Reviewerly/exHarmony huggingface dataset:
A Novel Non-Linear Transformation Based Multi User Identification Algorithm for Fixed Text Keystroke Behavioral Dynamics

In this paper, we propose a new technique to uniquely classify and identify multiple users accessing a single application using keystroke dynamics. This problem is usually encountered when multiple users have legitimate access to shared computers and accounts, where, at times, one user can inadvertently be logged in on another user’s account. Since the login processes are usually bypassed at this stage, we rely on keystroke dynamics in order to tell users apart. Our algorithm uses the quantile transform and techniques from localization to classify and identify users. Specifically, we use an algorithm known as ordinal Unfolding based Localization (UNLOC), which uses only ordinal data obtained from comparing distance proxies, by “locating” users in a reduced PCA/Kernel-PCA/t-SNE space based on their typing patterns. Our results are validated with the help of benchmark keystroke datasets and show that our algorithm outperforms other methods.
A Multi-FPGA Scalable Framework for Deep Reinforcement Learning Through Neuroevolution

The application of Deep Neural Networks (DNN) for reinforcement learning has proven effective in solving complex problems, such as playing video games or training robots to perform human tasks. Training based on reinforcement implies the continuous interaction of the agent powered by the DNN and the environment, vanishing the typical separation between the training and inference stages in deep learning. However, the high memory and accuracy requirements of gradient-based training algorithms prevent using FPGAs for these applications. As an alternative, this work demonstrates the feasibility of using Evolutionary Algorithms (EA) for training DNNs and their usage in reinforcement learning scenarios. Unlike backpropagation, EA-based training of neural networks, referred to as neuroevolution, can be effectively implemented on FPGAs. Moreover, this paper shows how the inherent parallelism of EAs can be effectively exploited in multi-FPGA scenarios to accelerate the learning process. The proposed FPGA-based neuroevolutionary framework has been validated by building a system capable of learning autonomously to play the Pong Atari game in less than 25 generations.
Content-Based Venue Recommender Approach for Publication

The recommendation system is used in every field where a user may observe several choices. A good recommendation may increase the user interest and lessen the resources concerning time and effort for selecting the most appropriate choice. Websites use cookies to know about users’ preferences, likeness, and utilize user experiences to know about trending values. In this paper, a venue recommendation approach is proposed for research paper publications. The proposed recommendation technique utilizes a content-based approach using machine learning techniques to generate an appropriate recommendation. Feature engineering techniques including vector space, term frequency-inverse document frequency is used in this study. To classify the content, the Naïve Bayes probabilistic algorithm and support vector machine (SVM) is used. To achieve the objective, abstracts of the scientific publications published by Springer are extracted to make the dataset. The dataset consists of 100 different classes. The experimental results of the proposed technique exhibit more efficiency compared to the existing state-of-the-art technique by achieving more than 80% accuracy on the given dataset.
Research on DPOS Consensus Mechanism Improvement

In view of the phenomenon of node voting not actively and node gang crime in the DPOS consensus mechanism, this paper proposes an improved scheme based on credit score, which comprehensively calculates credit score according to node behavior record and error rate, and equity holders vote according to credit score, so as to make the witnesses elected more credible. The recommendation algorithm is introduced to reduce the centralization of nodes. An incentive mechanism is introduced to award credit points to nodes that vote positively and produce correct blocks, and punish malicious nodes. Experimental results show that the improved Dpos consensus mechanism based on credit points can quickly eliminate malicious nodes, and improve the enthusiasm of equity holders to vote, with high security.
Computation of lattice isomorphisms and the integral matrix similarity problem

Abstract Let K be a number field, let A be a finite-dimensional K -algebra, let $\operatorname {\mathrm {J}}(A)$ denote the Jacobson radical of A and let $\Lambda $ be an $\mathcal {O}_{K}$ -order in A . Suppose that each simple component of the semisimple K -algebra $A/{\operatorname {\mathrm {J}}(A)}$ is isomorphic to a matrix ring over a field. Under this hypothesis on A , we give an algorithm that, given two $\Lambda $ -lattices X and Y , determines whether X and Y are isomorphic and, if so, computes an explicit isomorphism $X \rightarrow Y$ . This algorithm reduces the problem to standard problems in computational algebra and algorithmic algebraic number theory in polynomial time. As an application, we give an algorithm for the following long-standing problem: Given a number field K , a positive integer n and two matrices $A,B \in \mathrm {Mat}_{n}(\mathcal {O}_{K})$ , determine whether A and B are similar over $\mathcal {O}_{K}$ , and if so, return a matrix $C \in \mathrm {GL}_{n}(\mathcal {O}_{K})$ such that $B= CAC^{-1}$ . We give explicit examples that show that the implementation of the latter algorithm for $\mathcal {O}_{K}=\mathbb {Z}$ vastly outperforms implementations of all previous algorithms, as predicted by our complexity analysis.
Dense Embeddings Preserving the Semantic Relationships in WordNet

In this paper, we provide a novel way to generate low dimensional vector embeddings for the noun and verb synsets in WordNet, where the hypernym-hyponym relationship is preserved in the embeddings. We call this embedding the Sense Spectrum (and Sense Spectra for embeddings). In order to create suitable labels for the training of sense spectra, we designed a new similarity measurement for noun and verb synsets in WordNet. We call this similarity measurement the Hypernym Intersection Similarity (HIS), since it compares the common and unique hypernyms between two synsets. Our experiments show that on the noun and verb pairs of the SimLex-999 dataset, HIS outperforms the three similarity measurements in WordNet. Moreover, to the best of our knowledge, the sense spectra provide the first dense synset embeddings that preserve the semantic relationships in WordNet.
Listening to Users' Voice: Automatic Summarization of Helpful App Reviews

App reviews are crowdsourcing knowledge of user experience with the apps, providing valuable information for app release planning, such as major bugs to fix and important features to add. There exist prior explorations on app review mining for release planning; however, most of the studies strongly rely on predefined classes or manually annotated reviews. Also, the new review characteristic, i.e., the number of users who rated the review as helpful, which can help capture important reviews, has not been considered previously. In the article, we propose a novel framework, named SOLAR, aiming at accurately summarizing helpful user reviews to developers. The framework mainly contains three modules: the review helpfulness prediction module, topic-sentiment modeling module, and multifactor ranking module. The review helpfulness prediction module assesses the helpfulness of reviews, i.e., whether the review is useful for developers. The topic-sentiment modeling module groups the topics of the helpful reviews and also predicts the associated sentiment, and the multifactor ranking module aims at prioritizing semantically representative reviews for each topic as the review summary. Experiments on five popular apps indicate that SOLAR is effective for review summarization and promising for facilitating app release planning.
Xigmoid: An Approach to Improve the Gating Mechanism of RNN

This work proposes an innovative approach for the gating mechanism of RNN class models. A transfer function is embedded into the original sigmoid to form a new gate function called xigmoid. The purpose is to alleviate the gradient amplification problem when the models are trying to learn features at far end of a long time series. Using the xigmoid function, original LSTM and GRU are converted to xLSTM and xGRU, respectively. The initialization method for the trainable parameters of xigmoid is also derived and discussed as a necessary support to the new method. Verification experiments are conducted for xLSTM and xGRU against several baseline models, showing both faster convergency/training and better accuracy of the proposed xigmoid-based models. The code and datasets are available at https://github.com/privateos/xigmoid
Literature survey on automatic bug triaging using machine learning techniques

Amongst all the phases of software development life cycle software testing phase is the most crucial phase. in this phase the errors or bugs are reported by the tester. Any kind of unexpected behavior in the software is referred to bug. the same can be referred as error or flaw. As soon as the bug is reported by the software tester the next step is to fix the bug or we can say that assigning the bug to appropriate developer which is known as bug triaging. the companies spend large amount of costs in bug triaging process. The process of assigning the bug to appropriate developer is based on various factors like severity, priority and risk associated with the bug. The key idea behind the bug triaging process is to search the suitable developer who can fix the bug in short span of time. in this research paper we will address the factors which impact the performance of bug triaging process. in the later section the comparative analysis of various automatic bug triaging processes has been done.
New Wine Old Bottles: Feistel Structure Revised

This paper mainly investigates the iterative structures whose decryption is similar to the encryption. Firstly, we unify many well-known structures which share similar procedures between the decryption and the encryption, and give a sufficient and necessary condition for this structure to be bijective, which reveals many new insights into the Feistel structure as well as the Lai-Massey structure. Secondly, we analyze the security of the unified structure against the known cryptanalysis. By extending the dual structure from a Feistel structure to the unified structure, we prove that a differential of the unified structure is impossible if and only if it is a zero-correlation linear hull of its dual structure, which presents a generalized link between the impossible differential and zero-correlation linear cryptanalysis shown in CRYPTO 2015. Significantly, several constraints on the linear components of the cipher and the permutation on the branches of the cipher are specified to make the structure resilient to differential and linear cryptanalysis. Furthermore, in the case that the order of the permutation equals the number of the branches <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula> , we prove that there always exist a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$(3n-1)$ </tex-math></inline-formula> -round impossible differential and a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$(3n-1)$ </tex-math></inline-formula> -round zero-correlation linear hull of the structure, and also present an algorithm to construct these distinguishers. Finally, we propose some novel structures which might be used in future block cipher designs.
Numerical Simulations of Noisy Quantum Circuits for Computational Chemistry

The opportunities afforded by near-term quantum computers to calculate the ground-state properties of small molecules depend on the structure of the computational ansatz as well as the errors induced by device noise. Here we investigate the behavior of these noisy quantum circuits using numerical simulations to estimate the accuracy and fidelity of the prepared quantum states relative to the ground truth obtained by conventional means. We implement several different types of ansatz circuits derived from unitary coupled cluster theory for the purposes of estimating the ground-state energy of sodium hydride using the variational quantum eigensolver algorithm. We show how relative error in the energy and the fidelity scale with the levels of gate-based noise, the internuclear configuration, the ansatz circuit depth, and the parameter optimization methods.
Weak-Boundary Sensitive Superpixel Segmentation Based on Local Adaptive Distance

Superpixel segmentation provides a way to capture object boundaries unsupervised and has benefited many compute vision applications. However, under-segmentation for weak boundaries and poor compatibility with image feature representations often limit its wide application. In this paper, we propose a new weak-boundary sensitive superpixel generation method and provide an all-in-one solution for images with different feature representations. We first design a local adaptive distance (LAD) to be more sensitive to feature changes in low-contrast regions. LAD leverages image local standard deviation as region contrast clues. It adaptively increases the feature distances in low-contrast regions to avoid feature space distances of weak boundaries being inundated by regularity constraints. LAD is scale-invariant that can be compatible with high bit-depth and multi-feature images. Then, based on LAD, we introduce a novel morphological contour evolution model to generate superpixels iteratively. Leveraging morphological dilation of superpixel shapes, the new model is more conducive to the boundary detection of irregular or slender objects. Extensive experiments demonstrate that our method favorably outperforms state-of-the-art methods, especially regarding the under-segmentation error and segmentation accuracy.
Crowd Flow Detection from Drones with Fully Convolutional Networks and Clustering

Crowd analysis from drones has attracted increasing attention in recent times, thanks to the ease of deployment and affordable cost of these devices. However, how this technology can provide a solution to crowd flow detection is still an explored research question. In this paper, we contribute by proposing a crowd flow detection method for video sequences shot by a drone. The method is mainly based on a Fully Convolutional Network model for crowd density estimation, which aims to provide a good compromise between effectiveness and efficiency, and clustering algorithms aimed at detecting the centroids of high-density areas in density maps. The method was tested on the VisDrone Crowd Counting dataset-characterized not by still images but by video sequences-providing promising results. This direction may open up new ways of analyzing high-level crowd behavior from drones. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>
Code Aggregate Graph: Effective Representation for Graph Neural Networks to Detect Vulnerable Code

Deep learning, especially graph neural networks (GNNs), provides efficient, fast, and automated methods to detect vulnerable code. However, the accuracy could be improved as previous studies were limited by existing code representations. Additionally, the diversity of embedding techniques and GNN models can make selecting the appropriate method challenging. Herein we propose Code Aggregate Graph (CAG) to improve vulnerability detection efficiency. CAG combines the principles of different code analyses such as abstract syntax tree, control flow graph, and program dependence graph with dominator and post-dominator trees. This extensive representation empowers deep graph networks for enhanced classification. We also implement different data encoding methods and neural networks to provide a multidimensional view of the system performance. Specifically, three word embedding approaches and three deep GNNs are utilized to build classifiers. Then CAG is evaluated using two datasets: a real-world open-source dataset and the software assurance reference dataset. CAG is also compared with seven state-of-the-art methods and six classic representations. CAG shows the best performance. Compared to previous studies, CAG has an increased accuracy (5.4%) and F1-score (5.1%). Additionally, experiments confirm that encoding has a positive impact on accuracy (4–6%) but the network type does not. The study should contribute to a meaningful benchmark for future research on code representations, data encoding, and GNNs.
Dynamic Channel-Aware Subgraph Interactive Networks for Skeleton-Based Action Recognition

Skeleton-based action recognition has obtained remarkable success due to the rapid development of Graph Neural Networks (GNNs). Existing skeleton-based methods primarily focus on the spatial aspect of skeleton graphs, but rarely mine long-range temporal relationships to learn the intrinsic dependencies across different frames. The latter is crucial for extracting discriminative motion patterns. To acquire a more precise spatial-temporal representation for the human skeleton data, we develop a lightweight yet practical method termed Dynamic Channel-Aware Subgraph Interactive Network (DCA-SGIN), using the interactive motion to adaptively capture long-range temporal nuance among the skeleton sequences. Moreover, we unify a novel paradigm to address the inherent structural limitations of GCNs. Specifically, the proposed graph interactive learners utilize the collaborative channel-aware topology of multiple subgraphs to model spatial relations. Unlike previous methods, the global and local features are simultaneously considered in spatial modeling without calculating any adjacency matrix, making it more efficient. Each proposed individual component in DCA-SGIN can be treated as a plug-in module that can be easily applied to other GNNs. Extensive experiments on the three challenging datasets show that the performance of DCA-SGIN outperforms the state-of-the-art methods with fewer FLOPs.
Deep Saliency Map Generators for Multispectral Video Classification

Despite their black box nature, deep neural networks have been successfully used in practical applications lately. In areas where the results of these applications can lead to safety hazards or decisions of ethical relevance, the application provider is accountable for the resulting decisions and should therefore be able to explain, how, and why a specific decision was made. For image processing networks, saliency map generators are a possible solution. A saliency map gives a visual hint on what is of special importance for the network’s decision, can reveal possible dataset biases and give a more profound insight in the decision process of the black box.This paper investigates how 2D saliency map generators need to be adapted for 3D input data, and additionally, how the methods behave when applied not only to ordinary video input but rather multispectral 3D input data. This is exemplarily shown on 3D video input data in human action recognition in the infrared and visual spectrum and evaluated by using the insertion and deletion metrics. The dataset used in this work is the Multispectral Action Dataset, where each scene is available in the long-wave infrared as well as the visual spectrum. To be able to draw a more general conclusion, the two investigated networks, 3D-ResNet 18 and Persistent Appearance Network (PAN), follow a different mindset.It could be shown, that the saliency methods can also be applied to 3D input data with remarkable results. The results show that a combined training with both, infrared and RGB 3D input data, lead to more focused saliency maps and outperform a training with only RGB or infrared data.
Towards Uninterruptible Smart Home Processes

Today's Smart Home protocols specify the functionality and interfaces of Smart Home devices, but they cannot represent arbitrary, complex processes defined by the user. For example, if a speaker has to play music after a motion detector detects movement. This process has to be set up on a centralized automation system. However, if the connection to the Internet breaks down or the involved cloud systems crash, most Smart Home processes will stop working. This paper proposes a decentralized approach where the defined process descriptions are directly transferred to the devices. The devices interpret and execute the process steps themselves with the help of a component called the Distributed Process Engine. This architecture guarantees the reliable execution of Smart Home processes in the event of many possible types of failures.
Quantum Motion Segmentation

Motion segmentation is a challenging problem that seeks to identify independent motions in two or several input images. This paper introduces the first algorithm for motion segmentation that relies on adiabatic quantum optimization of the objective function. The proposed method achieves on-par performance with the state of the art on problem instances which can be mapped to modern quantum annealers.
SubGraph Networks Based Entity Alignment for Cross-Lingual Knowledge Graph

Entity alignment is the task of discovering entities representing the equal real-world object in two knowledge graphs (KGs). Cross-lingual knowledge graph entity alignment aims to discover the cross-lingual links in the multi-language KGs, which is of wonderful value to solve the NLP problems and integrate multi-language KGs. In the task of aligning cross-language knowledge graphs, the structures of the two graphs are very similar, and the equivalent entities often have the same subgraph structure characteristics. The traditional GCN method neglects to obtain structural features through representative parts of the original graph and the use of adjacency matrix is not enough to effectively represent the structural features of the graph. In this paper, we introduce the subgraph network (SGN) method into the GCN-based cross-lingual KG entity alignment method. In the method, we extracted the first-order subgraphs of the KGs to expand the structural features of the original graph to enhance the representation ability of the entity embedding and improve the alignment accuracy. Experiments show that the proposed method is advanced in the task of entity alignment.
DVF: Toward Semiautomatic Composition of Perceptual Images of a Virtual Scene Through Hand Gesture Interface

Human beings perceive scenes by enlarging an area of interest (AOI), whereas an ordinal camera captures the entire scene evenly. Therefore, it is not easy to share subjective impressions with others effectively only by sharing normal photos. Such challenges exist in cyberworlds as well. In this study, we design Digitus View Finder (DVF), a system that automatically outputs images close to the scenes a user perceives. A preliminary user study indicated that the DVF would be able to estimate a plausible scale factor of the AOI as a function of three parameters: the number of subjects in the AOI, the distance to the subjects, and a personal difference as a correction term.