Reviewerly/exHarmony huggingface dataset:
Radar Guided Dynamic Visual Attention for Resource-Efficient RGB Object Detection

An autonomous system's perception engine must provide an accurate understanding of the environment for it to make decisions. Deep learning based object detection networks experience degradation in the performance and robustness for small and far away objects due to a reduction in object's feature map as we move to higher layers of the network. In this work, we propose a novel radar-guided spatial attention for RGB images to improve the perception quality of autonomous vehicles operating in a dynamic environment. In particular, our method improves the perception of small and long range objects, which are often not detected by the object detectors in RGB mode. The proposed method consists of two RGB object detectors, namely the Primary detector and a lightweight Secondary detector. The primary detector takes a full RGB image and generates primary detections. Next, the radar proposal framework creates regions of interest (ROIs) for object proposals by projecting the radar point cloud onto the 2D RGB image. These ROIs are cropped and fed to the secondary detector to generate secondary detections which are then fused with the primary detections via non-maximum suppression. This method helps in recovering the small objects by preserving the object's spatial features through an increase in their receptive field. We evaluate our fusion method on the challenging nuScenes dataset and show that our fusion method with SSD-lite as primary and secondary detector improves the baseline primary yolov3 detector's recall by 14% while requiring three times fewer computational resources.
Applications and Enhancement of Document-Based Sentiment Analysis in Deep learning Methods: Systematic Literature Review

Sentiment analysis has become a highly effective research field in the natural language domain and has a large scope of real-world implementations. An existing active study concentration for sentiment analysis is the development of graininess at the document level, appearing with two featured objectives: subjectivity classification, which determines whether a document is objective or subjective and sentiment detection which defines whether or not a document has a sentiment. Deep learning approaches have featured as a chance for developing these objectives with their ability to present both syntactic and semantic characteristics of text without demands for high-level attribute engineering. In this paper, we focus to produce a systematic literature review of deep learning methods for document-based sentiment analysis to determine different features in the text. In addition, this systematic literature review presents a brief survey, evaluation, enhancement of recent developments in the field of sentiment analysis techniques and applications of documents for deep learning, starting with the Convolutional Neural Network, continues to cover the Recurrent Neural Network, including Long Short-Term Memory and Gated Repetitive Units. This review also contains the implementation and application of Recursive Neural Network, Deep Belief Network, Domain-Adversarial Network Models and Hybrid Neural Network. This work considers most of the papers published when the history of deep learning began, and specifically the sentiment analysis of the documents.
Measuring organizational information security awareness in South Africa

Information is a valuable resource that organization may utilize in the current business environment. It is critical to comprehend the importance of information protection, as it safeguards the lifeline of the organization. All employees within organization should be aware of the organizational information security culture. Organizations should promote an information security awareness culture, so as to secure data as part of their critical infrastructure. Organizations should monitor and measure information security awareness levels among employees, with a number of international instruments. However, the validity of those instruments has not yet been determined in the South African context. As a consequence, the aim of this article is to validate one internationally accepted measurement instrument – the Human Aspects of Information Security-Questionnaire (HAIS-Q) in South Africa. The research sought to determine employees’ awareness levels, in order to make recommendations aimed at improving awareness in organizations. A survey was conducted whereby the data from 356 respondents were collected across industries, with a web-based questionnaire. To determine the factor structure of the scale under investigation, an exploratory factor analysis (EFA) and Cronbach’s alpha was used to establish the internal reliability of the HAIS-Q. T-tests and ANOVAs were used to identify significant differences between demographic groups.
Performance Evaluation of Aggregation-based Group Recommender Systems for Ephemeral Groups

Recommender Systems ( RecSys ) provide suggestions in many decision-making processes. Given that groups of people can perform many real-world activities (e.g., a group of people attending a conference looking for a place to dine), the need for recommendations for groups has increased. A wide range of Group Recommender Systems ( GRecSys ) has been developed to aggregate individual preferences to group preferences. We analyze 175 studies related to GRecSys . Previous works evaluate their systems using different types of groups (sizes and cohesiveness), and most of such works focus on testing their systems using only one type of item, called Experience Goods (EG). As a consequence, it is hard to get consistent conclusions about the performance of GRecSys . We present the aggregation strategies and aggregation functions that GRecSys commonly use to aggregate group members’ preferences. This study experimentally compares the performance (i.e., accuracy, ranking quality, and usefulness) using four metrics (Hit Ratio, Normalize Discounted Cumulative Gain, Diversity, and Coverage) of eight representative RecSys for group recommendations on ephemeral groups. Moreover, we use two different aggregation strategies, 10 different aggregation functions, and two different types of items on two types of datasets (EG and Search Goods (SG)) containing real-life datasets. The results show that the evaluation of GRecSys needs to use both EG and SG types of data, because the different characteristics of datasets lead to different performance. GRecSys using Singular Value Decomposition or Neural Collaborative Filtering methods work better than others. It is observed that the Average aggregation function is the one that produces better results.
Cross-Domain Remaining Useful Life Prediction Based on Adversarial Training

Remaining useful life prediction can assess the time to failure of degradation systems. Currently, numerous neural network-based prediction methods have been proposed by researchers. However, most of the work contains an implicit prerequisite: the network training and testing data have the same operating conditions. To solve this problem, an adversarial discriminative domain adaption prediction method based on adversarial training is proposed to improve the accuracy of cross-domain prediction under different working conditions. First, an LSTM feature extraction network is constructed to mine the source domain data and the target domain data for deep feature representation. Subsequently, the parameters of the target domain feature extraction network are adjusted based on the idea of adversarial training to achieve domain invariant feature mining. The proposed scheme is experimented on a publicly available dataset and achieves state-of-the-art prediction performance compared to recent unsupervised domain adaptation prediction methods.
Special Issue “Cyber Risk and Security”

The COVID-19 pandemic and now the war in Ukraine, have raised the risks to levels not seen in the last 30 years [...]
TwinNet: Twin Structured Knowledge Transfer Network for Weakly Supervised Action Localization

Action recognition and localization in untrimmed videos is important for many applications and have attracted a lot of attention. Since full supervision with frame-level annotation places an overwhelming burden on manual labeling effort, learning with weak video-level supervision becomes a potential solution. In this paper, we propose a novel weakly supervised framework to recognize actions and locate the corresponding frames in untrimmed videos simultaneously. Considering that there are abundant trimmed videos publicly available and well-segmented with semantic descriptions, the instructive knowledge learned on trimmed videos can be fully leveraged to analyze untrimmed videos. We present an effective knowledge transfer strategy based on inter-class semantic relevance. We also take advantage of the self-attention mechanism to obtain a compact video representation, such that the influence of background frames can be effectively eliminated. A learning architecture is designed with twin networks for trimmed and untrimmed videos, to facilitate transferable self-attentive representation learning. Extensive experiments are conducted on three untrimmed benchmark datasets (i.e., THUMOS14, ActivityNet1.3, and MEXaction2), and the experimental results clearly corroborate the efficacy of our method. It is especially encouraging to see that the proposed weakly supervised method even achieves comparable results to some fully supervised methods.
Foreign ownership and firm performance in Sub-Saharan Africa

Our study investigates the effects of foreign ownership on firm performance with respect to profitability, productivity, export intensity, and skills acquisition in Sub-Saharan African (SSA) countries. This is particularly important given that in the last three decades, the stock of FDI has increased by over 20,000% in SSA countries. To achieve the objectives of this paper, we employed firm- and country-level data from the World Bank Enterprise Surveys and the World Bank Development Indicators, respectively. Results of the least squares dummy variable (LSDV) and propensity score matching (PSM) estimations showed that foreign ownership was positively associated with higher levels of financial profitability, productivity, exports, and skilled labour acquisition. Policy implications were deduced from the findings.
Chapter 13. Knowledge patterns in corpora

Knowledge patterns (KPs) have evolved as tools for detecting, extracting, and classifying occurrences of conceptual and semantic relations expressed in texts. KPs offer many strengths, including clear identification of relations present. The extraction of relation-containing contexts, often referred to as knowledge-rich, allows users to observe the items participating in the relation and nuances conveyed by other textual elements. Nevertheless, designing and implementing pattern-based approaches is challenging, in part because of patterns' linguistic (i.e., lexical, grammatical) or paralinguistic nature. KPs must be selected and represented with care for a given use, and their performance may vary. In this chapter, we will explore some benefits and challenges of pattern-based approaches to corpus analysis, and some prospects for future applications.
Self-Attention-Based Edge Computing Model for Synthesis Image to Text through Next-Generation AI Mechanism

Image synthesis based on natural language description has become a research hotspot in edge computing in artificial intelligence. With the help of generative adversarial edge computing networks, the field has made great strides in high-resolution image synthesis. However, there are still some defects in the authenticity of synthetic single-target images. For example, there will be abnormal situations such as “multiple heads” and “multiple mouths” when synthesizing bird graphics. Aiming at such problems, a text generation single-target model SA-AttnGAN based on a self-attention mechanism is proposed. SA-AttnGAN (Attentional Generative Adversarial Network) refines text features into word features and sentence features to improve the semantic alignment of text and images; in the initialization stage of AttnGAN, the self-attention mechanism is used to improve the stability of the text-generated image model; the multistage GAN network is used to superimpose, finally synthesizing high-resolution images. Experimental data show that SA-AttnGAN outperforms other comparable models in terms of Inception Score and Frechet Inception Distance; synthetic image analysis shows that this model can learn background and colour information and correctly capture bird heads and mouths. The structural information of other components is improved, and the AttnGAN model generates incorrect images such as “multiple heads” and “multiple mouths.” Furthermore, SA-AttnGAN is successfully applied to description-based clothing image synthesis with good generalization ability.
An Accurate Method of Determining Attribute Weights in Distance-Based Classification Algorithms

Weight determination aims to determine the importance of different attributes; determining accurate weights can significantly improve the accuracy of classification and clustering. This paper proposes an accurate method for attribute weight determination. The method uses the distance from the sample point of each class to the class center point. It can minimize the weights and determines the attribute weights of the constraints through the objective function. In this paper, the attribute weights obtained by the exact solution are applied to the K-means clustering algorithm; three classic machine learning data sets, the iris data set, the wine data set, and the wheat seed data set, are clustered. Using the normalized mutual information as the evaluation index, a confusion matrix was established. Finally, the clustering results are visualized and compared with other methods to verify the effectiveness of the proposed method. The results show that this method improves the normalized mutual information by 0.11 and 0.08, respectively, compared with the unweighted and entropy weighted methods for iris clustering results. Furthermore, the performance on the wine data set is improved by 0.1, and the performance on the wheat seed data set is improved by 0.15 and 0.05.
Data Augmentation Based on Generative Adversarial Network with Mixed Attention Mechanism

Some downstream tasks often require enough data for training in deep learning, but it is formidable to acquire data in some particular fields. Generative Adversarial Network has been extensively used in data augmentation. However, it still has problems of unstable training and low quality of generated images. This paper proposed Data Augmentation Based on Generative Adversarial Network with Mixed Attention Mechanism (MA-GAN) to solve those problems. This method can generate consistent objects or scenes by correlating the remote features in the image, thus improving the ability to create details. Firstly, the channel-attention and the self-attention mechanism are added into the generator and discriminator. Then, the spectral normalization is introduced into the generator and discriminator so that the parameter matrix satisfies the Lipschitz constraint, thus improving the stability of the model training process. By qualitative and quantitative evaluations on small-scale benchmarks (CelebA, MNIST, and CIFAR-10), the experimental results show that the proposed method performs better than other methods. Compared with WGAN-GP (Improved Training of Wasserstein GANs) and SAGAN (Self-Attention Generative Adversarial Networks), the proposed method contributes to higher classification accuracy, indicating that this method can effectively augment the data of small samples.
The Impact of IoT on News Media in the Smart Age

Public progress and people’s well-being need free and honest news media effective for educating the public, holding experienced authors accountable, and recording public debate of public officials on current events. News media’s evolution might be influenced by a variety of variables. Political understanding, economics, education, development corporation, and information technology are vital variables that will offer effective news. Due to the enactment of web and mobile technologies, the media world is now preparing for the next explorations with the Internet of Things (IoT). Because of IoT-based devices, this mix of technologies has already begun to spread. One of the industries most influenced by this next technological revolution will be news media. Internet of Things devices is enabling new methods of creating, disseminating, and consuming journalistic material, ushering the news business into a new paradigm: ubiquitous journalism. In this paper, we collected data from Chinese news organizations to improve security, team collaboration, high-speed network access, and public accessibility. We have used Rivest-Shamir-Adleman (RSA) encryption to contextualize IoT adoption inside the framework of technological advancements absorbed by media, and the process can be optimized by using Hybridized Fruitfly Bumblebee Optimization Algorithm (HFBOA). The suggested technique is compared to other existing methods, and its performance is assessed. The proposed method helps in increasing the security of the contents related to news media. The estimated results for our proposed technique are implementation cost (45), latency (40), security level (95), reliability (87), accuracy (89), throughput (6.7), execution time (1.05), and energy consumption (45).
Construction of Cross-Cultural College English Teaching System Based on Computer Simulation Technology

For a long time, college English instruction has emphasised language skills training and information transfer while neglecting to emphasise the development of cross-cultural communication skills. Theoretical study on cross-cultural communication ability, on the other hand, has grown in various dimensions and lacks empirical research methodologies. Meanwhile, in real teaching, a cross-cultural communicative competence training framework that is adequate for Chinese college English teaching practise and has a high degree of recognition and operability has yet to be constructed. This paper uses computer simulation technology to construct the teaching system of college English cross-cultural knowledge. At the same time, this paper proposes a cross-cultural communicative competence training model suitable for Chinese college English teaching based on the Chinese language communication environment and foreign language teaching environment. Finally, this paper constructs a cross-cultural university English teaching system model through computer simulation. Through teaching experiments, we can see that the system constructed in this paper has a certain effect.
Rotational Cryptanalysis on ChaCha Stream Cipher

In this paper we consider the ChaCha20 stream cipher in the related-key scenario and we study how to obtain rotational-XOR pairs with nonzero probability after the application of the first quarter round. The ChaCha20 input can be viewed as a 4×4 matrix of 32-bit words, where the first row of the matrix is fixed to a constant value, the second two rows represent the key, and the fourth some initialization values. Under some reasonable independence assumptions and a suitable selection of the input, we show that the aforementioned probability is about 2−251.7857, a value greater than 2−256, which is the one expected from a random permutation. We also investigate the existence of constants, different from the ones used in the first row of the ChaCha20 input, for which the rotational-XOR probability increases, representing a potential weakness in variants of the ChaCha20 stream cipher. So far, to our knowledge, this is the first analysis of the ChaCha20 stream cipher from a rotational-XOR perspective.
An Optimized Discrete Data Classification Method in <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mi>N</mi> </math>-Dimensional

We propose a discrete data classification method of scattered data in <math xmlns="http://www.w3.org/1998/Math/MathML" id="M2"> <mi>N</mi> </math> -dimensional by solving the minimax problem for a set of points. The current research is extended from 2-dimensional and 3-dimensional to <math xmlns="http://www.w3.org/1998/Math/MathML" id="M3"> <mi>N</mi> </math> -dimensional. The problem can be applied to artificial intelligence classification problems (machine learning, deep learning), point data analysis problems (data science problem), the optimized design of nanoscale circuits, and the location of facility problems, circle detection on 2D image, or sphere detection on depth image. We generalized the discrete data classification methodology in <math xmlns="http://www.w3.org/1998/Math/MathML" id="M4"> <mi>N</mi> </math> -dimensional. Finally, we resolved to find an exact solution of the location of a manifold for our suggested problem in <math xmlns="http://www.w3.org/1998/Math/MathML" id="M5"> <mi>N</mi> </math> -dimensional.
Improved conditional differential attacks on lightweight hash family QUARK

Abstract Nonlinear feedback shift register (NFSR) is one of the most important cryptographic primitives in lightweight cryptography. At ASIACRYPT 2010, Knellwolf et al. proposed conditional differential attack to perform a cryptanalysis on NFSR-based cryptosystems. The main idea of conditional differential attack is to restrain the propagation of the difference and obtain a detectable bias of the difference of the output bit. QUARK is a lightweight hash function family which is designed by Aumasson et al. at CHES 2010. Then the extended version of QUARK was published in Journal of Cryptology 2013. In this paper, we propose an improved conditional differential attack on QUARK. One improvement is that we propose a method to select the input difference. We could obtain a set of good input differences by this method. Another improvement is that we propose an automatic condition imposing algorithm to deal with the complicated conditions efficiently and easily. It is shown that with the improved conditional differential attack on QUARK, we can detect the bias of output difference at a higher round of QUARK. Compared to the current literature, we find a distinguisher of U-QUARK/D-QUARK/S-QUARK/C-QUARK up to 157/171/292/460 rounds with increasing 2/5/33/8 rounds respectively. We have performed the attacks on each instance of QUARK on a 3.30 GHz Intel Core i5 CPU, and all these attacks take practical complexities which have been fully verified by our experiments. As far as we know, all of these results have been the best thus far.
Twitter Data Analysis Using Hadoop and ‘R’ and Emotional Analysis Using Optimized SVNN

Standalone systems cannot handle the giant traffic loads generated by Twitter due to memory constraints.A parallel computational environment provided by Apache Hadoop can distribute and process the data over different destination systems.In this paper, the Hadoop cluster with four nodes integrated with RHadoop, Flume, and Hive is created to analyze the tweets gathered from the Twitter stream.Twitter stream data is collected relevant to an event/topic like IPL-2015, cricket, Royal Challengers Bangalore, Kohli, Modi, from May 24 to 30, 2016 using Flume.Hive is used as a data warehouse to store the streamed tweets.Twitter analytics like maximum number of tweets by users, the average number of followers, and maximum number of friends are obtained using Hive.The network graph is constructed with the user's unique screen name and mentions using 'R'.A timeline graph of individual users is generated using 'R'.Also, the proposed solution analyses the emotions of cricket fans by classifying their Twitter messages into appropriate emotional categories using the optimized support vector neural network (OSVNN) classification model.To attain better classification accuracy, the performance of SVNN is enhanced using a chimp optimization algorithm (ChOA).Extracting the users' emotions toward an event is beneficial for prediction, but when coupled with visualizations, it becomes more powerful.Bar-chart and wordcloud are generated to visualize the emotional analysis results.