Reviewerly/exHarmony huggingface dataset:
Design and Analysis of an Unmanned Aerial Vehicle Capable of Carrying the Camera

Today Unmanned aerial vehicles (UAV) have found widespread use and may have been using them to capture various events or take photographs of operational areas; but the fact is that UAVs or quadcopters, which are some of these flights, are still at the beginning of their journey and can be future applications will be more widely used.Given the geographical situation and the current situation in the world, the country needs to step up with the world in order to strengthen the defense forces.One of the tools that have been used in recent decades to other tools due to their characteristics and their unique features, including the absence of human casualties, the study and more precise missions, and ... are of interest to many countries, including our country, are unmanned aerial vehicles.Many researchers have been carried out on these devices today.In the same way, our researchers in our country have done a lot of researches on this device in recent years and tried to make these birds with the least cost and with the highest efficiency.This paper also attempts to analyze the flights with the ability to carry the camera.At first, its components, including wings, body, tail, and landing gear, are optimally designed and then aerodynamically and instrumentally analyzed, so that this research used to build these flights species in the future.
Steganography analysis techniques applied to audio and image files

The present work carries out a descriptive analysis of the main steganography techniques used in specific digital media such as audio and image files. For this purpose, a literary review of the domains, methods, and techniques as part of this set was carried out and their functioning, qualities, and weaknesses are identified. Hence, it is concluded that there is a wide relationship between audio and image steganography techniques in their implementation form. Nevertheless, it is determined that LSB is one of the weakest techniques, but the safest and the most robust technique within each type of the presented medium.
Automatic Arabic Part-of-Speech Tagging: Deep Learning Neural LSTM Versus Word2Vec

Part-of-speech (POS) tagging is the process of selecting an appropriate POS tag for each word in a natural language sentence.POS tagging is a vital part of most natural language processing (NLP) applications.In comparison to other languages, there is a dearth of studies on NLP applications for the Arabic language.Recently, neural networks (NNs) and deep learning technologies have shown excellent results for some English and Latin NLP applications.However, for Arabic, the practice is still in its infancy, and more work is needed to determine whether neural technologies will lead to convincing results for NLP applications.In this paper, a long short-term memory (LSTM) model has been used to investigate the effectiveness of NNs in Arabic NLP.The model has been specifically applied to identify the POS tags for Arabic words and morphemes taken from the Quranic Arabic Corpus (QAC) data set.QAC is a well-known gold standard dataset prepared by researchers from Leeds University.It is interesting to note that LSTM tagger achieved 99.72% accuracy for tagging morphemes and 99.18% for tagging words, while the Word2Vec tagger achieved 99.55% for tagging morphemes and 97.33% for tagging words.
A Novel Extraction Method for Wildlife Monitoring Images with Wireless Multimedia Sensor Networks (WMSNs)

In remote areas, wireless multimedia sensor networks (WMSNs) have limited energy, and the data processing of wildlife monitoring images always suffers from energy consumption limitations. Generally, only part of each wildlife image is valuable. Therefore, the above mentioned issue could be avoided by transmitting the target area. Inspired by this transport strategy, in this paper, we propose an image extraction method with a low computational complexity, which can be adapted to extract the target area (i.e., the animal) and its background area according to the characteristics of the image pixels. Specifically, we first reconstruct a color space model via a CIELUV (LUV) color space framework to extract the color parameters. Next, according to the importance of the Hermite polynomial, a Hermite filter is utilized to extract the texture features, which ensures the accuracy of the split extraction of wildlife images. Then, an adaptive mean-shift algorithm is introduced to cluster texture features and color space information, realizing the extraction of the foreground area in the monitoring image. To verify the performance of the algorithm, a demonstration of the extraction of field-captured wildlife images is presented. Further, we conduct a comparative experiment with N-cuts (N-cuts), the existing aggregating super-pixels (SAS) algorithm, and the histogram contrast saliency detection (HCS) algorithm. A comparison of the results shows that the proposed algorithm for monitoring image target area extraction increased the average pixel accuracy by 11.25%, 5.46%, and 10.39%, respectively; improved the relative limit measurement accuracy by 1.83%, 5.28%, and 12.05%, respectively; and increased the average mean intersection over the union by 7.09%, 14.96%, and 19.14%, respectively.
A Proposal for Evading the Measurement Uncertainty in Classical and Quantum Computing: Application to a Resonant Tunneling Diode and a Mach-Zehnder Interferometer

Measuring properties of quantum systems is governed by a stochastic (collapse or state-reduction) law that unavoidably yields an uncertainty (variance) associated with the corresponding mean values. This non-classical source of uncertainty is known to be manifested as noise in the electrical current of nanoscale electron devices, and hence it can flaw the good performance of more complex quantum gates. We propose a protocol to alleviate this quantum uncertainty that consists of (i) redesigning the device to accommodate a large number of electrons inside the active region, either by enlarging the lateral or longitudinal areas of the device and (ii) re-normalizing the total current to the number of electrons. How the above two steps can be accommodated using the present semiconductor technology has been discussed and numerically studied for a resonant tunneling diode and a Mach-Zehnder interferometer, for classical and quantum computations, respectively. It is shown that the resulting protocol formally resembles the so-called collective measurements, although, its practical implementation is substantially different.
Classification of Mobile Application Reviews using Word Embedding and Convolutional Neural Network

The app reviews are useful for app developers because they contain valuable information, e.g. bug, feature request, user experience, and rating. This information can be used to better understand user needs and application defects during software maintenance and evolution phase. The increasing number of reviews causes problems in the analysis process for developers. Reviews in textual form are difficult to understand, this is due to the difficulty of considering semantic between sentences. Moreover, manual checking is time-consuming, requires a lot of effort, and costly for manual analysis. Previous research shows that the collection of the review contains non-informative reviews because they do not have valuable information. Non-informative reviews considered as noise and should be eliminated especially for classification process. Moreover, semantic problems between sentences are not considered for the reviews classification. The purpose of this research is to classify user reviews into three classes, i.e. bug, feature request, and non-informative reviews automatically. User reviews are converted into vectors using word embedding to handle the semantic problem. The vectors are used as input into the first classifier that classifies informative and non-informative reviews. The results from the first classifier, that is informative reviews, then reclassified using the second classifier to determine its category, e.g. bug report or feature request. The experiment using 306,849 sentences of reviews crawled from Google Play and F-Droid. The experiment result shows that the proposed model is able to classify mobile application review by produces best accuracy of 0.79, precision of 0.77, recall of 0.87, and F-Measure of 0.81.&#x0D;
Blind Image Quality Assessment Based on Multi-Window Method and HSV Color Space

To improve the evaluation accuracy of the distorted images with various distortion types, an effective blind image quality assessment (BIQA) algorithm based on the multi-window method and the HSV color space is proposed in this paper. We generate multiple normalized feature maps (NFMs) by using the multi-window method to better characterize image degradation from the receptive fields of different sizes. Specifically, the distribution statistics are first extracted from the multiple NFMs. Then, Pearson linear correlation coefficients between spatially adjacent pixels in the NFMs are utilized to quantify the structural changes of the distorted images. Weibull model is utilized to capture distribution statistics of the differential feature maps between the NFMs to more precisely describe the presence of the distortions. Moreover, the entropy and gradient statistics extracted from the HSV color space are employed as a complement to the gray-scale features. Finally, a support vector regressor is adopted to map the perceptual feature vector to image quality score. Experimental results on five benchmark databases demonstrate that the proposed algorithm achieves higher prediction accuracy and robustness against diverse synthetically and authentically distorted images than the state-of-the-art algorithms while maintaining low computational cost.
A New Flexible Multi-flow LRU Cache Management Paradigm for Minimizing Misses

The Least Recently Used (LRU) caching and its variants are used in large-scale data systems in order to provide high-speed data access for a wide class of applications. Nonetheless, a fundamental question still remains: in order to minimize the miss probabilities, how should the cache space be organized to serve multiple data flows? Commonly used strategies can be categorized into two designs: pooled LRU (PLRU) caching and separated LRU (SLRU) caching. However, neither of these designs can satisfactorily solve this problem. PLRU caching is easy to implement and self-adaptive, but does not often achieve optimal or even efficient performance because its set of feasible solutions are limited. SLRU caching can be statically configured to achieve optimal performance for stationary workload, which nevertheless could suffer in a dynamically changing environment and from a cold-start problem. To this end, we propose a new insertion based pooled LRU paradigm, named I-PLRU, where the data flows can be inserted at different positions of a pooled cache. This new design can achieve the optimal performance of the static SLRU, but retains the adaptability of PLRU for resource sharing. Theoretically, we characterize the asymptotic miss probabilities of I-PLRU, and prove that, for any given SLRU design, there always exists an I-PLRU configuration that achieves the same asymptotic miss probability, and vice versa. We next design a policy to minimize the miss probabilities. However, the miss probability minimization problem turns out to be non-convex under the I-PLRU paradigm. Notably, we utilize an equivalence mapping between I-PLRU and SLRU to efficiently find the optimal I-PLRU configuration. We prove that I-PLRU outperforms PLRU and achieves the same miss probability as the optimal SLRU under a stationary request arrival process. From an engineering perspective, the flexibility of I-PLRU avoids separating the memory space, supports dynamic and refined configurations, and alleviates the cold-start problem, potentially yielding better performance than both SLRU and PLRU.
Irony and sarcasm expression in Twitter

Information extraction and sentiment analysis of social net contents is a relatively new field, but a very promising and quickly developing one. In the current work, we discuss the possibilities of automatic detection and analysis of ironic and sarcastic messages in one of the most popular (among both users and researches) social nets - Twitter. A particular trait of this research will consist in analyzing the languages content, which is rarely observed. We will focus on the material of Spanish, German and Russian languages, trying to implement the same analysis algorithms to all three as well and to define the differences.
Large-Scale Electronic Voting Based on Conflux Consensus Mechanism

Blockchain voting can bring many practical benefits. It is said that blockchain technology represents a revolution in security and transparency that makes electronic voting possible. It will undoubtedly have an unexpected and far-reaching impact on future democracy. Electronic voting can accelerate, simplify and reduce the cost of elections, and even lead to more participation and development into stronger democracies. Firstly, this paper analyses the problems existing in the current blockchain voting system, then introduces the concept and principle of Conflux consensus mechanism. Based on blockchain technology and ring signature, a large-scale voting electronic voting scheme integrating the advantages of Conflux consensus mechanism with blockchain voting system is proposed, which is decentralized, self-managed, non-interactive and free of charge.
WebPut: A Web-Aided Data Imputation System for the General Type of Missing String Attribute Values

In this demonstration, we present an end-to-end web-aided data imputation prototype system named WebPut. WebPut consults the Web for imputing the missing values in a local database when the traditional inferring-based imputation method has difficulties in getting the right answers. Specifically, WebPut investigates the interaction between the local inferring-based imputation methods and the web-based retrieving methods and shows that retrieving a small number of selected missing values can greatly improve the imputation recall of the inferring-based methods. Besides, WebPut also incorporates a crowd intervention component that can get advice from humans in case that the web-based imputation methods may have difficulties in making the right decisions. We demonstrate, step by step, how WebPut fills an incomplete table with each of its components.
Improving Critical Reading with E-Texts: A Controlled Study in a Collegiate Philosophy Course

This project investigated the impact of incorporating e-reader texts and annotation tools in an upper level philosophy course. This project adds to the body of literature that assesses gains/losses in conventional measures of performance (e.g., scores on graded assignments) and changes in student attitudes as reported in questionnaires. However, this project was unique in that it focused on training students to use e-reader tools for critical reading practices and it included assessment of student annotations and their relationship with the performance measures. We tested the hypothesis that, with intentional training and a course-design that provided multiple opportunities for practice and feedback, students using e-readers for critical engagement with their reading assignments would demonstrate (a) deeper understanding of the content of the texts, (b) improvement in their use of critical reading practices, and (c) improvement in their attitudes toward the use of e-readers for academic work. While we did not observe significant gains in graded assignments compared with control groups using printed texts, we found no evidence of losses for students using e-readers. At the same time, we found evidence of improvement in students’ critical reading practices, especially when paired with modeling and practice throughout the term. We also observed significant positive changes in student attitudes toward the use of e-readers for academic work, compared with controls. Our findings suggest that achieving the benefits of e-readers for the development of critical reading skills requires a course with design elements that are specifically tailored to this purpose.
How much topological structure is preserved by graph embeddings?

Graph embedding aims at learning representations of nodes in a low dimensional vector space. Good embeddings should preserve the graph topological structure. To study how much such structure can be preserved, we propose evaluation methods from four aspects: 1) How well the graph can be reconstructed based on the embeddings, 2) The divergence of the original link distribution and the embedding-derived distribution, 3) The consistency of communities discovered from the graph and embeddings, and 4) To what extent we can employ embeddings to facilitate link prediction. We find that it is insufficient to rely on the embeddings to reconstruct the original graph, to discover communities, and to predict links at a high precision. Thus, the embeddings by the state-of-the-art approaches can only preserve part of the topological structure.
Implementation of Zoning and K-Nearest Neighbor in Character Recognition of Wrésastra Script

Balinese script is an important aspect that packs the Balinese culture from time to time which continues to experience development along with technological advances. Balinese script consists of three types (1) Wrésastra, (2) Swalalita and (3) Modre which have different types of characters. The Wrésastra and Swalalita script are Balinese scripts which grouped into the script criteria that are used to write in the field of everyday life. In this research, the zoning method will be implemented in the feature extraction process to produce special features owned by Balinese script. The results of the feature extraction process will produce special features owned by Balinese script which will be used in the classification process to recognize the character of Balinese script. Special features are produced using the zoning method, it will divide the image characters area of ??Balinese scripts into several regions, to enrich the features of each Balinese script. The result of feature extractions is stored as training data that will be used in the classification process. K-Nearest Neighbors is implemented in the special feature classification process that is owned by the character of Balinese script. Based on the results of the test, the highest level of accuracy was obtained using the value K=3 and reference=10 with the accuracy of Balinese script recognition 97.5%.
Data-Driven Behavior-Based Negotiation Model for Cyber-Physical-Social Systems

This paper presents an agent-based fuzzy constraint-directed negotiation (AFCN) model for cyber-physical-social systems (CPSS). The proposed AFCN model is a behavior-based negotiation framework, which involves the agents perceive the opponents' behavior and market environment to evaluate the proposal and revise the intention to guide the behavior of agent which represent the goal the agents aim to achieve. The novelty of the proposed model is to add the fuzzy membership function to exchange information between the cyber, physical, and social worlds for representing the imprecise Quality of Service (QoS) preferences that must be satisfied. This added information sharing is of critical importance for the effectiveness of distributed coordination because it not only reveals the opponent's behavior preference but also can specify the possibilities prescribing the extent to which the feasible solutions are suitable for agent's behavior. Moreover, the AFCN model can flexibly adopt different behavioral strategies such as the competitive, win-win, collaborative, and hybrid strategies for classic market environments which enable an agent to reach an agreement benefit all participants without reducing any of an agent's desires. The experimental results demonstrate that the proposed behavior-based agent negotiation model outperforms other agent-based approaches in terms of the level of satisfaction, the ratio of successful negotiation, the total revenue of service providers, the buying price of the unit resource, and convergence speed.
Visual Identification and Extraction of Intrinsic Axes in High-Dimensional Data

Interactive axis extraction for high-dimensional data visualization has been demonstrated to be powerful in high-dimensional data exploring and understanding. The extracted axes help to yield new 2-D arrangements of data points, providing new insights into the data. However, the existing interfaces for extraction only support linear axes or non-linear axes without specific semantics. When the data points lie in a manifold, it is hard to capture intrinsic features of the manifold by either linear axes or non-linear axes without specific semantics. Furthermore, a dataset with complicated topology would contain holes and branches. While a branch often indicates a local trend, it may not make sense to project data points to an axis in a different branch. In this paper, we propose an interactive visual interface to identify and extract intrinsic axes in high-dimensional data. The system contains four major views. The topology view presents the skeleton-based topology of the dataset. The detail view provides a force-directed layout of a high-dimensional data and allows interactive extracting intrinsic axes. The characteristics of extracted axes are visualized in the intrinsic axes view. The projection view layouts data points aligning with extracted intrinsic axes. Case studies and comparative experiments demonstrate the usefulness of our visual analytics system.
Estimation of extrinsic parameters for dynamic binocular stereo vision using unknown-sized rectangle images

An online calibration method of extrinsic parameters for dynamic binocular stereo vision using unknown-sized rectangle images is presented herein. The two cameras can rotate freely in two-axis direction without zooming, and the cameras’ intrinsic parameters and initial relative position are known in advance. Hence, only the rotation angles of the cameras, namely, the yaw angle in the horizontal direction and the pitch angle in the vertical direction, must be solved. The approximate values of the rotation angles are obtained by the Ellipse-A attitude and heading reference system in real time, and the rotation angles of each camera are solved iteratively using only a single rectangular centroid according to interimage homography. The pitch and yaw angles are eventually modified based on the property of fundamental matrix by the corresponding rectangular centroids, obtained by the left and right cameras after rotation, respectively. Computer simulations and real experiments are performed to validate the efficiency of the proposed method, and we compare calibration results with that of the scale-invariant feature transform method and Zhang’s method. The quantitative results demonstrate that the root mean square (rms) of the absolute error of the attitude angles and the translation vector does not exceed 0.12° and 7.2 mm, respectively. Additionally, the rms of the relative error of the three-dimensional coordinates does not exceed 1.6%.