Reviewerly/exHarmony huggingface dataset:
ON OBDD-BASED ALGORITHMS AND PROOF SYSTEMS THAT DYNAMICALLY CHANGE THE ORDER OF VARIABLES

Abstract In 2004 Atserias, Kolaitis, and Vardi proposed $\text {OBDD}$ -based propositional proof systems that prove unsatisfiability of a CNF formula by deduction of an identically false $\text {OBDD}$ from $\text {OBDD}$ s representing clauses of the initial formula. All $\text {OBDD}$ s in such proofs have the same order of variables. We initiate the study of $\text {OBDD}$ based proof systems that additionally contain a rule that allows changing the order in $\text {OBDD}$ s. At first we consider a proof system $\text {OBDD}(\land , \text{reordering})$ that uses the conjunction (join) rule and the rule that allows changing the order. We exponentially separate this proof system from $\text {OBDD}(\land )$ proof system that uses only the conjunction rule. We prove exponential lower bounds on the size of $\text {OBDD}(\land , \text{reordering})$ refutations of Tseitin formulas and the pigeonhole principle. The first lower bound was previously unknown even for $\text {OBDD}(\land )$ proofs and the second one extends the result of Tveretina et al. from $\text {OBDD}(\land )$ to $\text {OBDD}(\land , \text{reordering})$ . In 2001 Aguirre and Vardi proposed an approach to the propositional satisfiability problem based on $\text {OBDD}$ s and symbolic quantifier elimination (we denote algorithms based on this approach as $\text {OBDD}(\land , \exists )$ algorithms). We augment these algorithms with the operation of reordering of variables and call the new scheme $\text {OBDD}(\land , \exists , \text{reordering})$ algorithms. We notice that there exists an $\text {OBDD}(\land , \exists )$ algorithm that solves satisfiable and unsatisfiable Tseitin formulas in polynomial time (a standard example of a hard system of linear equations over $\mathbb {F}_2$ ), but we show that there are formulas representing systems of linear equations over $\mathbb {F}_2$ that are hard for $\text {OBDD}(\land , \exists , \text{reordering})$ algorithms. Our hard instances are satisfiable formulas representing systems of linear equations over $\mathbb {F}_2$ that correspond to checksum matrices of error correcting codes.
Removal of Image Obstacles for Vehicle-mounted Surrounding Monitoring Cameras by Real-time Video Inpainting

One of the practical problems with surrounding view cameras (SMCs) of a vehicle is the degradation of image quality due to obstacles by substances adherent to their lens surface, such as raindrops and mud. Such image degradation could be improved by image restoration techniques that have been studied in the field of computer vision. However, to assist the driver, real time processing and fidelity of the recovered image are essential, which disqualifies most of the existing methods. In this study, we propose to adopt a recently developed video-inpainting method that can restore high-fidelity images in real time. It estimates optical flows using a CNN and use them to match occluded regions in the current frame to unoccluded regions in previous frames, restoring the former. Although the direct application does not lead to satisfactory results due to the peculiarities ofthe SMC videos, we show that two improvements make it possible to obtain good results that are useful in practice. One is to use a model-based flow estimation method to obtain target flows for training the CNN, and the other is to improve how the estimated flows are used to match the current and previous frames. We conducted experiments using real images mainly of parking spaces in urban areas. The results, including subjective evaluation, show the effectiveness of our approach.
A New Contrast Based Degraded Document Image Binarization

The binarization of badly degraded document images is very challenging job due to the presence of various degradations such as ink bleed through, stains, smear, blur, low contrast and nonuniform illumination. Many binarization techniques are proposed in the literature, most of these techniques are threshold based. This chapter proposes the binarization technique which uses the contrast feature to compute the threshold value with minimum parameter tuning. It computes the local contrast image using maximum and minimum pixel values in the neighbourhood. The high contrast text pixels in the image are detected using global binarization. Finally, the local thresholds are computed using high contrast image pixels within the local window to binarize the document image. It has been tested on benchmark datasets H-DIBCO-2010 and H-DIBCO-2016 in terms of F-measure, PSNR and NRM. The results are compared with the Bernsen’s and LMM contrast based binarization techniques and found to be outperforming these methods.
Dense Disparity Maps from RGB and Sparse Depth Information Using Deep Regression Models

A dense and accurate disparity map is relevant for a large number of applications, ranging from autonomous driving to robotic grasping. Recent developments in machine learning techniques enable us to bypass sensor limitations, such as low resolution, by using deep regression models to complete otherwise sparse representations of the 3D space. This article proposes two main approaches that use a single RGB image and sparse depth information gathered from a variety of sensors/techniques (stereo, LiDAR and Light Stripe Ranging (LSR)): a Convolutional Neural Network (CNN) and a cascade architecture, that aims to improve the results of the first. Ablation studies were conducted to infer the impact of these depth cues on the performance of each model. The models trained with LiDAR sparse information are the most reliable, achieving an average Root Mean Squared Error (RMSE) of 11.8 cm on our own Inhouse dataset; while the LSR proved to be too sparse of an input to compute accurate predictions on its own.
File Operations Information Collecting Software Package Used in the Information Security Incidents Investigation

The article describes file operations information collecting software package, that can be implemented in information system structure during information security incidents investigation. The package allows to collect and systematize accomplished file operations on all nodes of the information system structure, using the information obtained from the volume changes log - $UsnJrnl. This log possesses almost comprehensive information about files and actions that performed in relation to them. In addition, this log has several advantages compared to other logs: completeness, reliability and file identification information extraction speed. Software package also works as storage with file operations information backup availability that helps to speed up and simplify the process of information security incidents investigation. Stored information can be processed by SIEM system. It is necessary to say that SIEM system should be able to expand its functionality using e.g. scripts. Main goals of software package and SIEM system interaction are constructing a time line of information security events to accelerate the elimination of the incident consequences, and provide recommendations for applied information protection measures improving.
Style Transfer of Urban Road Images Using Generative Adversarial Networks With Structural Details

To evaluate the driving behavior of unmanned vehicles, the testing of driving algorithms using urban road images is necessary. In this article, we propose a framework using generative adversarial networks (GANs) with structural information for image style transfer: StructureGAN and GradientGAN. Different types of urban image transfers are generated using the proposed framework, such as day to night, sunny to foggy, and summer to winter transfers. The proposed method can well maintain the integrity of foreground objects and the image structural information. Artifacts such as image distortion and foreground disappearance are eliminated. The experiments with the baseline methods indicate the effectiveness of the proposed framework, which can produce transferred images with high quality.
Integrating Blockchain with Local Public Service System

A smart city is having various types of offices, which are having different types of data. These departments are related to each other. In some cases, if the user wants to access a particular type of data of any local governance department then that data is available for the user, during the process sometimes the data may be hacked and modified by third party entity. Therefore, for removing such drawback, we are using blockchain technology here. In this paper, a blockchain-based system is proposed for bringing trust and integration between different subunits of local public service systems. The existing systems are working in silos; the proposed system uses Azure Blockchain Workbench for integrating such units and bringing sync and trust between them. This results into faster and secure operation at both the administrative as well as consumer (public) end.
Wigner function and phase sensitivity of photon-added coherent states and number states as inputs of MZI

In this paper, we investigate the quantum interference of output port by considering any photon-number state and photon-added coherent states (PSCS) as inputs of Mach-Zehnder interferometer. We re-present the relationship between input and output operators obtained by Weyl ordering and study the quantum statistical properties of the output field by one output's Wigner function. Furthermore, we mainly analyse the phase sensitivity on the basis of photon-number parity measurements scheme. Finally, we also discuss the internal relationships between the output's Wigner function and phase sensitivity of interferometer. It is shown that the negativity of output's Wigner function, the parity signal resolution and the interferometer's phase sensitivity can be improved by adding the photon numbers to coherent states.
Effect of Mean Filter on Face Image from Video Frames

One of the most important studies is the identification of face from videos in case of low resolution. This article looks at the effect of a mean filter on facial pictures. The mean filter measures the number of pixels in successive images using the intensities of neighbouring pixels and temporal neighbouring pixels. The pixels of the original high-resolution image were used as reference values to measure the effect of the suggested technique on the picture peak signal noise ratio (PSNR) and mean square error (MSE) levels by calculating the noise and error figure of the pixels of the filtered low-resolution pictures. Outcomes indicate major impact on successive face video record frames from the suggested filter.
Combined Networks with Multi-level Attention for Distantly-Supervised Relation Extraction

Abstract Distantly-supervised relation extraction is expected to extract relational facts from very large corpora. However, it is inevitably accompanied by the problem of mislabeling, which affects the performance of relation extraction. To obtain richer information from sentences and make full use of the information of entity pairs, we propose the combined networks containing piecewise convolutional neural networks (PCNN) and bidirectional gated recurrent units (BiGRU) with multi-level attention. In this model, PCNN and BiGRU are utilized to obtain local features and sequence information. Then the multi-level attention is proposed to extract the most correlated information, which can affect the assignment of sentence attention weight. And it reduces the influence of wrong labeled instances indirectly. The experimental results show that our method can enhance the representation power of the network and prove the effectiveness of our model compared with several baseline methods.
Visualization Classification and Prediction Based on Data Mining

Abstract The paper aims to solve huge amounts and noise of current internet information. In order to better excavate the user’s interests and provide accurate recommendations, improve the Internet environment and services and accurately analyze and evaluate the behavior of large-scale user groups. This article compares web pages with sentences in natural language, and uses the natural language processing algorithm Word2vec to extract the feature vectors of massive web pages browsed by users. PCA has been reduced to two dimensions, and webpage visualization and logistic regression have been implemented to classify webpages. A series of user clicks on webpages can be viewed as a walk in n-dimensional space. Probabilistic language models and Monte Carlo simulations are used as the next step for users. Click behavior to make predictions. Experiments show that the machine learning processing natural language algorithm used in this paper has a good performance in classification and prediction accuracy.
DADIM: A distance adjustment dynamic influence map model

Influence map (IM) is often used as a decision supporting technology in game artificial intelligence (AI). However, the traditional influence map does not describe dynamic information. Some improved IM models can describe dynamic information, but not accurately enough. When an object moves, it would produce large influence in its moving direction than other directions. Therefore, the influence produce by the object to a location depends on the relation between the location and the object’s moving direction. This paper proposed a dynamic influence map model based on distance adjustment, DADIM. This model produces different influence values in different direction by adjusting the “distance” between two locations. This method can encode dynamic information into the influence map easily. Experiments show this model avoids the weakness of dynamic influence map with location prediction. Compared with other influence maps, this model could improve the performance of the game AI with time complexity being unchanged.
A Fast and Efficient Picking Algorithm for Earthquake Early Warning Application Based on the Variance Piecewise Constant Models

An earthquake warning system, or earthquake early warning system, is a system of accelerometers, seismometers, communication, computers, and alarms that is devised for notifying adjoining regions of a substantial earthquake while it is in progress. This is not the same as earthquake prediction, which is currently incapable of producing decisive event warnings. The implementation of efficient and computationally simple picking algorithm is necessary for this purpose, as well as automatic picking of seismic phases for seismic surveillance and routine earthquake location for fast hypocenter determination. In this paper, a picking method, based on the detection of signals changes in variance, is proposed, taking advantage of a generalized linear model formulation of the investigated problem. An application to simulated data is provided.
Extracting Iso-Disparity Strip Width using a Statistical Model in a Stereo Vision System

Disparity map images, as outputs of a stereo vision system, are known as an effective approach in applications that need depth information in their procedure. One example of such applications is extracting planes with arbitrary attributes from a scene using the concept of iso-disparity strips. The width and direction of strips depend on the plane direction and position in the 3D space. In this paper, a statistical analysis is performed to model the behavior of these strips. This statistical analysis as well as a frequency analysis reveal that for each group of iso-disparity strips, which are corresponding to a single plane in 3D, the width of strips can be represented by an average value superposed by an Additive Gaussian Noise (AGN). This means that a simple averaging technique can significantly reduce the measurement noise in applications such as ground detection using these strips. Results show that the width of iso-disparity strips can be measured with an average precision of 96% using the presented noise model.
Recurrent Attention Dense Network for Single Image De-Raining

The problem of single image rain removal has attracted tremendous attention as the blurry images caused by rain streaks can degrade the performance of many computer vision algorithms. Although deep learning based de-raining methods have achieved a significant success, there are still unresolved issues in terms of the performance. In this work, we propose a novel recurrent attention dense network (RADN) for single image de-raining. In RADN, a region-level attention module is first utilized to identify rain streaks regions for the subsequent removal task. As rain streaks have different sizes and shapes, a modified densely connected convolutional network (DenseNet) with dilation convolutions and reduced channels is developed for an effective feature representation. The rain streaks are removed stage by stage and a Gate Recurrent Unit (GRU) is incorporated to deliver useful information from previous stages to later stages for a better performance. Qualitative and quantitative evaluations on both synthetic and real-world datasets demonstrate that the proposed approach can achieve a remarkable performance in comparison with the state-of-the-art methods for single image rain removal.
E-textile Piano Fabricated using Several Textile Technologies

This paper presents research focused on the development of an e-textile piano made by several textile technologies. The textile piano was designed to combine different ways of contacting, such as embroidery, soldering, gluing, thermo-transfer. A blue cotton fabric was used as a substrate for textile piano. Three different samples of the textile piano were made and tested. Arduino LilyPad USB Plus and Arduino LilyPad SimpleSnap were used as a control unit for designed pianos. The advantages, disadvantages of different technologies and examples are shown and discussed in the paper.
High Capacity Text and Multimedia Data Hiding in Medical Images

The growth of high-speed computer networks and that of the internet, in particular, has increased the simplicity of communication. But this type of improvement in the field of data communication has a high fear of getting data snooped at the time of sending it from one medium to another medium. To overcome this issue steganography (data hiding) place an important role. In this paper, multimedia data have been fixed into a medical image by choosing the substitution method and difference value method because it has become a very essential issue to protect confidentiality. An algorithm for hiding a secret video inside a medical image is proposed. A comparative study between the two methods is also performed. Even though a large number of embedding techniques are reported, a combination of text and multimedia data is not embedded. In addition to that the retrieval process to be error-free.The main application of this method is in the field of medical and military purposes. This method is used in hospitals for reducing the storage space by keeping the patient's records safely under a cover image. This method maintains its transparency even after hiding the data into the host(medical) image.
P <sup>2</sup> KASE A <sup>2</sup> —privacy‐preserving key aggregate searchable encryption supporting authentication and access control on multi‐delegation

Delegation is a technique that allows a subject receiving a delegation (the delegatee) to act on behalf of the delegating subject (the delegator). Although the existing Key Aggregate Searchable Encryption (KASE) schemes support delegation of search rights over any set of ciphertexts using a key of constant-size, two critical issues still should be considered. Firstly, an adversary can intercept the aggregate key or query trapdoor from the insecure communication channels involving the cloud server and impersonate as an authorized user to the server for accessing the data. Secondly, the existing KASE schemes only discuss the delegation of rights from the data owner to other users. However, if a subject receiving a delegation cannot perform the time-critical task on the shared data because of the unavailability, it becomes necessary for the delegatee to further delegate his received rights to another user. In this paper, we propose a novel KASE scheme that allows a fine-grained multi-delegation, i.e., if the attributes of the delegatee satisfy the hidden access policy (defined by the data owner), the delegatee can delegate his received rights to another user, without compromising data privacy. The proposed scheme provides security against the impersonation attack by verifying the user's authentication.
Root-Hadamard transforms and complementary sequences

In this paper we define a new transform on (generalized) Boolean functions, which generalizes the Walsh-Hadamard, nega-Hadamard, 2k-Hadamard, consta-Hadamard and all HN-transforms. We describe the behavior of what we call the root-Hadamard transform for a generalized Boolean function f in terms of the binary components of f. Further, we define a notion of complementarity (in the spirit of the Golay sequences) with respect to this transform and furthermore, we describe the complementarity of a generalized Boolean set with respect to the binary components of the elements of that set.
Robust Federated Learning Under Worst-Case Model

Federated learning provides a communication- efficient training process via alternating between local training and averaging updated local model. Nevertheless, it requires perfectly acquisition of the model which is hard to achieve in wireless communication practically, and the noise will cause serious effect on federated learning. To tackle this challenge, we propose a robust design for federated learning to decline the effect of noise. Considering the noise in communication steps, we first formulate the problem as the parallel optimization for each node under worst-case model. We utilize the sampling-based successive convex approximation algorithm to develop a feasible training scheme, due to the unavailable maxima noise condition and non-convex issue of the objective function. In addition, the convergence rate of proposed design are analyzed from a theoretical point of view. Finally, the prediction accuracy improvement and loss function value reduction of the proposed design are demonstrated via simulation.
Code Smell Detection Tool for Java Script Programs

JavaScript is a client-side scripting language that is widely used in web applications. It is dynamic, loosely-typed and prototype-based with first-class functions. The dynamic nature of JavaScript makes it powerful and highly flexible in almost every way. However, this flexibility may result in what is known as code smells. Code smells are characteristics in the source code of a program that usually correspond to a deeper problem. They can lead to a variety of comprehension and maintenance issues and they may impact fault- and change-proneness of the application in the future. We present TAJS <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">lint</sub> , an automated code smell detection tool for JavaScript programs that is based on static analysis. TAJS <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">lint</sub> includes a set of 14 code smells, 9 of which are collected from various sources and 5 new smells we propose. We conduct an empirical evaluation of TAJS <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">lint</sub> on a number of JavaScript projects and show that TAJS <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">lint</sub> achieves an overall precision of 98% with a small number of false positives. We also study the prevalence of code smells in these projects.