Reviewerly/exHarmony huggingface dataset:
A study on the development and utilization of college English curriculum resources

College English curriculum has always been a difficult point in teaching.The active development and rational use of English curriculum resources plays an important role in improving the quality of college English teaching.Therefore, this paper studies the development and utilization strategy of college English curriculum resources.This paper analyzes the current situation of the development and utilization of college English curriculum resources, and expounds the problems in the process of curriculum development and utilization.In view of the above problems, this paper discusses the strategies for the development and utilization of English curriculum resources from the following aspects: establishing a community of English teachers' curriculum resources, capturing effective information to develop student resources, making full use of the extensive and deep college English classes, and building an online teaching platform to push English curriculum resources.Through the above research, it aims to improve the quality of college English curriculum and provide guarantee for the future development of college students.
Teaching practice of artificially lowering groundwater level in the course of hydraulic engineering construction

The course of hydraulic engineering construction involves many contents, wide scope and short class hours, and teachers often do not have enough time to teach many knowledge points that need to be mastered in practical projects, among which the knowledge point of "artificially lowering groundwater level" has this characteristic.Based on tube-well method and well-point method, this paper designs the teaching path based on the concept of case teaching method, and makes use of effect drawings, engineering drawings, PPT animation, FLASH animation and engineering cases in limited class hours to make students understand the basic principles, technical characteristics, scope of application, design and calculation of these two0 methods.Finally, the students can initially master the calculation of the displacement of foundation pit, the number and layout of pipe wells in artificially lowering groundwater level, and have the preliminary ability of engineering practice.
On the Application of Traditional Culture in Teaching Chinese as a Foreign Language

Chinese as a foreign language is one of the most important courses in the university teaching process, and its main purpose is to cultivate the composite Chinese talents needed by the international community and to spread Chinese culture to the world.For this reason, when teaching Chinese as a foreign language, teachers and leaders of institutions need to work together with education departments to strengthen the application of China's traditional cultural and integrate it into the teaching content, so that students can fully understand the charm of China's traditional culture, increase their interest in learning and national self-confidence, actively participate in the later learning process, and when they join the workforce, they can play their own value role in the dissemination of Chinese culture.In actual teaching, as there are many contents and types of traditional culture in China, teachers need to make scientific choices and follow the corresponding principles to develop teaching contents and objectives that are more suitable for students' learning, so as to lay a solid foundation for the overall development of the field of teaching Chinese as a foreign language.The following is an analysis and exploration of the application of traditional culture in teaching Chinese as a foreign language.
GEOSPATIAL MODELING OF LANDSLIDE SUSCEPTIBILITY IN AMBEGHAR VILLAGE SATARA DISTRICT, MAHARASHTRA

Landslides are one of the most common natural disasters that occur in hilly regions.The present study aims to delineate the landslide-susceptible zones along Ambeghar village, Satara District, Maharashtra, using geospatial techniques.The study area is located in the Western Ghats region of India, and it is prone to landslides due to the high rainfall and steep slopes.The methodology involves the collection of data on topography, geology, land use/land cover, and rainfall using remote sensing and GIS techniques.The data is processed and integrated using the Fuzzy Overlay Analysis technique to generate a landslide susceptibility map.The results show that the study area is moderately susceptible to landslides, with the majority of the area falling under the moderate and high susceptibility categories.The study can be further used for adoption of suitable land-use planning and mitigation measures to prevent or minimize the impact of landslides in the study area.
Predicting the minimum control time of quantum protocols with artificial neural networks

Abstract Quantum control relies on the driving of quantum states without the loss of coherence, thus the leakage of quantum properties into the environment over time is a fundamental challenge. One work-around is to implement fast protocols, hence the Minimal Control Time (MCT) is of upmost importance. Here, we employ a machine learning network in order to estimate the MCT in a state transfer protocol. An unsupervised learning approach is considered by using a combination of an autoencoder network with the k -means clustering tool. The Landau–Zener (LZ) Hamiltonian is analyzed given that it has an analytical MCT and a distinctive topology change in the control landscape when the total evolution time is either under or over the MCT. We obtain that the network is able to not only produce an estimation of the MCT but also gains an understanding of the landscape’s topologies. Similar results are found for the generalized LZ Hamiltonian while limitations to our very simple architecture were encountered.
STUDENT PERFORMANCE PREDICTION USING ML A SURVEY

There is a huge difference between traditional education system and current education system.Current Educational System has so many problems.Today's educational system has many challenging factors.Student's performance varies from one student with the other student.There are so many factors and parameters that affects student academic performances.Every college or education system aims for 100% results in their colleges.Factors like communication skills, student grasping capability, attendance status, family history, family income, pervious semester results etc. plays a vital role in the student academic performance.Result prediction system can be built with different approaches like Random Forest, CNN, Decision Tree, SVM Algorithm etc.We thought of using our own dataset for Indian students with Bayesian Classifier and Random Forest algorithms, they may yield better prediction about student performance.
Extending Rapid Class Augmentation to a Single-Shot-Detector Object Detection Framework

This paper describes how eXtending Rapid Class Augmentation (XRCA) optimization can be integrated into a modern single-shot detector (SSD) architecture to enable fast and efficient progressive learning of new objects. The key distinguishing property of XRCA optimization is the incorporation of memory from previously learned classes into its weight update equations. This allows XRCA models to optimally learn new types of objects using just the new object training data. The new XRCA-SSD object detection framework replaces the traditional SSD's prediction heads with the XRCA prediction heads that use different XRCA optimization modes to update the weights. The mean average precision (mAP) performance metric for a SSD model trained using XRCA versus stochastic gradient descent is compared and the XRCA-SSD trained model is shown to greatly outperform the SGD-SSD model by largely mitigating the impact of catastrophic forgetting during new object augmentation. We expect the new XRCA-SSD framework to be especially relevant for real-time progressive learning applications where rapid training times are critical, and compute and memory are often limited.
Data Lifecycle Management in Evolving Input Distributions for Learning-Based Aerospace Applications

Learning-enabling components are increasingly popular in many aerospace applications, including satellite pose estimation. However, as input distributions evolve over a mission lifetime, it becomes challenging to maintain performance of learned models. In this work, we present an open-source benchmark of a satellite pose estimation model trained on images of a satellite in space and deployed in novel input scenarios (e.g., different backgrounds or misbehaving pixels). We propose a framework to incrementally retrain a model by selecting a subset of test inputs to label, which allows the model to adapt to changing input distributions. Algorithms within this framework are evaluated based on (1) model performance throughout mission lifetime and (2) cumulative costs associated with labeling and model retraining. We also propose a novel algorithm to select a diverse subset of inputs for labeling, by characterizing the information gain from an input using Bayesian uncertainty quantification and choosing a subset that maximizes collective information gain using concepts from batch active learning. We show that our algorithm outperforms others on the benchmark, e.g., achieves comparable performance to an algorithm that labels 100% of inputs, while only labeling 50% of inputs, resulting in low costs and high performance over the mission lifetime.
An analysis of the Oracles used in Ethereum’s blockchain

Smart contracts are programs whose business logic runs in a decentralized architecture named blockchain where each of the executing nodes trusts and agrees with the execution outcomes. Blockchain is an isolated execution environment, so smart contracts cannot access external data by themselves. The blockchain Oracle, or simply Oracle, is the software that allows smart contracts to interact with the outside world. In the past, a wide variety of studies have examined the oracles from various perspectives such as their implementation characteristics and their reliability. Unlike some other studies, this paper analyzes the use of Oracles through a static analysis of smart contracts. First, we analyze the source code of a corpus of over 40K smart contracts to find the most blockchain Oracles used by the Ethereum blockchain. On the basis of previous works, we examine the features of the most used blockchain Oracles in Ethereum. Lastly, we discussed our findings and the possible reasons why some blockchain oracles are more widely used than others. The purpose of this study is to shed light on how and which oracles are actually used by Ethereum smart contracts, as an alternative to other very interesting studies that analysed the blockchain oracle characteristics.
Adaptive density peak clustering algorithm combined with sparse search

Abstract With the advantages of few parameters and the ability to deal with clusters of arbitrary shape, the density peak clustering algorithm has attracted wide attention since it came out. However, the algorithm has problems such as high time complexity, poor clustering effect on complex data sets, and the need to manually select cluster centers. Aiming at the above shortcomings, an improved density peak clustering algorithm is proposed. Combined with the sparse search algorithm, the calculation of the similarity between each point and its nearest neighbor is simplified, and the problem of the high time complexity of the algorithm is overcome. A new local density definition method is adopted to make the density of data points better reflect the spatial structure of data distribution and to improve the clustering accuracy of the algorithm. Finally, a strategy for automatically selecting cluster centers is proposed to improve the adaptability of the algorithm. The algorithm is used to compare with the other improved algorithm on artificial data sets and real data sets. The experimental results show that the proposed algorithm can show a better clustering effect and can quickly and accurately identify various complex clusters.
SOLAR POWERED AUTOMATIC GRASS CUTTER

Solar energy is a clean, freely available, and abundantly available renewable energy.It is also the most important of the non-conventional sources of energy because it is non-polluting and therefore helps decrease the greenhouse effect.Talking about the grass cutter that uses fuel for working, they emit gases contributing to the greenhouse effect as well as contribute to air pollution.Grass cutters operated through electric power also need manpower to operate.So, the best way to put an end to these problems is to use an automatic grass cutter that uses renewable energy resources for its charging as opposed to an electric cable extension.In this paper, we will show the technological progress in automatic solar grass cutters and how they have tackled some problems and also optimizations in solar power consumption.
Enhancing the job scheduling procedure to develop an efficient cloud environment using near optimal clustering algorithm

In this internet era, cloud computing and there are various problems in the cloud computing, where the consumers as well as the service providers facing in their day to day cloud activities. Job scheduling problem plays a vital role in the cloud environment. To provide an efficient job scheduling environment, it is necessary to perform efficient resource clustering. In this regard, the proposed system, concentrated on the resource clustering methodology by proposing an efficient resource clustering algorithm named identicalness split up periodic node size (ISPNS) in the cloud environment. The algorithm proposed helps in forming resource clusters with the help of cloud environment. The proposed system is compared with the existing systems to justify the performance of the proposed resource clustering algorithm and it produces near optimal solution for the resource clustering problem which helps to provide an efficient job scheduling in cloud environment.
Balanced homodyne detection circuit design analysis for high gain and low noise performance

The study, generation, and application of squeezed states of light has been a widely researched area in quantum optics. Vacuum fluctuations or photon shot noise quantified in terms of quadrature operators, impose a quantum limit on the sensitivity of quantum sensing and quantum communication systems. Thus, squeezing the quadratures reduces the quantum uncertainty in a particular quadrature. The amount of squeezing is, however, highly limited by the optics in the experiments and the performance of the balanced homodyne detection (BHD) systems employed for the detection of the squeezed states of light. In this paper, we have analytically studied the steady state, transient and noise responses of the balanced homodyne detection (BHD) circuitry by comparing two configurations i.e., a variable gain circuit with a differential amplifier and a current subtracting circuit with differential fine-tuning circuit (DFTC) and an adjustable bias (ABV). The current subtracting circuit design with DFTC and ABV has been adopted from Xiaoli et al. [1] in order to compensate for any differences in the photodiode performance characteristics in the two arms. A variable gain configuration is used to compensate for the unequal beam splitter ratio and uneven optical powers in the two arms. In order to further improve the noise performance of the system, a differential amplifier is used in the output of the variable gain configuration. A high transimpedance gain (~>70 dB) and a low noise (~<500nV/ √ Hz) performance can be achieved using these circuits as per our analysis.
A dialogue system based on ontology automatically built through a natural language text analysis

An integrated approach is created to the development of natural-language dialogue systems driven by an ontological graph database. Ontology here has a defined regular structure that contains typed semantic relationships between concepts, as well as related contexts, which may also have a multilevel structure and additional typing. The ontology is created automatically due to the semantic analysis of a natural language using a specially developed original software, which is set up to work with inflected languages, in particular Ukrainian. The ontology description is serialized in OWL format. To work as part of the dialog system, the ontology is transferred to the graph database Neo4j. The Cypher language is used for formal queries. The original phrases of the user are subject to a special method of semantic analysis, which determines the type of formal query to the database. The essence of the analysis is that the text of the user phrase goes through a series of checks. Based on their results, a set of basic templates for formal requests is determined, as well as additional constructions that are attached to the basic template. Some of the checks may also return the notion of substitution to certain specified positions of the formal query. Formal queries can return both contexts and lists of ontology concepts. In addition to concepts, queries can also return information about specific semantic predicates that connect them, which simplifies the synthesis of natural language responses. The synthesis of answers is based on special templates, the choice of which is directly related to the corresponding template of the formal query.
Transformation domain requirements specification into computation tree logic language

The requirements specification languages are frequently challenged in every domain - including the safety aspects of cognitive mobility. The correct formalization and communication of the expectations related to the systems is a decisively important step (i.e. the intra cognitive mobility aspect of this early design step) because the effects of the mistakes made during the development and analysis of the requirements are magnified in the later phases of the development life cycle. Formal description and modeling of the requirements become even more imperative with the increasing level of automation in transport as there is gradually less human supervision and intervention in case of undesired/erroneous behavior. For this reason, regulations and standards recommend the use of semi-formal and formal requirement description languages during the development of systems. However, it can be difficult for experts of a specific field to use a field-independent, completely formal method, partly due to their lack of necessary background knowledge, and partly because formal descriptions are difficult to read. It is, therefore worthwhile to strive for a compromise, and to develop a formalism that fits the specific field and is a little closer to natural language. This paper presents a possible methodology (transformation process) for developing and applying of such an intermediate language. The constructed intermediate language (we call it ‘restricted textual template’) provides an easy-to-apply, practice-oriented language compared to currently available solutions. We aim to support the work of the transportation engineers who work in the development of industrial control systems.
Can We Teach Functions to an Artificial Intelligence by Just Showing It Enough “Ground Truth”?

The term “artificial intelligence”, which in the past received very different interpretations, is nowadays being identified with deep learning. Deep neural networks bring the promise that, instead of hand-crafting data processing algorithms by mathematical reasoning based on formalized principles, we can simply feed enough data to a neural network, which will learn the right operator from it. In supervised learning, the data are either annotated by humans or obtained from a large set of observed pairs (xn, f(xn)). It is this association of an output f(xn) to an input xn in a learning dataset which is called a “ground truth”. The use of a “ground truth” annotated by humans raises a serious methodological problem, as humans are fallible. Worse even, the performance of these methods is evaluated and compared on subsets of the same annotations. Objective natural ground truths raise similar issues: raw data can be ambiguous or contradictory. In this paper, we shall examine two examples where machine learning methods were used to replicate aspects of human perception and logic: depth perception and the detection of straight lines or segments. We show that a strict control of the geometry in the learning data set, or a rigorous mathematical definition of the geometric task, lead to results widely different from those learned blindly from annotated datasets or from ground truths acquired in the wild. We conclude that a mathematical and principled analysis of learning datasets should precede their use. En gratitude à Catriona Byrne, mémorable, unique, et irremplaçable chef d’orchestre de l’édition mathématique.
Toward a Robust Segmentation Module Based on Deep Learning Approaches Resolving Historical Cursive Fonts Challenges

Historical documents are of major importance in conserving cultural and scientific heritage. In order to preserve this patrimony and to allow researchers from multiple fields to manipulate them, experts had opted to digitize these documents. In order to accelerate the process of classification while building logical connections between elements, the segmentation task should be treated carefully. Multiple non-textual blocks are presented within historical documents. Moreover, the non-consideration of the presence of columns generates major issues within the different processing stages. Based on deep learning segmentation approaches, this paper introduces the opted design for text/non-text separation and columns segmentations tasks. Moreover, a novel segmentation approach dealing with cursive fonts is proposed. The accuracy of the text/non-text blocks segmentation accuracy is equal to 97.32%. The columns segmentation approach accuracy is equal to 93.26%.
Automatic encoding and repair of reactive high-level tasks with learned abstract representations

We present a framework for the automatic encoding and repair of high-level tasks. Given a set of skills a robot can perform, our approach first abstracts sensor data into symbols and then automatically encodes the robot’s capabilities in Linear Temporal Logic (LTL). Using this encoding, a user can specify reactive high-level tasks, for which we can automatically synthesize a strategy that executes on the robot, if the task is feasible. If a task is not feasible given the robot’s capabilities, we present two methods, one enumeration-based and one synthesis-based, for automatically suggesting additional skills for the robot or modifications to existing skills that would make the task feasible. We demonstrate our framework on a Baxter robot manipulating blocks on a table, a Baxter robot manipulating plates on a table, and a Kinova arm manipulating vials, with multiple sensor modalities, including raw images.
IMAGE FEATURE EXTRACTION USING HOUGH TRANSFORM

Image feature extraction is a fundamental process in computer vision and image processing.A powerful technique for identifying and extracting geometric elements from digital photographs is the Hough Transform.In this study, we suggest a brand-new approach to extracting picture features using the Hough Transform.The suggested technique is based on the detection and extraction of circular features from images using a circular Hough Transform.Experimental results show that the suggested technique performs more accurately and efficiently than current methods.Overall, the proposed method demonstrates the potential of using Hough Transform for image feature extraction.