Miracle-dz/UniFAR-FTUs huggingface dataset:
Boosting Search Engines with Interactive Agents

This paper presents first successful steps in designing search agents that learn meta-strategies for iterative query refinement in information-seeking tasks. Our approach uses machine reading to guide the selection of refinement terms from aggregated search results. Agents are then empowered with simple but effective search operators to exert fine-grained and transparent control over queries and search results. We develop a novel way of generating synthetic search sessions, which leverages the power of transformer-based language models through (self-)supervised learning. We also present a reinforcement learning agent with dynamically constrained actions that learns interactive search strategies from scratch. Our search agents obtain retrieval and answer quality performance comparable to recent neural methods, using only a traditional term-based BM25 ranking function and interpretable discrete reranking and filtering actions.
Towards disease-aware image editing of chest X-rays

Disease-aware image editing by means of generative adversarial networks (GANs) constitutes a promising avenue for advancing the use of AI in the healthcare sector. Here, we present a proof of concept of this idea. While GAN-based techniques have been successful in generating and manipulating natural images, their application to the medical domain, however, is still in its infancy. Working with the CheXpert data set, we show that StyleGAN can be trained to generate realistic chest X-rays. Inspired by the Cyclic Reverse Generator (CRG) framework, we train an encoder that allows for faithfully inverting the generator on synthetic X-rays and provides organ-level reconstructions of real ones. Employing a guided manipulation of latent codes, we confer the medical condition of cardiomegaly (increased heart size) onto real X-rays from healthy patients. This work was presented in the Medical Imaging meets Neurips Workshop 2020, which was held as part of the 34th Conference on Neural Information Processing Systems (NeurIPS 2020) in Vancouver, Canada
IS NEUROLINGUISTIC PROGRAMMING A MYTH? A REVIEW OF NLP STUDIES

NLP has not been able to gain the status of a scientific model since its emergence in 1970’s with certain reservations by the experts in the various fields such as educators, linguists, English language teachers, and scientists. Despite the fact that more than fifty organizations are working in the UK solely on NLP, there are only a few conversations and studies available on the subject due to mere supposition about the lack of scientific support for its existence. In NLP history, much work has been ignored due to mere speculation. Also, many researchers did not pay any attention to the theoretical and conceptual underpinnings of the NLP ideas and researches via ignoring various studies in this filed. This article has aimed at the exploration of NLP via various studies in diverse fields dealing with the varied aspects of NLP toolkit to illustrate the mammoth significance it holds. The research circumspectly examines the fundamental role that NLP plays in education, sports, health, and English language teaching practices that is beyond the claims of experts who declare it a pseudoscience.
Speaker-Conditioned Hierarchical Modeling for Automated Speech Scoring

Automatic Speech Scoring (ASS) is the computer-assisted evaluation of a candidate's speaking proficiency in a language. ASS systems face many challenges like open grammar, variable pronunciations, and unstructured or semi-structured content. Recent deep learning approaches have shown some promise in this domain. However, most of these approaches focus on extracting features from single audio, making them suffer from the lack of speaker-specific context required to model such a complex task. We propose a novel deep learning technique for non-native ASS, called speaker-conditioned hierarchical modelling. In our technique, we take advantage of the fact that oral proficiency tests rate multiple responses for a candidate. We extract context vectors from these responses and feed them as additional speaker-specific context to our network to score a particular response. We compare our technique with strong baselines and find that such modelling improves the model's average performance by 6.92% (maximum = 12.86%, minimum = 4.51%). We further show both quantitative and qualitative insights into the importance of this additional context in solving the problem of ASS.
A randomised trial of a web-based physical activity self-management intervention in COPD

Improving exercise capacity is a primary objective in COPD. Declines in exercise capacity result in reduced physical activity and health-related quality of life (HRQoL). Self-management interventions can teach patients skills and behaviours to manage their disease. Technology-mediated interventions have the potential to provide easily accessible support for disease self-management. We evaluated the effectiveness of a web-based self-management intervention, focused on physical activity promotion, on exercise capacity in COPD. This 6-month randomised controlled trial (NCT02099799) enrolled 153 persons with COPD at two US sites (VABoston, n=108; VABirmingham, n=45). Participants were allocated (1:1) to the web-based self-management intervention (physical activity promotion through personalised, progressive step-count goals, feedback, online COPD-related education and social support via an online community) or usual care. The primary outcome was exercise capacity (6-min walk distance (6 MWD)). Secondary outcomes included physical activity (daily steps per day), HRQoL (St. George's Respiratory Questionnaire Total Score), dyspnoea, COPD-related knowledge and social support. Change in step-count goals reflected intervention engagement. Participants' mean age was 69 (sd=7), and mean forced expiratory volume in 1 s % predicted was 61% (sd=21%). Change in 6MWD did not differ between groups. Intervention participants improved their mean daily step counts by 1312 more than those in the usual care group (p<0.001). Groups did not differ on other secondary outcomes. VABirmingham participants were significantly more engaged with the intervention, although site did not modify the effect of the intervention on 6MWD or secondary outcomes. The intervention did not improve exercise capacity but improved physical activity at 6 months. Additional intervention modifications are needed to optimise its COPD self-management capabilities. A web-based self-management intervention improved physical activity but not exercise capacity. There is a need to develop and study accessible self-management interventions for COPD. https://bit.ly/3iT1yvU
Optimization and Sampling Under Continuous Symmetry: Examples and Lie Theory

In the last few years, the notion of symmetry has provided a powerful and essential lens to view several optimization or sampling problems that arise in areas such as theoretical computer science, statistics, machine learning, quantum inference, and privacy. Here, we present two examples of nonconvex problems in optimization and sampling where continuous symmetries play -- implicitly or explicitly -- a key role in the development of efficient algorithms. These examples rely on deep and hidden connections between nonconvex symmetric manifolds and convex polytopes, and are heavily generalizable. To formulate and understand these generalizations, we then present an introduction to Lie theory -- an indispensable mathematical toolkit for capturing and working with continuous symmetries. We first present the basics of Lie groups, Lie algebras, and the adjoint actions associated with them, and we also mention the classification theorem for Lie algebras. Subsequently, we present Kostant's convexity theorem and show how it allows us to reduce linear optimization problems over orbits of Lie groups to linear optimization problems over polytopes. Finally, we present the Harish-Chandra and the Harish-Chandra--Itzykson--Zuber (HCIZ) formulas, which convert partition functions (integrals) over Lie groups into sums over the corresponding (discrete) Weyl groups, enabling efficient sampling algorithms.
EVALUATION OF SEMI-SUPERVISED LEARNING FOR CNN-BASED CHANGE DETECTION

Over the past few years, many research works have utilized Convolutional Neural Networks (CNN) in the development of fully automated change detection pipelines from high resolution satellite imagery. Even though CNN architectures can achieve state-of-theart results in a wide variety of vision tasks, including change detection applications, they require extensive amounts of labelled training examples in order to be able to generalize to new data through supervised learning. In this work we experiment with the implementation of a semi-supervised training approach in an attempt to improve the image semantic segmentation performance of models trained using a small number of labelled image pairs by leveraging information from additional unlabelled image samples. The approach is based on the Mean Teacher method, a semi-supervised approach, successfully applied for image classification and for sematic segmentation of medical images. Mean Teacher uses an exponential moving average of the model weights from previous epochs to check the consistency of the model’s predictions under various perturbations. Our goal is to examine whether its application in a change detection setting can result in analogous performance improvements. The preliminary results of the proposed method appear to be compatible to the results of the traditional fully supervised training. Research is continuing towards fine-tuning of the method and reaching solid conclusions with respect to the potential benefits of the semi-supervised learning approaches in image change detection applications.
Public Acceptance on Hypnotheraphy as a Complementary Alternative Health Service

Despite the pros and cons, the practice of hypnotherapy is growing in Indonesia. How are the hypnotherapy clients aware about hypnotherapy? Is the process of acceptance in line with the pattern of adoption of the innovation? Theoretically, there are five stages of the process of adoption of an innovation: awareness, interest, evaluation, first-trial, and confirmation. This study was conducted in four major cities in Java. A sequential-explanatory mixed method was applied. There were two stages for this method, quantitative survey and then followed by qualitative study. The conclusion suggested that the acceptance of hypnotherapy by clients depended on its effectiveness in helping them to cope with their problems. The finding also corroborated and slightly added to the five-stages theory of the process of innovation adoption. The most effective communication channel in the process of adopting hypnotherapy was face-to-face and interpersonal, with the hypnotherapy clients as diffusion agents. The most important message in the context of developing the hypnotherapist profession was that hypnotherapy is an evidence-based professional service.
COMPARISON OF CRYOSAT-2 AND ICESAT-2 ON WATER LEVEL MONITORING OF NAM CO LAKE

There are more than 1,000 lakes(>1km2) on the Tibetan Plateau and lake level is an important physical feature of lake changes. Lake level change is an important indicator to reflect changes of climate and environment in a certain area. The development of satellite altimetry has provided data support for the monitoring of lake level and effectively compensated for the deficiencies of traditional water level monitoring in alpine regions. In this study, the laser altimeter of ICESat-2 and the radar altimeter of CryoSat-2 are used to provide lake level of the Nam Co lake during the period of 2010-2020. The result showed that the standard deviation (SD) of ICESat2 (0.0895m) was lower than the SD of CryoSat-2 (0.2556m) and the months with higher SD values were mostly during the ice period of Nam Co lake. ICESat-2 had a considerably decreased measurement uncertainty. There are systematic differences in lake levels extracted by different altimetry satellites and the mean bias between ICESat-2 and CryoSat-2 was around 0.45m. After removing interaltimeter biases, the continuous lake levels from 2010 to 2020 were constructed. The inter-annual changes in lake levels were flat or even slightly decreased and the lake level has dropped by about 0.80 m in general. The water level generally reached the highest from September to October of the year in terms of intra-annual changes. Besides, temperature and precipitation changes were closely related to lake level tendency.
A novel solution of an elastic net regularisation for dementia knowledge discovery using deep learning

ABSTRACT Accurate classification of Magnetic Resonance Images (MRI) is essential to accurately predict Mild Cognitive Impairment (MCI) to Alzheimer’s Disease (AD) conversion. Meanwhile, deep learning has been successfully implemented to classify and predict dementia disease. However, the accuracy of MRI image classification is low. This paper aims to increase the accuracy and reduce the processing time of classification through Deep Learning Architecture by using Elastic Net Regularisation in Feature Selection. The proposed system consists of Convolutional Neural Network (CNN) to enhance the accuracy of classification and prediction by using Elastic Net Regularisation. Initially, the MRI images are fed into CNN for features extraction through convolutional layers alternate with pooling layers, and then through a fully connected layer. After that, the features extracted are subjected to Principle Component Analysis (PCA) and Elastic Net Regularisation for feature selection. Finally, the selected features are used as an input to Extreme Machine Learning (EML) for the classification of MRI images. The result shows that the accuracy of the proposed solution is better than the current system. In addition to that, the proposed method has improved the classification accuracy by 5% on average and reduced the processing time by 30 ~ 40 seconds on average. The proposed system is focused on improving the accuracy and processing time of MCI converters/non-converters classification. It consists of features extraction, feature selection, and classification using CNN, FreeSurfer, PCA, Elastic Net, and Extreme Machine Learning. Finally, this study enhances the accuracy and the processing time by using Elastic Net Regularisation, which provides important selected features for classification.
Automated Computer Attacks Detection in University Environment

Since the massive expansion of the Internet into a commercial world, the security of computer systems has become a priority. There are other areas that see an increase in the inclusion of the Internet, like national governments, hospitals, and university systems. All these systems contain highly sensitive information. In an effort to increase the security of internal data, we propose a novel method for the detection of automated computer attacks. This method was tested on a custom dataset prepared from the logs of the university information system at Prague University of Economics and Business. Two datasets were used. The first dataset contained only simple attacks, while the second one comprised the advanced attacks. The compiled and anonymized datasets were uploaded to BigML framework, where K-means, Isolation Forest and Logistic Regression algorithms were used in order to validate the proposed novel method. Our results showed that the proposed method is viable in cases where the attack volume is high and the time spacing between the actions is similar, which was verified on both tested datasets. It reached the detection rate of 93.57% in the case of simple attacks dataset, and 95.37% in the case of advanced attacks dataset. It reached similar detection rates as other algorithms used in the commercial environment. Based on this project, the proposed method can be implemented into the university information system in order to prevent these types of attacks in the future.
Properly learning decision trees in almost polynomial time

We give an $n^{O(\log\log n)}$-time membership query algorithm for properly and agnostically learning decision trees under the uniform distribution over $\{\pm 1\}^{n}$. Even in the realizable setting, the previous fastest runtime was $n^{O(\log n)}$, a consequence of a classic algorithm of Ehrenfeucht and Haussler. Our algorithm shares similarities with practical heuristics for learning decision trees, which we augment with additional ideas to circumvent known lower bounds against these heuristics. To analyze our algorithm, we prove a new structural result for decision trees that strengthens a theorem of O'Donnell, Saks, Schramm, and Servedio. While the OSSS theorem says that every decision tree has an influential variable, we show how every decision tree can be “pruned” so that every variable in the resulting tree is influential.
HUGE INFORMATION INTEGRATION FOR EMERGENCY MANGAMENT IN BIG DATA ENIVROMNENT

: The sound locale of emergency association has been in the purpose of joining of thought for different demands particularly the item constructing. In the data focused and PC driven world, a basic motivation behind PC authorities is to oversee and dismantle Big Data, remove data from heterogeneous sources and store it in joined structure deals with that permit in addition dealing with. In this paper, Big Data examination techniques and instruments that are imperative in all seasons of emergency association are appeared. What's more, a structure arranging arrangement of an essential information association structure will be bankrupt down that contains four stages; information age, information securing, information storing up, and information examination. Purposes of enthusiasm of the utilization of Big Data for emergency association are reviewed. An imaginative perspective of open issues concerning Big Data in emergency association is shown.
Improving Multimodal fusion via Mutual Dependency Maximisation

Multimodal sentiment analysis is a trending area of research, and multimodal fusion is one of its most active topic. Acknowledging humans communicate through a variety of channels (i.e visual, acoustic, linguistic), multimodal systems aim at integrating different unimodal representations into a synthetic one. So far, a consequent effort has been made on developing complex architectures allowing the fusion of these modalities. However, such systems are mainly trained by minimising simple losses such as L_1 or cross-entropy. In this work, we investigate unexplored penalties and propose a set of new objectives that measure the dependency between modalities. We demonstrate that our new penalties lead to a consistent improvement (up to 4.3 on accuracy) across a large variety of state-of-the-art models on two well-known sentiment analysis datasets: CMU-MOSI and CMU-MOSEI. Our method not only achieves a new SOTA on both datasets but also produces representations that are more robust to modality drops. Finally, a by-product of our methods includes a statistical network which can be used to interpret the high dimensional representations learnt by the model.
Mortality risk from acute aortic dissection among hospital admissions during weekends and holiday season

Background Acute aortic dissection is a life-threatening condition associated with high mortality rate. Findings from previous studies addressing the “weekend effect” on the mortality rate from an acute aortic dissection mortality have been inconsistent. Furthermore, the effect of admission for acute aortic dissection during the holiday season has not been previously investigated. Objective Our aim was to evaluate the effect of admission for acute aortic dissection during holiday season or weekends on the risk of mortality. Methods We conducted a retrospective analysis of nationwide cohort data from the Taiwan’s National Health Insurance Research Database. We collected data on all adult patients hospitalized for acute aortic dissection between 2001 and 2017 in Taiwan and classified them into the following three groups based on day of admission: holiday season (at least 4 consecutive days; n = 280), weekend (n = 1 041), and weekday (n = 3 109). The following three outcomes were evaluated: in-hospital mortality, 7-day mortality, and 180-day mortality. Results A multivariable logistic regression was used to adjust for possible cofounders on the measured outcomes. Compared to weekday admissions for acute aortic dissection, weekend admissions resulted in a 29% increase in the risk of in-hospital death (aOR = 1.29; 95% CI, 1.05–1.59; P = 0.0153), with a 25% increase in the 7-day (aOR = 1.25; 95% CI, 1.001–1.563; P = 0.0492) and 20% increase in the 180-day mortality risk (aOR = 1.20; 95% CI, 1.01–1.42; P = 0.0395). Of note, admission over the holiday season did not result in a higher mortality risk than for weekday admissions; this finding, however, might reflect insufficient statistical power on subgroup analysis. Conclusion Patients admitted for acute aortic dissection during the weekends are at higher risk of mortality compared to those admitted on weekdays. Our finding likely reflects inadequate staffing and team experience of weekend staff and can guide healthcare policy makers to improve patient outcomes.
Coexisting conventional and inverse mechanocaloric effects in ferroelectrics

The mechanocaloric effect is the temperature change of a material upon application or removal of an external stress. Beyond its fundamental interest, this caloric response represents a promising and ecofriendly alternative to current cooling technologies. To obtain large mechanocaloric effects, we need materials whose elastic properties (e.g., strain, elastic compliance) are strongly temperature dependent. This is the case of ferroelectric perovskite oxides, where the development of the spontaneous electric polarization is accompanied by significant strains and lattice softening. Thus, in this work we study the mechanocaloric properties of model ferroelectric PbTiO3, by means of predictive atomistic (“second-principles”) simulations and a perturbative formalism here introduced. Our calculations reveal relatively large effects (up to −4 K for relatively small applied compressions of −0.1 GPa) and several striking features. In particular, we find that the mechanocaloric response is highly anisotropic in the ferroelectric phase, as it can be either conventional (temperature increases upon compression) or inverse (temperature decreases) depending on the direction of the applied stress. We discuss and explain these surprising results, which compare well with existing experimental information. Our analysis suggests that the coexistence of conventional and inverse mechanocaloric responses is probably common among ferroelectrics and materials displaying a negative thermal expansion.
RISK OF RETURN CHARACTERISTICS OF ISLAMIC BANK FINANCING PORTFOLIO IN INDONESIA

Correspondence: Name: Sulistya Rusgianto Email: sulistya@feb.unair. ac.id ABSTRACT The study aims to investigate the risk of return characteristics of Islamic bank financing portfolios. A quantitative approach of value at risk is used to estimate return volatility of Islamic commercial banks and Islamic business units in Indonesia for the period of May 2014 to June 2020. The finding suggests proportion of equity-based financing tends to increase in contrast to decreasing of debt and lease-based financing. While rate of return for all financing types tends to decline, the return of equity-based financing experiences higher volatility than other two financing types. The main finding regarding the decrease in value at risk indicates Islamic banks able to reduce risk of financing portfolio. The finding implies Islamic banks should continue to improve the best-practice of risk management based on their past experience to anticipate future risk. As for the regulators, the finding may be used to develop early warning system to anticipate a systemic failure in Islamic banking industry. Further research is recommended to explore impact of market behavior on the risk of each type of financing.