Reviewerly/exHarmony huggingface dataset:
Representing interlingual meaning in lexical databases

Abstract In today’s multilingual lexical databases, the majority of the world’s languages are under-represented. Beyond a mere issue of resource incompleteness, we show that existing lexical databases have structural limitations that result in a reduced expressivity on culturally-specific words and in mapping them across languages. In particular, the lexical meaning space of dominant languages, such as English, is represented more accurately while linguistically or culturally diverse languages are mapped in an approximate manner. Our paper assesses state-of-the-art multilingual lexical databases and evaluates their strengths and limitations with respect to their expressivity on lexical phenomena of linguistic diversity.
Detection of tamper forgery image in security digital mage

Every industry that uses digital photos is concerned about image security. Forensics and public safety have long relied on suspect photos, crime scene photos, biometric photos, and other images. As digital imaging has advanced, the use of digital images iun this field has grown significantly. While digital image processing has helped to develop many new approaches in forensic research, it has also simplified image manipulation. The public availability of various snipping image manipulation software has made digital image validity a problem. It's used as strong evidence in a variety of crimes, as well as documentation for a variety of reasons. The advancement of photo processing and editing software has simplified and made it more accessible to create and modify photographs. The most common types of picture forgery are copy-move forgery and splicing images. To conceal or display an error scenario, a portion of a photograph is duplicated and pasted further in the photograph, and splicing an image means two images in one image. This research looks at different types of digital image forgeries as well as forgery detection software. A review of existing approaches for detecting faked images was conducted.
A Machine Learning Study of High Robustness Quantum Walk Search Algorithm with Qudit Householder Coins

In this work, the quantum random walk search algorithm with a walk coin constructed by generalized Householder reflection and phase multiplier has been studied. The coin register is one qudit with an arbitrary dimension. Monte Carlo simulations, in combination with supervised machine learning, are used to find walk coins that make the quantum algorithm more robust to deviations in the coin’s parameters. This is achieved by introducing functional dependence between these parameters. The functions that give the best performance of the algorithm are studied in detail by numerical statistical methods. A thorough comparison between our modification and an algorithm, with coins made using only Householder reflection, shows significant advantages of the former. By applying a deep neural network, we make a prediction for the parameters of an optimal coin with an arbitrary size and estimate the algorithm’s stability for such a coin.
Declarative static analysis for multilingual programs using CodeQL

Summary Declarative static program analysis has become one of the widely‐used program analysis techniques. Declarative static analyzers perform three steps: creating databases of facts from program source code, evaluating rules to generate new facts, and running queries over facts to extract all information related to specific properties via query systems. Declarative static analyzers can easily target diverse programming languages by modifying only databases and rules for new languages. Because query systems are independent of programming languages, they are reusable for new languages. However, even when declarative analyzers support multiple programming languages they do not currently support the analysis of multilingual programs written in two or more programming languages. We propose a systematic methodology that extends a declarative static analyzer supporting multiple languages to support multilingual programs as well. The main idea is to reuse existing components of the analyzer. Our approach first generates a merged database of facts, consisting of multiple logical language spaces. It allows existing language‐specific rules to derive new facts for the corresponding language from the facts in the corresponding language space. Then, it defines language‐interoperation rules that handle the language interoperation semantics. Finally, it uses the same query system to get analysis results leveraging the language interoperation semantics. We develop a proof‐of‐concept declarative static analyzer for multilingual programs by extending CodeQL, which can track dataflows across language boundaries. Our evaluation shows that the analyzer successfully tracks dataflows across Java‐C and Python‐C language boundaries and detects genuine interoperation bugs in real‐world multilingual programs.
A Framework for Understanding Cognitive Biases in Technical Communication

Purpose: The communication of technical information is often susceptible to cognitive biases. Technical communicators need to understand cognitive biases and know how to tackle them accordingly. This article devises a framework of principles that provides technical communicators an operable affordance and a vocabulary to approach cognitive biases and to communicate empathetically. Method: I review a vast body of literature in technical communication with a focus on problems caused by cognitive biases. This work reveals significant problems in information visualization that can provide for a nuanced discussion on cognitive biases in technical communication. Using these problems as a guide, I draw upon cognitive theories in how people use information, the prospect theory about how people make decisions, and the self-determination theory about how such decisions are influenced by the social context. I then assemble a framework of principles that illuminates the workings of cognitive biases. I extrapolate sample questions that technical communicators can use to examine cognitive biases in information visualization and technical communication. Results: The framework of principles explains how cognitive biases affect technical communication. These principles are useful for gaining a deeper understanding of users from a cognitive bias perspective and optimizing for empathetic communication. Conclusion: Technical communicators and users are prone to cognitive biases. The framework of principles, cognitive biases, and sample questions presented in this article provide technical communicators a new lens to examine their work and improve user experience.
A deep learning model to detect foggy images for vision enhancement

Fog limits meteorological visibility, posing a significant danger to road safety. Poor visibility is considered as a significant contributor to road accidents in foggy weather conditions. Regardless, image defogging techniques can only work with foggy images. In a real-time system, however, defogging foggy images becomes difficult if they are not identified as foggy or clear. Because we cannot rely on human vision to distinguish between foggy and clear pictures, we need a robust model that classifies the input image as foggy or clear based on some features. This paper proposes a robust Deep Learning (DL) model based on Convolutional Neural Network (CNN) for classifying the input as foggy and clear. The proposed Deep Neural Network (DNN) architecture is efficient and precise enough to classify images as foggy or clear, with a training time complexity of O(n2) and a prediction time complexity of O(n). The experimental results reveal promising results in both qualitative and quantitative assessments. The model has an accuracy of 94.8%, precision of 91.8%, recall of 75.8% and F1 score of 80.3% when evaluated on the SOTS dataset, indicating that it might be utilized to mitigate the safety risk in vision enhancement systems.
Trace element composition and genesis mechanism of the Fuli Pb-Zn deposit in Yunnan: LA-ICP-MS and in situ S-Pb isotopic constraints

The Fuli Pb-Zn deposit in Yunnan is located in the southeast of the Sichuan-Yunnan-Guizhou (SYG) Pb-Zn metallogenic province in South China. Lead and zinc reserves total approximately 0.3 million tons with an average grade of 18.68% Pb+Zn. The stratiform ore occurs in the interlayer fracture zone of the middle Permian Yangxin formation dolomite. The main sulfides of the Fuli Pb-Zn deposit consist of sphalerite, galena, and pyrite, while dolomite and calcite are the main gangue minerals. Mineralization exhibits massive, disseminated, vein and breccia textures. Sphalerites of two colors (black and red) have been identified in the Fuli deposit. LA-ICP-MS analysis revealed that the black and red sphalerites were enriched in Cd, Cu, Ga, and Ge and depleted in Fe, Mn, and In to varying degrees. The aforementioned elements exhibit homogeneous patterns in the LA-ICPMS time resolution profiles, which is consistent with variations in the concentrations of major elements like Zn and S. This indicates that these elements may occur in sphalerite as a result of isomorphous substitution. However, elements such as As, Sb, Pb, and Ag fluctuate greatly in the LA-ICPMS time resolution profiles, suggesting that these elements may exist as fine inclusions. Thus, the different colors of the Fuli sphalerite may be attributed to various elements such as Ni, Cu, and Ga; Ni and Cu result in purple Sp, Cu renders sphalerite red, and Ga imparts a yellow color. The sulfur isotope compositions of the two sphalerites exhibit little variation, with δ 34 S values ranging from 15.57‰ to 16.91‰, indicating the enrichment of 34 S. These results are consistent with the sulfur isotopic compositions of Permian marine sulfates, indicating that thermochemical sulfate reduction was the main source of the reduced sulfur in the hydrothermal fluids. In situ Pb isotopic composition analysis revealed 208 Pb/ 204 Pb, 207 Pb/ 204 Pb, and 206 Pb/ 204 Pb values for galena in the range of 38.5–38.651, 15.666–15.733, and 18.539–19.124, respectively. The in situ Pb isotopic ratios of most galenas plot on the field of the basement metamorphic rocks of the Kunyang Group. These in situ Pb isotopic signatures reveal that the metallogenic metals are mainly derived from crustal basement. The findings of this study suggest that the Fuli Pb-Zn deposit is a MVT Pb-Zn deposit controlled by the interlayer compressional structure with characteristics of carbonate-hosted, epigenetic, simple mineral symbiosis, high Pb-Zn grade, and abundant Cd, Ga, and Ge along with other dispersed elements.
Differentiating depression using facial expressions in a virtual avatar communication system

Depression has a major effect on the quality of life. Thus, identifying an effective way to detect depression is important in the field of human-machine interaction. To examine whether a combination of a virtual avatar communication system and facial expression monitoring potentially classifies people as being with or without depression, this study consists of three research aims; 1) to understand the effect of different types of interviewers such as human and virtual avatars, on people with depression symptoms, 2) to clarify the effect of neutral conversation topics on facial expressions and emotions in people with depression symptoms, and 3) to compare verbal and non-verbal information between people with or without depression. In this study, twenty-seven participants-fifteen in the control group and twelve in the depression symptoms group-were recruited. They were asked to talk to a virtual avatar and human interviewers on both neutral and negative conversation topics and to score PANAS; meanwhile, facial expressions were recorded by a web camera. Facial expressions were analyzed by both manual and automatic analyses. In the manual analysis, three annotators counted gaze directions and reacting behaviors. On the other hand, automatic facial expression detection was conducted using OpenFace. The results of PANAS suggested that there was no significance between different interviewers' types. Furthermore, in the control group, the frequency of look-downward was larger in negative conversation topics than in neutral conversation topics. The intensity of Dimpler was larger in the control group than in the depression symptoms group. Moreover, the intensity of Chin Raiser was larger in neutral conversation topics than in negative conversation topics in the depression symptoms group. However, in the control groups, there was no significance in the types of conversation topics. In conclusion, 1) there was no significance between human and virtual avatar interviewers in emotions, facial expressions, and eye gaze patterns, 2) neutral conversation topics induced less negative emotion in both the control and depression symptoms group, and 3) different facial expressions' patterns between people with, or without depression, were observed in the virtual avatar communication system.
Selective quantum ensemble learning inspired by improved AdaBoost based on local sample information

Abstract In ensemble learning, random subspace technology not only easily loses some important features but also easily produces some redundant subspaces, inevitably leading to the decline of ensemble learning performance. In order to overcome the shortcomings, we propose a new selective quantum ensemble learning model inspired by improved AdaBoost based on local sample information (SELA). Firstly, SELA combines information entropy and random subspace to ensure that the important features of the classification task in each subspace are preserved. Then, we select the base classifier that can balance accuracy and diversity among a group of base classifiers generated based on local AdaBoost in each iteration. Finally, we utilize the quantum genetic algorithm to search optimal weights for base learners in the label prediction process. We use UCI datasets to analyze the impact of important parameters in SELA on classification performance and verify that SELA is usually superior to other competitive algorithms.
Interactive generation of new knowledge based on automatic means of logical inference

The work relates to the field of knowledge representation and algorithmization of their processing. To describe the subject of consideration by a set of statements and their processing, logical means of the first order type are used. Unlike the well-known achievements in the field of automatic proof (AP) in formal axiomatic theories, the automatic text synthesis of statements is complicated by the poor formalizability of the value of knowledge concept contained in the text. Based on the development of the calculus of positively-formed standardized formulas (pfs-formulas) the possibilities of analysis automation of the description of the subject matter and interactive generation of meaningful statements to improve this description are investigated. The proposed method of interactive knowledge generation integrates the capabilities of AP with algorithmized human-machine synthesis of new knowledge texts. AP in the calculus of pfs-formulas helps a person not only in the analysis of properties, but also in the choice of the required properties of the description of the subject matter. When the absence of a property desirable for a person in the analyzed knowledge is revealed by means of AP, the hypotheses is formed that logically guarantees the presence of the required property. Variants of hypotheses are automatically synthesized in a preliminary form, which are then refined interactively to improve the content of the generated text of new knowledge. The rules of synthesis are justified. Their application continues the inference in situations of inapplicability of the basic rule of inference in the calculus of pfs-formulas. The extension of calculus by the rules of synthesis transforms it as a first-order calculus of the deductive type into a means of deductive-abductive inference, which differs from the known works using abduction by the absence of a number of restrictions. Illustrative examples demonstrate the representation, analysis and generation of knowledge. These examples also show in interactive knowledge processing the importance of visualization of the processed text, provided by the large-block structure of pfs-formulas constructed from typical quantifiers. In conclusion, the results of the paper and possible directions for the development of the results are formulated.
<scp>MaNIACS</scp> : Approximate Mining of Frequent Subgraph Patterns through Sampling

We present MaNIACS , a sampling-based randomized algorithm for computing high-quality approximations of the collection of the subgraph patterns that are frequent in a single, large, vertex-labeled graph, according to the Minimum Node Image-based (MNI) frequency measure. The output of MaNIACS comes with strong probabilistic guarantees, obtained by using the empirical Vapnik–Chervonenkis (VC) dimension, a key concept from statistical learning theory, together with strong probabilistic tail bounds on the difference between the frequency of a pattern in the sample and its exact frequency. MaNIACS leverages properties of the MNI-frequency to aggressively prune the pattern search space, and thus to reduce the time spent in exploring subspaces that contain no frequent patterns. In turn, this pruning leads to better bounds to the maximum frequency estimation error, which leads to increased pruning, resulting in a beneficial feedback effect. The results of our experimental evaluation of MaNIACS on real graphs show that it returns high-quality collections of frequent patterns in large graphs up to two orders of magnitude faster than the exact algorithm.
Smudge Attack or: How to Secure Your Dirty Keypad

We present a concrete situation where there is a difference between the theoretical security of a cipher and its limitations when implemented in practice. Specifically, when entering a PIN, smudges on the keypad substantially reduce its security. We show how the number of possible keys in the presence of the “smudge attack” can be calculated. Specifically, it turns out that in all interesting situations, a key with fewer smudges increases security. Possible extensions for use in an undergraduate discrete mathematics or cryptography class are presented.
A Drone Teacher

Drones (micro unmanned aerial vehicles) are becoming more prevalent in applications that bring them into close human spaces. This is made possible in part by clear drone-to-human communication strategies. However, current auditory and visual communication methods only work with strict environmental settings. To continue expanding the possibilities for drones to be useful in human spaces, we explore ways to overcome these limitations through physical touch. We present a new application for drones--physical instructive feedback. To do this we designed three different physical interaction modes for a drone. We then conducted a user study (N=12) to answer fundamental questions of where and how people want to physically interact with drones, and what people naturally infer the physical touch is communicating. We then used these insights to conduct a second user study (N=14) to understand the best way for a drone to communicate instructions to a human in a movement task. We found that continuous physical feedback is both the preferred mode and is more effective at providing instruction than incremental feedback.
Quantum walks driven by quantum coins with two multiple eigenvalues

We consider a spectral analysis on the quantum walks on graph $$G=(V,E)$$ with the local coin operators $$\{C_u\}_{u\in V}$$ and the flip flop shift. The quantum coin operators have commonly two distinct eigenvalues $$\kappa ,\kappa '$$ and $$p=\dim (\ker (\kappa -C_u))$$ for any $$u\in V$$ with $$1\le p\le \delta (G)$$ , where $$\delta (G)$$ is the minimum degrees of G. We show that this quantum walk can be decomposed into a cellular automaton on $$\ell ^2(V;\mathbb {C}^p)$$ whose time evolution is described by a self adjoint operator T and its remainder. We obtain how the eigenvalues and its eigenspace of T are lifted up to as those of the original quantum walk. As an application, we express the eigenpolynomial of the Grover walk on $$\mathbb {Z}^d$$ with the moving shift in the Fourier space.
MAWKDN: A Multimodal Fusion Wavelet Knowledge Distillation Approach Based on Cross-View Attention for Action Recognition

The recognition performance of existing vision-based human action recognition (HAR) methods is greatly reduced in the case of low camera resolution or occlusion. Wearable sensors can provide complementary information to alleviate this problem. It is challenging to construct a robust HAR model using multimodal wearable-sensor data. In this paper, we propose a cross-Attention-based Multimodal fusion Wavelet Knowledge Distillation Network (MAWKDN) method to guide recognition from video data by acquiring complementary information from wearable sensors and reduce the noise effects through wavelet knowledge distillation, which improves the robustness of the model. A multi-attention dilated convolution kernel residual network including dilated convolution and an attention mechanism is constructed to extract features from various sensor modalities and fuse the various modal data through the cross-view attention method to acquire additional information from different modalities. To reduce the modal differences between different modalities of the teacher and student networks and acquire similar semantic knowledge, we learn the information between different modalities by constructing a graph structure of convolutional layer features, and computing the semantic preservation loss between the teacher and student networks. To reduce the influence of noise in the input data, we construct the loss of wavelet knowledge distillation, which transforms the image through the discrete wavelet transform and only retains the low frequency features to extract the useful information. The top-1 accuracy achieved on the UTD-MHAD (99.31%), Berkeley-MHAD (99.40%) and the F1-score on the MMAct (85.26% based on cross-session) dataset prove the superior performance of MAWKDN compared with the state-of-the-art HAR methods. Moreover, we demonstrate the robustness of the MAWKDN approach on the noise-added UTD-MHAD dataset.