Miracle-dz/UniFAR-FTUs huggingface dataset:
Math Word Problem Solving with Explicit Numerical Values

In recent years, math word problem solving has received considerable attention and achieved promising results, but previous methods rarely take numerical values into consideration. Most methods treat the numerical values in the problems as number symbols, and ignore the prominent role of the numerical values in solving the problem. In this paper, we propose a novel approach called NumS2T, which enhances math word problem solving performance by explicitly incorporating numerical values into a sequence-to-tree network. In addition, a numerical properties prediction mechanism is used to capture the category and comparison information of numerals and measure their importance in global expressions. Experimental results on the Math23K and APE datasets demonstrate that our model achieves better performance than existing state-of-the-art models.
ASCERTAINING OF TRUSTWORTHINESS IN THE NEWS ARTICLES ON SOCIAL MEDIA WITH DEEP NEURAL NETWORK ARCHITECTURES

Nowadays most people use the internet to access news because it is quick and inexpensive, but that results in very fast spread of fake news. Fake news is sometimes published with a hidden motive for benefiting financially, politically, etc., with a catchy headline much of the time attracting users, or it can even be unintentional. Yet it does affect people too much. Detection of fake news has become a difficult topic nowadays. We are focussing on automatically detecting fake content in online news. In this study, we considered the dataset for the identification of false news and it is available for public use, which offers links to source documents for each event. The accuracies are all small in all preceding works. In this study, Recurrent Neural Networks' various model, such as Hopfield Networks, Gated recurrent units, Recursive Neural Networks, are used to be more effective in predicting false news. For these models a comparative study is performed. Results obtained indicate that the issue of fake news identification can be effectively solved with deep learning methods.
Large-Scale Road Network Congestion Pattern Analysis and Prediction Using Deep Convolutional Autoencoder

The transportation system, especially the road network, is the backbone of any modern economy. However, with rapid urbanization, the congestion level has surged drastically, causing a direct effect on the quality of urban life, the environment, and the economy. In this paper, we propose (i) an inexpensive and efficient Traffic Congestion Pattern Analysis algorithm based on Image Processing, which identifies the group of roads in a network that suffers from reoccurring congestion; (ii) deep neural network architecture, formed from Convolutional Autoencoder, which learns both spatial and temporal relationships from the sequence of image data to predict the city-wide grid congestion index. Our experiment shows that both algorithms are efficient because the pattern analysis is based on the basic operations of arithmetic, whereas the prediction algorithm outperforms two other deep neural networks (Convolutional Recurrent Autoencoder and ConvLSTM) in terms of large-scale traffic network prediction performance. A case study was conducted on the dataset from Seoul city.
BOOST: Transport-Layer Multi-Connectivity Solution for Multi-Wan Routers

In this paper, we discuss the challenges faced by MPTCP when used to aggregate multiple WAN/Internet connections in Multi-WAN Routers (MWR). We observe that the two architectural variants, proxying and tunneling, used to deploy MPTCP in MWR suffer from key performance problems. First, the proxy variant creates one MPTCP connection for each TCP connection, which results in a large number of parallel uncoordinated MPTCP connections, which we explain leads to underutilization of the available capacity, suboptimal multipath scheduling, and increased loss rate. Second, the tunnel variant, which relies on encapsulating TCP over MPTCP, stacks two reliability layers and leads to a large number of spurious retransmissions, an issue known as TCP meltdown. Instead, we propose a new multi-path solution more suited to MWR, called BOOST. This solution eliminates the problems with both the proxy and tunnel approaches by multiplexing TCP connections over a single persistent multi-path connection. BOOST also takes a novel approach to multi-path scheduling that combines multipath load balancing and scheduling. In particular, short flows are transmitted across a single link to avoid HoL blocking while longer flows are opportunistically transmitted across multiple paths, utilizing left-over capacity. Evaluations show that BOOST provides better throughput, lower losses, and retransmissions while requiring less memory compared to both MPTCP variants
Analytical Hierarchy Process (Ahp) In Manufacturing And Non-Manufacturing Industries: A Systematic Literature Review

Analytical Hierarchy Process (AHP) is commonly used by many decision-makers in many industry to solve particular problems. However, the effective implementation of AHP for decision making in industries still becomes a question. This research aims at developing a systematic literature review using the real cases implementing AHP, both in manufacturing and non-manufacturing industries, from 2000 to 2020. To obtain research results, we select seventy-eight cases and utilize AHP combined with other methods. The results of this study show that twenty-two scientific works of literature combine AHP with other methods. GIS method and Expert choice software are the most frequently implemented methods combined with AHP. The finding of this study is expected to be a reference and recommendation for researchers or practitioners in using AHP in various industries.
A COMPARISON OF ML APPROACHES ON SENTIMENT ANALYSIS, BASED ON ONTOLOGIES, SARCASM AND SUBJECTIVITY DETECTIONS IN THE CASE STUDY OF US ELECTIONS.

Nowadays, Twitter has become one of the most excellent tools that give people the power to express their emotions. And also, to interact with other ordinary or political people. According to The Verge, as known as the American technology news website, more than 166 million users are using Twitter every day; this thing made Twitter one of the largest news sources and one of the places where most of the politicians publish their opinions or their thoughts. Sentiment analysis or opinion mining is a method that is used to understand the user's behavior based on their feelings in a given text, which can help to get a global idea of the expected outcomes of USA elections. Our research is based on extracting the data and analyzing tweets' sentiment to predict the USA elections results. As we know, most Americans use Twitter to interact with each other to explain their opinions and thoughts about the subjects related to their country. Also, the hashtag system on Twitter makes it easy to help people to interact and go viral. We also included other variables to make a significant comparison of our results, such as detecting sarcasm and subjectivity in a tweet. Also, we used two machine learning approaches: First known as Long Short-Term Memory (LSTM). The second is Bidirectional Encoder Representations from Transformers (BERT). In this work, we used more than 500,000 tweets to get a significant result. Moreover, our developed Framework consists of 5 steps: First, collecting data based on ontologies that we defined. Second, text pre-processing to clean data. Third, predicting subjectivity and sarcasm in a tweet. Fourth applying the two cited approaches to get the sentiment. And the last one is visualizing and analyzing the results.
Estimating and Monitoring Land Surface Phenology in Rangelands: A Review of Progress and Challenges

Land surface phenology (LSP) has been extensively explored from global archives of satellite observations to track and monitor the seasonality of rangeland ecosystems in response to climate change. Long term monitoring of LSP provides large potential for the evaluation of interactions and feedbacks between climate and vegetation. With a special focus on the rangeland ecosystems, the paper reviews the progress, challenges and emerging opportunities in LSP while identifying possible gaps that could be explored in future. Specifically, the paper traces the evolution of satellite sensors and interrogates their properties as well as the associated indices and algorithms in estimating and monitoring LSP in productive rangelands. Findings from the literature revealed that the spectral characteristics of the early satellite sensors such as Landsat, AVHRR and MODIS played a critical role in the development of spectral vegetation indices that have been widely used in LSP applications. The normalized difference vegetation index (NDVI) pioneered LSP investigations, and most other spectral vegetation indices were primarily developed to address the weaknesses and shortcomings of the NDVI. New indices continue to be developed based on recent sensors such as Sentinel-2 that are characterized by unique spectral signatures and fine spatial resolutions, and their successful usage is catalyzed with the development of cutting-edge algorithms for modeling the LSP profiles. In this regard, the paper has documented several LSP algorithms that are designed to provide data smoothing, gap filling and LSP metrics retrieval methods in a single environment. In the future, the development of machine learning algorithms that can effectively model and characterize the phenological cycles of vegetation would help to unlock the value of LSP information in the rangeland monitoring and management process. Precisely, deep learning presents an opportunity to further develop robust software packages such as the decomposition and analysis of time series (DATimeS) with the abundance of data processing tools and techniques that can be used to better characterize the phenological cycles of vegetation in rangeland ecosystems.
Production of truncated peptide (cellobiohydrolase Cel6A) by Trichoderma reesei expressed in Escherichia coli

Enzymatic cellulose hydrolysis is an important step for the production of second-generation biofuels. The filamentous fungus Trichoderma reesei is among the most important organisms for obtaining cellulolytic enzymes. The Cel6A (CBH II) cellulase from T. ressei plays an important role in cellulose hydrolysis and acts on the non-reducing end of cellulose, in contrast to Cel7B (CBH I), which acts on the reducing end of cellulose thus releasing cellobiose. Therefore, Cel6A deficiency becomes a limiting factor in cellulose saccharification. This work attempted to use codon optimization to enhance Cel6A expression in Escherichia coli. A plasmid expression vector, pUCITD04, was designed; this vector contains: the cel6a gene, regulatory regions (the promoter and terminator T7 sequences), the OmpT signal peptide that allows the secretion of proteins into the culture medium, and a 6His tail to allow purification of the protein by affinity chromatography. The protein expression experiment using a strain of E. coli transformed with pUCITD04 resulted in a 31 kDa polypeptide being secreted into the culture medium that did not possess enzymatic activity, meanwhile, the control strain transformed with the empty plasmid did not secrete any protein fragments, indicating that a truncated Cel6A was being produced by the experimental strain. This phenomenon has been reported during the production of recombinant cellulases in E. coli. In this research, we discuss probable causes of this phenomenon, as well as the drawbacks in the production of cellulases by E. coli, directing efforts to elucidate the causes of the production of truncated cellulases by this bacterial factory. Key words: Gene construct, recombinant cellobiohydrolase, Escherichia coli.
S erverless C omputing : F rom A n A pplication D eveloper ’ s P erspective

In the past few years, serverless computing has gained signiﬁcant popularity and became a go-to choice for deploying cloud applications and micro-services. Serverless computing with its unique ‘pay as you go’ pricing model and key performance beneﬁts over other cloud services, o ﬀ ers an easy and intuitive programming model to build cloud applications. In this model, a developer focuses on writing the code of the application while infrastructure management is left to the cloud provider who is responsible for the underlying resources, security, isolation, and scaling of the application. Recently, a number of commercial and open-source serverless platforms have emerged, o ﬀ ering a wide range of features to application developers. In this paper, ﬁrst, we present measurement studies demystifying various features and performance of commercial and open-source serverless platforms that can help developers with deploying and conﬁguring their server-less applications. Second, we discuss the distinct performance and cost beneﬁts of serverless computing and present a set of potential applications that can leverage the performance, cost, or both aspects of serverless computing. In the end, we discuss future research directions for serverless computing and suggest building tools and technologies, which would not only make serverless usage e ﬃ cient but will also accelerate serverless adoption.
Deep Reinforcement Learning for Visual Navigation and Active Tracking

—In this contribution, we present our research line on Deep Reinforcement Learning approaches for robot navigation, in particular: Target-Driven Visual Navigation and Visual Active Tracking. We assess our methods capabilities in several challenging scenarios and in a number of environments previously unseen during training. Finally, we also prove that they can be effectively deployed in real-world settings on real platforms.
AUTOMATIC DETECTION OF INVASIVE AND NON INVASIVE LOBULES OF THE BREAST IN MAMMOGRAM IMAGES

Breast cancer is a serious threat to women’s life and health. The morbidity and mortality of breast cancer are ranked first and second out of all female diseases. Early detection of lumps and tumors can effectively reduce the mortality rate of breast cancer. The mammogram is widely used in early screening of breast cancer due to its relatively low expense and high sensitivity to minor lesions. The majority of invasive breast carcinomas are categorized as Ductal carcinoma of the usual type. In the actual diagnosis process, however, the accuracy can be negatively affected by many factors, such as radiologist fatigue and distraction, the complexity of the breast structure, and the subtle characteristics of the early-stage disease. The computer-aided diagnosis (CAD) for breast cancer can help to address this issue. The simulation results show the better performance in detection as well as classification where the Sensitivity = 94.7 %
COVID-19 adversely affects the psychological status of healthcare workers in the emergency room

Aim: During the 2019 coronavirus disease (COVID-19) pandemic, healthcare workers experience intense levels of workload and pressure The exact psychological and mental impacts of this pandemic on healthcare workers and relevant factors remain to be elucidated Therefore, this study aimed to investigate the stress and anxiety of healthcare workers due to COVID-19 Material and Methods: This prospective multi-centered survey-based study was conducted on 205 participants working in the emergency room (ER) of two hospitals between May 15 and May 22, 2020 Besides demographic data such as age, gender, working history in ER, marital status and presence of children, the Perceived Stress Scale (PSS) and the Beck Anxiety Inventory (BAI) were used in the questionnaire Data were analyzed by either the Kruskal-Wallis test and Mann-Whitney U test, Chi-square test or Fisher’s exact test Results: Most of the participants were nurses (35 5%), and 53 2% of all participants were male The rate of COVID-19 infection among physicians and nurses was significantly higher than among personnel in other occupations The rate of the willingness of physicians and nurses to receive psychiatric support was significantly higher than that of other personnel (p &lt; 0 05) Physicians and nurses had higher BAI and PSS scores than other staff, and scores of nurses were significantly higher than physicians (p &lt; 0 05) Discussion: During the pandemic, the adverse psychological and mental impacts of COVID-19 were prevalent among the healthcare workers Adequate mea-sures at both organizational and national levels are required to improve the psychological state of frontline healthcare workers during the COVID-19 pandemic
Effect of aqueous extract of Iranian Sumac ( Rhus criaria L.) on the shelf life of Hypophthalmichthys molitrix fillet during storage at 4 ◦ C

Sumac ( Rhus coriaria L.), belonging to the Anacardiaceae family, grows in Mediterranean countries, North Africa, South Europe, Afghanistan and Iran. The sumac berries contain biological activities such as antioxidant, antimicrobial, hypoglycemic and antidiabetic. The present study examined the effects of water extract of sumac on the chemical, microbiological and sensory quality on silver carp ( Hypophthalmichthys molitrix ) fillets during storage at 4 ๐ C. Treatments in the present study included the following: Control without extract of sumac, 1, 2.5 and 5 % (w/v) the water extract of sumac. After 18 days of storage, the results showed that the lowest total viable count (TVC) (7.34 log 10 cfu g -1 ), psychrophilic total counts (PTC) (7.21 log 10 cfu g -1 ), pH (6.43), total volatile basic nitrogen (TVB-N) (23.38 mg N 100g -1 ), Thiobarbituric acid (TBA) (1.66 mg MDA kg -1 ) and peroxide value (PV) (5.28 meq kg -1 ) were recorded in 5% sumac water extract treated samples, while the highest TVC (9.01 log 10 cfu g -1 ), PTC (8.97 log 10 cfu g -1 ), pH (7.15), TVB-N (38.71 mg N 100g -1 ), TBA (2.82 mg MDA kg -1 ) and PV (9.41 mEq kg -1 ) occurred in the control group. The best outcome of sensory evaluation was acquired in samples that treated with 5% sumac water extract and was significantly different ( p ≤0/05), when compared to the control group after 18 days of storage. Natural preservatives such as sumac extract can be used as a safe method for storage of silver carp fillets, which is quite promising for food industry.
Automatic ingredient replacement in digital recipes: combining machine learning with expert knowledge

Over the past years, people have become increasingly aware of their eating habits and the corresponding eﬀects on mental and physical health. This thesis argues that eating habits could be further improved by user empowerment, which could be achieved by the possibility of people adjusting recipes to their needs and preferences. One of the most prominent examples of recipe adjustment is ingredient substitution when the proposed ingredient is unavailable or contains allergen substances. This thesis explores how to identify suitable ingredient substitutes in a broad range of recipes. The following two methods have been implemented and evaluated: the traditional use of domain expert knowledge and data-driven methods of using word embeddings. The main assumption of the word embedding method was inspired by the distributional hypothesis in linguistics, namely that words that are often used in similar contexts tend to convey similar meanings. In the context of cooking, this hypothesis suggests that ingredients consumed in similar recipes are likely to be suitable substitutes of each other. The two methods were extensively evaluated with a user test, which resulted in an adapted and therefore better performance of the expert knowledge model.
The Interactive Role of Organizational Learning in the Relationship Between Modest Leadership and Employee Green Behavior Strategy

The present study aims to investigate the interactive role of organizational learning in the relationship between modest leadership and the employee green strategy. In order to achieve the objectives of the study we collected data from commercial banks working in Muthanna Governorate (al-Rafidain, Al-Rasheed). In total 119 responses were collected from employees at different levels (Director, associate director, head of Department, Division manager, employee) of organization. The study found the most important conclusion that the indirect influence of organizational learning as an interactive role with the modest leadership in the behavior of green workers. The study recommends utmost need to develop modest leadership to motivate the employees toward green behavior. Further, this study draws useful insights for future research and recommendations for policy makers.
Chromatic and Morphological Differentiation of Triatoma dimidiata (Hemiptera: Reduviidae) with Land Use Diversity in El Salvador

Chagas disease is caused by the parasite Trypanosoma cruzi, which is transmitted by insect-vectors in the taxonomic subfamily Triatominae and affects approximately 8,000,000 people world-wide. Current mitigation strategies for Chagas focus on insecticides, infrastructure improvements, and management of symptoms, which are largely unsustainable in underserved communities where the disease is widespread. Transmission patterns of vector-borne diseases are known to adaptively respond to habitat change; as such, the objective of our study was to evaluate how the physical characteristics of Triatoma dimidiata would vary in relation to land use in El Salvador. We hypothesized that the color and morphology of T. dimidiata would change with municipal levels of urban and natural green space, natural green space, and agricultural space, as well as municipal diversity, richness, and evenness of land use types. Our results characterize how T. dimidiata color and morphology vary directly with anthropogenic changes to natural and agricultural environments, which are reflective of a highly adaptable population primed to respond to environmental change. Mitigation studies of Chagas disease should exploit the relationships between anthropogenic land use and T. dimidiata morphology to evaluate how the transmission pattern of T. cruzi and Chagas disease symptomology are impacted.
A ROBUST TECHNIQUE OF BRAIN MRI CLASSIFICATION USING CNN ALGORITHM

In the last decenniums, Counterfeiting and identity spoofing and have been noticed at a very high rate, it has become critical to develop a smart and developed biometric security system that is in sync with the advancement of information technology (IT) services. As a result, humans continue to conduct studies in the field of brain recognition. The fact that attacks on hidden biometric systems (such as brain fingerprints) are so difficult to succeed is the main motivation for using these biometrics. Biometric systems' privacy contributes greatly to improving the degree of robustness in terms of individual verification and identification. In this research, the convolutional neural network classifier, which is considered one of the most important classifiers of the deep education type, was relied on to classify data sets including 300 brain MRIs (Magnetic Resonance Imaging) into 30 classes. Note that the brain MRI scans are for the thirty patients, ten images to each patient, these patients real data base from Medical Yarmok Hospital. The classifier was combined with the linear discriminative analysis (LDA) the powerful feature extraction, extracting a brain signature, called “brainprint” from brain MRI images. The proposed algorithm is developed to classification brain by several stages including; data acquisition; preprocess; feature extraction using (LDA) to get important information of image. The proposed system show high accuracy with features extracted using Linear Discriminate Analysis with classifier CNN. The Experimental results getting after implementations and testing is of Precision 100%.