Reviewerly/exHarmony huggingface dataset:
TEACHING MEDIA IN EFL CLASSROOMS: WHAT ARE THEY AND WHY SELECT THEM

Teaching media are essential in the teaching and learning English. The use of appropriate media in teaching a new language will help teachers create more effective learning environment in the classrooms. Using a descriptive qualitative method, this study explores what kinds of teaching media used by the teachers in teaching English and what factors influencing English teachers’ selection of teaching media in the classroom. This study was conducted at SMP Unismuh Makassar, involving two English teachers who have worked there for several years. The researcher used interview and observation as instruments in data collection process. The data were analyzed using data reduction, data display and data verification. The results of the study show that the teachers make use of four kinds of media, namely graphic media such as textbook and picture, media video such as video, projected media such as Power Point and display media such as a board. In addition, the factors influencing the teachers’ selection of those media are time availability, media availability, students’ characteristics, and students’ needs
Soft Edge Detection by Mamdani Fuzzy Inference of Color Image

One of the most common image operation analyses is the edge detection technique. Edge detection is used for shaping the edge of an image. Also, it is used for enhancing images. This paper presents a new approach to detecting the edge of color image using the Mamdani fuzzy inference classifier based on the Fuzzy Set Membership Function (FSMF). Here, Gaussian Curve Membership Function (GCMF) is used as a FSMF. GCMF is used for each class to assign that class to each pixel. In this approach, two windows/filters are used in size (1x2) and (2x1). Several standard color images are used to test our proposed algorithm (City, Jelly_cc11, Baboon, Lena, and Peppers). In order to parametric evaluation of selected images, Peak Signal Noise Ratio (PSNR) and Mean Square Error (MSE) are considered. However, the performance of our proposed algorithm compared with other well-known approaches (Canny, Prewitt, and Sobel) is somehow very similar but significantly faster.
Fast Localization and Tracking of Visual Target

For the visual targets in the runway and taxiway, we designed a tracking system, which can effectively track the targets stably and smoothly, and ensure that the targets are always located in the central area of the picture. This paper will introduce the three main parts of the system in detail: target centralization, tracking algorithm and control strategy. Firstly, we proposed to quickly centralize the target before tracking, which can improve the stability. We also realized a fast centralization method with small deviation from the imaging principle of the camera. After that, we comprehensively balanced the advantages and disadvantages of classical tracking algorithm, selected the suitable one, and modified it to realize a method with a frame rate of 170 frames per second. In terms of control, we focused on the strategy of the direction and speed, which ensure the smooth movement of the running camera. Finally, we put forward some points which can be improved.
Witness Encryption from Smooth Projective Hashing System

Smooth projective hash functions, also designated as hash proof systems (Eurocrypt ’02) and witness encryption (STOC ’13), are two powerful cryptographic primitives. The former can produce a hash value corresponding to an NP language instance in two ways, and the latter allows encrypting a message using the description of an instance in an NP language. Mostly, witness encryption is constructed using computationally expensive tools like multilinear maps or obfuscation. In this work, we build a witness encryption scheme using the smooth projection hash function (SPHF), which achieves efficient encryption and decryption. Specifically, we generate a zero-knowledge proof for the witness w of the NP instance x and use this proof as an instance of SPHF to encrypt a message. Next, we instantiate SPHF, the key technique of the WE scheme, supporting the NP-complete circuit SAT problem proved in the NIZK argument. Furthermore, based on our instantiated SPHF, our WE produces a fixed-size ciphertext, and a theoretical comparison with Derler et al. (DCC ’18) instantiated SPHF from Gorth-Sahai proofs (GS-proofs) shows that our SPHF instantiation is more efficient than theirs.
Aerial Pruning Using a Multirotor Flying Robot with a Wire-Suspended Reciprocating Saw

We have developed a flying robot that hangs a reciprocating saw from a multi-rotor UAV with a wire. In this study, we propose a method of pruning a branch at a place away from the UAV as a work point and experiments aimed at verifying the possibility of pruning work and improving cutting speed and stability. In the experiment, some wood was cut with no force applied and with ducted fans pressed against the object to be cut. In order to measure force and vibration, we attached a force sensor to the fixed UAV and conducted the experiment. We confirmed that the proposed method can be applied to the actual flying robot and succeeded in significantly reducing the cutting time by pressing against the cutting target through experiments.
Improving Noise Robustness of Single Sensor Data in Human Activity Recognition With UMAP and Additional Data

In this paper, we propose a method for human activity recognition that can transfer information from multiple sensors to a single sensor with improving noise robustness by using a topological feature representation created by UMAP. With clean data, UMAP improves as much as 12% in macro F1-score compared to the Feature Agglomeration approach. With noisy data, UMAP is more stable, with only a 0.25% decrease in average macro F1-Score. By contrast, with Feature Agglomeration and Traditional approaches, the average macro F1-scores decline by 2.75% and 5.75%, respectively.
Learning to Rank with Capsule Neural Networks

Exploratory search scenarios are becoming common in recommender systems. Exploratory search is aimed at self-education and knowledge acquisition, its query often cannot be formulated in a short phrase but can be represented as one or several topically coherent documents. The results of exploratory search can be very extensive, but the ranking algorithms mostly use simple statistics which are not sufficient to rank documents in the convenient for further investigation and refinement order. In this work, we study capsule neural networks in combination with various textual representations applied to document ranking. The proposed model is tested on ArXiv triplets and Microsoft News Datasets and compared to classical approaches. Experiments show that capsule networks can significantly improve the quality of rankings in terms of search Presicion and Recall.
On evaluation of the solution of Gaussian impulse propagation

We consider the solution of the 2D wave equation with the initial data prescribing a Gaussian pulse. This solution has integral representations. Previously proposed algorithm of its evaluation is optimized by the use of more precise error bounds of numerical quadratures.
Alpha-Rooting Color Image Enhancement Method for Discrete Fourier Transform and Discrete Quaternion Fourier Transform

In this paper, color images enhancement using alpha-rooting will be compared with the discrete Fourier transform and discrete quaternion Fourier transform. The increase in color images was measured by Gray Level Co-Occurrence Matrix (GLCM) by looking at the contrast in each image. It was found that the selection of $$\alpha$$ values greatly affect the results of image in an image. The results of experiments conducted using several images inform that the discrete Fourier transform has a GLCM value at a higher contrast than the discrete quaternion Fourier transform.
Perspective Review on Deep Learning Models to Medical Image Segmentation

In recent days, deep learning is on rage and is gaining a huge amount of popularity due to its supremacy in terms of accuracy. Deep learning is being used for a vast number of applications out of which healthcare is an important category. In this paper, we discuss the role of deep learning in medical image segmentation. It is also known as the automated or semi-automated detection of edges within various medical image modalities so as to identify the region of interest. Furthermore, we also explore the various deep learning networks that are widely preferred for medical image segmentation along with the architecture and overview of each network. This paper covers the most recent and widely preferred deep learning networks such as Convolutional Neural Network (CNN) and other related networks such as Alexnet, Resnet, U-net and V-net. The challenges and limitations of the emerging DL networks is also studied.
Measuring Noticeability: Multi-scale Context Aggregation for Prioritizing Video Anomalies

Determining how impactful an anomaly is on the viewing experience of an audience is important to production studios, content creators, and content distributors. However, judging the impact of each anomaly is a highly manual and subjective task. To support automation of this, we propose the use of noticeability and introduce a method of prediction. We employed a psychophysical experimental method to capture the impact of various anomalies across various images. We then developed a multi-scale context aggregation model, trained on that data, to predict the noticeability of anomalies on novel images. This notice ability prediction can then be used to prioritize anomalies and ensure that remediation efforts are spent where it would most benefit the audience experience.
Face Recognition Using Gradient Texture Features

Nowadays,it is very important to recognize/identify a person for security reasons. A lot of people and organizations use biometrics for the identification of a person. Face recognition is one of the biometric identification techniques used a lot. The crucial part of the face recognition system is feature extraction and a good feature extraction technique has to be robust to noise and illumination. In this paper, we have proposed a new feature extraction technique using the texture of the image with the help of gradient features. In the proposed algorithm, at first, the face is detected using the MTCNN algorithm and the features are extracted from the detected face for the recognition purpose. Kirsch edge detection 8 kernels are applied to get edge response from the face detected and used ANOVA to find the edge similarity between each of the convoluted images of Kirsch edge response to get the final image as explained in the encoding face image section. Eigen values were obtained from the final image using Principal Component Analysis (PCA). The Chi-square method is used as a classification technique for matching the features of the test image with trained images present in the database. Here, the FERET dataset is used for training the model and testing the accuracy and we have achieved encoring results using the proposed method.
Naïve Bayes for Analysis of Student Learning Achievement

Student achievement is measured by the achievement index value obtained every semester,student achievement is measured by several factors, and in this research the author takes several factors including study paths, choice of majors, monthly living expenses, relationships with friends, relationships with family, motivation study, employment, scholarships, transportation, and internet services. Analysis and prediction of student achievement using Naïve Bayes Algorithm classification method, the result is this algorithm works very well using 14 student datasets to determine the grades of the 15th student. Based on theAnalysis, variables that affect student achievement include choice of majors, residence, relationships with friends, relationships with family, job, and scholarships. The accuracy of the naïve bayes algorithm for this student achievement case study model reaches 60%, precision 25%, and recall 100%.
On the Automatised Generation of a Japanese Banduke Table

Banduke tables are a traditional Japanese mean of communicating rankings and other organisation charts.They are well-known in Japan especially thanks to their continued usage to announce details of sumo wrestling competitions.Such tables were also used in earlier centuries for instance to publish the names of the wealthiest individuals in the country.Traditional banduke tables feature a characteristic, visually impacting design which is difficult to reproduce on computer systems.And, the automatisation of the generation of such a chart is even more challenging.In this paper, in order to keep this cultural heritage alive, we investigate this automatisation issue and propose a system which generates banduke tables.This work notably starts with an analysis of the various elements and zones that are essential to the structure of a banduke table.After presenting technical details of the proposed generation system, like the generation flow of such an organisation chart, we address the critical character rendering issue.The details of character box calculations are formally described.The obtained results are very promising in that the charts that can be produced by our proposal look very realistic.Besides, we have also quantitatively evaluated the generated tables by comparing them to original documents, five in total, some of which were obtained with the help of museums of Japanese history.Both traditional tables and ones of our modern, printing era were used for this comparison experiment.
Structured and tiled-based pruning of Deep Learning models targeting FPGA implementations

Model compression techniques have lead to a reduction of size and number of computations of Deep Learning models. However, techniques such as pruning mostly lack of a real co-optimization with hardware platforms. For instance, implementing unstructured pruning in dedicated hardware is not a straightforward task, which increases memory and reduces the effective bandwidth usage. Moreover, such pruning algorithms should be adapted to certain hardware requirements, such as the use of tiling. Therefore, in this work, we leverage the use of the Gumbel-Softmax relaxation sampling to structurally prune tiles, which benefits further hardware implementations, and additionally allows to jointly optimize with quantization. Additionally, we show that the combination of different pruning scenarios leads to a larger sparsity. Finally, we demonstrate the benefit of using structured pruning on fine-grained elements (weights) in an FPGA design.
Transfer Meta Learning

Transfer Learning methods aim to reuse previously acquired knowledge about a source task to facilitate learning of a target task. In this paper, we present a Meta Learning approach to find optimal hyperparameters for Transfer Learning processes given previously known metadata about the source task, the target task, and the pre-trained model. We collected metadata and model parameters from more than 15,000 Transfer Learning processes in a dataset, which we use to learn metamodels that predict a Transfer Learning process result in terms of accuracy on the validation sets, given prior information such as the number of epochs, learning rates, optimizers, etc. Using feedforward multilayer perceptrons (MLP), we show that and how our approach finds efficient hyperparameters for Transfer Learning for image classification.
Optimizing Winograd Convolution on GPUs via Partial Kernel Fusion

Convolution operations are the essential components in modern CNNs (Convolutional Neural Networks), which are also the most time-consuming. Several fast convolution algorithms include FFT and Winograd, have been proposed to solve this problem. Winograd convolution is used to improve the inference performance of the convolution operators with small kernels, which are the mainstream in the current popular CNNs. However, the implementations of Winograd convolution in many highly optimized deep neural network libraries and deep learning compilers are not efficient. Due to the complex data dependencies of the four stages of Winograd convolution, it is very challenging to optimize it. In this paper, we improve the inference performance of the Winograd convolution operator on GPUs. We propose a sync-free implementation of the calculation stage of Winograd and furtherly propose methods of PKF (Partial Kernel Fusion) utilizing different memory levels of GPUs. We implemented PKF-Reconstructor based on TVM for PKF Winograd convolution. Evaluations on convolution operators from real-world CNNs show that our method achieves a speedup of 8.22 $$\times $$ –13.69 $$\times $$ compared to cuDNN and 4.89 $$\times $$ –9.10 $$\times $$ to the fastest vanilla TVM Winograd implementation.
Population Management

Abstract After having generated several solutions, we can seek to learn how to combine them. This chapter review techniques for generating new solution from existing ones and for managing a population of solution. The most popular method in this field is undoubtedly genetic algorithms. However, the latter are less advanced metaheuristics than memetic algorithms or scatter search. The path relinking technique is also part of this chapter. Finally, among the last metaheuristics invented, we find the particle swarm methods, which seem adapted to continuous optimization.
Using 3D Topological Connectivity for Ghost Particle Reduction in Flow Reconstruction

Volumetric flow velocimetry for experimental fluid dynamics relies primarily on the 3D reconstruction of point objects, which are the detected positions of tracer particles identified in images obtained by a multi-camera setup. By assuming that the particles accurately follow the observed flow, their displacement over a known time interval is a measure of the local flow velocity. The number of particles imaged in a 1 Megapixel image is typically in the order of 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> -1 0 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">4</sup> , resulting in a large number of consistent but in-correct reconstructions (no real particle in 3D), that must be eliminated through tracking or intensity constraints. In an alternative method, 3D Particle Streak Velocimetry (3D-PSV), the exposure time is increased, and the particles' pathlines are imaged as “streaks”. We treat these streaks (a) as connected endpoints and (b) as conic section segments and develop a theoretical model that describes the mechanisms of 3D ambiguity generation and shows that streaks can drastically reduce reconstruction ambiguities. Moreover, we propose a method for simultaneously estimating these short, low-curvature conic section segments and their 3D position from multiple camera views. Our results validate the theory, and the streak and conic section reconstruction method produces far fewer ambiguities than simple particle reconstruction, outperforming current state-of-the-art particle tracking software on the evaluated cases.
Information-Theoretic Cryptography: A Maneuver in the Trade-Off Space of Cryptography in IoT

This chapter discusses the reason why we have chosen to study perfectly secure cryptography (as a branch of information-theoretic cryptography) among the existing approaches to cryptography in IoT. To this end, we take a systematic approach. We first introduce timeliness and resource constraint awareness as two critical requirements of cryptography in IoT and highlight the trade-off between them. Next, we use the existing research literature to establish an ecosystem as well as a life cycle for each of the following fields. In the next step, we demonstrate that all the developed ecosystems and life cycles are closely similar to each other. The ecosystems consist of similar enablers and application areas. Further, the life cycles include similar phases with similar challenges and issues in each phase. These findings lead to the formation of common ecosystems and life cycles for all of the aforementioned fields. Finally, we establish an ecosystem as well as a life cycle for information-theoretic cryptography and highlight their similarities to the common ecosystem and the common life cycle mentioned above. These similarities suggest information-theoretic cryptography as a promising solution for resolving the trade-off between the requirements of cryptography in IoT.
A Method for Predicting the State of Sensors and Meters Based on Improved Generative Adversarial Networks

Sensors and meters are used in a wide range of applications, the effects of anomalies or failures are increasing. Therefore, accurate and efficient state prediction methods are required. In this paper, the main problem of state prediction— imbalanced classification is studied. The traditional GAN and CatGAN are analyzed, and an improved model CatGAN-SP is proposed for the problem of state prediction. The relevant objective functions are deduced, and the specific expressions of the objective functions are given. The experimental verification was carried out using measured data of ultrasonic flowmeter. The effectiveness of the model in sensors state prediction problem is proved by comparison with related algorithms. In particular, the model further solves the class imbalance problem and improves the accuracy of sensors state prediction effectively.
Research on the Detection and Tracking of Moving Objects in Dynamic Scenes

Aiming at the poor robustness of the moving objects detection and tracking algorithm in the dynamic scenes, a new moving objects tracking algorithm in the dynamic scenes is proposed, which combines the optical flow method and Kalman predictor, can solve the occlusion problem in target tracking. The optical flow method solves the detection of the moving objects problem, and the Kalman predictor is used to complete the moving target prediction and association. The experimental results show that, the proposed algorithm can work well in the stationary scenes and the dynamic scenes, and the accuracy of detection of moving objects in dynamic scenes is more effective than the optical flow method only.
Description and Temporal Positioning of Historical Events and Periods by Using Links Between Temporal Resources

The temporal beginning and end boundaries of a historical event and period are often represented as another event or period rather than an accurate date and time (e.g., the early modern period is often defined as beginning from the Renaissance). This study proposed a procedure to describe time intervals of events and periods whose boundaries are defined by other events or periods, as resource description framework (RDF) data and to determine their temporal positions. This procedure enables an RDF data to have information about the original definition and semantics of the historical event or period in addition to temporal range. Calendar periods, such as dates, months, years and decades also can be considered as resources having a temporal range just like the historical events and periods, and can be used to specify boundaries of a time range. This means that the time intervals described according to the proposed procedure can be processed in the same framework, regardless of whether they are based on dates or events. Subsequently, this study examined the temporal position of a time interval by tracing links between resources used to specify the time interval.
A Consideration on Change the Transition Probability of SHAKE256 with Different Initial Values

NIST standardizes some cryptographies to develop unbreakable systems even with quantum computers namely post quantum cryptography. Some of them uses the pseudo random number generator, SHAKE256 in their key generation and encryption schemes. In this manuscript, the uniformity of sequences obtained from SHAKE256 is evaluated by checking the transition probability of 2-bit patterns to check its unpredictability.