Reviewerly/exHarmony huggingface dataset:
Performance Comparison of Moving Target Recognition between Faster R-CNN and SSD

Target detection algorithms based on deep learning and convolutional neural networks (CNN), such as Faster Regionbased Convolutional Neural Network method (R-CNN) and Single Shot Multi-Box Detector (SSD), are superior to traditional algorithms in detection accuracy and speed. However, there is still a lack of detailed research on the different performance between different algorithms. This paper introduces the Faster R-CNN algorithm and SSD algorithm respectively, and implements the algorithm function based on the open source code. In order to evaluate the performance of these two algorithms, a large number of videos containing cars in different scenarios were used for testing. Experimental results show that the former is superior to the latter in recognition accuracy, while the latter is superior to the former in recognition accuracy.
Advanced Encryption Standard-Cipher Blockchain Mode for File Cryptography Stint Control

Protecting files is common goal of individual, specially having data privacy on confidential and important files. Anyone may do illegal or it could be hackers who is always there to attack it, without the knowledge of the person on how to protect it from those illegal intention where they can keep these hackers from prying their eyes using a cryptographic system. This research focus on File Cryptography Stint Control Software using Advance Encryption Standard-Cipher Block Chain mode so outbreak will not infiltrate the security in order to create a strong cryptographic system that will accept the tract of its capabilities to different file types and the capacity to convert the given files into ciphertext format and back to its original form.
Stochastic batch size for adaptive regularization in deep network optimization

• Adaptive regularization for deep network optimization via parameter-wise batch size. • The stochastic batch size reflects local and global properties of each parameter. • Beneficial for practical studies where the number of training examples is small. We propose a first-order stochastic optimization algorithm incorporating adaptive regularization for pattern recognition problems in deep learning framework. The adaptive regularization is imposed by stochastic process in determining batch size for each model parameter at each optimization iteration. The stochastic batch size is determined by the update probability of each parameter following a distribution of gradient norms in consideration of their local and global properties in the neural network architecture where the range of gradient norms may vary within and across layers. We empirically demonstrate the effectiveness of our algorithm using an image classification task based on conventional network models applied to commonly used benchmark datasets. The quantitative evaluation indicates that our algorithm outperforms the state-of-the-art optimization algorithms in generalization while providing less sensitivity to the selection of batch size which often plays a critical role in optimization, thus achieving more robustness to the selection of regularity.
Developing the Bangladeshi National Corpus- a Balanced and Representative Bangla Corpus

The need for a balanced, representative national scale corpus has been skyrocketing for the already `low resource' tagged language-Bangla. Many sporadic empirical works have been done so far in the field of NLP and Computational Linguistics yet, and these are never enough. Moreover, none of these works can bear the best fruit without the help of a standard corpus. To address these issues, the goal of this research work was set to compile the Bangladeshi National Corpus (BDNC). This paper proposes the development process of the BDNC (first phase- Bangla monolingual corpus). In this work, the whole task was divided into three major phases, where the goal of the first phase is to build a representative monolingual corpus that will include at least 100 million Bangla words. Whereas, in the second phase, there will be a sub-corpora that will consist of a parallel corpus having 1 million words in Bangla and English. However, at the third and final phase, the parallel corpus will incorporate 15 foreign languages (including English) comprising a weighted corpus size of at least 15 million words.
Web Personalization Recommendation System Through Semantics

WPRS is a new approach as it works on discovering the precise web browsing behavior from uncertain keywords and defines the semantic measurement with user recommendation of keywords within the user query. It also worked on to eliminating ambiguity in keywords being matched. The proposed approach is standard as these can be combined with several proposals and are function autonomous. WPRS system when used to retrieving user browsing behavior information from the web database. The next stride will be the combine of this technique with a model that deal with the semantic enhancement of personal recommendation and serves its users more intelligently.
Architecture to Minimize Energy Consumption in Cloud Datacenter

As there is a huge requirement for IT resources in business world, cloud computing is used by various businesses to satisfy their requirements. Many businesses outsource their IT needs to cloud and get benefited from cloud services. Cloud represents datacenter which has full infrastructure that provides virtualized services. The main challenge in cloud datacenter is energy consumption by server and computer and cooling equipment in cloud datacenter. Cloud datacenter emits huge amount of carbon dioxide which affect the environment acts against green atmosphere. This paper attempts to reduce the energy consumption of computing resources in cloud datacenter. Architecture has different entities which are described in the concerned sections. The proposed architecture saves energy by clustering similar request from the clients and also switches off ideal resource in cloud datacenter. The proposed architecture is an efficient energy consumption resource management model for cloud datacenter.
VLSI design of Parity check Code with Hamming Code for Error Detection and Correction

In order to get error-free data in any wireless communication systems the data has to be transmitted and received in an efficient manner. But in case of some wireless networks such as cognitive radio(CR) technology ,while performing spectrum sensing and spectrum allocation, they may cause interferences which leads to creating error in transmitted data. To overcome such problem in CR technology, the efficient data transmission needs Error Detection and Correction method. Here various error detection and correction methods are implemented based on parity check code along with hamming code algorithm. Encoder block may calculate the parity bits for data bits in various parity check code methods and also parity bits are coded with hamming codes. Whereas in decoder block, data can be regenerated after calculating syndrome vector and parity checking also takes place. Such encoder and decoder block is developed using verilog coding in Xilinx ISE software. Finally the comparison of various parity check code with hamming code is distinguished.
Improving Tracking Soccer Players in Shaded Playfield Video

Soccer is the most popular sport in the world and the information extracted from this match has many uses. It can be used to extract players `paths, recognize the performance, and evaluate the players` statistics, evaluate the referee`s decision, and so on. One of the main steps in analyzing soccer video is tracking players that seeks to locate players when playing video. Player tracking involves various processes, such as playfield detection, player detection, tracking of players, apparent modeling of players, and identification of players overlapping. One of the challenges in this field is the tracing of players in the shaded play field, which challenges the tracking of players due to light changes in the field. In this paper, using the proposed algorithm to identify and Tracking players in television shows with two shaded playfield and sun shades. The proposed method is identified the playfield by using the saliency map algorithm and shadow elimination, which will minimize the noise from the stadium area. Then by using the features of the color, brightness and edge we will recognize the players. Using the combination of Top-hat transformation and the morphological operation the lines of the playfield is detected. Finally, by using the results of the detection step we will track players. The tracker used in this study is an improved particle filter that uses a combination of color and edge features. The results of the proposed method demonstrate that the detection of play field has 93% accuracy. Also the proposed method tracks the detected players with 90% precision. Therefore tracking accuracy shows that light variations have very little effect on it.
Testing Numbs Us to Our Loss of Intellectual Control

I've been reflecting on how we used to develop software in the 1990s compared to what I see today. One thing that stands out is that everyone is testing. Testing, testing, testing. What a difference! Having those tests gives us confidence that evolution of the code is not breaking anything. I don’t know what I would do if my project’s tests were lost. So, how did we ever get code out the door before we had automated regression testing? I think the answer has many parts, and it includes ideas that we should not resurrect, including individual ownership of modules, quarterly product releases (or worse), wholeprogram specifications, and lots of manual testing.
No-Reference Video Quality Assessment using Recurrent Neural Networks

The quality assessment is a vital routine in videorelated industries such as broadcast service providers. Due to the duration and the excessive number of the video files, case by case assessment of the files by operators is no longer feasible. Therefore, a computer-based video quality assessment mechanism is the only solution. While it is common to measure the quality of a video file at the compression stage by comparing it against the raw data, at later stages no reference video is available for comparison. Therefore, a no-reference (Blind) video quality assessment (NR-VQA) technique is essential. The common NRVQA methods learn a quality metric based on a number of features extracted from video frames or series of adjacent frames. In the training stage, the features are usually required all at once and the outcome is mainly insensitive to the frame order. For instance, most methods return the same quality score if the video is played in the reverse time order. In this work, we propose an in-the-wild NR-VQA method based on recurrent neural networks (RNN), which takes the frame order into account. Indeed, the RNN is responsible to combine frame-level features by preserving their order so as to form a single video quality metric. As the RNN receives the frame-level statistical features in a sequential manner, the method is also oblivious to the frame size and video length (duration). The experiments show comparable or better performance with previous methods on KonVid-1k dataset.
Hardware Implementation of Convolutional Neural Networks Based on Residue Number System

The paper examines the use of residue number system (RNS) for hardware implementation of neural networks based on VLSI or FPGA. Widely known mobile neural networks that are highly accurate and best suited for implementation at hardware level have been explored. Major difficulties in their RNS implementationare examined. Several methods for solving the related problems are proposed: convolutions with step value greater than one, rather than previously used MaxPooling layers, are considered; non-standard activation functions containing only addition, subtraction and multiplication are investigated; efficient algorithm for scaling implementation is proposed and compared with conventional algorithm. Finally, we propose complete flow for design and transfer of MobileNet neural network to hardware level on RNS basis.
Implementation of Fuzzy Mamdani For Recommended Tourist Locations In Madura - Indonesia

Abstract As a developing country, Indonesia is currently actively developing itself in all its main fields of tourism. Diversity of tourism in Indonesia, one of which is located on Madura Island which has approximately 60 attractions spread across four districts namely Bangkalan, Sampang, Pamekasan and Sumenep. However, the current condition, there are many attractions that are not explored in the outside community so that many tourists do not know which attractions are revised. This study focuses on how to make a decision support system that provides tourist attractions for tourists. The method used is Fuzzy Mamdani which can produce the best decision by determining the Min-Max value and finding the fuzzy value based on its center. This research succeeded in making a system that provides tourist attraction recommendations for tourists by using fuzzy mamdani based on three criteria, namely the number of visitors, the distance of the tourist location to the city center, and the review of visitors on the internet. A post-publication change was made to this article on 20 Apr 2020 as the previously published article was a duplicate.
Renal Cell Cancer Nuclei Segmentation from Histopathology Image Using Synthetic Data

Renal cell cancer nuclei segmentation from histopathology image is an important step in cancer diagnosis and treatment. However, an automatic cell nuclei segmentation in medical application is a difficult task. Particularly a large amount of annotated data is required for deep learning network. In this work, synthetic but annotate renal cell nuclei data are generated based on non-synthetic data reference. Furthermore, background texture is created according to reference image. The Random shapes of nuclei polygon are created where polygon regions are filled up with foreground nuclei color. The synthetic cell nuclei patches are refined with speeded-up robust feature selection algorithm. The non-synthetic reference patches are considered to assign a score for each synthetic patch where synthetic patches with highest scores are collected. The synthetic patches with corresponding masks are utilized to train U-net segmentation network, which provides better performance than existing methods. In this approach, manual data annotation does not require, which is time-consuming and expensive. The renal cell cancer histopathology data has been collected from Cancer Image Archive, USA which is used to produce non-synthetic reference patches. The proposed framework is validated with Kidney Renal Clear Cell Carcinoma (KIRC) datasets. We have achieved average 0.923 cell nuclei segmentation accuracy from histopathology patches where accuracy provides better segmentation result than other existing methods.
A cognitive system of elderly exercise evaluation with sensors and robots

As the aged society is approaching soon, importance of physical practice is increasing for elderly people more to keep their joint flexibility and cognition ability. Although such physical practice is conducted at care facilities in many cases, necessary care is not always available because the elderly population growth is quite rapid. Therefore it is important to introduce ICT (information and communications technology) into the elderly care support at the care facilities to supplement the care shortage. As the first step of introduction of ICT, we propose a cognitive system to watch elderly persons during exercising and to evaluate their exercise. The system consists of three processes. In the first process, body joint data of an elderly person are collected by a depth sensor. Then we extract features to evaluate the elderly person’s exercise from the measured data in the second process. Finally, in the third process, the evaluation is feedbacked to the elderly person by using an intimate robot interface. The cognition system can be extended by using multiple depth sensors to mitigate occlusion. When multiple depth sensors are used, there appear two problems: frame synchronization and calibration. We also approach these two problems.
Comparative Performance Analysis of Different Travelling Mechanisms Based on RecurDyn

Abstract According to the characteristics of different travelling mechanisms of the robot, the kinematic equations of wheeled mobile robot and tracked mobile robot are established by using the kinematic theory; the tire system and the triangle track system are established by using the multi-body dynamics simulation software RecurDyn, and the simulation analysis is carried out under the road conditions such as over the boss, according to the principle of single variable; manage the data, and we can get the difference of the robot’s performance in obstacle surmounting, speed and stability. The results show that when the mobile robot adopts the travelling mechanisms of wheel shoe combination, it can improve the overall motion performance, and provide a new idea and direction for the future design of the travelling mechanism.
Building User Trust of Critical Digital Technologies

Digital technology is permeating all aspects of human society and life. This leads to humans becoming highly dependent on digital devices, including upon digital: assistance, intelligence, and decisions. A major concern of this digital dependence is the lack of human oversight or intervention in many of the ways humans use this technology. This dependence and reliance on digital technology raises concerns in how humans trust such systems, and how to ensure digital technology behaves appropriately. This works considers recent developments and projects that combine digital technology and artificial intelligence with human society. The focus is on critical scenarios where failure of digital technology can lead to significant harm or even death. We explore how to build trust for users of digital technology in such scenarios and considering many different challenges for digital technology. The approaches applied and proposed here address user trust along many dimensions and aim to build collaborative and empowering use of digital technologies in critical aspects of human society.
Future of cognitive science

Cognitive science deals with the study of mind focusing on its representation and use of knowledge. It is a multidisciplinary technique, where researchers from different domains such as linguistic, philosophers, psychologist, or computer scientists contribute to this science. The chapter discusses the role of cognitive science in varied field and also explores the future of cognitive neuroscience with its enhancement techniques.
Correlation between Types of Parenting with the Development of Children Aged 1-5 Years

Important period in child development is at the age of 1-5 years old. Parenting is one of the factors that cause development of child aged 1-5 years old. The objective of this study is to find the correlation between types of parenting parents with the development of children aged 1-5 years old. This research method is cross-sectional design. Data were collected during June-September 2015. Respondents in this study were parents and children 1-5 years old who met the inclusion and exclusion criteria. We used systematic random sampling technique until total sample of 345 parents and children achieved. Independent variables studies was types of parenting and dependent variable was development of children aged 1-5 years old. Data types of parenting was collected by questionnaire and development of children aged 1-5 years old was measured by Pre-Development Screening Questionnaire (KPSP). Analysis was using Chi Square. The authoritarian parenting 11.6%, 14.5% democratic parenting, 12.7% passive parenting and mixed parenting 62%. 73% of the KPSP with a “correct” development, “doubt” (13.6%), and “possible disorder” (13.3%). There is a significant correlation between types of parenting and development children aged 1-5 years old (p = 0.000) with contingency coefficient value 0,661. Risk factors for the disorder development are 69.7% for the variable parenting variables, maternal education, maternal occupation and nutritional status of children. Types of parenting had correlation with development children aged 1-5 years old.
Improvement of Edge-Tracking Methods using Genetic Algorithm and Neural Network

One of the most basic and important operations in the field of image processing is image extraction and detection. Edge recognition is very important for image clarity and image segmentation. The importance of edge detection is that the human eye can recognizes the existence of different objects by observing the edges. So it makes sense to do edge detection before interpreting images in automated systems. Edges are points in the image where the two pixels are two different values, or two values very large in numerical value. It can be said that one of the goals of edge reduction in data size in images is to preserve the original structure and shape of the images. Editing has various applications, including object recognition, segmentation and image coding, in a variety of medical images. One of the problems we encounter when editing images has noises. In this paper, a combination of several standard edge- matching algorithms, neural network and multi-objective genetic algorithm NonDominated Sorting Genetic Algorithm (NSGA-II) is used to edge detection. In the proposed method, we give the standard multiedge finder to a forward propagating (FP) neural network as input. A multi-objective genetic algorithm with Non- Dominated Sorting Genetic Algorithm (NSGA-II was used to select the number of edge detectors to access the neural network and to select the most accurate ones. The genetic algorithm selects the least accurate number of edge detectors to enter the neural network. To evaluate the proposed method, TP, TN, FP, FN criteria were used to compare with other methods. Finally, by comparing the results obtained on different images using existing methods and the proposed method it is observed that the proposed method has better accuracy in detecting the edge of noise images.
Comparison of Image Smoothing Methods on Potholes Road Images

Abstract Before segmentation performed on potholes, road images is done first on the image smoothing. With the aim at the time of the next stage of the next process (segmentation) is expected to produce good accuracy. In this research, there will be a comparison of four smoothing methods consisting of lowpass filter, highpass filter, highboost filter and gaussian filter. The four smoothing methods applied to twenty pothole road image data that have been preprocessed using a combination of brightness and contrast methods. Comparative measurements of the four methods use the Means Square Error (MSE) value and Peak Signal to Noise Ratio (PSNR). Test results obtained from this research is the average value of MSE lowpass filter 68.12, MSE highpass filter is 569.99, MSE highboost filter is 838.54 and MSE Gaussian filter is 34.68. The average value of PSNR lowpass filter is 66.60 dB, PSNR highpass filter is 47.36 dB, PSNR highboost filter is 44.77 dB, and PSNR Gaussian filter is 75.94 dB. The measurement results obtained by the Gaussian filter method which is the best method for smoothing on potholes road images, because it produces the greatest PSNR value, and the smallest MSE value.
Efficient Image Compression for Cloud System

There has been an increasing demand to store a large quantity of images generated in social media, eHealth, and other popular domains. Due to the rapid uses of digital images, an efficient and faster compression technique is needed for any cloud-based applications. This paper introduces and describes an idea of compressing images using a distributed system to reduce the storage requirement of data (with images). The proposed strategy can provide storage efficiency with an easier way to compress and reconstruct the image in future when needed.