Reviewerly/exHarmony huggingface dataset:
An Algorithmic Analysis on Optimized Image Compression Model

“Image compression is the process of reducing the size of the image without deteriorating the image quality to an intolerable level”. The minimization of file size permits more storage in a given memory space. However, to develop the quality of the images during compression, this paper intends to present an enhanced image compression model, which is dependent on hybrid optimization algorithm by merging Lion Algorithm (LA) and Jaya Algorithm (JA). Here, the input image that has to be compressed is subjected to segmentation using Adaptive Active Contour Model (ACM) that split the image into Region of Interest (ROI) and non-ROI regions. Further, the ROI regions are compressed using JPEG-LS scheme and non-ROI regions are compressed by means of wavelet-oriented lossy compression scheme. After compression process, the decompression of the image is carried out by performing the reverse process of compression. Here, the adaptiveness of the presented scheme is influenced with the aid of proposed Jaya based Cubpool Formation in LA (JCF-LA). In addition, an algorithmic analysis is done for the adopted JCF-LA-based compression model to analyze its performance.
Displacements Towards Empathy

[POR NÃO SER EXISTINDO]: Displacements Towards Empathy consists of an interactive artwork that employs cognitive computing [1-3] and ubiquitous computing [4-6] concepts and techniques to revisit 'Narcissus' in a co-authored physical-digital space (interactor / artwork / artist) from contemporary references: social networks and public database platforms.
Object Detection Based On Decision Level Fusion

Object detection is widely used in different fields. In this paper, a new object detection model of decision level fusion based on Mask R-CNN is proposed. The model fuses the infrared images and the visible images at decision-making level. It is called as IVF-Mask R-CNN. In order to improve the stability and the accuracy of the IVF-Mask R-CNN model, the partial network freeze strategy, learning rate reduction strategy and test image up-sizing strategy are used. The fuse model IVF-Mask R-CNN compensates the limitation of single imaging model of object detection. It improves the accuracy of the detection. The experimental results show that IVF-Mask R-CNN model contributes to a better object detection effect compared with the single visible detection model and the infrared detection model.
Bilingual Lexicon Learning Using Tagged Images via Graph Trilateral Filter-based Feature Refinement

A novel method to realize bilingual lexicon learning (BLL) using tagged images is presented in this paper. Different from existing methods that require parallel corpora, the proposed method enables extraction of semantically similar words by utilizing not such corpora but tagged images on image sharing services. The main contribution of this paper is derivation of a novel framework to refine visual features of tagged images based on graph trilateral filter-based smoothing. This enables reduction of the influence of noisy tags that are irrelevant to contents of images. As a result, accurate BLL becomes feasible by nearest neighbor search using the refined visual features.
Interoperability Performance in Adaptive Middleware for Enterprise Business Applications

To improve the presentation of B2B (Business to Business) and B2C (Business to Consumer) regarding venture wide Service Oriented Architecture (SOA), we need middleware interoperability particularly with agent building to be specific CORBA (Common Object Request Broker Architecture) proposed by Object Management Group ORB programming named ORBeline. Unmistakable models for client server correspondences have just been created and executed specifically Handle Driven ORB (H-ORB), Forwarding ORB (F-ORB), and the Adaptive ORB (A- ORB). This paper concentrates how to improve the presentation of the interoperability in Adaptive ORB (A-ORB) as for client server collaboration in N-level engineering alongside multithreading condition. We have presented a strategy called linear discriminant interoperable support learning method and how it will in general be used for improving the presentation of interoperability is examined. The outcome gives the framework conduct especially the impact of message measure, between hub deferrals; torpidity and flexibility of solicitation/reaction administration times for the A-ORB engineering are broke down.
On the Integration of Deep Learning and Fuzzy Methods for Aspect-based Sentiment Analysis

Sentiment analysis is an important task in the field of big data and artificial intelligence and has a wide range of real-world applications. The majority of current approaches, however, attempt to detect the overall polarity of a sentence, or text span, irrespective of the entities mentioned and their aspects. A typical way for solve this is to employ a classifier that learns to identify the polarity labels given input text. Such methods require amount of training examples which were manually constructed. While most work in text mining in the field of sentiment analysis domain focus on the use of supervised machine learning technologies, in this paper, we investigate on aspect-based sentiment analysis for mobile game reviews. Our method integrates both deep learning learned features and support vector machines. The deep learning feature is mainly derived from inducting word relations in Wikipedia and the collected in-domain text. To prevent from assessing unknown missing words, we design a fuzzy-based approach to capture word-level sentiment scores. By using a fuzzy-based soft computing strategy with a small lexicon allows us to produce a system with better accuracy and precision than pure machine learning such as Naïve Bayes and SVMs. To demonstrate the effectiveness of the proposed approach, we conduct the experimental results on the collected Mobile game reviews. These results show that our method outperforms supervised systems. One good property of this method is that it does not need to perform Chinese word segmentation in testing time.
Research on Optimized Speed Measurement Algorithm Based on Error Correction

In this paper, for the non-contact speed measurement technology of maglev train, the speed measurement accuracy needs to be improved, and the relative positioning error is large. So the MEMS-IMU inertial measurement unit is introduced to compensate the speed of the original speed measurement system, and the error based on the MEMS-IMU speed measurement is performed analysis. Based on this, an error correction method based on wavelet threshold optimization and an optimized speed measurement algorithm are proposed to improve the speed measurement performance.
New Metric Based on SQuAD for Evaluating Accuracy of Enterprise Search Algorithms

Enterprise Search is a continuously evolving and important field, which is seeing a resurgence driven by artificial intelligence. Still, there is no objective, generally accepted way to compare various enterprise search systems. SQuAD is becoming popular for measuring algorithmic reading comprehension (MRC) but is ineffective for quantifying effectiveness of enterprise search in business-use situations. In this paper we modify the SQuAD scoring methodology to propose a scoring system for enterprise search systems that aligns with the real world expectations of users. Further, we use a search system based on Calibrated Quantum Mesh (CQM) to underscore the relevance of this metric.
Chaotic System Based on Mutually Connected Digital Oscillators

In this paper the experiments with two mutually coupled digital oscillators are described. The digital oscillators with quadrature outputs are used for experiment. On the beginning the amplitude stabilization and way of synchronization of digital oscillator are described. Then the behavior of the connected oscillators is described. From simulation results can be seen that oscillators connected together can produce chaotic signals. The digital oscillators were simulated and constructed by means of microcontroller. Results of simulations and measurements are presented.
The Influence of the Computer-Aided Environment on the English Translation Teaching Under the Network Teaching Mode

The network teaching refers to the use of the computer networks by teachers to transmit the teaching information to students, or students acquire the knowledge through the computer networks. Computer aided tools (CATs) are often used in the modern translation, and they are of great help to improve the efficiency of the translators. In order to improve the level of the translation teaching in the modern colleges and universities, it is necessary to apply the computer-aided translation tools to the modern translation teaching, use different translation software and hardware devices for the translation exercises, stimulate students’ interests in their learning, and improve students’ English translation practice abilities.
Regional brain development analysis through registration using anisotropic similarity, a constrained affine transformation

We propose a novel method to quantify brain growth in 3 arbitrary orthogonal directions of the brain or its sub-regions through linear registration. This is achieved by introducing a 9 degrees of freedom (dof) transformation called anisotropic similarity which is an affine transformation with constrained scaling directions along arbitrarily chosen orthogonal vectors. This gives the opportunity to extract scaling factors describing brain growth along those directions by registering a database of subjects onto a common reference. This information about directional growth brings insights that are not usually available in longitudinal volumetric analysis. The interest of this method is illustrated by studying the anisotropic regional and global brain development of 308 healthy subjects betwen 0 and 19 years old. A gender comparison of those scaling factors is also performed for four age-intervals. We demonstrate through these applications the stability of the method to the chosen reference and its ability to highlight growth differences accros regions and gender.
A Non-image-based Subcharacter-level Method to Encode the Shape of Chinese Characters

Most characters in the Chinese and Japanese languages are ideographic compound characters composedof subcharacter elements arranged in a planar manner. Token-based and image-based subcharacter-level modelshave been proposed to leverage the subcharacter elements. However, on the one hand, the conventional tokenbasedsubcharacter-level models are blind to the planar structural information; on the other hand, the image-basedmodels are weak with respect to ideographic characters that share similar shapes but have different meanings.These characteristics motivate us to explore non-image-based methods to encode the planar structural informationof characters. In this paper, we propose and discuss a method to encode the planar structural information by learningembeddings of the categories of the structure types. Our proposed model adds the structure embeddings to theconventional subcharacter embeddings and position embeddings before they are input into the encoder. In this way,the model learns the planar structural and positional information and retains the uniqueness of each character. Weevaluated the method in a text classification task. In the experiment, the embeddings were encoded by a CNN encoder,and then the encoded vectors were input into an LSTM classifier to classify product reviews as positive or negative.We compared the proposed model with models that use only the subcharacter embeddings, the structure embeddingor the position embeddings as well as with the conventional models in previous works. The results show that addingboth structure embeddings and position embeddings leads to more rich and representative features and better fittingon the dataset. The proposed method results in at best 1:8% better recall, 0:63% better F-score, and 0:55% better accuracy on the testing datasets compared to previous methods.
Improved Moth-Flame Optimization Based on Opposition-Based Learning for Feature Selection

In this paper, an improvement for the Moth-flame Optimization (MFO) algorithm is proposed based on Opposition-Based Learning (OBL), that enhances the exploration of the search space through computing the opposition values of solutions generated by MFO. Moreover, such an approach increases the efficiency of MFO as multiple regions in the search space are investigated at the same time. The proposed algorithm (referred to as OBMFO) avoids the limitations of MFO (and other swarm intelligence algorithms) that result from the moving in the direction of the best solution, especially if this direction does not lead to the global optimum. Experiments are run using classical six benchmark functions to compare the performance of OBMFO against MFO. Moreover, OBMFO is used to solve the feature selection problem, using eight UCI datasets, in order to improve the classification performance through removing irrelevant and redundant features. The comparison results show that the OBMFO superiors to MFO for the tested benchmark functions. It also outperforms another three swarm intelligence algorithms in terms of the classification performance.
User Operation Strategy of Bookstore APP Under the Background of Big Data

In a Internet product, different from one-time business relations of the traditional products, in addition to design drive the product features and experiences, user participation also constitutes the Internet is an important part of the core value of the products, the user’s use of time and attention to product added value, in the Internet ecological products, users create and provide value for each other, each other between user for recognition of products, is willing to participate in product design, improve and spread, even some contribution value in the service sectors. This article applies the method of case analysis, through the “WeChat reading” APP user operation strategy analysis, combines the actual conditions of five years on the Internet product operation and management experience, explore the book city class APP user operation strategies and methods, including set up the mechanism of high quality growth path and the product, establish incentive system, the user is grading fine operation, recall silent users, by some users to drive all the users.
A Scoring System of Oral English-Chinese Translation Based on Frame Semantic Analysis

In recent years, there has been an increasing interest in speech evaluation. However, most of the automatic evaluation systems focus on evaluating the quality of retelling questions and reading questions. In our best knowledge, research evaluating the quality of English-Chinese oral translation questions have not been found. This paper proposed an automatic method for scoring English-Chinese oral translation. This method leveraged three indicators, including key words, general idea of sentences and fluency, as the basis of scoring. The key words indicator was evaluated by the synonym analysis at the lexical level to evaluate while the general idea of sentences indicator was evaluated by the framework semantic analysis at the sentence level. Then, fluency indicator was scored by the speed of the speech. The experimental results demonstrated that using Mel-frequency cepstral coefficients (MFCC) and the Hidden Markov Model (HMM) algorithm to identify the key words, the system achieved average recognition rate in answering keywords of 97.3%. There is a good possibility that the proposed method may be effective for key word recognition.
Fuzzy Granular Computing-Based Controller Design for Inverted Pendulum System with Delay

Fuzzy control as a method used in control design has been predominant when the plant mathematical model is unavailable. Granular computing has been seen as a process which speeds up the fuzzy methodology when combined to the fuzzy control design. Inverted pendulum control and stabilization are a classical problem investigated in this work. The stabilization of the pendulum is achieved with an advanced fuzzy granular computing-based controller under the influence of time delay. The controller which was a combination of fuzzy type-2 controller along with classical PID controller achieved the control. However, it was seen that the impact of time delay with advanced fuzzy controllers is significant even if there was a unit transportation delay in the system the output response suffers which becomes a major drawback and which indicates the need for possible boundaries in time delay systems.
An Analytical Approach to Document Clustering Techniques

Clustering is a technique that group data together based on their similarity and apart based on their dissimilarity. When this technique is applied to documents and the terms within these documents retrieval of similar documents become easy and efficient. Document clustering is being researched and utilized for many years but is yet far from being optimal. To study and analyze different document clustering algorithm, a theoretical literature review and analysis was performed and the results are presented in this paper. This paper comprises of theoretical review of papers. 95 papers were identified and out of these 30 were selected. Various techniques or algorithms and modifications to previous algorithms proposed for document clustering by various researchers are compiled and presented with the intent that it will aid the researchers in finding out the current and future scope of research in information retrieval systems and document clustering technologies.
Evaluation of Effect on Walking Behavior by Seeing Augmented Reality Objects

In recent years, augmented reality (AR) technology has received attention. AR technology is expected to be widely used in various day-to-day activities. Many researchers have proposed methods that use AR technology for various purposes. However, there is a possibility that displayed AR objects cause anxiety or spoil scenic views for the user, as the objects can hinder the view and the appearances can change to several forms. Therefore, it is important to consider how we should display the AR objects depending on its purpose. In this study, we have investigated how participants behave when they are shown different AR objects to lead them to the left/right side on an outdoor road. As a result, we confirmed that we should use the AR object whose appearance blends with the surroundings when the object is a sign to lead people on outdoor.
Tensorshield: Tensor-based Defense Against Adversarial Attacks on Images

Recent studies have demonstrated that machine learning approaches like deep neural networks (DNNs) are easily fooled by adversarial attacks. Subtle and imperceptible perturbations of the data are able to change the result of deep neural networks. Leveraging vulnerable machine learning methods raises many concerns, especially in domains where security is an important factor. Therefore, it is crucial to design defense mechanisms against adversarial attacks. For the task of image classification, unnoticeable perturbations mostly occur in the high-frequency spectrum of the image. In this paper, we utilize tensor decomposition techniques as a preprocessing step to find a low-rank approximation of images that can significantly discard high-frequency perturbations. Recently a defense framework called Shield [1] could “vaccinate” Convolutional Neural Networks (CNN) against adversarial examples by performing random-quality JPEG compressions on local patches of images on the ImageNet dataset. Our tensor-based defense mechanism outperforms the SLQ method from Shield by 14% against Fast Gradient Descent (FGSM) adversarial attacks, while maintaining comparable speed.
A Low-Memory Time-Efficient Implementation of Outermorphisms for Higher-Dimensional Geometric Algebras

From the beginning of David Hestenes rediscovery of geometric algebra in the 1960s, outermorphisms have been a cornerstone in the mathematical development of GA. Many important mathematical formulations in GA can be expressed as outermorphisms such as versor products, linear projection operators, and mapping between related coordinate frames. Over the last two decades, GA-based mathematical models and software implementations have been developed in many fields of science and engineering. As such, efficient implementations of outermorphisms are of significant importance within this context. This work attempts to shed some light on the problem of optimizing software implementations of outermorphisms for practical prototyping applications using geometric algebra. The approach we propose here for implementing outermorphisms requires orders of magnitude less memory compared to other common approaches, while being comparable in time performance, especially for high-dimensional geometric algebras.
MDAnet

The majority of existing methods for Visual Question Answering are primarily based on Recurrent Neural Networks(RNNs) with attention to extract question features and outputs the last hidden state for modal fusion. However, only the last hidden state cannot preserve precise location information, which may lead to semantic confusion. Our work adopts Multiple Encoder Block to replace RNNs, and propose a Double Attention Module which includes Objective Attention and Spatial Attention to acquire more important regions or objects. The proposed method is evaluated on VQA 2.0 and CLEVR, and obtain competitive result.
A Planning Approach Based on APTL

This paper proposes a path planning approach for multi-agent systems (MASs) with Alternating Projection Temporal Logic (APTL). Our approach aims to calculate a path efficiently which satisfies specific properties in a MAS. The MAS is formalized in an interpreted system, then symbolically represented, and properties of paths are expressed by APTL formulas. We implement a robotic soccer game in order to illustrate the availability of this approach, actions and strategies of each robot are described in detail in interpreted system. The toolkit MCMAS_APTL2 has been developed, where we just enter an APTL formula of specific properties and the model of the robotic soccer game into the toolkit, then a path satisfies the formula is calculated and provided.
Supervised Key Terms Clustering for Regulatory Monitoring

Multivariate analysis, clustering, and classification are common machine learning challenges in food, beverage and consumer product regulation documentations. Using selective key terms from a food, beverage chemical and consumer product lexicon to perform supervised key term clustering helps on both predicting the relevance of regulation documents to their subjects and also explaining the classification and document ranking decisions. The results can also be used as feedback to improve the lexicon.