Miracle-dz/UniFAR-FTUs huggingface dataset:
Comparative Evaluation of the Effect of Different Post and Core Materials on Stress Distribution in Radicular Dentin by Three-Dimensional Finite Element Analysis

Objectives: The aim of this study was to investigate the stress distribution of different post and core materials in radicular dentin by three-dimensional finite element analysis (3D FEA). Materials and Methods: Twelve 3D models of a maxillary central incisor were simulated in the ANSYS 5.4 software program. The models were divided into three groups; the first group included: 1-Gold post and core and 2-Nickel-chromium (Ni-Cr) post and core restored with metal-ceramic restorations (MCRs). The second group included: 1-Stainless steel post, 2-Titanium post, 3-Carbon fiber post, 4-Glass fiber post, and 5-Quartz fiber post with composite cores and MCRs. The third group included: 1-Zirconia post and core, 2-Zirconia post, 3-Carbon fiber post, 4-Glass fiber post, and 5-Quartz fiber post; the last four models had composite cores restored with all-ceramic restorations (ACRs). Each specimen was subjected to a compressive load at a 45-degree angle relative to its longitudinal axis at a constant intensity of 100 N. The models were analyzed with regard to the stress distribution in dentin. Results: Two stress concentration sites were detected in the models. The first group showed the lowest stress levels in the cervical region, while the stress levels detected in the second group were higher than those in the first group and lower than those found in the third group. Fiber-reinforced posts induced a higher stress concentration between the middle and cervical thirds of the root compared to other posts. Conclusions: According to the results, since cast posts induce lower stresses in dentin, they are recommended for clinical use. Fiber-reinforced posts and ACRs caused the maximum stresses in dentin.
Towards a Smart Educational Environment Framework for mLearning in a Malaysian context

Due to rapid improvements in technology, university academics and students require high-quality learning experience via a smart educational environment. This study was conducted primarily due to the scarcity of local content in mLearning for the local context, particularly with respect to learning of local culture. Therefore, the aim of this study is to understand the impact of the smart educational environment of mLearning in the Malaysian context. This is done via garnering perspectives of students and academics from Malaysian universities. The methods used for this research are focus groups. The data were coded and analysed using thematic analyses. This paper presents the framework of the smart educational environment of mLearning for the Malaysian context. The smart educational mLearning environment entails benefits through student-generated activities and culturally appropriate design, thus indicating the potential for a better design quality of the smart educational mLearning environment for Malaysia.
Uncertainty Matters: Evidence from Close Elections

This paper uses a data-rich environment to produce direct econometric estimates of macroeconomic and financial uncertainty for 11 advanced nations. These indices exhibit significant independent variation from popular proxies. Using this new data we control for both first and second moment financial shocks in identifying the real effects of macro uncertainty shocks. We further separate the identified macro shocks from financial shocks using narrative information, requiring that macro uncertainty rises during close elections. These are events which are likely to lead to macro uncertainty but are disjoint from a weakening in financial conditions. We find that macro uncertainty shocks matter for the vast majority of countries and that the real effects of macro uncertainty shocks are generally larger conditioning on close elections. These results are robust to controlling for credit spreads, financial uncertainty, global uncertainty and a measure of the first moment of the business cycle as proxied by a composite leading indicator.
Neuroevolution of hierarchical reservoir computers

Reservoir Computers such as Echo State Networks (ESN) represent an alternative recurrent neural network model that provides fast training and state-of-the-art performances for supervised learning problems. Classic ESNs suffer from two limitations; hyperparameter selection and learning of multiple temporal and spatial scales. To learn multiple scales, hierarchies are proposed, and to overcome manual tuning, optimisation is used. However, the autonomous design of hierarchies and optimisation of multi-reservoir systems has not yet been demonstrated. In this work, an evolvable architecture is proposed called Reservoir-of-Reservoirs (RoR) where sub-networks of neurons (ESNs) are interconnected to form the reservoir. The design of each sub-network, hyperparameters, global connectivity and its hierarchical structure are evolved using a genetic algorithm (GA) called the Microbial GA. To evaluate the RoR and microbial GA, single networks and other hierarchical ESNs are evolved. The results show the microbial GA can dramatically increase the performance of single networks compared to other optimisation techniques. The evolutionary process also leads to competitive results with RoRs and other hierarchical ESNs, despite having fewer connections than a single network. In the final section, it is revealed that the RoR architecture may learn generalised features other architectures cannot, offering improvements in network generalisation to other tasks.
SpaceNet: A Remote Sensing Dataset and Challenge Series

Foundational mapping remains a challenge in many parts of the world, particularly in dynamic scenarios such as natural disasters when timely updates are critical. Updating maps is currently a highly manual process requiring a large number of human labelers to either create features or rigorously validate automated outputs. We propose that the frequent revisits of earth imaging satellite constellations may accelerate existing efforts to quickly update foundational maps when combined with advanced machine learning techniques. Accordingly, the SpaceNet partners (CosmiQ Works, Radiant Solutions, and NVIDIA), released a large corpus of labeled satellite imagery on Amazon Web Services (AWS) called SpaceNet. The SpaceNet partners also launched a series of public prize competitions to encourage improvement of remote sensing machine learning algorithms. The first two of these competitions focused on automated building footprint extraction, and the most recent challenge focused on road network extraction. In this paper we discuss the SpaceNet imagery, labels, evaluation metrics, prize challenge results to date, and future plans for the SpaceNet challenge series.
Neural Random Projections for Language Modelling

Neural network-based language models deal with data sparsity problems by mapping the large discrete space of words into a smaller continuous space of real-valued vectors. By learning distributed vector representations for words, each training sample informs the neural network model about a combinatorial number of other patterns. In this paper, we exploit the sparsity in natural language even further by encoding each unique input word using a fixed sparse random representation. These sparse codes are then projected onto a smaller embedding space which allows for the encoding of word occurrences from a possibly unknown vocabulary, along with the creation of more compact language models using a reduced number of parameters. We investigate the properties of our encoding mechanism empirically, by evaluating its performance on the widely used Penn Treebank corpus. We show that guaranteeing approximately equidistant (nearly orthogonal) vector representations for unique discrete inputs is enough to provide the neural network model with enough information to learn --and make use-- of distributed representations for these inputs.
The power of thinning in balanced allocation

Balls are sequentially allocated into $n$ bins as follows: for each ball, an independent, uniformly random bin is generated. An overseer may then choose to either allocate the ball to this bin, or else the ball is allocated to a new independent uniformly random bin. The goal of the overseer is to reduce the load of the most heavily loaded bin after $\Theta(n)$ balls have been allocated. We provide an asymptotically optimal strategy yielding a maximum load of $(1+o(1))\sqrt{\frac{8\log n}{\log\log n}}$ balls.
Ballistocardiogram Signal Processing: A Literature Review

Time-domain algorithms are focused on detecting local maxima or local minima using a moving window, and therefore finding the interval between the dominant J-peaks of ballistocardiogram (BCG) signal. However, this approach has many limitations due to the nonlinear and nonstationary behavior of the BCG signal. This is because the BCG signal does not display consistent J-peaks, which can usually be the case for overnight, in-home monitoring, particularly with frail elderly. Additionally, its accuracy will be undoubtedly affected by motion artifacts. Second, frequency-domain algorithms do not provide information about interbeat intervals. Nevertheless, they can provide information about heart rate variability. This is usually done by taking the fast Fourier transform or the inverse Fourier transform of the logarithm of the estimated spectrum, i.e., cepstrum of the signal using a sliding window. Thereafter, the dominant frequency is obtained in a particular frequency range. The limit of these algorithms is that the peak in the spectrum may get wider and multiple peaks may appear, which might cause a problem in measuring the vital signs. At last, the objective of wavelet-domain algorithms is to decompose the signal into different components, hence the component which shows an agreement with the vital signs can be selected i.e., the selected component contains only information about the heart cycles or respiratory cycles, respectively. An empirical mode decomposition is an alternative approach to wavelet decomposition, and it is also a very suitable approach to cope with nonlinear and nonstationary signals such as cardiorespiratory signals. Apart from the above-mentioned algorithms, machine learning approaches have been implemented for measuring heartbeats. However, manual labeling of training data is a restricting property.
Neuromemristive Circuits for Edge Computing: A Review

The volume, veracity, variability, and velocity of data produced from the ever increasing network of sensors connected to Internet pose challenges for power management, scalability, and sustainability of cloud computing infrastructure. Increasing the data processing capability of edge computing devices at lower power requirements can reduce several overheads for cloud computing solutions. This paper provides the review of neuromorphic CMOS-memristive architectures that can be integrated into edge computing devices. We discuss why the neuromorphic architectures are useful for edge devices and show the advantages, drawbacks, and open problems in the field of neuromemristive circuits for edge computing.
New Losses for Generative Adversarial Learning

Generative Adversarial Networks (Goodfellow et al., 2014), a major breakthrough in the field of generative modeling, learn a discriminator to estimate some distance between the target and the candidate distributions. This paper examines mathematical issues regarding the way the gradients for the generative model are computed in this context, and notably how to take into account how the discriminator itself depends on the generator parameters. A unifying methodology is presented to define mathematically sound training objectives for generative models taking this dependency into account in a robust way, covering both GAN, VAE and some GAN variants as particular cases.
Identifying Framing Bias in Online News

It has been observed that different media outlets exert bias in the way they report the news, which seamlessly influences the way that readers’ knowledge is built through filtering what we read. Therefore, understanding bias in news media is fundamental for obtaining a holistic view of a news story. Traditional work has focused on biases in terms of “agenda setting,” where more attention is allocated to stories that fit their biased narrative. The corresponding method is straightforward, since the bias can be detected through counting the occurrences of different stories/themes within the documents. However, these methods are not applicable to biases which are implicit in wording, namely, “framing” bias. According to framing theory, biased communicators will select and emphasize certain facts and interpretations over others when telling their story. By focusing on facts and interpretations that conform to their bias, they can tell the story in a way that suits their narrative. Automatic detection of framing bias is challenging since nuances in the wording can change the interpretation of the story. In this work, we aim to investigate how the subtle pattern hidden in language use of a news agency can be discovered and further leveraged to detect frames. In particular, we aim to identify the type and polarity of frame in a sentence. Extensive experiments are conducted on real-world data from different countries. A case study is further provided to reveal possible applications of the proposed method.
Neuro-guided genetic programming: prioritizing evolutionary search with neural networks

When search operators in genetic programming (GP) insert new instructions into programs, they usually draw them uniformly from the available instruction set. Prefering some instructions to others would require additional domain knowledge, which is typically unavailable. However, it has been recently demonstrated that the likelihoods of instructions' occurrence in a program can be reasonably well estimated from its input-output behavior using a neural network. We exploit this idea to bias the choice of instructions used by search operators in GP. Given a large sample of programs and their input-output behaviors, a neural network is trained to predict the presence of individual instructions. When applied to a new program synthesis task, the network is first queried on the set of examples that define the task, and the obtained probabilities determine the frequencies of using instructions in initialization and mutation operators. This priming leads to significant improvements of the odds of successful synthesis on a range of benchmarks.
Benchmarking Nature-Based Solution and Smart City Assessment Schemes Against the Sustainable Development Goal Indicator Framework

Increasing global urbanization yields substantial potential for enhanced sustainability through careful management of urban development and optimized resource use efficiency. Nature-based solutions (NBS) can provide a means for cities to successfully navigate the water-energy-climate relationship, thus enhancing urban resilience. Implementation of NBS can improve local or regional economic resilience underpinned by the sustainable use of natural resources. The innovative governance, institutional, business, and finance models and frameworks inherent to NBS implementation also provide a wealth of opportunity for social transformation and increased social inclusiveness in cities. The ultimate benefit of NBS implementation in cities is increased livability, which is typically measured as a function of multiple social, economic and environmental variables. Given the range of different interventions classified as NBS and the cross-sectoral character of their co-benefits, different assessment schemes can be used to evaluate NBS performance and impact. Herein, performance and impact indicators within three robust NBS- and Smart City-related assessment schemes – Mapping and Assessment of Ecosystems and their Services (MAES), Knowledge and Learning Mechanism on Biodiversity and Ecosystem Services (EKLIPSE), and Smart City Performance Measurement Framework (CITYkeys) – were critically analyzed with respect to Sustainable Development Goal (SDG) 11, ‘Make cities and human settlements inclusive, safe, resilient and sustainable’. Each selected assessment scheme was benchmarked with respect to the Inter-Agency Expert Group on SDG Indicators’ global indicator framework for the sub-objectives of SDG 11. The alignment between each of the selected NBS assessment schemes and the SDG indicator framework was mapped with particular emphasis on consistency with city-level framework indicators for each SDG 11 sub-objective. The results were illustrated as composite scores describing the alignment of the analyzed NBS and Smart city assessment schemes with the SDG 11 sub-objectives. These results facilitate NBS assessment scheme selection based on alignment between each analyzed assessment scheme and specific SDG 11 sub-objectives. Cities face multiple challenges amidst a complex hierarchy of legislative, regulatory and other stakeholder obligations. The present study showed that strategic selection of an NBS assessment scheme which closely aligns with one or more sub-objectives within SDG 11 can maximize operational efficiency by exploiting synergies between evaluation schemes.
Pregnancy Ketonemia and Development of the Fetal Central Nervous System

Glucose is the major source of energy for the human brain which in turn uses ketone bodies as a supplement for energy deficit in glucose cell deficiency conditions. Pregnancy complicated by gestational diabetes is a condition associated with significantly increased risk of ketonemia development. The data available proves a changing influence of ketones on the central nervous system during fetal life and in adults as well. Ketone bodies freely pass through the placenta. They can affect fetal growth and organ damage development, especially the central nervous system. As agreed in the current recommendation of the diabetes associations, it is not obligatory for the attending doctor to conduct a routine inspection of ketone bodies during diabetes treatment in pregnancy. This article is a literature review of ketones' effect on the central nervous system and an attempt to initiate discussion whether we should consider including ketonemia assessment into the standard care package for pregnant women with diabetes and begin some research on the explanation of its influence on fetal development.
Model Farm Services Center Approach: An Implication to Boost Farmer’s Yield

The instant study was conducted to estimate the Model Farm Services Center’s (MFSC) contribution in yield improvement of major crops/vegetables in district Dera Ismail Khan, Khyber Pakhtunkhwa, Pakistan, during the year 2014-15. Based on Sekaran sampling technique 306 respondents were selected and were personally interviewed through well prepared and pre-tested interview schedule. To find out the actual performance of yield improvement by Model Farm Services Center along with other repressors, step by step analysis was performed. The results of Wilcoxon Sign Rank Test showed significant improvement in yield of sugarcane, wheat, maize, rice and tomato by Model Farm Services Center. Pearson’s correlation coefficient showed highly significant positive relation of sugarcane and wheat yield difference before and after Model Farm Services Center registration with MFSC. Ordinary Least Square Multiple-Regression Model indicates that MFSC registration duration and landholding significantly define the variation in dependent variables at 1% and 5% level of significance respectively for sugarcane yield. Whereas solely Model Farm Services Center registration duration profoundly highly significantly (P ≤ 0.01) explains the variation in dependent variables in wheat yield.
A Ricardian Analysis of the Impact of Temperature and Rainfall Variability on Ag-riculture in Dosso and Maradi Regions of Niger Republic

The impact of climate variability on Maradi and Dosso agriculture was estimated taking into account farmer adaptations. The study used a Ricardian analysis of 200 farms to explore the effects of climate variability on net revenue. It also simulates the impact of different climate scenarios on agriculture incomes. This analysis bespeaks that if temperature increases 1°C annually, the annual crop net revenues for both frameworks will decrease up to 582170.7 FCFA2 for model without adaptation (M1) and up to 1316 FCFA for model with adaptation (M2). An increase of Precipitation of 1 mm/month will increase crop receipts for the frameworks up to 721,917 FCFA for M1 and 1,861,455 FCFA for M2. In order to predict climate change impacts for these regions, the RCP 4.5 and RCP 8.5 of IPCC scenarios were examined. The crop net receipts will fall between 10% and 26% if the scenarios happen. Another finding of this study is that each farmer who is practicing adaptation is able to cover the potential loss from climate variability up to 8.95% and 12.71% per ha respectively in Maradi and in Dosso. The study proposes that these regions should start planning measures for unexpected event of climate conditions. Irrigated systems need to be encouraged in order to minimize the vulnerability of the agricultural sector.
Emoji recommendation in private instant messages

Emojis are some of the most common ways to convey emotions and sentiments in social messaging applications. In order to help the user choose emojis among a vast range of possibilities, we aim at developing an automatic recommendation system based on user message analysis and real emoji usage, which goes beyond the simple dictionnary lookup that is done in the industry (mainly Android and iOS). For this purpose, we present a novel automatic emoji prediction model trained and tested on real data and based on sentiment-related features. Such a model differ from the ones learnt from tweets and can predict emojis with a 84.48% f1-score and a 95.49% high precision, using MultiLabel-RandomForest algorithm on real private instant message corpus. We want to determine the best discriminative features for this task.
Dynamic adaptation of byzantine consensus protocols

The problem of distributed consensus in the presence of Byzantine faults has received particular attention in recent decades. Today a variety of solution to this problem exist, each optimized for particular execution conditions. Given that, in most cases, real systems operate under dynamic conditions, it is important to develop mechanisms that allow the algorithms to be adapted or changed at runtime to optimize the system to the current conditions. The problem of dynamic adaptation of consensus algorithms is not new, but the literature is scarce for the Byzantine case and there is no comprehensive comparison of existing solutions. This work has two complementary objectives. First, it studies how the different dynamic adaptation techniques proposed for the crash failure model can be applied in the presence of Byzantine faults. Second, it presents a comparative study of the performance of these switching algorithms in practice. For that purpose, we have implemented the switching algorithms in a common software framework, based on the open source BFT-SMaRt library. Using this framework we have performed an extensive evaluation that offers useful insights on the practical effects of different mechanisms used to support the run-time switching among Byzantine protocols.