Reviewerly/exHarmony huggingface dataset:
Information Extraction from Twitter Using DBpedia Ontology: Indonesia Tourism Places

As the popular microblogs of social media, Twitter provides a number of short messages of interesting information, like the mention of entities to promote local cultures and tourism places in order for others to know and visit the places. However, the entities mentioned within the tweet may be interpreted differently and vary depending on the context of the tweet. To find the appropriate context of the entities, we need the system that can perform semantic annotation for every entity, especially for the tourism places. In this paper, we provide Named Entity Recognition used to define the candidate entities of the places. Furthermore, by using DBpedia Ontology as a semantic annotation resources we construct the information extraction from the context and semantic of the entities to collect the URI of Wikipedia page of the place. The experiment result showed the system was capable to extract the mentioned place and annotated the URI of Wikipedia with high accuracy which defined by 0.95 of Precision, 0.79 of Recall, and 0.87 of F-Measure.
MATLAB Machine Learning Toolboxes

The MathWorks sells several packages for machine learning. Their toolboxes work directly with MATLAB and Simulink. The MathWorks products provide high-quality algorithms for data analysis along with graphics tools to visualize the data. Visualization tools are a critical part of any machine learning system. They can be used for data acquisition, for example, for image recognition or as part of systems for autonomous control of vehicles, or for diagnosis and debugging during development. All of these packages can be integrated with each other and with other MATLAB functions to produce powerful systems for machine learning. The most applicable toolboxes that we will discuss are listed in the following; we will use only the deep learning and the Instrument Control toolboxes in this book.
Design and Implementation of an Improved DBSCAN Algorithm

DBSCAN algorithm is a density-based clustering algorithm, and it has been widely used in data clustering. DBSCAN algorithm needs to calculate the distance between each object and all the other objects when searching for core objects. The process leads to high computational overhead. This paper proposes an improved algorithm which takes no account of distant neighbors of an object and computes only the distances between the object and its nearby neighbors. This method reduces the distance measurements when searching for core objects, and the computational cost is obviously reduced. Four standard data sets of UCI are selected to verify the clustering process and algorithm performance of the improved DBSCAN algorithm. Experimental results show that the improved algorithm reduces the computational cost tremendously, while maintaining high clustering accuracy.
An Improved SURF in Image Mosaic Based on Deep Learning

Image mosaic is widely used in the machine vision and various projects, the mosaic image may sometimes in malformed, which is impossible to be predicted when it occurs, with the Speed-up Robust Feature (SURF). In order to get successful mosaic images at most of the time with SURF, an improved SURF based on deep learning with Convolutional Neural Network (CNN) is proposed in this paper. The improved SURF can feedback the mosaic procession by re-stitching the images automatically after eliminating the bad deformed one by the judging of the results through deep learning, and output the better, which free-data area of black will be continue to be detected and then be restored with the nearest neighbor interpolation, and finally outputs the ideal image. The experiment shows that the improved SURF can eliminate the deformed mosaic as effectively and can improve the mosaic as efficiency, and get the final perfect mosaic images all the time.
On the Solution of Poisson’s Equation using Deep Learning

We devise a numerical method for solving the Poisson's equation using a convolutional neural network architecture, otherwise known as deep learning. The method we have employed here uses both feedforward neural systems and backpropagation to set up a framework for achieving the numerical solutions of the elliptic partial differential equations - more superficially the Poisson's equation. Our deep learning framework has two substantial entities. The first part of the network enables to fulfill the necessary boundary conditions of the Poisson's equation while the second part consisting of a feedforward neural system containing flexible parameters or weights gives rise to the solution. We have compared the solutions of the Poisson's equation arising from our deep learning framework subject to various boundary conditions with the corresponding analytic solutions. As a result, we have found that our deep learning framework can obtain solutions which are accurate as well as efficient.
Active Sensing of Robot Arms Based on Zeroing Neural Networks: A Biological-Heuristic Optimization Model

Conventional biological-heuristic solutions via zeroing neural network (ZNN) models have achieved preliminary efficiency on time-dependent nonlinear optimization problems handling. However, the investigation on finding a feasible ZNN model to solve the time-dependent nonlinear optimization problems with both inequality and equality constraints still remains stagnant because of the nonlinearity and complexity. To make new progresses on the ZNN for time-dependent nonlinear optimization problems solving, this paper proposes a biological-heuristic optimization model, i.e., inequality and equality constrained optimization ZNN (IECO-ZNN). Such a proposed IECO-ZNN breaks the conditionality that the solutions via ZNN for solving nonlinear optimization problems can not consider the inequality and equality constraints at the same time. The time-dependent nonlinear optimization problem subject to inequality and equality constraints is skillfully converted to a time-dependent equality system by exploiting the Lagrange multiplier rule. The design process for the IECO-ZNN model is presented together with its new architecture illustrated in details. In addition, the conversion equivalence, global stability as well as exponential convergence property are theoretically proven. Moreover, numerical studies, real-world applications to robot arm active sensing, and comparisons sufficiently verify the effectiveness and superiority of the proposed IECO-ZNN model for the time-dependent nonlinear optimization with inequality and equality constraints.
License plate super-resolution reconstruction based on improved ESPCN network

This Due to the limitation of the imaging equipment or other conditions, the obtained license plate pixel resolution is low, which brings great difficulty to the license plate detection technology. In order to solve the above problems, the improved ESPCN network is used to extract the original high-definition picture detail features, and the super-resolution reconstruction of the license plate image with lower resolution is performed. The real-time convolution residual network method is compared with the classical reconstruction methods such as bilinear interpolation, nearest neighbor interpolation and ESPCN. The subjective visual evaluation and objective quantization index (PSNR, SSIM) are superior to the classical reconstruction method. It is proved that the real-time residual convolution network improves the resolution of the license plate image and also improves the license plate recognition rate to some extent.
Detecting Spam Emails/SMS Using Naive Bayes, Support Vector Machine and Random Forest

SMS spams are dramatically increasing year by year because of the expansion of movable users round the world. Recent reports have clearly indicated an equivalent. Mobile or SMS spam may be a physical and thriving drawback because of the actual fact that bulk pre-pay SMS packages are handily obtainable recently and SMS is taken into account as a trusty and private service. SMS spam filtering may be a relatively recent trip to deal such a haul. The amount of information traffic moving over the network is increasing exponentially and therefore the devices that are connected thereto are considerably vulnerable. Thus there’s a bigger have to be compelled to secure our system from this kind of vulnerability, here network security play a really vital role during this context. In this paper, a SMS spams dataset is taken from UCI Machine Learning repository, and after perform pre-processing and different machine learning techniques such as random forest (RF), Naive Bayes (NB), Support Vector Machine (SVM) are applied to the dataset are applied and compute the performance of these algorithms.
A Combined Edge Detection Analysis and Clustering based Approach for Real Time Text Detection

Recently, scene text detection has become an active research topic in computer vision and document analysis because of its great importance. However, due to the number of factors, such as variety in font size, style, colors, language, as well as background noise, blur, and occlusion, makes text detection is still challenging. The proposed methods directly run on the frame image of the recording video to detect text. Sobel operator used to perform edge detection on the image while K-means clustering used to extract text from the background of the image. After the image has been segmented based on the cluster, the text will be identified using the Maximum Stable External Region (MSER). Applications run on Android devices and built by using OpenCV for image retrieval processes, while algorithms written using C++. The experimental result shows 71.79%, 62.11%, and 64.56% for average recall, precision, and F1-score, respectively.
Generative adversarial networks for non-negative matrix factorization in temporal psycho-visual modulation

The image factorization problem is the key challenge in Temporal Psycho-Visual Modulation (TPVM). In this paper, we present an end-to-end learned model for image-based non-negative matrix factorization. We decompose a set of images into a small number of image bases which can be used to reconstruct all the images by linearly combining the bases. During the process, the image bases, as well as their weights for the linear combination, are unknown. The method is based on conditional GAN and a variational sample disturber. Traditional NMF methods suffer from slow computational speed and poor generalization ability. A deep neural network shows potential in these two aspects. We conduct adversarial training in our model to generate better bases for image restoration. Our method outperforms other CNN based methods on several public datasets. Compared to traditional NMF algorithms, our model generates image bases that preserve details. Our model has advantages in speed as well as generalization ability.
Digital education transaction object authentication service based on blockchain technology

With the rapid development of E‐Learning, the demand for high‐quality personalized education services and resources has increased rapidly. In order to meet this demand, a project called “Digital Education Crowdfunding and Crowdcreating Personalized Service Platform Development and Application Demonstration” was launched in China. The goal of the project is to provide a personalized education resource and service trading platform for producers, suppliers, and users by joint investment and collaborative creation. So how to ensure the authenticity and credibility of the objects (education resources and services) involved in this multi‐party participated service platform becomes a challenging task. Blockchain is emerging as a promising technology that enables people to make secure and reliable transactions because of the decentralization and collective maintenance features. Thus in this letter we proposed a novel blockchain‐based authentication service for digital education transaction object to resolve the problem of education transaction object authentication among different education institutions. We constructed information models for the two types object and stored the metadata information of the model on the blockchain to make it transparent and safe. And smart contracts are designed to authenticate education transaction object automatically among different education institutions. To improve the performance of blockchain‐based authentication service, an improved consensus process for blockchain network is proposed and incorporated into our prototype system. Performance simulation is carried out and the experimental results demonstrate the practicality of our blockchain‐based authentification service.
Ethics and Decisions in Distributed Technologies: A Problem of Trust and Governance Advocating Substantive Democracy

The distributed architecture of applications such as blockchains, wireless sensor networks, multi-agent platforms, and the Internet of Things charges technological development to face, by default, with two aspects of responsibility that were usually accorded to human beings: the “decision” (Who is enabled to make decisions in a decentralized system? What about the mechanism for deciding? Authorized by whom? With what kind of consensus?) and “ethics” (To which principles must respond the decision-making mechanism? And, if decisions are distributed, what is the role of ethics? To guide or to laissez-faire? Permissive or restrictive? For everyone or only for those who are authorized?). Responding to these epochal questions can lead to rethinking the distributed technologies in view of a game-changing transformation of models of trust-in-governance. From a model signed by centralization of trust, we have come to progressively more decentralized forms until the contemporary “distributed trust”. This paper constitutes an endeavor to introduce and address the main philosophical foundations of this historical passage.
Recognizing Word Gesture in Sign System for Indonesian Language (SIBI) Sentences Using DeepCNN and BiLSTM

SIBI is a sign language that is officially used in Indonesia. The use of SIBI is often found to be a problem because of the many gestures that have to be remembered. This study aims to recognize SIBI gestures by extracting hand and facial features which are then classified using Bidirectional Long ShortTerm Memory (BiLSTM). The feature extraction used in this research is Deep Convolutional Neural Network (DeepCNN) such as ResNet50 and MobileNetV2, where both models are used as a comparison. This study also compares the performance and computational time between the two models which is expected to be applied to smartphones later, where both models can now be implemented on smartphones. The results showed that the use of ResNet50-BiLSTM model have better performance than MobileNetV2-BiLSTM which is 99.89%. However, if it will be applied to mobile architecture, MobileNetV2-BiLSTM is superior because it has a faster computational time with a performance that is not significantly different when compared to ResNet50-BiLSTM.
Toning: New Experience of Sharing Music Preference with Interactive Earphone in Public Space

The users in their 20s are curious about the feedback after sharing the music that they like, as much as the act of sharing itself. Moreover, they wish to share their musical taste with various people not only with their acquaintance, in everyday spaces and when on the move. In this paper, we present an interactive music sharing service called Toning, that makes users express their musical taste and share music with people in the same space through the most common wearable device, bluetooth earbuds. Our design process started with user survey and interview, aiming to understand their opinion about proper way of expressing music preference, and define design guidelines. Based on this, early concept design was conducted.
Commodity Ecology: From Smart Cities to Smart Regions Via a Blockchain-Based Virtual Community Platform for Ecological Design in Choosing All Materials and Wastes

This article describes three points: (1) how a fine-grained yet systematic model of regional material sustainability called Commodity Ecology is being linked to current technological trends (2) of societies evenly saturated with ever cheaper and more mobile Information Communication Technologies (ICT) and (3) increasingly organized by decentralized ledgers like Blockchain. We live in a society connected by the Internet and increasingly served by and even ruled by online platforms. Particularly, an evenly shared mobile-phone revolution in ICT is putting platform access in the hands of all peoples around the world. Another disruptive technology after the Internet is blockchain technology which has revolutionized the exchange of information in areas such as cryptocurrency, supply chain management, healthcare and smart contracts. Thanks to these technological developments, instead of just smart cities, we now live among real/virtual ‘smart regions’ that on their own are capable of deliberating, finding, and buying/selling en masse toward their own better choices in sustainable material choices and better waste handling. These trends are utilized by the first online blockchain-based virtual community platform for ‘smart regions’ that facilitates this global, multi-regional drive to more democratic, holistic, and sustainable ecological design in all of our consumptive choices. Commodity Ecology is a model, rubric, and checklist for sustainability in 130 material categories, on a virtual platform for deliberation in over 860 distinct ecoregions simultaneously. The United Nations Academic Impact Office called Commodity Ecology a top global initiative for actually achieving Sustainable Development Goal #12, Responsible Consumption and Production.
Comparison of Different Spike Sorting Subtechniques Based on Rat Brain Basolateral Amygdala Neuronal Activity

Developing electrophysiological recordings of brain neuronal activity and their analysis provide a basis for exploring the structure of brain function and nervous system investigation. The recorded signals are typically a combination of action potentials (spikes) and noise. High amounts of background noise and possibility of electric signaling recording from several neurons adjacent to the recording site have led scientists to develop neuronal signal processing tools such as spike sorting to facilitate brain data analysis. Spike sorting plays a pivotal role in understanding the electrophysiological activity of neuronal networks. This process prepares recorded data for meaningful interpretations of interactions among neurons, investigating the connectivity between different brain regions, and ultimately understanding the overall structure of brain functions. Spike sorting consists of three steps: spike detection, feature extraction, and spike clustering. There are several methods to implement each of spike sorting steps. This paper provides a systematic comparison of various spike sorting sub-techniques applied to real extracellularly recorded data from a rat brain basolateral amygdala. An efficient sorted data resulted from careful choice of spike sorting sub-methods leads to better interpretation of the brain structures connectivity under different conditions, which is a very sensitive concept in diagnosis and treatment of neurological disorders. Here, spike detection is performed by appropriate choice of threshold level via three different approaches. Feature extraction is done through PCA and Kernel PCA methods, where Kernel PCA outperforms. We have applied four different algorithms for spike clustering including K-means, Fuzzy C-means, Bayesian and Fuzzy maximum likelihood estimation. As one requirement of most clustering algorithms, optimal number of clusters is achieved through validity indices for each method. Finally, the sorting results are evaluated using inter-spike interval histograms. Rat brain real data used in simulations was recorded in neuroscience research center, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
Knuth Morris Pratt - Boyer Moore Hybrid Algorithm for Knowledge Management System Model on Competence Employee in Petrochemical Company

Knowledge Management System (KMS) is a system that allows a company to create, obtain, store and use knowledge optimally which is useful for improving company progress. Knowledge storage is useful for maintaining knowledge, therefore a string search method is needed to access the existing knowledge in KMS, so that if the knowledge is needed then other employees can easily get it. In this research a knowledge search method will be conducted in the form of employee competencies in KMS, to get it back a string search must be performed to make it easily accessible to other employees. The search method in this study is a combination of the search Algorithm Knuth Morris Pratt and Boyer Moore. In this study, the combined Knuth Morris Pratt-Boyer Moore (KMP-BM) Hybrid Algorithm is used for modeling the knowledge management system in determining employee competencies in petrochemical industry companies. Employee competency data are the result of competency assessment of the company which is then grouped by human resource development in KMS. The results of this study are looking for a method of string matching with the application of the KMP-BM Hybrid Algorithm on the KMS model to store various knowledge about employee competencies in the company. By using a combination of the existing advantages KMP and BM Algorithm, searching for KMS can be done more quickly and accurately.
Open data repositories: Current risks and opportunities

As data sharing has become a more familiar obligation for academic researchers, there has been a correlating increase in the roles that librarians play supporting open data repositories and providing data management consulting and services. These repositories are sponsored by governments, funding agencies, academic institutions, professional societies, and scholarly publishers.
Transformer based Chinese Sentiment Classification

This paper deals with the task of Chinese sentiment classification. We propose the MITE (Multi-Inputs Transformer Encoder) model, draw on the transformer encoding thought, mining the emotional information of Chinese contents. MITE introduce self-attention to find the emotional dependence between words, which we think is important for analyzing text sentiment categories. Experiments prove that our method improve the correctness of sentiment classification, which proves the emotional tendency is influenced by the sentimental polarity of the words in the sentence.
Deep Learning Semantic Compression: IoT Support over LORA Use Case

Long Range (LORA) networks are serious candidates to support Internet of Things (IoT). Despite the scalability and range of LORA, yet many IoT devices need to send much more data than what is possible in this band. In this paper, a deep learning compression method to squeeze data and transfer its semantic is presented. Data from IoT equipment is considered as a time series and trains a neural network. Resulting neural network weights are periodically sent instead of sending all the IoT raw data. Anomalies are locally detected by a similar neural network and sent separately. The resulting architecture makes it feasible to use LORA for IoT devices that generate very large amounts of data.
InfraRed Images Augmentation Based on Images Generation with Generative Adversarial Networks

The challenge brought by small data sets is a common problem in many computer visual tasks. The method of images generation with generative adversarial networks is investigated as a solution to extend small infrared datasets in this paper. In such a solution, the generation of infrared images is realized by generative adversarial networks by which RGB images can be translated to infrared images. The images generated by the generator of generative adversarial networks are of high quality, excluding some cases that the scene include rivers. Experiment results suggest that such an approach is effective in images generation for infrared dataset augmentation.