Reciprocal Mean Research Articles

Sakinah Mawadah and Rahmah in QS. al-Rūm [30]: 21: Analysis of Qirāah Mubādalah Faqihuddin Abdul Kodir (L.1971)

The phenomenon of divorce in Indonesia increases every year with various problems faced in a family. As time goes by, problems and solutions to overcome these problems also change, so that solutions that were commonly used in the past to deal with current household disputes are not appropriate. If referring to the interpretation of QS. al-Rūm [30]: 21 does not yet fully contain an interpretation regarding the current phenomenon of the concepts of sakinah mawadah and rahmah. This paper tries to respond to this problem by analyzing the interpretation of the QS. al-Rūm [30]: 21 uses the Qirā'ah Mubādalah perspective. The results of this research include; First, the interpretation of QS. al-Rūm [30]: 21 in the perspective of mubādalah is Allah SWT. shows one of the signs of His greatness by creating for his servants couples (both husband and wife) of the same kind so that they both feel calm and at ease, and tend to each partner. Then, to obtain this calm and tranquility, Allah SWT. bring a sense of love and affection between the two. These are the signs of His greatness for a people who think. Second, in its literal pronunciation, the verse discusses men who obtain peace from their wives, but in terms of its meaning, this verse also applies to women who obtain peace from their husbands. This is emphasized in the phrase bainakum which shows the reciprocal meaning between husband and wife.

Middle, Reflexive, and Reciprocal Constructions in Nalögo: A Typological and Diachronic Account

Abstract: In many Oceanic languages, middle and reciprocal meanings are expressed by reflexes of Proto-Oceanic *paRi-, a polysemous and polyfunctional prefix with collective, associative, iterative, and reciprocal functions. Traditionally, reflexive constructions in Oceanic languages were marked differently from middles and reciprocals. Where reflexes of Proto-Oceanic *paRi- show a lower productivity or disappeared, Oceanic languages co-opted available morphemes and constructions to express the functions once performed by the prefix. In terms of polysemes, along with the middle/reciprocal one, which is rooted in the history of these constructions, two paths of extension developed over time: (i) from middle/reciprocal to reflexive (with reflexes of Proto-Oceanic *paRi-), and (ii) from reflexive to reciprocal, but different from middle (with innovated markers). The aim of this paper is twofold: (i) to provide a description of middles, reflexives, and reciprocals in Nalögo, a Reefs–Santa Cruz Oceanic language, and (ii) to contribute to the discussion on the typology and diachrony of such constructions within the Oceanic family. In particular, I show that Nalögo has two innovated markers, the reflexive =lëbu (maybe from Proto-Oceanic *[ta]bulo(s) 'turn round, turn back') and the reciprocal - welo . Furthermore, Nalögo displays a so-far unknown path of semantic extension within the Oceanic family: from reflexive to middle. While the reflexive–middle polysemy is widely attested in the languages of the world, it constitutes a typological rarity in Oceanic languages.

Reciprocal-reflective approach to learning: an approach to increase employee and manager learning

PurposeThe purpose of this paper is to present for practitioner (manager) consideration an approach to a collaborative learning and problem-solving effort by a manager in partnership with an employee. Fundamentally, in place of manager direction and expertise applied to employee learning, the approach is grounded on manager-employee collaboration that makes use of an approach to learning to: (1) address some work-based problem or issue, (2) share typical leadership roles of goal-setting and problem solving, and (3) jointly working to improve the knowledge, skills, and personal attributes of each partner.Design/methodology/approachThe concept of reciprocal reflective approach to learning (RRAL) is created by consideration of some factors that are consolidated, ultimately, to yield an approach to change. They include shared efforts in goal-setting and related tasks, identification of knowledge and skill areas linked to the performance and growth of each of the partners, and the use of a specific learning model.FindingsThe approach presented can be achieved yet requires much attention by each participant. Reciprocity means that each participant is involved in their own increases in knowledge and skills as well as aiding their partner in her/his improvements. Problem solving is also a large component of their efforts as is relationship building.Originality/valueThe approach embraces how reciprocal action can go well beyond problem solving and includes manager and employee interest and attention in helping each other to learn, change, and recognize growth in personal attributes.

Approches sémiotiques et langagières en physique et en mathématiques

In physics and mathematics, there is a growing interest in studying the meanings that teachers and students construct through the mobilization of several semiotic systems, including embodied action, such as gestures, body postures, rhythm and speech. In this article, we propose a comparison of two approaches, developed in parallel in each of these disciplines. In physics, it is the function of models in the scientific community of physicists that guides the consideration of semiotic systems to account for the reciprocal meaning of material situations and taught concepts. In mathematics, it is the very nature of activity - considered at the same time as ideal, material and sensible - which leads to a consideration of the semiotic systems which underlie it, a consideration which allows us to shed new light on the processes of teaching and learning

Research on Traditional Chinese Medicine: Domain Knowledge Graph Completion and Quality Evaluation.

Knowledge graphs (KGs) can integrate domain knowledge into a traditional Chinese medicine (TCM) intelligent syndrome differentiation model. However, the quality of current KGs in the TCM domain varies greatly, related to the lack of knowledge graph completion (KGC) and evaluation methods. This study aims to investigate KGC and evaluation methods tailored for TCM domain knowledge. In the KGC phase, according to the characteristics of TCM domain knowledge, we proposed a 3-step "entity-ontology-path" completion approach. This approach uses path reasoning, ontology rule reasoning, and association rules. In the KGC quality evaluation phase, we proposed a 3-dimensional evaluation framework that encompasses completeness, accuracy, and usability, using quantitative metrics such as complex network analysis, ontology reasoning, and graph representation. Furthermore, we compared the impact of different graph representation models on KG usability. In the KGC phase, 52, 107, 27, and 479 triples were added by outlier analysis, rule-based reasoning, association rules, and path-based reasoning, respectively. In addition, rule-based reasoning identified 14 contradictory triples. In the KGC quality evaluation phase, in terms of completeness, KG had higher density and lower sparsity after completion, and there were no contradictory rules within the KG. In terms of accuracy, KG after completion was more consistent with prior knowledge. In terms of usability, the mean reciprocal ranking, mean rank, and hit rate of the first N tail entities predicted by the model (Hits@N) of the TransE, RotatE, DistMult, and ComplEx graph representation models all showed improvement after KGC. Among them, the RotatE model achieved the best representation. The 3-step completion approach can effectively improve the completeness, accuracy, and availability of KGs, and the 3-dimensional evaluation framework can be used for comprehensive KGC evaluation. In the TCM field, the RotatE model performed better at KG representation.

Further evidence for an HPSG-based theory of the semantics of different and the same

Yatabe (2021) presents a theory according to which the meaning of a word like different in a sentence like Anna and Bill like different films contains the meaning of a reciprocal pronoun. Since the postulated reciprocal meaning inside the meaning of a word like different requires the presence of a semantic antecedent, the theory entails that the apparent internal reading of a sentence like John saw and reviewed different films, which does not contain a plural DP that could serve as the semantic antecedent of the postulated reciprocal meaning, must be licensed in a way that is entirely different from the way in which the internal reading of a sentence like Anna and Bill like different films is licensed. In the present paper, I adduce additional pieces of evidence for this theory. In order to enhance the plausibility of the proposed theory, I also show how the collective interpretation of reciprocals and the interaction of reciprocals and cumulative interpretation can be accounted for within the theory.

Triplet Label Based Image Retrieval Using Deep Learning in Large Database

Recent days, Image retrieval has become a tedious process as the image database has grown very larger. The introduction of Machine Learning (ML) and Deep Learning (DL) made this process more comfortable. In these, the pair-wise label similarity is used to find the matching images from the database. But this method lacks of limited propose code and weak execution of misclassified images. In order to get-rid of the above problem, a novel triplet based label that incorporates context-spatial similarity measure is proposed. A Point Attention Based Triplet Network (PABTN) is introduced to study propose code that gives maximum discriminative ability. To improve the performance of ranking, a correlating resolutions for the classification, triplet labels based on findings, a spatial-attention mechanism and Region Of Interest (ROI) and small trial information loss containing a new triplet cross-entropy loss are used. From the experimental results, it is shown that the proposed technique exhibits better results in terms of mean Reciprocal Rank (mRR) and mean Average Precision (mAP) in the CIFAR-10 and NUS-WIPE datasets.

Long Short-Term Memory for Non-Factoid Answer Selection in Indonesian Question Answering System for Health Information

Providing reliable health information to a community can help raise awareness of the dangers of diseases, their causes, methods of prevention, and treatment. Indonesians are facing various health problems partly due to the lack of health information; hence, the community needs media that can effectively provide reliable health information, namely a question answering (QA) system. The frequently asked questions are non-factoid questions. The development of answer selection based on the classical approach requires distinctive engineering features, linguistic tools, or external resources. It can be solved using deep learning approach such as Convolutional Neural Networks (CNN). However, this model cannot capture the sequence of words in both questions and answers. Therefore, this study aims to implement a long short-term memory (LSTM) model to effectively exploit long-range sequential context information for an answer selection task. In addition, this study analyses various hyper-parameters of Word2Vec and LSTM, such as the dimension, context window, dropout, hidden unit, learning rate, and margin; the corresponding values that yield the best mean reciprocal rank (MRR) and mean average precision (MAP) are found to be 300, 15, 0.25, 100, 0.01, and 0.1, respectively. The best model yields MAP and MRR values of 82.05% and 91.58%, respectively. These results experienced an increase in MAP and MRR of 18.68% and 46.11%, respectively, compared to CNN as the baseline model.

E-Learning Course Recommender System Using Collaborative Filtering Models

e-Learning is a sought-after option for learners during pandemic situations. In e-Learning platforms, there are many courses available, and the user needs to select the best option for them. Thus, recommender systems play an important role to provide better automation services to users in making course choices. It makes recommendations for users in selecting the desired option based on their preferences. This system can use machine intelligence (MI)-based techniques to carry out the recommendation mechanism. Based on the preferences and history, this system is able to know what the users like most. In this work, a recommender system is proposed using the collaborative filtering mechanism for e-Learning course recommendation. This work is focused on MI-based models such as K-nearest neighbor (KNN), Singular Value Decomposition (SVD) and neural network–based collaborative filtering (NCF) models. Here, one lakh of Coursera’s course review dataset is taken from Kaggle for analysis. The proposed work can help learners to select the e-Learning courses as per their preferences. This work is implemented using Python language. The performance of these models is evaluated using performance metrics such as hit rate (HR), average reciprocal hit ranking (ARHR) and mean absolute error (MAE). From the results, it is observed that KNN is able to perform better in terms of higher HR and ARHR and lower MAE values as compared to other models.

On the rise: The expansion of Serial Verb Constructions in Tariana

The emergence and the expansion of serial verbs can be affected by language contact. This paper focuses on a case study from Tariana, a highly endangered Arawak language spoken in the multilingual Vaupés River Basin Linguistic Area in Brazilian Amazonia. Tariana has numerous types of asymmetrical and symmetrical serial verbs highly frequent in discourse of all genres. Two kinds of serial verbs are on the rise. A construction involving a prefixed form -siwa with an emphatic, reciprocal, sociative, and reflexive meaning is developing into a serial verb construction. The motivation for this development lies in intensive language contact with the unrelated Tukano, now the major language in use by the extant speakers of Tariana, where reflexive and reciprocal meanings are expressed through serial verbs. The integration of recapitulating verb sequences with the verb -ni ‘do, make’ into the system of serial verbs is indirectly linked to the impact of Tucano where the verb meaning ‘do, make’ is used as a recapitulating device in bridging linkage. The development of recapitulating serial verbs in Tariana can be partly seen as an independent innovation, and as an outcome of language-internal pressure to create further serial verbs, expanding and extending the productive and much-deployed mechanism in the language.

MAKNA RESIPROSITAS TRADISI MBECEK PADA PEREMPUAN PEDESAAN JAWA DALAM ACARA PERNIKAHAN DI BANYUANGI JAWA TIMUR, INDONESIA

The mbecek tradition was born from the womb of solidarity of the people of Kalipait Village as a complete emotional bond. It has been a tradition for generations. The exciting thing about this research is that the mbecek tradition is an ancestral heritage that until now has remained sustainable due to cultural and social factors. As times have changed, the tradition of mbecek used to be based on the principle of helping but now experiencing a shift in business mixed with business is felt to be burdensome. Allows for the occurrence of family economic resistance. This research was conducted to determine the meaning of reciprocity of the mbecek tradition of Javanese rural women. This research uses a qualitative research method with a descriptive approach—data collection through observation, in-depth interviews, and documentation. Data analysis using an interactive model begins by continuously examining all data from primary and secondary sources until the data is saturated. The results showed that mbecek is a vehicle for exchange in the form of money and staples as a form of investment to maintain kinship ties.

O065 The impact of breaking up sitting on vigilant attention during the workday when sleep restricted

Abstract Introduction Sleep restriction is associated with impairments to cognitive performance. A current strategy to improve cognitive performance at work is breaking up prolonged sitting with light-intensity walking. However, it is unknown whether this strategy can counteract the impact of sleep restriction. This study investigated the effect of breaking up sitting on cognitive performance under conditions of sleep restriction. Method 84 healthy adults (age M± SD 23.1±4.5 years; 41 females) participated in a 7-day laboratory study, with 5 simulated workdays (0900-1700). Participants were randomly allocated to a condition: Breaking up sitting and 9-h sleep (Break9; n=20), Breaking up sitting and 5h sleep (Break5; n=20), Sedentary and 9-h sleep (Sit9; n=22), Sedentary and 5-h sleep (Sit5; n=22). Sleep opportunity prior to each workday was 9-h (2200-0700) or 5-h (0200-0700) depending on condition. Every 30-min during the workday participants in the breaking up sitting conditions completed 3-min bouts of light-intensity walking, while participants in the sedentary conditions remained sitting. At 0900, 1100, 1300, 1500, and 1700, participants completed a 10-min Psychomotor Vigilance Task and the Karolinska Sleepiness Scale. Results Mixed model ANOVAs revealed no significant 2-way or 3-way interactions between condition (Break9, Break5, Sit9 and Sit5), time of day, and workday for vigilant attention (reciprocal mean response time and mean lapses) or subjective sleepiness. Discussion Breaking up prolonged sitting with light-intensity walking did improve cognitive performance. Higher doses of physical activity may be needed to counteract the effects of sleep restriction on cognitive performance. Findings highlight the acute cognitive deficits associated with inadequate sleep.

Graph Embedding-Based Domain-Specific Knowledge Graph Expansion Using Research Literature Summary

Knowledge bases built in the knowledge processing field have a problem in that experts have to add rules or update them through modifications. To solve this problem, research has been conducted on knowledge graph expansion methods using deep learning technology, and in recent years, many studies have been conducted on methods of generating knowledge bases by embedding the knowledge graph’s triple information in a continuous vector space. In this paper, using a research literature summary, we propose a domain-specific knowledge graph expansion method based on graph embedding. To this end, we perform pre-processing and process and text summarization with the collected research literature data. Furthermore, we propose a method of generating a knowledge graph by extracting the entity and relation information and a method of expanding the knowledge graph using web data. To this end, we summarize research literature using the Bidirectional Encoder Representations from Transformers for Summarization (BERTSUM) model based on domain-specific research literature data and design a Research-BERT (RE-BERT) model that extracts entities and relation information, which are components of the knowledge graph, from the summarized research literature. Moreover, we proposed a method of expanding related entities based on Google news after extracting related entities through the web for the entities in the generated knowledge graph. In the experiment, we measured the performance of summarizing research literature using the BERTSUM model and the accuracy of the knowledge graph relation extraction model. In the experiment of removing unnecessary sentences from the research literature text and summarizing them in key sentences, the result shows that the BERTSUM Classifier model’s ROUGE-1 precision is 57.86%. The knowledge graph extraction performance was measured using the mean reciprocal rank (MRR), mean rank (MR), and HIT@N rank-based evaluation metric. The knowledge graph extraction method using summarized text showed superior performance in terms of speed and knowledge graph quality.

The National Front and Ultraliberalism (Extract from Pharmacologie du Front National, 2013)

The National Front and Ultraliberalism (Extract from <i>Pharmacologie du Front National</i>, 2013)

Financial customer classification by combined model

Abstract This paper explores the pros and cons of different algorithm models on the same selection problem, and then uses the combined prediction theory to obtain a new combined prediction model to explore its prediction accuracy. The actual problem to be solved is to help financial institutions to scientifically classify customers who choose financial products. We select the bank data set in the UCI database, which is derived from the survey data of a customer conducted by a financial institution in Portugal for a wealth management product. Decision tree C5.0 algorithm, naive Bayes classification algorithm and binary logit model are individually used to carry out a single model of empirical research on financial product customer classification. Through the empirical analysis of the five combination models, it is concluded that in the model that uses the least squares weighting method to determine the weight, the weight appears negative, which does not conform to the actual situation. The model that is based on the least squares weighting method and the model that is based on the simple weighting method are excluded. In contrast, the arithmetic mean weighted model is better than the reciprocal variance weighted model and the reciprocal mean square model. The accuracy reaches 89.91%, which is 0.43% higher than the accuracy of a single model. It can be concluded that the model that is based on the arithmetic average weighting is a better combination forecasting model.

A treatment recommender clinical decision support system for personalized medicine: method development and proof-of-concept for drug resistant tuberculosis

BackgroundPersonalized medicine tailors care based on the patient’s or pathogen’s genotypic and phenotypic characteristics. An automated Clinical Decision Support System (CDSS) could help translate the genotypic and phenotypic characteristics into optimal treatment and thus facilitate implementation of individualized treatment by less experienced physicians.MethodsWe developed a hybrid knowledge- and data-driven treatment recommender CDSS. Stakeholders and experts first define the knowledge base by identifying and quantifying drug and regimen features for the prototype model input. In an iterative manner, feedback from experts is harvested to generate model training datasets, machine learning methods are applied to identify complex relations and patterns in the data, and model performance is assessed by estimating the precision at one, mean reciprocal rank and mean average precision. Once the model performance no longer iteratively increases, a validation dataset is used to assess model overfitting.ResultsWe applied the novel methodology to develop a treatment recommender CDSS for individualized treatment of drug resistant tuberculosis as a proof of concept. Using input from stakeholders and three rounds of expert feedback on a dataset of 355 patients with 129 unique drug resistance profiles, the model had a 95% precision at 1 indicating that the highest ranked treatment regimen was considered appropriate by the experts in 95% of cases. Use of a validation data set however suggested substantial model overfitting, with a reduction in precision at 1 to 78%.ConclusionOur novel and flexible hybrid knowledge- and data-driven treatment recommender CDSS is a first step towards the automation of individualized treatment for personalized medicine. Further research should assess its value in fields other than drug resistant tuberculosis, develop solid statistical approaches to assess model performance, and evaluate their accuracy in real-life clinical settings.

Velocity reciprocity and the relativity principle

Velocity reciprocity means that the velocity of an inertial frame S with respect to another inertial reference frame S′ is the opposite of the velocity of S′ with respect to S. While velocity reciprocity holds true for both the Lorentz and Galilean transformations, it need not be true in general, and it is a widely held misconception that velocity reciprocity is a sole consequence of the relativity of motion principle. The situation is, in fact, much more complicated, and it is the purpose of this paper to call attention to and hopefully help to rectify the matter regarding the misconception. We show that if we ascribe universal and unrestricted validity to the relativity of motion principle, then velocity reciprocity cannot possibly follow only from the relativity principle, and we detail the additional assumptions that must be made in order for velocity reciprocity to hold.

Quantifying test-retest reliability of repeated objective attentional measures in Lewy body dementia

Objective cognitive impairment is a feature of Lewy body dementia (LBD), and computerised attentional tasks are commonly used as outcome measures in interventional trials. However, the reliability of these measures, in the absence of interventions, are unknown. This study examined the reliability of these attentional measures at short-term and longer-term follow-up stages. LBD patients (n = 36) completed computerised attentional tasks [simple and choice reaction time, and digit vigilance (SRT, CRT, DV)] at short-term (Day 0–Day 5) and longer-term (4 and 12 weeks) follow-up. Intra-class correlations (ICCs) were calculated to assess test–retest reliability. At short-term, the reciprocal SRT, CRT and DV mean reaction time to correct answers, the reciprocal DV coefficient of variation, and reciprocal power of attention (PoA) all showed excellent levels of reliability (all ICCs > 0.90). The reciprocal PoA showed the highest level of reliability (ICC = 0.978). At longer-term follow-up, only the reciprocal PoA had excellent levels of reliability (ICC = 0.927). Reciprocal SRT, CRT and DV reaction time to correct answers, and the CRT coefficient of variation values, showed good levels of test–retest reliability (ICCs ≥ 0.85). Contrary to expectations, most attentional measures demonstrated high levels of test–retest reliability at both short-term and longer-term follow-up time points. The reciprocal PoA composite measure demonstrated excellent levels of test–retest reliability, both in the short-term and long-term. This indicates that objective attentional tasks are suitable outcome measures in LBD studies and that the composite PoA measure may offer the highest levels of reliability.

TRANSLATION EQUIVALENCE OF INDONESIAN VERBREDUPLICATION INTO ENGLISH

In terms of morphology, reduplication is called unique because it duplicates the similar words twice or more. In Indonesia, reduplication is often used in daily communication. It can make Indonesian language sound unique. It is possible to say “kecil-kecil” to emphasize “something (plural) in small size”. On the other hand, when Indonesian reduplication is translated into English, it is impossible to find “*small-small” as its translation. This paper aims to investigate about translation equivalence of Indonesian verb reduplication into English. The Natural Semantic Metalanguage approach proposed by Wierzbicka (1996) was used to find translation equivalence of Indonesian verb reduplication meaning and its translation into English. By using descriptive qualitative methods in analyzing data, the finding shows that verb reduplication emphasizes on reciprocal meaning, iterative meaning, and the meaning of doing something for happiness. Translating Indonesian verb reduplication into English means transferring the message or emphasizes meaning rather than preserving their unique form. &#x0D; &#x0D; Keywords: Reduplication, Translation Equivalence, NSM Approach

A major step in developing Mainland China's cross‐border insolvency law

AbstractSection 5 of Mainland China's Enterprise Bankruptcy Law provides that, in the absence of treaties, Chinese courts may recognise foreign insolvency proceedings based on reciprocity. The meaning of reciprocity is very uncertain, and no rules have been laid down on how recognition or assistance may be given to foreign insolvency proceedings. The Mainland has not entered into any treaty or arrangement on the matter with another jurisdiction. On 14 May 2021, the Mainland signed an agreement with Hong Kong, under which the Mainland issued an Opinion on the rules for the recognition of and assistance to Hong Kong insolvency proceedings. This article argues that this is a momentous development for both the Mainland and Hong Kong. It explains how the Opinion adapted provisions and ideas from the UNCITRAL Model Law on Cross‐Border Insolvency with modifications to suit the Mainland's needs and the objectives it wanted to achieve; in particular, the Mainland's concern with mitigating the risks arising from the Opinion's implementation. At the same time, however, cases decided after the coming into force of the arrangement revealed the hurdles posed by the corporate structure that Chinese groups in Hong Kong adopted to the utility of the arrangement, foreshadowing dynamic developments in practice and debate in the doctrinal and theoretical spheres.