Reviewerly/exHarmony huggingface dataset:
Predicting Cryptocurrency Price Movements Based on Social Media

Predicting cryptocurrency price movements is a challenging task due to the highly stochastic nature of the market. This paper exploits features from social media, combining with the historical price to build an accurate model for predicting trending of the Bitcoin, the most popular cryptocurrency these days. The novelty of this work is introducing a new feature called 'interaction' which helps improve the model's performance significantly. Our approach shows a very promising result, which outperforms other recent works by a large margin.
Modification of Standard Kalman Filter Based on Augmented Input Estimation and Deadbeat Dissipative FIR Filtering

This paper presents modification of standard Kalman filter (KF) based on augmented input estimation (AIE) and deadbeat dissipative FIR filtering (DDFF) for maneuvering target tracking. Although KF is a well-known tool in estimation and tracking but it has two weaknesses, disability in maneuvering motions and lack of robustness against temporary model uncertainty. For the first problem, the AIE is proposed to cover both the non-maneuvering and maneuvering parts of motion and so the maneuver detection procedure is eliminated. This model with an input estimation (IE) approach is a special augmentation in the state space model which considers both the state vector and the unknown input vector as a new augmented state vector. Also, KF is based on infinite impulse response (IIR) structure and cannot be robust against temporary model uncertainty. Unlike IIR, finite impulse response (FIR) filter is robust and stable against model uncertainty. So, for the second problem, an extra type of FIR filter named DDFF is introduced in this paper that not only has the intrinsic properties of FIR but also can improve these features by tuning some weighting parameters. In the last examples of the paper, the advantages of the proposed model against other models are shown.
A Comparative Analysis of Predictive Modeling Techniques: A Case Study of Device Failure

In the modern lifestyle, a person’s life is full of gadgets and appliances. At some period of time, every device will have downtime. During the downtime of a device, its performance keeps on degrading up to any level. Preventive maintenance in every regular interval can only save a machine from performance degradation or sometimes any kind of accidental hazard. Hence, if the device downtime can be detected automatically, then some kind of preventive measure can be taken to recover it from its degradation state. Here, a machine learning-based approach is proposed to detect the state of any device. The Kaggle dataset is used here for experimentation. The whole system is divided into three different phases. In the first phase, the data is collected and cleaned using various statistical methods. In the next phase, the model is trained using the training dataset. Finally, the trained model is evaluated using the testing dataset. In this work, various machine learning algorithms are used to find the best algorithm showing the best performance against the given dataset.
An Efficient Scheme of Network English Teaching Based on Psycholinguistics

From the perspective of the information processing system of psycholinguistics, this paper studies the students' psychological motivation for learning a foreign language and the function of communication. It focuses on the basic characteristics of emotional analysis by psycholinguistics and the way of assigning eigenvalues. Then, by analyzing the difference between the false and the real opinions of different emotional tendencies, this paper explores the relationship between emotional analysis and deception of new spam information recognition. It integrate the existing data set, and set the standard deceptive false comment data with the tendency of multi-sentiment. Through the study of the theoretical basis of web-based learning activities and the information processing model of web-based learning, this paper discusses the factors and process of web-based learning flow, and obtains the causal model of web-based interface of English teaching. It also combines the relevant theoretical methods and guiding principles of web-based interface design. Finally, under the guidance of the theoretical basis of psycholinguistics, combined with its own teaching practice, a college network English teaching program is constructed to create a fully interactive learning space for students and teachers.
Drill hole spacing optimization of non-stationary data for seam thickness and total sulfur: A case study of coal deposits at Balikpapan Formation, Kutai Basin, East Kalimantan

Abstract The characteristics of coal deposits are relatively homogeneous in general; therefore, their geometric and quality parameters have stationary distributions. However, the geological setting could change this stationarity characteristic; thus, the coal may contain spatially different data trends. These data trends should be treated to derive an accurate estimation of the available coal resources. The general objective of this study is to optimize the drill hole spacing for the non-stationary dataset by applying some approaches to detrend the data. These approaches include spatially dividing the data into several domains (i.e., spatial clustering), extending the existing grid size (drill hole spacing), transforming the data, and using residuals data. The geometric and quality parameters used in this study were the seam thickness and total sulfur, respectively, from one coal seam in the Balikpapan Formation, Kutai Basin, East Kalimantan, Indonesia. The treated dataset was statistically and spatially analyzed to identify the data distribution changes. Nonetheless, the extended spacing, transformation, and residuals approaches resulted in almost similar data trends to the original. The study results indicated that the best approach to detrend the seam thickness and total sulfur at the study site was by dividing the dataset into three spatial domains (i.e., clusters). The global estimation variance method was used in the clustered dataset to produce optimum drill hole spacings of 1000 m, 2000 m, and 2600 m for the northern, central, and southern clusters, respectively, and the measured coal resources at the study site were obtained.
Critical reading skill and discovery learning method at elementary schools based on an Android-application: A computerization approach

Abstract This study discusses the implementation of the method of discovery learning in critical reading learning in Indonesian language lessons in elementary schools. This study aims to determine the improvement of critical reading skills of fifth-grade students of elementary school through discovery learning methods and to find out the implementation of discovery learning methods in improving critical reading skills of fifth-grade students in elementary school. The method used in this study is the Elliott model action research method. The action research was carried out on 32 fifth graders at one of the Islamic private elementary schools in Palembang, Indonesia. Based on the results of the study, it can be concluded that the use of discovery learning methods in critical reading learning can improve students’ skills in critical reading. Thus the results of this study can be recommended to elementary school educators to use this discovery learning method in critical reading learning in Indonesian language lessons in elementary school. In addition, the discovery learning guide can also be used to facilitate the learning process.
A Ciphertext Policy Attribute Based Signcryption Scheme with Secure and Flexible Key Evolving

Data encryption and signature technique can provide secure data protection and verification during data transmission. A data owner can choose to generate a legal signature first, then encrypt it before data sharing. However, the traditional sign-then-encrypt method not only occupies much more computation resources on digital devices, but also brings about high computation cost to the whole communication systems when data verification and key updating happens. In this paper, we utilize CP-ABE and ABS to propose a CP-ABSC scheme, which reduces the calculation burden for users in multi-party communication scenario. By security analysis, our scheme is demonstrated to realize confidentiality, unforgeability with resistance of collusion attack and traitor tracing.
Image Inpainting by Adaptive Fusion of Variable Spline Interpolations

There are many methods for image enhancement. Image inpainting is one of them which could be used in reconstruction and restoration of scratch images or editing images by adding or removing objects. According to its application, different algorithmic and learning methods are proposed. In this paper, the focus is on applications, which enhance the old and historical scratched images. For this purpose, we proposed an adaptive spline interpolation. In this method, a different number of neighbors in four directions are considered for each pixel in the lost block. In the previous methods, predicting the lost pixels that are on edges is the problem. To address this problem, we consider horizontal and vertical edge information. If the pixel is located on an edge, then we use the predicted value in that direction. In other situations, irrelevant predicted values are omitted, and the average of rest values is used as the value of the missing pixel. The method evaluates by PSNR and SSIM metrics on the Kodak dataset. The results show improvement in PSNR and SSIM compared to similar procedures. Also, the run time of the proposed method outperforms others.
Random-Key Cuckoo Search (RKCS) Applications

This chapter represents the random-key encoding scheme originally performed by Bean (1994) to solve, using genetic algorithms, combinatorial optimization problems for which the solutions are a sequence of integers. It is based on the process of interpreting random real numbers from the continuous space to encode a solution for a given combinatorial space. Based on the approach of Ouaarab et al. (2015a). It is combined random keys with cuckoo search to solve combinatorial problems such as traveling salesman and quadratic assignment problems (Ouaarab et al. 2015b). The idea is to try the transition from continuous to discrete space by avoiding the passage of traditional adaptation operators that can affect the performance of the algorithm and to rely firmly on a direct interpretation of operators used by metaheuristics in the continuous search space.
Street Sign Recognition Algorithm Based on Deep Learning

The complex background, uneven illumination and object occlusion have increased the difficulty of scene texts detection. In this paper, we improved the existing object detection algorithm SSD, and made it possible to detect text objects in traffic guidance sign. We used a deep neural network CRNN to identify the text. This network is a combination of Convolution Neural Network and Recurrent Neural Network. At the same time, we proposed a new idea to optimize the detection algorithm through the text recognition result, so that the whole network can be trained end-to-end. According to the experimental results, the detection network achieves 88% mAP on our dataset at 11.6FPS, which has a good recognition effect.
Object Segmentation in Video Sequences by using Single Frame Processing

Object segmentation, detection and tracking in videos is one of the most important task of computer vision. It is necessary in all of the real time deployed surveillance systems. Various unsupervised and semi-supervised video object segmentation techniques have been implemented and shown efficient results. But all of these techniques process all of the frames of a video sequence, which requires a huge training data and results in a large computational time. In this paper, a semi-supervised technique is proposed which segments an object in a video by just processing a single frame of the sequence. In this framework, a fully convolutional network is used to separate the foreground from the image, create the mask of the object and then segments the object with the help of this mask. The foreground separation in a frame is done by using pre-trained network while, training and testing of rest of the network is done using a specified dataset named as DAVIS. The results show that, the proposed framework takes less computational time and has also improved the overall accuracy of video object segmentation by 10% as compared to previous techniques.
Cryptocurrency Mining Games with Economic Discount and Decreasing Rewards.

In the consensus protocols used in most cryptocurrencies, participants called miners must find valid blocks of transactions and append them to a shared tree-like data structure. Ideally, the rules of the protocol should ensure that miners maximize their gains if they follow a default strategy, which consists on appending blocks only to the longest branch of the tree, called the blockchain. Our goal is to understand under which circumstances are miners encouraged to follow the default strategy. Unfortunately, most of the existing models work with simplified payoff functions, without considering the possibility that rewards decrease over time because of the game rules (like in Bitcoin), nor integrating the fact that a miner naturally prefers to be paid earlier than later (the economic concept of discount). In order to integrate these factors, we consider a more general model where issues such as economic discount and decreasing rewards can be set as parameters of an infinite stochastic game. In this model, we study the limit situation in which a miner does not receive a full reward for a block if it stops being in the blockchain. We show that if rewards are not decreasing, then miners do not have incentives to create new branches, no matter how high their computational power is. On the other hand, when working with decreasing rewards similar to those in Bitcoin, we show that miners have an incentive to create such branches. Nevertheless, this incentive only occurs when a miner controls a proportion of the computational power which is close to half of the computational power of the entire network.
Reversible Watermarking for Protecting Patient’s Data Privacy Using an EPR-Generated QR Code

As a result of the modern communication technology and the transmission of medical images from one place to another, watermarking has become urgent to protect copyright, authentication and integrity of medical images and patient’s information. This paper proposes a solution to the authentication and integrity problems by proposing a fragile reversible watermarking scheme. The proposed scheme generates a Quick Response (QR) code from the patient’s data and uses it as a watermark hidden in the medical image. The proposed scheme resulted in an average Peak Signal to Noise Ratio (PSNR) of 51.25 dB for a payload of one bit per pixel (1 bpp), Structural Similarity Index Measure (SSIM) of 0.99 and Mean Squared Error (MSE) of 0.488 indicating that the watermarked images obtained are of high visual quality.
On Distributed Consensus by a Cascade Of Generalized Graph Filters

One of the main results of finite-time distributed consensus is that a network can compute exactly the average of its node values by a sequence of local operations. However, in the worst case the number of required communications exchanges can be of the order of the network size. To reduce the number of exchanges, generalized graph filters were introduced to approximate arbitrary linear operations, consensus included. Unfortunately, as for classical graph filters, the design of generalized graph filters rapidly becomes ill-conditioned with increasing filter order. This leads to numerical instabilities and, ultimately, to error saturation effects. Therefore, in this work, we propose a cascade implementation of generalized graph filters and an efficient algorithm for learning the filter coefficients. By the proposed approach, a further error reduction and better design stability are achieved.
SynthLog: A Language for Synthesising Inductive Data Models (Extended Abstract)

We introduce SynthLog, an extension of the probabilistic logic programming language ProbLog, for synthesising inductive data models. Inductive data models integrate data with predictive and descriptive models, in a way that is reminiscent of inductive databases. SynthLog provides primitives for learning and manipulating inductive data models, it supports data wrangling, learning predictive models and constraints, and probabilistic and constraint reasoning. It is used as the back-end of the automated data scientist approach that is being developed in the SYNTH project. An overview of the SynthLog philosophy and language as well as a non trivial example of its use, is given in this paper.
Research on User Privacy Protection Algorithm in Location Service

With the continuous improvement of positioning technology and wireless communication technology, and the rapid spread of smartphones, mobile users can use various types of location-based services anytime, anywhere. At the same time, the privacy issues brought by location services are getting more and more attention from researchers. In order to improve the efficiency of the algorithm and improve the ability to resist attacks, this paper studies the user location privacy protection technology under the mobile peer-to-peer architecture. This paper proposes a location information protection algorithm based on bidirectional active concealment space. The user location privacy protection is implemented by using a dummy location privacy algorithm. The experiment shows that this algorithm can protect the privacy of users in the location service well, and can resist both passive and active attacks, and provide accurate location service at the same time.
Image Fusion-Based Tone Mapping Using Gaussian Mixture Model Clustering

Tone mapping (TM) algorithms convert high dynamic range (HDR) images into low dynamic range (LDR) images to represent on conventional display devices. Most TM methods compress the dynamic range of input HDR images by using a global transformation function (TF), and then improve local detail by applying contrast enhancement techniques. However, these approaches often fail to restore local detail lost in the dynamic range compression. To solve this problem, we propose a novel image fusion-based TM method. We use Gaussian mixture model clustering algorithm to estimate the dark and bright distributions in the luminance histogram of the input HDR image. Then, we generate two LDR images using two locally-adaptive TFs obtained by the components of each distribution. Finally, the output image is produced by the image fusion technique employing a brightness weight and a local contrast weight. The experimental results show that the proposed algorithm achieves high performance compared to state-of-the-art methods in terms of detail preservation and brightness adjustment.
Research on Development Strategy of Test and Evaluation Assessment for Equipment Software

How to check and assess the whole process of Test and Evaluation on equipment software is one of the major challenges faced by the development of equipment software at present. In order to solve above problems, this paper proposed the development strategies of Test and Evaluation assessment for equipment software in terms of the overall design, data-driven, assessment system and method construction. Then the process of Test and Evaluation assessment on equipment software was checked to verify the development ability, design and organization ability. Therefore, the assessment loop of quality for equipment software as well as the assessment loop of design, development, and stereotyping for equipment software could be gotten through. In this way, the assessment of Test and Evaluation provides supports and traction for equipment software. Moreover, the development strategy of Test and Evaluation assessment on equipment software were made for the operational and in-service usage, which could finally and greatly improve the quality and operational effectiveness of equipment software.
Performance-efficient CPU resource management algorithm on Heterogeneous multi-processor

The biggest difference of mobile devices over AC powered such as servers and desktops is that their power budget is very limited. In other words, the CPU resource management solution for mobile devices makes a greater contribution to product quality. Their main algorithm was to choose the minimum energy or the best-fit performance to accommodate a given job. However, in real world, performance and energy are essential to each other and cannot be treated in these independent and consecutive referencing methods. In this document, we would like to give you new ideas for how to consider performance efficiency in CPU resource management solution.
SAR Seeker Deception Jamming and Anti-jamming Measures

Considering repeater jamming for anti-ship SAR seeker, this paper proposes a mathematical model of repeater jamming for SAR. The influence of parameter estimation accuracy on the effectiveness of repeater jamming is analyzed. In the end, several anti-jamming measures of SAR seeker are summarized in anti-ship scenario.
Fast Light Field Reconstruction Using Convolutional Neural Network to Double Angular Resolution

Light field imaging is the best given the amount of information it provides when compared to conventional photography, it captures angular and spatial information from all directions. Despite that, its limited resolution poses great difficulty in the use of these enormous capabilities. In this paper, we tried to lessen the impact of this drawback by using a deep-learning algorithm. We adopted the idea of dividing the process into disparity estimation and color prediction. Our system was trained to double the angular resolution fast and accurately. Experimental results demonstrate that our system can reconstruct high-quality images faster than the state-of-the-art techniques.
Meta-Analysis of Global Activities in Augmented Reality (AR) and Virtual Reality (VR)

Motivated by Mark Billinghurst’s work about where AR (Augmented Reality) research is taking place, we investigated where VR (Virtual Reality) and AR (Augmented Reality) research is happening around the world based on scholarly articles published in the IEEE VR (Institute of Electrical and Electronics Engineers Virtual Reality) and ISMAR (International Symposium on Mixed and Augmented Reality) proceedings. We analysed a total of 414 papers published in the last six years from 2012 to 2017 at the ISMAR and IEEE VR. The study shows that the USA leads both VR and AR research followed by Germany and France. The number of published VR papers seems increasing, on the other hand, the number of published AR papers seems slightly decreasing.
String Indexing with Compressed Patterns.

Given a string S of length n, the classic string indexing problem is to preprocess S into a compact data structure that supports efficient subsequent pattern queries. In this paper we consider the basic variant where the pattern is given in compressed form and the goal is to achieve query time that is fast in terms of the compressed size of the pattern. This captures the common client-server scenario, where a client submits a query and communicates it in compressed form to a server. Instead of the server decompressing the query before processing it, we consider how to efficiently process the compressed query directly. Our main result is a novel linear space data structure that achieves near-optimal query time for patterns compressed with the classic Lempel-Ziv 1977 (LZ77) compression scheme. Along the way we develop several data structural techniques of independent interest, including a novel data structure that compactly encodes all LZ77 compressed suffixes of a string in linear space and a general decomposition of tries that reduces the search time from logarithmic in the size of the trie to logarithmic in the length of the pattern.