Reviewerly/exHarmony huggingface dataset:
When do Contrastive Word Alignments Improve Many-to-many Neural Machine Translation?

Word alignment has proven to benefit many-to-many neural machine translation (NMT). However, high-quality ground-truth bilingual dictionaries were used for pre-editing in previous methods, which are unavailable for most language pairs. Meanwhile, the contrastive objective can implicitly utilize automatically learned word alignment, which has not been explored in many-to-many NMT. This work proposes a word-level contrastive objective to leverage word alignments for many-to-many NMT. Empirical results show that this leads to 0.8 BLEU gains for several language pairs. Analyses reveal that in many-to-many NMT, the encoder's sentence retrieval performance highly correlates with the translation quality, which explains when the proposed method impacts translation. This motivates future exploration for many-to-many NMT to improve the encoder's sentence retrieval performance.
Double or half reading, double or full meaning: Amphibological and anacoluthic syntax through the lens of Qur’an translators

The Qur’an abounds in multifaceted ambiguous and elliptical structures which sometimes attest its idiosyncratic rhetorical style and challenging formal correspondence and dynamic/functional equivalence between Arabic and English. Although previous translation studies on Qur’anic ambiguity and ellipsis are manifold, there is a paucity of past literature on amphibol(og)y and a dearth of previous research on anacoluthon in the Qur’an in particular. Therefore, the need for this study arises from the necessity to examine these two understudied syntactic phenomena, technically al-labs an-nahwi: (‘amphibology’) and fuqda:n at-tata:buc (‘anacoluthon’), through the lens of Qur’an translators and to assess the translational quality of transposing meaning duplicity and interpretive multiplicity from Qur’anic Arabic (in)to English. The specific objectives are to investigate how Qur’an translators resolved amphibolies while rendering verses superscripted by the interchangeable pause sign (∴ ∴) and to explore how they sequentialised anacolutha when translating anacoluthic verses marked by the elliptical sign (…). The study employs the qualitative contrastive method for a contrastive translational analysis of a typologically limited number of amphibolous and anacoluthic verses retrieved from the Qur’anic Arabic Corpus (QAC). Findings show that amphibology and anacoluthon are so challenging to Qur’an translators that there are remarkable variations in ambiguity resolution and anacoluthon sequentialisation. Qur’an translators act occasionally as explicitators, implicitators and neutralisers of its message and epitomise heterosubjectivity and asymmetricity in interpretative choices. The implications of these findings for Qur’an Translation Studies (QTS) highlight the importance of paratexts and epitexts for amphibological and anacoluthic syntax in translation. According to Genette (1997), paratexts and epitexts are thresholds of interpretation that add haunting subtexts to texts in translation. Subtexts are necessary to provide essential information or commentary on the translation of the original text.
Homo Hallucinas: Review of the Book «Introduction to Singular Philosophy» of F.I. Girenok

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Disambiguation of morpho-syntactic features of African American English -- the case of habitual be

Recent research has highlighted that natural language processing (NLP) systems exhibit a bias against African American speakers. The bias errors are often caused by poor representation of linguistic features unique to African American English (AAE), due to the relatively low probability of occurrence of many such features in training data. We present a workflow to overcome such bias in the case of habitual "be". Habitual "be" is isomorphic, and therefore ambiguous, with other forms of "be" found in both AAE and other varieties of English. This creates a clear challenge for bias in NLP technologies. To overcome the scarcity, we employ a combination of rule-based filters and data augmentation that generate a corpus balanced between habitual and non-habitual instances. With this balanced corpus, we train unbiased machine learning classifiers, as demonstrated on a corpus of AAE transcribed texts, achieving .65 F$_1$ score disambiguating habitual "be".
HUMAN MODEL IN THE CONTEXT OF THE RELATIONAL PARADIGM OF THEORETICAL PHYSICS

The main problems of the concepts of consciousness: explanation of the mechanism of the psyche; substantiation of free will; tabula rasa problem; the nature of the principles of scientific theories and morality - do not have a satisfactory solution within the framework of materialistic concepts. There are reasons to believe that consciousness goes beyond space-time, which leads to the idea of substantiating the nature of consciousness from the standpoint of the relational paradigm of theoretical physics.
A NEW SYSTEM OF KNOWLEDGE IN THE SCIENCE AND PHILOSOPHY OF COSMISM

The work is devoted to important aspects of the cardinal phenomenon of scientific and philosophical thought, with other words cosmism, or cosmic thinking, which began to form in the late 19th - early 20th centuries and continues to develop today. The work reveals the actual problem of understanding two main ways of knowledge - empirical scientific and metascientific (the comprehension of objects and phenomena by a person through his inner, spiritual, space), the synthesis of which is presented in the works of Russian scientists and cosmist philosophers. This synthesis is a new system of knowledge, which is the subject of research and allows us to understand a phenomenon or process in the most complete and deep way. This understanding is particularly relevant, because science faces a number of major problems that cannot be explained in terms of traditional, outdated approaches.
Broadening the ontology of threats verbalized in written communication on the Internet

The topicality of the research is justified by intensification of distractive communication between Internet users and by lack of understanding how to regulate such speech behavior, as the social function of internet-mediated communicative environment is still undetermined. The paper explores communicative features of threats-punishments and threats-warnings verbalized through written communication mediated by gadgets with access to the Internet. The aim of the research is to define the perlocutionary effect produced by verbalized threats based on written communication. Threats are understood as utterances containing a message about harmful consequences of the action of a threat sender towards a recipient of the threat if he or she will or will not do some action demanded in the threat. Verbalization means that threat is articulated in speech communication and realized in verbal form. The research hypothesis is tested on the basis of 68 threatening messages in the Russian language retrieved from contexts of speech communication in internet-messengers and on social network sites. We prove that the addresser (threatening person) verbalizes treats mostly in the form of punishments describing possible damage to the addressee’s (recipient’s) life and or health. We found out that threatening speech acts verbalized on the Internet are mostly similar in form and content to those realized in face-to-face communication. However, Internet threats are mostly determined as a reflection of emotional-modal state of the addresser in response to the actions of the addressee. Men are more active in the verbalization of threats-punishments, whereas women verbalize more threats-warnings. People tend to protect themselves by ignoring threats addressed to them. The obtained results clarify the idea of Internet-threat as affected speech behavior, the regulation of which is impossible within the framework of the existing legislation; therefore it is necessary to develop a criteria base consistent with public morality, with the participation of expert and academic communities and technological companies.
Cluster approach to criteria evaluation of the quality of a student’s educational outcome

Problem and goal . The issues of criteria-based evaluation of the student's educational results remain relevant for the modern theory and practice of education. As a rule, measures to monitor educational results and resources in educational institutions are carried out by expert, manual, non-automated methods. In accordance with the directions of digital transformation of education, it is necessary to create a technological assessment system that meets the requirements of modern society, subject to automation and intellectualization. The purpose of the work is to substantiate a new model of criteria-based assessment of the quality of the educational result, based on the mathematical methods of the theory of clustering and pattern recognition and allowing to automate the procedures for assessing the quality of educational objects, resources, educational and personal achievements of students. Methodology. The quality of an educational result or resource is determined by criteria indicators, which can be represented as features of the evaluated object using the information vector. By clustering the set of acceptable objects into three classes - with low, medium and high quality - it is possible to evaluate an object by its belonging to one of these classes. Clustering is carried out on the basis of a mining algorithm, the metric of city blocks is taken as a measure of the similarity of objects. Results. A program has been developed that consists of a source data module, a clustering module, and a recognition and training module. The model results of the program correlate with traditional rating assessments, in which the quality of the object is determined by a point scale. The obtained test results confirm the validity of the recognition algorithm and the correctness of the software product. Conclusion. Thus, the proposed model based on clustering and the recognition method showed the possibility of automated assessment of the quality of educational results of trainees and educational resources.
LaMemo: Language Modeling with Look-Ahead Memory

Although Transformers with fully connected self-attentions are powerful to model long-term dependencies, they are struggling to scale to long texts with thousands of words in language modeling. One of the solutions is to equip the model with a recurrence memory. However, existing approaches directly reuse hidden states from the previous segment that encodes contexts in a uni-directional way. As a result, this prohibits the memory to dynamically interact with the current context that provides up-to-date information for token prediction. To remedy this issue, we propose Look-Ahead Memory (LaMemo) that enhances the recurrence memory by incrementally attending to the right-side tokens, and interpolating with the old memory states to maintain long-term information in the history. LaMemo embraces bi-directional attention and segment recurrence with an additional computation overhead only linearly proportional to the memory length. Experiments on widely used language modeling benchmarks demonstrate its superiority over the baselines equipped with different types of memory.
Reformulating Speaker Diarization as Community Detection With Emphasis On Topological Structure

Clustering-based speaker diarization has stood firm as one of the major approaches in reality, despite recent development in end-to-end diarization. However, clustering methods have not been explored extensively for speaker diarization. Commonly-used methods such as k-means, spectral clustering, and agglomerative hierarchical clustering only take into account properties such as proximity and relative densities. In this paper we propose to view clustering-based diarization as a community detection problem. By doing so the topological structure is considered. This work has four major contributions. First it is shown that Leiden community detection algorithm significantly outperforms the previous methods on the clustering of speaker-segments. Second, we propose to use uniform manifold approximation to reduce dimension while retaining global and local topological structure. Third, a masked filtering approach is introduced to extract "clean" speaker embeddings. Finally, the community structure is applied to an end-to-end post-processing network to obtain diarization results. The final system presents a relative DER reduction of up to 70 percent. The breakdown contribution of each component is analyzed.
Multiset Neurons

The present work reports a comparative performance of artificial neurons obtained in terms of the real-valued Jaccard and coincidence similarity indices and respectively derived functionals. The interiority index and classic cross-correlation are also included for comparison purposes. After presenting the basic concepts related to real-valued multisets and the adopted similarity metrics, including the generalization of the real-valued Jaccard and coincidence indices to higher orders, we proceed to studying the response of a single neuron, not taking into account the output non-linearity (e.g.~sigmoid), respectively to the detection of gaussian two-dimensional stimulus in presence of displacement, magnification, intensity variation, noise and interference from additional patterns. It is shown that the real-valued Jaccard and coincidence approaches are substantially more robust and effective than the interiority index and the classic cross-correlation. The coincidence-based neurons are shown to have the best overall performance respectively to the considered type of data and perturbations. The potential of the multiset neurons is further illustrated with respect to the challenging problem of image segmentation, leading to impressive cost/benefit performance. The reported concepts, methods, and results, have substantial implications not only for pattern recognition and machine learning, but also regarding neurobiology and neuroscience.
Application of Neural Network Variations for Determining the Best Architecture for Data Prediction

Abstract &#x0D; This study focuses on the application and comparison of the epoch, time, performance/MSE training, and performance/MSE testing of variations of the Backpropagation algorithm. The main problem in this study is that the Backpropagation algorithm tends to be slow to reach convergence in obtaining optimum accuracy, requires extensive training data, and the optimization used is less efficient and has performance/MSE which can still be improved to produce better performance/MSE in this research—data prediction process. Determination of the best model for data prediction is seen from the performance/MSE test. This data prediction uses five variations of the Backpropagation algorithm: standard Backpropagation, Resistant Backpropagation, Conjugate Gradient, Fletcher Reeves, and Powell Beale. The research stage begins with processing the avocado production dataset in Indonesia by province from 2016 to 2021. The dataset is first normalized to a value between 0 to 1. The test in this study was carried out using Matlab 2011a. The dataset is divided into two, namely training data and test data. This research's benefit is producing the best model of the Backpropagation algorithm in predicting data with five methods in the Backpropagation algorithm. The test results show that the Resilient Backpropagation method is the best model with a test performance of 0.00543829, training epochs of 1000, training time of 12 seconds, and training performance of 0.00012667.&#x0D;
Learning Path Optimization based on Multi-Attribute Matching and Variable Length Continuous Representation

Abstract Personalized learning path considers both learner and resource attributes. The evolutionary algorithm approach usually forms the learning path generation problem into a problem that optimizes the matching degree of the learner and the generated learning path. The proposed work considers the learner attributes as ability level, learning objective, learning style, and expected learning time. The learning path attributes include difficulty level, covered concept, supported learning styles, required learning time, and prerequisites. A variable-length representation of the learning path is adopted based on floating numbers, which significantly reduces the encoding length and simplifies the learning path generating process. An improved differential evolution algorithm is applied to optimize the matching degree of learning path and learner. The quantitative experiments on different problem scales show the proposed system outperforms the binary-based representation approaches in scaling ability and outperforms the comparative algorithms in efficiency.
Integration of EHR and PHR leveraging cloud services for approving treatments

Historically clinicians have been prescribing treatment to patients based on their visit to the hospital without referring to previous health records of the patients. This is primarily because clinicians do not have access to patient’s medical records. Though, with digital revolution in healthcare domain, patient’s medical history is available via Patient Health Records using Electronic Health Records (EHR) and Personal Health Records (PHR) systems. However, as EHR and PHR are maintained as separate systems in isolated manner, efficient accessibility of these systems is still very limited. To provide a unified view of patient data to the providers and the patients, integrated data resulting from such system not only allow to access past health records of patients, but further leverage PHRs data to provide quality treatment by examining the effect of treatment on patient’s health. Data integration techniques such as ETL and Cloud based technologies can be used to develop a system which provides real-time integration of healthcare data sourced &amp; streamed from various healthcare systems. It will leverage the recommendations of quality treatments to the patients as well as the clinicians from cohesive view of integrated EHR and PHR data.
Correlation Between Soil Nitrogen Content and NDVI Derived from Sentinel-2A Satellite Imagery

The United Nations Educational, Scientific, and Cultural Organization (UNESCO) has recognized the Balinese agricultural irrigation system known as subak as part of the world's cultural heritage. Subak is the driver of Bali’s agricultural and tourism sectors and, therefore, must be preserved. Population growth triggers the conversions of land functions, from subak to built-up lands, such as those transpiring in Denpasar City. On the other hand, with the population continuously increasing, the demand for food becomes inevitably higher. This has caused farmers to intensify their agricultural practices through, for instance, applying chemical fertilizers excessively-potentially decreasing soil fertility. An example is urea fertilizer that contains a macronutrient, i.e., nitrogen (N). This study aimed to analyze the soil N content and its correlation with rice growth using the Normalized Difference Vegetation Index (NDVI). The Kjeldahl method was conducted to measure the N levels in the soil laboratory. NDVI was extracted from remote sensing data, namely Sentinel-2A imagery, on a cloud computing platform, Google Earth Engine (GEE), using Band 8 (NIR) with a wavelength of 0.842 m and Band 4 (Red) with 0.665 m. The results showed that the N levels varied from 0.09% to 0.31% and the average NDVI values ranged from 0.47 to 0.54. There is a strong correlation (r = 0.75 to 0.78) between the NDVI values derived from the Sentinel-2A Satellite Imagery and the soil nitrogen content. Spatially, based on the analysis results of the 2019‒2021 data, parts of existing subak systems, i.e., Subak Kerdung, Mergaya, Padanggalak, and Sembung, have high soil N contents and NDVI values.
JN-Logo: A Logo Database for Aesthetic Visual Analysis

Data are an important part of machine learning. In recent years, it has become increasingly common for researchers to study artificial intelligence-aided design, and rich design materials are needed to provide data support for related work. Existing aesthetic visual analysis databases contain mainly photographs and works of art. There is no true logo database, and there are few public and high-quality design material databases. Facing these challenges, this paper introduces a larger-scale logo database named JN-Logo. JN-Logo provides 14,917 logo images from three well-known websites around the world and uses the votes of 150 graduate students. JN-Logo provides three types of annotation: aesthetic, style and semantic. JN-Logo’s scoring system includes 6 scoring points, 6 style labels and 11 semantic descriptions. Aesthetic annotations are divided into 0–5 points to evaluate the visual aesthetics of a logo image: the worst is 0 points; the best is 5 points. We demonstrate five advantages of the JN-Logo database: logo images as data objects, rich human annotations, quality scores for image aesthetics, style attribute labels and semantic description of style. We establish a baseline for JN-Logo to measure the effectiveness of its performance on algorithmic models of people’s choices of logo images. We compare existing traditional handcrafted and deep-learned features in both the aesthetic scoring task and the style-labeling task, showing the advantages of deep learning features. In the logo attribute classification task, the EfficientNet _B1 model achieved the best results, reaching an accuracy of 0.524. Finally, we describe two applications of JN-Logo: generating logo design style and similarity retrieval of logo content. The database of this article will eventually be made public.
OVERHEAD OPERATIONAL WATER FOOTPRINT DAN KUALITAS AIR PADA GEDUNG BERTINGKAT (STUDI KASUS GRAHA REKTORAT UNIVERSITAS NEGERI MALANG)

Graha Rektorat Building at the State University of Malang (UM) significantly contributes to water usage. Water usage in the building must be managed and appropriately considered, so it can highly contribute to UM's ranking of UI Green Metric for water conservation indicators. Water usage can be calculated by analysis of overhead operational water footprint. This research aims to analyse green, blue, and grey water footprint, overhead operational water footprint, and the quality of green water on the Graha Rektorat building. The methods used in this research are the quantitative method. This research showed the blue water footprint, green water footprint, and grey water footprint of Graha Rektorat building each by 5628 m3 during January – September 2021, 4361.43 m3/year 18.575 m3/day. Overhead operational water footprint on Graha Rektorat building equal with the value of blue water footprint which is 5628 m3 during January – September 2021. The quality of green water on the Graha Rektorat building does not meet the requirements of Indonesian Minister of Health No. 32 of 2017 concerning environmental health quality standards and water health requirements for hygienic and sanitation purposes. Swimming Pools, Solus Per Aqua, and Public Baths, so as another alternative, green water can be used as a substitute for flushing toilets and watering plants to reduce blue water usage.