Reviewerly/exHarmony huggingface dataset:
A Novel Thresholding Methodology Using WSI EMD and Adaptive Homomorphic Filter

In this brief, we propose a novel thresholding methodology using a combination an adaptive homomorphic filter (AHF) and a weighted sinc interpolated empirical mode decomposition (EMD) model for medical and nonuniformly illuminated image datasets. In this methodology, we use an AHF to correct the illumination of an image. Later, we obtain a smooth data component from the histogram of an illumination corrected image using weighted sinc interpolated EMD model. From this smooth data component, we obtain an optimal threshold value using global minimum criteria. By using this optimal threshold value, we obtain a thresholded image from the illumination corrected image. The proposed methodology is very simple and more robust to noise and easy for hardware implementation. The performance of the proposed thresholding methodology using weighted sinc interpolated EMD model is compared with other methods in the literature and the results clearly demonstrate the superiority of the proposed methodology for thresholding.
Ultrasound Signal Processing Using the Julia Programming Language

Julia is a programming language for numerical computing aiming at the combination of the usability features of a high level programming language with a good execution performance, allowing users to prototype and deploy their application using the same code. In this work, we evaluated the use of Julia for processing ultrasound signals and generation of a B-Mode image, being the input the raw signal acquired with an Ultrasonix platform using the Texo toolbox. We present the acquisition process using Texo and the signal processing chain implemented using Julia that encompassed: (1) reading data from files; (2) channel summation to obtain the scanlines (RF signal); (3) IQ demodulation; (4) envelope detection and logarithmic compression. We present the comparison of the execution time and the output image obtained with both Julia code and MATLAB®. In this particular application, we achieved a speedup of 2.7 times using Julia, but if we take into account only the ultrasound-related operations (channel summation to logarithmic compression), there is no speed up (0.56), i.e. MATLAB® code is faster. Besides that, the frame rate we achieved using Julia was about 7 frames per second. Based on these values, we conclude that it is not yet an alternative for a real-time medical ultrasound imaging system, but it can be an alternative for MATLAB® when performing simulations.
A Hybrid Graph Model for Distant Supervision Relation Extraction

Distant supervision has advantages of generating training data automatically for relation extraction by aligning triples in Knowledge Graphs with large-scale corpora. Some recent methods attempt to incorporate extra information to enhance the performance of relation extraction. However, there still exist two major limitations. Firstly, these methods are tailored for a specific type of information which is not enough to cover most of the cases. Secondly, the introduced extra information may contain noise. To address these issues, we propose a novel hybrid graph model, which can incorporate heterogeneous background information in a unified framework, such as entity types and human-constructed triples. These various kinds of knowledge can be integrated efficiently even with several missing cases. In addition, we further employ an attention mechanism to identify the most confident information which can alleviate the side effect of noise. Experimental results demonstrate that our model outperforms the state-of-the-art methods significantly in various evaluation metrics.
Enhanced Hash Algorithm Using a Two-Dimensional Vector to Improve Data Search Performance

Hashing is a procedure that can be used for a program to gain access to data quickly. It is very popular in many IT areas such as route lookup, cryptography development, and string data indexing for fast searching among others. Open Addressing and Separate Chaining are the generally two known collision resolutions if a hash function fails to plot a unique index. Engaging any collision resolution will result in multiple key comparisons and memory accesses causing time to increase linearly thus the lookup latency becomes indeterminate, posing an unpredictable outcome to some time-critical systems. Although the collision problem is inevitable, the proposed hashing algorithm using 2D vector and hash functions on its collision resolution can achieve lookup time at one or two memory accesses while resolving the collision.
An Analysis View of Component-Based Software Architecture Reconfiguration

Dynamically reconfigurable software is a class of software which is able to dynamically modify at run-time its own internal structure and hence its behavior in response to changes in its operating environment. Many existing approaches propose reconfiguration mechanism to identify the moment when realizing reconfiguration actions based on the concept of “transaction”. However, the replacement of components by another ones at runtime remains a complex task since in addition to the validity of the new version, it must preserve the correct completion of ongoing activities. In order to provide a comprehensive view of component-based architecture reconfiguration, in this paper we provide an analysis of the state of the art and attempt to generalize software architecture reconfiguration issue towards a design solution for developing dynamically reconfigurable software architectures (DRSAs).
Research on Robot Control Based on Reinforcement Learning

This paper reviews the rise and the development of the deep reinforcement learning (DRL). Then, the deep reinforcement learning algorithms for the high-dimensional continuous action space are divided into three categories of the algorithm based on the value function approximation, the algorithm based on the strategy approximation and the algorithm based on other structures. The latest representative algorithms and their characteristics of the deep reinforcement learning are explained in details, and their ideas, advantages and disadvantages are emphasized. Finally, combined with the development direction of the deep reinforcement learning algorithm, the control mechanism of using the deep reinforcement learning method to solve the control mechanism in the robotics problems is prospected.
Method of optimizing the dimensional features in sentiment analysis

The scope to express opinion by the end-user or customer about a product, service, administrative decisions, or events to the world is phenomenal, which is due to the internet access feasibility and the ease of using social web. The opportunity to identify the divergence of the opinion about the corresponding entity (product, service, decision, or event) enables to conclude the opportunity to stabilize, revise, improve, or withdrawal of that entity in regard to amaze the target audience. However, the quantity of the opinions about an entity elevates the difficulty of process to identify the divergence of the opinion using human resource. Hence, the computer-aided methods are the crux to find the divergence of the opinions. Nevertheless, the projection of opinion is not mandate to be in similar passion. Hence, the divergence in nature of expressing the opinion is substantial due to the ambiguity in natural languages and the dimensions of opinion expression feasibilities. In regard to this argument, the design of computer-aided methods to identify the opinion scope is intended in recent past contributions of the research contributions related to natural language processing, which are still inadequate. In order to this argument, the contribution of this manuscript ‘Method of Optimizing the Dimensional Features (MODF)in Sentiment Analysis’, this is a machine learning technique that enables to deal the multiple dimensions of the expressions of the opinions to train the target classifier. The experimental study portraying the significance and scalability of the proposal scaled against the other contemporary models.
Computationally efficient training of deep neural networks via transfer learning

Transfer learning is a highly successful approach to train deep neural networks for customized image recognition tasks. Transfer learning leverages deep learning models that are previously trained on massive datasets and re-trains the network for a novel image recognition dataset. Typically, the advantage of transfer learning has been measured in sample efficiency, but instead, we investigate the computational efficiency of transfer learning. A good pre-trained model provides features that can be used as input to a new classifier (usually the top layers of a neural network). We show that if a good pre-trained model is selected, that training a new classifier can be much more computationally efficient than training a deep neural network without transfer learning. The first step in transfer learning is to select a pre-trained model to use as a feed-forward network for generating features. This selection process either uses human intuition/convenience or a methodical but computationally intensive validation loop. Instead, we would prefer a method to select the pre-trained model that will produce the best transfer results for the least amount of computation. To this end, we provide a computationally efficient metric for the fit between a pre-trained model and a novel image recognition task. The better the fit, the less computation will be needed to re-train the pre-trained model for the novel task. As machine learning becomes ubiquitous, highly-accurate trained models will proliferate and computationally efficient transfer learning methods will enable rapid development of new image recognition models.
A 300-mm-wafer silicon photonics technology for advanced information systems

State-of-the-art 300-mm-wafer-based silicon photonics technology features superior fabrication accuracy, in-wafer uniformity, and reproducibility. Using this technology, various photonic devices, such as a large-scale optical switch matrix with a very low energy consumption, high-efficiency optical modulators, are developed for energy-efficient network systems. Novel functional photonic circuits, such as those for optical path-gate logic processing are now being developed. These photonic devices and circuits will be key elements in the energy-efficient and advanced information systems.
muRedder

Sound naturally fleets away after it has been generated, unless recorded by media. We designed muRedder to reinstate the ephemerality of sound by shredding a song ticket that embeds a sound source while playing the song simultaneously. In this study, we explored ordinary music listening activities by turning intangible music content into tangible artefacts, making the music unable to be replayed, and representing the sound-fading process by shredding the ticket. By showing the process of consuming the invisible auditory content in a way that is tangibly perceivable, the user experience of our design implies new value for slow consumption of digital content and musical participation in public spaces.
Deep learning visual programming

These last years, we have witnessed considerable improvements in machine learning and deep learning. Many advanced techniques are now based on deep neural networks. Although many software libraries are available, the development of deep neural networks requires a good level of mathematical knowledge and high programming skills. In this work, we present a visual tool to help simplify the programming of deep learning networks. The developed framework DeepViP is comprised of a node editor that provides users with a toolbox representing different types of neural layers. It allows the connection between the different blocks and the configuration of important hyper parameters of each layer. Thus, speeding-up experimentation with different architectures. Additionally, the developed solution offers users the possibility to generate a python script of the designed network that can be run using specific libraries such as keras or tensorflow.
Robust Moving Object Detection and Tracking Framework Using Linear Phase FIR Filter

Moving Object detection is a technique in computer vision in which multiple consecutive frames from a video are compared by applying various detection techniques to determine movement of an object. The most challenging task in motion detection is object tracking. In this paper we have proposed an effective framework for tracking down the moving object in which least square linear-phase Finite Impulse Response Filter is used for smoothing of image while optical flow estimation is used to calculate the motion between two images, median filter removes noise from a frame and motion vector estimation is used to find the variation in pixel movement using successive frames.
The Upgraded Democracy Adjusted for the 21&lt;sup&gt;st&lt;/sup&gt; Century

The author examines how does Democracy, the leading form of government in the world, fit itself to the tremendous changes occurred in the world toward the 21 st Century.Technology causes many changes that frequently occur before people and their organizations are able and competent to digest them.The author suggests some techniques and models for assisting nations to carry out the initiative of revising and enhancing their running systems.The article uses for the analysis of a two-aspect technique, constructed of Philosophical and Psychological methods for promoting understanding.Thus, the Philosophical aspect represents the Brain-the Rational way of thinking while the Psychological approach represents the inner-self feelings-the Emotional tactic toward actual occurrences.By using these two complimentary approaches, the picture accepted and the knowledge accumulated are much more completed and relevant.
Document Representation for Text Analytics in Finance

The automated analysis of unstructured data that is directly or indirectly relevant to developments on financial markets has attracted attention from researchers and practitioners alike. Recent advances in natural language processing enable a richer representation of textual data with respect to semantical and syntactical characteristics. Specifically, distributed representations of words and documents, commonly referred to as embeddings, are a promising alternative. Consequently, this paper investigates the utilization of these approaches for text analytics in finance. To this end, we synthesize traditional and more recent text representation techniques into a coherent framework and provide explanations of the illustrated methods. Building on this distinction, we systematically analyze the hitherto usage of these methods in the financial domain. The results indicate a surprisingly rare application of the outlined techniques. It is precisely for this reason that this paper aims to connect both finance and natural language processing research and might therefore be helpful in applying new methods at the intersection of the respective research areas.
The Effect of Two-Term Parameters on the Performance of Third-Order Neural Network on Medical Classification Problems

Artificial neural networks (ANN) also known as backpropagation (BP) algorithm has recently been applied in many areas and applications. BP algorithm is known as an excellent classifier for nonlinear input and output numerical data. However, the popularity of BP comes with some weaknesses which are moderate in learning and easily getting stuck in local minima. A vigorous area of research and papers on enlightening the performance of BP algorithm are revised especially in terms of literature. Furthermore, the performance of BP algorithm also highly influenced by the order of learning and the parameters that been chosen. This paper presents an improvement of BP algorithm by adjusting the two-term parameters on the performance of third-order neural network methods. The effectiveness of the proposed method is proven through the methods of stimulation on medical classification issues. The results show that the proposed implementation significantly improves the learning speed of the general BP algorithm.
A Novel Performance Metric for the Video Stabilization Method

Video stabilization methods cannot compensate entire unwanted motions of an unstable video. In this paper, we present a new performance metric which can effectively measure the stability of a video. Our hypothesis is that in case of non dynamic background, the background motion is zero without the foregrounds of a stable video when a video taken by a fixed camera. Firstly, the foregrounds of a stabilized video are discarded, then, each background pixel motion is determined between the two consecutive background frames in two directions separately, afterwards, an average motion of each pixel is computed. These mean motions determine the stability of a video. The more unstable video will have more background motions. The background motion is a criterion to evaluate the stability of a video. The experimental results prove the efficacy of our proposed approach.
On the Possibilities of Using BYOD Technology in Computer Science Lessons in 7–8 Grades

The article is devoted to the use of BYOD technology in computer science lessons in grades 7-8. The essence of the technology BYOD, the application form is given. Examples of the use of technology and the experience of using this technology in a computer science class at school are given.
An Interpretable Classification Model Based on Characteristic Element Extraction

The process of a classification application is usually dynamic and long. During the process of an application, better classification application effect can be acquired by enlarging and adjusting the training dataset continuously, for example, modifying the wrong labels of original instances. For this kind of dynamic classification applications, how to build an interpretable classifier which can help domain experts to understand each label's meanings reflected from the dataset, then to compare and discriminate them with their own mastered domain knowledge, and finally to adjust and optimize the training set to enhance the effect of classification applications, is a neglected but worth studying issue. Therefore, an interpretable classification model based on characteristic element extraction is proposed in this paper. The proposed classifier is constructed by extracting positive and negative characteristic elements for all class labels which can intuitively reflect their instinct characteristics. Thus, it has high interpretability obviously and can effectively help domain experts optimize classification effect. At the same time, experiment results show that our classifier also has higher accuracy compared with other kinds of classical classifiers. Consequently, the classification model proposed in this paper is effective and efficient, especially in practical applications.
Tiered Document Clustering Through Finest Centroid Assortment

Data growth is enormously higher today and difficult to handle in an organized manner and in effective time limit. In data mining, a wide range of research is available for managing data effectively. Similarly, document clustering is one of the notable firm process in the data mining utilized to assemble the related documents. In this article, we generate an algorithm for clustering by means of Adaptive Pillar K-means and Gaussian Firefly Algorithm (GFA). For determining the proper centroid in order to attain the proper clustered documents, Adaptive Pillar K-means Algorithm is utilized. Subsequently, GFA is exploited for the optimization process and also for enhancing the precision that results in reducing the sum of squared errors and computational time. Here, the performance of the proposed methodology is compared with various algorithms such as Genetic Algorithm, Ant colony optimization, and gravity clustering. The attained results shows the performance of the proposed methodology and the simulation results illustrated the betterment in quality with low sum of squared errors.
A New Equating Method Through Latent Variables

Comparability of measurements is an important practice in different fields. In educational measurement, equating methods are used to achieve the goal of having comparable scores from different test forms. Equated scores are obtained using the equating transformation which maps the scores on the scale of one test form into their equivalents on the scale of another for the case of sum scores. Such transformation has been typically computed using continuous approximations of the score distributions, leading to equated scores that are not necessarily defined on the original discrete scale. Considering scores as ordinal random variables, we propose a latent variable formulation based on a flexible Bayesian nonparametric model to perform an equipercentile-like equating that is capable to produce equated scores on the original discrete scale. The performance of our model is assessed using simulated data under the equivalent groups equating design. The results show that the proposed method has better performance with respect to a discrete version of estimated equated scores from traditional equating methods.
Eye-hand Coordination in Reaching and Grasping Vertically Translating Targets

Previous research in our lab has revealed a strong visual bias toward the eventual index finger contact location when grasping stationary or horizontally moving targets. However, the unique properties of the thumb may have an increased role when grasping vertically moving targets. Using their index finger and thumb, right-handed individuals reached for and grasped 2-D rectangular targets moving upward or downward on a vertically oriented computer screen. It was expected that fixations would be directed toward the index finger when grasping upward moving targets, and to the thumb when grasping downward moving targets. In trials involving upward moving targets, initial fixations were positioned well above the target, in anticipation of the eventual movement, and remained above the target even after the target started to move. Gaze shifted to the leading (upper) edge of the target when participants initiated their reaching movement, suggesting that participants were primarily attending to the eventual contact location of the index finger. Initial fixations toward downward moving targets were positioned well below its bottom edge, once again anticipating eventual movement. When the targets started to move, gaze shifted upward to a position just below the target’s centre of mass (COM) and remained there during reach onset. Final fixations for both upward and downward moving targets were positioned slightly above the target’s COM, a location that would allow the monitoring of both index and thumb when grasping. These results suggest that while index finger placement is prioritized when grasping upward moving targets, visual attention is directed toward more central locations when grasping downward moving targets, thus allowing both contact points to be monitored simultaneously. This is suggestive of an increased importance of the thumb, in addition to the index finger placement, not shown previously when grasping stationary or horizontally translating targets.