Reviewerly/exHarmony huggingface dataset:
Bridging the Gap of Dimensions in Distillation: Understanding the knowledge transfer between different-dimensional semantic spaces

In recent years, knowledge distillation has been widely used in the field of deep learning in order to reduce the model size and save time and space. The student-teacher paradigm is a framework for knowledge distillation, and knowledge distillation proposed to minimize the KL divergence between the probabilistic outputs of a teacher and student network. However, apart from the probabilistic outputs, there are much valuable information contained in the middle layers of the teacher network. As for NLP tasks, the hidden vectors from different layers of a model have different semantic information, but the vectors' dimension of the student network is different from that of the teacher network in many cases, which makes hidden layer distillation hard to be performed directly. We propose to simply use a transition matrix to project the student's vector to a space of the same dimension as the teacher's vector, and we theoretically prove the effectiveness of this method. Our analysis shows how the transition matrix preserve important semantic information, which is closely related to the vector's characteristic in Euclidean space. We provide a geometric method for the interpretability of shared knowledge space for student-teacher architectures. Our experiments show that this method can significantly improve the performance of a small model in different tasks with different models.
Mastication Detection Method by Chin Movement Using Image Processing

Our ability to bite and swallow declines with advancing age, and the risk of aspiration increases. In this study, as premises for the development of an aspiration prevention system, we will detect mastication by chin movement from moving images. At the same time, we will count the number of times that a person masticates simply, and think about validity of this method. In order to detect mastication, we focused on the time variation of the distance between the point at the tip of the nose and the chin. First of all, we start to detect face organ points. From them, the distance between the two points is calculated for each frame. When it exceeds the threshold, it is regarded as the mastication. At this time, we apply a moving average in order to increase the robustness of outliers. As a result of the experiment, the detection frequency of “mastication as usual” were close to the actual frequency. Similarly, it can be said that we could detect “mastication slower than usual” almost exactly. However, violent noises occurred frequently in “mastication faster than usual”. They are thought to be the aftereffects of the blurring of the image caused by the intense movement of subjects in the stage of detecting the face organ points. Besides, unclear chin movement made the detection difficult.
Extended Shadow Suppression Method for Outdoor Scenes Using CLAHE with OTSU Binarization and Assorted Color Spaces

Background modeling is an important step for video processing applications. The varying illumination conditions deteriorate the visual data representation. The video image sequences with very existence of shadow effect deteriorates the overall visual quality. This existence if disregarded, it may cause major performance issues in crucial computer vision applications like segmentation, object detection and tracking. If this could be handled, the facilitation of computer vision tasks can be boosted. However, it is a challenging research problem to suppress shadows by computer. It is therefore crucial to suppress shadows and keep details of original image intact. The proposed work focuses on improved shadow suppression model to improve the subjective perception with enhancing image using Contrast Limited Adaptive Histogram Equalization (CLAHE) and image binarization using OTSU with YCbCr and HSV color space. The objective evaluation using entropy measure and subjective results have shown better result.
Unambiguous State Discrimination with Intrinsic Coherence

We investigate the simulation of pure-mixed (quantum filtering) and mixed-mixed state discriminations, by discriminating a pair of pure states and comparing the optimal success probability with the ones for pure-mixed and mixed-mixed discriminations. It is shown thatunder the equal-fidelity condition, the pure-pure state discrimination scheme is superior to the pure-mixed (mixed-mixed) one. With respect to the simulation of filtering, the coherence is detrimental to the state discrimination for lower dimensional systems; while it is the opposite for the mixed-mixed case with symmetrically distributed coherence. This result is due to the different distribution of the intrinsic coherence in the two schemes. Extending this problem to infinite-dimensional systems, we find that the coherence which is detrimental to state discrimination may become helpful and vice versa.
California and Alabama haven't joined the Google antitrust lawsuit

Two very different states, California and Alabama, are sitting out a nearly nationwide antitrust probe of Google.
Examining the Bug Prediction Capabilities of Primitive Obsession Metrics

Bug prediction is an approach that helps make bug detection more automated during software development. Based on a bug dataset a prediction model is built to locate future bugs. Bug datasets contain information about previous defects in the code, process metrics, or source code metrics, etc. As code smells can indicate potential flaws in the source code, they can be used for bug prediction as well.In our previous work, we introduced several source code metrics to detect and describe the occurrence of Primitive Obsession in Java. This paper is a further study on three of the Primitive Obsession metrics. We integrated them into an existing, source code metrics-based bug dataset, and studied the effectiveness of the prediction built upon it. We performed a 10 fold cross-validation on the whole dataset and a cross-project validation as well. We compared the new models with the results of the original dataset. While the cross-validation showed no significant change, in the case of the cross-project validation, we have found that the amount of improvement exceeded the amount of deterioration by 5%. Furthermore, the variance added to the dataset was confirmed by correlation and PCA calculations.
Proyectos en el proyecto de internacionalización del IES Felipe Solís, Cabra (Córdoba)

espanolEl Instituto Felipe Solis Villechenous cuenta con un Programa de Internacionalizacion que reune proyectos Erasmus+ y eTwining. Es en aquel ambito donde comenzo su andadura ligado a proyectos KA 103 de Educacion Superior y KA 102 para alumnado de Ciclos Formativos de Grado Medio. En la actualidad, ademas de los proyectos de movilidad de alumnado y profesorado, alrededor de 7 profesores/as de Lenguas extranjeras y profesorado bilingue de materias no linguisticas del centro estan desarrollando proyectos eTwinning con diversas tematicas. EnglishFelipe Solis Villechenous Secondary and Vocational School has an Internationalization Program that brings together eTwinning and Erasmus + projects. It is in this area where it began its journey linked to projects KA 103 for Higher Education and KA 102 for Vet students. Currently, in addition to the mobility projects for students and teachers, around 7 foreign language teachers and bilingual teachers of non-linguistic subjects are developing e-Twinning projects about different and motivating topics.
Verification of Menzerath-Altmann law in Different Chinese Registers based on corpus

Menzerath-Altmann law is an important law in the field of quantitative linguistics, which has been verified in many languages and linguistic hierarchies. While up to now, little research has been done into Chinese language and sentence level. Therefore this paper builds a corpus containing several literary forms based on the Chinese texts, using Python programming language and NLREG to verify the law of MA at the level of Chinese sentences and try to give explanations. This paper hopes to provide knowledge support for all kinds of linguistic research and natural language processing tasks.
Applications of Mathematical Modelling Techniques

The primary objective of this research article is to discuss the applications of mathematical modeling techniques with difference equations and ordinary differential equations. Mathematical modeling is the art of translating problems from an application area into tractable mathematical formulations whose theoretical and numerical analysis provides insight, answers, and guidance useful for the originating application. Learning about mathematical modeling is an important step from a theoretical mathematical training to an application-oriented mathematical expertise, and makes the researcher fit for mastering the challenges of our modern technological culture. This research paper mainly focuses on characteristics, assumptions and limitations of mathematical modeling. Mathematical modeling with optimization techniques and mathematical modeling with simulations are proposed here. Exponential modeling and its differential equation, Logistic modeling with differential equations and mathematical modeling with Lotka-Volterra equations have been presented in a lucid manner. Moreover this discussion will certainly give an insight about mathematical modeling using integration and mathematical model building with linear programming problem.
Deep-learning-based remote sensing video super-resolution for Jilin-1 satellite

Due to far imaging distance and relatively harsh imaging conditions, the spatial resolution of remote sensing data are relatively low. Images/videos super-resolution is of great significance to effectively improve the spatial resolution and visual effect of remote sensing data. In this paper, we propose a deep-learning-based video super-resolution method for Jilin-1 remote sensing satellite. We use explicit motion compensation method by calculating the optical flow through the optical flow estimation network and compensating the motion of the image through warp operation. After obtaining the multi-frame images after motion compensation, it is necessary to use multi-frame image fusion for super-resolution reconstruction. We performed super-resolution experiments with scale factor 4 on Jilin-1 video dataset. In order to explore suitable fusion method, we compared two kinds of image fusion methods in the super-resolution network, i.e. concatenation by channel and 3D convolution, without motion compensation. Experimental results show that 3D convolution achieves better super-resolution performance, and video super-resolution result is better than the compared single image super-resolution method. We also performed experiments with motion compensation by optical flow estimation network. Experimental results show that the difference between the image after motion compensation and the reference frame becomes smaller. This indicates that the explicit motion compensation method can compensate the difference between the frames due to the target motion to a certain extent.
Intuitive Manipulation of Virtual Clay in Augmented Reality

In recent years, there has been a lot of research on how to achieve interaction between users and virtual objects using augmented reality. Interaction technologies in augmented reality need to enable users to handle virtual objects intuitively. In addition, since hands are the main means of interaction with objects in real life, it is also necessary to enable interaction operations with hands on virtual objects [1]. In order to make it possible to intuitively handle objects in virtual space using hands in real space, it is necessary to consider whether physical phenomena in real space and virtual space are correctly superimposed (physical consistency). In this study, we proposed a system that allows users to intuitively handle the deformation, movement, and merging of virtual objects in augmented reality. The system was then used by four university students to compare it with existing studies [2].
Results of Formation Monitoring Professional Competencies Geography Teachers

The article deals with the results of diagnostics of the level of formation of professional competencies of geography teachers. In the course of the study professional deficits in the subject, methodological, psychological, pedagogical and communicative training of teachers were revealed, which should be paid special attention to when building individual educational trajectories and professional development system.
Particle Swarm Optimization Enhanced with Kernel Principal Component Analysis

Particle swarm optimization (PSO) converges quickly in the initial stage of the search, and is essentially a random search algorithm. Such random search will inevitably lead to a premature convergence problem. In this study, we propose a novel particle swarm optimization enhanced by means of kernel principal component analysis (KPSO). The idea comes from particle swarm optimization imitates human social behavior. By introducing human social behavior, the optimal solution is searched from the overall driving swarm instead of considering only a single optimal particle, preventing particles premature. KPSO is tested on low-dimensional and high-dimensional benchmark functions. Experimental results show that compared with other PSO variants, the KPSO algorithm exhibits competitive performance in terms of accuracy and convergence speed, especially on high-dimensional problems. The KPSO algorithm is also applied to multi-fuel economic dispatch, and the results prove the effectiveness of the proposed method.
Application and Empirical Analysis of Project Teaching Method in Computer Professional Courses

This paper studies the application characteristics of project teaching method in computer course teaching in universities, and the mechanism of project driven teaching quality evaluation, and establishes the teaching quality evaluation system based on network platform. Under the framework of. Net+Ajax, this paper discusses the interaction mode of web application. Starting from the demand of specific examples, we also analyze the application development under the traditional mode and point out the shortcomings, and applies related technology to the project teaching system. Through the processing of the evaluation data, the teaching administrators, students and teachers can understand the relevant information in real time, to improve the teaching effect of project driven teaching course. The results of empirical analysis show that the teaching scheme is easy to operate, intuitive, from the whole design project to the implementation of the project, students' post self-confidence has been greatly improved, the degree of cooperation in class is also significantly enhanced, in a real sense, it realizes the universality and maintainability.
Environmental and Ergonomic Considerations for Augmented Reality User Experiences in Vehicle Diagnostics Tools

The advent of augmented reality technology has been a boon for automotive segment, particularly the vehicle diagnostics. Augmented reality enables the overlay of critical diagnostic digital data on the field of view of the real-world vehicle parts thus providing valuable information with the context of diagnosis and repair thus greatly reducing the digital-analog divide. Another key advantage in augmenting critical information like DTC codes, RT measurements over the actual vehicle parts helps in reducing the cognitive load for the technician in remembering the details from service manuals and can help in lowering the human error factor as well. This is one of the reasons why many of the tier 1 vehicle OEMs are eagerly embracing this technology. However, the platform on which most of the tools are designed are predominantly smartphone and tablet based since the smartphone hardware and software provide a cost effective as well as comprehensive ecosystem for rendering the augmented content. This, in reality, poses challenges from an environmental and ergonomic standpoint for the technician who needs to use these tools in conditions which is dynamic and may not always be linear. The paper details study ongoing across multiple geographies including US, Europe and India involving contextual observations as well as moderated in person interviews conducted with the technicians with an aim to capture the environmental conditions and ergonomic factors pertaining to the technicians’ work zone and how it influences the user experience of designing augmented reality tools for vehicle diagnostics. For the purpose of this study, application of readily available and cost-effective AR platforms is being considered and high-end mixed reality headsets like Microsoft’s Hololens are excluded.
LiDAR-based localization and automatic control of UAVs for mobile remote reconnaissance

Sensor-based monitoring of the surroundings of civilian vehicles is primarily relevant for driver assistance in road traffic, whereas in military vehicles, far-reaching reconnaissance of the environment is crucial for accomplishing the respective mission. Modern military vehicles are typically equipped with electro-optical sensor systems for such observation or surveillance purposes. However, especially when the line-of-sight to the onward route is obscured or visibility conditions are generally limited, more enhanced methods for reconnaissance are needed. The obvious benefit of micro-drones (UAVs) for remote reconnaissance is well known. The spatial mobility of UAVs can provide additional information that cannot be obtained on the vehicle itself. For example, the UAV could keep a fixed position in front and above the vehicle to gather information about the area ahead, or it could fly above or around obstacles to clear hidden areas. In a military context, this is usually referred to as manned-unmanned teaming (MUM-T). In this paper, we propose the use of vehicle-based electro-optical sensors as an alternative way for automatic control of (cooperative) UAVs in the vehicle’s vicinity. In its most automated form, the external control of the UAV only requires a 3D nominal position relative to the vehicle or in absolute geocoordinates. The flight path there and the maintaining of this position including obstacle avoidance are automatically calculated on-board the vehicle and permanently communicated to the UAV as control commands. We show first results of an implementation of this approach using 360° scanning LiDAR sensors mounted on a mobile sensor unit. The control loop of detection, tracking and guidance of a cooperative UAV in the local environment is demonstrated by two experiments. We show the automatic LiDAR-controlled navigation of a UAV from a starting point A to a destination point B. with and without an obstacle between A and B. The obstacle in the direct path is detected and an alternative flight route is calculated and used.
Visual Servo Control of Underwater Vehicles Based on Image Moments

To solve the dynamic positioning problem of underwater vehicles for executing autonomous operation tasks, an image moments-based six degrees of freedom (DOF) visual servo control method is proposed. At first, the equations of motion of underwater vehicles are presented, and the image moments of underwater objects are introduced. Then the Jacobian matrix of image moments is derived, and the image- based visual servo control algorithm is designed, in which the feedback states are constructed by the image moments and attitude angles of the vehicle. To estimate the pitch and roll angles, a multi-layer neural network is trained to approximate the angles with image moments. The stability of the proposed visual servo control is analyzed by a Lyapunov-based method. The simulation results prove that the proposed control method has satisfactory performances for decoupled control of different DOFs with underwater targets with different shapes.
MAIN: Multihead-Attention Imputation Networks

The problem of missing data, usually absent in curated and competition-standard datasets, is an unfortunate reality for most machine learning models used in industry applications. Recent work has focused on understanding the nature and the negative effects of such phenomena, while devising solutions for optimal imputation of the missing data, using both discriminative and generative approaches. We propose a novel mechanism based on multi-head attention which can be applied effortlessly in any model and achieves better downstream performance without the introduction of the full dataset in any part of the modeling pipeline. Our method inductively models patterns of missingness in the input data in order to increase the performance of the downstream task. Finally, after evaluating our method against baselines for a number of datasets, we found performance gains that tend to be larger in scenarios of high missingness.
Opinion Mining Analysis on Online Product Reviews Using Naïve Bayes and Feature Selection

For profit companies that are centered on customers, every opinion of them is very important because knowing this opinion will be able to help the company to consider in making decisions about whether a product will be added to the production or not. It is necessary to know whether this opinion is positive or negative. This study discusses the classification of positive and negative opinions on online products using Naïve Bayes method. And to overcome a large number of datasets (having a lot of features/words) for increasing the accuracy of Naïve Bayes, it is combined with feature selection. In this case, the feature selection technique used is Chi-Square. This research is devoted to comparing system performance to see the performance of feature selection on small and large datasets. From the results of these tests, it is evident that feature selection combined with Naïve Bayes results in better system performance than not using feature selection for datasets that have many word features (large datasets) which can increase the accuracy around 11%. On the other hand, the use of feature selection has less effect on small datasets.
Data Diversification Revisited: Why Does It Work?

Data Diversification is a recently proposed method of data augmentation for Neural Machine Translation (NMT). While it attracts broad attention due to its effectiveness, the reason for its success is unclear. In this paper, we first establish a connection between data diversification and knowledge distillation, and prove that data diversification reduces the modality complexity. We also find knowledge distillation has a lower complexity of data modality than data diversification, but challenging to boost performance. Our analysis reveals that knowledge distillation has a negative impact on the word frequency distribution where increasing rare words with unreliable representations. Furthermore, data diversification trains multiple models to further decrease the modality complexity, suffering from unbearable computational expenses. To reduce the computational cost, we propose adjustable sampling, which samples a model multiple times instead of training multiple models. Different from other sampling methods, our method introduces entropy to adjust the quality and diversity of the generated sentences, achieving the goal of reducing modality complexity and noise introduction. Extensive experimental results show our method dramatically reduces the computational cost of data diversification without loss of accuracy, and achieves improvements over other strong sampling methods.