Miracle-dz/UniFAR-FTUs huggingface dataset:
Efficient Weight factorization for Multilingual Speech Recognition

End-to-end multilingual speech recognition involves using a single model training on a compositional speech corpus including many languages, resulting in a single neural network to handle transcribing different languages. Due to the fact that each language in the training data has different characteristics, the shared network may struggle to optimize for all various languages simultaneously. In this paper we propose a novel multilingual architecture that targets the core operation in neural networks: linear transformation functions. The key idea of the method is to assign fast weight matrices for each language by decomposing each weight matrix into a shared component and a language dependent component. The latter is then factorized into vectors using rank-1 assumptions to reduce the number of parameters per language. This efficient factorization scheme is proved to be effective in two multilingual settings with $7$ and $27$ languages, reducing the word error rates by $26\%$ and $27\%$ rel. for two popular architectures LSTM and Transformer, respectively.
Learning Models for Suicide Prediction from Social Media Posts

We propose a deep learning architecture and test three other machine learning models to automatically detect individuals that will attempt suicide within (1) 30 days and (2) six months, using their social media post data provided in the CL-Psych-Challenge. Additionally, we create and extract three sets of handcrafted features for suicide detection based on the three-stage theory of suicide and prior work on emotions and the use of pronouns among persons exhibiting suicidal ideations. Extensive experimentations show that some of the traditional machine learning methods outperform the baseline with an F1 score of 0.741 and F2 score of 0.833 on subtask 1 (prediction of a suicide attempt 30 days prior). However, the proposed deep learning method outperforms the baseline with F1 score of 0.737 and F2 score of 0.843 on subtask2 (prediction of suicide 6 months prior).
Diagnostic analysis of US fashion brand’s Bangladeshi outsourcing

ABSTRACT The purpose of this paper is to examine Bangladesh’s performance as an outsourcing destination from the perspectives of buyers and suppliers involved in a representative US apparel company which has outsourced from Bangladesh. This study employed a sequential mixed method research design which consisted of quantitative evaluation and qualitative interviews. The sample was comprised of three buyers and three suppliers in the Phillip’s Van-Heusen (PVH). From the results of quantitative evaluation, it was found that Bangladesh has a greater advantage in the cost and quality clusters than Vietnam. The interviews revealed that the US buyers have some concerns in geographic location, trade restrictions, country’s political situation, warranty policies, and currency exchange situations. To strengthen Bangladeshi capability, Bangladeshi government’s investment to improve facilities, nomination of supplier’s backward linkage, improvement of manufacturing capacity, running improvement programs, negotiation to reduce tariff, and building mutual trust between buyers and suppliers were suggested. These diagnosis results provide the US buyers and the Bangladeshi suppliers with not only valuable information on the current status of Bangladesh’s performance but guidance to successful strategies for improvement of outsourcing performance.
Evaluation of Carrageenan, Xanthan Gum and Depolymerized Chitosan Based Coatings for Pineapple Lily Plant Production

Some natural polysaccharides and their derivatives are used in horticulture to stimulate plant growth. This study investigated the effects of coating bulbs with carrageenan-depolymerized chitosan (C-DCh) or xanthan-depolymerized chitosan (X-DCh) on growth, flowering, and bulb yield as well as physiological and biochemical attributes of pineapple lily (Eucomis autumnalis). The results showed that treatment with C-DCh or X-DCh significantly increased all growth parameters, bulb yield, greenness index, stomatal conductance, total N, total K, and total sugar content of bulbs and accelerated anthesis as compared with untreated bulbs. The positive impact of coatings on plant growth and physiological attributes depended on the type of biopolymer complexes. The X-DCh treatment exhibited the greatest plant height, fresh weight, daughter bulb number, greenness index, stomatal conductance, total N, K, and sugar content. However, this treatment induced a significant decrease in L-ascorbic acid, total polyphenol content and antioxidant activity. Overall, the results of this study indicated high suitability of C-DCh and X-DCh as bulb coatings for pineapple lily plant production.
A DLT-based Smart Contract Architecture for Atomic and Scalable Trading

Distributed Ledger Technology (DLT) has an enormous potential but also downsides. One downside of many DLT systems, such as blockchain, is their limited transaction throughput that hinders their adoption in many use cases (e.g., real-time payments). State channels have emerged as a potential solution to enhance throughput by allowing transactions to process off-chain. While current proposals can increase scalability, they require high collateral and lack support for dynamic systems that require asynchronous state transitions. Additionally, the latency of channel initialisations can cause issues especially if fast interactions are required. In this paper, we propose an atomic, scalable and privacy-preserving protocol that enables secure and dynamic updates. We develop a smart contract-based Credit-Note System (CNS) that allows participants to lock funds before a state channel initialisation, which enhances flexibility and efficiency. We formalise our model using the Universal Composability (UC) framework and demonstrate that it achieves the stated design goals of privacy, scalability, and atomicity. Moreover, we implement a dispute process in the state channel to counter availability attacks. Finally, we analyse the protocol in the context of an asynchronous smart grid-based marketplace.
An Intelligent Passive Food Intake Assessment System with Egocentric Cameras

Malnutrition is a major public health concern in low-and-middle-income countries (LMICs). Understanding food and nutrient intake across communities, households and individuals is critical to the development of health policies and interventions. To ease the procedure in conducting large-scale dietary assessments, we propose to implement an intelligent passive food intake assessment system via egocentric cameras particular for households in Ghana and Uganda. Algorithms are first designed to remove redundant images for minimising the storage memory. At run time, deep learning-based semantic segmentation is applied to recognise multi-food types and newly-designed handcrafted features are extracted for further consumed food weight monitoring. Comprehensive experiments are conducted to validate our methods on an in-the-wild dataset captured under the settings which simulate the unique LMIC conditions with participants of Ghanaian and Kenyan origin eating common Ghanaian/Kenyan dishes. To demonstrate the efficacy, experienced dietitians are involved in this research to perform the visual portion size estimation, and their predictions are compared to our proposed method. The promising results have shown that our method is able to reliably monitor food intake and give feedback on users' eating behaviour which provides guidance for dietitians in regular dietary assessment.
Quantify the Non-Markovian Process with Intervening Projections in a Superconducting Processor

A Markov assumption considers a physical system memoryless to simplify its dynamics. Whereas memory effect or the non-Markovian phenomenon is more general in nature. In the quantum regime, it is challenging to define or quantify the non-Markovianity because the measurement of a quantum system often interferes with it. We simulate the open quantum dynamics in a superconducting processor, then characterize and quantify the non-Markovian process. With the complete set of intervening projections and the final measurement of the qubit, a restricted process tensor can be determined to account for the qubit-environment interaction. We apply the process tensor to predict the quantum state with memory effect, yielding an average fidelity of $99.86\%\pm 1.1\unicode{x2030}$. We further derive the Choi state of the rest process conditioned on history operations and quantify the non-Markovianity with a clear operational interpretation.
PRESTIGIOUS PERCEPTION OF POTATO FARMING: AN OVERVIEW OF THE ECONOMY, SOCIO-CULTURE, AND ITS EXISTENCE

Potato farming has been developed by the Tengger Tribe in Ngadisari Village, Sukapura District, Probolinggo Regency. These tribal people cultivate various agricultural commodities, but they feel prestige when farming potatoes. This prestigious perception underlines the research that aims: 1. To determine the existence of the prestigious perception, 2. To analyze economically whether potato farming supports the prestigious perception that it can provide higher profits than other plants cultivated on the same land, and 3. Are there socio-cultural reasons for the prestigious perception of potato farming. This study used quantitative methods with 30 respondents selected by purposive sampling. The description method used to know the existence and identify social-cultural factors of the prestigious perception. To prove that economically, analyzed by comparing the potato’s farming income with non-potato on the same land. The result showed: 1) 77 % of respondents still feel prestige if they cultivate potatoes, 2) economically, potato farming had a higher income than non-potato farming, and 3) socio-cultural factors that support Tengger Tribe feel prestige: a. passed down from generation to generation, b. farming complexity challenges, and c. the needs and equality of female workers.
Hone as You Read: A Practical Type of Interactive Summarization

We present HARE, a new task where reader feedback is used to optimize document summaries for personal interest during the normal flow of reading. This task is related to interactive summarization, where personalized summaries are produced following a long feedback stage where users may read the same sentences many times. However, this process severely interrupts the flow of reading, making it impractical for leisurely reading. We propose to gather minimally-invasive feedback during the reading process to adapt to user interests and augment the document in real-time. Building off of recent advances in unsupervised summarization evaluation, we propose a suitable metric for this task and use it to evaluate a variety of approaches. Our approaches range from simple heuristics to preference-learning and their analysis provides insight into this important task. Human evaluation additionally supports the practicality of HARE. The code to reproduce this work is available at https://github.com/tannerbohn/HoneAsYouRead.
Protection of Coenzyme Q-10 Against Contrast-induced Acute Kidney Injury in Diabetic Rats

Background: Diabetes Mellitus (DM) is a important risk factor for Contrast-induced acute kidney injury (CI-AKI). DM and CI-AKI share oxidative damage and inflammation mechanisms that induction of protective and cellular adaptation enzymes as coenzyme Q-10 (COQ-10). The aim of this study was to investigate the therapeutic potential of COQ-10 in renal function, renal hemodynamics, oxidative profile and renal histology in diabetic rats submitted to the CI-AKI model. Methods: Wistar rats, male, randomized into four groups: Citrate- control animals, received citrate buffer (streptozotocin vehicle, 0.4 ml); DM- animals that received streptozotocin (60 mg/kg); DM+IC: DM animals, treated with iodinated contrast (IC, 6 ml/kg); DM+IC+COQ-10: DM animals treated with COQ-10 (10 mg/kg) and that received with IC (6 ml/kg). The protocols lasted 4 weeks. Were evaluated the renal function by inulin clearance and serum creatinine, renal hemodynamics by renal blood flow (RBF) and renal vascular resistance (RVR), markers of oxidative stress such as urinary peroxides and nitrate, lipid peroxidation, thiols in renal tissue and renal histological analysis.Results: DM animals showed reduced renal function which was reflected with an increase of serum creatinine and significant reduced of inulin clearance, as well as a reduction on RBF, increased RVR and redox imbalance with a higher urinary peroxides, nitrate lipid peroxidation levels and depletion of thiols in renal tissue. IC treatment exacerbated theses changes in DM + IC. COQ-10 administration ameliorates renal function, prevented hemodynamic changes, neutralize oxidative damage and progression of the histologic damage in the DM+IC+COQ-10 group. Conclusion: This study is the first that demonstrated a renoprotection of COQ-10 in experimental model of risk factor of DM for CI-AKI. COQ-10 presented an antioxidant effect on the CI-AKI in diabetic rats, by improving function and renal hemodynamics, preserving morphology and reducing oxidative stress.
Cole-Impedance Model Representations of Right-Side Segmental Arm, Leg, and Full-Body Bioimpedances of Healthy Adults: Comparison of Fractional-Order

The passive electrical properties of a biological tissue, referred to as the tissue bioimpedance, are related to the underlying tissue physiology. These measurements are often well-represented by a fractional-order equivalent circuit model, referred to as the Cole-impedance model. Objective: Identify if there are differences in the fractional-order (α) of the Cole-impedance parameters that represent the segmental right-body, right-arm, and right-leg of adult participants. Hypothesis: Cole-impedance model parameters often associated with tissue geometry and fluid (R∞, R1, C) will be different between body segments, but parameters often associated with tissue type (α) will not show any statistical differences. Approach: A secondary analysis was applied to a dataset collected for an agreement study between bioimpedance spectroscopy devices and dual-energy X-ray absoptiometry, identifying the Cole-model parameters of the right-side body segments of N=174 participants using a particle swarm optimization approach. Statistical testing was applied to the different groups of Cole-model parameters to evaluate group differences and correlations of parameters with tissue features. Results: All Cole-impedance model parameters showed statistically significant differences between body segments. Significance: The physiological or geometric features of biological tissues that are linked with the fractional-order (α) of data represented by the Cole-impedance model requires further study to elucidate.
fAshIon after fashion: A Report of AI in Fashion

In this independent report fAshIon after fashion, we examine the development of fAshIon (artificial intelligence (AI) in fashion) and explore its potentiality to become a major disruptor of the fashion industry in the near future. To do this, we investigate AI technologies used in the fashion industry through several lenses. We summarise fAshIon studies conducted over the past decade and categorise them into seven groups: Overview, Evaluation, Basic Tech, Selling, Styling, Design, and Buying. The datasets mentioned in fAshIon research have been consolidated on one GitHub page for ease of use. We analyse the authors' backgrounds and the geographic regions treated in these studies to determine the landscape of fAshIon research. The results of our analysis are presented with an aim to provide researchers with a holistic view of research in fAshIon. As part of our primary research, we also review a wide range of cases of applied fAshIon in the fashion industry and analyse their impact on the industry, markets and individuals. We also identify the challenges presented by fAshIon and suggest that these may form the basis for future research. We finally exhibit that many potential opportunities exist for the use of AI in fashion which can transform the fashion industry embedded with AI technologies and boost profits.
Artificial Intelligence-Based Sensors for Next Generation IoT Applications: A Review

Sensors play a vital role in our daily lives and are an essential component for Internet of Things (IoT) based systems as they enable the IoT to collect data to take smart and intelligent decisions. Recent advances in IoT systems, applications, and technologies, including industrial Cyber-Physical Systems (CPSs), are being supported by a wide range of different types of sensors based on artificial intelligence (AI). These smart AI-based sensors are typically characterized by onboard intelligence and have the ability to communicate collaboratively or through the Internet. To achieve the high level of automation required in today’s smart IoT applications, sensors incorporated into nodes must be efficient, intelligent, context-aware, reliable, accurate, and connected. Such sensors must also be robust, safety- and privacy-aware for users interacting with them. Sensors leveraging advanced AI technologies, new capabilities have recently emerged which have the potential to detect, identify, and avoid performance degradation and discover new patterns. Along with knowledge from complex sensor datasets, they can promote product innovation, improve operation level, and open up novel business models. We review sensors, smart data processing, communication protocol, and artificial intelligence which will enable the deployment of AI-based sensors for next-generation IoT applications.
Disembedded or Deeply Embedded? A Multi-Level Network Analysis of Online Labour Platforms

This article extends the economic-sociological concept of embeddedness to encompass not only social networks of, for example, friendship or kinship ties, but also economic networks of ownership and control relationships. Applying these ideas to the case of digital platform labour pinpoints two possible scenarios. When platforms take the role of market intermediaries, economic ties are thin and workers are left to their own devices, in a form of ‘disembeddedness’. When platforms partake in intricate inter-firm outsourcing structures, economic ties envelop workers in a ‘deep embeddedness’ which involves both stronger constraints and higher rewards. With this added dimension, the notion of embeddedness becomes a compelling tool to describe the social structures that frame economic action, including the power imbalances that characterize digital labour in the global economy.
Distance Learning as a Resilience Strategy during Covid-19: An Analysis of the Italian Context

The purpose of this paper is to analyze the strategic model of distance learning adopted by Italian higher education, showing how the health emergency due to Covid-19 has transformed it from an “optional” for traditional universities to the only means to ensure public health protection and continuity in education programs. Comparing two situations (before and during the pandemic), the aim is to identify best practices that, even after the end of the emergency, can be adopted by Italian higher education institutions to boost their digital supply and compete in an international context. After a general context analysis, aimed to underline benefits and risks connected to the development of distance learning, the case of the Italian higher education system has been analyzed. Data were collected through a documentary analysis, looking at what Italian higher education institutions disclosed through their official websites and documents: every form of communication about digital strategy was taken into account. Then, they were analyzed qualitatively, in order to individuate which platforms have been combined to ensure quality in education provided. Research findings demonstrate the resilience of the Italian higher education, able to react and to re-organize itself in only one week: the results of the pandemic may be a stronger university, able to combine quality in education with the potential of technological devices and to compete at the international level. Distance learning represents a complex field, still characterized by separated understandings and in a context where limited attention has been dedicated to its development for what concerns the Italian context, the choice to examine it represents the originality of this paper.
Evaluation of Temporomandibular Disorders Among Dental Students of Saudi Arabia Using Diagnostic Criteria/temporomandibular Disorders (DC/TMD) and Its Association With Biographic, Academic, and Psychosocial Parameters – A Cross-sectional Study

Background: Temporomandibular disorders (TMD) are a board category of conditions arising from the various components of the temporomandibular joint (TMJ) complex. Bio-psychosocial model is the most accepted theory describing the etiopathogenesis of TMD. Dental students are vulnerable to psychological disorders including anxiety, depression and stress. Hence, the aim of the current study was to evaluate the TMD among dental students of various academic levels and explore the association of TMD with biographic, academic, and psychosocial parameters. Methods: A total of 246 students of a dental school in Saudi Arabia were chosen for the study. After getting consent, all students were examined as per the diagnostic criteria/Temporomandibular disorders (DC/TMD) including components from axis-I and axis-II. Results: The overall cross-sectional prevalence of TMD was found to be 36.99%. Pain arising from the jaw, temple, and peri-auricular area was the most commonly reported symptom and elicited sign during examination. Among the pain-related TMD, myalgia was the commonest diagnosed condition, whereas disc displacement with reduction was found prevalent in intra-articular disorder category. Female (OR=1.94; P=0.004), married (OR=1.74; P=0.04), and students in clinical levels (OR=1.65; P=0.03) were shown to have significantly higher risk to develop TMD. Among the psychosocial parameters, parafunctional habits (OR=2.10; P<0.001) and anxiety (OR=1.55; P=0.04) are shown to increase risk of developing TMD. Students with any TMD reported to have significantly higher pain intensity (OR=1.68; P=0.01) and jaw functional limitations (OR=1.45; P=0.008). Conclusion: Dental students especially in the clinical levels were shown to poses higher risk of developing TMD, hence strategies such as academic counselling and objective evaluation via rubrics should be planned to modify the administration of the curriculum, training methods and evaluation process.
Magnetization loss of no-insulation coil for an electrodynamic suspension system

A high-temperature superconducting no-insulation (NI) coil, with its self-protection property, high engineering current density and unique demagnetization property, becomes a potential candidate for an electrodynamic suspension (EDS) system. Compared with the applications in high field magnets, the NI coil used in the EDS system is considered as working in a dynamic state, the magnetization loss generated in the NI coil is essential for the design of a cryogenic system. This paper presents the study on AC magnetization loss of NI coils by both numerical and experimental methods. Firstly, a 3D finite element numerical model representing the full geometry of the NI coil is built to analyse the effect of field frequency, field magnitude, as well as the radial characteristic resistance. Then, systematic discussions are conducted to figure out the working mechanism of NI coils. Finally, a calibration-free method testing platform is installed to validate the numerical model, and a modified model is proposed to represent the non-uniform radial characteristic resistance caused by stress distribution. The conclusions of this paper will be used in the future optimization of NI coils and the cryogenic design of the EDS system.