Reviewerly/exHarmony huggingface dataset:
Exploiting Variable Precision Computation Array for Scalable Neural Network Accelerators

In this paper, we present a flexible Variable Precision Computation Array (VPCA) component for different accelerators, which leverages a sparsification scheme for activations and a low bits serial-parallel combination computation unit for improving the efficiency and resiliency of accelerators. The VPCA can dynamically decompose the width of activation/weights (from 32bit to 3bit in different accelerators) into 2-bits serial computation units while the 2bits computing units can be combined in parallel computing for high throughput. We propose an on-the-fly compressing and calculating strategy SLE-CLC (single lane encoding, cross lane calculation), which could further improve performance of 2-bit parallel computing. The experiments results on image classification datasets show VPCA can outperforms DaDianNao, Stripes, Loom-2bit by 4.67×, 2.42×, 1.52× without other overhead on convolution layers.
Exploring Uncertain Samples through Active Learning To Enhance Text Emotion Classification

The traditional supervised learning of emotion classification models requires large number of training examples, which are usually very expensive to acquire. However, without a careful selection there may exist many irrelevant and duplicate examples for the experts to annotate, which wastes the human labor and affects the learning efficiency. In this paper we propose a novel method to improve the quality of training data and enhance the efficiency of training through active learning. Specifically, we use the information entropy to measure the uncertainty of model predictions on raw text samples, and choose those with the highest uncertainty for human experts to annotate as new training samples. Learning from these examples could most significantly solve these uncertain problems and therefore efficiently improve the model. Our experiment of multi-label emotion classification on Chinese microblog shows that the proposed method can effectively find out emotion statements with high uncertainty, and with these new training examples the emotion classification model gets improved in a steady and efficient manner.
Identification of correspondents between a standard series collection and an experimental series collection

The article proposes a method of comparing a collection of series representing the experimental data characterizing a physical phenomenon with a collection of series representing data archived in a catalog, of the same phenomenon, having in addition to the experimental data collection a number of characteristics for each series included. The goal is to associate each series of the sample collection with a series in the catalogue collection through a "proximity" relationship based on a statistical estimator. Once the "closest" series in the catalogue is determined, for each series in the sample collection, the additional features corresponding to the catalogue series, "closest", are associated with the series in the sample collection. The described process is one of the steps that attempt to improve a method of identifying physical properties of physical environments or objects.
Discretization of the Crime Rate from Numerical Into Categorical

The objective of this study is to discretize the crime rate figures into categorical values and to find associations between different crime rates in multiple cities. Regression modeling is used to regress the value of one crime rate on the basis of all others or some of the other crimes rates. As per US statistics on crime, this research focused on identifying the variable crime in multiple cities of the US and categorized the data of crime by applying machine learning techniques with the establishment of data discretization and preprocessing process.
Speech Corpus Development for Voice-Controlled MAV

The main idea of this paper is to bring out the uniqueness of the speech corpus required for development of command and control applications such as voice-controlled MAV. Since MAV finds application in adverse environments, the effect of noise degrades ASR performance. Since English words uttered are greatly influenced by user’s mother tongue, there is a necessity to create a customized speech corpus. The corpus creation is accomplished by a NALVoiceCorpus tool, which is designed to capture the specific requirements of the corpus. The tool is quite generic in nature and it can find application in development of any ASR system.
Parallelization Framework of Root Cause Analysis Based on Global Cache and Global Lock

The end-to-end resolution time cost of system failure can be split into the time of root cause analysis (RCA) and the problem resolution. In complex enterprise applications and large-scale cluster fault diagnosis activities, the RCA has a long duration due to the scale and complexity of the business, directly causing miss SLAs. To enhance the efficiency of problem discovery, an asynchronous parallel framework is presented in this paper, which establishes concurrent detection or orchestration jobs based on the dependency graph of various types of resources and parallel analysis jobs based on the parallel access capability of a specific resource bearer. Since there is one or two root causes of most anomalies, the framework's message notification mechanism was also designed as a pattern that gives reports immediately after discovery and as a full-path coverage pattern to achieve early reporting of root cause analysis and early intervention by human. Thus, the root cause analysis duration is reduced and the end-to-end solution time of system failure is shortened.
The FI Theory: Understanding Factual Knowledge and Information

The FI theory is a theory about factual knowledge and about information in general. FI stands for Factual Information. The basis for the FI theory consists of Ogden and Richard’s semiotic triangle and Stamper’s semiotic ladder. They clarify that information is a dyadic notion: it is the inseparable combination of content (the communicated thought) and form (the sign that serves to signify the thought). The main contribution of the semiotic ladder is that it distinguishes between the semantics and the pragmatics of thoughts and in doing so clarifies that a (elementary) thought consists of a fact and an intention. Intentions correspond with commitments in the social world. The core of the FI theory is the semiotic mill, refined into the ontological mill, which is a framework for understanding perception and conception. It explains how factual knowledge is created from perceptions of concrete things, directed by (fact) types, which operate as conceptual sieves. Three topics are elaborated. The first one is the recognition that most of the types that people use are functional types. Regarding functional types, the important fact is that they are inherently subjective, and therefore hard to define precisely. The second topic is the problem of sameness and change, illustrated with the well-known paradox of Theseus. The third topic concerns the composition and decomposition of things, based on the part-of relationship between things. Lastly, two issues in current programming and modelling practice are discussed. The first one is the duality of types, as opposed to the synonymy of signs. The second issue is the value types in software. Most programming and modelling languages offer four ‘types’: integer, real, boolean, and string. The first three are true types; the fourth is only a sign ‘type’.
Character action recognition based on deep convolutional neural network and action sequence

Object motion recognition has been widely used in video tracking, motion analysis, medical assistance and human-computer intelligent interaction, etc., which has become a hot research field in the field of computer vision. One of the main difficulties in character action recognition is to extract powerful features from video input to describe character action. It is not only necessary to detect the characters from the complex background, but also to accurately identify the changes between the movements [1]. In the process of character action research, the accurate description of human skeleton points makes the research on the key points have high accuracy and real-time. This paper proposes a motion recognition method based on depth feature and motion sequence, and adds depth convolution information on the basis of motion unit. The powerful feature extraction and description ability of convolutional network are employed to further describe and analyze the action objects[2]. The Transfer learning and structure optimization of the deep convolutional network were carried out, and the training and data testing were carried out on the new data set, so as to improve the recognition rate of human actions.
Transformation based image de-noising

In the processing of image de-noising, wavelet thresholding is an imported technique to refine the image from noisy components. Images are such as information which is transmitted from one source to another. During this transmission different noises are also added in the original information. The purpose is to introduce such type of technique to remove noises such as Gaussian noise from the images so that least extent of data carrying information diminish with the extreme removal of unwanted noisy components. We use Generalized Gaussian distribution for modeling. For the extraction of the original image, a hybrid scheme of thresholding is proposed. This algorithm is implemented and simulated in MATLAB using parameters like mean square error (MSE), peak signal to noise ratio (PSNR), visual quality and structural similarity index (SSIM). It is observed from the analysis that the hybrid technique gives result better than existing de-noising techniques. The computational complexity is less and provides better edge preserving during filtration.
Basics for the Process and Requirements of Ethical Hackers: A Study

Hacking is the act of breaking down into someone else PC for the cause of stealing data. Presently, hacking is majorly utilized in pessimistic practice. Through the proper utilization of ethics of hacking, we can apply it for superior motives, i.e., for the welfare of the society, it can be turned into ethical hacking. Before starting hacking, one should have proper knowledge about it—the attacks, its methods, its past experience, and then only a person can be a good ethical hacker. To be more precisely said, ethical hacking is the creativity in which we can enhance and help organizations to be secure and give a pathway for their growth.
Mechanism for Removal of Rain From Digital Images

The rain streaks in an image generally cause an imparity in the processing of the image in any image processing algorithm, this is a huge problem under a neural network that would consider these pixels as a part of the image, this might lead to error in the output. To overcome this imparity in images we use the L0 gradient smoothing method to remove the layer of the image, this layer corresponds to the rain pixels, which are typically low amplitude structures and hence removing these pixels by increasing the number of jumps in pixel format causing the overlap of unwanted pixels with much more important pixels and retaining the necessary sharpness of the image. This method also allows the removal of unwanted noise and increases the prediction rate with retention of the structures necessary for processing the image. This method is also useful in removing noise produced in the compression of animated pictures into JPEG format. The removal of rain pixels in any image could improve its processing quality and drastically improve the prediction probability. Finally, after removing these rain pixels, we have an image with low gradient and lower contrast, to improve the contrast and enhance the image we use HOG (Histogram Oriented Gradient System), this allows proper prediction and improves picture quality, this paper also aims at detecting persons in a frame after removal of noise from the input image, thus prediction of people in the given picture is much easier when the HOGS is implemented to it after L0 gradient smoothing is applied.
A study on the corresponding aspects and characteristics of grammatical features in “Godeung Gugeo Dokbon” and “Godeung Joseoneo Geub Hanmundokbon”

This study examines the special features and (dis)similarities of grammar items between Korean and Japanese at the beginning of the 20th century by comparing a Korean language textbook, “Godeung Joseoneo Dokbon ‘Advanced Korean Readers’” (1913; 1924), and a ‘national language’ textbook, “Godeung Gugeo Dokbon ‘Advanced National Language Readers’”(1913; 1924), which were used during the Japanese colonial era. Results are as follows. There is little change observed in grammar items between the 1st and the 3rd education decrees in Japanese. In contrast, grammatical forms became diverse during the 3rd education decree period in the case of Korean. For stylistic, the "da" form appeared in the 1st educational rule, and was used frequently in the 3rd, and the formal style "sumnida" also appeared during the 3rd educational rule. In the case of negation, the change of Korean"s negative expression from ‘leading’ negation to ‘trailing’ negation, may have been influenced by the translation of Japanese that had the ‘trailing’ negation only. In addition, the double negation ‘ji anichi moshada’ was also influenced by Japanese. As a result, the fact that such changes appeared in materials translated from Japanese to Korean shows that Korean at the beginning of the 20th century had a significant relevance to Japanese.
Bagging Techniques to Reduce Misclassification of Breast Cancer Prediction Base on Gradient Boosted Trees (GBT) Algorithm

Abstract Breast cancer is a malignant tumour that grows in breast cells and has a risk of death. Breast cancer has levels ranging from stage 0 to stage 4. The higher the stage of breast cancer, the higher the risk of death and is difficult to treat. The application of machine learning algorithms has been proposed to help predict breast cancer. Predictions made by classifying patients tend to have breast cancer or not. This research proposes to implement bagging techniques to reduce misclassification in the Gradient Boosting Trees (GBT) algorithm. The experimental results show that the application of bagging techniques can reduce misclassification and improve prediction accuracy.
Object Detection Approach Using Single Shot Multibox Detector for Sprinting Movement Recognition

The current movement detection tool is using the motion analysis system by attaching the reflective balls to specific places on the body and special cameras will measure the three-dimensional location of the reflective balls. Object detection in sports for athletes movement recognition is still new in this era. Thus, the object detection is one of the Convolution Neural Network (CNN) approaches that use image processing and computer vision to detect the type of object within a certain class in digital images and videos. Sprinting is the movement selected specifically for this study because of the biomechanics of its movement. The dataset was created based on the digital images per frame from athletes sprinting. Single Shot Multibox Detector is the most accurate algorithm for the object detection model and it has been proven by PASCAL VOC 2012. Hence, this algorithm helped to train the images and create the dataset for the system to run the object detection. An evaluation process to measure the accuracy of sprinting detection was tested and the value was greater than 80% by using intersection over union (IOU) method. Several videos of sprinting from the subjects had been tested with the machine learning and thus the sprinting detection was detected in that video with the scoring label above 70%. A comparison result was discussed in term of speed taken from two different athletes to finish the sprint regarding the score value and number detection produce in a video. The detection of sprinting would a stepping stone to make a real-time movement recognition in sports and also could be advanced to the prediction of talent in junior athletes.
An Improved Deep Convolutional Model for Segmentation of Nucleus and Cytoplasm from Pap Stained Cell Images

For the early detection of cervical dysplasia, automated cervical cell analysis system requires an accurate segmentation of nucleus and cytoplasm from cells. The segmentation of cellular materials from pap stained cytology image is open issue due to touching and crowded cells, presence of inflammatory cells, mucus and blood in the image. In this paper, for detecting and analyzing cell components from cervical smears, we developed a deep convolution framework using FC-Densenet56. Here images from Herlev dataset are trained and tested in deep architectures. A combination of FC-DenseNet56 and ResNet101 were used in proposed method to get an accurate result. For the comparison purpose, the results of proposed segmentation were evaluated with Precision and Dice coefficient, that achieves better results than the works reported in the literature. The performance parameters such as Precision and Dice coefficient is obtained greater than 90% and Recall and IoU got values greater than 85%. Besides cervical smear images, the proposed methodology can be adopted for segmentation of other cytology images.
A New Approach For Expansion the Throughput Capacity of the Quick Response Code

QR code has emerged as strong platforms, and powerful for a large-scale field of scientific and industrial processes.it can execute an essential part in addressing the subject of authentication. QR codes are applied in domains that affect the transfer of text information, and these include-mail messages, phone numbers, hyperlinks or another text file, This pursuit has thus been helpful in our knowledge towards obtaining an adequate and inexpensive method that does not require a high level of training. A key aspect of this work is thus to propose a new method to succeed in the deficiencies of the limited size of the traditional QR code. It relies on the features of the Shannon-Fano encoding algorithm to decrease the size of the input data and the principles of the XOR function. The main structure of this paper can be summarised in the following sections. First, it deals with the handling of an authentication system. The second section deals with the enhancement of the QR code to promote the data storage capacity. The last section of this paper deals with the display of the experiment's results and discusses. The suggested algorithm was explained in the schemes of several aspects, such as the secrecy aspect, the durability aspect, the complexity aspect, and the storage capacity. In this work, the proposed QR code does not reproduce the configuration or structure of the QR code. Indeed, the experimental results of this paper produced better findings of a great deal of the previous work.
Secure Primitive for Big Data Utilization

In this chapter, we describe two security primitives for big data utilization. One is a privacy-preserving data integration among databases distributed in different organizations. This primitive integrates the same data among databases kept in different organizations while keeping any different data in an organization secret to other organizations. Another is a privacy-preserving classification. This primitive executes a procedure for server’s classification rule to client’s input database and outputs only the result to the client while keeping the client’s input database secret to the server and server’s classification rule to the client. These primitives can be executed not only independently but also jointly. That is, after we integrate databases from distributed organization by executing the privacy-preserving data integration, we can execute a privacy-preserving classification.
Discriminative Metric Learning with Convolutional Feature Descriptors for Age-Invariant Face Recognition and Verification

Aging includes internal and external factors that cause variation in appearance of face and, consequently, it is a difficult problem to handle in person identification and verification using face images. In this paper, we propose a method for face recognition and verification that is robust against variation of facial appearance caused by aging. Our proposed method uses discriminative metric learning over convolutional feature descriptors extracted from frontal face images. The results of an experiments for performance evaluation on the FG-Net and CACD face aging datasets empirically clarify that the proposed method is effective for improving the performance of person identification and verification in the scenario where input face images contain appearance variation due to aging.
Introduction to Algorithms

This chapter and the next will serve as an introduction to two major topics in computer science: algorithms and data structures. Many computer science programs have a sequence of two to four courses on these topics, so our treatment here will barely scratch the surface. The purpose is not to make you an expert, but to get you thinking algorithmically.
Beyond Bitcoin: The Impact of Blockchain on The World - A Survey

Blockchain has become one of the new buzzwords in technology in the last decade, mainly because of Bitcoin, a cryptocurrency that had an incredible rise in value in 2017. Despite its value falling since then, many have begun realizing that the peer-to-peer network behind it, Blockchain, can be used in a multitude of other technologies and industries. This paper focuses on summarizing Blockchain's applications beyond Bitcoin and cryptocurrencies, as well as discussing its features that allow it to effectively do so. Blockchain has shown the world that it is a trusted and decentralized way to compute, for transactions and to transfer data. Hence, a variety of fields are now utilizing this emerging technology, from financial services to music to healthcare, and this paper has successfully compiled and evaluated a list of a few of these applications.
Automated mythological scene recognition using machine learning and graphs

This paper presents a method to automate the identification of scenes from Indian mythology in works of art such as paintings and line drawings. Artificial neural networks were used to detect mythological characters, animals, landscapes and weapons in the input image to aid scene detection. The mythological texts associated with the image were used to discern the strength of the relationships between characters and build a Character Association Graph, which was used to improve the predictions made by the neural networks. These predictions and a graph-based algorithm were used to map the input image to a set of likely scenes. Experiments were carried out on images from the ancient Indian epic, The Ramayana, with prediction accuracy of 76% on high definition (HD) images and 64% on non-HD images.
Faster R-CNN with Attention Feature Map for Robust Object Detection

This paper describes the improved object detection method from Faster R-CNN using an attention feature map in RPN. The research work adopts Faster R-CNN is used as the input feature map of the RPN using the last block in the backbone network, but the proposed method takes the created feature maps from the combination of dilated convolution and attention feature map for RPN networks. Attention feature map takes the high probability to object and emphasize the location of object. With the new feature map, the proposed bounding box and class determine the final output for object detection. As backbone network, ResNet50, ResNet101 and ResNet152 have trained on ImageNet. On PASCAL VOC 2007, our proposed method achieves 79.83% mAP that is 73.2% mAP.