Reviewerly/exHarmony huggingface dataset:
Self-distribution binary neural networks

In this work, we study network binarization (i.e., binary neural networks, BNNs), which is one of the most promising techniques in network compression for convolutional neural networks (CNNs). Although prior work has introduced many binarization methods that improve the accuracy of BNNs by minimizing the quantization error, there remains a non-negligible performance gap between the binarized model and the full-precision model. Given that feature representation is critical for deep neural networks and that in BNNs, the features only differ in signs, we argue that the impact on the accuracy of BNNs may be strongly related to the sign distribution of the network parameters in addition to the quantization error. To this end, Self-Distribution Binary Neural Network (SD-BNN) is proposed. First, we utilize Activation Self Distribution (ASD) to adaptively adjust the sign distribution of activations, thereby improving the sign differences of the outputs of the convolution. Second, we adjust the sign distribution of weights through Weight Self Distribution (WSD) and then fine-tune the sign distribution of the outputs of the convolution. Extensive experiments on the CIFAR-10 and ImageNet datasets with various network structures show that the proposed SD-BNN consistently outperforms state-of-the-art (SOTA) BNNs (e.g., 92.5% on CIFAR-10 and 66.5% on ImageNet with ResNet-18) with lower computational cost. Our code is available at https://github.com/pingxue-hfut/SD-BNN .
A New Method for Absolute Pose Estimation with Unknown Focal Length and Radial Distortion

Estimating the absolute pose of a camera is one of the key steps for computer vision. In some cases, especially when using a wide-angle or zoom lens, the focal length and radial distortion also need to be considered. Therefore, in this paper, an efficient and robust method for a single solution is proposed to estimate the absolute pose for a camera with unknown focal length and radial distortion, using three 2D-3D point correspondences and known camera position. The problem is decomposed into two sub-problems, which makes the estimation simpler and more efficient. The first sub-problem is to estimate the focal length and radial distortion. An important geometric characteristic of radial distortion, that the orientation of the 2D image point with respect to the center of distortion (i.e., principal point in this paper) under radial distortion is unchanged, is used to solve this sub-problem. The focal length and up to four-order radial distortion can be determined with this geometric characteristic, and it can be applied to multiple distortion models. The values with no radial distortion are used as the initial values, which are close to the global optimal solutions. Then, the sub-problem can be efficiently and accurately solved with the initial values. The second sub-problem is to determine the absolute pose with geometric linear constraints. After estimating the focal length and radial distortion, the undistorted image can be obtained, and then the absolute pose can be efficiently determined from the point correspondences and known camera position using the undistorted image. Experimental results indicate this method's accuracy and numerical stability for pose estimation with unknown focal length and radial distortion in synthetic data and real images.
The problem-based benchmark suite (PBBS), V2

The Problem-Based Benchmark Suite (PBBS) is a set of benchmark problems designed for comparing algorithms, implementations and platforms. For each problem, the suite defines the problem in terms of the input-output relationship, and supplies a set of input instances along with input generators, a default implementation, code for checking correctness or accuracy, and a timing harness. The suite makes it possible to compare different algorithms, platforms (e.g. GPU vs CPU), and implementations using different programming languages or libraries. The purpose is to better understand how well a wide variety of problems parallelize, and what techniques/algorithms are most effective.
An Efficient Sampling-Based Path Planning for the Lunar Rover with Autonomous Target Seeking

This paper presents an efficient path planning method for the lunar rover to improve the autonomy and exploration ability in the complex and unstructured lunar surface environment. Firstly, the safe zone for the rover’s motion is defined, based on which a detecting point selection strategy is proposed to choose target positions that meet the rover’s constraints. Secondly, an improved sampling-based path planning method is proposed to get a safe path for the rover efficiently. Thirdly, a map extension strategy for the unstructured and continually varying environment is included to update the roadmap, which takes advantage of the historical planning information. Finally, the proposed method is tested in a complex lunar surface environment. Numerical results show that the appropriate detecting positions can be selected autonomously, while a safe path to the selected detecting position can be obtained with high efficiency and quality compared with the Probabilistic Roadmap (PRM) and A* search algorithm.
A New Quantum Multiparty Simultaneous Identity Authentication Protocol with the Classical Third-Party

To guarantee information security in communication, quantum identity authentication plays a key role in politics, economy, finance, daily life and other fields. In this paper, a new quantum multiparty simultaneous identity authentication protocol with Greenberger-Home-Zeilinger (GHZ) state is presented. In this protocol, the authenticator and the certified parties are the participants with quantum ability, whereas the third party is a classical participant. Here, the third-party is honest and the other two parties may be dishonest. With the help of a classical third-party, a quantum authenticator and the multiple certified parties can implement two-way identity authentication at the same time. It reduces the quantum burden of participants and lowers down the trustworthiness, which makes the protocol be feasible in practice. Through further security analysis, the protocol can effectively prevent an illegal dishonest participant from obtaining a legitimate identity. It shows that the protocol is against impersonation attack, intercept-measure-resend attack and entangle-measure attack, etc. In all, the paper provides positive efforts for the subsequent security identity authentication in quantum network.
Dual-Arm Robot Trajectory Planning Based on Deep Reinforcement Learning under Complex Environment

In this article, the trajectory planning of the two manipulators of the dual-arm robot is studied to approach the patient in a complex environment with deep reinforcement learning algorithms. The shape of the human body and bed is complex which may lead to the collision between the human and the robot. Because the sparse reward the robot obtains from the environment may not support the robot to accomplish the task, a neural network is trained to control the manipulators of the robot to prepare to hold the patient up by using a proximal policy optimization algorithm with a continuous reward function. Firstly, considering the realistic scene, the 3D simulation environment is built to conduct the research. Secondly, inspired by the idea of the artificial potential field, a new reward and punishment function was proposed to help the robot obtain enough rewards to explore the environment. The function is consisting of four parts which include the reward guidance function, collision detection, obstacle avoidance function, and time function. Where the reward guidance function is used to guide the robot to approach the targets to hold the patient, the collision detection and obstacle avoidance function are complementary to each other and are used to avoid obstacles, and the time function is used to reduce the number of training episode. Finally, after the robot is trained to reach the targets, the training results are analyzed. Compared with the DDPG algorithm, the PPO algorithm reduces about 4 million steps for training to converge. Moreover, compared with the other reward and punishment functions, the function used in this paper will obtain many more rewards at the same training time. Apart from that, it will take much less time to converge, and the episode length will be shorter; so, the advantage of the algorithm used in this paper is verified.
User oppression in human-computer interaction: a dialectical-existential perspective

Purpose This research theorizes the condition of human beings reduced to being users (and only users) in human-computer interaction (HCI), a condition that favors them becoming objects or targets of commercial dark patterns, racialized profiling algorithms, generalized surveillance, gendered interfaces and heteromation. Design/methodology/approach The reconceptualization of the users’ condition is done by confronting HCI theories on users with a dialectical-existential perspective over human ontology. The research is presented as a conceptual paper that includes analyzing and revising those theories to develop a conceptual framework for the user oppression in HCI. Findings Most HCI theories contribute to the user oppression with explicit or implicit ontological statements that denies their becoming-more or the possibility of users developing their handiness to the full human potential. Put together, these statements constitute an ideology called userism. Social implications HCI needs to acknowledge its role in structuring oppression not just in sexism, racism, classism and ableism, but also the specific relation that pertains to HCI: userism. Similar to other fields, acknowledging oppression is the first step toward liberating from oppression. Originality/value The user is an existential condition for HCI theories, yet few theories can explain in depth how this condition affects human development. From the dialectical-existential perspective, the user condition can be dehumanizing. Computers may intensify existing oppressions through esthetic interactions but these interactions can be subverted for liberation.
Rumour influence minimization and topic modelling for twitter dataset using machine learning schemes

We advocate a joint combined trend sentiment categorization method to guide sentiment classifiers for a couple of tweets at the same time. Distinctively, putrefy the sentiment classifier of all trends linked to two additives, namely the specific trend and the global trend. Our method gives a green way to as it should sort out trendy subjects devoid of the lack of outside facts making news channels to find out the infringement news in a particular time or to rush out viral memes to enhance marketing decisions with competitors. The analysis of social functions also reveals styles associated with all kinds of trends, such as tweets with approximately ongoing activities of trendsetters. The unique version of the Trends Greedy and Dynamic Blocking Algorithms can capture the precise expressions of sentiment in each Trend. In addition, we extract trends unique sentimental knowledge from each of the classified and unmarked samples in each Trend and use it to embellish the mastery of Trends precise sentiment classifiers. In addition, we are introducing green algorithms to resolve the version of our technique. These schemes can effectively boost the performance of the combined trends group and outperform traditional methods with experimental effects on benchmark datasets.
Student assessment in cybersecurity training automated by pattern mining and clustering

Hands-on cybersecurity training allows students and professionals to practice various tools and improve their technical skills. The training occurs in an interactive learning environment that enables completing sophisticated tasks in full-fledged operating systems, networks, and applications. During the training, the learning environment allows collecting data about trainees' interactions with the environment, such as their usage of command-line tools. These data contain patterns indicative of trainees' learning processes, and revealing them allows to assess the trainees and provide feedback to help them learn. However, automated analysis of these data is challenging. The training tasks feature complex problem-solving, and many different solution approaches are possible. Moreover, the trainees generate vast amounts of interaction data. This paper explores a dataset from 18 cybersecurity training sessions using data mining and machine learning techniques. We employed pattern mining and clustering to analyze 8834 commands collected from 113 trainees, revealing their typical behavior, mistakes, solution strategies, and difficult training stages. Pattern mining proved suitable in capturing timing information and tool usage frequency. Clustering underlined that many trainees often face the same issues, which can be addressed by targeted scaffolding. Our results show that data mining methods are suitable for analyzing cybersecurity training data. Educational researchers and practitioners can apply these methods in their contexts to assess trainees, support them, and improve the training design. Artifacts associated with this research are publicly available.
Nonlinear Optical Rectification in an Inversion-Symmetry-Broken Molecule near a Metallic Nanoparticle

We study the nonlinear optical rectification of an inversion-symmetry-broken quantum system interacting with an optical field near a metallic nanoparticle, exemplified in a polar zinc-phthalocyanine molecule in proximity to a gold nanosphere. The corresponding nonlinear optical rectification coefficient under external strong field excitation is derived using the steady-state solution of the density matrix equations. We use ab initio electronic structure calculations for determining the necessary spectroscopic data of the molecule under study, as well as classical electromagnetic calculations for obtaining the influence of the metallic nanoparticle to the molecular spontaneous decay rates and to the external electric field applied to the molecule. The influence of the metallic nanoparticle to the optical rectification coefficient of the molecule is investigated by varying several parameters of the system, such as the intensity and polarization of the incident field, as well as the distance of the molecule from the nanoparticle, which indirectly affects the molecular pure dephasing rate. We find that the nonlinear optical rectification coefficient can be greatly enhanced for particular incident-field configurations and at optimal distances between the molecule and the metallic nanoparticle.
Security, integrity, and privacy of cloud computing and big data

Big data is crucial in the modern world because more datasets are falling in its domain. The term refers to the enormously growing complex heterogeneous datasets requiring powerful technologies and algorithms. It is identified by its huge volume, high velocity of generation, and variety of data formats; identified as 3Vs. Data can be from smart grids, e-health systems, IoT, transportation, or government monitoring data, which must be sorted, analyzed, and stored. The challenge arises in storing and managing unlimited data coping with high read and write rates flexibly and efficiently while dealing with various data models. Cloud computing, an open standard model, enabling ubiquitous computing with shared network access of a group of computing assets, serves to manage the big data resources. However, the use of distributed resources also poses a threat to the security and integrity of data. The security and integrity of big data resources is an open research topic of prime significance. This chapter will introduce big data, the need for cloud and distributed computing, data storage on the cloud resources, and the security concerns with its storage. Under these emblems, different datasets that form the big data will be discussed, along with the complexities in handling these datasets. Cloud computing architecture will be presented along with different types of clouds and services in current designs. The chapter will also outline cloud data security and storage security domains from the perspective of cloud data owners and service providers.
VisGNN: Personalized Visualization Recommendationvia Graph Neural Networks

In this work, we develop a Graph Neural Network (GNN) framework for the problem of personalized visualization recommendation. The GNN-based framework first represents the large corpus of datasets and visualizations from users as a large heterogeneous graph. Then, it decomposes a visualization into its data and visual components, and then jointly models each of them as a large graph to obtain embeddings of the users, attributes (across all datasets in the corpus), and visual-configurations. From these user-specific embeddings of the attributes and visual-configurations, we can predict the probability of any visualization arising from a specific user. Finally, the experiments demonstrated the effectiveness of using graph neural networks for automatic and personalized recommendation of visualizations to specific users based on their data and visual (design choice) preferences. To the best of our knowledge, this is the first such work to develop and leverage GNNs for this problem.
DiriE: Knowledge Graph Embedding with Dirichlet Distribution

Knowledge graph embedding aims to learn representations of entities and relations in low-dimensional space. Recently, extensive studies combine the characteristics of knowledge graphs with different geometric spaces, including Euclidean space, complex space, hyperbolic space and others, which achieves significant progress in representation learning. However, existing methods are subject to at least one of the following limitations: 1) ignoring the uncertainty, 2) incapability of complex relation patterns. To address the above issues simultaneously, we propose a novel model named DiriE, which embeds entities as Dirichlet distributions and relations as multinomial distributions. DiriE employs Bayesian inference to measure the relations between entities and learns binary embeddings of knowledge graphs for modeling complex relation patterns. Additionally, we propose a two-step negative triple generation method that generates negative triples of both entities and relations. We conduct a solid theoretical analysis to demonstrate the effectiveness and robustness of our method, including the expressiveness of complex relation patterns and the ability to model uncertainty. Furthermore, extensive experiments show that our method outperforms state-of-the-art methods in link prediction on benchmark datasets.
A Discriminative Cross-Aligned Variational Autoencoder for Zero-Shot Learning

Zero-shot learning (ZSL) aims to classify unseen samples based on the relationship between the learned visual features and semantic features. Traditional ZSL methods typically capture the underlying multimodal data structures by learning an embedding function between the visual space and the semantic space with the Euclidean metric. However, these models suffer from the hubness problem and domain bias problem, which leads to unsatisfactory performance, especially in the generalized ZSL (GZSL) task. To tackle such a problem, we formulate a discriminative cross-aligned variational autoencoder (DCA-VAE) for ZSL. The proposed model effectively utilizes a modified cross-modal-alignment variational autoencoder (VAE) to transform both visual features and semantic features obtained by the discriminative cosine metric into latent features. The key to our method is that we collect principal discriminative information from visual and semantic features to construct latent features which contain the discriminative multimodal information associated with unseen samples. Finally, the proposed model DCA-VAE is validated on six benchmarks including the large dataset ImageNet, and several experimental results demonstrate the superiority of DCA-VAE over most existing embedding or generative ZSL models on the standard ZSL and the more realistic GZSL tasks.
An ensemble design of a cash crops-warehouse receipt system (WRS) based on blockchain smart contracts

Purpose Warehouse receipt system (WRS) schemes aim to revitalize the market of cash crops, particularly those of the smallholder farmers. However, the existing government schemes for the smallholder farmers have not been much successful on account of issues related with transparency and trust deficit, thereby limiting farmers' access to credit for their farming activities as channeled via the formal financial institutions. Conceding that blockchain is an emerging technology, this study aims to theoretically posit the potential to optimize WRS in real time to bolster farmers' trust and privacy. Design/methodology/approach The research bases itself on the design science research (DSR) approach. Using the DSR approach backed by the literature review and evolutionary prototyping, a blockchain-based WRS has been advanced in the study. Findings This study provides a design of a digital WRS, which serves as a proof of concept of blockchain technology (BCT) applications in improving transparency in the WRS in the agricultural sector. The findings from this study present the possibility of leveraging blockchain smart contracts to improve the linkages between the WRS stakeholders to promote trust and transparency that would eventually unlock the potentials of WRS schemes. Furthermore, the findings inform decision-makers and practitioners to prioritize the emerging technologies in revamping the classical WRS. Research limitations/implications The study leaves research pointers for implementing BCT in agricultural supply chain and furthering the findings from the present study with an empirical agenda. Originality/value Whilst research on the BCT applications across myriad sectors abounds, research on the BCT applications to address societal needs is conspicuously limited. Furthermore, research on the practical implementation of the BCT in real-world scenarios is few and far between. The present study seeks to plug these gaps by underlining the application and utility of blockchain in WRS. The study makes a significant contribution to the theory and practice of the agrarian supply chain management, specifically the WRS, by invoking design knowledge obtained through an iterative design process besides serving as an edifice for the integration of emerging technologies, notably BCT in the agricultural sector.
Urbanized knowledge syndrome—erosion of diversity and systems thinking in urbanites’ mental models

Abstract Coastal ecosystems nearby human societies collectively shape complex social-ecological systems (SESs). These ecosystems support high levels of ecological biodiversity while providing resources and services to humans. However, shoreline armoring, land transformation, and urban homogenization across urbanized coastal areas may degrade natural ecosystems and alter how humans and nature are connected. We hypothesize that these alterations extend to residents’ knowledge of SESs. We explore evidence of such cognitive outcomes in graphical mental models of more than 1350 coastal residents across eight states in the Northeast United States. Our results revealed that, in more urbanized areas, residents’ mental models underrepresented complex interdependence between humans and natural components, indicating limited systems thinking. Additionally, urbanization and shoreline armoring were associated with homogenization of mental models. We refer to these results as Urbanized Knowledge Syndrome (UKS). Importantly, respondents with more symptoms of UKS were less likely to self-report adoption of pro-environmental behaviors. These results indicate a potential societal-level erosion of ecological knowledge associated with urbanization in the same way more urbanized areas are associated with diminishing ecological function. Thus, diagnosing and treating UKS is an essential component of urban sustainability.