Reviewerly/exHarmony huggingface dataset:
MARSHAL

Secure messaging applications are deployed on devices that can be compromised, lost, stolen, or corrupted in many ways. Thus, recovering from attacks to get back to a clean state is essential and known as healing. Signal is a widely-known, privacy-friendly messaging application, that uses key-ratcheting mechanism updates keys at each stage to provide end-to-end channel security, forward secrecy, and post-compromise security. We strengthen this last property, by providing a faster healing. Signal needs up to two full chains of messages before recovering, our protocol enables recovery after the equivalent of a chain of only one message. We also provide an extra protection against session-hijacking attacks. We do so, while building on the pre-existing Signal backbone, without weakening its other security assumptions, and still being compatible with Signal's out-of-order message handling feature. Our implementation results show that, while slower than Signal (as expected), MARSHAL's spectacular gain in healing speed comes at a surprisingly low cost, with individual stages (including key-derivation, encryption, and decryption) taking less than 6 ms.
Cross-Domain Traffic Scene Understanding by Integrating Deep Learning and Topic Model

Understanding cross-domain traffic scenarios from multicamera surveillance network is important for environmental perception. Most of existing methods select the source domain which is most similar to the target domain by comparing entire domains for cross-domain similarity and then transferring the motion model learned in the source domain to the target domain. The cross-domain similarity between overall different scenarios with similar local layouts is usually not utilized to improve any automatic surveillance tasks. However, these local commonalities, which may be shared across multiple traffic scenarios, can be transferred across scenarios as prior knowledge. To address these issues, we present a novel framework for cross-domain traffic scene understanding by integrating deep learning and topic model. This framework leverages the labeled samples with activity attribute labels from the source domain to annotate the target domain, where each label represents the local activity of some objects in the scene. When labeling the activity attributes of the target domain, there is no need to select the source domain, which avoids the phenomenon of performance degradation or even negative transfer due to wrong source domain selection. The effectiveness of the proposed framework is verified by extensive experiments carried out using public road traffic data.
The alternative method to speed up RSA’s decryption process

The aim of this paper is to propose the special methods to speed up RSA’ s decryption process. In fact, this method gives the option of selecting the best parameters to recover the plaintext. Assuming that the new integer has a lower Hamming Weight than the private key and is mathematically related to the private key, it will be used as the exponent in the decryption process instead of the private key. Furthermore, this method can be used in conjunction with the Chinese Remainder Theorem (CRT) to determine the best exponents. In addition, a method derived from the Square and Multiplication Algorithm is proposed to compute modular exponentiation in order to reduce computation time. However, this method can be performed when number 1 of the exponent’s binary value is not adjacent to each other. Assuming that the new integers for CRT have the lowest Hamming Weight, the experimental results show that if the proposed integers meet the condition, the proposed method is the best algorithm for completing the task. Furthermore, when 2048 bits of modulus is chosen and compared to CRT, the average time is reduced about 8%. Moreover, three-fourths of the cases require the proposed method with CRT in order to save the most computation costs on the decryption side. As a result, the proposed method is very likely to be chosen to complete the decrypting task.
A Greek Alphabet Guide for Research Methodology, Measurement, and Statistics

OPINION article Front. Educ., 25 March 2022Sec. Educational Psychology https://doi.org/10.3389/feduc.2022.831344
Similarity-Based Explanations meet Matrix Factorization via Structure-Preserving Embeddings

Embeddings are core components of modern model-based Collaborative Filtering (CF) methods, such as Matrix Factorization (MF) and Deep Learning variations. In essence, embeddings are mappings of the original sparse representation of categorical features (e.g., user and items) to dense low-dimensional representations. A well-known limitation of such methods is that the learned embeddings are opaque and hard to explain to the users. On the other hand, a key feature of simpler KNN-based CF models (aka user/item-based CF) is that they naturally yield similarity-based explanations, i.e., similar users/items as evidence to support model recommendations. Unlike related works that try to attribute explicit meaning (via metadata) to the learned embeddings, in this paper, we propose to equip the learned embeddings of MF with meaningful similarity-based explanations. First, we show that the learned user/item embeddings of MF do not preserve the distances between users (or items) in the original rating matrix. Next, we propose a novel approach that initializes Stochastic Gradient Descent (SGD) with user/item embeddings that preserve the structural properties of the original input data. We conduct a broad set of experiments and show that our method enables explanations, very similar to the ones provided by KNN-based approaches, without harming the prediction performance. Moreover, we show that fine-tuning the structure-preserving embeddings may unlock better local minima in the optimization space, leading simple vanilla MF to reach competitive performances with the best-known models for the rating prediction task.
Mapping Cultural Distances in a Catnet Approach

Abstract This article studies national-ethnic images and cultural distances utilizing contemporary network methodology. The conceptual framework related to cognitive maps of perceived in-group and out-group stereotypes builds on an extended application of the catnet concept introduced by Harrison White. Employing an adapted version of the classical comparative approach by Buchanan and Cantril, the survey took place online in 2016/17, in a joint project with Greek and Hungarian samples. The analyses build on a two-mode network approach of attributes attached to these two, and four other nationalities, highlighting emotional and competence dimensions and skills/knowledge styles in an attribute/nation perspective. The impact of recent crises is also treated by contrasting the cognitive maps of various segments of the Greek and Hungarian populations.
Improved Scalability of Demand-Aware Datacenter Topologies With Minimal Route Lengths and Congestion

The performance of more and more cloud-based applications critically depends on the performance of the interconnecting datacenter network. Emerging reconfigurable datacenter networks have the potential to provide an unprecedented throughput by dynamically reconfiguring their topology in a demand-aware manner. This paper studies the algorithmic problem of how to design low-degree and hence scalable datacenter networks optimized toward the current traffic they serve. Our main contribution is a novel network design which provides asymptotically minimal route lengths and congestion. In comparison to prior work, we reduce the degree requirements by a factor of four for sparse demand matrices. We further show the problem to be already NP-hard for tree-shaped demands, but permits a 2-approximation on the route lengths and a 6-approximation for congestion. We further report on a small empirical study on Facebook traces.
Aggregating risks from aquifer contamination and subsidence by inclusive multiple modeling practices

Aggregating risks to aquifers is investigated in this chapter, where risk exposures may originate from as wide causes as nitrate-N contamination studied by DRASTIC and subsidence by ALPRIFT frameworks. The aim is to present a proof of concept for aquifer risk aggregation problems under sparse data toward developing planning tools for aquifer management with sparse data. This chapter pools together two separate published results of one aquifer on mapping vulnerability to contamination and subsidence. Although vulnerability invokes measures needed to avoid problems, it does not reveal if aquifer conditions are amenable to triggering risk exposures but this chapter shows that this can be studied by a new set of general-purpose data layers to reveal the spontaneous capacity of the aquifers for risks. This chapter details the new data layers to enable the transformation of vulnerability indices into risk indices and thereby three strategies are employed to aggregate risks without using supervisory learning algorithms for lacking observed measures of risks. Risk indexing reveals that the methodology is fit-for-purpose but suggests that data fusion may be used to identify proxy aggregated risk indices to serve supervised learning from the results and the application of artificial intelligence to reduce inherent subjectivities.
LineGAN: An image colourisation method combined with a line art network

The work on grayscale image colourisation has been significantly improved. Currently, learning-based methods have achieved some great colourisation effects, but existing colour edge bleeding, especially when colourful cartoon characters. In this paper, we focus on the colourisation of cartoon characters from a series in an adversarial environment with a line art network, whose name is LineGAN. LineGAN learns the corresponding colour mapping from datasets, improving the accuracy of image colourisation. Our methods limit the colour boundary overflow by adding a line art frame in the generator. Extensive experiment results on cartoon image colourisation tasks demonstrate that the proposed method can achieve effective results.
A Bridge-Based Compression Algorithm for Topological Quantum Circuits

Topological quantum error correction (TQEC) is promising for scalable fault-tolerant quantum computation. The required resource of a TQEC circuit can be modeled as its space-time volume of a three-dimensional geometric description. Implementing a quantum algorithm with a reasonable physical qubit number and computation time is challenging for large-scale complex problems. Therefore, it is desirable to minimize the space-time volume for large-scale TQEC circuits. Previous work proposed bridge compression, which can significantly compress a TQEC circuit, but it was performed manually. This article presents the first automated tool that can perform bridge compression on a large-scale TQEC circuit. Our proposed algorithm applies the bridge compression technique to compactify TQEC circuits with modularization. Besides, we offer a time-ordering-aware 2.5-D placement for compacting TQEC circuits and satisfying time-ordered measurement constraints. On the other hand, we suggest friend net-aware routing to effectively reduce the required routing resource under topological deformation. Compared with the state-of-the-art work, experimental results show that our proposed algorithm can averagely reduce space-time volumes by 84%.
TExSS 22: Transparency and Explanations in Smart Systems

Smart systems, such as decision support or recommender systems, continue to prove challenging for people to understand, but are nonetheless ever more pervasive based on the promise of harnessing rich data sources that are becoming available in every domain. These systems tend to be opaque, raising important concerns about how to discover and account for fairness or bias issues. The workshop on Transparency and Explanations in Smart Systems (TExSS) welcomes researchers and practitioners interested in exchanging ideas for overcoming the design, development, and evaluation issues in intelligent user interfaces. Specifically, we will focus on barriers preventing better reliability, trainability, usability, trustworthiness, fairness, accountability, and transparency. This year's theme is “Responsible, Explainable AI for Inclusivity and Trust”, emphasizing the importance of responsibility that tech-industry and developers have towards the design, implementation and evaluation of explainable, inclusive and trustworthy human-AI interaction.
Operator Behavior Analysis System for Operation Room Based on Deep Learning

Human behavior analysis has been a leading technology in computer vision in recent years. The station operation room is responsible for the dispatch of trains when they enter and leave the station. By analyzing the behaviors of the operators in the operation room, we can judge whether the operators have violations. However, there is no scheme to analyze the operator’s behavior in the operation room, so we propose an operator behavior analysis system in the station operation room to detect operator’s violations. This paper proposes an improved target tracking algorithm based on Deep-sort. The proposed algorithm can improve the target tracking performance through the actual test compared with the traditional Deep-sort algorithm. In addition, we put forward the detection scheme for common violations in the operation room: off-position, sleeping, and playing mobile phone. Finally, we verify that the proposed algorithm can detect the behaviors of operators in the station operation room in real time.
One Possibility of a Neuro-Symbolic Integration

Deep learning (DL) technologies automate the labor of theoreticians, developers, and programmers, that is otherwise needed to design knowledge representations and algorithms for each particular domain. At the same time, DL has limitations rooted in the statistical nature of the method, which largely ignores the available domain knowledge. An example of a DL-hard domain is human social-emotional behavior. In contrast, biologically inspired cognitive architectures (BICA) are based on the scientific knowledge accumulated in cognitive and neuro-sciences, and therefore can be successful in generating believable socially-emotional behavior, although at great expense of intellectual human labor. The challenge is to integrate the two approaches, adding their strengths and compensating their weaknesses. Here a particular form of such integration is proposed, that also involves methods of evolutionary programming. The expected result could be a self-developing artificial socially-emotional intelligence, capable of growing autonomously to the human level and beyond. Potential future applications of the method can be expected to have a global impact on the society.
Effective Outlier Detection using Scalable and Robust Clustering (SRC) Algorithm

Clustering large datasets with high dimensionality effectively and efficiently is a challenging task. The authors made an attempt to face the challenge by developing a Scalable and Robust Clustering(SRC) algorithm by hybridizing the grid and density based clustering algorithms. In contrast to the data points belonging to various clusters based on their similarity, the outliers are the data points with dissimilar / abnormal behavior from the rest of the points. Hence this paper investigates the applicability of the SRC algorithm developed by the authors for Outlier Detection (SRC-OD) from large datasets with high dimensionality. A framework is developed to analyze the performance of proposed SRC algorithm for outlier detection compared with that of the existing outlier detection (OD) algorithms using Jaccard Similarity metric and execution time. The results produced by SRC-OD algorithm are comparable with Isolation forest Outlier detection algorithm (ISO) which is the best known OD algorithm, based on Jaccard similarity, while the proposed SRC_OD algorithm is more scalable than all other Outlier detection algorithms except ISO as its execution time grows slower. Moreover, SRC-OD framework detects the outliers as a by product while clustering large datasets.
Data Analysis of Culture Boosting Civic Moral Construction Based on K-Means Algorithm

Based on network data analysis technology, this paper constructs a network data analysis process consisting of data collection, data pre-processing, data storage, data analysis and data presentation steps, focusing on data collection technology and data analysis technology. Data collection technology is based on web crawlers, which automatically grabs the program or script of World Wide Web information according to certain rules. The data analysis technique is based on the K-means clustering algorithm, which helps to classify the same and different views of Internet users into different classes, and managers quickly find useful information. Suggestions are put forward for regional culture based on network data analysis technology to contribute to the moral construction of citizens’ cyberspace: giving full play to the role of network data analysis technology, correctly guiding the freedom and self-discipline of citizens’ behaviour in cyberspace, strengthening rational thinking on the basis of perceptual understanding, comprehensively strengthening the construction of network content, and strengthening the institutionalisation of moral construction in cyberspace.
Experimental camera calibration study for 3D localisation

In this paper, we present a camera calibration technique, which consists in determining the intrinsic and extrinsic parameters of the camera. This approach has the advantage of being straightforward and uses simple objects from a 3-D scene (3-D test pattern). After the implementation of the approach, experimental tests were carried out in our laboratory, in order to evaluate intrinsic and extrinsic parameters of the camera. The experimental results obtained show the effectiveness of the approach used, in terms of precision of the 3-D localization. To further improve the accuracy of the results obtained, we have introduced a nonlinear mathematical model of the camera's optical distortions. Such precision is necessary for any application requiring knowledge of the 3-D environment of the said application
Multi-Camera Vehicle Tracking Based on Deep Tracklet Similarity Network

Multi-camera vehicle tracking at the city scale has received lots of attention in the last few years. It has large-scale differences, frequent occlusion, and appearance differences caused by the viewing angle differences, which is quite challenging. In this research, we propose the Tracklet Similarity Network (TSN) for a multi-target multi-camera (MTMC) vehicle tracking system based on the evaluation of the similarity between vehicle tracklets. In addition, a novel component, Candidates Intersection Ratio (CIR), is proposed to refine the similarity. It provides an associate scheme to build the multi-camera tracking results as a tree structure. Based on these components, an end-to-end vehicle tracking system is proposed. The experimental results demonstrate that an 11% improvement on the evaluation score is obtained compared to the conventional similarity baseline.
Omen: discovering sequential patterns with reliable prediction delays

Abstract Suppose we are given a discrete-valued time series $$X $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>X</mml:mi> </mml:math> of observed events and an equally long binary sequence $$Y $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>Y</mml:mi> </mml:math> that indicates whether something of interest happened at that particular point in time. We consider the problem of mining serial episodes, sequential patterns allowing for gaps, from $$X $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>X</mml:mi> </mml:math> that reliably predict those interesting events. With reliable we mean patterns that not only predict that an interesting event is likely to follow, but in particular that we can also accurately tell how how long until that event will happen. In other words, we are specifically interested in patterns with a highly skewed distribution of delays between pattern occurrences and predicted events. As it is unlikely that a single pattern can explain a complex real-world progress, we are after the smallest, least redundant set of such patterns that together explain the interesting events well. We formally define this problem in terms of the Minimum Description Length principle, by which we identify the best patterns as those that describe the occurrences of interesting events $$Y $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>Y</mml:mi> </mml:math> most succinctly given the data over $$X $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>X</mml:mi> </mml:math> . As neither discovering the optimal explanation of $$Y $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>Y</mml:mi> </mml:math> given a set of patterns, nor the discovery of optimal pattern set are problems that allow for straightforward optimization, we break the problem in two and propose effective heuristics for both. Through extensive empirical evaluation, we show that both our main method, Omen , and its fast approximation fOmen , work well in practice and both quantitatively and qualitatively beat the state of the art.
Exploration for Countering the Episodic Memory

Reinforcement learning is a prominent computational approach for goal-directed learning and decision making, and exploration plays an important role in improving the agent's performance in reinforcement learning. In low-dimensional Markov decision processes, table reinforcement learning incorporated within count-based exploration works well for states of the Markov decision processes that can be easily exhausted. It is generally accepted that count-based exploration strategies turn inefficient when applied to high-dimensional Markov decision processes (generally high-dimensional state spaces, continuous action spaces, or both) since most states occur only once in deep reinforcement learning. Exploration methods widely applied in deep reinforcement learning rely on heuristic intrinsic motivation to explore unseen states or unreached parts of one state. The episodic memory module simulates the performance of hippocampus in human brain. This is exactly the memory of past experience. It seems logical to use episodic memory to count the situations encountered. Therefore, we use the contextual memory module to remember the states that the agent has encountered, as a count of states, and the purpose of exploration is to reduce the probability of encountering these states again. The purpose of exploration is to counter the episodic memory. In this article, we try to take advantage of the episodic memory module to estimate the number of states experienced, so as to counter the episodic memory. We conducted experiments on the OpenAI platform and found that counting accuracy of state is higher than that of the CTS model. At the same time, this method is used in high-dimensional object detection and tracking, also achieving good results.
Wavelet network-based deep learning system for image classification

The problem addressed in this paper is feature extraction and classification of images. As a solution, we proposed a Deep Wavelet Network architecture based on the Wavelet Network and the Stacked Auto-encoders. In this work, we shifted from the deep learning based on neural networks to deep learning based on wavelet networks. The latter doesn’t change the general form of the Deep Learning based on the Neural Network but it is a novel method that shows the process of feature extraction and explains the system of image classification. Our Deep Wavelet Network is created for the training and the classification phase. After the training phase, a linear classifier is applied. Finally, the experimental test of our method is in the COIL-100 dataset.