Reviewerly/exHarmony huggingface dataset:
Deceiving Deep Neural Networks-Based Binary Code Matching with Adversarial Programs

Deep neural networks (DNNs) have achieved a major success in solving challenging tasks such as social networks analysis and image classification. Despite the prosperous development of DNNs, recent research has demonstrated the feasibility of exploiting DNNs using adversarial examples, in which a small distortion is added into the input data to largely mislead prediction of DNNs.Determining the similarity of two binary codes is the foundation for many reverse engineering, re-engineering, and security applications. Currently, the majority of binary code matching tools are based on DNNs, the dependability of which has not been completely studied. In this research, we present an attack that perturbs software in executable format to deceive DNN-based binary code matching. Unlike prior attacks which mostly change non-functional code components to generate adversarial programs, our approach proposes the design of several semantics-preserving transformations directly toward the control flow graph of binary code, making it particularly effective to deceive DNNs. To speedup the process, we design a framework that leverages gradient- or hill climbing-based optimizations to generate adversarial examples in both white-box and black-box settings. We evaluated our attack against two popular DNN-based binary code matching tools, asm2vec and ncc, and achieve reasonably high success rates. Our attack toward an industrial-strength DNN-based binary code matching service, BinaryAI, shows that the proposed attack can fool remote APIs in challenging black-box settings with a success rate of over 16.2% (on average). Furthermore, we show that the generated adversarial programs can be used to augment robustness of two white-box models, asm2vec and ncc, reducing the attack success rates by 17.3% and 6.8% while preserving stable, if not better, standard accuracy.
Reflection Removal Using Multiple Polarized Images with Different Exposure Times

When we take a photograph through glass windows or doors, the foreground scene is reflected in the captured image. The reflected components overlap with the background scene and make object recognition and identification more difficult. This paper proposes a novel reflection removal method using multiple polarized images taken with different exposure times. To achieve a high accuracy reflection removal in high dynamic range scenes, in which photographed images have under-/over-exposed pixels, we introduce a minimization problem of weighted nonnegative matrix factorization (WNMF) with total variation regularization. To solve this minimization problem, we also introduce an alternating optimization scheme with the alternating direction method of multipliers (AO-ADMM). The advantages of the proposed method over some conventional methods are demonstrated in experiments of reflection removal using real-world images.
Learning Contextual Embedding Deep Networks for Accurate and Efficient Image Deraining

The existing state-of-the-art deraining methods rely on a bulky network structure to accurately capture rain streaks, resulting in prohibitive memory consumption and computation cost. In addition, most of them destroy background details along with the process of rain removal. This paper presents a simple but effective network to address the above problems. Firstly, we developed a lightweight model, called Cross-Scale Contextual Embedding Network (CSCEN), to achieve the cross-scale embedding of rain streaks in different scale contexts. We also introduced a new deeply-supervised rain detection loss, making the training process of the intermediate layers be direct and transparent thus the transfer of rain information layer-by-layer would be under appropriate supervision. Qualitative and quantitative experiments show that the proposed network is superior to the advanced deraining methods and inherits better generalization in real-world rain removal.
LabXscape: A Prototype for Enhancing Player Experience in Cross-Reality Gameplay

Interest in multiplayer games that allow players to connect and play together using different technologies, such as virtual or augmented reality (VR/AR) has increased. Research has shown that in cross-reality gaming experiences (eg. where there are differences in players' abilities, user interface (UI), and methods of interaction) it is possible to achieve an enhanced player experience (PX) through various interdependencies. However, most of the previous work focuses on co-located scenarios, where the space and proximity of the players are local and utilized. In this study we present a cross-reality prototype called LabXscape. Through the prototype we are researching how asymmetries of space, UI, methods of interaction, information access, and narrative impact the PX for players using different technologies (eg. VR, AR, PC). In this cross-reality prototype, players can connect with different devices found on the Milgram and Kishino's Reality-Virtuality Continuum [1]. Their interactions, movements, and information influence and are shared with each other, creating a cross-reality experience [2]. Our observations reveal that there are factors that allow non-VR players to have as engaging experience as VR players, despite using a less immersive device.
Noe: Norm Emergence and Robustness Based on Emotions in Multiagent Systems

Social norms characterize collective and acceptable group conducts in human society. Furthermore, some social norms emerge from interactions of agents or humans. To achieve agent autonomy and make norm satisfaction explainable, we include emotions into the normative reasoning process, which evaluates whether to comply or violate a norm. Specifically, before selecting an action to execute, an agent observes the environment and infers the state and consequences with its internal states after norm satisfaction or violation of a social norm. Both norm satisfaction and violation provoke further emotions, and the subsequent emotions affect norm enforcement. This paper investigates how modeling emotions affect the emergence and robustness of social norms via social simulation experiments. We find that an ability in agents to consider emotional responses to the outcomes of norm satisfaction and violation (1) promotes norm compliance; and (2) improves societal welfare.
An Empirical Feature Selection Approach for Phishing Websites Prediction with Machine Learning

The proportion of phishing attacks has soared worldwide amid the Covid-19 crisis since people started using the internet more actively. Browsing phishing websites can cause immense damage to user privacy. In this article, investigating the attributes of URLs to detect the possible legitimate and phishing websites, we presented a feature selection framework that improves the efficacy of machine learning models. In feature selection, considering the filter and wrapper method, we introduced an empirical hybrid framework that comprises two phases. To derive the accumulative feature subset, in the early stage, we performed a function perturbation ensemble using four filter techniques. Finally, to select the best features, we employed the wrapper method, in which the feature subset is passed into a statistical model to perform a p-value test (conforming 95% confidence). We used two phishing datasets, and applying this proposed hybrid ensemble framework, we derived only 45.95% of the initial features from each dataset. Thereafter, the optimized (hyperparameters) models such as Artificial Neural Network, XGBoost Classifier, Random Forest Classifier are applied to conduct 10-folds cross-validation on Data-I, the XGBoost Classifier outran with the accuracy of 96.08%. Besides, the XGBoost model performed prediction on Data-II, achieved a notable accuracy of 97.29%.
High Efficiency Image Correction for Low Power Underwater Dron

In this paper, a high efficiency underwater image correction method based on the evaluation of structural similarity and real-time performance of a traditional FUnIEGAN algorithm is proposed. The proposed method can be implemented on an embedded GPU module (Jetson Xavier NX). The proposed method also enhanced the color correction performance by a data generation methodology. In addition, AMP is used to reduce computational redundancy, which enables fast operation. As a result, object detection results are improved, and FPS is increased to 41.12.
The Case for Algorithmic Marketing

This chapter discusses the philosophy behind algorithmic marketing. This is an umbrella term for techniques that aspire to make marketing efforts more personalized and effective. The meaning of customer centricity and its implications for algorithmic marketing are discussed at length.
National Customs and Traditions in the Educational Process for Primary School Students

The main direction of the educational process is to develop the education system in accordance with the strategic directions of social and economic development of the republic, integrating it into the world educational space, preserving its national essence through the use of national customs and traditions, as well as cultural values, creating conditions for the formation of a personality in the national spirit, development of a high level of outlook and creative potential of the personality, cognitive competences. The implementation of these tasks requires a review of the content of the educational process in the country's schools from a new methodological standpoint, based on the use of elements of national customs and traditions. Such a new methodological system shows the need for radical changes in the traditional educational process, the development of a creative approach to teaching, and the updating of the content of education on a national basis.In the modern period of the development of society in the educational process of educational institutions the principle of education is implemented, taking into account the comprehensive development of students, such a system of education and upbringing provides an opportunity for the formation of cultural and ethnic identity, it is aimed at an in-depth study of the spiritual culture of the people and the ability to connect it with modern values. This system of education and training is based on the link between national customs and traditions (customs and traditions related to children's upbringing, household customs and traditions, social customs and traditions) and the educational process.
Reachability Based Trajectory Generation Combining Global Graph Search in Task Space and Local Optimization in Configuration Space

In this paper, we propose a trajectory planning framework for a robot that exploits a pre-computed database of end-effector trajectories as the guidance of optimization-based inverse kinematics. We constructed a reachable graph of a robot offline, which represents feasible end-effector paths with corresponding configurations. When performing the online trajectory planning, we applied A* search to the reachable graph to find a feasible path between input start and goal globally in the task space. Its cost function has the separated term dependent on the robot, which comes from the manipulability of configurations preserved in the reachable graph, and that is dependent on the environment. Then, we solve optimization-based inverse kinematics to generate an optimal joint trajectory while utilizing the end-effector trajectory and its corresponding configurations as the guidance to avoid local optimum. We evaluated our framework quantitatively by comparing it with existing methods to confirm that it achieved a high success rate and quality of results while suppressing its computational time. We also qualitatively proved its practicality by applying it to the material handling task in the real-world. This result shows that it improved the performance of the optimization-based inverse kinematics avoiding local optimum and applicability to the different environments of the pre-computed motion database.
A Study on the Virtual Delivery of Cultural Courtesy: AR Services for Packaging

For a long time, gift packaging has been a means of conveying the sender’s feeling and message just as important as the gift itself. This has been a part of social communication mean for a long time especially in East Asian societies. Therefore, packaging used to be a form of cultural communication which carries much personal and social meaning by using all kinds of material. In the twenty-first century, under the social atmosphere of sustainability, such cultural communication is regarded as vain, and the use of recycled packaging materials or the omission of packaging itself is increasingly common. Also, due to the COVID-19 pandemic, it is even more difficult to meet face-to-face and convey thankful and beloved feelings. The aim of this research is to study how non-material communication can contribute to people’s emotional satisfaction within the object exchange. The purpose is to develop a virtual packaging service using augmented reality (AR) technology, while reflecting people’s cultural and personal needs, as a sustainable user experience (UX) approach. This research analyzes user perceptions and attitudes towards virtual packaging through a quantitative study of 200 participants and evaluates users’ acceptability and cultural needs for immaterial packaging. Based on this, a new AR packaging service is developed which can bring psychological and spiritual satisfaction to people, unlike virtual messages and material packaging which have simple delivery purposes. This research will promote the use of AR technology as a cultural bridge between physical packaging and virtual communication delivering people’s hopes and cheerful minds, as well as a factor for sustainable development and the green economy.
Cluster Decomposition of the Body of Evidence

Two algorithms for the body of evidence clustering are developed and studied in this paper. The first algorithm is based on the use of the distribution density function of conflicting focal elements of the body of evidence. The second algorithm is similar to the k-means algorithm, but it uses the external conflict measure instead of the metric. It is shown that cluster decomposition can be used to evaluate the internal conflict of the body of evidence.
Pattern Matching Under $$\textrm{DTW}$$ Distance

In this work, we consider the problem of pattern matching under the dynamic time warping ( $$\textrm{DTW}$$ ) distance motivated by potential applications in the analysis of biological data produced by the third generation sequencing. To measure the $$\textrm{DTW}$$ distance between two strings, one must “warp” them, that is, double some letters in the strings to obtain two equal-lengths strings, and then sum the distances between the letters in the corresponding positions. When the distances between letters are integers, we show that for a pattern P with m runs and a text T with n runs: As a corollary of the second result, we also derive an approximation algorithm for general metrics on the alphabet.
Optimizing Parameters of Tone-Mapping Operation for Maximizing an Objective Function

Tone-Mapping Operators (TMOs) are usually used to produce Low Dynamic Range (LDR) versions of the High Dynamic Range (HDR) images that encapsulate better perceptual quality. Many TMOs have been proposed in literature as displaying HDR images on LDR screen is one major requirement, as long as LDR displays are in use. On the other hand, there are also many high performance current Computer Vision System (CVS) applications in use such as those for Optical Character Recognition (OCR), Automated Driver Assistance System (ADAS), etc. that are based on processing LDR images. These are incapable of processing the vast dynamic range of HDR imaging. Many times performance of these systems is excellent when images with optimal illumination are used and degrade badly for underexposed or overexposed images. HDR imaging do provide the avenue to combat non-optimal lighting condition and generate LDR images that maximizes image features and quality that is best suited for the targeted CVS application. In this work we have used a high performance state-of-the-art TMO (ATT) algorithm and has designed an automated training based mechanism to tune its parameters for optimizing the performance of a popular and free online available OCR engine Tesseract. Here we focus on a specific scenario of vision-based industrial product inspection. LDR imaging in a batch processing of product-line may suffer from glare, light source in background or poor illumination due to variation in position, orientation and movement of the product on conveyor belt based system with respect to camera and light source. HDR imaging can help reduce false rejection if optimal LDR images can be obtained for text reading by the OCR engine. Our proposed learning-based Application Specific TMO (ASTMO) significantly improves the OCR accuracy for diverse illumination conditions. Though we are reporting early results but they can pave the way for further research in this direction.
A Lightweight Backbone Used for Scene Text Recognition

This paper presents AutoMLPMixer which is a lightweight backbone based on the mixer-multi-layer percep-trons(MLPMixer) and automated machine learning. Secondly, in order to deal with parameter surging caused by a large dictionary in Chinese scene text recognition, this paper introduces grouped linear structure to process the embedding layers and prediction layers of the model. Compared with ResNet, the AutoMLPMixer can effectively reduce parameters by 16.4%. By introducing a grouped linear structure to solve the oversized dictionary problem, we can further reduce parameters by 14.89M in scene text recognition. Under coexist condition of both AutoMLPMixer and grouped linear structure, the model image processing speed increased by 29%. The code and models will be made publicly.
Recognition of Chinese Medical Named Entity Using Multi-word Segmentation Method

In recent years, with the continuous increase of electronic medical record data, people have begun to pay attention to medical named entity recognition. Medical named entity recognition can transform the free text in an electronic medical record from information to data, so it has high research value and application value. However, most of the current deep learning methods use character-level segmentation for semantic feature extraction, which leads to the loss of local dependencies between characters. In order to solve this problem, this paper proposes a multi-word segmentation method, which mainly uses “jieba” to segment as many sentences as possible, and then uses the BILSTM model to train the words after segmentation. The purpose of this method is to enrich the semantic features that can be used in the model learning process, so as to further improve the entity recognition ability of the model. Experimental results show that this method can improve the performance of the model.
A Time-Frequency-Domain Method of TDOA Estimation for Passive Radar Localization

A time-frequency-domain method of TDOA estimation is proposed in this paper. Time-domain correlation and frequency-domain estimation are used together to improve the accuracy of estimation. In this method, a new strategy consists of time shifting, slicing and least squares approximation is applied to eliminate the limitation of radar sampling frequency. A Monte Carlo simulation is conducted to evaluate the performance of this method. Additionally, the typical radar signal of AN/SPY-1 is chosen to be testing signal. The result indicates that the time-frequency-domain method could obtain 71 % performance improvements. In addition, it proves to be an effective solution to TDOA estimation for passive radar localization.
Visual Exploration of Rheological Test Results from Soft Materials

Rheological testing of soft materials aims to characterize them through a complex combination of elastic and viscous properties. Its fundamental idea is to apply a force and register the resulting deformation of the material. These tests have applications in a wide range of domains from adjusting oil drilling muds for different rock strata to developing fast-drying paints and quality control in food processing. Yet, visual-interactive means for the explorative analysis of the resulting data are scarce if they exist at all. Hence, we present <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$VAOS$</tex> - an open-source visual analysis software for exploring the complex rheological properties of soft materials subjected to oscillatory tests. <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$VAOS$</tex> offers a specific focus on the visual analysis across multiple samples and test runs at once, which is currently not well supported by existing software. We showcase the utility of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$VAOS$</tex> in a usage scenario and two expert interviews, effectively demonstrating the improved ability of scientists to swiftly obtain a rheological fingerprint of complex materials.
PMGNN: A Pioneer-Master Graph Neural Network for Graph Classification

Graph classification task has attracted increasing interest in recent years due to its application in a wide range of domains, and graph neural networks (GNNs), especially the attention-based ones, are proved to be highly effective in this task. However, existing attention-based GNNs have a limited receptive field since the attention weights are computed only based on the information obtained from the direct neighbor nodes. Moreover, they cannot flexibly adjust vectors for attention weight computation considering both current and history node embeddings. In this paper, we propose a novel graph neural network named Pioneer-Master Graph Neural Network (PMGNN) to achieve a better attention mechanism, which is composed of two modules. The first is a pioneer network, which utilizes a GNN to collect context information from multi-hop neighborhoods to increase the receptive field and keep the history node embeddings. The second module is a master network, which exploits the hierarchical attention module to generate the global view from the context information, and combines the global view with the instant view to achieve a better attention mechanism. Extensive experimental results on four real-world datasets demonstrate the effectiveness of our model compared with a series of state-of-the-art baselines.
Place of the discipline "Russian oral folk art" in the modern educational space: the pedagogical model of teaching focused on the student's personality

The purpose of the study is to determine the place of the classical humanitarian discipline "Russian oral folk art" in the university educational space in the aspect of an innovative pedagogical teaching model. The article analyzes components of the course and formed competencies of the student's personality. For the first time, it is shown how this model combines traditions developed by folklorists of the Bashkir State University (professors Lev Grigorievich Barag and Boris Gaiseevich Akhmetshin, associate professor Lyudmila Ivanovna Bryantseva), and modern approaches, forms, innovations. The experience introduced into the learning process, based on the scientific foundations of domestic pedagogy, as well as the system of goal-setting and competencies, is described. The conclusions are illustrated with examples from the teaching practice of the author. As a result, a model was developed of teaching the discipline "Russian oral folklore" allows students to master the subject deeper and better, makes it attractive and personally close to them, which is confirmed by surveys of the student audience. The conclusions and observations obtained can be widely and flexibly used in university pedagogical practice.