Reviewerly/exHarmony huggingface dataset:
Analyzing Prediction Performance between Wavelet Neural Network and Product-Unit Neural Network

Abstract Analyzing the performance of a particular approach in a field very dependent on the problem it’s aimed to solve. Artificial Neural Network (ANN) widely used for prediction in many areas including medical, environment, business intelligence and education. The uniqueness of ANN is the dynamic of hidden layer can be improvised mapped with the data problem and the structure of architecture can be enhanced such as Wavelet Artificial Neural Network (WANN) and Product Unit Neural Network (PUNN). This research aimed to analyzed the performance between WANN and PUNN towards water quality data of Chini Lake. Real world data comes with dynamic stream data and dynamic parameters based on its area of data collection method. Handling dynamic data would be misleading if the approach used very dependent towards data classes. The measurement to analyze the data based on performance accuracy, data sensitivity, data precision and specification of both method with regards of the regular ANN. The findings demonstrate the ability to obtain satisfactory prediction accuracy for both WANN and PUNN compared to regular ANN. The model accuracy for this case study by using WANN and PUNN were 75.34 % and 66.86 %, respectively. Therefore, WANN would be a competitive tool for prediction with conventional ANN.
Mechanical test study in composites using digital holographic interferometry and optical coherence tomography simultaneously

A dual optical configuration to inspect the internal and external mechanical response of a composite specimen is presented. The inspection simultaneously uses two equally aligned optical techniques, digital holographic interferometry and Fourier domain optical coherence tomography, to retrieve surface and internal data, respectively. The sample under study is a composite specimen of poly-methyl-methacrylate reinforced with metallic particles. Two different sets of samples are analyzed to compare their mechanical behavior. A homemade, fully controlled testing machine is used to apply a controlled compression load while each technique registers an image. In this form, the surface and internal optical phase measurements are correlated to the same compression value for comparison purposes. Results for each technique are directly presented as simultaneous displacement maps, and a discussion and conclusion of this proposed dual method of inspection are presented.
Agent Component Reuse Based on Semantics Concept Similarity

In a certain field, there is a need to create a large number of agent components, so there may be insufficient agent resources. In other words, there are a lot of tasks that need to be solved by Agent software in a certain period of time. At this time, the solution tree or mesh structure that have been completed tasks are put into the queue. Each time a new task is processed, all tree or mesh structure completed tasks in the queue are traversed from the root node, if there is a node in the queue are highly similar with the new task node. The node is directly used to solve the new task node without to create new Agent component. This paper proposes a tree structure based method for computing the similarity of ontology concepts to reuse Agent component. The experimental results show that this method effectively utilizes the semantic information of ontology concepts and obtains reasonable calculation results, thus reducing the creation of Agents.
Effectiveness of Handout Notes to Group Discussion in a General Chemistry Course at a Two-Year College

This study looks at the effectiveness of handout notes in a General Chemistry course at a two-year college. To help increase student participation in class, handout notes were made available 48 hour in advance. Entry quizzes measured student’s preparedness for class session whereas exit quizzes measured effectiveness of discussion session. Student poll showed 59% of students considered handout notes to have been very effective in helping them prepare for class session. Performance on entry quizzes, engaged discussion groups, and willingness to work problems on the board supported students’ belief that handout notes were very effective in preparing for lecture session.
Deep Learning Using for Fall Detection on the Rehabilitation Walking-Aid Robot

Unexpected falls can result in severe injuries to disabled individuals who can barely maintain the balance of themselves. In this paper, a deep learning-based method for fall detection on the rehabilitation walking-aid robot was proposed. The goal of this method is to provide a real-time detector for the walking-aid robot so it can take preventive measures when the fall happened. The deep neural network with error back propagation to update the weight of each feature. The data set used in this paper was obtained upon the prototype of a walking-aid robot. The real-time motion data of the user's waist can be obtained by multiple sensors mounted on the walking-aid robot. Extract the motion features from the acquired data and tag it as normal or abnormal (potential to falling) feature, these two parts of data compose to a sample. Divide the whole data set into two parts to train and test the neural network. It turns out the accuracy of this trained neural network can be 95.8%. In the end, the effect of the parameter and hyper-parameters of the neural network was discussed.
Competing Topic Naming Conventions in Quora

Quora is a popular Q&A site which provides users with the ability to tag questions with multiple relevant topics which helps to attract quality answers. These topics are not predefined but user-defined conventions and it is not so rare to have multiple such conventions present in the Quora ecosystem describing exactly the same concept. In almost all such cases, users (or Quora moderators) manually merge the topic pair into one of the either topics, thus selecting one of the competing conventions. An important application for the site therefore is to identify such competing conventions early enough that should merge in future. In this paper, we propose a two-step approach that uniquely combines the anomaly detection and the supervised classification frameworks to predict whether two topics from among millions of topic pairs are indeed competing conventions, and should merge, achieving an F-score of 0.711. We also develop a model to predict the direction of the topic merge, i.e., the winning convention, achieving an F-score of 0.898. Our system is also able to predict ~ 25% of the correct case of merges within the first month of the merge and ~ 40% of the cases within a year. This is an encouraging result since Quora users on average take 936 days to identify such a correct merge.
Classroom pedagogical projects (CPP) with ICT for the development of research proficiency in higher education: a classroom experience

This article aims to analyze the relevance of Classroom Pedagogical Projects (CPP), developed with ICT, for the consolidation of research competence within the framework of innovative educational management. It shows a critical analysis of the author's classroom experience as a mediator in the Bachelor's degree in Basic Education program, with an emphasis on computer science, from the Universidad del Magdalena, Colombia, in which a pedagogical proposal is implemented, with a qualitative approach, which corresponded to the principles of action research, for the development of research competence in higher education. For the analysis, the criteria, theoretical, legal and extensive experience in the subject were considered, among other aspects, in addition, the methodology of systematization of own experiences and the hermeneutic study of information were used. From the classroom work, the processes of classification, systematization, categorization and triangulation of data were applied in order to obtain the results and final reflections. It is concluded that the development of CPP with ICT contributes to the consolidation of the research skills demanded by the knowledge society, promotes a comprehensive training, for the development of culture and citizenship.
Task Allocation and Path Planning of a Multi-Robot System Using Heuristic Coupled Particle Swarm Optimization Algorithm

This study deals with a plant layout where there were ninety predefined locations which have to be inspected by using three multiple robots in such a way that there would not be any collisions between the robots. A heuristic integrated multiobjective particle swarm optimization algorithm (HPSO) is developed for allocating tasks to each robot and planning of path while moving from one task location to another. For optimal path planning of each robot the research utilized A* algorithm. The task allocation for each robot is carried out using a modified multiobjective particle swarm optimization algorithm where the earliest completion time (ECT) inspired technique is used to make the algorithm applicable in multi robot task allocation problems. At the later stage of this study, in order to test the capability of HPSO an instance is solved by the algorithm and is compared with the existing solutions of a genetic algorithm with the A* algorithm. The computational results showed the superiority of the proposed algorithm over existing algorithms.
Query-Based Summarization using Reinforcement Learning and Transformer Model

Query-based summarization is an interesting problem in the text summarization field. The reinforcement learning technique is popular for robotics and become accessible for the text summarization problem in the recent years. The lack of significant works with reinforcement learning in this field inspired us to solve the query-based summarization problem using this technique. While doing so, we have introduced a different approach for sentence ranking and clustering to avoid the existence of redundant data in the summaries. We have proposed an unsupervised extractive summarization method, which provides some state-of-the-art results. We also developed two abstractive multi-document summarization models using reinforcement learning technique and the transformer model. We consider the importance of information coverage and diversity under a fixed sentence limit for our summarization models. We have done several experiments for our proposed models, which bring significant results across different evaluation metrics.
Stripe removal method for remote sensing images based on multi-scale variation model

Remote sensing images are often affected by severe stripe noise, which degrades image quality. In this paper, a destriping method is proposed with the multi-scale variation model on the basis of stripe noise analysis. First, the stripe noise model is established. Then, the multi-scale energy function is constructed with the unidirectional characteristics of stripe noise. Last, the function is optimized with the split Bregman approach, and the destriped image is obtained by the accumulation of the component in each scale. Experimental results on MODIS level 1B data show that the image quality and quantitative evaluation index can be improved. Compared with the typical destriping method, the proposed method is proved to remove stripe noise effectively while preserve detail information.
Blind Motion Deblurring Based on Generative Adversarial Networks

In the past two years, GAN (Generative Adversarial Networks) has emerged and been applied to the image deblurring problem, showing good results, especially in restoring high-frequency texture details of the image. However, there are very few papers on GAN-based image deblurring so far, and it is not ideal for the processing of edge features. In this paper, an end-to-end blind image motion deblurring algorithm based on GAN is proposed. In the pixel domain, we use the weighted sum of cross entropy and L1 loss as the loss function. In the feature domain, the weighted sum of the features extracted by VGG and DenseNet is used to calculate the loss. And we add "deconvolution + PixelShuffle" module to the network. Experiments show that our method achieves the excellent performance in terms of PSNR (Peak Signal to Noise Ratio) and SSIM (Structural Similarity Index), simultaneously eliminates the checkerboards effectively.
Remote Facial Emotion Recognition System

This paper describes a method for collecting information of user's emotional state based on its facial expressions and linking the data acquired to the user's actions in a time. Methods and Algorithms: At client side, we use reactive programming model implemented by RxJS library to manage data streams. Our client library includes logic to combining events from different sources such as MediaStream APIs, classic DOM event API. We have developed our own neural network architecture that allows us to map the facial expressions of people into a continuous space of arousal/valence. The same architecture for classification of emotions was used. We used inception and residual blocks and batch normalization layers to achieve higher accuracy. In addition, we augment train data by adding pose variations using 3D face reconstruction. Results: We have developed a system that allows us to collect various data such as video stream and events of user interaction with the webpage. We also trained a neural network that shows high accuracy. One part of the system is a client library that does not require the user to install any special software and can be easily integrated into the website. The other part is a server application that enables the site owner to analyze the data collected.
Investigation and analysis of hyper and hypo neuron pruning to selectively update neurons during unsupervised adaptation

Unseen or out-of-domain data can seriously degrade the performance of a neural network model, indicating the model's failure to generalize to unseen data. Neural net pruning can not only help to reduce a model's size but can improve the model's generalization capacity as well. Pruning approaches look for low-salient neurons that are less contributive to a model's decision and hence can be removed from the model. This work investigates if pruning approaches are successful in detecting neurons that are either high-salient (mostly active or hyper) or low-salient (barely active or hypo), and whether removal of such neurons can help to improve the model's generalization capacity. Traditional blind adaptation techniques update either the whole or a subset of layers, but have never explored selectively updating individual neurons across one or more layers. Focusing on the fully connected layers of a convolutional neural network (CNN), this work shows that it may be possible to selectively adapt certain neurons (consisting of the hyper and the hypo neurons) first, followed by a full-network fine tuning. Using the task of automatic speech recognition, this work demonstrates how the removal of hyper and hypo neurons from a model can improve the model's performance on out-of-domain speech data and how selective neuron adaptation can ensure improved performance when compared to traditional blind model adaptation.
Intelligent and Disaster Prevention Hard Hat Based on AIOT and Speeches Recognition

Technology always comes from human nature and life safety is more important than anything. In recent years natural disasters and work safety accidents happened frequently. So we put what have learned into practice. With the technology of IoT, our team has developed the Intelligent and Disaster Prevention Hard Hat, which improve the life safety more than tradition hard hat. Different from traditional Hard Hats, we combine Raspberry 3 and various sensors to transform into an Intelligent and Disaster Prevention Hard Hat, with Global Positioning System and MQ2 toxic gas detection, which is the best to apply in a variety of disaster situations. The main control terminal is built with a Responsive Web Design (RWD), which can change the webpage frame with various devices to provide the best visual effect. With 21 kinds of multi-hazard instant alarms, users can instantly know whether there will be a secondary disaster in near future, and combine voice and face recognition, etc. The Hakka's speech recognition is included for communication between client and backsite center.
Password Cracking Algorithm using Probabilistic Conjunctive Grammar

In this paper a password cracking algorithm is designed using Probabilistic Conjunctive Grammar (PCG). Here PCG is used for deriving the base structure of target password by combining multiple parse trees. After analysing the base structure of each password in the data-set, the base structures which are similar to the base structure of the targeted passwords are collected. In the final step, the password is cracked with its cracking probability.
Improving LSTM Based Acoustic Model with Dropout Method

The deep neural network model applied on speech recognition has become one of the most successful application of deep learning. Speech recognition can be used for surgical record. Among all kinds of neural networks, recurrent neural network (RNN) is the best one for sequence modeling, because of its capacity of molding long term dependency. As a specific unit of RNN, long short-term memory (LSTM) has been widely used for speech recognition, especially for acoustic modeling. Also, when training a neural network, dropout is often used to prevent overfitting and improve model's generalization capacity. This paper explores the application of dropout with LSTM based acoustic modeling. Through combining the per-element and per-frame dropout methods, the accuracy of speech recognition is finally improved by more than 5% relatively. The experiments were done on THCHS-30 corpus.
Aerial-Ground Collaborative Pathfinding with HLSTL using FPGAs

In this work, it is proposed an scenario of collaboration between an aerial and a terrestrial vehicles devoted to obtain the best possible route for the terrestrial vehicle, as well as the adaptation to possible unexpected situations on the ground. In order to get the best response time, a solution for realtime computing of the optimal routes is built on an FPGA-based device. Though the proposed system is applicable in multiple scenarios, this paper focuses on smart farming applications. The evolution of the design flows for high-performance, heterogeneous solutions has reached a high point with the widespread adoption of design flows based on High Level Synthesis (HLS) technology for FPGAs. However, the claim about the potential use of legacy code, or code written by non-FPGA experts, almost straightforwardly and smoothly, needs to be further developed. This work contributes to this objective, introducing the concept of a generic-programming framework for FPGA-based solutions. The HLS Template Library supports a quick and easy development flow from models written in standard high-level languages that make it use of software abstract data types. These structures are well-known artifacts provided by many software libraries such as Glib (ANSI C) or STL (C++) and are intensively used in multiple engineering and scientific fields.
An Attack Scenario Generation Method Using the Behavior Model

To achieve high traceability from requirements to test scenarios, a test scenario generation method has been widely recognized as being an integral aspect of model-based development. In this study, we propose a test scenario generation method from formalized functional requirements which employs the approach with the "fluent" proposition. Based on results from a hazard analysis, this technique is able to generate test scenarios that reveals hazardous situations by specifying a hazardous state as their end-state of a scenario. Furthermore, by modeling attack targets and methods, test scenarios leading to a hazardous state by malicious external attacks are generated.
The Data Cleaning of Electric Industrial Control Terminal Based on the iForest and Genetic BP Neural Network Algorithms

This paper focuses on the data cleaning of the electric industrial control terminal. First, we introduce the isolation forest algorithm for locating the dirty data. In order to improve the detection accuracy for higher dependent data in power system, the isolation forest is modified. Second, we use the BP neural network to predict the dirty data. To optimize the performance, the genetic algorithm is introduced for optimizing the weight of BP neural network. Finally, we provide a case study for illustrating the effectiveness of the proposed method.
Morphological Residue Encoding and Piecewise Approximation Techniques for Lossless Binary Image Compression

In this work, we propose an advanced framework for lossless compression of binary images. The proposed framework is organized in two steps: contour approximation and residue encoding. First, object contours are approximated by piecewise cubic polynomial curves and Improved Adaptive Arithmetic Coding are applied to encode the coefficients. Then, we propose an efficient algorithm based on morphological operation to detect the residues, i.e. the points of the original image that are not in the reconstructed image. After encoding the residue by the proposed column by column method or the Chebyshev distance method, a very high compression ratio can be achieved.
Personalized Recommendation via Enhanced Redundant Eliminated Network-Based Inference

An efficient recommendation system is fundamental to solve the problem of information overload in modern society. In physical dynamics, material diffusion based on binary networks has a wide range of applications in recommendation systems. However, material diffusion has the problem of excessive diffusion and redundant similarity. In the past studies, most people focused on reducing the popularity of popular items. This paper mitigates the problem of redundant similarity by considering the second-order similarity of the enhanced items. It evaluates the algorithm through three real datasets (MovieLens, Netflix and RYM), which proves the method is superior to other algorithms in accuracy, diversity and novelty.
A General Framework for the Related-Key Linear Attack Against Block Ciphers with Linear Key Schedules

We present a general framework for the related-key linear attack that can be applied to iterative block ciphers with linear key schedules. The attack utilizes a newly introduced related-key linear approximation that is obtained directly from a linear trail. The attack makes use of a known related-key data consisting of triplets of a plaintext, a ciphertext, and a key difference such that the ciphertext is the encrypted value of the plaintext under the key that is the xor of the key to be recovered and the specified key difference. If such a block cipher has a linear trail with linear correlation $$\epsilon $$, it admits attacks with related-key data of size $$O(\epsilon ^{-2})$$ just as in the case of classical Matsui’s Algorithms. But since the attack makes use of a related-key data, the attacker can use a linear trail with the squared correlation less than $$2^{-n}$$, n being the block size, in case the key size is larger than n. Moreover, the standard key hypotheses seem to be appropriate even when the trail is not dominant as validated by experiments. The attack can be applied in two ways. First, using a linear trail with squared correlation smaller than $$2^{-n}$$, one can get an effective attack covering more rounds than existing attacks against some ciphers, such as Simon48/96, Simon64/128 and Simon128/256. Secondly, using a trail with large squared correlation, one can use related-key data for key recovery even when the data is not suitable for existing linear attacks.