Miracle-dz/UniFAR-FTUs huggingface dataset:
Identifying the determinants of individual scientific performance: A perspective focused on AMO theory

Purpose: The aim of this study is to empirically analyse how motivation and the opportunity to investigate enhance the direct relation between the researcher’s human capital and individual scientific performance.Design/methodology/approach: Following recent investigations of strategic human capital and the abilities-motivation-opportunity (AMO) theory, we propose a double quantitative-qualitative methodology to identify the determinants of individual scientific performance.Findings: Applying regression analysis to a sample of 471 Spanish academic researchers, we confirm the moderating role of a researcher’s motivation and opportunities.Originality/value: Drawing on the empirical evidence obtained, this work discusses the relevant determinants of scientific productivity, providing practical recommendations for research management and policy making.
A Review on Chemometrics in Pharmaceutical Analysis

Chemometrics is a science which involves the application of statistical and mathematical methods to the analytical data for the collection and extraction of information which is useful for analyzing the results. Chemometrics is an essential tool for multivariate data collection and analysis protocols, calibration, process modelling, pattern recognition and classification, signal correction and compression, and statistical process control. It involves various chemometric techniques like bilinear and multivariate models by using spectroscopic, chromatographic and electrochemical methods which helps in the identification and quantitative analysis of active substances in complex mixtures. This review describes various chemometric methods which is mainly applicable for interpretation of UV/Visible spectra, and also the data obtained by other instrumental methods including near infrared (NIR), attenuated total reflectance Fourier transform infrared (ATR-FTIR), terahertz pulse spectroscopy, fluorescence spectroscopy, electroanalysis, highperformance liquid chromatography (HPLC), and flow-injection analysis for the analysis of drugs in pharmaceutical preparations.
Effect of Oral Iron Therapy in Moderately Affected Anemianic Pregnant Women

In India, Iron deficiency anemia is one of the major causes of maternal deaths, over the past years, various oral and intra muscular & intravenous preparations of iron have been used for correction of iron muscular are associated with significant side effects; Intramuscular (Iron dextran) was used as an alternative to oral iron therapy for those who were not compliant to oral therapy. Iron dextran has a lot of side effects such as fever, arthralgia, even anaphylactic reactions extending to pulmonary edema and even death. Further it is not possible to achieve the target rise in Hemoglobin level in a limited time period, when the patient is approaching term. Whereas Intravenous (Iron sucrose complex) is a relatively new drug which is a BOON to medical therapy and is the BEST OPTION of iron therapy when used as an alternative to oral therapy as it restores iron stores more promptly and is able to raise the hemoglobin to satisfactory level .
THE USAGE OF BALANCED SCORECARD MEASURES, BUSINESS STRATEGY AND FIRM PERFORMANCE

Sole reliance on financial or accounting based performance measures is inadequate in the new manufacturing environment. Increase awareness of the importance of nonfinancial performance measures in providing long-term value creation and long-term strategic focus as well as their effects on firm performance has led to several innovations in the area of performance measurement system. One of the widely known innovations in this area is called balanced scorecard (BSC) which has been originated by Kaplan and Norton in 1992. The BSC supplements the traditional financial measures with non-financial measures focused on at least three other perspectives – customers, internal business processes, and learning and growth. The main objective of this study is, therefore, to investigate empirically the extent of usage of performance measures, conceptualized as the BSC measures, within an organizational context. Also, the objective of this study is, in part, to determine whether factors such as perceived environmental uncertainty and firm size can act as antecedent variables to the BSC measures and business strategy. Specifically, this study investigated the relationship between business strategy (using Miles and Snow strategic types) and the BSC measures usage and their main and alignment effects on firm performance. The sample was obtained from 120 Malaysian manufacturing firms operating in West Malaysia. The results revealed that the usage of financial measures is still high and ranked first among the four perspectives of the BSC measures. However, the usage of non-financial measures is gaining momentum. Also, there was indication that firm size can be antecedent variable to the BSC measures and business strategy. The results of the selection approach to fit provide enough evidence for the proposition that
INDICATORS OF SCIENTIFIC PUBLICATIONS ON ENTRENCHMENT: ANALYSIS OF ARTICLES FROM THE SPELL1

The aim of this paper is to carry out a bibliometric analysis of the literature on organizational entrenchment in the SPELL database between 2011 and 2020. Our methodology is descriptive, quantitative-qualitative, and the data are analyzed using bibliometrics. 32 articles were identified, submitted to selection and analysis criteria. The research identified a variation on the professional profile of its participants, featuring civil servants with a higher percentage. Methodological characteristics that stand out include the empirical nature, the quantitative approach, the use of questionnaires, multivariate data analysis, and studies in an organization. These results contribute to expand knowledge by allowing a broader view and analysis of the literature by indicating the main authors, journals, participating workers, and methodological aspects used.
"Knights": First Place Submission for VIPriors21 Action Recognition Challenge at ICCV 2021

This technical report presents our approach"Knights"to solve the action recognition task on a small subset of Kinetics-400 i.e. Kinetics400ViPriors without using any extra-data. Our approach has 3 main components: state-of-the-art Temporal Contrastive self-supervised pretraining, video transformer models, and optical flow modality. Along with the use of standard test-time augmentation, our proposed solution achieves 73% on Kinetics400ViPriors test set, which is the best among all of the other entries Visual Inductive Priors for Data-Efficient Computer Vision's Action Recognition Challenge, ICCV 2021.
Linear Maximum Rank Distance Codes of Exceptional Type

Scattered polynomials of a given index over finite fields are intriguing rare objects with many connections within mathematics. Of particular interest are the exceptional ones, as defined in 2018 by the first author and Zhou, for which partial classification results are known. In this paper we propose a unified algebraic description of $\mathbb {F}_{q^{n}}$ -linear maximum rank distance codes, introducing the notion of exceptional linear maximum rank distance codes of a given index. Such a connection naturally extends the notion of exceptionality for a scattered polynomial in the rank metric framework and provides a generalization of Moore sets in the monomial MRD context. We move towards the classification of exceptional linear MRD codes, by showing that the ones of index zero are generalized Gabidulin codes and proving that in the positive index case the code contains an exceptional scattered polynomial of the same index.
Tracing Origins: Coref-aware Machine Reading Comprehension

Machine reading comprehension is a heavily-studied research and test ﬁeld for evaluating new pre-trained models and ﬁne-tuning strategies, and recent studies have enriched the pre-trained models with syntactic, semantic and other linguistic information to improve the performance of the model. In this paper, we imitated the human’s reading process in connecting the anaphoric expressions and explicitly leverage the coreference information to enhance the word embeddings from the pre-trained model, in order to highlight the coreference mentions that must be iden-tiﬁed for coreference-intensive question answering in QUOREF, a relatively new dataset that is speciﬁcally designed to evaluate the coreference-related performance of a model. We used an additional BERT layer to focus on the coreference mentions, and a Relational Graph Convolutional Network to model the coreference relations. We demonstrated that the explicit incorporation of the coreference information in ﬁne-tuning stage performed better than the incorporation of the coreference information in training a pre-trained language models.
Towards a Broad-Persistent Advising Approach for Deep Interactive Reinforcement Learning in Robotic Environments

Deep Reinforcement Learning (DeepRL) methods have been widely used in robotics to learn about the environment and acquire behaviours autonomously. Deep Interactive Reinforcement 2 Learning (DeepIRL) includes interactive feedback from an external trainer or expert giving advice to help learners choose actions to speed up the learning process. However, current research has been limited to interactions that offer actionable advice to only the current state of the agent. Additionally, the information is discarded by the agent after a single use, which causes a duplicate process at the same state for a revisit. In this paper, we present Broad-Persistent Advising (BPA), an approach that retains and reuses the processed information. It not only helps trainers give more general advice relevant to similar states instead of only the current state, but also allows the agent to speed up the learning process. We tested the proposed approach in two continuous robotic scenarios, namely a cart pole balancing task and a simulated robot navigation task. The results demonstrated that the agent’s learning speed increased, as evidenced by the rising reward points of up to 37%, while maintaining the number of interactions required for the trainer, in comparison to the DeepIRL approach.
Wasserstein Unsupervised Reinforcement Learning

Unsupervised reinforcement learning aims to train agents to learn a handful of policies or skills in environments without external reward. These pre-trained policies can accelerate learning when endowed with external reward, and can also be used as primitive options in hierarchical reinforcement learning. Conventional approaches of unsupervised skill discovery feed a latent variable to the agent and shed its empowerment on agent’s behavior by mutual information (MI) maximization. However, the policies learned by MI-based methods cannot sufficiently explore the state space, despite they can be successfully identified from each other. Therefore we propose a new framework Wasserstein unsupervised reinforcement learning (WURL) where we directly maximize the distance of state distributions induced by different policies. Additionally, we overcome difficulties in simultaneously training N(N>2) policies, and amortizing the overall reward to each step. Experiments show policies learned by our approach outperform MI-based methods on the metric of Wasserstein distance while keeping high discriminability. Furthermore, the agents trained by WURL can sufficiently explore the state space in mazes and MuJoCo tasks and the pre-trained policies can be applied to downstream tasks by hierarchical learning.
Span Detection for Vietnamese Aspect-Based Sentiment Analysis

Aspect-based sentiment analysis plays an essential role in natural language processing and artificial intelligence. Recently, researchers only focused on aspect detection and sentiment classification but ignoring the sub-task of detecting user opinion span, which has enormous potential in practical applications. In this paper, we present a new Vietnamese dataset (UIT-ViSD4SA) consisting of 35,396 human-annotated spans on 11,122 feedback comments for evaluating the span detection in aspect-based sentiment analysis. Besides, we also propose a novel system using Bidirectional Long Short-Term Memory (BiLSTM) with a Conditional Random Field (CRF) layer (BiLSTM-CRF) for the span detection task in Vietnamese aspect-based sentiment analysis. The best result is a 62.76% F1 score (macro) for span detection using BiLSTM-CRF with embedding fusion of syllable embedding, character embedding, and contextual embedding from XLM-RoBERTa. In future work, span detection will be extended in many NLP tasks such as constructive detection, emotion recognition, complaint analysis, and opinion mining. Our dataset is freely available at https://github.com/kimkim00/UIT-ViSD4SA for research purposes.
Crisis Domain Adaptation Using Sequence-to-sequence Transformers

User-generated content (UGC) on social media can act as a key source of information for emergency responders in crisis situations. However, due to the volume concerned, computational techniques are needed to effectively filter and prioritise this content as it arises during emerging events. In the literature, these techniques are trained using annotated content from previous crises. In this paper, we investigate how this prior knowledge can be best leveraged for new crises by examining the extent to which crisis events of a similar type are more suitable for adaptation to new events (cross-domain adaptation). Given the recent successes of transformers in various language processing tasks, we propose CAST: an approach for Crisis domain Adaptation leveraging Sequence-to-sequence Transformers. We evaluate CAST using two major crisis-related message classification datasets. Our experiments show that our CAST-based best run without using any target data achieves the state of the art performance in both in-domain and cross-domain contexts. Moreover, CAST is particularly effective in one-to-one cross-domain adaptation when trained with a larger language model. In many-to-one adaptation where multiple crises are jointly used as the source domain, CAST further improves its performance. In addition, we find that more similar events are more likely to bring better adaptation performance whereas fine-tuning using dissimilar events does not help for adaptation. To aid reproducibility, we open source our code to the community1.
Multi-modal Aggregation Network for Fast MR Imaging

—Magnetic resonance (MR) imaging is a commonly used scanning technique for disease detection, diagnosis and treatment monitoring. Although it is able to produce detailed images of organs and tissues with better contrast, it suffers from a long acquisition time, which makes the image quality vulnerable to say motion artifacts. Recently, many approaches have been developed to reconstruct full-sampled images from partially observed measurements in order to accelerate MR imaging. However, most of these efforts focus on reconstruction over a single modality or simple fusion of multiple modalities, neglecting the discovery of correlation knowledge at different feature level. In this work, we propose a novel Multi-modal Aggregation Network, named MANet, which is capable of discovering complementary representations from a fully sampled auxiliary modality, with which to hierarchically guide the reconstruction of a given target modality. In our MANet, the representations from the fully sampled auxiliary and undersampled target modalities are learned independently through a speciﬁc network. Then, a guided attention module is introduced in each convolutional stage to selectively aggregate multi-modal features for better reconstruction, yielding comprehensive, multi-scale, multi-modal feature fusion. Moreover, our MANet follows a hybrid domain learning framework, which allows it to simultaneously recover the frequency signal in the k -space domain as well as restore the image details from the image domain. Extensive experiments demonstrate the superiority of the proposed approach over state-of-the-art MR image reconstruction methods.
Scribosermo: Fast Speech-to-Text models for German and other Languages

Recent Speech-to-Text models often require a large amount of hardware resources and are mostly trained in English. This paper presents Speech-to-Text models for German, as well as for Spanish and French with special features: (a) They are small and run in real-time on microcontrollers like a RaspberryPi. (b) Using a pretrained English model, they can be trained on consumer-grade hardware with a relatively small dataset. (c) The models are competitive with other solutions and outperform them in German. In this respect, the models combine advantages of other approaches, which only include a subset of the presented features. Furthermore, the paper provides a new library for handling datasets, which is focused on easy extension with additional datasets and shows an optimized way for transfer-learning new languages using a pretrained model from another language with a similar alphabet.
Direct simultaneous speech to speech translation

We present a direct simultaneous speech-to-speech translation (Simul-S2ST) model, Furthermore, the generation of translation is independent from intermediate text representations. Our approach leverages recent progress on direct speech-to-speech translation with discrete units, in which a sequence of discrete representations, instead of continuous spectrogram features, learned in an unsupervised manner, are predicted from the model and passed directly to a vocoder for speech synthesis on-the-fly. We also introduce the variational monotonic multihead attention (V-MMA), to handle the challenge of inefficient policy learning in speech simultaneous translation. The simultaneous policy then operates on source speech features and target discrete units. We carry out empirical studies to compare cascaded and direct approach on the Fisher Spanish-English and MuST-C English-Spanish datasets. Direct simultaneous model is shown to outperform the cascaded model by achieving a better tradeoff between translation quality and latency.
Assessing Risks and Modeling Threats in the Internet of Things

Threat modeling and risk assessments are common ways to identify, estimate, and prioritize risk to national, organizational, and individual operations and assets. Several threat modeling and risk assessment approaches have been proposed prior to the advent of the Internet of Things (IoT) that focus on threats and risks in information technology (IT). Due to shortcomings in these approaches and the fact that there are significant differences between the IoT and IT, we synthesize and adapt these approaches to provide a threat modeling framework that focuses on threats and risks in the IoT. In doing so, we develop an IoT attack taxonomy that describes the adversarial assets, adversarial actions, exploitable vulnerabilities, and compromised properties that are components of any IoT attack. We use this IoT attack taxonomy as the foundation for designing a joint risk assessment and maturity assessment framework that is implemented as an interactive online tool. The assessment framework this tool encodes provides organizations with specific recommendations about where resources should be devoted to mitigate risk. The usefulness of this IoT framework is highlighted by case study implementations in the context of multiple industrial manufacturing companies, and the interactive implementation of this framework is available at http://iotrisk.andrew. cmu.edu.
A Survey on State-of-the-art Techniques for Knowledge Graphs Construction and Challenges ahead

Global datasphere is increasing fast, and it is expected to reach 175 Zettabytes by 20251. However, most of the content is unstructured and is not understandable by machines. Structuring this data into a knowledge graph enables intelligent applications such as deep question answering, recommendation systems, semantic search, etc. The knowledge graph is an emerging technology that allows logical reasoning and uncovers new insights using content and context. Thereby, it provides necessary syntax and reasoning semantics that enable machines to solve complex healthcare, security, financial institutions, economics, and business problems. As an outcome, enterprises are putting their effort into constructing and maintaining knowledge graphs to support various downstream applications. Manual approaches are too expensive. Automated schemes can reduce the cost of building knowledge graphs up to 15–250 times. This paper critiques state-of-the-art automated techniques to produce knowledge graphs of near-human quality autonomously. Additionally, it highlights different research issues that need to be addressed to deliver high-quality knowledge graphs.
Relation Preserving Triplet Mining for Stabilizing the Triplet Loss in Vehicle Re-identification

Object appearances often change dramatically with pose variations. This creates a challenge for embedding schemes that seek to map instances with the same object ID to locations that are as close as possible. This issue becomes signiﬁcantly heightened in complex computer vision tasks such as re-identiﬁcation(re-id). In this paper, we suggest these dramatic appearance changes are indications that an object ID is composed of multiple natural groups and it is counter-productive to forcefully map instances from different groups to a common location. This leads us to introduce Relation Preserving Triplet Mining (RPTM), a feature matching guided triplet mining scheme, that ensures triplets will respect the natural sub-groupings within an object ID. We use this triplet mining mechanism to establish a pose-aware, well-conditioned triplet cost function. This allows a single network to be trained with ﬁxed parameters across three challenging benchmarks, while still providing state-of-the-art re-identiﬁcation results.
Application of Sequence Embedding in Protein Sequence-Based Predictions

In sequence-based predictions, conventionally an input sequence is represented by a multiple sequence alignment (MSA) or a representation derived from MSA, such as a position-specific scoring matrix. Recently, inspired by the development in natural language processing, several applications of sequence embedding have been observed. Here, we review different approaches of protein sequence embeddings and their applications including protein contact prediction, secondary structure, prediction, and function prediction.