Reviewerly/exHarmony huggingface dataset:
Neural Translation for European Union (NTEU)

The Neural Translation for the European Union (NTEU) engine farm enables direct machine translation for all 24 official languages of the European Union without the necessity to use a high-resourced language as a pivot. This amounts to a total of 552 translation engines for all combinations of the 24 languages. We have collected parallel data for all the language combinations publickly shared in elrc-share.eu. The translation engines have been customized to domain,for the use of the European public administrations. The delivered engines will be published in the European Language Grid. In addition to the usual automatic metrics, all the engines have been evaluated by humans based on the direct assessment methodology. For this purpose, we built an open-source platform called MTET The evaluation shows that most of the engines reach high quality and get better scores compared to an external machine translation service in a blind evaluation setup.
Concept of universality within the framework of the second order cybernetics

Ideological essence of the second-order cybernetics is generally understood as a “view from within”. This requires certain conceptual revision of most fundamental notions. Particularly, an interpretation of a ‘universal law’ is subject to such a revision. Formally speaking, any universal law may be coded in the form АхР(х), where Ax is the universal quantifier. However, its meaning changes significantly, for it could be shown that it must be treated probabilistically. The reason for this is in that the standard phrase ‘for any x’, which discloses the meaning of the formula Ax, must be seen here as an arbitrariness of choice, while ‘the choice arbitrariness’ must be seen as a constructive procedure within the second-order cybernetics. Otherwise, basic presumption of the second-order cybernetics will not hold. Indeed, classical science assumes that when one says ‘Let us pick up an arbitrary x from the model M’ then the very procedure of ‘picking up an arbitrary x’ is not assumed to be a part of model M. Classical scientists think of this act as of some agent’s act who is completely external to model M. Second order scientists take it differently. All agent’s acts — observations, measurements, picks, experiment organizations, etc — must be seen as a part of an appropriate model. This is the core of the ‘view from within’: theoretical agents, their acts as well as their theories must be considered as internal events, or properties, of the intended models. Going back to formula Ax, it is easier to see now that an intention ‘to pick up an arbitrary x’ must be treated as a real process within an appropriate model M. The only way to do it is to assume that such models have (truly) random events generators G as a necessary part of their structure. All above implies that an interpretation of AxP(x) within the framework of the second-order cybernetics must be the following: P(x) is universally true on M iff there exists G in M such that at any time t G may randomly choose any element from M with probability р(х)≠0, and P(x) will appear to be true. As a result of this, we may claim that the very idea of justifiable universality is inconsistent with deterministic ontologies (in the second-order science framework). Indeed, deterministic ontologies do not assume that at any time t it is possible to pick up an arbitrary x from the model M, for, by definition, they are limited only to certain choices through time, which are pre-determined by deterministic schedule of choices.
On the Interaction between Annotation Quality and Classifier Performance in Abusive Language Detection

Abusive language detection has become an important tool for the cultivation of safe online platforms.We investigate the interaction of annotation quality and classifier performance.We use a new, fine-grained annotation scheme that allows us to distinguish between abusive language and colloquial uses of profanity that are not meant to harm.Our results show a tendency of crowd workers to overuse the abusive class, which creates an unrealistic class balance and affects classification accuracy.We also investigate different methods of distinguishing between explicit and implicit abuse and show lexicon-based approaches either over-or under-estimate the proportion of explicit abuse in data sets.
Turkey

This chapter focuses on legislative speechmaking patterns in Turkey over four election cycles between 1995 and 2011. Specifically, the chapter aims to test the theory of legislative speechmaking outlined earlier in this volume, where political parties in closed-list proportional electoral systems are hypothesized to have full control over the speakers’ list to ensure the party discipline and unity. Results based on 35,000 legislative speeches made by over 2100 MPs do not support some elements of the outlined theory. In particular, I show that as the number of terms served in the parliament increases, the propensity to take the legislative floor decreases. Additionally, government party MPs are significantly less likely to take the floor. However, in line with the theory, cabinet ministers take the floor much more frequently, relative to others. The implications of these findings are discussed in the context of the theory of legislative speechmaking around the world.
Discussion Structure Prediction Based on a Two-step Method

Conversations are often held in laboratories and companies.A summary is vital to grasp the content of a discussion for people who did not attend the discussion.If the summary is illustrated as an argument structure, it is helpful to grasp the discussion's essentials immediately.Our purpose in this paper is to predict a link structure between nodes that consist of utterances in a conversation: classification of each node pair into "linked" or "not-linked."One approach to predict the structure is to utilize machine learning models.However, the result tends to over-generate links of nodes.To solve this problem, we introduce a two-step method to the structure prediction task.We utilize a machine learning-based approach as the first step: a link prediction task.Then, we apply a score-based approach as the second step: a link selection task.Our two-step methods dramatically improved the accuracy as compared with one-step methods based on SVM and BERT.
834 A robust deep learning approach for precisely segmenting cells in multiplex tissue images

Background Multiplex images are becoming pivotal in tissue pathology because they provide positional location and multidimensional phenotype of every cell. The heterogeneity of cells, morphologies, and densities makes the identification of the millions of cells in a tissue slice challenging. There is an urgent need for a robust, yet flexible, algorithm to automatically demarcate each cell that accurately defines cellular boundaries. We have developed a method to extend a DL nuclear identification algorithm beyond the nucleus and to the outer boundary of the cell using biological signals from multiplex panels. Methods All image analysis was performed in the Visiopharm image analysis platform. Three human observers provided ground truth (GT) annotations by outlining cells in predefined areas each containing ~30 cells in six different images from two different multiplex instruments: mIF = 8-plex via Vectra Polaris from Akoya and IMC = 13-plex via Hyperion from Fluidigm. Images were subsequently segmented by different AI methods: Machine Learning Nuclear Detection (ML), Deep Learning Nuclear Detection (DL), and DL that incorporates biological signals (DL+). Each set of computer-generated annotations was compared to GT using common evaluation metrics DICE, Precision and Sensitivity. Results Overall, we found a high degree of concordance between the computer-generated and human annotations (DICE = 0.73±0.08, n=12) and between imaging modalities (mIF: 0.76±0.07; IMC: 0.71±0.08; n=6). Comparison of DICE scores for the AI methods indicated a superior delineation of cell boundaries using the DL+ method (DL+: 0.79±0.07; ML: 0.74±0.08; DL: 0.74±0.03;). Precision, which compares true vs false positive annotated regions to GT, was also high for all images (0.77±0.11) (mIF: 0.76±0.10; IMC: 0.78±0.11). Sensitivity, which compares true positives vs false negative annotated regions GT, was also high for all images (0.77±0.09) (mIF: 0.76±0.09; IMC: 0.79±0.09). Conclusions We developed a flexible DL based strategy that enables the most comprehensive segmentation of cells in multiplex tissue images. Each AI approach shows a high concordance with segmentation annotations from human observers as measured by the industry standards DICE, Precision and Sensitivity. The DL+ method did achieve the highest DICE score indicating a more accurate delineation of cell boundaries. Expectedly, precision and sensitivity metrics are similar between all methods while DICE Coefficient better accounts for the annotations at the cell edge. The DL+ cell segmentation algorithm will yield an improved accuracy when phenotyping cells in downstream analysis as the precise biomarker composition is more accurately contained within each cell.
Twintimacy and Twinships

In the dominant individualistic U.S. culture, twins face a double bind. On one hand, their twinness is celebrated as special and mystical. On the other hand, closeness in twinships is heavily monitored and often positioned as problematic. Because of this paradox, communication surrounding twins is an excellent case study to explore the dark side (DS) metaphor of communication. The DS metaphor takes a functionalist approach arguing that communication phenomena can be viewed as light (productive/functional) or dark (destructive/dysfunctional) depending on context. To elucidate the DS metaphor, we juxtapose our experience as twins at two parades: our 4th grade Halloween parade and the annual parade at the Twinsburg Twins Festival.
RANCANG BANGUN ALAT CETAK KOMPOSIT SAMPAH PLASTIK DENGAN SABUT KELAPA

The plastic waste composite molding tool with coconut fiber is designed with the aim of converting HDPE plastic waste, especially drink bottle covers mixed with coconut fiber into a product in the form of a composite board (panel) that is more useful. The dimensions of the mold are 410 x 540 mm, the tool height is 850 mm, the width is 600 mm, the length is 600 mm, the temperature of the installed heating elements is around 200-250 degrees for 12 pieces, 220 V 1000 watts, while the press uses a 10 ton hydraulic jack. While the heating temperature for pressing the composite board is set in the range of 130 – 180 oC. From the results of the first printing press with iron plate material, the resulting product is sticky on the plate surface, the second test is coated with aluminum foil, the resulting product is less than perfect because the surface is uneven. , while the third test was coated with a stainless steel plate, the resulting product was better than the previous results. This research needs to be improved in order to assist the government in handling plastic waste. In addition, the results of products with this tool will help improve people's skills and improve the economy of the products of this tool.
A Comparative Study of Neural Networks and Logistic Regression for High Energy Physics

Neural networks are programs that run based on machine learning algorithms and resources to mirror the function of the brain in its roughest capacity. Neural networks are used primarily for the management and manipulation of large quantities data to form classifications, more efficient searches, and prediction of the data. Neural networks exist as part of the larger field of machine learning that exists. Linear regression in turn serves as the statistics based solution to the classification issue, an alternative to neural networks that are also a form of machine learning. The focus of this research was to observe whether neural networks or linear regression models are more effective for classification of a supersymmetry dataset. The supersymmetry dataset is made up of the results gathered particle collision events within a particle accelerator. Supersymmetry itself is a theory within particle physics that suggests the particles that are absent in the standard model are symmetric, or balancing, counterparts to the particles that have been already discovered.
Counselors as leaders: the mediating role of stress in the relationship between psychological capital and transformational leadership

Leadership is a fundamental factor in organizational success (Koene, Voglaar, & Soeter, 2012). Leader effectiveness occurs when individuals in leadership positions impact groups or followers in ways that aid them in performing their roles with positive organizational outcomes (Dhar & Mishra, 2001). Thus, successful leaders impact organizational effectiveness, goal attainment, employee satisfaction, and organizational well-being (Kaiser & Overfield, 2010). Leadership performance has also been closely linked to stress (Harms, Crede, Tynan, Leon, and Jueng, 2017); which, according to Seaward (2015), can overload cognitive pathways, decreasing the processing and cognitive recall abilities that are necessary to make sound decisions. Therefore, it is imperative that effective leaders understand the concept of stress and how to manage it. Given the counseling profession’s increasing interest in leadership (ACA, 2009; CACREP, 2016), it is surprising that, other than a declaration by Paradise, et al (2010), the counseling profession has not made more efforts to examine how counselors are effective leaders. Given the established link between leadership and effective stress management, the dearth of research related to counselors as effective leaders is even more confounding, as counseling programs produce graduates who have training, skills, and attributes related to wellness, self-care, and approaches to influencing human behavior (e.g., stress management). To date, no other studies exist that have quantitatively examined how counselors may be effective leaders. The purpose of this study was to examine the relationships between Psychological Capital (PsyCap), Perceived Stress, and Transformational Leadership among counselors in leadership positions using Hobfoll’s (1989) Model of Conservation of Resources (MCR). A hierarchical regression model was created to determine predictability and relationship of variables. Four hypotheses were tested in this study. There was a strong positive relationship discovered between PsyCap and Transformational Leadership among counselors, and a strong negative relationship was discovered between PsyCap and Perceived Stress. These findings were consistent with research in the field of leadership and organizational science. However, an insignificant relationship between Perceived Stress and Transformational Leadership precluded Perceived Stress from acting as a mediator in the relationship between PsyCap and Transformational Leadership. Overall, findings from the study indicate that counselors are effective leaders, maintain significant psychological resources as well as low stress while inhabiting leadership roles.
Effect Generation Based on Causal Reasoning.

Causal reasoning aims to predict the future scenarios that may be caused by the observed actions. However, existing causal reasoning methods deal with causalities on the word level. In this paper, we propose a novel event-level causal reasoning method and demonstrate its use in the task of effect generation. In particular, we structuralize the observed cause-effect event pairs into an event causality network, which describes causality dependencies. Given an input cause sentence, a causal subgraph is retrieved from the event causality network and is encoded with the graph attention mechanism, in order to support better reasoning of the potential effects. The most probable effect event is then selected from the causal subgraph and is used as guidance to generate an effect sentence. Experiments show that our method generates more reasonable effect sentences than various well-designed competitors.
Joint Hashing Feature and Classifier Learning for Cross-Modal Retrieval

To solve the problem of low retrieval accuracy and long training time in cross-modal retrieval algorithms,a cross-modal retrieval algorithm joining hashing feature and classifier learning(HFCL)is proposed.Uniform hash codes are utilized to describe different modal data with the same semantics.In the training stage,label information is utilized to study discriminative hash codes.And the kernel logistic regression is adopted to learn the hash function of each modal.In the testing stage,for any sample,the hash feature is generated by learned hash function,and another modal datum related to its semantics is retrieved from the database.Experiments on three public datasets verify the effectiveness of HFCL.
Improved Named Entity Recognition for Noisy Call Center Transcripts

We explore the application of state-of-the-art NER algorithms to ASR-generated call center transcripts.Previous work in this domain focused on the use of a BiLSTM-CRF model which relied on Flair embeddings; however, such a model is unwieldy in terms of latency and memory consumption.In a production environment, end users require low-latency models which can be readily integrated into existing pipelines.To that end, we present two different models which can be utilized based on the latency and accuracy requirements of the user.First, we propose a set of models which utilize state-of-the-art Transformer language models (RoBERTa) to develop a high-accuracy NER system trained on a custom annotated set of call center transcripts.We then use our bestperforming Transformer-based model to label a large number of transcripts, which we use to pretrain a BiLSTM-CRF model and further fine-tune on our annotated dataset.We show that this model, while not as accurate as its Transformer-based counterpart, is highly effective in identifying items which require redaction for privacy law compliance.Further, we propose a new general annotation scheme for NER in the call-center environment.
Modern Museum and Paramuseum: Existential Alienation and Actualization

The article is devoted to the study of the problem of alienation of culture in a modern museum and the processes of actualization of objects and phenomena of history in the space of the paramuseum. In the center of the author’s attention is the theme of creating the illusion of existential comfort. It explores the contradiction between the need for museification of culture in order for a modern person to be able to appeal to it when building one’s own identity, and the constant desire to place the culture of the past on a safe reservation. The issue of aestheticization of cultural objects in the museum space and the role of a museum in interpreting, preserving and distorting their meaning is raised. The museum, created as a repository of antiquities, a collection of masterpieces, today has become the most sensitive system that responds to changes in the life of culture and society. An axiological analysis of modern museums shows their growing popularity as an element of the entertainment industry, while their aesthetic, analytical, and intellectual role is becoming obscure. Respect for the museum as a keeper of cultural memory, for the focus of scientific life is disappearing. Instead, a simplified “attraction museum” and paramuseum is coming to the fore, creating endless games with historical objects, reconstructions, visitors and interpretations of the events of history and culture. The authors of the article are among the first to turn to the concept of “paramuseum” and give it a comprehensive assessment. For the first time, a scientific classification of paramuseums (on the example of paramuseums of northwestern Russia) is proposed. Their main features and characteristics are identified. A synergistic approach to the processes of actualization and alienation of cultural objects in the museum environment made it possible to include the viewer, the recipient, as the third, necessary component of this system. This made it possible to conclude that museum values are alienated or updated not by themselves, but only in relation to the “person watching.” Thus, modern museums and paramuseums are a form of value-based self-consciousness of society, demonstrating the total stratification of post-culture society, its fragmentation into value clusters that can represent culture as a whole only in the process of analytical consciousness, but not in the collection of subject series.
Socio-economic situation of the region as a factor in the dynamics of the institute of education in the new social reality

In the article, the authors have analyzed the problems of providing educational services during the pandemic. The position of the determination of the institution of education from the level of socio-economic well-being of the region is justified. The authors have substantiated the position on the formation of new challenges to the institute of education, caused by the formation of a new social reality in the conditions of isolation and restrictions of the communicative space. The social, economic and psychological factors of reducing the degree of satisfaction with educational services during the period of large-scale restrictions of full-time education have been considered. The problems of organizing a distant learning mode for the heads of educational organizations, the teaching staff and the children's audience have been identified. The authors consider the most significant problems to be: increasing stratification of the Russian regions, polarity of their economic condition, which reveals differentiation in the technical equipment of educational organizations and the availability of educational services and resources to provide a modern educational space; the polarity of the technical equipment of educational organizations; organizational and technical difficulties; the lack of knowledge of teaching staff about the rules of organizing video content.
Learning curves of generic features maps for realistic datasets with a teacher-student model

Teacher-student models provide a framework in which the typical-case performance of high-dimensional supervised learning can be described in closed form. The assumptions of Gaussian i.i.d. input data underlying the canonical teacher-student model may, however, be perceived as too restrictive to capture the behaviour of realistic data sets. In this paper, we introduce a Gaussian covariate generalisation of the model where the teacher and student can act on different spaces, generated with fixed, but generic feature maps. While still solvable in a closed form, this generalization is able to capture the learning curves for a broad range of realistic data sets, thus redeeming the potential of the teacher-student framework. Our contribution is then two-fold: First, we prove a rigorous formula for the asymptotic training loss and generalisation error. Second, we present a number of situations where the learning curve of the model captures the one of a realistic data set learned with kernel regression and classification, with out-of-the-box feature maps such as random projections or scattering transforms, or with pre-learned ones - such as the features learned by training multi-layer neural networks. We discuss both the power and the limitations of the framework.