Reviewerly/exHarmony huggingface dataset:
Conclusions, Implications, and Viewpoints

This chapter shows how users can use Socrates DigitalTM to analyze information, identify assumptions, and apply concepts to determine conclusions. After the user determines the conclusions, the next step for users is to predict the implications of those conclusions. Predicting the implications is followed by a step where the user summarizes the conclusions and implications into a viewpoint. Afterward, the user evaluates this viewpoint, and Socrates DigitalTM presents the opportunity to create another viewpoint. After the user decides there is no more evidence to gather, Socrates DigitalTM will calculate a score that provides a confidence level in the current viewpoint for the user.
A Two-domain Data Hiding Scheme for Data Signature in Integrated Online Services Applications

Abstract There is confidential data in integrated online services applications. The data needs to be hidden before transmission on the Internet, and the data authenticity should be assured. Information hiding technology is an effective way of protecting the confidential data and may be used for assuring data authenticity. A Two-domain data hiding scheme (TDHS) is introduced in the paper to protect contract document authenticity in grid system. TDHS aims to embed confidential data into signature image in both spatial and frequency domain. LSB-like algorithm is used to generate a signature image H s by embedding feature data of contract document into the original signature image H in the spatial domain. Then DCT algorithm is adopted to embed the watermark in signature image H s in the frequency domain, and generate signature image H s ready . The signature image H s ready is inserted into the contract document. When received the document with signature, the recipient can verify its authenticity. The feasibility of TDHS is verified through experiments.
Project Kappa: An augmented-reality based sensor data crowdsourcing platform for environmental monitoring

Recent advancement in sensor technologies and internet of things (IoT) paved ways for the deployment of sensor networks for environmental monitoring. However, current sensor network requires the use of networking modules, such as WiFi, LoRaWan, NB-IoT or SigFox (LP-WAN), which requires higher power demand. The main feature of Project Kappa the synergy between the use of augmented reality (AR), gamification, Bluetooth and cloud-based IoT servers to crowdsource environmental sensor data acquisition. The application incentivizes users to connect their smartphones to low-powered environmental sensors via an augmented reality game. Once connected, data transmission between the sensor node and the cloud server will be made via the phone’s internet connection, reducing the need for power hungry networking modules. As a proof-of-concept, this paper describes the development of an air-quality monitoring sensor node, the augmented reality app, and its subsequent integration the cloud database. The prototype was tested with a group of 80 volunteers, demonstrating the viability of deploying the system for real world application.
Image Super-Resolution Through Compressive Sensing-based Recovery

The primary aim of image super-resolution techniques is to produce a high resolution (HR) image from a low resolution (LR) image efficiently. Deep learning algorithms are being extensively used to address the ill-posed problem of single image super-resolution which requires extremely large data-sets and high processing power. When one does not have access to large data-sets or have limited processing power, an alternative technique may be in order. In this study, we have developed a novel positive scale image resizing method inspired by compressive sensing (CS). We have considered the image super-resolution as a CS recovery problem in which a low resolution image is assumed as a compressed measurement and the required interpolated image is treated as output of the CS-based recovery. In the proposed HR recovery method, a deterministic binary block diagonal measurement matrix, (DBBD), is used as measurement matrix since it maintains the visual similarity between the low and high resolution images. Then along with a sparsification matrix, the sparse representation of HR image is first recovered and subsequently the dense HR image is obtained. The proposed method is applied to medical and non-medical images. The HR images obtained using the traditional proximal, bilinear and bi-cubic interpolation techniques are compared with those obtained using the proposed method. The proposed CS inspired method delivers superior HR images than the traditional techniques. The superiority of the proposed method is attributed to the unique usage of the DBBD matrix and the CS recovery algorithm to obtain a high resolution image without any prior training data-set.
Sender Reputation Construction method using Sender Authentication

Spam is not just annoying, it is a serious problem that causes security problems. Mail filters are effective and widely used as anti-spam measures. However, spam senders are also becoming more sophisticated in their content and transmission methods, and countermeasures are becoming more difficult. In addition, if the mail filter makes a false judgment, there is also the problem that the necessary mail will not be delivered. In this paper, we propose a method for constructing sender reputation using sender authentication technologies. The sender of the forwarded mail is used as a method to determine the legitimate mail server. To determine the sender of the forwarded mail, use the authentication result of SPF and DKIM authentication. In addition, we propose a method using DKIM’s block list as a countermeasure against forwarded spam. We used these methods to validate the sender reputation using the mails we actually received.
Packet-Sequence and Byte-Distribution is Enough for Real-Time Identification of Application Traffic

With the development of Internet technology, application traffic identification based on Machine learning has taken much attention in the past decade. However, there are still some problems even if many traffic classification schemes have been proposed, which will affect the QoS (Quality of Service) of application traffic identification. First, the complete-flow-based methods will fail when facing incomplete flows and multiple flows in real-time detection. Second, the model generalization ability will decrease if using raw traffic data as input directly, especially the representation learning model. For these reasons, in this paper, we developed a new traffic classification method based on packet-sequence and byte-distribution, which can be used for application traffic identification. The byte-distribution of packet-sequence can be obtained through statistics directly without parsing the application protocol. The packet-sequence analysis unit avoids problems caused by the incomplete network flow, which is more suitable for real-time detection. Using packet payload byte-distribution instead of raw data balances the relationship between raw input and manual intervention to a certain extent. In order to prove that packet-sequence and byte-distribution are enough for application traffic classification, we tested our traffic classification method on the dataset we collected in the environment of Ethernet. We achieved an accuracy of 97.485%, which is a good performance. Our method also demonstrated good real-time performance and an acceptable detection result.
Self-interpretable Bayesian Rule Lists Model with Deep Prototype Layer

Bayesian rule lists (BRL) model aims to provide interpretability for prediction and decision-making process, where researchers have conducted sufficient work to make such probabilistic model applicable to be integrated into more complex architecture. Deep neural net with prototype layers initially created to deal with dimension reduction, visualization and image data interpretability in computer vision tasks, is now also widely used in other fields due to its pluggable interpretability. However, pure BRL is lack of model expressiveness especially for settings where approximation for highly complex non-linear mapping is requisite. Meanwhile, the prototype deep net, although can output certain kind of prototypes, relies on other interpretable models or problem definition to provide concrete interpretable logics for the generated prototypes. In this paper, we mainly deal with difficulty of providing both interpretability and decent prediction precision for decision process, proposing a deep neural network with prototype layers based on BRL model, known as self-interpretable Bayesian rule lists (SiBRL). This model provides interpretability through Bayesian rules and prototype with decent model performance based on expressive deep net. We first use truncated Poisson distribution and uniform distribution to generate rules and decision points which are put into deep net together with training data, and then compute the likelihood based on deep net architecture with Bayesian rule process. Finally, cutting plane algorithm (CPA) is used to optimize the parameters and hyperparameters in the BRL model with the objective being the likelihood given by the deep net part. Experiment based on a default prediction dataset shows that our model can provide reasonable interpretability while keeping prediction precision superior to non-ensembled machine learning models such as decision tree and BRL.
An analysis of the possibility of realization steganography in C#

The computing power of modern computers is sufficient to break many cryptographic keys, therefore it is necessary to create an additional security layer which hides the very fact of transmitting a secret message. For this purpose, steganographic methods can be used. The article is devoted to the analysis of the possibility of implementing digital images steganography with the use of the C # programming language. Firstly, existing libraries and mathematical transformations which can help with performing steganography were found. Also, own code solutions were implemented. In order to objectively evaluate the methods of data hiding, the parameters describing the degree of distortion of transforms and hidden images were calculated. Subsequently, optimal solutions for specific problems were identified and demonstrational data hiding was performed. Based on the obtained results, it can be concluded that it is possible to successfully implement steganography in the C # language. There are many ready-made libraries and tools, the effectiveness of which has been verified in the conducted analysis. Due to the contradictory of stenographic requirements, it is not possible to meet all of them optimally, i.e. undetectability, resistance to destruction and information capacity. For this reason, it is not possible to clearly indicate the best solutions. In order to achieve satisfactory results, one should look for compromises between the set requirements.
Improved Remote Sensing Image Registration of Residual Block Densely Connected Network Based on Reinforcement Learning

As one of the vital research in terms of image processing, NIR (NIR) and RGB (RGB) remote sensing image registration (RSIR) has attracted the attention of many scholars. We proposed the densely connected neural network improved by residual blocks (RBDCNN) to extract features to generate Q values, which improves the performance of registration, and uses the distance difference between the transformation matrix of the reference image and the float image. Compared with the existing method, our registration result is closer to the reference image. Our method was tested on five data sets in Landsat8, and the simulation results show that our network effectively improved the recognition of NIR and RGB remote sensing images and the accuracy of registration.
Personalized Semantic Retrieval System based on Statistical Language Model

The semantic relationship in thesaurus is introduced into the current network information retrieval tool, which can realize semantic retrieval. Using a statistical language model to express query statements and return results in the form of probability distribution can more effectively complete the construction of user model and realize personalized retrieval. Firstly, this paper proposes a similarity calculation method based on the relationship between words in the thesaurus. On the basis of this method, combined with the idea of query expansion and weighted sorting, this paper proposes a semantic retrieval method of forestry information based on the thesaurus. Secondly, this paper uses a statistical language model to propose personalized retrieval methods based on three different user models: topic model, historical model and mixed model. Finally, a forestry information personalized semantic retrieval system is realized by using semantic retrieval and personalized retrieval method. Experimental results indicate that the proposed personalized semantic retrieval method can effectively improve the retrieval performance.
Strategic Approaches to Planning School Development and Improving Educational Outcomes

The article presents an example of strategic planning of school development in order to improve educational results. The author provides arguments and justifications for the choice of specific strategic approaches from understanding the categories of "school performance", "development factors", "management team", "social capital", "innovative activity" to researching the potential of the school, specific management decisions, demonstrating the structure and content developed development models. The material of the article reflects in detail the strategy of planning the educational process in a particular school in terms of development. The model of the innovative project "Educational Navigator" is described. A model of intraschool education quality assessment is presented. The article provides examples of the results of the study.
Emulation of HTTP Adaptive Video Streaming over SDN

Video streaming is growing into a well-known technology for media transmission over the Internet. Dynamic Adaptive Streaming over HTTP (DASH) permits transmitting data streams to a user with the largest feasible bit rate in different bandwidth situations which is especially important for wireless networks. The purpose of this paper is to emulate and analyze DASH technology as far as live video streaming is concerned in an SDN environment, as well as to examine the Quality of Experience (QoE) on SDN networks, namely to understand the user&#x2019;s perspective. Thus, a virtual SDN network is developed in the Mininet environment to simulate DASH technology. In each experiment, one server transmits a video live to a client while the throughput is changing by adding extra traffic to the network. As a result, the impact of the added traffic on the quality of the broadcast video is examined, and the impact in terms of QoE is measured.
Simulators for Deep Neural Network Accelerator Design and Analysis: A Brief Review

The rapid development of machine learning techniques, especially deep learning, has led to a drastic increase of research attention for domain-specific processors such as deep neural networks (DNN) accelerators. The surge in scale and complexity of DNN accelerators poses great design challenge. Simulators with high simulation speed and accurate performance evaluation capability are pivotal for DNN accelerator design. A number of simulators targeting DNN accelerators have emerged, yet they are not summarized and classified. This paper presents a systematic review of state-of-the-art DNN accelerator simulator from the perspective of simulator performance, target platform, evaluation indicators, input/output characteristics, and implementation details.
Development of a mobile system using augmented reality technologies for school and preschool children for training mental arithmetic

Abstract This paper examines the problem of using augmented reality technologies to teach children of school and preschool age the skills of mental arithmetic. The main features of the development of training systems with augmented reality have been studied. The developed mobile system allows you to get and consolidate the basic skills of mental arithmetic using a set of game levels in the form of a 3D simulator. The application can be used for children with different initial training.
A Novel Distributed Adaptive Cubature Kalman Filter with Variational Bayesian Approach

A distributed cubature Kalman filter (CKF) is proposed for multi-sensor systems with uncertain measurement noise statistics. Firstly, the CKF is exploited to handle nonlinear state estimation. By choosing inverse Wishart priors, the joint posterior distribution of the state vector and measurement noise covariance matrix is approximated to a free-form variational distribution based on the variational Bayesian approach. By communicating with neighbors, all the sensor nodes can approximate global estimation via performing arithmetic average fusion within the framework of consensus filtering. Simulations of a target tracking problem are performed to illustrate the effectiveness of the proposed algorithm.
Long Text Filtering in English Translation based on LSTM Semantic Association

Translation studies is one of the fastest growing interdisciplinary research fields in the world today. Business English is an urgent research direction in the field of translation studies. To some extent, the quality of business English translation directly determines the success or failure of international trade and the economic benefits. On the basis of sequence information encoding and decoding model of LSTM, this paper proposes a strategy combining attention mechanism with bidirectional LSTM model to handle the question of feature extraction of text information. The proposed method reduces the semantic complexity and improves the overall correlation accuracy. The experimental results show its advantages.
The Rise and Fall of Bitcoin: Predicting Market Direction Using Machine Learning Models

Bitcoin's dominance in the cryptocurrency market has only increased in recent years. However, it experiences rapid spikes and declines that creates difficulty in predicting its future behavior. Much research has been done to find efficient models that predict with high accuracy, but with limited results. The goal of this study was to determine if higher accuracy can be achieved by focusing on a broader perspective of numeric ranges as opposed to specific time series price predictions. The predictions were concentrated on reporting the expected market direction for the following hour. In using one hour interval trading data and creating discrete classes of levels of hourly changes, five different Machine Learning models were trained and tested. Except for one model, cross validation accuracy ranging from 96-100% was achieved.
Current Status and Prospects of Mobile AR Applications

Objective Investigating the popularity of mobile AR applications has laid the foundation for the exploration of mobile AR applications. Methods The questionnaire survey selected AR experimental samples and divided the samples into 4 categories: mobile AR games, mobile AR advertisements, mobile AR videos, and mobile AR shopping. Establish the evaluation team to analyze the user experience data feedback results of the four types of AR applications. Conclusion Users have the most experience with mobile AR games, and users have a higher degree of experience with AR games than other categories.
Digital Abuse: Awareness is Preparedness

Technology allows us to stay connected. Whether you are re-connecting with a lost friend from elementary school, video chatting with a partner that is far away, or making new connections with people that you’ve never met, technology allows us to reach out and touch people in ways that would have been impossible before these advancements. 1 Even though many of us spend a lot of time on our phones and computers, digital abuse isn’t as widely-known as it should be. Part of the reason for that is because it’s so interwoven with the other kinds of abuse that you might have heard about — like physical, sexual, emotional, financial, and verbal — that it’s not often singled out. It’s unlikely that someone will experience digital abuse without also experiencing another kind of abuse, but digital abuse itself is very serious and can be detrimental to a person’s wellbeing. 2
Error Distribution-based Anomaly Score for Forecasting-based Anomaly Detection of PV Systems

For forecasting-based anomaly detection for PV systems, in this paper, we propose a way for calculating anomaly scores. The basic idea of our approach is to utilize the distribution of forecasting errors of normal data to derive relative anomaly score, which is limited to from 0 to 100. To further improve the anomaly score, we apply our basic idea to each month separately because the distribution of forecasting errors changes over time. Through experiments using the real data, we examine some aspects of our approach preliminarily.
A survey of graph neural network with meta-learning for dealing with data in non-Euclidean space

Graph Neural Network (GNN) is proposed to deal with data in non-Euclidean space with neural network technology, such as social network, chemical molecular structure, protein and disease correlation structure, etc. But in our daily life, the data we get are usually sparse, that is, we face the problem of few-shot learning. To address this problem, recently researchers apply meta-learning, which perform well in the field of few-shot learning, to GNN and prove through a large number of experiments that their combination can improve the accuracy. The main work of this paper is to introduce the methods, GNN with meta-learning, proposed in recent years. The general idea of these methods is to apply the training mode of meta-learning to the GNN model or GNN variants. The difference among the methods is that they solve the problems at different levels, such as node level, graph level and edge level. At the same time, these methods are classified according to the problems at different levels. In this paper, we sort out the literatures about the GNN with meta-learning for researchers' convenience.
Measuring Semantic Similarity of Vietnamese Sentences Based on Lexical and Distribution Similarity

Measuring the semantic similarity of sentence pairs is an important natural language processing (NLP) problem and has many applications in many NLP systems. Sentence similarity is used to improve the performance of many systems such as machine translation, speech recognition, automatic question and answer, text summarization. However, accurately evaluate the semantic similarity between sentences is still a challenge. Up to now, there are not sentence similarity methods, which exploit Vietnamese specific characteristics, have been proposed. Moreover, there are not sentence similarity datasets for Vietnamese that have been published. In this paper, we propose a new method to measure the semantic similarity of Vietnamese sentence pairs based on combining lexical similarity score and distribution semantic similarity score of two sentences. The experimental results have shown that our proposed model has high performance for the Vietnamese semantic similarity problem.