Reviewerly/exHarmony huggingface dataset:
Improving the Parallelism and Balance of RNS with Low-Cost and $$2^{{\varvec{k}}} + 1$$ Modules

In this paper, extending the set of low-cost modules with modules of $$2^{k} + 1$$ form, which are considered next in the complexity of implementation after modules of $$2^{k} - 1$$ proposed to consider. An experiment was carried out on the construction of various RNSs for the ranges from 16 to 64 bits and the number of modules from 3 to 8. The results of the experiment showed a significant increase in parallelism and improvement in the balance of RNS with modules of the $$2^{k}$$ , $$2^{k} - 1$$ and $$2^{k} + 1$$ forms, compared to using only modules of the $$2^{k}$$ and $$2^{k} - 1$$ forms. In the discussion of the experiment, we raised the questions of more effective measurement of the balance of the RNS, as well as the problem of using the diminished-1 encoding. This problem is a subject to a thorough study both at the theoretical level, for example, using a unit-gate model, and practical tests on modern FPGA and ASIC microelectronic devices.
Analysis of Neural Networks for Image Classification

The article explores the option of using information theory’s mathematical tools to model artificial neural networks. The two primary network architectures for image recognition, classification, and clustering are the feedforward network and convolutional networks. The study investigates the use of orthogonal transformations to enhance the effectiveness of neural networks and wavelet transforms in convolutional networks. The research proposes practical applications based on the theoretical findings.
Modulo 2k + 1 Truncated Multiply-Accumulate Unit

Digital signal processing requires the calculation of large data volume. To increase the speed of data processing, a Residue Number System is used. This number system provides performing calculations in parallel, reducing time costs. In practice, moduli of the Residue Number System of a special form ( $$2^{k} ,2^{k} - 1,2^{k} + 1$$ ) are most often used. The article proposes a method for calculating modulo $$2^{k} + 1$$ using the Diminished-one coding technique and developed the Inverted End-Around Carry Truncated Multiply-Accumulate unit (IEAC-TMAC). This approach increases the number of moduli, affecting a decrease in the capacity of the moduli and the delay. Hardware modeling showes that, compared with the existing varieties of TMAC blocks, the proposed block demonstrates worse results in terms of hardware costs by 27–231%, depending on the block being compared. However, using two blocks of 2 times less bit width of the form $$(2^{k} - 1,2^{k} + 1)$$ provides reducing the occupied area of a device in comparison with the modulo $$(2^{2k} - 1)$$ by 24–48% times and decreasing the execution speed by 1.20–1.24 times. A promising direction for further research will be the development of digital signal processing devices with moduli of a special type ( $$2^{k} ,2^{k} - 1,2^{k} + 1$$ ).
RodNet: An Advanced Multidomain Object Detection Approach Using Feature Transformation With Generative Adversarial Networks

Advanced object detection (OD) techniques have been widely studied in recent years and have been successfully applied in real-world applications. However, existing algorithms may struggle with nighttime image detection, especially in low-luminance conditions. Researchers have attempted to overcome this issue by collecting large amounts of multidomain data, but performance remains poor because these methods train images from both low- and sufficient-luminance domains without a specific training policy. In this work, we present a lightweight framework for multidomain OD using feature domain transformation with generative adversarial networks (GANs). The proposed GAN framework trains a generator network to transform features from the low-luminance domain to a sufficient-luminance domain, making the discriminator networks unable to distinguish whether the features were generated from a low-luminance or a normal image and thus achieving luminance-invariant feature extraction. To preserve semantic meaning in the transformed features, a training policy has been introduced for OD and feature transformation in various domains. The proposed method achieves the state-of-the-art performance with a 9.95 improvement in average precision without incurring additional computational costs.
Performance-Based Analysis of K-Medoids and K-Means Algorithms for the Diagnosis and Prediction of Oral Cancer

In many research communities, clustering plays a major role in real-time applications such as health care, financial analysis, crime rate prediction, and customer trend analysis. Clustering offers a variety of potential techniques for determining a diagnosis and making prognosis regarding oral cancer. In this particular investigation, we used two distinct clustering strategies known as K-medoids and K-means to examine a dataset on oral cancer and to predict the likelihood of oral cancer sickness. The amount of time needed to complete each algorithm's computation is tallied, and the algorithms' overall effectiveness is evaluated. In order to do the analysis of our research, we employed R programming. Using significant amounts of data pertaining to oral cancer, an analysis of two different clustering methods is carried out in order to determine whether the method is superior. We have at last reached the point where the K-Medoids and K-Means clustering algorithms are accurate. According to the results of the experiments, the K-Means algorithm appears to produce the best results when contrasted with the K-Medoids method.
Formation Planning for Tethered Multirotor UAV Cooperative Transportation With Unknown Payload and Cable Length

This study investigates the formation planning problem of tethered multirotor unmanned aerial vehicle (UAV) cooperative transportation with unknown payload and cable length. Normally, the transportation formation and trajectory are given in advance or designed based on the coupled system model. It is challenging to dynamically generate flexible formations in response to changing environments when the payload and cable length are unknown. This paper proposes an online formation planning method for multirotor UAVs. First, by analyzing the tension on cables, we propose some formation criteria and further construct a corresponding performance function of optimization. Then, desired trajectories/formations that can reduce the cost functions are generated by using the admittance model. Next, an estimation-based formation tracking control is designed, which ensures that multirotor UAVs follow the desired trajectories/formations. Finally, numerical simulations and experiments are conducted to demonstrate the effectiveness of the proposed method. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This paper is motivated by the formation planning problem of tethered multirotor UAV cooperative transportation. In industry and production applications, a team of multirotor UAVs has a larger load capacity than a single one. Nevertheless, the formation planning of the tethered cooperative transportation is challenging, especially when the payload and cable length are unknown. Rather than give a predefined formation or trajectory, this paper suggests an online formation planning method for multirotor UAVs in case of unknown payload and cable length. The method is implemented through the following three parts: 1) By analyzing the tension on cables, we propose some formation criteria and further construct a corresponding performance function of optimization. 2) By using the admittance model, we generate desired trajectories/formations that can minimize the proposed cost function. 3) By estimating cable tension, we design formation tracking control laws for multirotor UAVs to follow the desired trajectories/formations. The proposed formation planning method does not rely on the knowledge of the payload and length of cables, which makes it can be easily applied to extensive industry, production, and military practice. Finally, numerical simulations and experiments are conducted to demonstrate the feasibility of the proposed method.
3D Cyst Sonomammogram Projection Using Reflection Coefficient and Mass Density in Python

The formation of a breast cyst is a common problem among females nowadays, and there is a possibility of developing a complex cyst that consists of fluid with a solid deposit. Appropriate identification can be achieved, nevertheless, by adapting life signs universally rather than just focusing on characteristics that signify regional responses to survival-related problems. The models for health statistics should take into account the chronological and ecological variables that are unique to each planetary and period of time. Thus, there is a non-invasive method needed to project a cyst’s pattern in 3D to calculate the cyst’s size. In this paper, a novel algorithm is used to project the segmented region extracted from ultrasound breast images through the calculation of the reflection coefficient as a threshold. Initially, the acquired images are binned twice, and the reflection coefficient is calculated for the second level bin. Then, for the extracted region of interest (RoI), the value of mass density is calculated, which has a range of 0.92–1.14 g/cm3. Then, the mapping between the reflection coefficient to mass density is performed to confirm the RoI in second-level binning. Finally, the segmented RoI is projected as a 3D pattern by considering the size of second-level bins on the X and Y axis and the range of reflection coefficient on the Z axis.
Interactive Methods in the Study of the Discipline “Mathematics” for Non-mathematical Specialties

The paper considers some aspects of the use of interactive methods in the study of the discipline “Mathematics” for non-mathematical specialties at the North Caucasus Federal University. The discipline “Mathematics” is included in the block of the mandatory part of the undergraduate curriculum in the natural sciences, and interactive teaching methods are designed to help students in the study of a non-core discipline. The use of interactive technologies contributes to the implementation of the competence-based approach. The paper gives example of the use of interactive methods in the practical classes of training groups of training areas 03.19.01 - Biotechnology and 05.30.01 - Medical Biochemistry, which contribute to mastering the modern mathematical apparatus for further use in solving theoretical and applied tasks in professional activity. A questionnaire was conducted among students of these areas in order to determine the effectiveness of the interactive teaching methods used. The results of the interview were analyzed. It was revealed that the use of interactive methods in the learning process helps to increase motivation and develop independence, stimulates an independent search for new knowledge, improves student performance and makes it possible to apply mathematical knowledge, skills and abilities in practical research.
Localization and Motion Planning of Industrial Tractor–Trailers Vehicles

This brief presents real-time configuration estimation and motion planning for the industrial tractor–trailers vehicle composed of a full-scale car-like tractor and multiple full trailers. For the real-life vehicle, determining the configuration is challenging. With only on-tractor sensors, we solve this problem by fusing information from system dynamics propagation, geometrical constraints among the articulated units, and matching with the prebuilt environment map. The solution is efficiently achieved by formulating and solving a maximum a posterior (MAP) estimation problem in the pose-graph optimization framework. With the complicated tractor–trailers’ structure, small mismatch between actual and planned trajectories is crucial to inherit obstacle-free guarantee from planning to execution. We consider the dynamics and focus on facilitating the reduction of the mismatch by proposing a controller-based smoothing method to perform online motion planning. The given waypoint path is smoothed by forward propagation using a deliberately designed controller to generate the trajectory. The efficiently computed trajectories are also obstacle-free and dynamically feasible. The controller is also applied in execution to precisely reproduce the planned system evolvement. We demonstrate the performance with the real-life industrial tractor–trailers’ vehicle.
Robust Analysis of Almost Sure Convergence of Zeroth-Order Mirror Descent Algorithm

This letter presents an almost sure convergence of the zeroth-order mirror descent (ZOMD) algorithm. The algorithm admits non-smooth convex functions and a biased oracle which only provides noisy function value at any desired point. We approximate the subgradient of the objective function using Nesterov’s Gaussian Approximation (NGA) with certain alternations suggested by some practical applications. We prove an almost sure convergence of the iterates’ function value to the neighbourhood of optimal function value, which can not be made arbitrarily small, a manifestation of a biased oracle. This letter ends with a concentration inequality, which is a finite time analysis that predicts the likelihood that the function value of the iterates is in the neighbourhood of the optimal value at any finite iteration.
Model distillation for high-level semantic understanding：a survey

Abstract： Computer vision tasks aim to construct computational models in relevant to functions-like of human visual sys• tems.Current deep learning models are progressively improving upper bounds of performances in multiple computer vision tasks， especially for analysis and understanding of high-level semantics， i. e. ， multimedia-based descriptors for human recognition.Typical tasks to understand high-level semantics include image classification， object detection， instance seg• mentation， semantic segmentation， and video' s recognition and tracking.With the development of convolutional neural networks (CNNs)， deep learning based high-level semantic understanding have all been benefiting from increasingly deeper and cumbersome models， which is also challenged for the problem of storages and computational costs.To obtain
Scholarly societies partly to blame for post-truth age?

Reading the reactionary defense of the digital stone age in AAAS’ flagship magazine Science, I felt reminded of the now infamous “Make American Science Great Again” letter to Trump and all the other public statements by scholarly societies over the last 30 years on how this internet thing is a threat to their revenue and hence must be opposed. The development of the internet by scholarly institutions should have been the opening bell for a broader thought process within scholarly societies...
Learning to Live with Machine Translation

Rapid advancements in technologies of text and image generation have increasingly put the perceived autonomy of human creativity under threat. Even before ChatGPT and other large-language models sent such anxieties into overdrive, literary critics were arguing for a hermeneutics of automatic writing and revisiting long-held assumptions about artistic originality. Few, however, gave much thought to these model's quirky cousins—a family branch that once ruled over the utopian dreams invested in AI: machine translation (MT). This essay reflects on why translation has been lost in all the recent talk about these models and offers a necessary corrective. It considers what a critical response to MT might look like when reframed around an understanding of current technologies and a vision of MT as potential collaborator rather than human replacement. First, it offers an overview of current neural-based MT and the theories of translation that underwrite it. It then uses literary texts as a limit case for surveying the technology's most visible gaps, providing a deep, qualitative analysis of Japanese literary texts machine translated into English. Finally, it takes a speculative turn and considers what "good enough" machine translation of a large corpus of world literature might be good for in a future of ubiquitous and ever more accessible MT. The results hint at more immediate ways that MT invites inquiry into the present conditions of world literature, but also to a future where the entanglement of human translation and agency with the material agency of the technology bring forth potentials in both.
A Structure Editor with Type-Safe Copy/Paste

The editor calculus of Godiksen et al [2] describes the edit primitives of a syntax-directed editor for functional programs in a simply typed lambda calculus. However, a main shortcoming is that only top-down programming is allowed, meaning that we can only construct the AST from the root. This makes it difficult to refactor previously written code without having to potentially delete and re-insert large parts of a program.
The Foil: Capture-Avoiding Substitution With No Sharp Edges

Correctly manipulating program terms in a compiler is surprisingly difficult because of the need to avoid name capture. The rapier from Peyton Jones and Marlow [9] is a cutting-edge technique for fast, stateless capture-avoiding substitution for expressions represented with explicit names. It is, however, a sharp tool—its invariants are tricky and need to be maintained throughout the whole compiler that uses it. We describe the foil, an elaboration of the rapier that uses Haskell’s type system to enforce the rapier’s invariants statically, preventing a class of hard-to-find bugs, but without adding any run-time overheads.
Compiling a functional array language with non-semantic memory information

We present a technique for introducing a notion of memory in the compiler representation for a parallel functional array language, in a way that allows for transformation and optimization of memory access patterns and uses, while preserving value-based semantics.
First-Class Data Types in Shallow Embedded Domain-Specific Languages using Metaprogramming

Functional programming languages are excellent for hosting embedded domain specific languages (eDSLs) because of their rich type systems, minimal syntax, and referential transparency. However, data types defined in the host language are not automatically available in the embedded language. To do so, all the operations on the data type must be ported to the eDSL resulting in a lot of boilerplate.
ANALYSIS OF FACTORS AFFECTING CRYPTOCURRENCY RETURN DURING THE COVID-19 PANDEMIC

This study aims to analyze the effect of Asset Price, Transaction Volume and Market Capitalization on Cryptocurrency Return. This study uses secondary data in the form of a 2020 weekly report accessed at www.Indodax.com. The data analysis method in this study uses panel data regression analysis which is processed using eviews 10. The partial results show that asset price has a negative and insignificant effect on cryptocurrency returns, transaction volume has a positive and insignificant effect on cryptocurrency returns, market capitalization has a negative and negative effect and not significant to the return of cryptocurrencies. The results of the study simultaneously show that asset price, transaction volume and market capitalization have a negative and insignificant effect on the dependent variable, namely cryptocurrency returns with an R-squared value of 1.1682%. Suggestions for further research are to add other variables that affect cryptocurrency returns such as macroeconomics.
SFCA: A Scalable Formal Concepts Driven Architecture for Multi-Field Knowledge Graph Completion

With the proliferation of Knowledge Graphs (KGs), knowledge graph completion (KGC) has attracted much attention. Previous KGC methods focus on extracting shallow structural information from KGs or in combination with external knowledge, especially in commonsense concepts (generally, commonsense concepts refer to the basic concepts in related fields that are required for various tasks and academic research, for example, in the general domain, “Country” can be considered as a commonsense concept owned by “China”), to predict missing links. However, the technology of extracting commonsense concepts from the limited database is immature, and the scarce commonsense database is also bound to specific verticals (commonsense concepts vary greatly across verticals, verticals refer to a small field subdivided vertically under a large field). Furthermore, most existing KGC models refine performance on public KGs, leading to inapplicability to actual KGs. To address these limitations, we proposed a novel Scalable Formal Concept-driven Architecture (SFCA) to automatically encode factual triples into formal concepts as a superior structural feature, to support rich information to KGE. Specifically, we generate dense formal concepts first, then yield a handful of entity-related formal concepts by sampling and delimiting the appropriate candidate entity range via the filtered formal concepts to improve the inference of KGC. Compared with commonsense concepts, KGC benefits from more valuable information from the formal concepts, and our self-supervision extraction method can be applied to any KGs. Comprehensive experiments on five public datasets demonstrate the effectiveness and scalability of SFCA. Besides, the proposed architecture also achieves the SOTA performance on the industry dataset. This method provides a new idea in the promotion and application of knowledge graphs in AI downstream tasks in general and industrial fields.
Penerapan Variasi Metode Pembelajaran Pada Pelaksanaan Pembelajaran Aksara Dasar Pegon

The purpose of this research is to find out and understand the application of various learning methods that are implemented to the success of students in the basic learning process of the Pegon script at the fourth grade elementary school level at SDI Terpadu Al Badr Ploso. The method used in this study is the Field Research method and uses a qualitative approach, because this research will emphasize more on collecting qualitative data and using data analysis in making conclusions.&#x0D; The results of this study indicate that (1) the application of learning methods in the implementation of Pegon learning, students look conducive and enthusiastic in participating in learning, their enthusiasm becomes motivated by the convenience of using the method. (2) The method used at SDI Terpadu Al Badr Ploso is the lecture method, quiz, reward, stupid, question and answer, teacher observation, approach to students, demonstration method, cooperative, drill method. (3) Students are said to be successful if students are able to write without seeing examples, memorize Tarqib (an abbreviation in Pegon learning), understand the meaning of the books, and are able to apply what is obtained in Pegon learning. The purpose of using various methods is so that students do not get bored quickly in learning. In addition, so that the learning conditions of students are comfortable and fun. The importance of learning the Pegon script from an early age is to hope that there will be future generations of the ancestors to make a tradition of learning the meaning of the books. Because in the book there are extraordinary life lessons, from life in the mother's belly to life in another realm