Reviewerly/exHarmony huggingface dataset:
CASPER: Covert Channel Using Internal Speakers

In recent years, researchers have studied various methods for transferring data in a network-separated environment, and the most representative method is the use of inaudible frequency signals like ultrasonic waves. This method has the advantage of being able to transfer data without other people noticing, but it has the disadvantage that speakers must exist. In a laboratory or company, external speakers may not be attached to each computer. Therefore, this paper presents a new covert channel attack that transfers data using internal speakers on the computer's motherboard. The internal speaker can also produce a sound of the desired frequency, and, therefore, data can be transferred using high frequency sounds. We encode data into Morse code or binary code and transfer it. Then we record it using a smartphone. At this time, the location of the smartphone can be any distance within 1.5 m when the length per bit is longer than 50 ms, such as on the computer body or on the desk. Data are obtained by analyzing the recorded file. Our results show that data is transferred from a network-separated computer using an internal speaker with 20 bits/s in maximum.
Bayesian scale mixtures of normals linear regression and Bayesian quantile regression with big data and variable selection

Quantile regression, which estimates various conditional quantiles of a response variable, including the median (0.5th quantile), is particularly useful when the conditional distribution is asymmetric or heterogeneous or fat-tailed or truncated. Bayesian methods for the inference of quantile regression have been receiving increasing attention from both theoretical and empirical viewpoints but facing the challenge of scaling up when the data are too large to be processed by a single machine under many big data environments nowadays. In this paper, we develop a structure link between Bayesian scale mixtures of normals linear regression and Bayesian quantile regression (BQR) via normal-inverse-gamma (NIG) distribution type of likelihood function, prior distribution and posterior distribution. We further explore the detailed methods of BQR for big data, variable selection and posterior prediction. The performance of the proposed techniques is evaluated via simulation studies and a real data analysis.
A hybrid cognitive model for machine agents

Collaborative teams pursue common goals, completing tasks and making decisions with various levels of interdependence. A shared mental model (SMM) is a foundational structure in high performing, production teams and aids humans in predicting their teammate’s goals and intentions. Advice teams utilize a transactive memory system (TMS) that integrate sources of knowledge and the source’s credibility. SMMs and TMSs elevate human performance when the nature of emergence complements the associated team type. However, project and action teams require both behavioral and knowledge integration. We present a hybrid cognitive model (HCM) for machine agents that unifies SMM and TMS characteristics. The HCM enables anytime selection over the two cognitive representations with the computational complexity of a single model. Furthermore, the pliant nature of credibility modeling in TMSs can represent expertise, thoroughness, or trust simultaneously in the team context. Results in a multi-agent project domain demonstrate the HCM’s efficacy for machine agent teams and potential for applications in human–machine teams.
End-to-end deep speaker embedding learning using multi-scale attentional fusion and graph neural networks

As an attractive research in biometric authentication, Text Independent Speaker Verification (TI-SV) problem aims to specify whether two given unconstrained utterances come from the same speaker or not. As state-of-the-art solutions, end-to-end approaches using deep neural networks seek to learn a highly discriminative speaker embedding space. In this paper, we propose a novel end-to-end approach for speaker embedding learning by focusing on two crucial factors: speaker embedder architecture and objective function. The proposed module in the speaker embedder is composed of an Efficient Multi-resolution feature Representation (EMR) block followed by a Multi-scale Channel Attention Fusion (MCAF) block. The EMR effectively addresses the issue of fixed resolution convolutional kernels which commonly used in most embedder architectures. Moreover, the MCAF significantly improves the simple summation-based feature fusion used in residual embedder networks. Regarding the objective function, we conduct the speaker embedding space towards learning the embedding-to-embedding relations, in addition to only embedding-to-training class relations employed by most previous methods. So, we propose to employ a dynamic graph attention network, on top of the proposed embedder to learn all informative relations between embeddings, and then learn both embedder and graph-based networks in an end-to-end manner. We conduct various experiments on a large-scale benchmark dataset called VoxCeleb1&2. The effectiveness of all proposed components is verified through an ablation study. We show the superior or competitive performances of the proposed approach compared to seven well-known embedding architectures and 32 SV systems, regarding two evaluation metrics, EER and minDCF, as well as the number of embedder parameters.
A generality hard channel pruning with adaptive compression rate selection for HRNet

Network pruning extends a promising prospect to neural network compression. However, applications of existing methods to various vision tasks, e.g., human pose estimation, are limited by the parameter distribution difference, shortage of general pruning strategies and high pruning cost. As a complex and widely used backbone network, HRNet is an effective benchmark for studying these issues. We propose an adaptive Hard channel pruning method with an Adaptive Compression Rate to replace manual experience in pruned model selection, called HACR. It first designs a hard criterion that takes γ and β to measure channel importance with the insight of the consistency relationship between the distribution adjustment of the channel and the final output distribution. Then, a partial pruning strategy is advanced for the complex structural network by only pruning channels without cross-layer integration and keeping the network infrastructure. Next, we adaptively predict an optimal compression rate based on the validation metric of unfine-tuned models, providing a reference for compression parameter selection. The processes of HACR are simplified to adaptive pruned model selection and fine-tuning with lower pruning cost. Furthermore, the numerical distribution of γ and β is used to extend HACR in common pruning task benchmarks, based on the a principle that lower level channels should be preferentially retained. We conduct HRNet pruning experiments on COCO2017 and MPII, and promote extension study on CIFAR-10 to verify the generality of strategy. The results show that HACR is effective on multiple tasks, e.g. HRNet-W48 reduces 58.2% Params and 50.1% FLOPS, and VGG-16 reduces 91.5% Params and 67.5% FLOPS with only 0.3% precision loss.
DEVOPS AND MODERN SERVER ARCHITECTURE

An integral part of any multi-user service is its server part. Data storage, authorization logic, algorithm requiring&#x0D; security so that no user can find a vulnerability in the system. All this is assigned to the backend - the server&#x0D; logic of the application. Without exaggeration, thousands and millions of users around the world depend on&#x0D; security, stability, and continuous updating o f the server code. It is the responsibility o f each developer to ensure&#x0D; the maximum performance o f the above properties.&#x0D; The development o f any application depends on its architecture and the architecture o f each component. Previously,&#x0D; setting up the server environment and developing the backend o f the service part were inseparable. But&#x0D; applications are becoming more complex, system requirements are becoming more difficult to implement. Setting&#x0D; up a server environment, automating work processes and updates are highlighted in a separate area o f&#x0D; activity and the IT industry - DevOps. There are containerization tools, process automation tools, web servers&#x0D; and much more in the world. And none o f these technologies are used in isolation. For a software developer&#x0D; without appropriate DevOps skills, it is difficult to implement a competent server configuration
Quantum rebound attacks on reduced‐round ARIA‐based hash functions

Abstract ARIA is a block cipher proposed by Kwon et al. at ICISC 2003 that is widely used as the national standard block cipher in the Republic of Korea. Herein, we identify some flaws in the quantum rebound attack on seven‐round ARIA‐DM proposed by Dou et al. and reveal that the limit of this attack is up to five rounds. Our revised attack applies to not only ARIA‐DM but also ARIA‐MMO and ARIA‐MP among the PGV models, and it is valid for all ARIA key lengths. Furthermore, we present dedicated quantum rebound attacks on seven‐round ARIA‐Hirose and ARIA‐MJH for the first time. These attacks are only valid for the 256‐bit key length of ARIA because they are constructed using the degrees of freedom in the key schedule. All our attacks are faster than the generic quantum attack in the cost metric of the time–space tradeoff.
Delineation of Integrated Anomaly with Generative Adversarial Networks and Deep Neural Networks in the Zhaojikou Pb‐Zn Ore District, Southeast China

Abstract Geochemical maps are of great value in mineral exploration. Integrated geochemical anomaly maps provide comprehensive information about mapping assemblages of element concentrations to possible types of mineralization/ore, but vary depending on expert's knowledge and experience. This paper aims to test the capability of deep neural networks to delineate integrated anomaly based on a case study of the Zhaojikou Pb‐Zn deposit, Southeast China. Three hundred fifty two samples were collected, and each sample consisted of 26 variables covering elemental composition, geological, and tectonic information. At first, generative adversarial networks were adopted for data augmentation. Then, DNN was trained on sets of synthetic and real data to identify an integrated anomaly. Finally, the results of DNN analyses were visualized in probability maps and compared with traditional anomaly maps to check its performance. Results showed that the average accuracy of the validation set was 94.76%. The probability maps showed that newly‐identified integrated anomalous areas had a probability of above 75% in the northeast zones. It also showed that DNN models that used big data not only successfully recognized the anomalous areas identified on traditional geochemical element maps, but also discovered new anomalous areas, not picked up by the elemental anomaly maps previously.
Approximate solutions to several classes of Volterra and Fredholm integral equations using the neural network algorithm based on the sine-cosine basis function and extreme learning machine

In this study, we investigate a new neural network method to solve Volterra and Fredholm integral equations based on the sine-cosine basis function and extreme learning machine (ELM) algorithm. Considering the ELM algorithm, sine-cosine basis functions, and several classes of integral equations, the improved model is designed. The novel neural network model consists of an input layer, a hidden layer, and an output layer, in which the hidden layer is eliminated by utilizing the sine-cosine basis function. Meanwhile, by using the characteristics of the ELM algorithm that the hidden layer biases and the input weights of the input and hidden layers are fully automatically implemented without iterative tuning, we can greatly reduce the model complexity and improve the calculation speed. Furthermore, the problem of finding network parameters is converted into solving a set of linear equations. One advantage of this method is that not only we can obtain good numerical solutions for the first- and second-kind Volterra integral equations but also we can obtain acceptable solutions for the first- and second-kind Fredholm integral equations and Volterra-Fredholm integral equations. Another advantage is that the improved algorithm provides the approximate solution of several kinds of linear integral equations in closed form (i.e., continuous and differentiable). Thus, we can obtain the solution at any point. Several numerical experiments are performed to solve various types of integral equations for illustrating the reliability and efficiency of the proposed method. Experimental results verify that the proposed method can achieve a very high accuracy and strong generalization ability.
Can blockchain enabled green bond issuance lead to intent to invest? A moderated mediation model

While financial literacy supports enhanced investment decisions, the study of blockchain enabled green bond issuance and its influence on intent to invest has not been investigated to date. To address this research gap, the present study developed a moderated mediation model, based on planned behaviour theory, and organizational information processing theory. This model describes the influence of blockchain enabled green bond issuance on the intent to invest via perceived behavioural control, conditioned by the moderating role of financial literacy. 304 responses were collected to test the hypothesized relationship. The results revealed that perceived behavioural control partially mediates the relationship between blockchain enabled green bond issuance and the intent to invest. Besides, the effect of mediation was discovered to be substantial at higher levels of financial literacy.
A true random number generator with high bit rate and low energy efficiency

Summary In this paper, a novel feedback architecture of true random number generator based on tetrahedral ring oscillator is proposed. The random raw bits are fed back to produce control voltages, which control the transmission gates in ring oscillators. Since random fluctuations of the control voltages increase the phase jitter of the oscillator, this architecture improves the bit rate and randomness of random sequence. Besides, a post‐processor is designed to enhance the randomness further. The circuit is implemented in TSMC 40‐nm complementary metal oxide semiconductor (CMOS) process, and it takes up an area of 85 × 51 μm. Measurement results show that the random number sequences pass the statistical randomness tests, with a low power consumption of 67 μW, high speed of 45 Mbps, and low energy efficiency of 1.48 pJ/bit.
An Improved Confounding Effect Model for Software Defect Prediction

Software defect prediction technology can effectively improve software quality. Depending on the code metrics, machine learning models are built to predict potential defects. Some researchers have indicated that the size metric could cause confounding effects and bias the prediction results. However, evidence shows that the real confounder should be the development cycle and number of developers, which could bring confounding effects when using code metrics for prediction. This paper proposes an improved confounding effect model, introducing a new confounding variable into the traditional model. On multiple projects, we experimentally analyzed the effect extent of the confounding variable. Furthermore, we verified that controlling confounding variables helps improve the predictive model’s performance.
Semantic piecewise convolutional neural network with adaptive negative training for distantly supervised relation extraction

Distantly Supervised Relation Extraction (DSRE) aligns existing knowledge bases with unstructured text to extract relation facts, and its automatically generated training data is inevitably noisy. Most existing works identify and reduce the impact of noise by enhancing semantic features. However, they only consider the semantic information in a single instance and ignore the semantic information between different instances. In this work, we propose a Semantic Piecewise Convolutional Neural Network (SPCNN), which uses the similarity between different entity pairs as semantic information to improve relation extraction. Specifically, to learn better semantic vector representations, we combine position features with entity pair features and entity similarity features in a high-dimensional space respectively, and generating two different semantic-aware representations. Then we unify these two representations to form a high-quality bag representation for training. Moreover, we design an Adaptive Negative Training (ANT) strategy, which facilitates the network to further exploit the rich semantic features to reduce the interference of noisy labels. Extensive experimental results on a large-scale benchmark dataset show that our method significantly outperforms other baselines.
Toward understanding the effectiveness of attention mechanism

Attention mechanism (AM) is a widely used method for improving the performance of convolutional neural networks (CNNs) on computer vision tasks. Despite its pervasiveness, we have a poor understanding of what its effectiveness stems from. It is popularly believed that its effectiveness stems from the visual attention explanation, i.e., attention weights indicate the importance of feature and AM advocates focusing on the important part of an input image rather than ingesting the entire input. However, we find only a weak consistency exists between the attention weights of features and their importance. We verify the feature map multiplication that brings about high-order non-linearity into CNNs is crucial for the effectiveness of AM. Furthermore, we show an essential impact of feature map multiplication on the learned surfaces of CNNs. With the high-order non-linearity, feature map multiplication plays a regularization role on CNNs, which makes the learned curves smoother and more stable in-between real samples (test/training samples in datasets). Thus, compared to vanilla CNNs, CNNs equipped with AM are more robust to noises and yield smaller model sensitivity scores, which is the reason for their better performance.
Hybrid planning for challenging construction problems: An Answer Set Programming approach

We study construction problems where multiple robots rearrange stacks of prefabricated blocks to build stable structures. These problems are challenging due to ramifications of actions, true concurrency, and requirements of supportedness of blocks by a surface or a robot and stability of the overall structure at all times. We propose a general elaboration tolerant method to solve a wide range of construction problems, based on the knowledge representation and reasoning paradigm of Answer Set Programming. This method not only (i) determines a stable final configuration of the structure, but also (ii) computes the order of manipulation tasks for multiple autonomous robots to build the structure from an initial configuration, (iii) while simultaneously ensuring the requirements of supportedness and stability at all times. We prove the soundness and completeness of our method with respect to these properties. We introduce a set of challenging construction benchmark instances, including construction of (uneven) bridges and overhangs, and discuss the usefulness of our framework over these instances. Furthermore, we perform experiments to investigate the computational performance of our hybrid method, and demonstrate the applicability of our method using a bimanual Baxter robot.
Item Analysis of Multiple Choice Questions (MCQs) for Dangerous Goods Courses in Air Transportation Management Department

Analysis of question items is an assessment of the questions that are evaluated to measure quality. Analysis of question items has two ways, namely quantitative and qualitative question item analysis. The goal to be achieved is to find out the quality of the multiple-choice form of Final Semester Exam questions and also to detect sound and wrong questions. The method used in this study is the quantitative descriptive method. Data collection uses documentation, interviews, and observations. The Excel program uses data analysis to detect the quality of good and bad questions. The results of the quantitative analysis of the reliability of multiple-choice questions of 0.76 mean reliable questions. Difficulty index: 3 medium questions (6%), 47 easy questions (76%), and 9 very easy questions (18%). Discrimination index: 8 questions are very low (16%), 22 questions are low (44%), 9 questions are medium (18%), and t10 questions are high (20%). Distractor efficiency: 125 items of malfunctioning questions (83.33%) and 25 items of functioning questions (16.67%).
Privacy-preserving distributed deep learning via LWE-based Certificateless Additively Homomorphic Encryption (CAHE)

Current privacy-preserving schemes proposed for distributed deep learning (DDL) are based on public key infrastructure (PKI) which has management of keys problem and also key escrow problem for those that may use identity-based public key cryptography (ID-PKC). This presents the possibility for the privacy guarantee of the distributed/collaborative deep learning system to be flawed due to the presence of a Trusted Third Party (TTP) (i.e. a certificate authority (CA) for public key cryptography (PKC) and a key generation center (KGC) for an ID-PKC), that must be trusted. Additionally, the presence of high communication cost associated with the transmission of homomorphically encrypted gradients from participants to the cloud server in distributed deep learning needs more attention. Furthermore, few schemes have been proposed for DDL that have post-quantum robustness. We, therefore, propose and design a new privacy-preserving distributed deep learning solution. First, a new LWE-based certificateless additively homomorphic encryption scheme called CAHE is proposed and designed. Secondly, partial sharing algorithms based on random selection and selection of the largest (or topmost) gradients in terms of magnitude are proposed. Thirdly, we put forward a new privacy-preserving distributed deep learning framework that combines the certificateless additively homomorphic encryption and partial sharing in the DDL. Our approach provides better privacy (i.e. not relying solely on the TTP) and also reduces communication cost for distributed deep learning. We note emphatically that this is the first time certificateless HE is being proposed, designed, and implemented in DDL. Results from our work indicate that the accuracy of the CAHE-based deep learning does not deteriorate (97.20%) while greatly preserving the privacy of participant’s dataset. With partial sharing, our solution records 97.17% and 97.12% accuracy for the selection of topmost gradients and random selection of gradients scenarios respectively, and with a great reduction in communication cost for transmitting encrypted gradients.