Reviewerly/exHarmony huggingface dataset:
Global Pooling, More than Meets the Eye: Position Information is Encoded Channel-Wise in CNNs

In this paper, we challenge the common assumption that collapsing the spatial dimensions of a 3D (spatial-channel) tensor in a convolutional neural network (CNN) into a vector via global pooling removes all spatial information. Specifically, we demonstrate that positional information is encoded based on the ordering of the channel dimensions, while semantic information is largely not. Following this demonstration, we show the real world impact of these findings by applying them to two applications. First, we propose a simple yet effective data augmentation strategy and loss function which improves the translation invariance of a CNN's output. Second, we propose a method to efficiently determine which channels in the latent representation are responsible for (i) encoding overall position information or (ii) region-specific positions. We first show that semantic segmentation has a significant reliance on the overall position channels to make predictions. We then show for the first time that it is possible to perform a `region-specific' attack, and degrade a network's performance in a particular part of the input. We believe our findings and demonstrated applications will benefit research areas concerned with understanding the characteristics of CNNs.
CORRESPONDENCE OF THE STAFF OF THE MANUSCRIPT DEPARTMENT OF THE LIBRARY OF THE RUSSIAN ACADEMY OF SCIENCES WITH I.N. ZAVOLOKO (1972–1983)

Ivan Nikiforovich Zavoloko (1897–1984) – a well-known figure of the Old Believer movement, historian, local historian, folklorist, collector of antiquities, educator, who had great authority both among the Old Believers and the scientific community. He actively collaborated with the Pushkin House and the Library of the USSR Academy of Sciences (BAN) in Leningrad in collecting manuscripts in the Baltic States and studying them. The published correspondence covers the period from 1972 to 1983 and includes 46 documents. Those are letters from I.N. Zavoloko to the curator of the manuscripts of the BAN N.Yu. Bubnov, to other employees of the Manuscript Department; and some response letters.
Automatic estimation of road visibility in foggy weather based on image feature statistics

We propose a method that uses the visibility feature extraction module and support vector machine (VFEM SEM) classification algorithm for visibility estimation. First, five statistical image features are extracted to fit a multivariate Gaussian (MVG) model; then, the visibility indexes and Mahalanobis distance are calculated from the MVG model and the reference samples, which are selected from the highest and lowest visibility levels, respectively. Finally, the SVM classifier is trained for visibility classification. Due to the lack of real scene foggy datasets, we collect a dataset called foggy highway from highway surveillance scenes. To evaluate the proposed algorithm’s performance fairly and objectively, we synthesize the foggy virtual KITTI image dataset with continuous annotations using the atmospheric scattering model and use another public synthetic dataset called “foggy road sign images.” Comprehensive experiments are conducted under three datasets. The experimental results show that the proposed method has achieved better classification accuracy compared with the other traditional image processing methods. Moreover, compared with deep learning-based methods, the proposed method has also achieved competitive performance and requires much less computational resources and image data. All of these advantages make the proposed algorithm more suitable for mobile device deployment.
Generating Attribution Maps With Disentangled Masked Backpropagation

Attribution map visualization has arisen as one of the most effective techniques to understand the underlying inference process of Convolutional Neural Networks. In this task, the goal is to compute an score for each image pixel related with its contribution to the final network output. In this paper, we introduce Disentangled Masked Backpropagation (DMBP), a novel gradient-based method that leverages on the piecewise linear nature of ReLU networks to decompose the model function into different linear mappings. This decomposition aims to disentangle the positive, negative and nuisance factors from the attribution maps by learning a set of variables masking the contribution of each filter during back-propagation. A thorough evaluation over standard architectures (ResNet50 and VGG16) and benchmark datasets (PASCAL VOC and ImageNet) demonstrates that DMBP generates more visually interpretable attribution maps than previous approaches. Additionally, we quantitatively show that the maps produced by our method are more consistent with the true contribution of each pixel to the final network output.
To Handling The Missing Values By Using Pre-Processing In Embedding In Dimensionality Reduction By Using Data Mining Techniques

As of now, data mining is one of the regions of incredible intrigue since it permits us to find covered up and frequently fascinating examples with regards to enormous volumes of information. In this present reality, enormous datasets are gotten from numerous sources and contain information that will, in general, be deficient, boisterous, and conflicting. In this unique circumstance, it is critical to get ready crude information to meet the prerequisites of information mining calculations. This is the job of the data pre- processing stage, in which information cleaning, change, and coordination, or information dimensionality decrease are performed. Hence in this process has detected the missing values in data cleaning by using the data pre-processing techniques. It finds and detects and removes the tuples without noise and replaces them to finds the value which helps normalization which helps data transformation. Data reduction is used to perform dimensionality reduction in the pre-processing methods. It aggregates to the selective data sets through selective processing and reduces the size with supports of numerosity respectively. It overcomes the existing drawbacks which support embedding in data mining techniques to provide the best solution to dimensionality reduction by using PCA and T-SNE.
Professor Brian Kennett, Head of Seismology Group, Research School of Earth Sciences

Kennett, Brian Prof - RSES, Head of Seismology Group - Seismological methods for the detection of nuclear testing. Photographer: Hay, Stuart
AI-UPV at IberLEF-2021 DETOXIS task: Toxicity Detection in Immigration-Related Web News Comments Using Transformers and Statistical Models

This paper describes our participation in the DEtection of TOXicity in comments In Spanish (DETOXIS) shared task 2021 at the 3rd Workshop on Iberian Languages Evaluation Forum. The shared task is divided into two related classification tasks: (i) Task 1: toxicity detection and; (ii) Task 2: toxicity level detection. They focus on the xenophobic problem exacerbated by the spread of toxic comments posted in different online news articles related to immigration. One of the necessary efforts towards mitigating this problem is to detect toxicity in the comments. Our main objective was to implement an accurate model to detect xenophobia in comments about web news articles within the DETOXIS shared task 2021, based on the competition's official metrics: the F1-score for Task 1 and the Closeness Evaluation Metric (CEM) for Task 2. To solve the tasks, we worked with two types of machine learning models: (i) statistical models and (ii) Deep Bidirectional Transformers for Language Understanding (BERT) models. We obtained our best results in both tasks using BETO, an BERT model trained on a big Spanish corpus. We obtained the 3rd place in Task 1 official ranking with the F1-score of 0.5996, and we achieved the 6th place in Task 2 official ranking with the CEM of 0.7142. Our results suggest: (i) BERT models obtain better results than statistical models for toxicity detection in text comments; (ii) Monolingual BERT models have an advantage over multilingual BERT models in toxicity detection in text comments in their pre-trained language.
Analysis Of Economic Factors in Juvenile Delinquency

Juvenile delinquency is a long-standing problem that exists globally. Juvenile delinquency usually refers to juvenile delinquency that includes various offenses such as theft, fighting, drug use, sexual assault, intentional injury, and more. Many factors are behind juvenile delinquency, including socioeconomic background, family environment, education, mental health, etc. The present research thesis aims to explore the current state of juvenile delinquency in various countries, the causes of juvenile delinquency, and recommendations for reducing juvenile delinquency from an economic perspective. According to research, childhood has a significant impact on adolescents; parents need to timely care about the psychological state of adolescents and give timely guidance; the government should allow poor adolescents the opportunity to educate, to reduce the rise of juvenile crime rate.
RANCANGAN DEKORATIF SIMETRIS BERDASARKAN MODEL FRAKTAL POLA SIERPINSKY GASKET ASIMETRIS

A symmetrical decorative design is always attractive to be used in many decoration needs. Fractal model gives a simple way in designing a decorative design that can be recycled later to have other design products easily and efficiently. The iterated function system fractal model used the code that can be modified by affine transformation operation. Sierpinsky gasket is one of a simple but attractive fractal object.
ELDOC - Design of Electric Dolly Camera for Video Recording Using the Omni-Direction Wheel

The dolly camera has function as a regulator of camera movement in making video productions such as films. The movement system on the dolly camera uses the thrust of humans and the running track rail for its movement. The process of preparing the dolly camera also requires a long process, such as assembling the dolly camera and then installing the track, etc. In this case study, we conducted research that designed an electric vehicle to carry a camera but with a simple way of working such as moving without rails and can be controlled with a mobile android. We named this robot vehicle ELDOC: Electric Dolly Camera which uses a four Omni-direction wheels system in the flexibility of direction and motion for taking videos and pictures. Contributions to the design of ELDOC in addition to the multimedia/cinematography field can also be used in the medical field as a monitoring tool for patient activities in isolation or ICU room with a mobile android from different rooms. In the results of the process running on the motor, we take error data from the calculation speed and measurement speed, wherein the duty circle is 19.60%, 38.50%, 58.8%, 78.66%, and the average error is 0,08. The stability level of the ELDOC when carrying the camera is stable but has vibrations or oscillations. The results of the data orientation (gyroscope) show the total average of the three samples Y (Roll) orientation is 0.6°/s and X (Pitch) orientation is 0.18°/s.
Elevating Software Quality Through Product Discovery Techniques: Key Findings from a Grey Literature Review

In the era of digital transformation, the notion of software quality transcends its traditional boundaries, necessitating an expansion to encompass the realms of value creation for customers and the business. Merely optimizing technical aspects of software quality can result in diminishing returns. Product discovery techniques can be seen as a powerful mechanism for crafting products that align with an expanded concept of quality—one that incorporates value creation. Previous research has shown that companies struggle to determine appropriate product discovery techniques for generating, validating, and prioritizing ideas for new products or features to ensure they meet the needs and desires of the customers and the business. For this reason, we conducted a grey literature review to identify various techniques for product discovery. First, the article provides an overview of different techniques and assesses how frequently they are mentioned in the literature review. Second, we mapped these techniques to an existing product discovery process from previous research to provide concrete guidelines for establishing product discovery in their organizations. The analysis shows, among other things, the increasing importance of techniques to structure the problem exploration process and the product strategy process. The results are interpreted regarding the importance of the techniques to practical applications and recognizable trends.
Formation of professional competence for future social educators based on an integrated approach

The objective of this study is to assess the efficacy of a comprehensive strategy for developing the professional abilities of social educators. Additionally, it seeks to identify the problems encountered by high schools in this regard and examine the process of cultivating professional competencies. The research comprised a sample of 253 individuals from L. Gumilyov Eurasian National University, located in Astana, Kazakhsta. The study compares the diagnostic results on a quantitative and qualitative level. The assessment standards of Fisher and the descriptive statistics were acquired. The study's data was gathered by requesting questions from teachers and students, testing, interviews, and evaluations from experts who fit this profile. The findings indicated that future social educators will have problems with their lack of high-level professional competence. Additionally, the majority of employers have requirements that are related to how well students develop their skills as well as their motivation to engage in extracurricular activities, work on themselves, and engage in future professional endeavors. The study revealed that employing an integrated strategy for the training of prospective social teachers yielded the most significant impact on the development of their professional abilities. The study's findings can be used to improve vocational training for social educators.
Increasing the Power of Two-Sample T-tests in Health Psychology Using a Compositional Data Approach

Increasing the statistical power of two-sample t-tests is of fundamental interest in health psychology and health economics. For example, grant funding depends on quality-adjusted life years (QALY) index values associated with a therapeutic intervention. Increasing the statistical power of t-tests corresponds to increasing effect sizes and, thus, increased grant funding and incentives. Improved psychometric profiling can help to reduce costs, improve resource usage, increase patient welfare and reduce mental health risks. Recently, the compositional structure (i.e., the Simplex) of bipolar Likert scales data was revealed pointing out the bias inherent in traditional evaluations. We propose to convert the compositional data towards the real valued interval scale using the isometric log-ratio (ilr) transformation providing unbiased parameter estimates (e.g., means, standard deviations), standards and improving psychometric patient profiling. Via simulation we show that the ilr approach increases the statistical power of the paired and unpaired two-samples t-test affecting effect size and grant funding. Re-analyzing real data we demonstrate the practical relevance of the approach. The results generalize to more complex statistical procedures, e.g. analysis of variance (ANOVA).
Investigating the Generative Dynamics of Energy-Based Neural Networks

Generative neural networks can produce data samples according to the statistical properties of their training distribution. This feature can be used to test modern computational neuroscience hypotheses suggesting that spontaneous brain activity is partially supported by top-down generative processing. A widely studied class of generative models is that of Restricted Boltzmann Machines (RBMs), which can be used as building blocks for unsupervised deep learning architectures. In this work, we systematically explore the generative dynamics of RBMs, characterizing the number of states visited during top-down sampling and investigating whether the heterogeneity of visited attractors could be increased by starting the generation process from biased hidden states. By considering an RBM trained on a classic dataset of handwritten digits, we show that the capacity to produce diverse data prototypes can be increased by initiating top-down sampling from chimera states, which encode high-level visual features of multiple digit classes. We also find that the model is not capable of transitioning between all possible digit states within a single generation trajectory, suggesting that the top-down dynamics is heavily constrained by the shape of the energy function. We also study the generative dynamics on a more challenging dataset containing pictures of faces, showing that the exploration of stable states also partially depends on complexity of the training data distribution.
The Impact of the Application of Algorithms in Social Media Marketing on Society

As algorithms have evolved, they have been heavily used in social media, and more brands are using them as an effective tool for social media marketing. The use of algorithms in social media marketing has become increasingly prevalent as brands seek to maximize their impact and reach on these platforms. However, there has been a growing concern over the impact of algorithms on society, including issues such as filter bubbles, echo chambers, and algorithmic bias. This research is important as it seeks to provide insights into the social impact of algorithmic applications in social media marketing. The findings could be useful to both brands and consumers, enabling them to make more informed decisions about their use of algorithms in social media marketing. This research aims to investigate the social impact of the use of algorithms in social media marketing. The study collected 135 samples through a qualitative research method using a questionnaire for social media users. The collected data was analyzed to derive both positive and negative impacts of algorithmic applications in social media marketing on society. The application of algorithms on social media can affect society due to efficient marketing and enhanced access to information. However, it can also have negative impacts on society, for example, potential for manipulation, loss of privacy, reinforcement of stereotypes and biases, and amplification of harmful content. And the paper builds on previous research on the subject of algorithms and social media by exploring their potential impact in more depth. By doing so, the study intends to enable both brands and consumers to effectively avoid its negative effects.
A Study of the Business Modes of Lululemon and Alo Yoga

Fitness brands are on the rise in recent days, with the increading demand of peoples requirements of health. These fitness brands are further developed under the era of the pandemic. With their rapid development, the business modes of them are therefore of particular interest to the author. Furthermore, it is found that few research have been conducted in this area. Therefore, there existed a research gap in it. This research aims to take on this opportunity to build up on existing research in the business modes of fitness brands. It uses a comparative and case study approach to study two fitness brands, Lululemon and Alo yoga, to investigate their business modes and patterns. The result found that there are some similar modes and patterns while they remain unique in other asepcts of their businesses.
A Comparative Corpus-linguistic Study of Thick Adjectives: Cases of the PRC Criminal Law and the Model Penal Code

With the promotion of law-based governance in China, laws and regulations play an ever more important role in our country whilst placing further demands on PRC law translation. Guided by previous research, this paper conducts a comparative study into the adjective phenomenon from the two selected corpus--the Model Penal Code and the PRC Criminal Law, respectively representing professional legal discourse and Chinese law translation. Concerning the research result, the author concludes the legal norms drawn from Model Penal Code and puts forward improvement proposals for the official Chinese-English law translations in terms of adjective distribution and category choices. The findings demonstrate that legal language tends to avoid adjectives and that thick concepts are more commonly used due to their evaluative intensity. By contributing to the promotion of future C-E legal translation, this paper aims to support China's legal system in its increasingly important role on the global stage.
Using Internet Access for Service Continuity amid the Covid-19 Pandemic in the North West Provincial Public Libraries of South Africa

The Covid-19 pandemic has exposed the many ICT infrastructural challenges entrenched in community libraries. This study determined how public libraries in the North West Province of South Africa could use internet access to continue the provision of information services to clients amid the Covid-19 pandemic and beyond. This was guided by key research objectives, namely to ascertain the status of internet access in North West provincial public libraries; determine the effect of the Covid-19 lockdown on services in North West provincial public libraries; and identify ways to use technology for service continuity in these libraries. The study adopted a quantitative research approach and employed a survey design. A questionnaire was used to collect data from a purposive sample of 15 public libraries in the North West Province. This sample was derived from a population of 123 community libraries falling under the provincial Department of Arts, Culture, Sports and Recreation. The findings revealed that there was a lack of funding to develop and support the ICT infrastructure in community libraries. It was further revealed that most of the province’s community libraries did not have access to the internet due to expired contracts with internet service providers (ISPs). Furthermore, the study revealed a lack of business continuity planning in community libraries. The study recommended that the province provide funding to community libraries for the development and maintenance of ICT infrastructure. The development of a central point in the province to manage internet access to community libraries was further recommended. It was further recommended that community libraries develop business continuity plans in consultation with stakeholders.
An Investigation of the Use of Augmented Reality in Public Art

Augmented reality offers many artistic possibilities when it comes to the creation of place-based public artworks. In this paper, we present a series of works around the topic of augmented reality (AR) art and place-based storytelling, including the use of walking as a creative method, a series of workshops with emerging artists, public AR art collaborations and a study to examine user experience when interacting with such artworks. Our findings from these works show the potential of integrating augmented reality with public physical artworks and offer guidance to artists and AR developers on how to expand this potential. For artists, we show the importance of the space in which the artwork will be placed and provide guidance on how to work with the space. For developers, we find that there is a need to create tools that work with artists’ existing practices and to investigate how to expand augmented reality past the limitations of site- or piece-specific apps.