Reviewerly/exHarmony huggingface dataset:
Feature Selection by Improved Sand Cat Swarm Optimizer for Intrusion Detection

The rapid growth of number of devices that are connected to internet of things (IoT) networks, increases the severity of security problems that need to be solved in order to provide safe environment for network data exchange. The discovery of new vulnerabilities is everyday challenge for security experts and many novel methods for detection and prevention of intrusions are being developed for dealing with this issue. To overcome these shortcomings, artificial intelligence (AI) can be used in development of advanced intrusion detection systems (IDS). This allows such system to adapt to emerging threats, react in real-time and adjust its behavior based on previous experiences. On the other hand, the traffic classification task becomes more difficult because of the large amount of data generated by network systems and high processing demands. For this reason, feature selection (FS) process is applied to reduce data complexity by removing less relevant data for the active classification task and therefore improving algorithm's accuracy. In this work, hybrid version of recently proposed sand cat swarm optimizer algorithm is proposed for feature selection with the goal of increasing performance of extreme learning machine classifier. The performance improvements are demonstrated by validating the proposed method on two well-known datasets - UNSW-NB15 and CICIDS-2017, and comparing the results with those reported for other cutting-edge algorithms that are dealing with the same problems and work in a similar configuration.
Deep reinforcement learning-based algorithms selectors for the resource scheduling in hierarchical Cloud computing

Cloud computing environment is becoming increasingly complex due to its large-scale information growth and increasing heterogeneity of computing resources. Hierarchical Cloud computing dividing the system into multi-levels with multiple subsystems to support the adaptability to abundant requests from users has been widely applied and brings great challenges to resource scheduling. It is critical to find an effective way to address the complex scheduling problems in hierarchical Cloud computing, whose scenarios and optimization objectives often change with the types of subsystems. In this paper, we propose a scheduling framework to select the scheduling algorithms (SFSSA) for different scheduling scenarios considering no algorithm well suitable to all scenarios. To concretize SFSSA, we propose deep learning-based algorithms selectors (DLS) trained by labeled data and deep reinforcement learning-based algorithms selectors (DRLS) trained by feedback from dynamic scenarios to complete the algorithms selection regarding the scheduling algorithms as selectable tools. Then, we apply strategies including pre-trained model, long experience reply and joint training to improve the performance of DRLS. To enable the quantitative comparison of selectors, we introduce a weighted cost model for the trade-off between solution and complexity. Through multiple sets of experiments in hierarchical Cloud computing with multi subsystems for five types of scheduling problems and varying weights of cost, we demonstrate DLS and DRLS outperform baseline strategies. Compared with random selector, greedy selector, round-robin selector, single best selector, virtual best selector and single fast selector, DLS reduces the cost by 47.4%, 46.1%, 33.9%, 47.9%, 19.3%, 18.8% under stable parameter ranges, and DRLS reduces the cost by 41.1%, 40.6%, 11.7%, 42.3%, 11.5%, 12.5% in dynamic scenarios respectively. In experiments, we also validate DRLS has stronger adaptability than DLS in dynamic scheduling scenarios and DRLS using all of strategies achieves the best performance.
Joint event causality extraction using dual-channel enhanced neural network

Event Causality Extraction (ECE) plays an essential role in many Natural Language Processing (NLP), such as event prediction and dialogue generation. Recent research in NLP treats ECE as a sequence labeling problem. However, these methods tend to extract the events and their relevant causality using a single collapsed model, which usually focuses on the textual contents while ignoring the intra-element transitions inside events and inter-event causality transition association across events. In general, ECE should condense the complex relationship of intra-event and the causality transition association among events. Therefore, we propose a novel dual-channel enhanced neural network to address this limitation by taking both global event mentions and causality transition association into account. To extract complete event mentions, a Textual Enhancement Channel(TEC) is constructed to learn important intra-event features from the training data with a wider perception field. Then the Knowledge Enhancement Channel(KEC) incorporates external causality transition knowledge using a Graph Convolutional Network (GCN) to provide complementary information on event causality. Finally, we design a dynamic fusion attention mechanism to measure the importance of the two channels. Thus, our proposed model can incorporate both semantic-level and knowledge-level representations of events to extract the relevant event causality. Experimental results on three public datasets show that our model outperforms the state-of-the-art methods.
A user-centric privacy-preserving authentication protocol for IoT-AmI environments

Ambient Intelligence (AmI) in Internet of Things (IoT) has empowered healthcare professionals to monitor, diagnose, and treat patients remotely. Besides, the AmI-IoT has improved patient engagement and gratification as doctors’ interactions have become more comfortable and efficient. However, the benefits of the AmI-IoT-based healthcare applications are not availed entirely due to the adversarial threats. IoT networks are prone to cyber attacks due to vulnerable wireless mediums and the absentia of lightweight and robust security protocols. This paper introduces computationally-inexpensive privacy-assuring authentication protocol for AmI-IoT healthcare applications. The use of blockchain & fog computing in the protocol guarantees unforgeability, non-repudiation, transparency, low latency, and efficient bandwidth utilization. The protocol uses physically unclonable functions (PUF), biometrics, and Ethereum powered smart contracts to prevent replay, impersonation, and cloning attacks. Results prove the resource efficiency of the protocol as the smart contract incurs very minimal gas and transaction fees. The Scyther results validate the robustness of the proposed protocol against cyber-attacks. The protocol applies lightweight cryptography primitives (Hash, PUF) instead of conventional public-key cryptography and scalar multiplications. Consequently, the proposed protocol is better than centralized infrastructure-based authentication approaches.
Multiregional Coverage Path Planning for Multiple Energy Constrained UAVs

In recent years, we have witnessed a growing use of unmanned aerial vehicles (UAVs) in a variety of civil, commercial and military applications. Among these applications, many require the UAVs to scan or survey one or more regions, such as land monitoring, disaster assessment, search and rescue. To realize such applications, path planning is a key step. Although the coverage path planning (CPP) problem for a single region has been extensively studied in the literature, CPP for multiple regions has gained much less attention. This multi-regional CPP problem can be considered as a variant of the (multiple) traveling salesman problem (TSP) enhanced with CPP. Previously, we have studied the case of a single UAV. In this paper, we extend our previous studies to further consider multiple UAVs with energy constraints. To solve this new path planning problem, we develop two approaches: 1) a branch-and-bound (BnB) based approach that can find (near) optimal tours and 2) a genetic algorithm (GA) based approach that can solve large-scale problems efficiently under different objectives. Comprehensive theoretical analyses and computational experiments demonstrate the promising performance of the proposed approaches in terms of optimality and efficiency.
A knowledge graph completion model based on contrastive learning and relation enhancement method

The rapid development in knowledge graph (KG) technology and its popularity in the field of artificial intelligence (AI) have significantly increased the support for similar KG-based applications. However, there is a concerning problem regarding KGs; most of them are often incomplete. This motivated us to study knowledge graph completion (KGC). Some recent studies have used graph neural networks (GNN) such as graph convolutional networks (GCN) to model graph-structured data, providing good results on KGC tasks. However, the edge weights in GCN models are controlled by degree, a measure that moderately ignores the differences among relation information. To address the above limitations and obtain better KGC, we propose a model based on graph attention networks (GATs) and contrastive learning (CL), called the CLGAT-KGC model. This model introduces the graph attention mechanism and adds different representations of entities under the same entity corresponding to different relations to enhance the entity-relation message function. Additionally, a new CL method is proposed under the CLGAT-KGC model to better learn the embedding of entities and relations in the KG domain. We have completely verified the effectiveness of this model through extensive experiments.
Test smells 20 years later: detectability, validity, and reliability

Abstract Test smells aim to capture design issues in test code that reduces its maintainability. These have been extensively studied and generally found quite prevalent in both human-written and automatically generated test-cases. However, most evidence of prevalence is based on specific static detection rules. Although those are based on the original, conceptual definitions of the various test smells, recent empirical studies indicate that developers perceive warnings raised by detection tools as overly strict and non-representative of the maintainability and quality of test suites. This leads us to re-assess test smell detection tools’ detection accuracy and investigate the prevalence and detectability of test smells more broadly. Specifically, we construct a hand-annotated dataset spanning hundreds of test suites both written by developers and generated by two test generation tools ( EvoSuite and JTExpert ) and performed a multi-stage, cross-validated manual analysis to identify the presence of six types of test smells in these. We then use this manual labeling to benchmark the performance and external validity of two test smell detection tools—one widely used in prior work and one recently introduced with the express goal to match developer perceptions of test smells. Our results primarily show that the current vocabulary of test smells is highly mismatched to real concerns: multiple smells were ubiquitous on developer-written tests but virtually never correlated with semantic or maintainability flaws; machine-generated tests actually often scored better, but in reality, suffered from a host of problems not well-captured by current test smells. Current test smell detection strategies poorly characterized the issues in these automatically generated test suites; in particular, the older tool’s detection strategies misclassified over 70% of test smells, both missing real instances (false negatives) and marking many smell-free tests as smelly (false positives). We identify common patterns in these tests that can be used to improve the tools, refine and update the definition of certain test smells, and highlight as of yet uncharacterized issues. Our findings suggest the need for (i) more appropriate metrics to match development practice, (ii) more accurate detection strategies to be evaluated primarily in industrial contexts.
Development and innovation of enterprise knowledge management strategies using big data neural networks technology

To strengthen the development of enterprises and optimize knowledge management strategies, the current situation of enterprise knowledge management (EKM) is investigated and the evaluation indicators of EKM strategies are analyzed. The specific structure and principles of neural network algorithms are studied using big data. Finally, neural networks (NNs) technology is used to evaluate EKM strategies and calculate the specific weight and strategy application of EKM using big data. The results show that with the support of big data, the use of NNs technology can analyze not only the knowledge management strategies, but also the different strategies of knowledge management used by different enterprises. When analyzing EKM strategies, enterprises indicators collected by big data vary greatly. The highest and lowest values are approximately 0.94 and 0.28, respectively. It indicates that NNs technology can be used to study different knowledge management strategies. Using this technology, the knowledge management strategies of different enterprises are calculated and optimized. The error between the final calculation and the actual result is relatively small, with a maximum and minimum of approximately 0.197 and 0.012, respectively. With the support of big data, the innovation and development of EKM strategy using NNs technology provides technical support for EKM and a reference.
Methods to Detect an Event using Artificial Intelligence and Machine Learning

Machine Learning can improve and automate how anomalous events can be detected and reported faster than before. Some of the ways events can be easily detected through the use of Artificial Intelligence and Machine Learning Models are, using big data analysis on Twitter and Facebook which are examples of social media sites. to get huge event data and a machine learning model such as SVM to process them to segregate and identify an event as anomalous or not, using CNN ( Convolutional neural network ). This network performs feature extraction and classification in a very optimum way and detects fire hazards at a higher pace. While Convolution Neural Networks are good at extracting 2D localised features, a new 3D feature extraction Model called T-CNN or Tube Convolutional Neural Network can also be used for Event Detection. This paper looks at various methods mentioned above and explains the best way to detect Events using Machine Learning.
Fast image encryption algorithm based on multi-parameter fractal matrix and MPMCML system

This paper studies chaotic image encryption technology and fractal matrix theory, and proposes a synchronous permutation-diffusion encryption algorithm based on multi-parameter fractal matrix. First, this paper proposes a new Multiple Parameter Mixed Coupled Map Lattices system (MPMCML). Then, the concept of fractal matrix is introduced, and the construction process of multi-parameter fractal matrix is given. Finally, based on the multi-parameter fractal matrix, a new synchronous permutation-diffusion image encryption algorithm is proposed. Most importantly, the multi-parameter fractal matrix is very sensitive to the change of parameters, any slight change of parameters will produce a completely different fractal matrix, making the encryption result completely different. Compared with the methods of permutation before diffusion and diffusion before permutation, the synchronous permutation-diffusion method greatly improves the privacy of permutation and diffusion, and improves the anti-attack ability of the algorithm. By analyzing the encryption performance and the ability to resist various attacks, it can be determined that the proposed algorithm exhibits good security characteristics.
P-Detector: Real-Time P-Wave Detection in a Seismic Waveform Recorded on a Low-Cost MEMS Accelerometer Using Deep Learning

Recently, the Internet of Things (IoT) systems have been widely used for earthquake detection because of the easy construction of a dense seismic network, communication capabilities, and low cost of sensors. However, when utilizing MEMS sensors as seismic sensors, earthquake detection capabilities are often affected by various types of noises because such sensors are installed in heterogeneous environments. In earthquakes, P-waves first arrive, but their lengths are only a few seconds, and their amplitudes are also relatively smaller than to S-waves. As a result, it is difficult to accurately detect P-waves in IoT systems where environmental noises are always present. Furthermore, when using deep learning approaches for earthquake detection, inference time usually becomes a critical factor for real-time processing because of the complex architecture of a detection model. To that end, in this letter, we present a deep learning model that can detect P-waves in noisy environments. The model outputs the detection probability before the arrival of strong shakes. We tested our model on earthquakes recorded by the IoT-based seismic sensors deployed in South Korea. Our model can detect P-waves within 1.5&#x2013;2.5 s after the first arrival of P-wave with the accuracy of 98.8&#x0025;, making it applicable in real-time earthquake detection.
Least Significant Bit Steganography for Text and Image hiding

Digital image attestation is powerful regard for the digital rebellion. To fulfill this need, different watermarking methods have been established. Though, it is hard to accomplish a watermarking system that is robust and secure. In this paper, one of the spatial domain approaches has been considered for image hiding and text hiding. The technique used in this work is the least-significant bit steganography which is easy to implement and gives a high mean square error and low peak signal to noise ratio. In this approach, one to eight bits of the first component of the pixels in the carrier image is replaced with the most significant bits of the secret data. To see the effect of the given approach in text hiding and image hiding, experimental results has been performed for different target bit-planes.
HIT HAR: Human Image Threshing Machine for Human Activity Recognition Using Deep Learning Models

In recent days, research in human activity recognition (HAR) has played a significant role in healthcare systems. The accurate activity classification results from the HAR enhance the performance of the healthcare system with broad applications. HAR results are useful in monitoring a person's health, and the system predicts abnormal activities based on user movements. The HAR system's abnormal activity predictions provide better healthcare monitoring and reduce users' health issues. The conventional HAR systems use wearable sensors, such as inertial measurement unit (IMU) and stretch sensors for activity recognition. These approaches show remarkable performances to the user's basic activities such as sitting, standing, and walking. However, when the user performs complex activities, such as running, jumping, and lying, the sensor-based HAR systems have a higher degree of misclassification results due to the reading errors from sensors. These sensor errors reduce the overall performance of the HAR system with the worst classification results. Similarly, radiofrequency or vision-based HAR systems are not free from classification errors when used in real time. In this paper, we address some of the existing challenges of HAR systems by proposing a human image threshing (HIT) machine-based HAR system that uses an image dataset from a smartphone camera for activity recognition. The HIT machine effectively uses a mask region-based convolutional neural network (R-CNN) for human body detection, a facial image threshing machine (FIT) for image cropping and resizing, and a deep learning model for activity classification. We demonstrated the effectiveness of our proposed HIT machine-based HAR system through extensive experiments and results. The proposed HIT machine achieved 98.53% accuracy when the ResNet architecture was used as its deep learning model.
Deep Rectangling for Image Stitching: A Learning Baseline

Stitched images provide a wide field-of-view (FoV) but suffer from unpleasant irregular boundaries. To deal with this problem, existing image rectangling methods devote to searching an initial mesh and optimizing a target mesh to form the mesh deformation in two stages. Then rectangular images can be generated by warping stitched images. However, these solutions only work for images with rich linear structures, leading to noticeable distortions for portraits and landscapes with non-linear objects. In this paper, we address these issues by proposing the first deep learning solution to image rectangling. Concretely, we predefine a rigid target mesh and only estimate an initial mesh to form the mesh deformation, contributing to a compact one-stage solution. The initial mesh is predicted using a fully convolutional network with a residual progressive regression strategy. To obtain results with high content fidelity, a comprehensive objective function is proposed to simultaneously encourage the boundary rectangular, mesh shape-preserving, and content perceptually natural. Besides, we build the first image stitching rectangling dataset with a large diversity in irregular boundaries and scenes. Experiments demonstrate our superiority over traditional methods both quantitatively and qualitatively.
Saliency in Augmented Reality

With the rapid development of multimedia technology, Augmented Reality (AR) has become a promising next-generation mobile platform. The primary theory underlying AR is human visual confusion, which allows users to perceive the real-world scenes and augmented contents (virtual-world scenes) simultaneously by superimposing them together. To achieve good Quality of Experience (QoE), it is important to understand the interaction between two scenarios, and harmoniously display AR contents. However, studies on how this superimposition will influence the human visual attention are lacking. Therefore, in this paper, we mainly analyze the interaction effect between background (BG) scenes and AR contents, and study the saliency prediction problem in AR. Specifically, we first construct a Saliency in AR Dataset (SARD), which contains 450 BG images, 450 AR images, as well as 1350 superimposed images generated by superimposing BG and AR images in pair with three mixing levels. A large-scale eye-tracking experiment among 60 subjects is conducted to collect eye movement data. To better predict the saliency in AR, we propose a vector quantized saliency prediction method and generalize it for AR saliency prediction. For comparison, three benchmark methods are proposed and evaluated together with our proposed method on our SARD. Experimental results demonstrate the superiority of our proposed method on both of the common saliency prediction problem and the AR saliency prediction problem over benchmark methods. Our dataset and code are available at: https://github.com/DuanHuiyu/ARSaliency.
Search-oriented Micro-video Captioning

Pioneer efforts have been dedicated to the content-oriented video captioning that generates relevant sentences to describe the visual contents of a given video from the producer perspective. By contrast, this work targets at the search-oriented one that summarizes the given video via generating query-like sentences from the consumer angle. Beyond relevance, diversity is vital in characterizing consumers' seeking intention from different aspects. Towards this end, we devise a large-scale multimodal pre-training network regularized by five tasks to strengthen the downstream video representation, which is well-trained over our collected 11M micro-videos. Thereafter, we present a flow-based diverse captioning model to generate different captions from consumers' search demand. This model is optimized via a reconstruction loss and a KL divergence between the prior and the posterior. We justify our model over our constructed golden dataset comprising 690k <query, micro-video> pairs and experimental results demonstrate its superiority.