Reviewerly/exHarmony huggingface dataset:
A Robust Traffic Video Stabilization Method Assisted by Foreground Feature Trajectories

This paper considers stabilizing traffic videos recorded by vehicle-mounted cameras. Compared with videos recorded by handheld cameras, traffic videos suffer from more challenges, such as dynamic scenes, dominant foreground objects, and significant parallax. As conventional video stabilization methods usually estimate a homography only from background feature trajectories and warp frames with that homography, they may not obtain enough number of background feature trajectories, especially in the existence of large moving vehicles or strong parallax, and cannot obtain a precise homography to stabilize videos. To resolve this issue, this paper proposes a novel method that makes use of foreground feature trajectories to assist background feature trajectories in stabilizing traffic videos. More specifically, the stabilized views of foreground trajectories are obtained through solving a spatial-temporal optimization. Then, background feature trajectories, together with foreground feature trajectories and their stabilized views, are used to compute the desired homography, which is implemented to stabilize videos. To further improve the stabilization performance, this paper proposes a block-based strategy, which divides each frame into a few equal blocks and attempts to maintain the balance of the numbers of feature trajectories of blocks by evenly extracting feature points and compensating discontinuous feature trajectories if necessary. The experimental results confirm that the proposed method is superior to several state-of-the-art methods in stabilizing traffic videos, especially when there are large moving objects and/or parallax.
Smooth Trajectory Planning for Autonomous Leader-Follower Robots

Path planning is a key factor that determines how well a robotic vehicle performs in executing automated formations and maneuvers as in multi-vehicle platooning and self- organizing leader following with safe and graceful movements. Many types of path- planning schemes have been employed in the autonomous robotics and driving systems. In this paper, we will focus on the application of a smooth path-planning (SPP) algorithm that produces simple-to-implement robotic maneuvers. The algorithm is derived from using a well-established Lyapunov stability criterion and a clever dynamical control synthesis. We show that the SPP can be adapted to many autonomous guidance scenarios. Simulations show that the SPP resulted in autonomous behaviors similar to that parallel those of human or animal actions. The paper presents results using Matlab simulations as well as Gazebo animation. The results will provide a foundation for an implementation of SPP on actual robotic vehicles.
Recursive Conditional Generative Adversarial Networks for Video Transformation

Conditional generative adversarial networks (cGANs) are used in various transformation applications, such as super-resolution, colorization, image denoising, and image inpainting. So far, cGANs have been applied to the transformation of still images, but their use could be extended to the transformation of video contents, which has a much larger market. This paper considers problems with the cGAN-based transformation of video contents. The major problem is flickering caused by the discontinuity between adjacent image frames. Several postprocessing algorithms have been proposed to reduce that effect after transformation. We propose a recursive cGAN in which the previous output frame is used as an input in addition to the current input frame to reduce the flickering effect without losing the objective quality of each image. Compared with previous postprocessing algorithms, our approach performed better in terms of various evaluation metrics for video contents.
An integration of uml use case diagram and activity diagram with Z language for formalization of library management system

Unified Modeling Language (UML) is the effective standard for modeling object-oriented software systems. However, the ambiguity of semantics and the absence of consistency among UML diagrams lead to lack of precisely defining the requirements of a system. On the other hand, formal methods are techniques and tools use the mathematical notations, and they involve the precise syntax and semantics of the unambiguous software requirements specification. It applied in early stages of Software Development Life Cycle (SDLC). Therefore, an integrated between UML specification and formal specification is required to reduce the requirements' ambiguity and error, and to improve the quality and security of software systems. This paper proposes an approach involves the combining UML use-case diagram and activity diagrams with Z language for formalization of Library Management System (LMS). The focus of this paper is on consistency between the UML diagrams to Z Schema, and then verified by using the Z / EVEs tool.
Compiling Sandboxes: Formally Verified Software Fault Isolation

Software Fault Isolation (SFI) is a security-enhancing program transformation for instrumenting an untrusted binary module so that it runs inside a dedicated isolated address space, called a sandbox. To ensure that the untrusted module cannot escape its sandbox, existing approaches such as Google’s Native Client rely on a binary verifier to check that all memory accesses are within the sandbox. Instead of relying on a posteriori verification, we design, implement and prove correct a program instrumentation phase as part of the formally verified compiler CompCert that enforces a sandboxing security property a priori. This eliminates the need for a binary verifier and, instead, leverages the soundness proof of the compiler to prove the security of the sandboxing transformation. The technical contributions are a novel sandboxing transformation that has a well-defined C semantics and which supports arbitrary function pointers, and a formally verified C compiler that implements SFI. Experiments show that our formally verified technique is a competitive way of implementing SFI.
Application of Artificial Intelligence in Autonomous English Learning among College Students

To improve the English proficiency of college students, this paper attempts to promote autonomous English learning among these students using artificial intel-ligence technology. Specifically, the principles, features and application fields of artificial intelligence were introduced in details, and the requirements, structure, database and processes of autonomous English learning systems were discussed at length. On this basis, the backpropagation (BP) neural network, a typical artifi-cial intelligence technique, was selected to design the modules of the autonomous English learning system for college students. The proposed system was then im-plemented in integrated open environment of Visual Studio, the architecture of the browser/server system and the SQL Server database on the Windows platform. The results show that the proposed system provides college students with intelli-gent and open self-evaluation tests and effective learning feedbacks. The research findings shed new light on improving the English learning of college students and reliving the pressure on college English teachers.
Blind quantum computation with a heralded single-photon source

Blind quantum computation is a scheme that adds unconditional security to cloud quantum computation. In the protocol proposed by Broadbent, Fitzsimons, and Kashefi, the ability to prepare and transmit a single qubit is required for a user (client) who uses a quantum computer remotely. In case a weak coherent pulse is used as a pseudo-single-photon source, however, we must introduce decoy states, owing to the inherent risk of transmitting multiple photons. In this paper, we demonstrate that by using a heralded single-photon source and a probabilistic photon number resolving detector we can gain a higher blind state generation efficiency and longer access distance, owing to noise reduction on account of the heralding signal.
IMPLEMENTASI ALGORITMA APRIORI TERHADAP DATA PENJUALAN PADA PERUSAHAAN RETAIL

The development of the business world is increasingly rapid, so it needs a special strategy to increase the turnover of the company, in this case the retail company. In increasing the company's turnover can be done using the Data Mining process, one of which is using apriori algorithm. With a priori algorithm can be found association rules which can later be used as patterns of purchasing goods by consumers, this study uses a repository of 209 records consisting of 23 transactions and 164 attributes. From the results of this study, the goods with the name CREAM CUPID HEART COAT HANGER are the products most often purchased by consumers. By knowing the pattern of purchasing goods by consumers, the company management can increase the company's turnover by referring to the results of processing sales transaction data using a priori algorithm
Improved Real-Time Facial Expression Recognition Based on a Novel Balanced and Symmetric Local Gradient Coding

In the field of Facial Expression Recognition (FER), traditional local texture coding methods have a low computational complexity, while providing a robust solution with respect to occlusion, illumination, and other factors. However, there is still need for improving the accuracy of these methods while maintaining their real-time nature and low computational complexity. In this paper, we propose a feature-based FER system with a novel local texture coding operator, named central symmetric local gradient coding (CS-LGC), to enhance the performance of real-time systems. It uses four different directional gradients on 5 × 5 grids, and the gradient is computed in the center-symmetric way. The averages of the gradients are used to reduce the sensitivity to noise. These characteristics lead to symmetric of features by the CS-LGC operator, thus providing a better generalization capability in comparison to existing local gradient coding (LGC) variants. The proposed system further transforms the extracted features into an eigen-space using a principal component analysis (PCA) for better representation and less computation; it estimates the intended classes by training an extreme learning machine. The recognition rate for the JAFFE database is 95.24%, whereas that for the CK+ database is 98.33%. The results show that the system has advantages over the existing local texture coding methods.
A Model-Checking-Based Framework for Analyzing Ambient Assisted Living Solutions

Since modern ambient assisted living solutions integrate a multitude of assisted-living functionalities, out of which some are safety critical, it is desirable that these systems are analyzed at their design stage to detect possible errors. To achieve this, one needs suitable architectures that support the seamless design of the integrated assisted-living functions, as well as capabilities for the formal modeling and analysis of the architecture. In this paper, we attempt to address this need, by proposing a generic integrated ambient assisted living system architecture, consisting of sensors, data collection, local and cloud processing schemes, and an intelligent decision support system, which can be easily extended to suit specific architecture categories. Our solution is customizable, therefore, we show three instantiations of the generic model, as simple, intermediate, and complex configurations, respectively, and show how to analyze the first and third categories by model checking. Our approach starts by specifying the architecture, using an architecture description language, in our case, the Architecture Analysis and Design Language, which can also account for the probabilistic behavior of such systems, and captures the possibility of component failure. To enable formal analysis, we describe the semantics of the simple and complex architectures within the framework of timed automata. We show that the simple architecture is amenable to exhaustive model checking by employing the UPPAAL tool, whereas for the complex architecture we resort to statistical model checking for scalability reasons. In this case, we apply the statistical extension of UPPAAL, namely UPPAAL SMC. Our work paves the way for the development of formally assured future ambient assisted living solutions.
A Novel Nonlinear Partial Least Square Integrated With Error-Based Extreme Learning Machine

This paper proposes a novel nonlinear partial least square (PLS) approach for dealing with the modeling problem of industrial processes with input variables in collinearity. The new method combines the external linear PLS framework with the internal extreme learning machine (ELM) function. First, PLS is used as the outer framework to extract the input and output latent variables as well as eliminating the collinearity of the original variables, and then ELM is employed to describe the nonlinear relation between pairs of latent variables. Besides, the weight updating strategy based on errors minimization is also involved to improve the prediction accuracy. Then, the pH-neutralization process is taken as a benchmark to verify the validity of the new model. Finally, this method is applied to model the NO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><i>x</i></sub> emission of a 1000-MW coal-fired boiler, and root-mean-square error (RMSE) values are 5.9541 for the training dataset and 6.8323 for the testing dataset. Compared with linear PLS and another two nonlinear PLS methods, smaller prediction errors is obtained. The results indicate new nonlinear PLS model can be a better choice for establishing the model of NO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><i>x</i></sub> emission for coal-fired boilers.
FLUID SYSTEMS OF THE MAMSKO-BODAIBIN MINERAGENIC ZONE IN NORTH TRANSBAIKAL

The article presents the results of the study of fluid systems in the Earth’s crust within the Mamsko-Bodaibin mineragenic zone of the northern Transbaikal using magnetic field anomalies, gravity and seismic data. Two-dimensional models of the density and magnetization of schistosity zones and deep sections of gold deposits allowed us to identify the location of fluid systems and possible paths of magmatic thermofluid flows in ancient rocks. The authors have revealed peculiarities of fluid systems at different depth levels and the role of vertical ore-controlling formations of the earth crust of the Mamsko-Bodaibin zone. Diagnostic features are proposed to control the paths of fluid transport to the surface and the location of the ore-generating structures, taking into account the fluid-magmatic activity of the earth’s crust and mantle. The analysis showed that the Mamsko-Bodaibin zone is prospective in the search for new ore objects. Based on the results of the study, we propose a refined model of the ore localization of the Sukholozhskoye ore field, which includes the influence of deep factors.
The Relationship Between Virtual and Actual Reality: Phenomenological-Ontological Approach

The article addresses the relationship between virtual and actual reality as the external world of users. The objective is to study the relationship between two realities: virtual reality and actual reality in their ontological uniqueness and distinctness. The set objective implies that virtual reality should be seen as a specific conditional reality. The applied methodological principle incorporates concepts of ontological/ functional/ artistic conditionalities. The development of modern VR technology contributing to the formation of cyberculture is shown to bring the functional conditionality to the forefront, putting into action and emphasizing the leading part of an individual, the importance of his or her self-subject position (as the Participant or the Observer) in social construction of reality. The specific nature of the boundaries of conditionality as well as their penetrability/ impenetrability (characterizing openness or closeness of realities) make it possible to draw a demarcation line between the actual reality and other realities.The comparative analysis given in the article revealed a number of distinctive features inherent in virtual realities. Due to these features, virtual realities can be differentiated both from the actual reality and the realities (mental, artistic) generally referred to as virtual by a number of scholars. As defined, the actual reality, as long as its social and regulatory pressure is imperative and unconditional, can compete with virtual realities, which, on the other hand, due to their interactivity can have such an impact on the actual reality that any concept of two autonomous worlds makes no sense.
Method of Feature Reduction in Short Text Classification Based on Feature Clustering

One decisive problem of short text classification is the serious dimensional disaster when utilizing a statistics-based approach to construct vector spaces. Here, a feature reduction method is proposed that is based on two-stage feature clustering (TSFC), which is applied to short text classification. Features are semi-loosely clustered by combining spectral clustering with a graph traversal algorithm. Next, intra-cluster feature screening rules are designed to remove outlier feature words, which improves the effect of similar feature clusters. We classify short texts with corresponding similar feature clusters instead of original feature words. Similar feature clusters replace feature words, and the dimension of vector space is significantly reduced. Several classifiers are utilized to evaluate the effectiveness of this method. The results show that the method largely resolves the dimensional disaster and it can significantly improve the accuracy of short text classification.
The Incomplete Linguistic Features and Schematic Structure in EFL University Students’ Narrative Texts

This study attempts to investigate the incomplete linguistic features and schematic structure in university students’ narrative text. Qualitative content analysis method was utilized as the research design. The instrument of the research is the writing sheet. The data were taken from a narrative text written by 34 university students of second-year English education study program under the theme of folktales from North Sumatera. The data were analyzed by using the transitivity system to identify linguistic features in narrative text. The findings present that there are 4 different titles of folktales found in the data. From the data, it was commonly found that there is an incomplete part of the schematic structure of narrative text written by students namely complication, evaluation, resolution, and coda. In terms of linguistic features, it is discovered that most of the students have difficulties in differing the tense used covering the use of regular and irregular verbs, and the absence of dialogue. Then, the conclusion can be drawn that the incomplete schematic structure and linguistic features exist in university students’ narrative text. It is also suggested that students keep practicing to write and lecturers should devote higher attention in teaching this genre so that it is able to be comprehended and properly produced by students.
Privacy-preserving outsourced image feature extraction

Abstract Privacy-protected signal and image processing for outsourced computing has hence recently been popular. We present a framework for privacy-preserving outsourced image feature Extraction(POIFE) in the semi-honest setting. Also we prove that the construction is simulation-based secure under the semi-honest adversaries. In our scheme, the user can be flexible to outsource his computation to the cloud without real-time online. Compared with the previous works, our solution improves privacy.
Towards Scalability Trade-off and Security Issues in State-of-the-art Blockchain

Blockchain is the latest technology developed in recent years for storing and sharing valuable information about transactions.This technology applies different methods for storing information that is unlike other existing traditional ways.Blockchain technology is an excellent example of maintaining privacy and security (in terms of immutability).As it does not need any control of central unit, it has tremendous impacts on many organizations involving from business to education and finance.This technology has a lot of attractive features that make it very popular from day to day in the domain of technology.But then also we have to pertain to the scalability and security challenges in this technology for more honorable and reliable in state-of-the-art Blockchain technology.
Textual genres and writing difficulties in specialized domains

Writing is widely believed to be more difficult than reading comprehension. This difficulty is even greater for specialized texts; as a type of specialized communication, they must obey concrete norms, which can vary based on domain or textual genre. This article aims to identify the most frequently written genres and greatest writing challenges among different groups (specialists, students, and laypersons) in the specialized domains of medicine, tourism and the public administration and to identify the major writing difficulties they pose. A series of in-person interviews and online surveys were conducted. Quantitative and qualitative analysis shed light on the textual genres posing the greatest writing difficulties for the target groups and the causes thereof.
Misspecified CRB on Parameter Estimation for a Coupled Mixture of Polynomial Phase and Sinusoidal FM Signals

This paper studies parameter estimation of a coupled mixture of polynomial phase signal (PPS) and sinusoidal frequency modulated (FM) signal, a newly introduced model motivated by industrial applications. Particularly, we analytically evaluate the estimation performance (or performance loss) via the misspecified Cramér-Rao bound (CRB) when system designers choose existing efficient estimation algorithms designed for an independent (decoupled) mixture model due to hardware limits. Our analysis provides an analytical tool to conveniently evaluate performance loss if the implemented system ignores the coupling effect. The achievability of the misspecified CRB is verified by numerical examples.
A Blockchain Enhanced Framework for Social Networking

Social networking sites have given users unprecedented opportunities for the generation and dissemination of content. A variety of social networking sites exist for different purposes, to afford users a range of anonymous and non-anonymous options for self-expression, and the ability to be a part of a virtual community. These “affordances” enable users to create and share content; however, the ability to partially or wholly detach user identity from the content has resulted in unique challenges for content access and content attribution. This paper proposes a framework for secure, trustworthy social networking that also creates value for user-generated content by using a blockchain-enhanced framework for social networking. This work explains the application of such a framework for collocated spaces of robots and IoT devices and identifies key challenges that result as a consequence of merging social networking sites and blockchain technology.
Extractive Summarization with Very Deep Pretrained Language Model

Recent development of generative pretrained language models has been proven very successful on a wide range of NLP tasks, such as text classification, question answering, textual entailment and so on.In this work, we present a two-phase encoder decoder architecture based on Bidirectional Encoding Representation from Transformers(BERT) for extractive summarization task.We evaluated our model by both automatic metrics and human annotators, and demonstrated that the architecture achieves the stateof-the-art comparable result on large scale corpus -CNN/Daily Mail 1 .As the best of our knowledge, this is the first work that applies BERT based architecture to a text summarization task and achieved the stateof-the-art comparable result.