Reviewerly/exHarmony huggingface dataset:
Moderators of Social Facilitation Effect in Virtual Reality: Co-presence and Realism of Virtual Agents

Social facilitation has been researched for decades, but in the face of the development of virtual reality technology, new questions arise regarding the possibility of its occurrence in this environment in the presence of computer-generated agents. Past research provided inconclusive answers: several experiments confirmed this possibility, but several others disagreed. On the other hand, previous studies have shown the important role of VR characteristics, such as realism or co-presence, in evoking other psychological phenomena. However, no study has investigated the interplay between the presence of computer-generated agents and perceived social realism in evoking social facilitation in virtual reality. To this end, the present study was conducted. The sample consisted of 48 professional firefighters. Subjects were instructed to perform a rescue procedure in a virtual reality headset. The performance of participants was logged. The obtained results confirmed the role of social realism as a moderator of the occurrence of social facilitation in the presence of computer-generated agents. At the same time, the main effect of facilitation was not confirmed. These results support predictions that the subjective feeling of being in a realistic company of others may be more important in evoking social facilitation than objective facts. Furthermore, the results contribute to the debate regarding the mechanism of social facilitation, suggesting that simple augmentation of the environment with social distractors is not enough, thus questioning the attentional explanation of the effect. Taken together, our results extend previous findings on social facilitation and open up new possibilities for designing effective virtual environments.
LEEP: A New Measure to Evaluate Transferability of Learned Representations

We introduce a new measure to evaluate the transferability of representations learned by classifiers. Our measure, the Log Expected Empirical Prediction (LEEP), is simple and easy to compute: when given a classifier trained on a source data set, it only requires running the target data set through this classifier once. We analyze the properties of LEEP theoretically and demonstrate its effectiveness empirically. Our analysis shows that LEEP can predict the performance and convergence speed of both transfer and meta-transfer learning methods, even for small or imbalanced data. Moreover, LEEP outperforms recently proposed transferability measures such as negative conditional entropy and H scores. Notably, when transferring from ImageNet to CIFAR100, LEEP can achieve up to 30% improvement compared to the best competing method in terms of the correlations with actual transfer accuracy.
Reasoning about Political Bias in Content Moderation

Content moderation, the AI-human hybrid process of removing (toxic) content from social media to promote community health, has attracted increasing attention from lawmakers due to allegations of political bias. Hitherto, this allegation has been made based on anecdotes rather than logical reasoning and empirical evidence, which motivates us to audit its validity. In this paper, we first introduce two formal criteria to measure bias (i.e., independence and separation) and their contextual meanings in content moderation, and then use YouTube as a lens to investigate if the political leaning of a video plays a role in the moderation decision for its associated comments. Our results show that when justifiable target variables (e.g., hate speech and extremeness) are controlled with propensity scoring, the likelihood of comment moderation is equal across left- and right-leaning videos.
Antimagicness of subdivided fans

A graph Γ (simple, finite, undirected) with an Ω-covering has an (α, δ)-Ω-antimagic labeling if the weights of all subgraphs Ω of graph Γ constitute an arithmetic progression with the common difference δ.Such a graph is called super (α, δ)-Ω-antimagic if ν(V(Γ)) = {1, 2, 3, . . ., |V(Γ)|}.In the present paper, the cycle coverings of subdivision of fan graphs has been considered and results are proved for several differences.
To compress or not to compress: Understanding the Interactions between Adversarial Attacks and Neural Network Compression

As deep neural networks (DNNs) become widely used, pruned and quantised models are becoming ubiquitous on edge devices; such compressed DNNs are popular for lowering computational requirements. Meanwhile, recent studies show that adversarial samples can be effective at making DNNs misclassify. We, therefore, investigate the extent to which adversarial samples are transferable between uncompressed and compressed DNNs. We find that adversarial samples remain transferable for both pruned and quantised models. For pruning, the adversarial samples generated from heavily pruned models remain effective on uncompressed models. For quantisation, we find the transferability of adversarial samples is highly sensitive to integer precision.
On Model Evaluation Under Non-constant Class Imbalance

Many real-world classification problems are significantly class-imbalanced to detriment of the class of interest. The standard set of proper evaluation metrics is well-known but the usual assumption is that the test dataset imbalance equals the real-world imbalance. In practice, this assumption is often broken for various reasons. The reported results are then often too optimistic and may lead to wrong conclusions about industrial impact and suitability of proposed techniques. We introduce methods (Supplementary code related to techniques described in this paper is available at: https://github.com/CiscoCTA/nci_eval ) focusing on evaluation under non-constant class imbalance. We show that not only the absolute values of commonly used metrics, but even the order of classifiers in relation to the evaluation metric used is affected by the change of the imbalance rate. Finally, we demonstrate that using subsampling in order to get a test dataset with class imbalance equal to the one observed in the wild is not necessary, and eventually can lead to significant errors in classifier’s performance estimate.
A novel image encryption algorithm based on fractional order 5D cellular neural network and Fisher-Yates scrambling

This paper proposes a new chaotic image encryption algorithm. Firstly, an original phased composite chaotic map is used. The comparative study shows that the map cryptographic characteristics are better than the Logistic map, and the map is used as the controller of Fisher-Yates scrambling. Secondly, with the higher complexity of the fractional-order five-dimensional cellular neural network system, it is used as a diffusion controller in the encryption process. And mix the secret key, mapping and plaintext, we can obtain the final ciphertext. Finally, the comparative experiments prove that the proposed algorithm improves the encryption efficiency, has good security performance, and can resist common attack methods.
Building Low-Interactivity Multifactor Authenticated Key Exchange for Industrial Internet of Things

Industrial Internet of Things (IIoT) brings together computers, devices, advanced analytics, and people in industries, such as transportation, oil plant, and power grid that leads to major efficiency and productivity gains for almost any industrial procedures. Due to the interconnection of devices in IIoT, communication security has become a critical issue to address in many emerging industry standards that require the authentication and key exchange procedure to be done to guarantee the authorized machine access (e.g., from users) and secure the data transmission between machines. To overcome the shortcoming (i.e., low entropy) of the memorable password in user authentication, it is rightfully recommended by industry standards (such as IEC-62443 family) to use multifactor authentication (MFA) for higher security levels. Notably, latency is one of the main sources of inefficiency when a device is communicating with other machines on IIoT. To mitigate latency, a smooth projective hash function (SPHF) built from well-studied standard assumptions is used to achieve a lowinteractivity multifactor authenticated key exchange protocol (MFAKE) because SPHF allows each party to prove to the others that he knows the right authentication factor(s). In this article, we are, therefore, motivated to build a new MFAKE named “secure remote multifactor (SRMF)” to achieve the humaninvolved “machine-to-machine” secure communication in IIoT. That is, SRMF leverages multiple user-centric authentication factors (such as password, biometric fingerprints, and PIN), and it can synergistically support multifactor registration (MFR), MFA, and multifactor key exchange (MFKE). Furthermore, to prevent authentication factors stored at the server exposing to attackers, the password-harden service (i.e., Pythia-PRF and USENIX'15) inspires us to develop a multifactor hardening service (MFHS) utilizing an oblivious pseudorandom function (OPRF). The balanced security of the proposed protocol is proved under the model of Bellare-Pointcheval-Rogaway (EUROCRYPTO'00) along with theoretical and experimental evaluations.
A quantum-computing advantage for chemistry

Error-mitigated Hartree-Fock computation is performed with up to 12 superconducting qubits
Forecasting Solar Power Output Generation: A Systematic Review with the Proknow-C

This paper presents a systematic review of the solar output power generation forecasting using the Proknow-C methodology for the development of a bibliographic portfolio which allows identifying the state of the art for use as a scientific reference and encouraging further research and development of new models. The execution of the process identified 292 papers relevant to the theme from the two important international scientific databases. The portfolio creation process and bibliometric analysis of the recognition of articles, authors, keywords, journals and review articles are described. The theme of this research is current and of interest in the international scientific community, however, in Brazil and Latin America few related researches were observed. The annual historical profile of publications has shown a growing number of recent research on the theme, which motivated the proposition of an adjustment to Proknow-C to create more concise repositories. This paper's contributions have an exploratory and descriptive meaning, which provides the state of the art in the solar power output generation forecasting for use as a theoretical framework in future researches in Latin America, and another methodological meaning that discusses the process using a case study.
An Underwater Image Enhancement Algorithm Based on MSR Parameter Optimization

The quality of underwater images is often affected by the absorption of light and the scattering and diffusion of floating objects. Therefore, underwater image enhancement algorithms have been widely studied. In this area, algorithms based on Multi-Scale Retinex (MSR) represent an important research direction. Although the visual quality of underwater images can be improved to some extent, the enhancement effect is not good due to the fact that the parameters of these algorithms cannot adapt to different underwater environments. To solve this problem, based on classical MSR, we propose an underwater image enhancement optimization (MSR-PO) algorithm which uses the non-reference image quality assessment (NR-IQA) index as the optimization index. First of all, in a large number of experiments, we choose the Natural Image Quality Evaluator (NIQE) as the NR-IQA index and determine the appropriate parameters in MSR as the optimization object. Then, we use the Gravitational Search Algorithm (GSA) to optimize the underwater image enhancement algorithm based on MSR and the NIQE index. The experimental results show that this algorithm has an excellent adaptive ability to environmental changes.
An Image Encryption Algorithm Based on Hyperchaotic System and Variable-Step Josephus Problem

In this paper, an image encryption algorithm based on a hyperchaotic system and variable-step Josephus problem is proposed. Based on an in-depth analysis of the classic Josephus problem, a new variable-step Josephus problem that combines the pseudorandom sequence with the Josephus problem is proposed. Firstly, the hash value of the plaintext image is calculated, which is converted to the initial value of the chaotic system. Secondly, the chaotic system is iterated to generate four pseudorandom sequences X, Y, Z, and W. The sequences X, Y, and Z are input as parameters into the variable-step Josephus function to scramble the positions of the rows, pixel bits, and columns of the image, respectively. Finally, the elements of the sequence W and the image pixels are used to perform the addition operation. According to the experiments, the information entropy of the encrypted image with size 256 <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <msup> <mrow /> <mrow> <mi>∗</mi> </mrow> </msup> </math> 256 reaches 7.997 and the adjacent correlations in three directions are within ±0.01. The experimental results show that image encryption algorithm proposed in this paper has plaintext sensitivity and can resist the common attacks.
NeuPDE: Neural Network Based Ordinary and Partial Differential Equations for Modeling Time-Dependent Data

We propose a neural network based approach for extracting models from dynamic data using ordinary and partial differential equations. In particular, given a time-series or spatio-temporal dataset, we seek to identify an accurate governing system which respects the intrinsic differential structure. The unknown governing model is parameterized by using both (shallow) multilayer perceptrons and nonlinear differential terms, in order to incorporate relevant correlations between spatio-temporal samples. We demonstrate the approach on several examples where the data is sampled from various dynamical systems and give a comparison to recurrent networks and other data-discovery methods. In addition, we show that for MNIST and Fashion MNIST, our approach lowers the parameter cost as compared to other deep neural networks.
On Inverses of Permutation Polynomials of Small Degree Over Finite Fields

Permutation polynomials (PPs) and their inverses have applications in cryptography, coding theory and combinatorial design theory. In this paper, we make a brief summary of the inverses of PPs of finite fields, and give the inverses of all PPs of degree $\leq 6$ over finite fields $\mathbb{F}_{q}$ for all $q$ and the inverses of all PPs of degree $7$ over $\mathbb{F}_{2^n}$. The explicit inverse of a class of fifth degree PPs is the main result, which is obtained by using Lucas' theorem, some congruences of binomial coefficients, and a known formula for the inverses of PPs of finite fields.
Monocular 3D Object Detection With Sequential Feature Association and Depth Hint Augmentation

Monocular 3D object detection, with the aim of predicting the geometric properties of on-road objects, is a promising research topic for the intelligent perception systems of autonomous driving. Most state-of-the-art methods follow a keypoint-based paradigm, where the keypoints of objects are predicted and employed as the basis for regressing the other geometric properties. In this work, a unified network named as FADNet is presented to address the task of monocular 3D object detection. In contrast to previous keypoint-based methods, we propose to divide the output modalities into different groups according to the estimation difficulty of object properties. Different groups are treated differently and sequentially associated by a convolutional Gated Recurrent Unit. Another contribution of this work is the strategy of depth hint augmentation. To provide characterized depth patterns as hints for depth estimation, a dedicated depth hint module is designed to generate row-wise features named as depth hints, which are explicitly supervised in a bin-wise manner. The contributions of this work are validated by conducting experiments and ablation study on the KITTI benchmark. Without utilizing depth priors, post optimization, or other refinement modules, our network performs competitively against state-of-the-art methods while maintaining a decent running speed.
Cooperative co-evolution for feature selection in Big Data with random feature grouping

Abstract A massive amount of data is generated with the evolution of modern technologies. This high-throughput data generation results in Big Data, which consist of many features (attributes). However, irrelevant features may degrade the classification performance of machine learning (ML) algorithms. Feature selection (FS) is a technique used to select a subset of relevant features that represent the dataset. Evolutionary algorithms (EAs) are widely used search strategies in this domain. A variant of EAs, called cooperative co-evolution (CC), which uses a divide-and-conquer approach, is a good choice for optimization problems. The existing solutions have poor performance because of some limitations, such as not considering feature interactions, dealing with only an even number of features, and decomposing the dataset statically. In this paper, a novel random feature grouping (RFG) has been introduced with its three variants to dynamically decompose Big Data datasets and to ensure the probability of grouping interacting features into the same subcomponent. RFG can be used in CC-based FS processes, hence called Cooperative Co-Evolutionary-Based Feature Selection with Random Feature Grouping (CCFSRFG) . Experiment analysis was performed using six widely used ML classifiers on seven different datasets from the UCI ML repository and Princeton University Genomics repository with and without FS. The experimental results indicate that in most cases [i.e., with naïve Bayes (NB), support vector machine (SVM), k -Nearest Neighbor ( k -NN), J48, and random forest (RF)] the proposed CCFSRFG-1 outperforms an existing solution (a CC-based FS, called CCEAFS) and CCFSRFG-2, and also when using all features in terms of accuracy, sensitivity, and specificity.
An Improving Majority Weighted Minority Oversampling Technique for Imbalanced Classification Problem

Minority oversampling techniques have played a pivotal role in the field of imbalanced learning. While traditional oversampling algorithms can cause problems such as intra-class imbalance of samples, ignoring important information of boundary samples, and high similarity between new and old samples. Based on the situation, we proposed a new type of over-sampling method, BIRCH and Boundary Midpoint Centroid Synthetic Minority Over-Sampling Technique (BI-BMCSMOTE). First of all, the algorithm used the BIRCH clustering method to achieve quick cluster of the minority samples. After identifying and removing the noise, it marked the boundary minority samples in the label by probability. Secondly, it generated a density function for each sample cluster, calculated its density and sampling weight, performed midpoint composite sampling among the minority samples marked by probability and other minority samples in each cluster, and then calculated and analyzed the specific value of composite sampling to improve the accuracy of the model. According to the experimental results, the algorithm was proved to be valid.
Share or Not? Learning to Schedule Language-Specific Capacity for Multilingual Translation

Using a mix of shared and language-specific (LS) parameters has shown promise in multilingual neural machine translation (MNMT), but the question of when and where LS capacity matters most is still under-studied. We offer such a study by proposing conditional language-specific routing (CLSR). CLSR employs hard binary gates conditioned on token representations to dynamically select LS or shared paths. By manipulating these gates, it can schedule LS capacity across sub-layers in MNMT subject to the guidance of translation signals and budget constraints. Moreover, CLSR can easily scale up to massively multilingual settings. Experiments with Transformer on OPUS-100 and WMT datasets show that: 1) MNMT is sensitive to both the amount and the position of LS modeling: distributing 10%-30% LS computation to the top and/or bottom encoder/decoder layers delivers the best performance; and 2) one-to-many translation benefits more from CLSR compared to many-to-one translation, particularly with unbalanced training data. Our study further verifies the trade-off between the shared capacity and LS capacity for multilingual translation. We corroborate our analysis by confirming the soundness of our findings as foundation of our improved multilingual Transformers. Source code and models will be released.
A new approach to optimal smooth path planning of mobile robots with continuous-curvature constraint

Smooth path planning is very important to mobile robots with continuous-curvature constraint, but there are still some limitations and drawbacks on traditional planning approach. To deal with this problem, a new approach combined with parametric cubic Bezier curve (PCBC) and particle swarm optimization with adaptive delayed velocity (PSO-ADV), is developed to plan the smooth path of mobile robots. Unlike the traditional smooth path consisting of several linear and curve segments with discontinuous curvature at the joints, the smooth path composed of PCBC segments has equivalent curvature at the segment joints, thereby it is able to attain continuous curvature along the whole smooth path. In terms of the mathematical formulation of PCBC, the smooth path planning is essentially an optimization problem to seek the optimal control points and parameters of PCBC segments. To handle this intractable problem and some frequently encountered troubles (e.g. premature convergence and local trapping), a new PSO-ADV algorithm is developed by blending the term of adaptive delayed velocity, and its superiority can be confirmed by several simulation experiments. The new approach is finally applied to produce the smooth path with continuous-curvature constraint, and can achieve superior performance in comparison with traditional method.
Variational Bayesian adaptation of process noise covariance matrix in Kalman filtering

Adaptive Kalman filtering with unknown constant or varying process noise covariance matrix is studied. A resolution is proposed to directly estimate or tune the process noise covariance matrix in Kalman filtering using variational Bayesian technique. By state augmentation, conjugacy of the process noise covariance matrix's inverse-Wishart distribution is realized in the estimation at each time instant. The methodological development is given. Illustration examples are presented to demonstrate the improved state filtering performance and the process noise covariance tracking performance of the new method.
3D-TDC: A 3D temporal dilation convolution framework for video action recognition

Video action recognition is a vital area of computer vision. By adding temporal dimension into convolution structure, 3D convolution neural network owns the capacity to extract spatio-temporal features from videos. However, due to computing constraints, it is hard to input the whole video into the convolution network at one time, resulting in a limited temporal receptive field of the network. To address this issue, we propose a novel 3D temporal dilation convolution (3D-TDC) framework, to extract spatio-temporal features of actions from videos. First, we deploy the 3D temporal dilation convolution as the shallow temporal compression layer, enabling an effective capture of spatio-temporal information in a larger time domain with the reduced computational load. Then, an action recognition framework is constructed by integrating two networks with different temporal receptive fields to balance the long-short time difference. We conduct extensive experiments on three widely-used public datasets (UCF-101, HMDB-51, and Kinetics-400) for performance evaluation, and the experimental results demonstrate the effectiveness of our proposed framework in video action recognition with low computational load.