Reviewerly/exHarmony huggingface dataset:
The game of Flipping Coins

We consider Flipping Coins, a partizan version of the impartial game Turning Turtles, played on lines of coins. We show the values of this game are numbers, and these are found by first applying a reduction, then decomposing the position into an iterated ordinal sum. This is unusual since moves in the middle of the line do not eliminate the rest of the line. Moreover, when $G$ is decomposed into lines $H$ and $K$, then $G=(H:K^R)$. This is in contrast to Hackenbush Strings where $G= (H:K)$.
"I'm a Professor, which isn't usually a dangerous job": Internet-Facilitated Harassment and its Impact on Researchers

While the Internet has dramatically increased the exposure that research can receive, it has also facilitated harassment against scholars. To understand the impact that these attacks can have on the work of researchers, we perform a series of systematic interviews with researchers including academics, journalists, and activists, who have experienced targeted, Internet-facilitated harassment. We provide a framework for understanding the types of harassers that target researchers, the harassment that ensues, and the personal and professional impact on individuals and academic freedom. We then study preventative and remedial strategies available, and the institutions that prevent some of these strategies from being more effective. Finally, we discuss the ethical structures that could facilitate more equitable access to participating in research without serious personal suffering.
List-GRAND: A practical way to achieve Maximum Likelihood Decoding

Guessing Random Additive Noise Decoding (GRAND) is a recently proposed universal Maximum Likelihood (ML) decoder for short-length and high-rate linear block-codes. Soft-GRAND (SGRAND) is a prominent soft-input GRAND variant, outperforming the other GRAND variants in decoding performance; nevertheless, SGRAND is not suitable for parallel hardware implementation. Ordered Reliability Bits-GRAND (ORBGRAND) is another soft-input GRAND variant that is suitable for parallel hardware implementation, however it has lower decoding performance than SGRAND. In this paper, we propose List-GRAND (LGRAND), a technique for enhancing the decoding performance of ORBGRAND to match the ML decoding performance of SGRAND. Numerical simulation results show that LGRAND enhances ORBGRAND's decoding performance by $0.5-0.75$ dB for channel-codes of various classes at a target FER of $10^{-7}$. For linear block codes of length $127/128$ and different code-rates, LGRAND's VLSI implementation can achieve an average information throughput of $47.27-51.36$ Gbps. In comparison to ORBGRAND's VLSI implementation, the proposed LGRAND hardware has a $4.84\%$ area overhead.
Deforming the Loss Surface

In deep learning, it is usually assumed that the shape of the loss surface is fixed. Differently, a novel concept of deformation operator is first proposed in this paper to deform the loss surface, thereby improving the optimization. Deformation function, as a type of deformation operator, can improve the generalization performance. Moreover, various deformation functions are designed, and their contributions to the loss surface are further provided. Then, the original stochastic gradient descent optimizer is theoretically proved to be a flat minima filter that owns the talent to filter out the sharp minima. Furthermore, the flatter minima could be obtained by exploiting the proposed deformation functions, which is verified on CIFAR-100, with visualizations of loss landscapes near the critical points obtained by both the original optimizer and optimizer enhanced by deformation functions. The experimental results show that deformation functions do find flatter regions. Moreover, on ImageNet, CIFAR-10, and CIFAR-100, popular convolutional neural networks enhanced by deformation functions are compared with the corresponding original models, where significant improvements are observed on all of the involved models equipped with deformation functions. For example, the top-1 test accuracy of ResNet-20 on CIFAR-100 increases by 1.46%, with insignificant additional computational overhead.
A converging reputation ranking iteration method via the eigenvector

Ranking user reputation and object quality in online rating systems is of great significance for the construction of reputation systems. In this paper we put forward an iterative algorithm for ranking reputation and quality in terms of eigenvector, named EigenRank algorithm, where the user reputation and object quality interact and the user reputation converges to the eigenvector associated to the greatest eigenvalue of a certain matrix. In addition, we prove the convergence of EigenRank algorithm, and analyse the speed of convergence. Meanwhile, the experimental results for the synthetic networks show that the AUC values and Kendall's τ of the EigenRank algorithm are greater than the ones from the IBeta method and Vote Aggregation method with different proportions of random/malicious ratings. The results for the empirical networks show that the EigenRank algorithm performs better in accuracy and robustness compared to the IBeta method and Vote Aggregation method in the random and malicious rating attack cases. This work provides an expectable ranking algorithm for the online user reputation identification.
METHODICAL ASPECTS OF GRAPHIC COMPETENCE FORMATION FUTURE TEACHER OF LABOR TRAINING AND TECHNOLOGY AT STUDY OF VOCATIONAL TRAINING SUBJECTS

The article attempts to investigate the structure and content of the graphic competence of teachers of labor education and technology as an important component of training. Graphic competence of a teacher of labor education and technology is characterized by a conscious need and ability of specialists to use graphic tools to solve professionally oriented problems based on a system of graphic knowledge, skills and experience of graphic activity and individual qualities of teachers, set of external and internal incentives. Currently, in the preparation of bachelors in the course 014.10 Secondary education (Labor training and technology) great importance is attached to the study of professional and practical disciplines related to professional and practical training and determine the formation of graphic competence, such as: «Fundamentals of design and modeling», «Drawing and basics of compositio», «Drawing and painting». Graphics as an independent type of design has a significant pedagogical impact on the cultural and professional development of the future teacher of labor training and technology, in particular, the development of his creative abilities. The graphic activity of higher education seekers is determined by the peculiarities of the three levels of knowledge. The first - perception, the second - imagination as a sign of psychological action; third - as a practical way of technical and technological display of the picture at the conditional level in the form of a graphic composition. Graphic training of bachelors in the qualification «Teacher of labor education and technology» is carried out in the in-depth study of design, drawing and painting, the subject of which is the study of the theoretical foundations of art education. For the effective formation of graphic competence of future teachers of labor education and technology in the direction of 014.10 Secondary education (Labor education and technology), in filling the subjects of professional training of Poltava National Pedagogical University named after V.G. Korolenko methodological aspects are taken into account. Keywords: graphic knowledge, graphic skills, graphic competence, professional training, teacher of labor education and technology.
Exploring Long- and Short-Range Temporal Information for Learned Video Compression

Learned video compression methods have gained a variety of interest in the video coding community since they have matched or even exceeded the rate-distortion (RD) performance of traditional video codecs. However, many current learning-based methods are dedicated to utilizing short-range temporal information, thus limiting their performance. In this paper, we focus on exploiting the unique characteristics of video content and further exploring temporal information to enhance compression performance. Specifically, for long-range temporal information exploitation, we propose temporal prior that can update continuously within the group of pictures (GOP) during inference. In that case temporal prior contains valuable temporal information of all decoded images within the current GOP. As for short-range temporal information, we propose a progressive guided motion compensation to achieve robust and effective compensation. In detail, we design a hierarchical structure to achieve multi-scale compensation. More importantly, we use optical flow guidance to generate pixel offsets between feature maps at each scale, and the compensation results at each scale will be used to guide the following scale's compensation. Sufficient experimental results demonstrate that our method can obtain better RD performance than state-of-the-art video compression approaches. The code is publicly available on: https://github.com/Huairui/LSTVC.
Label Flipping Data Poisoning Attack Against Wearable Human Activity Recognition System

Human Activity Recognition (HAR) is a problem of interpreting sensor data to human movement using an efficient machine learning (ML) approach. The HAR systems rely on data from untrusted users, making them susceptible to data poisoning attacks. In a poisoning attack, attackers manipulate the sensor readings to contaminate the training set, misleading the HAR to produce erroneous outcomes. This paper presents the design of a label flipping data poisoning attack for a HAR system, where the label of a sensor reading is maliciously changed in the data collection phase. Due to high noise and uncertainty in the sensing environment, such an attack poses a severe threat to the recognition system. Besides, vulnerability to label flipping attacks is dangerous when activity recognition models are deployed in safety-critical applications. This paper shades light on how to carry out the attack in practice through smartphone-based sensor data collection applications. This is an earlier research work, to our knowledge, that explores attacking the HAR models via label flipping poisoning. We implement the proposed attack and test it on activity recognition models based on the following machine learning algorithms: multi-layer perceptron, decision tree, random forest, and XGBoost. Finally, we evaluate the effectiveness of K-nearest neighbors (KNN)-based defense mechanism against the proposed attack.
Success of Uncertainty-Aware Deep Models Depends on Data Manifold Geometry

For responsible decision making in safety-critical settings, machine learning models must effectively detect and process edge-case data. Although existing works show that predictive uncertainty is useful for these tasks, it is not evident from literature which uncertainty-aware models are best suited for a given dataset. Thus, we compare six uncertainty-aware deep learning models on a set of edge-case tasks: robustness to adversarial attacks as well as out-of-distribution and adversarial detection. We find that the geometry of the data sub-manifold is an important factor in determining the success of various models. Our finding suggests an interesting direction in the study of uncertainty-aware deep learning models.
Pemodelan faktor-faktor yang memengaruhi angka kesembuhan tuberkulosis di Jawa Barat menggunakan regresi spline truncated

Tuberculosis is a bacterial infection caused by Mycobacterium tuberculosis. Transmission of tuberculosis (TBC) can occur due to environmental factors and community behavior. West Java is Indonesia's province with the highest number of tuberculosis cases. Curing tuberculosis is critical to reducing cases and breaking the transmission chain. The Human Development Index (IPM), good sanitation, comprehensive tuberculosis treatment, public spaces (PS) meeting health criteria, and residents having health insurance are all assumed to influence the tuberculosis cure rate. This research aimed to model the elements that have a substantial impact on tuberculosis cure rates.The tuberculosis cure rate in West Java in 2020 was modeled using nonparametric spline truncated linear regression with a combination of knot points (3,3,3,3,2). The lowest Generalized Cross Validation (GCV) value of 26.7579 was used to find the best knot point. The adjusted coefficient of determination for this study was 96.35 percent, indicating that the linear truncated spline regression model with a combination of knot points is feasible to use in modeling. The five predictor variables simultaneously affect the tuberculosis cure rate of 96.35 percent, while 3.65 percent is influenced by other variables not used in the study.&#x0D; Keywords: Spline truncated, tuberculosis cure, knots, GCVMSC2020: 62G08
Graph? Yes! Which one? Help!

Amazon Neptune is a graph database service that supports two graph (meta)models: W3C's Resource Description Framework (RDF) and Labeled Property Graphs (LPG). Customers opt in for one or the other model, and this choice determines which data modeling features can be used, and - perhaps more importantly - which query languages are available to query and manipulate the graph. The choice between the two technology stacks is difficult and requires consideration of data modeling aspects, query language features, their adequacy for current and future use cases, as well as many other factors (including developer preferences). Sometimes we see customers make the wrong choice with no easy way to reverse it later. It is therefore highly desirable that the choice of the query language can be made without consideration of what graph model is chosen, and can be easily revised or complemented at a later point. In this paper, we advocate and explore the idea of a single, unified graph data model that embraces both RDF and LPGs, and naturally supports different graph query languages on top. We investigate obstacles towards unifying the two graph data models, and propose an initial unifying model, dubbed "one graph" ("1G" for short), as the basis for moving forward.
Central Bank Digital Currencies: considerations for a not-too-distant future

The central bank digital currencies (CBDC) are subjects of the rising interest of central banks from advanced and emerging economies. Given the relevance and topicality of the subject, this paper seeks to organize the debate that encompasses what is understood as CBDC in the recent international literature, as well as to analyze the possible implications of its implementation. Given that CBDC may assume various forms, there’s still high uncertainty regarding its potential costs and benefits. The decisions concerning the features of the CBDC may have deep implications on various aspects such as the resilience of the domestic financial system, monetary policy effectiveness, functioning of the credit system, and means of payments, among others. Despite the uncertainty associated with the range of possible CBDC architectures, we argue that the strategies of the central banks regarding CBDC may be seen as defensive, as a way to keep up with the technological and cultural changes whilst managing to conduct the process and sustain its central role on the financial system.
Comparative Analysis of Environmental Accounting and Corporate Performance in Nigeria

The study analyzed the performance of Nigerian publicly traded corporations in terms of environmental accounting (green accounting). The investigation covers the years 2012 through 2020 using secondary data and an ex-post-facto approach. This methodology was chosen since the data had already been documented by reputable organizations like the Central Bank of Nigeria and other financial institutions. Five publicly traded firms were chosen at random from among the research's overall population: Oando, Shell, Agip, Coin Oil, and ALCON Oil. The study comprised all publicly traded Nigerian enterprises. Using Ordinary Least Square (OLS) methodology. The findings of the study exposed that restoration, redemption, and compensation expenses positively affect the companys’ performance, on performance. That is, green accounting has aided in the improvement of organizational performance in the majority of Nigeria's publicly traded companies. It also demonstrates that the majority of the firms mentioned are active in restoration, redemption, and compensation testing. The article recommends that, a standard should be created for analyzing, treating, and reporting a company's environmental actions. For the purposes of performance control and assessment, publicly traded companies should establish common environmental reporting and disclosure standards. Companies' competitiveness and overall performance can be enhanced by making their environmental practices public in their annual reports
El futuro de la educación o la educación del futuro

In this paper, the authors will talk about the development of technological platforms and tools that will support the creation and implementation of techno-pedagogical paradigms to tech the XXI Century generations. By proving how the innovative model of the open and distance learning universities (UNAD, for its Spanish acronym) is not run out and can be renewed, taking as an example the COVID-19 pandemic and analyzing the carriers that provide change to a new education, where the need of innovation in higher education in open and distance modalities is an urgent matter. Undoubtedly, learning is one of the human capacities that has most influenced their development and evolution. Thus, the e-education concept is presented as an effort to develop talents and human capacities using the Internet and new information and communication technologies (ICT). This will allow introducing a generalized remote mode, which will create a different culture adjusting the school curriculum to an online strategy, and not a rigid one, that involves technological improvements, so general and specialized knowledge are combined to the needs of scientific, technological, and social development. Teachers work with students differently and provide help through more knowledge and experience. Recent studies are showing that there are more open and online options for teacher professional development and teachers access the Internet to expand their development opportunities through social media platforms. In this sense, the non-attendance process of education has improved its relevance globally. Although, the COVID-19 pandemic caused an accelerator effect on the digital transformation of the educational institutions.
As ferramentas de branding como um importante recurso para as marcas de moda durante a pandemia de Sars Covid 19

This article aims to analyze how fashion brands used branding tools in their communication and how these strategies were important resources during the confnement periods of the pandemic, in order to maintain their connec-tion with consumers and/or attract new customers. for the brand. Through a literature review, it is possible to observe the infuence of social networks in strengthening the emotional bond between the consumer and the brand, even though its physical point of sale was closed during the frst confnement. Thus, it is possible to understand how a well-constructed communication can bring positive results and loyalty to a fashion brand even in periods of fnancial instability.
Application prospects for wearable body surface microfluidic system in sports

&lt;p&gt;The wearable body surface microfluidic system has great application potential in the field of sports. The use of the wearable body surface microfluidic system to monitor the physiological state of athletes can solve problems faced such as long inspection cycle in sports monitoring, difficulties in continuous monitoring, dependence on laboratory platforms, athlete resistance and other problems faced in technological integration to promote the development of the sports field. In recent years, the development of key technologies such as microfluidic chips and microneedle delivery provides an ideal solution for real-time monitoring and even immediate intervention of physiological states during exercise. This paper summarizes the latest research progress of wearable body surface microfluidic systems and focuses on eight wearable body surface microfluidic systems that may be applied in the field of sports, with their application prospects in sports analyzed and discussed. Finally, the application direction of the wearable body surface microfluidic system that may achieve breakthroughs is illustrated with the prospect demonstration of the future research and development direction of wearable sports equipment. This paper aims to focus on technical problems in the development of the sports field, provide multi-disciplinary solutions and advocate technology integration as well as provide scientific and technological assistance for the development of the sports field.&lt;/p&gt;
Realisasi Metode MOORA Untuk Menentukkan Kelayakan Bakal Calon Kepala Desa

Villages are part of the existing government order in the region. The presence of leaders who have good qualities will bring progress from the village itself. At this time the village has received serious attention from the central government, this aims to promote local wisdom in each village. However, not infrequently the attention given by the central government to the village is not used as well as possible. Unfinished infrastructure development, village services to the community are not good. This problem certainly gives a bad impression. In this case, based on the case study, it is necessary to select a candidate for the village head. The election or selection aims to get candidates for the village head election. Based on this, this study has the aim of realizing the Moora method to determine the feasibility of prospective village heads. The results of the moora method are then made a prototype of the website-based selection information system for the election of village head candidates. With the implementation of the moora method and the design of a website-based system, it provides an update in the selection of prospective village heads.
Bayesian ranking and selection with applications to field studies, economic mobility, and forecasting

Decision-making often involves ranking and selection. For example, to assemble a team of political forecasters, we might begin by narrowing our choice set to the candidates we are confident rank among the top 10% in forecasting ability. Unfortunately, we do not know each candidate's true ability but observe a noisy estimate of it. This paper develops new Bayesian algorithms to rank and select candidates based on noisy estimates. Using simulations based on empirical data, we show that our algorithms often outperform frequentist ranking and selection algorithms. Our Bayesian ranking algorithms yield shorter rank confidence intervals while maintaining approximately correct coverage. Our Bayesian selection algorithms select more candidates while maintaining correct error rates. We apply our ranking and selection procedures to field experiments, economic mobility, forecasting, and similar problems. Finally, we implement our ranking and selection techniques in a user-friendly Python package documented here: https://dsbowen-conditional-inference.readthedocs.io/en/latest/.