Reviewerly/exHarmony huggingface dataset:
Text classification model based on Word2vec and SF-HAN

With the rapid development and application of the electric power Internet of Things, the number of electronic texts in the system has increased dramatically. However, due to inadequate system management and the complexity of information sources, the current electronic text management in power grid systems has the problems of disordered format, redundant information, and unclear classification. Aiming at these problems, this paper designs and constructs a power grid text classification model based on Word2vec and SF-HAN. The model uses the CBoW model in Word2vec to generate word vectors, and then uses the improved SF-HAN network to predict and classify the extracted word vectors. The accuracy of the method is verified by experiments.
Robust and Accelerated Frequency-Tuned Salient Object Detection via Color Quantization

Abstract Frequency-tuned (FT) saliency detector has been widely used in various image processing and computer vision applications. However, removing high frequency noise and textures by a Difference of Gaussians (DoG) filter is still time-consuming, which hinders real-time execution of FT. In this paper, an improved algorithm is proposed to tackle the obstacles to performance improvement. It exploits color quantization within natural images and substitute Gaussian filtering process. Experimental results demonstrate that the proposed method runs up to about two times as fast as FT while resulting in almost the same saliency map.
Construction of Intelligent Maternity Interrogation Model Based on Ontology Knowledge Base

Most of the existing maternal information management is still in an independent system, forming a relatively serious island of medical information, a large number of diagnosis and treatment data, massive literature, etc. lack of effective collation, analysis and utilization. Based on the existing massive literature, guidelines and other data, using ontology technology and graphical modeling tools, using seven steps to build the intelligent ontology of pregnancy and childbirth, using Protégé tool to import pregnancy and childbirth cases to build the knowledge base, realizing the construction of the knowledge base of pregnancy and childbirth, building the intelligent diagnosis model of pregnancy and childbirth, providing data knowledge support for clinical decision support and intelligent diagnosis.
Shot Boundaries Detection Based Video Summary Using Dynamic Time Warping and Mean Shift

Recently, an increasing in videos amount presents in cyberspace in conjunction with growth of multimedia communications and technologies, and low-cost storage devices; it has become obligatory to have the capability to rapidly browse and search through huge multimedia databases. In this context, video summarization-based video retrieval has become a critical issue needs to be addressed. The main objective of shots boundary detection SBD methods pursues to detect the transitions between consecutive frames along video sequences in term of shots boundary detection. Hence, shots with abundant information are employed in the content-based video retrieval. This paper presents a new approach for shot boundary detection with two main challenges; static and dynamic objects with static and dynamic camera motion. The proposed method employed the Dynamic Time Warping DTW method for identifying the differences between sequenced frames. The abrupt and gradual changes are captured along frame's sequences within and between shots based on mean shift technique. From the experiments, the proposed shot boundary detection based key frame extraction method exhibits an efficient performance represented by more informative frames with less redundancy.
Relation Extraction of Minority Cultural Information Resources based on Bi-LSTM and Double Attention Mechanism

In recent years, the research of minority cultural information resources based on knowledge graph has become a research hotspot in academia. Relation extraction is an integral part of building a knowledge graph. In order to improve the accuracy of the relationship extraction of minority cultural information resources, this article first introduces Bidirectional Long Short-Term Memory (Bi-LSTM) to extract feature values. Secondly, it adds word-level and sentence-level attention mechanism to reduce the noise interference of the text. Finally, on the minority cultural information resources contrast experiments were carried out. The experimental results show that the Bi-LSTM neural network relation extraction model with double attention mechanism has better performance in accuracy, recall rate and comprehensive value index (F-Measure).
An Efficient Lossless Compression Method for Periodic Signals Based on Adaptive Dictionary Predictive Coding

This paper reports on an efficient lossless compression method for periodic signals based on adaptive dictionary predictive coding. Some previous methods for data compression, such as difference pulse coding (DPCM), discrete cosine transform (DCT), lifting wavelet transform (LWT) and KL transform (KLT), lack a suitable transformation method to make these data less redundant and better compressed. A new predictive coding approach, basing on the adaptive dictionary, is proposed to improve the compression ratio of the periodic signal. The main criterion of lossless compression is the compression ratio (CR). In order to verify the effectiveness of the adaptive dictionary predictive coding for periodic signal compression, different transform coding technologies, including DPCM, 2-D DCT, and 2-D LWT, are compared. The results obtained prove that the adaptive dictionary predictive coding can effectively improve data compression efficiency compared with traditional transform coding technology.
Integrating clustering and regression for workload estimation in the cloud

Abstract Workload prediction has been widely researched in the literature. However, existing techniques are per‐job based and useful for service‐like tasks whose workloads exhibit seasonality and trend. But cloud jobs have many different workload patterns and some do not exhibit recurring workload patterns. We consider job‐pool‐based workload estimation, which analyzes the characteristics of existing tasks' workloads to estimate the currently running tasks' workload. First cluster existing tasks based on their workloads. For a new task J , collect the initial workload of J and determine which cluster J may belong to, then use the cluster's characteristics to estimate J ′ s workload. Based on the Google dataset, the algorithm is experimentally evaluated and its effectiveness is confirmed. However, the workload patterns of some tasks do have seasonality and trend, and conventional per‐job‐based regression methods may yield better workload prediction results. Also, in some cases, some new tasks may not follow the workload patterns of existing tasks in the pool. Thus, develop an integrated scheme which combines clustering and regression and utilize the best of them for workload prediction. Experimental study shows that the combined approach can further improve the accuracy of workload prediction.
Deep Learning Approach for Arbitrary Image Style Fusion and Transformation using SANET model

For real-time applications of arbitrary style transformation, there is a trade-off between the quality of results and the running time of existing algorithms. Hence, it is required to maintain the equilibrium of the quality of generated artwork with the speed of execution. It's complicated for the present arbitrary style-transformation procedures to preserve the structure of content-image while blending with the design and pattern of style-image. This paper presents the implementation of a network using SANET models for generating impressive artworks. It is flexible in the fusion of new style characteristics while sustaining the semantic-structure of the content-image. The identity-loss function helps to minimize the overall loss and conserves the spatial-arrangement of content. The results demonstrate that this method is practically efficient, and therefore it can be employed for real-time fusion and transformation using arbitrary styles.
Generalized Perfect Codes for Symmetric Classical-Quantum Channels

We define a new family of codes for symmetric classical-quantum channels and establish their optimality. To this end, we extend the classical notion of generalized perfect and quasi-perfect codes to channels defined over some finite dimensional complex Hilbert output space. The resulting optimality conditions depend on the channel considered and on an auxiliary state defined on the output space of the channel. For certain <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$N$ </tex-math></inline-formula> -qubit classical-quantum channels, we show that codes based on a generalization of Bell states are quasi-perfect and, therefore, they feature the smallest error probability among all codes of the same blocklength and cardinality.
Comparative Study of the Maturity Models C2M e MR-MPS-SW

Research efforts have been increasingly necessary to understand how organizations should deal with the various maturity models, given the heterogeneity of the characteristics of these models. In this context, this work aims to propose a mapping between MR-MPS-SW and C2M models. The proposed mapping was reviewed and evaluated by five experts with experience in software process improvement. This mapping allowed us to identify that 82.2 % of C2M practices has some type of coverage for the expected MR-MPS-SW results. Finally, it is possible to see that the models here mapped are not conflicting, but rather complementary, with C2M being partially adherent to MR-MPSsw.
Personalized Multilingual Search - Predicting Search Result List Language Preferences

With estimates suggesting that half of the world's population learns or speaks at least two languages, Web information access systems such as Web search engines need to cater for an increasing variety of individual language proficiencies and preferences. However, while significant advances have been made regarding the handling, retrieval, and automatic translation of multilingual information, there has been a relative lack of user-centered research aiming to support individual users' multilingual abilities. To address this research gap, this paper presents a series of user studies and experiments that aim to inform novel search solutions that specifically support multilingual users. In particular, the experiments presented in this paper examine the extent to which a system can predict, for a given query, what language(s) a multilingual user would prefer the search results to be in. Results from our studies show that such predictions can statistically significantly outperform a baseline model, and that users' languages and proficiencies, their current location, as well as the search topic domain and type all influence the prediction results.
Machine learning approach to quick incident response

Tracking the evolution from the first DARPA set designed for IDS ML solutions, more than twenty years later, it can be noticed, that every time a new cybersecurity problem is discovered, unconsidered by previous solutions, a higher-level system is developed to solve it. Training on data specific to the defended system is more effective than training on publicly available datasets. This fact is arguable for the security solutions reviewed, but it is sure for solutions dedicated to incident response and forensics operations. This paper's objective is to design a machine learning-based schema for triage solutions used in quick incident response. More precisely, we evaluated the applicability of machine learning techniques for classifying unknown web access logs.
Chatbot with Touch and Graphics

Use of chatbots for emotional exchange is recently increasing in various domains. However, as existing chatbots have been considered in terms of natural language processing techniques for interaction with text-based chatting, chatbot interaction with users is lacking in terms of considering the emotions of users and managing turn-taking in conversation. This paper suggests an interaction technique having touch interactions with graphic interfaces (TwG) to solve these problems. In the system, users send their emotions and manage turn-taking through TwG technique. We conducted a Wizard of Oz study to evaluate user experience on emotional expression and turn-taking with TwG technique. Results showed that TwG interaction improved emotional expression compared to a traditional text-based chatbot interaction. Furthermore, the results showed that TwG positively affects natural turn-taking of the conversation.
Research on Small Vehicle Detection in Natural Traffic Scene

Aiming at the problem of small vehicle detection in natural traffic scenes, this paper improves the SSD algorithm. We use K-means algorithm to cluster the Ground Truth of the KITTI dataset to make the default box closer to vehicle. Introduce the FPN framework to achieve the fusion of multi-scale feature maps, and make full use of the feature information contained in deep feature maps and shallow feature maps. And we use Focal Loss to reduce the weight of easy negatives examples, make training focus on hard examples, and improve detection accuracy. Finally, we implement data augmentation for small vehicles so that the training process can extract more small vehicle features to enhance the detection performance. The improved SSD has a mAP of 85.16% on KITTI dataset, which is 6.56% higher than SSD, and an FPS of 43.87, which meets the real-time detection requirements.
On the behavioral implications of differential privacy

Differential privacy is commonly used in the computer science literature as a mathematical definition of privacy for the purpose of quantifying and bounding privacy loss. It induces a preference order over the set of privacy-jeopardizing mechanisms which, in turn, adhere to some properties of this order. We show that a set of five such properties uniquely captures the ordinal implications of prioritizing the alternatives in agreement with differential privacy. The model can also be applied to evaluate the appropriateness of differential privacy in different settings.
Aircraft Maintenance Training Simulation System Based on “CAN Bus + Ethernet” Architecture

Abstract Aiming at the problem of centralized data collection scheme, a distributed data acquisition and control scheme based on “CAN bus + Ethernet” is proposed. According to the functional structure design, the cockpit control equipment is defined as independent data acquisition and control nodes on the communication network. The architecture and related key technologies improve the reliability, maintainability and testability of the system. The application of this system to carry out training shortens the training period of aviation ground handling personnel and saves a large amount of maintenance and support funds.
A sentence sentiment classification method based on Self-supervised and Self-attention

In order to extract the semantic and grammatical information of sentences more effectively, this paper proposes a sentence sentiment classification method based on Self-supervised and Self-attention mechanism (SS-SAtt-BiLSTM). In this method, BiLSTM network is used to extract the feature of text context relationship, and self-supervised (SS) learning mode is introduced into the supervised sentence representation model. The sentence itself is used as the label data information of current words, and an improved self-attention mechanism (SA) is used to calculate the attention weight of each moment. The experimental results of MR and Stanford sentient treebank (sst-5) data sets show that this method reduces the dependence on tagged data, and the improved self-attention mechanism enables the model to learn more key features of sentences and improve the classification performance.
ethVote: Towards secure voting with distributed ledgers

The topic of performing safe and secure elections is a long-standing debate. Regardless, of the various attempts for electronic or Internet-based voting, the majority of countries still use paper ballots. Nevertheless, with major advancements occurring over the last years in both cryptography and distributed ledgers we believe that there is space now for re-investigating this area. In this paper, we propose ethVote an Internet voting system that makes use of the Ethereum blockchain, state of the art cryptographic mechanisms and a P2P-based front-end to ensure a secure voting process. In addition, we provide an open-source proof of concept implementation that features the majority of the needed components for securely using ethVote. Our proposal is tested both in terms of unit testing, requirement verification, and with regard to the feasibility to perform such an operation in a public distributed ledger.
Predicting Rainfall Intensity using Naïve Bayes and Information Gain Methods (Case Study: Sleman Regency)

Abstract Climate change, which has an impact on environmental problems in tropical countries, is still a severe problem, and efforts to prevent and manage it are continuously pursued. Indonesia, as a tropical country with topographical conditions and strategic geographical position, causes Indonesia to have different weather and climate patterns. Climatologically there are significant differences between the rainy season and the dry season. Both these seasons can bring blessings but also disasters if not appropriately handled, flooding in the rainy season and drought in the dry season. High rainfall can cause floods and landslides, whereas if using excess water in the rainy season can be a solution for water shortages in the dry season. The purpose of this study is to predict the rainfall intensity with the Naïve Bayes method and what parameters are considered the most dominant causes of heavy rainfall using the information gain method. The source of the data in this study came from BMKG data, which was during the rainy season between October to March from 2016 - 2019 in the Sleman Regency. The results of this study showed that the Naïve Bayes method could be used to predict rainfall intensity in Sleman Regency. Also, the most influential parameter on rainfall intensity is the average temperature with an information gain value of 0.047811028.
Comparative analysis of two steering modes using a car-like mobile robot

Nowadays, the steering system in automotive vehicles still is Ackerman type, which maneuverability is limited, but this dominant system has forced drivers to deal with the maneuverability restrictions that it implies. In this paper is tested and compare two steering modes using a carlike mobile robot. The vehicle was operated by the user through a joystick and two different algorithms were programmed in the vehicle, one is the classic Ackerman steering mode and independent steerability. It was possible to perform the same tests in a workspace for each management mode and then a comparison was made between them. The result of this study allows us to deal with the implementation of a new steering system in cars and / or means of transport to improve the current driving system.
Virtual bodies (avatars) and sport exercises: some important thoughts

In sport sociology several studies and theoretical research examine the body and emerging technologies, whereas discussions on exercise and virtual bodies (Avatars) are limited. In line with the accelerated developments in computer graphics and especially advanced computer interfaces, this study seeks to explain how the sport exercises of avatars could be a significant theme in sport sociological analysis. We are looking for explanations and an understanding of the novel forms of (sport) embodiment which could unavoidably arise in the case of avatar exercises through very complicated, and in many ways, paradoxical interactions and interpenetrations between the physical body and the virtual body. This theoretical study, based on interdisciplinary literature and research data, adds to the formulation of a more coherent theoretical model, which could be useful to future researchers.
Visual Environment Design of VR Space for Sequential Reading in Web Browsing

Recently, the use of VR is diversifying with the spread of the relatively cheap VR equipment. VR has been used in games, entertainment facilities, educational fields and so on. For example, web browsing in VR space is one of the focused fields. Some of its typical applications are Bigscreen and Virtual Desktop. Since these applications can create a big size screen for the web browser, users could easily browse the web in the VR space. Web browsing can be classified into information search and information acquisition (sequential reading). However, research on the information acquisition has not been conducted yet. Therefore, the purpose of this study is to verify the optimal visual environment for information acquisition task in the VR space. In the experiment, we created six conditions' web pages using Unity. They were consisted of two font size conditions and three screen size conditions. In the experimental task, the subjects were asked to loudly read the text on the screen, and they were asked to answer a questionnaire for subjective evaluation. In order to compare readability of each conditions, invisible area and reading time were measured. From the experimental results, we could verify the suitable visual environment for web browsing in VR space.
Forgery Numeral Handwriting Detection based on Convolutional Neural Network

Handwriting forgery detection is one of the hot spots in forensic science, and economic cases of handwritten forged numbers are increasing. At the same time, forgery identification of documents is an important evidence in criminal proceedings. For the problem of tedious and low degree of automation of manual document inspection, put forward a method for handwritten forged numeral detection based on convolutional neural networks. The experiment convened 50 volunteers and collected image samples of 6 types of forged and normal handwritings with 50 different brand pens, and established a total of more than 7,200 data sample sets. Perform parameter optimization on the basis of Alexnet, while training and testing the data set. The experimental results show that the highest accuracy rate of the six types of handwritten forged numeral recognition is 97.84%, and the average accuracy is 95.35%, which is better than the SVM feature classifier. It provides a new method for forgery handwriting identification.
The amortized analysis of a non-blocking chromatic tree

A non-blocking chromatic tree is a type of balanced binary search tree where multiple processes can concurrently perform search and update operations. We prove that a certain implementation has amortized cost O(c˙+log⁡n) for each operation, where c˙ is the maximum number of concurrent operations during the execution and n is the maximum number of keys in the tree during the operation. This amortized analysis presents new challenges compared to existing analyses of other non-blocking data structures.