Miracle-dz/UniFAR-FTUs huggingface dataset:
Machine Learning Applications on Agricultural Datasets for Smart Farm Enhancement

This work aims to show how to manage heterogeneous information and data coming from real datasets that collect physical, biological, and sensory values. As productive companies—public or private, large or small—need increasing profitability with costs reduction, discovering appropriate ways to exploit data that are continuously recorded and made available can be the right choice to achieve these goals. The agricultural field is only apparently refractory to the digital technology and the “smart farm” model is increasingly widespread by exploiting the Internet of Things (IoT) paradigm applied to environmental and historical information through time-series. The focus of this study is the design and deployment of practical tasks, ranging from crop harvest forecasting to missing or wrong sensors data reconstruction, exploiting and comparing various machine learning techniques to suggest toward which direction to employ efforts and investments. The results show how there are ample margins for innovation while supporting requests and needs coming from companies that wish to employ a sustainable and optimized agriculture industrial business, investing not only in technology, but also in the knowledge and in skilled workforce required to take the best out of it.
Label super-resolution networks

We present a deep learning-based method for super-resolving coarse (low-resolution) labels assigned to groups of image pixels into pixel-level (high-resolution) labels, given the joint distribution between those low- and high-resolution labels. This method involves a novel loss function that minimizes the distance between a distribution determined by a set of model outputs and the corresponding distribution given by low-resolution labels over the same set of outputs. This setup does not require that the high-resolution classes match the low-resolution classes and can be used in high-resolution semantic segmentation tasks where high-resolution labeled data is not available. Furthermore, our proposed method is able to utilize both data with low-resolution labels and any available high-resolution labels, which we show improves performance compared to a network trained only with the same amount of high-resolution data. We test our proposed algorithm in a challenging land cover mapping task to super-resolve labels at a 30m resolution to a separate set of labels at a 1m resolution. We compare our algorithm with models that are trained on high-resolution data and show that 1) we can achieve similar performance using only low-resolution data; and 2) we can achieve better performance when we incorporate a small amount of high-resolution data in our training. We also test our approach on a medical imaging problem, resolving low-resolution probability maps into high-resolution segmentation of lymphocytes with accuracy equal to that of fully supervised models.
A Review: Improved Method of Load Balancing Algorithms

: Wireless sensor networks have received increasing attention in the recent few years. In many military and civil applications of sensor networks, sensors are constrained in onboard energy supply and are left unattended. Energy, size and cost constraints of such sensors limit their communication range . In this paper, we examine the proposed load balancing algorithms for wireless sensor network . It is used to extend the lifetime of a wireless sensor network which is the main issue in WSN by reducing energy consumption. It can also increase network scalability. In this paper, we implemented proposed method on Load Balancing algorithm for wireless sensor networks which can achieve both energy balancing and energy efficiency for all sensor nodes.
Computational thinking and tinkering : exploration study of primary school students in robotic and graphical programming

The study was conducted to analyse the effect of a robotic program to primary school children. In order to succeed in a world increasingly dependent on technology, computational thinking is important. Computational thinking is considered as an important skill for students in 21-st learning century. Computational thinking provides basic knowledge in the design of generalization problem; decomposition, data representation, generalization, modeling, and algorithm. Educational robotics associated much with computational thinking and the subject of computer science through programming module has been emphasized by the education ministry recently and was introduced formally in the primary school curriculum, which focuses on solving technological problems. The instrument used to measure the technological problem solving is Technological Problem Solving Inventory (PSI-TECH). Quasi-experiments was implemented in this study, involved experimental group and control group which were equal in selected characteristics. The robotic and basic visual programming program conducted for 10 weeks, consistent with the school syllabus and activities. Data were collected before and after the program, and quantitative analysis of t-test and ANOVA were used. Result had shown a significance positive value for the experimental group after the program. This study contributes in the field of education, especially teachers in investigating the problem-solving skills among students. In addition, diversification of study in the field of robotics.
Politeness Counts: Perceptions of Peacekeeping Robots

The “intuitive” trust people feel when encountering robots in public spaces is a key determinant of their willingness to cooperate with these robots. We conducted four experiments to study this topic in the context of peacekeeping robots. Participants viewed scenarios, presented as static images or animations, involving a robot or a human guard performing an access-control task. The guards interacted more or less politely with younger and older male and female people. Our results show strong effects of the guard's politeness. The age and sex of the people interacting with the guard had no significant effect on participants' impressions of its attributes. There were no differences between responses to robot and human guards. This study advances the notion that politeness is a crucial determinant of people's perception of peacekeeping robots.
Controlling Energy Consumption by Internet of Things (IoT) Applications

The concept of Internet-of-Things (IoT) emerged around two decades ago but has gained momentum during the last few years. The modern technology has infused this innovative paradigm on several scales but still lacks shadowing many aspects of our daily life. Several businesses have attempted its interjection in their processes, however, some of the features of IoT have been avoided by most of them due to its cryptic nature and requiring technical skills prior to its replete use. The development of IoT is well ahead of its need to be and linked to an inability of adopting its fast-paced changes along with the lack of strong business operational models. This paper contributes to the developing of a smart home where electrical appliances would react according to the environment while users would be able to control them through smart gadgets. The conventional setup of arranging and organising the electrical appliances is compared to the newly-developed, several patterns have been observed through which carbon footprints and electricity consumption could be reduced. The collected data has been analysed and it proves that IoT does not only helps save energy consumption but also to save the environment. This paper also highlights the challenges faced by consumers who are affected by the penetration of IoT into their lives along with benefits of infusing IoT applications. The overall theme of this paper is, therefore, to make the consumer understand the benefits of IoT applications into their lives using Arduino setup to control electrical home appliances as a benchmark.
MEK Pathway Inhibition Increases the Efficacy of a PI3K and HDAC Inhibitor in Endometrial Cancer Cells

Background: Endometrial cancer (EC) is the commonest gynaecologic malignancy in many countries and its incidence is rising. Although excellent prognosis is associated with early stage disease, response to systemic treatment for metastatic or recurrent EC is often low and treatment options are limited.Aims-Methods: The aim of the study was to propose improved targeted drug treatments suitable for subsequent testing in pre-clinical models of EC. Cell proliferation assay (MTS) was used to assess viability of EC cell lines following treatment with drug inhibitors and Western blotting to explore the effect of inhibitors in molecular pathways. Results: We identified that CUDC-907, a PI3K and HDAC inhibitor, was the most effective monotherapy treatment of a panel of drugs screened in EC cells. Moreover, several combination treatments showed synergism in EC cell lines, with the most efficacious being CUDC-907 combined with the MEK inhibitor PD0325901. This indicates that simultaneous inhibition of two main oncogenic pathways, PI3K and MEK, could improve drug sensitivity in EC.Conclusions: In summary, we propose a range of targeted inhibitory drugs, alone or in combination, showing in vitro efficacy in endometrial cancer cells, which could provide novel therapeutic strategies for advanced EC.
Analysis and Compilation of Parallel Programming Languages

Traditional compilation faces numerous challenges with program optimizations for parallel architectures. A particular challenge is the design of proper intermediate languages and representations to enable the application of relevant optimization techniques.Various parallel intermediate representations and languages have been proposed.To overcome this issue, different alternatives are more and more exploitedsuch as empirical autotuning or interactive compilation. Such alernatives require fondamentally different typesof intermediates languages such as transformation meta-languages. In this thesis, we study transformation meta-languages for numerical applications: wa particularly address four questions:(i) How do we introduce domain-specific expressiveness? (ii) How do we rethink their design to enhance their flexibility in composing optimizations paths and generating multiple program variants? (iii) How far can we introduce NUMA (Non-Uniform Memory Access) awareness? (iv) As a new class of meta-languages, how do we formalize their semantics? We answer these questions through the design and semantics of TeML, a tensor optimizations meta-language.
Action Recognition Based on Weight BOVW

To improve the accuracy and robustness of human action recognition, a kind of human action recognition algorithm based on the weighing bad-of-word model was put forward. The features extracted from video sequences through existing algorithms contained HOG feature, HOF feature, and MBH feature, and relevant visual vocabulary table was acquired through the K-means clustering method. Statistics of the word frequency of all visual words of each category was made, and the words that rank at the front in the word frequency were selected to conduct the weighted normalization processing and acquire the weight of the words. The test samples conducted weighing expressions on the words whose frequency ranked at the front positions during the generalization process of bad of words, and it conducted classification and recognition of the characteristics of the bag of words through SVM. The result of UCF Sports DS experiment showed that the new algorithm had a good recognition capacity.
Log2

normalized and filtered data is then evaluated for bioactive compound activity using the two-tiered strategy below: The normalized and filtered data is then evaluated for bioactive compound activity using the two-tiered strategy below: Quantitative high throughput screening (qHTS) assays play an important role in the Tox21 community efforts to advance toxicology from a predominantly observational science at the level of disease-specific models to a predominantly predictive science based on broad inclusion of target-specific, mechanism-based, biological observations. However, the analysis of qHTS data has largely been motivated by the conservative focus of pharmaceutical applications (i.e., minimizing the risk of Type I error) and generally has relied on heuristics rather than statistical tests to make activity calls. To integrate statistical significance with toxicological relevance within qHTS studies, we fit normalized concentration-response data from twelve different agonist nuclear receptor assays the ToxCast™ 320 compounds to four-parameter Hill equations. An overall F-test comparing the best fit to the Hill equation and a horizontal line (no response) for each substance, using different significance thresholds, was used to identify active compounds within the tested concentration range. Compounds active below the minimum tested concentration were found by comparing the distribution of measured responses to a control value. We compare this statistical method to a previously utilized heuristic approach. Quantitative high throughput screening (qHTS) assays play an important role in the Tox21 community efforts to advance toxicology from a predominantly observational science at the level of disease-specific models to a predominantly predictive science based on broad inclusion of target-specific, mechanism-based, biological observations. However, the analysis of qHTS data has largely been motivated by the conservative focus of pharmaceutical applications (i.e., minimizing the risk of Type I error) and generally has relied on heuristics rather than statistical tests to make activity calls. To integrate statistical significance with toxicological relevance within qHTS studies, we fit normalized concentration-response data from twelve different agonist nuclear receptor assays the ToxCast™ 320 compounds to four-parameter Hill equations. An overall F-test comparing the best fit to the Hill equation and a horizontal line (no response) for each substance, using different significance thresholds, was used to identify active compounds within the tested concentration range. Compounds active below the minimum tested concentration were found by comparing the distribution of measured responses to a control value. We compare this statistical method to a previously utilized heuristic approach. Abstract Abstract Our goal is to develop a general statistical framework …
Stochastic optimization approaches to learning concise representations

We propose and study a method for learning interpretable features via stochastic optimization of feature architectures. Features are represented as multi-type expression trees using a set of activation functions common in neural networks in addition to other elementary functions. Continuous features are trained via gradient descent, and the performance of features in ML models is used to weight the rate of change among subcomponents of representations. The search process maintains an archive of representations with accuracy-complexity trade-offs to assist in generalization and interpretation. We compare several stochastic optimization approaches within this framework. We benchmark these variants on many real-world regression problems in comparison to other machine learning approaches. The best results across methods are obtained by search methods that directly optimize the accuracy-complexity trade-off in order to find simple architectures that generalize well.
Don't Settle for Average, Go for the Max: Fuzzy Sets and Max-Pooled Word Vectors

Recent literature suggests that averaged word vectors followed by simple post-processing outperform many deep learning methods on semantic textual similarity tasks. Furthermore, when averaged word vectors are trained supervised on large corpora of paraphrases, they achieve state-of-the-art results on standard STS benchmarks. Inspired by these insights, we push the limits of word embeddings even further. We propose a novel fuzzy bag-of-words (FBoW) representation for text that contains all the words in the vocabulary simultaneously but with different degrees of membership, which are derived from similarities between word vectors. We show that max-pooled word vectors are only a special case of fuzzy BoW and should be compared via fuzzy Jaccard index rather than cosine similarity. Finally, we propose DynaMax, a completely unsupervised and non-parametric similarity measure that dynamically extracts and max-pools good features depending on the sentence pair. This method is both efficient and easy to implement, yet outperforms current baselines on STS tasks by a large margin and is even competitive with supervised word vectors trained to directly optimise cosine similarity.
VLSP Shared Task: Named Entity Recognition

Named entities (NE) are phrases that contain the names of persons, organizations, locations, times and quantities, monetary values, percentages, etc. Named Entity Recognition (NER) is the task of recognizing named entities in documents. NER is an important subtask of Information Extraction, which has attracted researchers all over the world since 1990s. For Vietnamese language, although there exists some research projects and publications on NER task before 2016, no systematic comparison of the performance of NER systems has been done. In 2016, the organizing committee of the VLSP workshop decided to launch the first NER shared task, in order to get an objective evaluation of Vietnamese NER systems and to promote the development of high quality systems. As a result, the first dataset with morpho-syntactic and NE annotations has been released for benchmarking NER systems. At VLSP 2018, the NER shared task has been organized for the second time, providing a bigger dataset containing texts from various domains, but without morpho-syntactic annotation. These resources are available for research purpose via the VLSP website vlsp.org. vn/resources. In this paper, we describe the datasets as well as the evaluation results obtained from these two campaigns.
Multimodal deep learning methods for person annotation in video sequences

The purpose of this project is to focus in exploring deep learning methods for multimodal person recognition in video sequences. The project will be carried out in the context of unsupervised person annotation systems for broadcast TV programs. Person annotation aims to determining who and where appears in a video sequence. We decide to improve the detection, recognition and track of the text in that systems. We examine different techniques and implementations obtaining a great results for each block of the project and this evaluations gives us some future developments to improve more the system.
Interpretable Perceived Topics in Online Customer Reviews for Product Satisfaction and Expectation

Online customer reviews contain useful and important information, in particular, for product management--because customers tend to praise or criticize certain features or attributes of goods in their reviews. We propose a model that extracts the perceived topics from textual reviews by natural language processing under the restrictions of their interpretability and predictability of product satisfaction as current product evaluation and expectation as future possible demand by supervised learning. The empirical analysis on user-generated content of food reviews shows that our proposed model performs better than alternative models, and it suggests product managers the necessity of improving some specific attributes and focus their advertising on these attributes as fulfilling customer needs.
Hide and Seek: Mitigating the Jammers by Hiding Packets to Seek Secure Transmission

Wireless networks are most emerging with its fascinating applications that make our day to day works most compact and easy. Security in such network is important, as these networks are prone to intentional interference attacks. Such attacks lead to denial of service attacks which is known as jamming. Jamming was previously addressed based on external threat model however for internal threat model attacks are made on messages of high interest such as route request reply by knowing the network secrets and protocol specifications. Such kind of jamming is known as selective jamming. In this paper we address the problem of selective jamming by making it random one. To mitigate this selective jamming attack real time classification is prevented by combining cryptographic methods with strong hide commitment scheme, cryptographic puzzle, all or nothing transformation and hash based hiding scheme along with PHY layer attributes that is addressed using TCP protocol.
Wireless battery management system

Measurement and control of individual charge can result in a longer battery use and more reliable operation of the physical properties of battery cells. The present invention discloses a system, cell unit, control means and a method by a wireless communication link for an automatic management of the battery pack. According to the present invention, a single cycle of the battery pack by the external control unit may track and record member. Advantageously it is provided a battery pack unit activation control, including the ability to short-circuit disposed between the respective electrodes of the battery cell group.
Comprehensive Effect of Salt Stress and Peanut Cultivars on the Bacterial Community Structure and Diversity of Peanut Rhizosphere Soils

Background: Plant rhizosphere bacterial communities influence plant growth and stress tolerance, which differs across cultivars and external environments. Peanut (Arachis hypogaea. L) as an important oil crop cultivated worldwide. However, relatively little is known about the comprehensive effects of environmental conditions and peanut cultivars on rhizosphere bacterial community structure and diversity. Results: Here, bacterial community structure diversity from rhizosphere soils of various susceptible and resistant peanut cultivars with or without salt stress was analyzed by 16S rRNA gene deep sequencing and quantitative PCR assays. Taxonomic analysis showed that the bacterial community predominantly consisted of phyla Actinobacteria, Proteobacteria, Chloroflexi, Acidobacteria, and Cyanobacteria. Among these bacteria, numbers of beneficial bacteria Cyanobacteria and Proteobacteria increased, while that of Acidobacteria decreased after salt treatment. Metabolic function prediction showed that the percentages of reads categorized to signaling transduction and inorganic ion transport and metabolism were higher in the salt-treated soils, which may be beneficial to plant survival and salt tolerance. Conclusions: Overall, rhizosphere bacterial community structure and population metabolism are affected by salt stress, which may be conducive to peanut stress tolerance in saline-alkali soil. The study is therefore crucially important to develop the foundation for improving the salt tolerance of peanuts via modifying the soil bacterial community.
Compressing higher-order co-occurrences for texture analysis using the self-organizing map

Texture analysis is useful in many computer vision applications. One of the most useful texture feature sets is based on second-order co-occurrences of gray levels of pixel pairs. An extension of the co-occurrences to higher orders is prevented by the large size of the multidimensional arrays. We quantize the higher-order co-occurrences by the self-organizing map, called the co-occurrence map, which allows a flexible two-dimensional representation of co-occurrence histograms of any order. Experiments with natural gray level and color textures show that the method is effective in texture classification and segmentation.