Miracle-dz/UniFAR-FTUs huggingface dataset:
Towards a Component Reference Implementations Frame for Achieving Multi-disciplinary Coherent Conceptual and Chorological Contextualisation in Prehistory and Prehistoric Archaeology

— This paper presents contributions towards a new component reference implementations frame, which can be used with conceptual knowledge models. The extended research allows a general chorological and coherent conceptual knowledge contextualisation across disciplines. The frame will enable to coherently address multi-disciplinary knowledge by employing conceptual knowledge frameworks and component reference implementations. The presented achievements and scenarios concentrate on new information science approaches for multi-disciplinary contexts in prehistory and archaeology, targeting the use of inherent aspects and creation of new insight, strategies, and perspectives. The new advanced approach can be applied to any type of knowledge, e.g., factual, conceptual, procedural, metacognitive, and structural. The integration of chorological knowledge includes integration of spatial and geospatial knowledge and features. Goal of this research is a new component reference implementations frame for multi-disciplinary coherent conceptual and chorological contextualisation. For the case scenarios, we are focussing on coherent knowledge in contexts with prehistory and archaeology disciplines, natural sciences, and advanced geoscientiﬁc information systems. The solution also integrates knowledge from satellite data and soil diversity and respective properties. The knowledge approach allows a multi-directional utilisation of coherent conceptual knowledge. Future research concentrates on further continued development of components, analysis, and application in multi-disciplinary scenarios of prehistory and archaeology.
Study on uniaxial impact compression characteristics of basalt fiber cement soil

: In order to study the dynamic mechanical properties of cement soil, uniaxial impact compres- sion tests with diﬀerent strain rates of cement soil with no ﬁber and with 0.2% basalt ﬁber were carried out by using a 50 mm steel split Hopkinson pressure bar device. The test results show that the impact compressive strength, dynamic increase factor and peak strain increase with the increase of strain rate under the same basalt ﬁber content, showing obvious strain rate eﬀect. The dynamic stress-strain curve of basalt ﬁber cement soil underwent elastic deformation stage, plastic deformation stage and failure stage. With the increase of strain rate, the degree of fracture of cement soil samples gradually increases, which shows that the number of fragments increases, the size decreases and tends to be uniform. After adding basalt ﬁber in cement soil, the crack can be delayed, the degree of fracture is smaller than that without ﬁber and the plasticity of the samples is enhanced. It shows that basalt ﬁber can improve the impact compressive strength of cement soil.
Exploring Data Augmentation for Classification of Climate Change Denial: Preliminary Study

In order to address the growing need of monitoring climate-change denial narratives in online sources, NLP-based methods have the potential to automate this process. Here, we report on preliminary experiments of exploiting Data Augmentation techniques for improving climate change denial classification. We focus on a selection of both known techniques, and augmentation transformations not reported elsewhere that replace certain type of named entities with high probability of preserving labels. We also introduce a new benchmark dataset consisting of text snippets extracted from online news labeled with fine-grained climate change denial types.
Extracting Impact Model Narratives from Social Services' Text

Named entity recognition (NER) is an important task in narration extraction. Narration, as a system of stories, provides insights into how events and characters in the stories develop over time. This paper proposes an architecture for NER on a corpus about social purpose organizations. This is the first NER task specifically targeted at social service entities. We show how this approach can be used for the sequencing of services and impacted clients with information extracted from unstructured text. The methodology outlines steps for extracting ontological representation of entities such as needs and satisfiers and generating hypotheses to answer queries about impact models defined by social purpose organizations. We evaluate the model on a corpus of social service descriptions with empirically calculated score.
The Impact of Supply Chain Integration on Corporate Social Responsibility

Purpose : Corporate Social Responsibility is among the most desired features of global operation in supply chains. Despite there is a societal urge for socially responsible supply chain, yet the supply chain players (manufactures) may only be motivated being socially responsible given it warrants them improved reputation, performance, profitability. Thus, the purpose of this study is to investigate the impact of Supply Chain Integration (suppliers, internal processes, customers) on CSR at the Pharmaceutical Manufacturing Industry. Design/Methodology/Approach : To actualize this study the data collected from managers who are working at Jordanian Pharmaceutical Manufacturing Companies by questionnaire. After confirming the normality, validity and reliability of the tool, descriptive analysis carried out, and correlation between variables checked. Finally, the impact tested by multiple regressions. Findings : The results show that there is a significant and positive impact of Supply Chain Integration on Corporate Social Responsibility of Jordanian Pharmaceutical Manufacturing Industry, where the internal integration dimension has the highest impact, contributing 55.6%. Next, customers' integration has the highest impact. On the other hand, the supplier’s integration dimension has no significant impact. Practical and Managerial Implications : Implementing the Supply Chain Integration in Pharmaceutical Industry is mandatory not option. Therefore, including Supply Chain within vision, mission and strategies will direct plans and daily activities towards CSR. Social Implications : This study recommends companies to consider Corporate Social Responsibility with their Supply Chain Integration, starting from selecting the suppliers, internal processes and selling to customers. Limitations/Recommendations : The current study conducted on Jordanian Pharmaceuticals Manufacturing Companies. Therefore, it recommends the future researches to collect more data over a longer time to check the current model validity and measuring instrument. It also recommends carrying out similar studies on other industries in Jordan and same industry outside Jordan to test its results generalizability. Originality/Value : This study may be considered as one of few studies that tackle the issue of Supply Chain Integration, and investigates its impact on Corporate Social Responsibility of Jordanian Pharmaceutical Manufacturing Industry.
Transformation of Public Services through Digital Services in the Covid-19 Era: Efforts Towards Good Governance in Indonesia

As a result of the Covid-19 pandemic, public services have become more adaptive and interactive, where adequate digital technology is considered to support the improvement of public services during the pandemic. However, a significant obstacle is that there are still people who are not technologically literate and the ineffectiveness of digital services in various local government agencies. This study aims to explain how public services can be transformed through digital services towards good governance in Indonesia. Then, this research method uses a qualitative research approach where data sources are taken from various online media and research journals related to changes in public services through digital services and analyzed using the NVivo12 Plus application. This study found that in an effort towards good governance in Indonesia, the transformation of public services through digital services has been carried out by the Provincial Governments of West Java, Central Java, and DKI Jakarta. In an aspect of transparency, each government agency has carried out transparent procedures in digital services to the public. Then, in the aspect of accountability, each stakeholder in each agency has a good orientation in digital services and has various obstacles that need to be fixed to make it more optimal. Furthermore, effectiveness and efficiency, each government agency has won awards for public service innovations such as the Jogo Tonggo application in Central Java Province, the JAKI application in DKI Jakarta Province, and the PIKOBAR application in West Java. Therefore, public services through digital services will help the government towards good governance in Indonesia.
Food Waste Reduction through Sales Forecasting using Temporal Fusion Transformers A deep machine learning predicting grocery

Food waste contributes signiﬁcantly to the world’s greenhouse gas emissions, and reducing it is an important part of tackling climate change. Sales forecasting is one way in which waste can be reduced, allowing grocery stores to place more accurate orders and to avoid surpluses. We implement a Temporal Fusion Transformer (TFT) for this purpose, a recent deep learning architecture based on the increasingly popular Transformer. The TFT can learn an entity embedding for each food product, but only supports a ﬁxed number of entities. We extend the TFT to be able to learn embeddings for a variable number of products by processing the name of a product using subword segmentation. This enables it to be used in applications where the number of entities frequently changes, such as when new products are added. Results show that the TFT can perform better than some well-known baseline models, and that its performance can be increased further by using our extension to let it process the name of a product. The embeddings learned for each product are shown to be meaningful, as products belonging to the same category receive similar embeddings even when the model is given no information about product category. We also show that the forecasts are interpretable. A strong weekly pattern is learned by the model and is reﬂected by the model’s attention weights. The model is also capable of learning to attend to similar promotion periods in the past when predicting sales during a new promotion, and learns to ignore these days during the absence of promotions.
Verification Witnesses

Over the last years, witness-based validation of verification results has become an established practice in software verification: An independent validator re-establishes verification results of a software verifier using verification witnesses, which are stored in a standardized exchange format. In addition to validation, such exchangable information about proofs and alarms found by a verifier can be shared across verification tools, and users can apply independent third-party tools to visualize and explore witnesses to help them comprehend the causes of bugs or the reasons why a given program is correct. To achieve the goal of making verification results more accessible to engineers, it is necessary to consider witnesses as first-class exchangeable objects, stored independently from the source code and checked independently from the verifier that produced them, respecting the important principle of separation of concerns. We present the conceptual principles of verification witnesses, give a description of how to use them, provide a technical specification of the exchange format for witnesses, and perform an extensive experimental study on the application of witness-based result validation, using the validators CPAchecker, UAutomizer, CPA-witness2test, and FShell-witness2test.
Autologous Cellular Therapy

search for placebo arms of injectate OA treatment. The review included 34 studies entered into three subgroups of studies: Group I included WOMAC and VAS score outcomes (n=29), Group 2 included studies that measured outcomes using other than WOMAC or VAS scores (n=5) and Group 3 included randomized trials using 1-3 injections of saline as a placebo arm (n=18). All AMSCI cohorts showed improvement at mean 15.3 months post-treatment. Mean WOMAC and VAS scores improved at 6-months and at final follow-up (p < 0.0001 for all). Scores > 2 years were also significant (WOMAC p = 0.001/VAS p = 0.004). Results greatly exceeded the minimal clinically important difference (MCID) at each time point. AMSCI improvement also substantially exceeded previously published 6-month placebo-treatment improvement. No dose-response relationship was seen. AMSCI cohorts showed continuing improvement ≥ 6 months, and continued upward at one year. Placebo scores were already trending downward by 6 months. The authors concluded that AMSCI is a consistently significantly effective treatment for osteoarthritis and it should no longer be stated that data is insufficient to establish AMSCI efficacy for OA. According to the authors, given its excellent safety profile, AMSCI should be widely used for the treatment of osteoarthritis. The limitations of this review are the limited number of cohorts available for analysis. The heterogeneity of the studies also limited the ability to compare treatment types
The Increase in Circulating Levels of Pro-Inflammatory Chemokines, Cytokines, and Complement C5 in Canines with Impaired Kidney Function

Chronic low-grade inflammation is a key contributor to the progression of kidney disease. The release of cytokines and other pro-inflammatory proteins may further contribute to detrimental kidney health by increasing interstitial edema and renal fibrosis. The aim of the present study was to investigate the inflammatory markers in canines who developed renal disease naturally and were diagnosed with renal disease either during life or following necropsy, as assessed by a veterinarian. RNA was isolated from canine blood obtained at necropsy and stored as bioarchived samples from ten canines with renal disease (9.6–14.7 yr) and ten controls (10.1–14.8 yr). At the time of death, the mean blood creatinine concentration and BUN were elevated in dogs with renal disease compared to control (both p < 0.01). Samples were assessed for changes in gene expression using the Canine cytokine RT2 Profiler PCR Array for inflammation. There was a significant increase in C-C Motif Chemokine Ligand 16 (CCL16), C-X-C Motif Chemokine Ligand 5 (CXCL5), Interleukin 16 (IL-16), and Complement Component 5 (C5) (all p < 0.05 vs. con). In addition, there was also a statistically non-significant increase in 49 genes and a down-regulation in 35 genes from a panel of total 84 genes. Pro-inflammatory genes including CCL16, CXCL5, IL-16, and C5 can all contribute to renal inflammation and fibrosis through different signaling pathways and may lead to a progressive impairment of kidney function. Blockade of their activation may be important in ameliorating the initiation and/or the progression of renal disease.
BabelDr: a web platform for rapid construction of phrasebook-style medical speech translation applications

BabelDr (http://babeldr.unige.ch/) is a joint project of Geneva’s Faculty of Translation and Interpretation (FTI) and University Hospitals (HUG), that has been active since July 2015. The goal is to develop methods that allow rapid prototyping of medium-vocabulary web-enabled medical speech translators, with particular emphasis on languages spoken by victims of the current European refugee crisis. A demonstrator system freely available on the project site translates spoken French medical examination questions into four languages.
Title: Stimulus-induced narrowband gamma oscillations are test-retest reliable 1 in healthy elderly in human EEG 2

26 Visual stimulus-induced narrowband gamma oscillations in (EEG) 27 recordings have been recently shown to be compromised in subjects with Mild Cognitive 28 Impairment or Alzheimer’s Disease (AD), suggesting that gamma could be an inexpensive 29 and easily accessible biomarker for early diagnosis of AD. However, to use gamma as a 30 biomarker, its characteristics should remain consistent across multiple recordings, even when 31 separated over long intervals. Previous magnetoencephalography studies in young subjects 32 have reported that gamma power remains consistent over recordings separated by a few 33 weeks to months. Here, we assessed the consistency of slow (20-35 Hz) and fast gamma (36- 34 66 Hz) oscillations induced by static full-field gratings in male (N=20) and female (N=20) 35 elderly subjects (>49 years) in EEG recordings separated by more than a year, and tested the 36 consistency in the magnitude of gamma power, its temporal evolution and spectral profile. 37 Gamma oscillations had distinct spectral and temporal characteristics across subjects, which 38 remained consistent across recordings (average intraclass correlation, ICC of ~0.7). Alpha 39 oscillations (8-12 Hz) and steady-state-visually-evoked-potentials (SSVEPs) were also found 40 to be reliable. We further tested how EEG features can be used to identify two recordings as 41 belonging to the same versus different subjects and found high classifier performance (area 42 under ROC curve of ~0.89), with the temporal evolution of slow gamma and spectral profile 43 emerging as the most informative features. These results suggest that EEG gamma 44 oscillations are reliable across recordings and can be used as a clinical biomarker as well as a 45 potential tool for subject identification.
University of Birmingham Decision-making experiences of health professionals in withdrawing treatment for children and young people

Objective: To explore factors that influence professionals in deciding whether to withdraw treatment from a child and how decision making is managed amongst professionals as an individual and as a team. Study Design: Semi-structured interviews were conducted with a purposive sample of health professionals working at a UK Children's Hospital, with children with life-limiting illnesses whose treatment has been withdrawn. Data were transcribed verba-tim, anonymized and analysed using a thematic framework method. Results: A total of 15 participants were interviewed. Five interrelated themes with associated subthemes were generated to help understand the experiences of health professionals in decision making on withdrawing a child's treatment: (1) understanding the child's best interests, (2) multidisciplinary approach, (3) external factors, (4) psychological well-being and (5) recommendations to support shared decision making. Conclusion: A shared decision-making approach should be adopted to support professionals, children and their families to make decisions collectively.
Constructing a “Cloud of Logistics Support” to Realize “Knowledge Centered”Logistics Support

With the improvement of informatization and technicalization, the support for ordnance equipment becomes harder. The essential cause reason is lack of effective way to get and transmit support knowledge. Based on analyzing the challenge of technicalization for ordnance equipment, a new support mode, Cloud Support, is proposed, which centers on support knowledge. In addition, the connotation, framework and basic run mode of Cloud Support are also introduced.
Measuring Public Perceptions of Transportation Services in Indonesia’s City through Social Media

This paper examines public perceptions of transportation services in Indonesia’s big city through social media such as news channels, social networking sites, Facebook, and Twitter. The objectives are twofold: first, extract the pros and cons of public perceptions through mainly social media; second, such perception can be useful inputs for the government to provide a better service for society. This qualitative research applies five SERVQUAL indicators to analyze transportation services with two opposing perceptions: positive and negative. The five indicators are tangibles, reliability, responsiveness, assurance, and empathy. The finding demonstrates that most people deliver positive perceptions rather than negative complaints. The wordclouds analysis supports this thesis. The majority of public comments always mention these words: friendly, easy, safety, cheap, complete, good, rapid, and clean. Nonetheless, some protests still exist because the words “jam, accident, not maintained well, and lack of punctuality,” are discovered. In addition to positive inputs, negative inputs can be utilized by the government to improve service quality.
Automated surface quality inspection with ARGOS: a case study

The commercial availability of automated inspection systems for optical surfaces specified according to ISO 10110-7 promises unsupervised and automated quality control with reproducible results. In this study, the classification results of the ARGOS inspection system are compared to the decisions by well-trained inspectors based on manual-visual inspection. Both are found to agree in 93.6% of the studied cases. Exemplary cases with differing results are studied, and shown to be partly caused by shortcomings of the ISO 10110-7 standard, which was written for the industry standard manual-visual inspection. Applying it to high resolution images of the whole surface of objective machine vision systems brings with it a few challenges which are discussed.
Robots and Unions: The Moderating Effect of Organised Labour on Technological Unemployment

Any expressed in are those of the author(s) and not those of IZA. Research in this series may include views on policy, but IZA takes no institutional policy positions. The IZA research network is committed to the IZA Guiding Principles of Research Integrity. evidence-based policy on issues. IZA of aims to provide answers to the global labor market challenges of our time. Our key objective is to build bridges between academic research, policymakers and society. ABSTRACT We analyse the moderating effect of trade unions on industrial employment and unemployment in countries facing exposure to industrial robots. Applying random effects within-between regression to a pseudo-panel of observations from 28 advanced democracies over 1998-2019, we find that stronger trade unions in a country are associated with a greater decline in the industry sector employment of young and low-educated workers. We also show that the unemployment rates for low-educated workers remain constant in strongly unionised countries with increasing exposure to robots, whereas in weakly unionised countries, low-educated unemployment declines with robot exposure but from a higher starting point. Our results point to unions exacerbating the insider-outsider effects of technological change within the industrial sector, which however is not fully passed on to unemployment.
Multi-object Detection for Autonomous Motion Planning based on Convolutional Neural Networks

We present a multi-object detection approach based on deep learning for motion planning. The multi-object detector is based on efﬁcient convolutional neural network, and the used approach outperforms state of the art methods in the detection of objects leading to efﬁcient autonomous motion planning. The multi-detector model is more efﬁcient the reason that it: a) detects multiple objects given one input image. b) uses one neural network that makes it computationally cheaper, and faster in test time. c) makes a robot able to perform a process of goal motions given only one detection input carrying multiple objects.