Miracle-dz/UniFAR-FTUs huggingface dataset:
Time of Day for Harvest Affects the Fermentation Parameters, Bacterial Community, and Metabolic Characteristics of Sorghum-Sudangrass Hybrid Silage

Ensiling is a common way for preserving green forages worldwide. Silage fermentation quality can vary greatly depending on the chemical and microbial characteristics of forage crop being ensiled. ABSTRACT To characterize the effects of time of day for harvest on the fermentation parameters, bacterial community, and metabolic characteristics of sorghum-sudangrass hybrid (SSG) silage, SSG (vegetative stage) harvested at 7:00 (AM), 12:00 (M), and 17:00 (PM) on three sunny days were ensiled for 1, 3, 7, 14, 30, and 60 days. Compared to AM silage, M and PM silages were characterized by delayed fermentation, unnormal lower final pH, and lower acetic acid production. In addition, PM silage contained higher residual water-soluble carbohydrates than other silages. After 60 days of ensiling, AM silage was dominated by Lactobacillus, whereas the bacterial communities of M and PM silages were complex and mainly composed of bacteria such as Delftia, Methylobacterium-Methylorubrum, Enhydrobacter, Acinetobacter, and Bacillus. The harvest time affected a wide range of metabolic pathways including “Metabolism” and “Cellular Processes” and “Organismal Systems” in SSG silage. Particularly, at the late stage of ensiling M silage exhibited highest relative abundances of amino acid metabolisms including “glycine, serine, and threonine metabolism,” “phenylalanine metabolism,” and lowest relative abundances of “lysine biosynthesis. ” These results suggest that the time of day for harvest could affect the fermentation parameters, bacterial community, and metabolic characteristics of SSG silage. Better SSG silage characteristics could be achieved through morning harvest. IMPORTANCE Ensiling is a common way for preserving green forages worldwide. Silage fermentation quality can vary greatly depending on the chemical and microbial characteristics of forage crop being ensiled. It is well documented that forages exhibit considerable variations in chemical composition and epiphytic microbiota during daylight. However, the effects of the time of day for harvest on silage fermentation is less investigated. Our results demonstrate that the time of day for harvest could affect the fermentation parameters, bacterial community, and metabolic characteristics of SSG hybrid silage. Harvesting SSG late in the day delayed fermentation process, lowered acetic acid production and final pH, and increased the residual water-soluble carbohydrates content in silage. Moreover, the delayed harvest time increased the relative abundances of bacteria such as Delftia, Methylobacterium-Methylorubrum, Acinetobacter, Enhydrobacter, and Bacillus, and amino acid metabolisms at the late stage of SSG ensiling. This study highlights the importance of diurnal changes in forage to fermentation characteristics, providing a strategy to improve silage quality through optimizing the harvest time.
Draft Genome Sequence of Nocardioides alcanivorans NGK65T, a Hexadecane-Degrading Bacterium

The Gram-positive bacterium Nocardioides alcanivorans NGK65T was isolated from plastic-polluted soil and cultivated on medium with polyethylene as the single carbon source. Nanopore sequencing revealed the presence of candidate enzymes for the biodegradation of polyethylene. Here, we report the draft genome of this newly described member of the terrestrial plastisphere. ABSTRACT The Gram-positive bacterium Nocardioides alcanivorans NGK65T was isolated from plastic-polluted soil and cultivated on medium with polyethylene as the single carbon source. Nanopore sequencing revealed the presence of candidate enzymes for the biodegradation of polyethylene. Here, we report the draft genome of this newly described member of the terrestrial plastisphere.
On the Explainability of Natural Language Processing Deep Models

Despite their success, deep networks are used as black-box models with outputs that are not easily explainable during the learning and the prediction phases. This lack of interpretability is significantly limiting the adoption of such models in domains where decisions are critical such as the medical and legal fields. Recently, researchers have been interested in developing methods that help explain individual decisions and decipher the hidden representations of machine learning models in general and deep networks specifically. While there has been a recent explosion of work on Explainable Artificial Intelligence (ExAI) on deep models that operate on imagery and tabular data, textual datasets present new challenges to the ExAI community. Such challenges can be attributed to the lack of input structure in textual data, the use of word embeddings that add to the opacity of the models and the difficulty of the visualization of the inner workings of deep models when they are trained on textual data. Lately, methods have been developed to address the aforementioned challenges and present satisfactory explanations on Natural Language Processing (NLP) models. However, such methods are yet to be studied in a comprehensive framework where common challenges are properly stated and rigorous evaluation practices and metrics are proposed. Motivated to democratize ExAI methods in the NLP field, we present in this work a survey that studies model-agnostic as well as model-specific explainability methods on NLP models. Such methods can either develop inherently interpretable NLP models or operate on pre-trained models in a post hoc manner. We make this distinction and we further decompose the methods into three categories according to what they explain: (1) word embeddings (input level), (2) inner workings of NLP models (processing level), and (3) models’ decisions (output level). We also detail the different evaluation approaches interpretability methods in the NLP field. Finally, we present a case-study on the well-known neural machine translation in an appendix, and we propose promising future research directions for ExAI in the NLP field.
Model-driven Self-adaptive Deployment of Internet of Things Applications with Automated Modification Proposals

Today’s Internet of Things (IoT) applications are mostly developed as a bundle of hardware and associated software. Future cross-manufacturer app stores for IoT applications will require that the strong coupling of hardware and software is loosened. In the resulting IoT applications, a quintessential challenge is the effective and efficient deployment of IoT software components across variable networks of heterogeneous devices. Current research focuses on computing whether deployment requirements fit the intended target devices instead of assisting users in successfully deploying IoT applications by suggesting deployment requirement relaxations or hardware alternatives. This can make successfully deploying large-scale IoT applications a costly trial-and-error endeavor. To mitigate this, we have devised a method for providing such deployment suggestions based on search and backtracking. This can make deploying IoT applications more effective and more efficient, which, ultimately, eases reducing the complexity of deploying the software surrounding us.
Design and Research of Intelligent Welcome Platform Based on Deep Learning

In this paper, firstly, the present situation of the welcome platform in practical application was described, and then, aiming at the problems of lack of intelligence, inaccurate face recognition and incomplete functions, a highly integrated intelligent welcome platform was developed by using face recognition technology, deep learning algorithm and related technologies, through the combination of software and hardware. The platform uses Haar cascade detection method to detect human faces, FaceNet neural network algorithm to identify human faces, and convolution neural network (CNN) method to predict various indicators of guests, which not only greatly improves the efficiency of face recognition, but also greatly improves the intelligence of the platform in the aspects of automatically identifying categories of guests and displaying different welcome speech, predicting indicators such as age, gender and emotion of guests and counting the number of guests. At present, the platform has been used in the university to make statistics and judgments on freshmen's registration, and it can also be widely used in commercial banks, schools, shopping malls, automatic control and other industries, bringing great commercial value.
Towards Bilingual Word Embedding Models for Engineering: Evaluating Semantic Linking Capabilities of Engineering-Specific Word Embeddings Across Languages

Word embeddings represent the semantic meanings of words in high-dimensional vector space. Because of this capability, word embeddings could be used in a wide range of Natural Language Processing (NLP) tasks. While domain-specific monolingual word embeddings are common in literature, domain-specific bilingual word embeddings are uncommon. In general, large text corpora are required for training high quality word embeddings. Furthermore, training domain-specific word embeddings necessitates the use of source texts from the relevant domain. To train bilingual domain-specific word embeddings, the domain-specific texts must also be available in two different languages. In this paper, we use a large dataset of engineering-related articles in German and English to train bilingual engineering-specific word embedding models using different approaches. We will evaluate our trained models, identify the most promising approach, and demonstrate that the best performing one is very capable of representing semantic relationships between engineering-specific words and mapping languages in a shared vector space. Moreover, we show that the additional use of an engineering-specific learning dictionary can improve the quality of bilingual engineering-specific word embeddings.
Deep Learning in Sentiment Analysis: Recent Architectures

Humans are increasingly integrated with devices that enable the collection of vast unstructured opinionated data. Accurately analysing subjective information from this data is the task of sentiment analysis (an actively researched area in NLP). Deep learning provides a diverse selection of architectures to model sentiment analysis tasks and has surpassed other machine learning methods as the foremast approach for performing sentiment analysis tasks. Recent developments in deep learning architectures represent a shift away from Recurrent and Convolutional neural networks and the increasing adoption of Transformer language models. Utilising pre-trained Transformer language models to transfer knowledge to downstream tasks has been a breakthrough in NLP. This survey applies a task-oriented taxonomy to recent trends in architectures with a focus on the theory, design and implementation. To the best of our knowledge, this is the only survey to cover state-of-the-art Transformer-based language models and their performance on the most widely used benchmark datasets. This survey paper provides a discussion of the open challenges in NLP and sentiment analysis. The survey covers five years from 1st July 2017 to 1st July 2022.
A Classification Scheme in Evaluating Undergraduate Students’ Feedback Towards Service Quality of Private University

Student feedback is an important part of service quality of a private university. The purpose of the study was to evaluate undergraduate students’ feedback towards service quality of private university. A total of 1555 questionnaires and a total of 201 negative comments were collected from one private university in the Philippines during one academic year. For students’ comments, classification schemes were utilized to organize the meaningful categories. It showed that negative personality traits of the employees and poor student accommodation were two most frequent negative comments. Classification schemes were found as a powerful tool to evaluate the students’ feedback towards service quality. Finally, that this approach can be applied and extended to analyze students’ feedback in different private universities worldwide.
An Intelligent Detection Method for Civil Aircraft Skin Damage Based on YOLOv3

Visual inspection is the main method of non-destructive testing for civil aircraft skin damage. With the development of digitization and intelligence in industry, the assistance of UAV and AGV is of great application value in civil aircraft visual inspection. In this paper, an automatic detection method of aircraft surface damage based on YOLOv3 is proposed. Experimental results show that the proposed method has great accuracy and speed in the detection of four typical injuries, which can meet the real-time inspection requirements of UAV/AGV around civil aircrafts.
Construction Quality Inspection and Evaluation of Abutment Back Transition Section

The engineering prevention and control of vehicle jumping at bridgehead is a systematic highway construction quality problem. Its disease has a serious impact on the service quality and driving comfort during road operation. Combined with the research results of the previous project, this paper carries out the structural scheme design of the transition section of the abutment back, and puts forward the technical index of the abutment back filling material and the index requirements of the compactness of the abutment back filling, so as to systematically guide the actual engineering construction, improve the construction quality and reduce the probability of vehicle jumping at the bridge head during the road operation.
Zallocator: A High Throughput Write-Optimized Persistent Allocator for Non-Volatile Memory

Non-volatile main memory (NVRAM) is likely to break the bottleneck caused by data transferring between main memory and extern storage, and fundamentally change the way applications do data persistence. We can build persistent data structures directly on NVRAM. To do this correctly and efficiently, we need the support of persistent allocators guaranteeing failure-consistency. However, existing persistent allocators pay huge persistence overhead for failure-consistency. In this article, we present Zallocator, a write-optimized failure-consistency allocator for NVRAM. Zallocator hardly brings write operations to NVRAM. It keeps all the heap management metadata in DRAM, and rebuild them after a crash. Experimental results show that Zallocator achieves a throughput comparable to state-of-the-art transient allocators, and reduces the average number of NVRAM writes per allocation/deallocation to almost zero.
Google project ara

The project Ara is an initiative of Google in the field of smartphones. The main focus of Project Ara is to bring the revolution of modular concept in smartphones. The platform will include a structural frame or endoskeleton that holds smartphone modules of the owner's choice, such as a display, camera or an extra battery. The project will focus on to reduce the yearly generated e-waste which is contributed by electronic industry especially mobile phones. This page aims to review the project’s goals, challenges, implementation and its scope in the near future.
Ethical, Legal & Participatory Concerns in the Development of Human-Robot Interaction

Abstract Research on Human-Robot Interaction is increasing as system become widely available and reached a level that enables smooth interactions. Yet, many research projects act in a silo mentality, in regard of participatory, ethical, or legal matters of social robotics. Knowledge about specific challenges is not universal and has often to be transferred from non-robotic contexts. We present findings in the three dimensions: participatory design, ethics for social robots and legal aspects. We accompanied eight research projects on social robots in real-world scenarios for three years. During that time, we spoke, observed, and helped (where possible) the research projects. This gave us specific insights into their work. Our findings work in three dimensions. In participatory design we observed that the trust relationship to users is essential to gain truthful insights and that a mixed method approach is promising. Regarding ethical aspects, we could see that ethical matters should be answered early on. And in the legal dimensions we noticed that the GDPR regulations are a challenge that often requires the help of experts. This work is reflecting on the observation of eight projects and is collecting lessons learned to help future projects and to learn from previous work.
Object-Based Image Comparison Algorithm Development for Data Storage Management Systems

Continuous development of big image storage management systems requires development and introduction of efficient searching and image comparison algorithms. In the scope of this article we analyze modern object detection models and image search algorithms based on their metadata and processed images object localization area comparison. The main advantages and disadvantages of such algorithms are discovered. Article offers objects data extraction algorithm from image using CNN, storage model of this information in image file service fields as metadata, cascade object-oriented image comparison and fast search algorithm using modern solutions in the field of parallel computing. A series of experiments is being set up to apply the proposed algorithms for comparing and searching images in large data storages. The results of experiments, analysis of the effectiveness of the application, conclusions and recommendations for the practical application of the proposed algorithms are given.
Word2Vec Model Analysis for Semantic and Morphologic Similarities in Turkish Words

The study presents the calculation of the similarity between words in Turkish language by using word representation techniques. Word2Vec is a model used to represent words into vector form. The model is formed using articles from Wikipedia dump Turkish service as the corpus and then Cosine Similarity calculation method is used to determine the similarity value. The open-source Python programming language and Gensim library are used to obtain high quality word vectors with Word2Vec and calculate the cosine similarity of the vectors. Continuous Bag-of-words (CBOW) algorithm is used to train high quality word vectors. The cosine similarity values in the results are derived from the weight (dimension values) of the vector dimensions. The Window size 10 and 300 vector dimension configurations are taken. Increasing the number of cycles contributes to the vectors getting more accurate values. The corpus is trained in five cycles (EPOCH) with the same parameters. The Turkish corpus contains more than one hundred and sixty one million words. The dictionary of words (unique words), obtained from the corpus, is more than three hundred and sixty-seven thousand. Such a big data gives an opportunity to conduct high quality semantic and morphologic analysis and arithmetic operations of the word vectors.
Keyword-based Study of Thematic Vocabulary in British Weather News

The survey centers on the examination of keywords and related themes representing weather in four daily British newspapers (The Times, The Guardian, The Daily Mail, The Sun) between 2014 and 2017. The articles in this period mentioning weather news represent the corpus of our research. The goals of the research are the following: expose frequently occurring words (keywords) in the corpus, categorize them into groups according to relevant themes in the text, identify the quantitative content of each lexical-thematic group, as well as determine dominant themes of weather news. The computer software that was used to establish keywords is WordSmith Tools 7.0 with the British National Corpus as a reference corpus. A method for automatic cataloging of keywords is described. The corpus contains 746 324 words taken from 180 newspaper articles under research. Despite the necessity of keyword study, thematic and quantitative analyses provide deeper insight into text-specific weather-related vocabulary and its textualizing role. The analysis of quantitative data helps to select two dominant lexical-thematic groups ‒ “Weather extremes” and “Weather and people”, giving evidence of central themes discussed in weather news. Hence, the resulting major themes are the depiction of adverse weather conditions affecting people’s daily life; the representation of the effects of weather disasters on people and their environment. The obtained results highlight the link between a theme/themes and lexical level of the text proving the efficiency of keyword analysis in the research.
Using word vector models to trace conceptual change over time and space in historical

Linking large digitized newspaper corpora in different languages that have become available in national and state libraries opens up new possibilities for the computational analysis of patterns of information flow across national and linguistic boundaries. The significant contribution this article presents is to demonstrate how word vector models can be used to explore the way concepts have shifted in meaning over time, as they migrated across space, by comparing newspapers from different countries published between 1840 and 1914. We define a concept, rather pragmatically, as a key term or core idea that has been used in historical discourse: an abstraction or mental representation that has served as a building block for thoughts and beliefs. We use historical newspapers in English, Finnish, German and Swedish from collections in the UK, US, Germany, and Finland, as well as the Europeana collection. As use cases, we analyze how the different conceptual constructs of “nation” and “illness” emerged and changed between 1840 and 1920. Conceptual change over time is simulated by creating a series of overlapping word vector models, each spanning ten years. Historical vocabularies are retrieved on the basis of vector space proximity. Conceptual change across space is simulated by comparing the historical change of vocabularies in newspaper collections from different nations in several languages. This computational approach to conceptual history opens up new ways to identify patterns in public
Text Tonality Classification Using a Hybrid Convolutional Neural Network with Parallel and Sequential Connections Between Layers

The analysis of the tonality of texts is an urgent problem in the field of natural language processing, which is often solved with the help of convolutional neural networks. However, most of these CNN models focus only on the study of local functions, ignoring global features. In this paper, a hybrid convolutional neural network with parallel-sequential connections between layers and from the layer of maximum pulling obtained from the matrix of the original text is proposed for the analysis of text tonality. The proposed hybrid convolutional neural network extracts text features using a parallel-connected convolutional block. Then the neural network classifies the features and combines these features with the original text features. The model of the proposed neural network is able to study both local and global features of short texts and has less convergence time and computing resource compared to the parallel DenseNet. Hybrid convolutional neural network with parallel-sequential connections between layers has a higher efficiency of text tone classification in 6 different databases compared to the base models CNN, TextCNN, FastText, DPCNN