Reviewerly/exHarmony huggingface dataset:
Truncated Fractional-Order Total Variation for Image Denoising under Cauchy Noise

In recent years, the fractional-order derivative has achieved great success in removing Gaussian noise, impulsive noise, multiplicative noise and so on, but few works have been conducted to remove Cauchy noise. In this paper, we propose a novel nonconvex variational model for removing Cauchy noise based on the truncated fractional-order total variation. The new model can effectively reduce the staircase effect and keep small details or textures while removing Cauchy noise. In order to solve the nonconvex truncated fractional-order total variation regularization model, we propose an efficient alternating minimization method under the framework of the alternating direction multiplier method. Experimental results illustrate the effectiveness of the proposed model, compared to some previous models.
DNN Intellectual Property Extraction Using Composite Data

As state-of-the-art deep neural networks are being deployed at the core level of increasingly large numbers of AI-based products and services, the incentive for "copying them" (i.e., their intellectual property, manifested through the knowledge that is encapsulated in them) either by adversaries or commercial competitors is expected to considerably increase over time. The most efficient way to extract or steal knowledge from such networks is by querying them using a large dataset of random samples and recording their output, which is followed by the training of a student network, aiming to eventually mimic these outputs, without making any assumption about the original networks. The most effective way to protect against such a mimicking attack is to answer queries with the classification result only, omitting confidence values associated with the softmax layer. In this paper, we present a novel method for generating composite images for attacking a mentor neural network using a student model. Our method assumes no information regarding the mentor's training dataset, architecture, or weights. Furthermore, assuming no information regarding the mentor's softmax output values, our method successfully mimics the given neural network and is capable of stealing large portions (and sometimes all) of its encapsulated knowledge. Our student model achieved 99% relative accuracy to the protected mentor model on the Cifar-10 test set. In addition, we demonstrate that our student network (which copies the mentor) is impervious to watermarking protection methods and thus would evade being detected as a stolen model by existing dedicated techniques. Our results imply that all current neural networks are vulnerable to mimicking attacks, even if they do not divulge anything but the most basic required output, and that the student model that mimics them cannot be easily detected using currently available techniques.
MineSweeper: a “clean sweep” for drop-in use-after-free prevention

Low-level languages, which require manual memory management from the programmer, remain in wide use for performance-critical applications. Memory-safety bugs are common, and now a major source of exploits. In particular, a use-after-free bug occurs when an object is erroneously deallocated, whilst pointers to it remain active in memory, and those (dangling) pointers are later used to access the object. An attacker can reallocate the memory area backing an erroneously freed object, then overwrite its contents, injecting carefully chosen data into the host program, thus altering its execution and achieving privilege escalation.
Analyzing semantic similarity amongst textual documents to suggest near duplicates

&lt;span&gt;Data deduplication techniques removing repeated or redundant data from the storage. In recent days, more data has been generated and stored in the storage environment. More redundant and semantically similar content of the data occupied in the storage environment due to this storage efficiency will be reduced and cost of the storage will be high. To overcome this problem, we proposed a method hybrid bidirectional encoder representation from transformers for text semantics using graph convolutional network hybrid bidirectional encoder representation from transformers (BERT) model for text semantics (HBTSG) word embedding-based deep learning model to &lt;span&gt;identify near duplicates based on the semantic relationship between text documents. In this paper we hybridize the concepts of chunking and&lt;/span&gt; &lt;span&gt;semantic analysis. The chunking process is carried out to split the documents into blocks. Next stage we identify the semantic relationship between&lt;/span&gt; &lt;span&gt;documents using word embedding techniques. It combines the advantages of the chunking, feature extraction, and semantic relations to provide better results.&lt;/span&gt;&lt;/span&gt;
The phenomena of epidemic crime, deepfakes, fake news, and the role of forensic linguistics

The present study analyses the phenomena of deepfakes and fake news, together with their linguistic fingerprints, to understand how these may influence the public, including in their decision making. Nowadays, linguistic fingerprints are present mostly in digital forms; therefore, forensic linguistics was also recently introduced as a subject and involves the analysis of linguistic fingerprints. The present study provides an insight into the contribution of linguist and forensic linguist experts in the work of investigative authorities. Linguistic fingerprints can convey messages and provide evidence to support an investigation, such concerning the following questions: Who could the perpetrator be? Who could have written the message? The linguist expert can also help develop a profile of perpetrators, including their likely age, sex, ethnicity, or help prove the validity of news versus fake news as well as other attributes of sources. These aspects are all covered in the present study.
A Two-Phase Evolutionary Method to Train RBF Networks

This article proposes a two-phase hybrid method to train RBF neural networks for classification and regression problems. During the first phase, a range for the critical parameters of the RBF network is estimated and in the second phase a genetic algorithm is incorporated to locate the best RBF neural network for the underlying problem. The method is compared against other training methods of RBF neural networks on a wide series of classification and regression problems from the relevant literature and the results are reported.
Comparison of Latent Semantic Analysis and Vector Space Model for Automatic Identification of Competent Reviewers to Evaluate Papers

The assignment of reviewers to papers is one of the most important and challenging tasks in organizing scientific events.A major part of it is the correct identification of proper reviewers.This article presents a series of experiments aiming to test whether the latent semantic analysis (LSA) could be reliably used to identify competent reviewers to evaluate submitted papers.It also compares the performance of the LSA, the vector space model (VSM) and the method of explicit document description by a taxonomy of keywords, in computing accurate similarity factors between papers and reviewers.All the three methods share the same input datasets, taken from real-life conferences and the produced paper-reviewer similarities are evaluated with the same evaluation methods, allowing a fair and objective comparison between them.Experimental results show that in most cases LSA outperforms VSM and could even slightly outperform the explicit document description by a taxonomy of keywords, if the term-document matrix is composed of TF-IDF values, rather than the raw number of term occurrences.
GIS-based urban village regional fire risk assessment and mapping

Fires in residential areas are one of the threats out of 13 disasters in Indonesia. Fires are disasters based on their causes, classified as disasters caused by human negligence. This research aims to identify residential fire incidents, assess fire risk levels, and map the risk level. We used the geographic information system (GIS) analysis approach and direct observation of the study area. The research location was in the Tamansari sub-district in Bandung City. The subdistrict of Tamansari consists of 20 neighborhood units (rukun warga/ RW) with 22,995 people and 6,598 households. We conducted a field survey from December 2019 to March 2020. We used a spatial approach to analyze fire risk in this residential area by using GIS to map urban-village regional fire incidents and assess the risk level. There were four fire hazard variables: population density, building density, building quality, road class. On the other hand, vulnerability variables are based on the community's social parameters: population density, percentage of old age and children under five, people with disabilities, and the population's sex ratio. The hazard and vulnerability maps overlay showed three neighborhood units (rukun warga/ RW) with a high risk of fire, eight RWs with a moderate risk of residential fires, and nine RWs with a low risk of residential fires. The areas with low-risk categories must remain vigilant because the width of the roads in these areas is relatively narrow.
Interpretable instance disease prediction based on causal feature selection and effect analysis

Abstract Background In the big wave of artificial intelligence sweeping the world, machine learning has made great achievements in healthcare in the past few years, however, these methods are only based on correlation, not causation. The particularities of the healthcare determines that the research method must comply with the causality norm, otherwise the wrong intervention measures may bring the patients a lifetime of misfortune. Methods We propose a two-stage prediction method (instance feature selection prediction and causal effect analysis) for instance disease prediction. Feature selection is based on the counterfactual and uses the reinforcement learning framework to design an interpretable qualitative instance feature selection prediction. The model is composed of three neural networks (counterfactual prediction network, fact prediction network and counterfactual feature selection network), and the actor-critical method is used to train the network. Then we take the counterfactual prediction network as a structured causal model and improve the neural network attribution algorithm based on gradient integration to quantitatively calculate the causal effect of selection features on the output results. Results The results of our experiments on synthetic data, open source data and real medical data show that our proposed method can provide qualitative and quantitative causal explanations for the model while giving prediction results. Conclusions The experimental results demonstrate that causality can further explore more essential relationships between variables and the prediction method based on causal feature selection and effect analysis can build a more reliable disease prediction model.
Design of Intelligent Perception Module Based on Wireless Sensor Network and Basketball Sports Attitude

In recent years, along with microelectromechanical (MEMS) technique and wireless body area network (WBAN), wearable health monitoring systems have emerged. With sustainability posture detection technique, people pay more and more attention to mankind posture detection. Human posture detection technique has been widely used in medical, film and television, industry, sports, and other fields. Motion capture is a technique for measuring the motion of moving objects in three-dimensional space with great accuracy. It is the most efficient method for producing computer 3D animation and collecting human motion data. The use of motion capture systems in animation is becoming increasingly common. The problem of a single sensor having a large error in monitoring human motion and attitude is addressed. The use of multisensor data fusion technology is used to propose a human motion pattern recognition method based on data fusion of accelerometer and gyroscope. The system must effectively integrate the information of various sensors in order to achieve the goal of accuracy, timeliness, and reliability processing, and multisensor information fusion systems for various complex application objects are constantly emerging.
The Quantum Condition Space

Abstract The fundamental properties of quantum physics are exploited to evaluate event probabilities with projection measurements. Next, to study what events can be specified by quantum methods, the concept of the condition space is introduced, which is found to be the dual space of the classical outcome space of bit strings. Like the classical outcome space generates the quantum state space, the condition space generates the quantum condition space being the central idea of this work. The quantum condition space permits the existence of entangled conditions having no classical equivalent. In addition, the quantum condition space is related to the quantum state space by a Fourier transform guaranteed by the Pontryagin duality, and therefore an entropic uncertainty principle can be defined. The quantum condition space offers a novel perspective of understanding quantum states with the duality picture. Furthermore, the quantum conditions have physical meanings and realizations of their own and thus may be studied for purposes beyond the original motivation of characterizing events for probability evaluation. Finally, the relation between the condition space and quantum circuits provides insights into how quantum states are collectively modified by quantum gates, which may lead to deeper understanding of the complexity of quantum circuits.
Cryptanalysis of a Hamming Code and Logistic-Map based Pixel-Level Active Forgery Detection Scheme

In this paper, we analyze the security of a fragile watermarking scheme for tamper detection in images recently proposed by S. Prasad et al.The chaotic functions are used in the scheme to exploit its pseudo-random behavior and its sensibility to initial condition and control parameter, but despite that, security flaws have been spotted and cryptanalysis of the scheme is conducted.Experimental results shows that the scheme could not withstand the attack and watermarked images were manipulated without triggering any alarm in the extraction scheme.In this paper, two different approaches of attacks are demonstrated and conducted to break the scheme.This work falls into the context of improving the quality of the designed cryptographic schemes taking into account several cryptanalysis techniques.
Pride and Professionalization in Volunteer Moderation: Lessons for Effective Platform-User Collaboration

While most moderation actions on major social platforms are performed by either the platforms themselves or volunteer moderators, it is rare for platforms to collaborate directly with moderators to address problems. This paper examines how the group-chatting platform Discord coordinated with experienced volunteer moderators to respond to hate and harassment toward LGBTQ+ communities during Pride Month, June 2021, in what came to be known as the "Pride Mod" initiative. Representatives from Discord and volunteer moderators collaboratively identified and communicated with targeted communities, and volunteers temporarily joined servers that requested support to supplement those servers' existing volunteer moderation teams. Though LGBTQ+ communities were subject to a wave of targeted hate during Pride Month, the communities that received the requested volunteer support reported having a better capacity to handle the issues that arose. This paper reports the results of interviews with 11 moderators who participated in the initiative as well as the Discord employee who coordinated it. We show how this initiative was made possible by the way Discord has cultivated trust and built formal connections with its most active volunteers, and discuss the ethical implications of formal collaborations between for-profit platforms and volunteer users.
Generative Adversarial Network for Synthetic Imaging Data of Sub-optimal Conditions in Photovoltaic Panels

Image detection technology is currently used to perform inspection and detection of defects in solar panels. Deep learning models are typically used to process visual light images, thermal or infrared images. Building machine learning models takes miles or millions of images. Currently, there are few freely available or freely accessible image data sets that contain suboptimal conditions in solar panels. This paper proposed an Adversary Generative Network (GAN) model to build a data set of photovoltaic panels under suboptimal conditions for Deep Learning training. We use a data set created at the Tecnológico de Costa Rica to generate an adversary neural network model. In addition, we evaluate our model using a convolutional neural network that classifies whether or not a visual light image or thermal images contains suboptimal conditions.
Random-Valued Impulse Noise Detection and Removal based on Local Statistics of Images

Random-valued impulse noise removal from images is a challenging task in the field of image processing and computer vision.In this paper, an effective three-step noise removal method was proposed using local statistics of grayscale images.Unlike most existing denoising algorithms that assume the noise density is known, our method estimated the noise density in the first step.Based on the estimated noise density, a noise detector was implemented to detect corrupted pixels in the second step.Finally, a modified weighted mean filter was utilized to restore the detected noisy pixels while leaving the noise-free pixels unchanged.The noise removal performance of our method was compared with 10 well-known denoising algorithms.Experimental results demonstrated that our proposed method outperformed other denoising algorithms in terms of noise detection and image restoration in the vast majority of the cases.
The Development of Learning Solar System Augmented Reality for Elementary School

Along with today's technological developments, applications can be used as a medium of learning. This study aims to create a learning application about the solar system by utilizing technology Augmented Reality which is expected to be used to support the Natural Science (IPA) learning program by an elementary school educational institution to make the teaching and learning system more creative and interactive. In this study, the method augmented reality used is Image Based Tracking, which uses images as markers. The tools that help in the making of this AR are the Vuforia SDK which is used as a storage area marker. By doing this research, the results obtained in the form of the Solar System Learning application Augmented Reality that runs on the platform Android and testing respondents using the method User Acceptance Testing (UAT)with a value of 73.5%.
Event-Triggered, Adaptive, Exponentially Asymptotic Tracking Control of Stochastic Nonlinear Systems

This paper investigates the problem of event-triggered, adaptive, asymptotic tracking control for a class of non-strict feedback stochastic nonlinear systems with symmetrical structures and sensor faults. Based on the negative exponential function, the event-triggered adaptive tracking control strategy deals with the problem of exponentially asymptotic convergence for the first time. The radial basis function neural network (RBFNN) mechanism addresses uncertain factors and unknown external disturbances in the system. The developed strategy ensures that all the signals of the closed-loop system are semi-globally uniformly bounded in probability, and that the tracking error can exponentially converge to zero. Finally, a simulation example demonstrates the effectiveness of the proposed method.
Perovskite tetragonality modeling for functional properties enhancement using Newtonian search based support vector regression computational method

Tetragonality occurs as a result of stretching the crystal structural lattice of perovskite along one of its lattice vectors such that the three axes are mutually perpendicular with two of the axes having equal lengths. This tetragonality distortion easily triggers functional properties such as pyroelectricity, ferroelectricity, capacitance and piezoelectricity among others, while synthesizing functional ceramics for a particular application. This work addresses and circumvents the challenges of experimental stress involved in functional ceramics synthesis by developing Newtonian search based support vector regression (GSB-SVR) model for perovskite tetragonality prediction using dopants concentration and ionic radii as the model predictors. The performance of proposed GSB-SVR model is compared with the existing method and better performance is obtained. The influence of lanthanides and zirconium incorporation on functional ceramics on the material tetragonality is also modeled by the developed GSB-SVR model. The precision of the developed model, its easily fetched predictors and pre-laboratory ability to effectively and efficiently model the perovskite tetragonality are of high importance in tailoring and enhancing functional properties of materials for desired applications.
Evaluation of Re-identification Risk using Anonymization and Differential Privacy in Healthcare

In the present scenario, due to regulations of data privacy, sharing of data with other organization for research or any medical purpose becomes a big hindrance for different healthcare organizations.To preserve the privacy of patients seems like a crucial challenge for Healthcare Centre.Numerous techniques are used to preserve the privacy such as perturbation, anonymization, cryptography, etc. Anonymization is well known practical solution of this problem.A number of anonymization methods have been proposed by researchers.In this paper, an improved approach is proposed which is based on k-anonymity and differential privacy approaches.The purpose of proposed approach is to prevent the dataset from re-identification risk more effectively from linking attacks using generalization and suppression techniques.
Két eljárás magyar nyelvű versek metrumának gépi felismertetéséhez

This paper presents two methods for the machine recognition of meter in Hungarian poetry, one for quantitative and another for qualitative meter. The algorithm analysing the former classifies the poems as dactylic, anapestic, iambic or trochaic, and defines the degree of regularity of the rhythm in the poems. The algorithm detecting the Hungarian qualitative meter returns the number of syllables of each beat. The paper also demonstrates the applicability of the two algorithms by using a test corpus containing the poems of eleven authors written between 1838 and 1941.
A HYBRID APPROACH TO DETECT SECURITY VULNERABILITIES IN WEB APPLICATIONS

The presence of security flaws allows deceitful operators to exploit web application weaknesses. The researcher brings a novel vulnerability assessment technique in this study that can enhance exposure detection rates while also improving efficiency by lowering the number of test results that reports the presence of a condition wrongly and tests result that implies the absence of a condition when it is actually present. The purpose of the experiment is on a cutting-edge tool that uses a hybrid method that combines white-box and black-box testing practices. The amalgamation in building the hybrid algorithm is not done blindly as it is based on extraordinary aspects like optimization and complexity amid others to make bigger effectivity. The algorithm viably identifies SQL injections, XSS injection and can be utilized in any genuine application that run on a web server, wherever the client and the database interrelates. Crawling and parsing to discover vulnerabilities are part of the scanning process. The process is done repeatedly until all vulnerabilities have been discovered. A prototype was done to test and validate the hybrid method. Simulation was done using a tool developed in Python and the researcher included in this paper a comparison table and graph that pits the new scanner versus two other web - based scanners.