Reviewerly/exHarmony huggingface dataset:
Performance Enhancement in Big Data by Guided Map Reduce

In recent years, due to the emergence of the Internet and communication technology data is generated at an increasing rate. Everything around us is connected to the internet and seems to generate data. Health care is one of the biggest booming data producing sector. To efficiently fetch the required information form the large volume of data, it is required to store the data in a distributed fashion so that the retrieval time should be very less and for that, a massive storage cluster of tens of thousands of connected machines with built-in analytics tools are required to systematically store and fast retrieve of the data. Hadoop, a well-known analytical application, divides the data into small blocks and with the help of Hadoop Distributed File System (HDFS), it distributes among the cluster node for storage and future processing. If there is a need to run a query on a specific item, Hadoop runs its query in all of its thousands of nodes for fetching a particular dataset, which increases the overall execution time. To overcome this issue, we have made an application Node Guided Map-Reduce (NGMR) based on Node.js in a distributed cluster with movable nodes, where this issue has been resolved. To build and run this application and make research on it, a huge amount of capital is required to build a data storage service. Due to this limitation, we have made a small scale model of the replica of a large storage cluster using low-end computational power and limited resource ARM-based single-board computers (Raspberry Pi). The work was done over the past few years is the implementation of the proposed scheme to enhance the performance in big data handling by Guided Map-Reduce with the help of Node.js and to present and create efficient cost-effective data storage and retrieval system for the health-care industry.
A Novel Formal Representation and Reasoning for Commonsense Knowledge

Commonsense knowledge reasoning is an important an important research field in artificial intelligence, and there has been a lot of research. But there is still much work to be done on the basic theory of commonsense knowledge formalization. In this article, we introduce a novel quantifier in first- order logic, called "collective quantifier", in order to construct a logical framework for the formal representation and reasoning of commonsense knowledge. After giving the semantic explanation of the collective quantifier, the complete formal expression of commonsense knowledge is realized. The proposed quantifier is slightly weaker than the universal quantifier. The universal quantifier describes the "characteristics of all individuals in the universe", while the collective quantifier describes the "characteristics of most individuals in the universe". In addition, we also propose a multi-level commonsense knowledge representation and reasoning, which divides the attribute values of commonsense knowledge into multiple levels for processing. For example, the commonsense knowledge "rainfall can cause floods" is divided into "heavy rains can cause catastrophic floods" or "heavy rains can cause major floods". Multi-level commonsense knowledge reasoning broadens the application scope of Classical commonsense knowledge reasoning.
Selecting the Optimal Configuration ofSwarm Algorithms for an NLP Task

The present article involves the generation of phrase boundaries in unconstrained free text. Particle Swarm Optimisation (PSO) is applied to determine the optimal values for a set of parameters, based on a limited amount of training data. The starting point is a detailed analysis of generated solutions, which leads to a reformulation of the phrasing task. Based on this reformulation, the optimal swarm configuration is investigated, including the interconnection between swarm particles as well as velocity initialisation. Another aspect studied is whether a homogeneous or a heterogeneous swarm provides a better optimisation, established by comparing three PSO variants. The experimental results are further analysed with statistical tests to determine their significance.
Design and Implementation of a Business Domain Requirement Collection System

With the normalization of requirement collection in the field of information and communication business, it is found that it is difficult to collect and manage requirement. In order to solve this problem, this paper analyzes the requirements of the collection system, designs the system architecture and functional modules, realizes the requirement collection system based on B / S architecture, and focuses on standardizing the requirement collection template and requirement data management, so as to improve the quality and efficiency of requirement collection as a whole.
Sustainability and Time Complexity Estimation of Сryptographic Algorithms Main Operations on Elliptic Curves

This paper presents the time complexity estimates for the methods of points exponentiation, which are basic for encrypting information flows in computer systems. As a result of numerical experiments, it is determined that the method of doubling-addition-subtraction has the lowest complexity. Mathematical models for determining the execution time of each considered algorithm for points exponentiation on elliptic curves were developed, which allowed to conduct in-depth analysis of their performance and resistance to special attacks, in particular timing analysis attack. The dependences of the cryptographic operations execution time on the key length and the sustainability of each method on the Hamming weight are investigated. It is proved that under certain conditions the highest sustainability of the system is achieved by the doubling-addition-subtraction algorithm. This allows to justify the choice of algorithm and its parameters for the implementation of cryptographic information security, which is resistant to special attacks.
The Detection of Hela Cells in Brightfield Images

Among all the rapidly developing pathological diagnosis methods, particular cell therapy has become one of the most popular research tasks. In some occasions of cell detection and segmentation, several methods of separating presented touching and overlapping cell structures need to be utilized. Applying and developing these methods has become one of the most crucial and error-prone tasks in further analysis of brightfield images. In this work, we choose HeLa cells in a specific cell tracking dataset to detect HeLa cells in brightfield images and describe an approach to do cell detection and further analysis. Given a set of brightfield HeLa cell images in the cell cycle, we separate them into the border, centre, and blank sessions as the labels. Patches are extracted from images after binarization. When they are distinguished and labelled, we utilize different filters as pre-process labels and carry on data augmentation to obtain abundant patches as our training dataset. We find that SVM is a desirable model for classification since it performs well in most datasets, and LeNet, which is able to respond to a part of the surrounding units, can also be applied in our experiment. Therefore, we prefer SVM and LeNet as our models to do classification and prediction. In optical microscopy, especially when transmitted light and fluorescence microscopy are related to the specific cell structure segmentation, the distinct approach that we introduced in this work about separating touching and overlapping cell structures represents a desirable performance with high efficiency and robustness
Towards Story-based Summarization of Narrative Multimedia

This study aims at summarizing narrative works (i.e., creative works that contain stories) in the consideration of their stories and types of required summaries. Various methods for story-based summarization have been proposed as a practical application of the character network analysis (i.e., a social network among characters that appeared in a story). However, the existing methods do not consider that summaries have different requirements according to their types (e.g., trailers, highlights, and recaps). These methods consist of three parts: (i) discretizing narrative works into regular units (e.g., scenes or shots), (ii) measuring the narrative significance of each unit, and (iii) generating summaries based on the narrative significance. Most of the existing studies have proposed their unique significance measurements based on individual narrative features. Also, since these methods have not considered the diverse types of summaries, they have simply selected top-N narrative units according to the measurements. In this study, we first introduce and redefine the narrative significance measurements. Subsequently, we propose a method for summarizing a narrative work regarding the requirements of the summaries by integrating the various significance measurements.
Slice: an algorithm to assign fixations in multi-line texts

When analyzing eye movement data from the reading of multi-line texts, it is important to ensure that fixations are assigned correctly to the lines of the text. This is a non-trivial problem as eye movement data are noisy and often show complex non-linear distortions. Here, we introduced Slice, a new algorithm to assign fixations in multi-line text. We describe how Slice operates and evaluate it using a data set of natural reading data. Results show that Slice performs better than the default method used by many software packages, is robust to many forms of distortions, and approximates manual coding decisions.
Remote sensing aircraft detection method based on lightweight YOLOv4

Remote sensing aircraft target detection is an important task in the field of remote sensing images interpretation. The general target detection network often includes a large number of parameters, slow detection speed, and poor performance when directly applied to aircraft detection tasks. In order to solve the issues, a novel remote sensing aircraft target detection method based on lightweight YOLOv4 is proposed in this paper. Firstly, Lightweight YOLOv4 adopts the MobileNetV3 and depthwise separable convolution to greatly reduce the amount of model parameters. Then, to further enhance the feature extraction ability of the network and make the network more lightweight, this paper uses the feature enhancement module (FEM) and residual fusion module (RFM). Extensive experiments on the DOTA aircraft dataset demonstrate that the Lightweight YOLOv4 can significantly improve the detection accuracy and efficiency, as well as owns fewer model parameters.
Towards an Anatomy of Software Craftsmanship

Context: The concept of software craftsmanship has early roots in computing, and in 2009, the Manifesto for Software Craftsmanship was formulated as a reaction to how the Agile methods were practiced and taught. But software craftsmanship has seldom been studied from a software engineering perspective. Objective: The objective of this article is to systematize an anatomy of software craftsmanship through literature studies and a longitudinal case study. Method: We performed a snowballing literature review based on an initial set of nine papers, resulting in 18 papers and 11 books. We also performed a case study following seven years of software development of a product for the financial market, eliciting qualitative, and quantitative results. We used thematic coding to synthesize the results into categories. Results: The resulting anatomy is centered around four themes, containing 17 principles and 47 hierarchical practices connected to the principles. We present the identified practices based on the experiences gathered from the case study, triangulating with the literature results. Conclusion: We provide our systematically derived anatomy of software craftsmanship with the goal of inspiring more research into the principles and practices of software craftsmanship and how these relate to other principles within software engineering in general.
HyRa: An Effective Hybrid Ranking Model

Information retrieval (IR) systems are concerned of the processing of the textual content of the document, the query, and issues of system effectiveness. IR models are means to integrate many sources of evidence on documents to achieve an effective retrieval system. Unlike traditional parameters such as word frequency in documents, semantic information retrieval is an environment that requires the application of semantic techniques. These techniques calculate a degree of query-document relevance to assign the highest rankings to the documents which are semantically closer to the query. To meet the user’s information needs and to optimize the performance of IR system, an innovative model was proposed to calculate the relevance of query-document pairs. The proposed model is a hybrid one that based on a query likelihood language model and a conceptual weighting model. It exploits the semantic similarity between document and query concepts and some document/collection statistics. The conducted experiments on the proposed model show improvement of the standard performance measures over the benchmark competitors. A statistical significance test is conducted to exclude the performance random variation of the compared models. Paired t-tests show that the proposed model is highly significant improves the most important measures.
Printing House of Ministry of Internal Affairs of Russian Empire in Mid-19th — Early 20th Centuries: structure, functions, personnel

The general characteristics of the government printing house that served the interests of Ministry of Internal Affairs in the middle of the 19 th and the beginning of the 20 th centuries, from the moment the printing house was created in 1836 until the 1910s, when the government was forced to join the struggle for public opinion. The staff of the printing house of the Ministry of Internal Affairs, its structure and changes in personnel and functions are investigated. It is shown that the outbreak of the First Russian Revolution accelerated the transformation of the printing house of the Ministry of Internal Affairs from an institution dealing with printing works on the document circulation of the Ministry of Internal Affairs into a structure that performs important functions in the ideological struggle for public opinion. It is proved that at the beginning of the twentieth century the priority direction for the printing house of the Ministry of Internal Affairs was the task of printing government newspapers “Government Bulletin”, “Russian State”, “Evening addition to the Government Bulletin”, etc. The difference between the servants and the workers of the printing house of the central state institution is emphasized. It is noted that if the servants of the printing house of the Ministry of Internal Affairs initially had the right to receive a social package, then the workers of the printing house had to earn this right.
Lyapunov exponent analysis for multilayer neural networks

A multilayer neural network can be regarded as a type of discrete-time dynamical system in the sense that layer-to-layer information propagation can be expressed as the time evolution of a particular dynamical system. In this study, we investigate the stability of information propagation in multilayer neural networks for classification problems using finite-time maximum Lyapunov exponents, and we discuss how multilayer neural networks classify inputs. The dynamical stability analysis in this study reveals the input-dependent stability of trained multilayer neural networks. Multilayer neural networks are trained such that the information propagation is highly sensitive to input data vectors near a decision boundary for classification whereas it is less sensitive to input data vectors far from the decision boundary. This implies that the decision boundary in classification problems is characterized by a set where the finite-time maximum Lyapunov exponents of the information propagation are relatively large. These results offer a new perspective on the estimation of uncertainty of classification using multilayer neural networks.
An Improved MSRCR Low Illumination Image Enhancement Algorithm Combined with Residual Fusion

In view of the problems of edge blur and detail loss in the process of enhancing low illumination image by traditional MSRCR algorithm, an improved MSRCR low illumination image enhancement algorithm combined with residual fusion is proposed in this paper. Firstly, adaptive multi-scale guided filtering is used to estimate illumination instead of Gaussian filter. According to the theory of multi-scale Retinex, the reflected image is obtained in logarithmic domain. Then, the image residual is extracted by L <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</inf> norm and fused with the reflected image. Finally, a new color recovery factor is introduced, and the fusion result is processed by color recovery to obtain the final enhanced image. Experimental results show that this algorithm can effectively improve the brightness and contrast of low illumination images, and has a stronger ability to express edge details, while color distortion is avoided.
Hepatocellular Carcinoma Segmentation from Digital Subtraction Angiography Videos Using Learnable Temporal Difference

Automatic segmentation of hepatocellular carcinoma (HCC) in Digital Subtraction Angiography (DSA) videos can assist radiologists in efficient diagnosis of HCC and accurate evaluation of tumors in clinical practice. Few studies have investigated HCC segmentation from DSA videos. It shows great challenging due to motion artifacts in filming, ambiguous boundaries of tumor regions and high similarity in imaging to other anatomical tissues. In this paper, we raise the problem of HCC segmentation in DSA videos, and build our own DSA dataset. We also propose a novel segmentation network called DSA-LTDNet, including a segmentation sub-network, a temporal difference learning (TDL) module and a liver region segmentation (LRS) sub-network for providing additional guidance. DSA-LTDNet is preferable for learning the latent motion information from DSA videos proactively and boosting segmentation performance. All of experiments are conducted on our self-collected dataset. Experimental results show that DSA-LTDNet increases the DICE score by nearly 4% compared to the U-Net baseline.
Event-Triggered Finite-Time Contrtol of IT2 T-S Fuzzy Interconnected Nonlinear Systems with Time-Delays

The event-triggered finite-time decentralized control design problem for the nonlinear interconnected systems with time-delays is studied in this paper. Firstly, an interval type-2 Takagi-Sugeno (IT2 T-S) fuzzy model in the framework of IF-THEN rules is utilized to represent the nonlinear interconnected systems. Secondly, an event-triggered mechanism is given depending on the systems input errors, and τ <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">M</inf> -dependent controllers are developed based on the parallel distributed compensation algorithm. Finally, based on the finite-time stabilization theory and Lyapunov function theory, the system is of asymptotic stability in finite-time, and the estimate of setting time of the system is obtained.
A 360-Degree Video Shooting Technique that Can Avoid Capturing the Camera Operator in Frame

In this research, we propose a shooting method and video processing method that uses two fisheye cameras to avoid capturing the camera operator in frame. 360-degree video is rapidly becoming widespread with it being supported on YouTube since 2015. In the future, 360-degree videos are expected to be used in all parts of society. However, when a camera operator usually shoots while holding a 360-degree camera, they themselves will unavoidably by captured in frame. This is a crucial problem with 360-degree cameras. Of course, some techniques have been proposed to avoid this, but all of them are limited and impractical. In this research, in order to solve this problem, we will refer to the conventional methods and then propose a new technique.
Multi-domain Reversible Data Hiding in JPEG Images

Images reversible data hiding is a technique to embed additional data by modifying carrier images in a recoverable way, that has attracted more and more attention, especially for the most widely used JPEG format. As data carrier or modification domain, researchers usually choose one of two, DCT coefficients or entropy-coded data, that may result in different image filesize expansion or visual quality distortion. To this end, a new reversible data hiding method for JPEG images in multi-domain is proposed. Unlike existing methods, both of DCT coefficients and entropy-coded data can be used to embed data at the same time. In order to make trade-off between distortion of DCT coefficients domain and entropy-coded data domain, a reasonable payload distribution algorithm is also proposed. Experimental results demonstrate the superior to state-of-the-art works.
Mobile Device Approach for the Measurement of Jump Flight Time

Teenagers are mainly sedentary with the use of technological devices. However, technological devices may be part of recreational activities to challenge people to achieve better results. This paper described the acquisition of accelerometer data from mobile devices and BioPlux devices, measuring the correct jump flight time with the help of a pressure sensor in a jump platform. Also, this paper tested different methods for adjusting the calculation of the jump flight time, including the least-squares method, the subtraction of average error, and the multilayer perceptron. Currently, a mobile application is available with the Least Squares methods, but it will use the best method soon. Also, the method presented can be also incorporated in a game to stimulate the physical activity practice.
Optimizing input mask for maximum memory performance of time-delay reservoir subjected to state noise

Reservoir computing is a brain-inspired machine-learning framework that has been successfully used in information processing. A state-of-the-art methodology, called time-delay reservoir (TDR), realizes the reservoir using a single nonlinear physical node with delayed self-feedback. Memory capacity of the TDR is sensitive to the time-multiplexing procedure for input signals with a random mask. The existing memory optimization methods with respect to the masks are limited to the case without state noise, and their computational cost is large. In this article, we optimize the input mask to maximize the memory performance of the TDR with a white Gaussian state noise in a computational time efficient manner within the context of the Fisher memory curve, and then a TDR based on the Mackey-Glass system is used to illustrate our proposed method. The memory-nonlinearity trade-off of the TDRs regarding the input masks is also investigated in view of spectral properties of the spatial Fisher information matrix.
Ts-Unet: A Temporal Smoothed Unet for Video Anomaly Detection

A Temporal-Smoothed Unet (TS-Unet) to detect video anomalies is proposed in this paper. The model is trained on regular videos to predict future frames, and uses prediction errors of test videos to detect anomalies. In addition to constraining model’s prediction result, particularly, a temporal-smooth loss is presented to force time-continuous feature maps changing smoothly in the latent space. By utilizing the temporal-smooth loss, the model learns more videos’ temporal regularity information; and achieve better anomaly detection performances. Experiments on multiple datasets show the superiority and validate the effectiveness of the proposed model.