Reviewerly/exHarmony huggingface dataset:
Neural-Network-Based Feature Learning: Convolutional Neural Network

Convolutional neural network (CNNs) are a kind of feedforward neural network with convolutional computation and deep structure. In recent years, the application of CNN is very extensive, such as visual images, video recognition, and natural language processing. In this chapter, we first introduce the basic architecture of CNN, including convolutional layers, pooling layers, batch normalization layers, and dropout layers, and pay more attention to the illustration of backpropagation of convolutional layers. Then we introduce transfer feature learning to use of similarities between data, tasks, or models to apply a model that has been learned in one field to a learning problem in another field. Later we introduce some popular and widely-used deep convolutional models, including AlexNet, VggNet, and GoogleNet. Finally, we use case studies to deepen the understanding of CNNs.
Autonomous Obstacle Avoidance Vehicle using LIDAR and an Embedded System

Self-driven vehicles have many type of algorithms which simulate the driving behaviour in the real world majorly in autonomous miniature vehicles. In the last decade, several approaches have been proposed for obstacle avoidance in selfdriving vehicles. In this, our team is implementing a vehicle which is capable of navigating in an unknown environment and detecting obstacle by using Raspberry Pi 3+ and a LIDAR module avoiding Computer Vision (CV) techniques. The further enhancement is being done by implementing a mapping feature to generate an image of environment during navigation.
ELECTRICAL NOISES REDUCTION IN NANOPORES EXPERIMENTS BASED ON CONSENSUS FILTER

Nanopore has developed into an indispensable tool for single molecules experiments, where useful information about the characteristics of DNA/RNA can be obtained from the flowing of ionic current elicited by individual molecules as they traverse a single nanometer-scale aperture.However, due to the relatively high background current noise and low measurement bandwidth, it is difficult to distinguish the molecule-specific signals from the intrinsic noises of the nanopore recording systems, which impeded its application in DNA sequencing.In this paper, we present a novel consensus filter-based noise reduction approach, which considerably improves the accuracy of the noise-filtered current traces and the signal-to-noise ratios (SNRs).The new approach is compared to the conventional Bessel filters and validated by analyzing simulated representative translocation data as well as alpha-Hemolysin (α-HL) nanopore experimental data.It is demonstrated that the high frequency noise generated by the voltage noise of the amplifier and Analog-to-Digital converter circuits (ADC) can be significantly reduced and some hidden short-lived translocation events can be recovered with satisfying accuracy beyond the conventional limitations of the Bessel filters.
A Gentle Introduction to Feature Learning

Feature learning is the process of using domain knowledge and special techniques to transform raw data into features. Feature learning can build derived features , eliminate irrelevant, redundant, or noisy data, accelerate the speed of data processing, and improve the understanding of the data. In this chapter, firstly, we show that feature learning is fundamental to the applications of machine learning and pattern recognition. Then we go over the life cycle of data, including data collection, data cleaning, data sampling, and feature learning. For each topic, we give a brief introduction to the related methods. Feature learning has become an essential research content for machine learning and pattern recognition. Through this chapter, readers can have a general understanding of feature learning.
Book of insects’ immune system: development and implementation with pbl in increasing students’ learning outcome

The purpose of this research was to develop a research-based reference book and applied it through the problem-based learning (PBL) using reference books in learning activities. The method for the reference book used the ADDIE development models. The reference book's effectiveness test results were conducted using a purposive sampling technique, with a total sample of 55 students and analyzed by t-test. The research results showed that the reference books of the development results were validated by media experts and material experts. The integration of the development of reference books in learning activities has an impact on increasing student understanding as demonstrated through the pre-test and post-test scores that differ significantly with t-value&gt; t-table (4.149&gt; 2.045) at p &lt;0.05. Therefore, the development of the insect's immune system book with the PBL model increasthe student learning outcomes significantly. Also, this research has been able to improve students' ability and competence in solving problems in insects' immune system subject matter.
Novel Deep Level Image State Ensemble Enhancement Method for M87 Imaging

Standard spatial domain filters fail to adequately denoise and enhance the contrast of an image. These filters have drawbacks like oversmoothing, diminished texture, and lack of generative capabilities. This paper proposes a new method of image reconstruction, Image State Ensemble Enhancement (ISEE), based on our previous work, Image State Ensemble Decomposition (ISED). Deep level ISEE and ISED have been developed to produce a class of filters that can address these issues. Full-reference and no-reference quality metrics are used to assess the image, and the full reference metrics showed a marked improvement, while the no-reference metrics were often better than the test image. The test image was taken from the Spitzer Space Telescope (SST), and ISEE reconstruction yielded improved structural detail over that of ISED and the original test image. Glare and noise were reduced in a narrow bandwidth, which led to the discovery of a vortex-shaped structure and an outburst in M87′s dusty infrared core. The vortex is located over M87′s visible core and black hole. This is verified with an SST and Hubble Space Telescope (HST) overlay, ISEE processed image. A counter-jet channel was also discovered, and it appears to be the path of the unobservable superluminal counter-jet.
A Manual for Web Corpus Crawling of Low Resource Languages

Since the seminal publication of “Web as Corpus” [1], the potential of creating corpora from the web has been realized for good for the creation of both online and offline corpora: noisy vs. clean, balanced vs. convenient, annotated vs. raw, small vs. big are only some antonyms that can be used to describe the range of possible corpora that can be and have been created. In our case, in the wake of the project Under Resourced Language Content Finder (URLCoFi), we describe a systematic approach to the compilation of corpora for low (or under) resource(d) languages (LRL) from the web in connection with a free eLearning course funded by studiumdigitale at Goethe University, Frankfurt. Despite the ease of retrieval of documents from the web, some characteristics of the digital medium introduce certain difficulties. For instance, if someone was to collect all documents on the web in a certain language, firstly, the collection could only be a snapshot since the web constantly changes content and secondly, there would be no way to ascertain completeness. In this paper, we show ways to deal with such difficulties in search scenarios for LRLs presenting experiences springing from a course about this topic. [1] A. Kilgarriff and G. Grefenstette, “Web as corpus,” in Proceedings of Corpus Linguistics 2001 , 2001, pp. 342–344.
Geographic information system and process-based modeling of soil erosion and sediment yield in agricultural watershed

BACKGROUND AND OBJECTIVES: The study explored the capability of the geographic information system interface for the water erosion prediction project, a process-based model, to predict and visualize the specific location of soil erosion and sediment yield from the agricultural watershed of Taganibong.METHODS: The method involved the preparation of the four input files corresponding to climate, slope, land management, and soil properties. Climate file processing was through the use of a breakpoint climate data generator. The team had calibrated and validated the model using the observed data from the three monitoring sites.FINDINGS: Model evaluation showed a statistically acceptable performance with coefficient of determination values of 0.64 (probability value = 0.042), 0.85 (probability value = 0.000), and 0.69 (probability value = 0.001) at 95% level, for monitoring sites 1, 2, and 3, respectively. A further test revealed a statistically satisfactory model performance with root mean square error-observations standard deviation ratio, Nash-Sutcliffe efficiency, and percent bias of 0.62, 0.61, and 44.30, respectively, for monitoring site 1; 0.65, 0.56, and 25.60, respectively, for monitoring site 2; and 0.60, 0.65, and 27.90, respectively, for monitoring site 3. At a watershed scale, the model predicted the erosion and sediment yield at 89 tons per hectare per year and 22 tons per hectare per year, respectively, which are far beyond the erosion tolerance of 10 tons per hectare per year. The sediment delivery ratio of 0.20 accounts for a total of 126,390 tons of sediments that accumulated downstream in a year.CONCLUSION: The model generated maps that visualize a site-specific hillslope, which is the source of erosion and sedimentation. The study enables the researchers to provide information helpful in the formulation of a sound policy statement for sustainable soil management in the agricultural watershed of Taganibong.
Face Recognition for Attendance Management

Uniqueness or distinction of an individual is his face.Right now, of an individual is utilized with the end goal of participation making consequently.Maintaining participation is very critical for student success tests at all learning institutes, Uniqueness or distinction of an individual is his face.Right-now of an individual is utilized with the end goal of participation making consequently attendances are manually taken using unit leaders provided appointment sheets as part of the regulations.Expect that the term for one subject is around an hour or 1 hour and to record participation takes 5 to 10 minutes.For each guide this is utilization of time.This method is recursive, extremely particular but mistaken by certain understudies sign up regularly with lost mates.The strategy in fact, it allows it impossible to track participant involvement understudies just one enormous homeroom condition.To avoid these misfortunes, a programmed procedure is utilized right now depends on picture preparing.Right now, location and face acknowledgment is utilized.Right now, propose the structure and utilization of a face identification and acknowledgment framework to naturally identify understudies going to a talk in a homeroom and imprint their participation by perceiving their appearances.
Designing and Creating Digital Interactive Content Framework: Description and Evaluation of the Almekhlafi Digital Interactive Content Model

The design and creation of digital interactive content for teaching and learning are gaining increasing importance. As this technology advances, the need to cope with everyday demands necessitates teachers and content developers to accommodate their students in this aspect. Interactive digital content provides opportunities for students to study anywhere and anytime, with classmates or independently, and within conventional or blended teaching and learning environments. The Almekhlafi Digital Interactive Content (ADIC) model is a systematic guide that could be used to produce interactive digital content on any subject. This model can serve as a framework for developing such content. The model consists of four major phases: Planning, designing, production, and evaluation. Each of these phases consists of a number of steps. The model was content-validated and tested for its reliability in real contexts. The model was piloted with 28 university faculty members and implemented with 81 university students. The results support the implementation of the model for designing and creating interactive digital content. The paper recommends that K-12 schools and higher education institutions should train students, teachers, and university faculty members to use the model effectively for producing sound, worthwhile, and interactive digital educational content. &nbsp
A novel pooling layer based on gaussian function with wavelet transform

&lt;p&gt;Convolution represent basic layer in the convolutional neural network, but it&lt;br /&gt;can result in big size of the data, which may increase the complexity of the&lt;br /&gt;network. Different pooling methods are used to perform down sample these&lt;br /&gt;data. In this paper, we have proposed a novel pooling method by using&lt;br /&gt;Gaussian function to determine the wavelet filter coefficients . At first, the&lt;br /&gt;basic statistics are determined for each pool size of the signal, then Gaussian&lt;br /&gt;probability distribution function is determined . According to the procedure&lt;br /&gt;of extracting the features , three methods are proposed , the first method is&lt;br /&gt;used the normalized values of basic statistics as wavelet filter to be&lt;br /&gt;multiplied by original signal, the second method used the determined&lt;br /&gt;statistics as features of the original signal ,then multiplied it with constant&lt;br /&gt;wavelet filter based on Gaussian ,while the third method is similar to first&lt;br /&gt;method, except it depend on entire signal instead of each pool size. The&lt;br /&gt;proposed methods are combined with other standard methods such as max&lt;br /&gt;and pooling. The experiments are performed on different datasets , and the&lt;br /&gt;results show that the proposed methods perform or outperform other&lt;br /&gt;methods and can increase performance of the (CNN).&lt;/p&gt;
Multi-modal RGB-D Image Segmentation from Appearance and Geometric Depth Maps

Classical image segmentation algorithms exploit the detection of similarities and discontinuities of different visual cues to define and differentiate multiple regions of interest in images. However, due to the high variability and uncertainty of image data, producing accurate results is difficult. In other words, segmentation based just on color is often insufficient for a large percentage of real-life scenes. This work presents a novel multi-modal segmentation strategy that integrates depth and appearance cues from RGB-D images by building a hierarchical region-based representation, i.e., a multi-modal segmentation tree (MM-tree). For this purpose, RGB-D image pairs are represented in a complementary fashion by different segmentation maps. Based on color images, a color segmentation tree (C-tree) is created to obtain segmented and over-segmented maps. From depth images, two independent segmentation maps are derived by computing planar and 3D edge primitives. Then, an iterative region merging process can be used to locally group the previously obtained maps into the MM-tree. Finally, the top emerging MM-tree level coherently integrates the available information from depth and appearance maps. The experiments were conducted using the NYU-Depth V2 RGB-D dataset, which demonstrated the competitive results of our strategy compared to state-of-the-art segmentation methods. Specifically, using test images, our method reached average scores of 0.56 in Segmentation Covering and 2.13 in Variation of Information.
Fuzzy logic controller design for PUMA 560 robot manipulator

The dynamic model of the robot manipulator contain from equations, these equations are nonlinear and contained from variations parameters due to variations in load, friction, and disturbance. The conventional computed torque (PD and PID) controllers are not highly suitable for nonlinear, complex, time-variant systems with delay. In this paper, the fuzzy logic controllers (FLC) has been used because it is efficient tools for control of nonlinear and uncertain parameters systems. This paper aims to design a fuzzy logic controller for position control of a PUMA 560 robot manipulator. Based on simulation results we conclude that the performance of the fuzzy logic controller in term of position tracking error in case of disturbance or load is better than the conventional computed torque (PD-CTC and PID-CTC) controllers.
Robust baggage detection and classification based on local tri-directional pattern

In recent decades, the automatic video surveillance system has gained significant importance in computer vision community. The crucial objective of surveillance is monitoring and security in public places. In the traditional local binary pattern, the feature description is somehow inaccurate, and the feature size is large enough. Therefore, to overcome these shortcomings, our research proposed a detection algorithm for a human with or without carrying baggage. The local tri-directional pattern descriptor is exhibited to extract features of different human body parts including head, trunk, and limbs. Then with the help of support vector machine (SVM), extracted features are trained and evaluated. Experimental results on INRIA and MSMT17_V1 datasets show that LtriDP outperforms several state-of-the-art feature descriptors and validate its effectiveness.
Exploration and Consideration on Teaching Reform of Data Structure Course for the Engineering Education Professional Certification

As a core curriculum of computer science, data structure plays a key role in connecting the preceding with the following in the professional curriculum system. This paper analyses the problems existing in the traditional teaching method of data structure course. Based on the results-oriented concept of engineering education for professional certification, the student-centered and continuous improvement concept of teaching, some ideas and measures for the reform of data structure course are presented, including adjusting teaching aims and improving teaching methods and reforming assessment methods, etc. Through the reform, we can effectively improve the teaching effect of data structure course, learning interest and initiative of the student and improve engineering practice ability for them.
Online Active Learning for Offensive Language Detection

Recent success in deep learning-based offensive language detection, however, the off-line learning strategy cannot cope with the challenge of rapid growth and evolution of offensive language. In this paper, we introduce an online active learning method to offensive language detection model. Online learning makes use of the user’s report feedbacks to continue training the detection model. In addition, active learning methods can filter out the mislabeled feedbacks to ensure the safety of the online learning process. Extensive experiments demonstrate that the proposed method can achieve promising results on the offensive response dataset.
ПСИХОЛОГІЧНІ ОСОБЛИВОСТІ АДАПТИВНОГО ПОТЕНЦІАЛУ ОСІБ, КОТРІ ПРАЦЮЮТЬ В ЕКСТРЕМАЛЬНИХ СИТУАЦІЯХ

The performance of professional activities in extreme situations has a distinctive nature due to a range of risk factors, which can adversely affect mental health, the state of emotional and mental stability, as well as dramatically increase the level of emotional or professional burnout. Work under such circumstances requires the employee to possess a certain degree of some psychological characteristics so that he/she would not get harmed personally while performing it. In the modern world, the psychology of professional activity and a number of other applied sciences face the situation that stress has virtually invaded the daily life of individuals becoming one of the most common causes of a range of diseases and diverse disorders. Today, it is virtually impossible to determine the criteria of a profession or an occupation which is safe for an individual's mental health since all of them are closely associated with some natural or technological risks which fill our lives. The primary goal of this research is to attempt and analyse what degree of adaptive capacity persons whose professional activity is closely associated with extreme situations have. The main thesis is that persons with different degrees of adaptive capacity possess certain psychological characteristics and individual features which do not only help them to go easily through the phase of adaptation to new, extreme conditions in which the task is performed but also considerably influence the degree of their satisfaction with the job associated with risks.It is stated that, undoubtedly, the degree of adaptive capacity is closely related to such factors as age, work experience, emotional-and-volitional sphere, motivational aspect of performing professional activities, etc. It was discovered that persons working as rescuers in the contemporary conditions of society demonstrate a low degree of adaptive capacity, which can actually have negative consequences both for the individual's mental health (bad mood, lack of attention, irritation, negative and occasionally suicidal thoughts, and so on) and for the effective performance of professional activities. It is stressed that in order to develop their adaptive capacity, it is absolutely necessary for the persons whose professional activity is closely associated with extreme situations to work on the development of such personal qualities as psychic flexibility, sense of humour, ability to think critically and access adequately the risks which representatives of extreme professions face. Such a perspective on the development of personal qualities is required in the first place to maintain personal mental stability and balance in the modern world, which is a powerful stress factor for our mental health.
Guest Editors’ Introduction to the Special Issue on Machine Learning Architectures and Accelerators

The twelve papers in this special section focus on machine learning architectures and accelerators. Deep learning or deep neural networks (DNNs), as one of the most powerful machine learning techniques, has achieved extraordinary performance in computer vision and surveillance, speech recognition and natural language processing, healthcare and disease diagnosis, etc. Various forms of DNNs have been proposed, including Convolutional Neural Networks, Recurrent Neural Networks, Deep Reinforcement Learning, Transformer model, etc. Deep learning exhibits an offline training phase to derive the weight parameters from an excessive training dataset, as well as an online inference phase to perform classification/prediction/perception/ control tasks based on the trained model. The paper in this section aim to find a convergence of software and hardware/architecture. It aims at DNN algorithms, parallel computing, and compiler code generation techniques that are hardware/architecture friendly, as well as computer architectures that are universal and consistently highly performant on a wide range of DNN algorithms and applications. In this co-design and co-optimization framework we can mitigate the limitation of investigating in only a single direction, shedding some light on the future of embedded, ubiquitous artificial intelligence.