Reviewerly/exHarmony huggingface dataset:
A Framework to Predict Social Crime through Twitter Tweets By Using Machine Learning

An increasing amount of data and information coming from social networks that can be used to generate a variety of data patterns for different types of investigation such as human social behavior, system security, criminology etc. A framework is developed to predict major types of social media crimes (Cyber stalking, Cyber bullying, Cyber Hacking, Cyber Harassment, and Cyber Scam) using the data obtained from social media website. The proposed framework is consist of three modules; data (tweet) pre-processing, classifying model builder and prediction. To build the prediction model Multinomial Naïve Bayes (MNB), K-Nearest Neighbors (KNN) and Support Vector Machine (SVM) is used that classify given data into different classes of crime. Further N-Gram language model is used with these machine learning algorithms to identify the best value of n and measure the accuracy of the system at different levels such as Unigram, Bigram, Trigram, and 4-gram. Results shows that all three algorithm attain the precision, Recall and F-measure above than 0.9 however Support vector machine performed slightly better. The proposed system produced better accuracy result as compared to existing network-based feature selection approach.
Predicting learners' effortful behaviour in adaptive assessment using multimodal data

Many factors influence learners' performance on an activity beyond the knowledge required. Learners' on-task effort has been acknowledged for strongly relating to their educational outcomes, reflecting how actively they are engaged in that activity. However, effort is not directly observable. Multimodal data can provide additional insights into the learning processes and may allow for effort estimation. This paper presents an approach for the classification of effort in an adaptive assessment context. Specifically, the behaviour of 32 students was captured during an adaptive self-assessment activity, using logs and physiological data (i.e., eye-tracking, EEG, wristband and facial expressions). We applied k-means to the multimodal data to cluster students' behavioural patterns. Next, we predicted students' effort to complete the upcoming task, based on the discovered behavioural patterns using a combination of Hidden Markov Models (HMMs) and the Viterbi algorithm. We also compared the results with other state-of-the-art classification algorithms (SVM, Random Forest). Our findings provide evidence that HMMs can encode the relationship between effort and behaviour (captured by the multimodal data) in a more efficient way than the other methods. Foremost, a practical implication of the approach is that the derived HMMs also pinpoint the moments to provide preventive/prescriptive feedback to the learners in real-time, by building-upon the relationship between behavioural patterns and the effort the learners are putting in.
BLWN: Blockchain-Based Lightweight Simplified Payment Verification in IoT-Assisted e-Healthcare

Recent advancement of Bitcoin revolution has propelled unlimited opportunities for futuristic digital ecosystem design. Transactions belonging to Bitcoin are being fully supported, realized, and disseminated by a set of innovative hot and cold wallet technologies within the blockchain framework. While deploying smart applications under the niche of Internet of Things (IoT) plethora, such Bitcoin transactions aroused the issue of unsupported resource-constrained tool sets and demanded for a novel architecture. To cater this challenge, this article proposes a novel Bitcoin lightweight IoT node-based system model while incorporating the improved simplified payment verification (SPV) process for e-healthcare application. This article, first formulates necessary models of background arts utilized in the work that include inhomogeneous Poisson point process, block header structure formulation, depth and height relationship, bloom filter mechanism, pay-to-public key hash (P2PKH) base stack process, and transaction management. Second, developed models are deployed through several key algorithms and associated procedures. Finally, analysis and discussions are made that encompass the block confirmation time, appropriateness of the byzantine fault tolerance, smart contract policy, and SPV response in the proposed scenario.
Egalito

For comprehensive analysis of all executable code, and fast turn-around time for transformations, it is essential to operate directly on binaries to enable profiling, security hardening, and architectural adaptation. Disassembling binaries is difficult, and prior work relies on a process virtual machine to translate references on the fly or inefficient binary code patching. Our Egalito recompiler leverages metadata present in current stripped x86_64 and ARM64 binaries to generate a complete disassembly, and allows arbitrary modifications that may affect program layout without any constraints from the original binary. We utilize our own layout-agnostic intermediate representation, which is low-level enough to make the regeneration of output code predictable, yet supports a dual high-level representation for sophisticated analysis. We demonstrate nine binary tools including a novel continuous code randomization technique where Egalito transforms itself, and software emulation of the control-flow integrity in upcoming hardware. We evaluated Egalito on a large set of Debian packages, completely analyzing 99.9% of a selection of 867 executables and libraries; a majority of 149 applicable Debian packages pass all tests under Egalito. On SPEC CPU 2006, thanks to our binary optimizations, Egalito actually observes a 1.7% performance speedup.
Critical infrastructures cybersecurity and the maritime sector

The paper addresses cyber-security in the maritime field, a sector increasingly vulnerable to cyber-attacks due to advances that are already in the process of implementation. This paper explores the level of knowledge and training required on the subject and its interaction with marine ecosystem. For this reason, we will carry out a deep bibliographic review in which we will support our later study. We will analyze the results obtained in an online questionnaire answered by experienced maritime professionals. The results show a lack of general knowledge in the field of maritime cybersecurity. Therefore, it is necessary to increase training levels in the maritime sector and the port interface connection with the supply chain.
Practical Data Poisoning Attack against Next-Item Recommendation

Online recommendation systems make use of a variety of information sources to provide users the items that users are potentially interested in. However, due to the openness of the online platform, recommendation systems are vulnerable to data poisoning attacks. Existing attack approaches are either based on simple heuristic rules or designed against specific recommendations approaches. The former often suffers unsatisfactory performance, while the latter requires strong knowledge of the target system. In this paper, we focus on a general next-item recommendation setting and propose a practical poisoning attack approach named LOKI against blackbox recommendation systems. The proposed LOKI utilizes the reinforcement learning algorithm to train the attack agent, which can be used to generate user behavior samples for data poisoning. In real-world recommendation systems, the cost of retraining recommendation models is high, and the interaction frequency between users and a recommendation system is restricted. Given these real-world restrictions, we propose to let the agent interact with a recommender simulator instead of the target recommendation system and leverage the transferability of the generated adversarial samples to poison the target system. We also propose to use the influence function to efficiently estimate the influence of injected samples on the recommendation results, without re-training the models within the simulator. Extensive experiments on two datasets against four representative recommendation models show that the proposed LOKI achieves better attacking performance than existing methods.
Compressed $$\varSigma $$-Protocol Theory and Practical Application to Plug &amp; Play Secure Algorithmics

$$\varSigma $$ -Protocols provide a well-understood basis for secure algorithmics. Recently, Bulletproofs (Bootle et al., EUROCRYPT 2016, and Bünz et al., S&P 2018) have been proposed as a drop-in replacement in case of zero-knowledge (ZK) for arithmetic circuits, achieving logarithmic communication instead of linear. Its pivot is an ingenious, logarithmic-size proof of knowledge BP for certain quadratic relations. However, reducing ZK for general relations to it forces a somewhat cumbersome “reinvention” of cryptographic protocol theory. We take a rather different viewpoint and reconcile Bulletproofs with $$\varSigma $$ -Protocol Theory such that (a) simpler circuit ZK is developed within established theory, while (b) achieving exactly the same logarithmic communication. The natural key here is linearization. First, we repurpose BPs as a blackbox compression mechanism for standard $$\varSigma $$ -Protocols handling ZK proofs of general linear relations (on compactly committed secret vectors); our pivot. Second, we reduce the case of general nonlinear relations to blackbox applications of our pivot via a novel variation on arithmetic secret sharing based techniques for $$\varSigma $$ -Protocols (Cramer et al., ICITS 2012). Orthogonally, we enhance versatility by enabling scenarios not previously addressed, e.g., when a secret input is dispersed across several commitments. Standard implementation platforms leading to logarithmic communication follow from a Discrete-Log assumption or a generalized Strong-RSA assumption. Also, under a Knowledge-of-Exponent Assumption (KEA) communication drops to constant, as in ZK-SNARKS. All in all, our theory should more generally be useful for modular (“plug & play”) design of practical cryptographic protocols; this is further evidenced by our separate work (2020) on proofs of partial knowledge.
Cloud Computing: History and Overview

Cloud computing has emerged as a new technology and business paradigm in the last couple of years. Cloud computing platforms provide easy access, scalability, reliability, reconfigurability, and high performance from its resources over the Internet without complex infrastructure management by customers. This article presents, a brief history of cloud computing from 1961 when McCarthy at MIT introduced about cloud computing, the evolution of cloud computing from its predecessors such as Utility computing and Grid computing, and development of cloud computing. We have also presented various directions in cloud computing along with advantages, cloud-centric design, mobile cloud, and security. It covers the characteristics, service models, and deployment models of cloud computing. We have presented the applications and security aspects associated with cloud computing.
Cultural heritage, tourist attractiveness and augmented reality: insights from Italy

In recent years, cutting-edge information and communication technologies (ICTs) have been implemented at different scales in order to improve tourists’ experiences of cultural heritage. In particular, advanced technologies combining augmented reality can offer a deeply immersive visitor’s experience aiming both at providing multifaceted information about the site and enhancing the attractiveness of a tourist destination at the different scales. Thus, the study aims at critically scrutinising the intersection of AR and tourist practices through the analysis of tourism destination stakeholders’ perceived value of AR solutions. The aim is to understand how it is crucial to take into account stakeholders’ perceptions about cutting-edge technology development, apart from providing practical implications for industry practitioners and local institutional actors.From the methodological standpoint, the article is based on a multi-method approach encompassing field-work observation, in addition to a stakeholder and case study approach, aiming at evaluating internal and external stakeholders’ perceived value through a qualitative perspective based on semi-structured interviews with institutional actors, entrepreneurs, visitors and local community representatives. The results suggested some critical reflections about the inherent elusiveness of technological innovation in the case it is not incorporated in a long-term sustainable vision.
PacGAN: The Power of Two Samples in Generative Adversarial Networks

Generative adversarial networks (GANs) are innovative techniques for learning generative models of complex data distributions from samples. Despite remarkable improvements in generating realistic images, one of their major shortcomings is the fact that in practice, they tend to produce samples with little diversity, even when trained on diverse datasets. This phenomenon, known as mode collapse, has been the main focus of several recent advances in GANs. Yet there is little understanding of why mode collapse happens and why recently-proposed approaches mitigate mode collapse. We propose a principled approach to handle mode collapse called packing. The main idea is to modify the discriminator to make decisions based on multiple samples from the same class, either real or artificially generated. We borrow analysis tools from binary hypothesis testing-in particular the seminal result of (Blackwell, 1953)-to prove a fundamental connection between packing and mode collapse. We show that packing naturally penalizes generators with mode collapse, thereby favoring generator distributions with less mode collapse during the training process. Numerical experiments on benchmark datasets suggests that packing provides significant improvements in practice as well.
An Improved Grey Wolf Optimizer Based on Tracking and Seeking Modes to Solve Function Optimization Problems

Grey wolf optimizer (GWO) is a new meta-heuristic algorithm. The GWO algorithm mimics the leadership hierarchy and hunting mechanism of grey wolves in nature. Three main stages of hunting include: encircling, tracking and attacking. It is easy to fall into local optimum when used to optimize high-dimensional data, and there is imbalance between exploration and exploitation. An improved grey wolf optimizer based on tracking mode and seeking mode is proposed to improve the diversity of the population and the ability of the algorithm to balance exploration and exploitation. The algorithm is verified by simulation experiments in three parts. Firstly, the proposed grey wolf optimizer based on tracking mode (TGWO), the improved grey wolf optimizer based on seeking mode (SGWO), the improved grey wolf optimizer based on tracking and seeking mode (TSGWO), Grey Wolf Optimizer (GWO), Particle Swarm Optimization (PSO), Salp Swarm Algorithm (SSA), Sine Cosine Algorithm (SCA), Ant Lion Optimizer (ALO), Whale Optimization Algorithm (WOA) and Moth-flame Optimization (MFO) are adopted to optimize 21 typical benchmark functions respectively, and the obtained statistical simulation results are compared; Secondly, the improved algorithm proposed in this paper is compared with Binary Grey Wolf Optimizer (BGWO), Hybrid PSOGWO Optimization (PSOGWO) and GWO Algorithm Integrated with Cuckoo Search (GWOCS); Finally, it is applied to the lightest design engineering problem of pressure vessels. Simulation results show that the superior performance of the proposed algorithm for exploiting the optimum and it has advantages in terms of exploration. The improved grey wolf optimizer based on tracking mode and seeking mode can better solve function optimization and classical engineering problems with constraints. It was found the improved grey wolf optimizer based on tracking mode has the high precision and the characteristics of balanced exploration and exploitation.
Visual complexity analysis using deep intermediate-layer features

In this paper, we focus on visual complexity, an image attribute that humans can subjectively evaluate based on the level of details in the image. We explore unsupervised information extraction from intermediate convolutional layers of deep neural networks to measure visual complexity. We derive an activation energy metric that combines convolutional layer activations to quantify visual complexity. To show the effectiveness of our proposed metric for various applications, we introduce Savoias, a visual complexity dataset that compromises of more than 1,400 images from seven diverse image categories (e.g., advertisement and interior design). We demonstrate high correlations of our deep neural network-based measure of visual complexity with human-curated ground-truth (GT) scores on various widely used network architectures, e.g., VGG16, ResNet-v2-152, and EfficientNet, and in networks trained on two classification tasks (object and scene classification). This result reveals that intermediate convolutional layers of deep neural networks carry information about the complexity of images that is meaningful to people. Furthermore, we show that our method of measuring visual complexity outperforms traditional methods on Savoias and two other state-of-the-art benchmark datasets. Moreover, we perform extensive analysis on the performance difference between our unsupervised method and a supervised method trained on the feature map, and show that by supervision, we can improve the prediction. Finally, we demonstrate that, within the context of a category, visually more complex images are also more memorable to human observers.
Enabling Intelligent Environment by the Design of Emotionally Aware Virtual Assistant: A Case of Smart Campus

With the advent of the 5G and Artificial Intelligence of Things (AIoT) era, related technologies such as the Internet of Things, big data analysis, cloud applications, and artificial intelligence have brought broad prospects to many application fields, such as smart homes, autonomous vehicles, smart cities, healthcare, and smart campus. At present, most university campus app is presented in the form of static web pages or app menus. This study mainly developed a Deep Neural Network (DNN) based emotionally aware campus virtual assistant. The main contributions of this research are: (1) This study introduces the Chinese Word Embedding to the robot dialogue system, effectively improving dialogue tolerance and semantic interpretation. (2) The traditional method of emotion identification must first tokenize the Chinese sentence, analyze the clauses and part of speech, and capture the emotional keywords before being interpreted by the expert system. Different from the traditional method, this study classifies the input directly through the convolutional neural network after the input sentence is converted into a spectrogram by Fourier Transform. (3) This study is presented in App mode, which is easier to use and economical. (4) This system provides a simple voice response interface, without the need for users to find information in complex web pages or app menus.
Crowd Detection in Aerial Images Using Spatial Graphs and Fully-Convolutional Neural Networks

Unmanned aerial vehicles (UAVs) also known as drones are increasingly populating our skies.This represents a relevant issue both for the legislator and the researcher.While the regulation plans often assume precautionary approaches, stating restrictive conditions of use for the sake of public safety, the applied research is exploring novel strategies to develop autonomous vehicles endowed with trusty operating mechanisms.The challenge is to let drones overflying even populated areas while keeping a steady control of the situation on the ground, thus enabling the possibility of safe landing with no harm for people.This can be done employing on-board cameras and embedded GPUs which allow for the execution in real-time of computer vision applications.In this paper, we introduce a crowd detection method for drone safe landing.The pivotal points of our proposal are related to the computational limitations imposed by the currently available hardware resources of UAVs.In this sense, our method is based on the light-weight scheme of a fully-convolutional neural network which conjugates nimble computations and effectiveness.We propose a two-loss model where a classification task (oriented to distinguish between crowded/non-crowded scenes) is supported by a regression task (aimed at better focusing the agglomeration tendency of the persons).This latter job is realized by resorting to the construction of a spatial graph for each analysed image and to the evaluation of the corresponding clustering coefficient.As a further element, our model is endowed with the capability to produce class activation heatmaps which contribute to the semantic enrichment of the flight maps.We tested our model on a large dataset of aerial images and we observed how it compares favorably with other approaches proposed in literature.
Neuron Linear Transformation: Modeling the Domain Shift for Crowd Counting

Cross-domain crowd counting (CDCC) is a hot topic due to its importance in public safety. The purpose of CDCC is to alleviate the domain shift between the source and target domain. Recently, typical methods attempt to extract domain-invariant features via image translation and adversarial learning. When it comes to specific tasks, we find that the domain shifts are reflected in model parameters' differences. To describe the domain gap directly at the parameter level, we propose a neuron linear transformation (NLT) method, exploiting domain factor and bias weights to learn the domain shift. Specifically, for a specific neuron of a source model, NLT exploits few labeled target data to learn domain shift parameters. Finally, the target neuron is generated via a linear transformation. Extensive experiments and analysis on six real-world data sets validate that NLT achieves top performance compared with other domain adaptation methods. An ablation study also shows that the NLT is robust and more effective than supervised and fine-tune training. Code is available at https://github.com/taohan10200/NLT.
TDHPPIR: An Efficient Deep Hashing Based Privacy-Preserving Image Retrieval Method

With the rapid development of multimedia techniques, mobile Internet and cloud computing, large-scale image retrieval service has become a necessity in our daily life. To overcome the challenges of privacy-preserving and data security during the image retrieval, we propose a novel deep hashing based privacy-preserving image retrieval method named TDHPPIR that can generate high quality hash codes of image and provide an efficient index structure for fast image retrieval in a security manner in cloud. A triplet Deep CNN structure model is introduced to learn deep visual representations and hash codes of images simultaneously, which can generate higher quality hash codes than the traditional way that mainly utilizes hand-crafted features. Besides, A novel hierachical bit-scalable hash codes based S-Tree, named H2S-Tree, is developed to increase the search efficiency. Comprehensive experiments on four benchmarks show that our method can combat the state-of-the-arts in both aspects of accuracy and efficiency.