of various metrics. It also provides very less deflection in cover image so that it becomes very difficult for a third person to differentiate between cover image and stego image. ImageData HidingSteganographyCryptography
JIG AND FIXTURE PENGELASAN SUDUT SIKU UKURAN LEBAR PROFIL (3 - 60 mm) DI JURUSAN TEKNIK MESIN POLITEKNIK NEGERI MALANG

The resulting angle deviation is the result of improper welding methods, in addition, there is also a distortion factor in the workpiece during the SMAW welding process at the workshop in the Department of Mechanical Engineering, State Polytechnic of Malang.The purpose of making this tool is to facilitate and provide time efficiency in the welding process, it is necessary to manufacture jig and fixture tools in the angle welding process. The way the angle welding jig and fixture works is by gripping the workpiece to be welded in a plane that forms an angle so that the object to be welded will follow the angled plane.
The Role of the Paper-Based Journal in an Era of Electronic Information

This presentation is a review of the changes in publishing methods that electronic publishing and distribution will involve. It presents a publisher’s view of the methodology needed to exploit the potential that online and other electronic technologies offer. Included is a discussion on the role of the paper-based journal in an era of electronic publication, and the ambivalence of scholars and of learned societies toward electronic information. Additionally, the role of universities is examined, as they become partners of publishers in delivering published material to customers on the Internet: by hosting servers, by providing access to bandwidth, and by adding value through participation in the publishing process. [Article copies available for a fee from The Haworth Document Delivery Service: 1-800-342-9678. E-mail address: getinfo@haworth.com]
PORTFOLIO PERFORMANCE ANALYSIS of REAL ESTATE INVESTMENT FUNDS: A STUDY on REAL

Real estate investment funds (REIF) took their place in the Turkish capital market with the communiqué of the Capital Markets Board CMB dated 03.01.2014. REIFs are institutions that bring together the construction industry in Turkey with corporate finance. The first fund in Turkey was established in 2016 and currently 80 funds are managed by 21 fund management companies. The aim of this study is to examine the financial and operational performances of real estate investment funds, which are expected to contribute to the institutionalization and acceleration of real estate investments and the elimination of financial problems such as the middle income trap in the sector. In this study, whether these funds can provide the expected contributions, fund development processes, investment strategies, structural problems in the sector, legal regulations, sectoral expectations and portfolio performance are analyzed as a whole. Due to limited number of the listed REIFs in Borsa İstanbul sample size is relative small with 6 observations. After providing all current statistics regarding REIFs stiuations in Turkey, portfolio performance is measured by Sharpe and Treynor ratios. In order to find out relative portfolio performance, indicators of listed REIFs are compared with returns of the Bist-100 national stock index and REIT index which are used as benchmark indicator. According to results of the analysis, it has been found out that most of funds provided better performance than Bist-100 national stock index and REIT Index. Based on the findings obtained in this study, it is strictly recommended that legal regulations should be implemented as soon as possible to increase REIF’s profitability and operational efficiency and make them sustainable.
Enhancing Keyphrase Extraction from Academic Articles with their  Reference Information

With the development of Internet technology, the phenomenon of information overload is becoming more and more obvious. It takes a lot of time for users to obtain the information they need. However, keyphrases that summarize document information highly are helpful for users to quickly obtain and understand documents. For academic resources, most existing studies extract keyphrases through the title and abstract of papers. We find that title information in references also contains author-assigned keyphrases. Therefore, this article uses reference information and applies two typical methods of unsupervised extraction methods (TF*IDF and TextRank), two representative traditional supervised learning algorithms (Na\"ive Bayes and Conditional Random Field) and a supervised deep learning model (BiLSTM-CRF), to analyze the specific performance of reference information on keyphrase extraction. It is expected to improve the quality of keyphrase recognition from the perspective of expanding the source text. The experimental results show that reference information can increase precision, recall, and F1 of automatic keyphrase extraction to a certain extent. This indicates the usefulness of reference information on keyphrase extraction of academic papers and provides a new idea for the following research on automatic keyphrase extraction.
Combinatorial Relationship Between Finite Fields and Fixed Points of  Functions Going Up and Down

We explore a combinatorial bijection between two seemingly unrelated topics: the roots of irreducible polynomials of degree $m$ over a finite field $F_p$ for a prime number $p$ and the number of points that are periodic of order $m$ for a continuous piece-wise linear function $g_p:[0,1]\rightarrow[0,1]$ that \emph{goes up and down $p$ times} with slope $\pm 1/p$. We provide a bijection between $F_{p^n}$ and the fixed points of $g^n_p$ that naturally relates some of the structure in both worlds. Also we extend our result to other families of continuous functions that goes up and down $p$ times, in particular to Chebyshev polynomials, where we get a better understanding of its fixed points. A generalization for other piece-wise linear functions that are not necessarily continuous is also provided.
Region Enhancement Methods of Color Blurred Image Based on Visual Communication

Aiming at the problems of poor effect of adding items, fuzziness of feature region boundary and low practicability in traditional color texture image fuzzy enhancement methods, a color image enhancement method based on visual communication is designed in this paper. Through the determination of the color blurred image region and the preprocessing of the determined region blurred image data, an image region color fuzzy enhancement algorithm based on visual communication is designed to realize the color blurred image region enhancement. The results show that the designed method can effectively improve the fuzzy region.
A Simple And Effective Filtering Scheme For Improving Neural Fields

Recently, neural fields, also known as coordinate-based MLPs, have achieved impressive results in representing low-dimensional data. Unlike CNN, MLPs are globally connected and lack local control; adjusting a local region leads to global changes. Therefore, improving local neural fields usually leads to a dilemma: filtering out local artifacts can simultaneously smooth away desired details. Our solution is a new filtering technique that consists of two counteractive operators: a smoothing operator that provides global smoothing for better generalization, and conversely a recovering operator that provides better controllability for local adjustments. We have found that using either operator alone can lead to an increase in noisy artifacts or oversmoothed regions. By combining the two operators, smoothing and sharpening can be adjusted to first smooth the entire region and then recover fine-grained details in regions overly smoothed. In this way, our filter helps neural fields remove much noise while enhancing details. We demonstrate the benefits of our filter on various tasks and show significant improvements over state-of-the-art methods. Moreover, our filter also provides better performance in terms of convergence speed and network stability.
Sensitivity of a Chaotic Logic Gate

Chaotic logic gates or ‘chaogates’ are a promising mixed-signal approach to designing universal computers. However, chaotic systems are exponentially sensitive to small perturbations, and the effects of noise can cause chaotic computers to fail. Here, we examine the sensitivity of a simulated chaogate to noise and other parameter variations (such as differences in supply voltage). We find that the regions in parameter space corresponding to chaotic dynamics coincide with the regions of maximum error in the computation. Further, this error grows exponentially with a maximum likelihood rate constant of 3.5 iterations of the chaotic map. As such, we discuss the fundamental challenges of chaotic computing, and suggest potential improvements. Our Python simulation methods are open-source and available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/Noeloikeau/chaogate</uri> .
Lyndon Interpolation for Modal $$\mu $$-Calculus

It is known that the modal $$\mu $$ -calculus has the Craig interpolation property, indeed uniform interpolation. We prove Lyndon interpolation for the calculus, a strengthening of Craig interpolation which is not implied by uniform interpolation. The proof utilises ‘cyclic’ sequent calculus and provides an algorithmic construction of interpolants from valid implications. This direct approach enables us to derive a correspondence between the shape of interpolants and existence of sequent calculus proofs.
Multimodal Interactions Using Pretrained Unimodal Models for SIMMC 2.0

This paper presents our work on the Situated Interactive MultiModal Conversations 2.0 challenge held at Dialog State Tracking Challenge 10. SIMMC 2.0 includes 4 subtasks, and we introduce our multimodal approaches for the subtask \#1, \#2 and the generation of subtask \#4. SIMMC 2.0 dataset is a multimodal dataset containing image and text information, which is more challenging than the problem of only text-based conversations because it must be solved by understanding the relationship between image and text. Therefore, since there is a limit to solving only text models such as BERT or GPT2, we propose a multimodal model combining image and text. We first pretrain the multimodal model to understand the relationship between image and text, then finetune our model for each task. We achieve the 3rd best performance in subtask \#1, \#2 and a runner-up in the generation of subtask \#4. The source code is available at https://github.com/rungjoo/simmc2.0.
EDUCATIONAL AND SCIENTIFIC LABORATORY OF INNOVATIVE EDUCATIONAL SOLUTIONS IN PRIMARY EDUCATION: PEDAGOGICAL TRENDS AND CREATIVE IDEAS

The experience of educational and scientific laboratory of Innovative educational solutions developed on the chair of primary education, natural and mathematical disciplines and teaching methods of PNPU named after V. G. Korolenko has been generalized and its brief haracteristic activity has been presented. The basic principles of the laboratory functionality which determine its place and role in the system of training specialists in the field of primary education have been distinguished. Emphasis is placed on the fact that the laboratory is an open platform for creating the preconditions for an effective combination of education, science and innovation. It has been pointed out that the laboratory environment meets the modern requirements of the new Ukrainian school regarding the organization of the educational space for junior students. The model variants and their use methods of the primary classroom hubs equipping built on the principles of pedagogical ergodesign have been illustrated.
Effectiveness of Variable Distance Quantum Error Correcting Codes

Quantum error correction is capable of digitizing quantum noise and increasing the robustness of qubits. Typically, error correction is designed with the target of eliminating all errors - making an error so unlikely it can be assumed that none occur. In this work, we use statistical quantum fault injection on the quantum phase estimation algorithm to test the sensitivity to quantum noise events. Our work suggests that quantum programs can tolerate non-trivial errors and still produce usable output. We show that it may be possible to reduce error correction overhead by relaxing tolerable error rate requirements. In addition, we propose using variable strength (distance) error correction, where overhead can be reduced by only protecting more sensitive parts of the quantum program with high distance codes.
Digital Signature and Digital Envelope

Now that we’ve covered the simple cryptographic primitives, its time to build more complex things using those primitives. The first of those is a digital signature, which provides message integrity, detection of tampering and non-repudiation. This is created using a hash function and asymmetric cryptography. The second of these is a digital envelope that provides privacy. This is created using symmetric key cryptography and asymmetric key cryptography.