Reviewerly/exHarmony huggingface dataset:
Cyber situational awareness issues and challenges

Today, most enterprises are increasingly reliant on information technology to carry out their operations. This also entails an increasing need for cyber situational awareness—roughly, to know what is going on in the cyber domain, and thus be able to adequately respond to events such as attacks or accidents. This chapter argues that cyber situational awareness is best understood by combining three complementary points of view: the technological, the socio-cognitive, and the organizational perspectives. In addition, the chapter investigates the prospects for reasoning about adversarial actions. This part also reports on a small empirical investigation where participants in the Locked Shields cyber defense exercise were interviewed about their information needs with respect to threat actors. The chapter is concluded with a discussion regarding important challenges to be addressed along with suggestions for further research.
Energy- and Quality of Experience-Aware Dynamic Resource Allocation for Massively Multiplayer Online Games in Heterogeneous Cloud Computing Systems

Massively multiplayer online games (MMOGs) are a new type of large-scale interactive applications providing a seamless virtual world for millions of gamers from all over the world. Traditionally, MMOGs are implemented using a client-server architecture. With the maturity of cloud computing, many MMOG providers such as Blizzard Entertainment and Jagex Limited have begun to utilize virtualized machines to serve their consumers due to the elasticity, adaptability, and cost advantages of clouds. It is a major challenge for MMOG providers how to dynamically allocate resources in an on-demand pattern in order to reduce the energy cost associated with operating a MMOG cloud while providing good-enough quality of experience (QoE) for MMOG gamers. In this paper, we propose a dynamic resource allocation scheme for MMOGs in heterogeneous cloud computing systems, which makes use of both dynamic virtual machine (VM) consolidation among physical machines (PMs) and long short-term memory based VM resizing at physical machine level to achieve the energy efficiency and desired QoE requirements. Our scheme considers multiple types of resources, characteristic of AFK (away from keyboard) gamers, heterogeneous PMs and VMs, strict QoE requirements and overheads incurred due to migrating VMs. Furthermore, a novel hybrid algorithm based on differential evolution and modified first-fit heuristic is presented for dynamic consolidation of VMs in heterogeneous cloud data centers. The experiment results show that, compared to the traditional over-provisioning policy, our resource allocation scheme can achieve up to 44.8&#x0025; energy savings while ensuring the QoE requirements of MMOG gamers under rapidly changing workloads.
TSSRD: A Topic Sentiment Summarization Framework Based on Reaching Definition

Exposure to massive information in daily lives makes it necessary for people to obtain major points efficiently, promoting the development of text summarization technology. However, existing sentiment-based text summarization methods only pay attention to the sentiment polarity of either a single sentence or a whole document, ignoring changes of sentiments along with sentences or sentiment flow across the whole document. To incorporate the above two aspects into the summarization process to generate high-quality summaries, we propose a topic sentiment summarization framework based on reaching definition (TSSRD). In the framework, we first use topic models to model documents and calculate topic sentiment embeddings. Then, we analyze document structures from different perspectives to design data flow diagrams, in which improved reaching definition is used to analyze sentiment changes and sentiment flow. Finally, topic sentiment summaries are generated based on sentiments in steady states of the reaching definition. To evaluate our summarization framework, we introduce an extrinsic evaluation method. In this method, a sentiment classifier is trained by the topic sentiment summaries, and accuracy of the sentiment classification is used as a quality score. Experimental results demonstrate that our summarization framework is at least 2.32% better than baselines on IMDb and Amazon datasets.
Application of Denoising Weighted Bilateral Filter and Curvelet Transform on Brain MR Imaging of Non-cooperative Patients

The use of a volume phase array type coil in the brain magnetic resonance imaging (MRI) examination of non-cooperative patients is associated with image noise arising from random electrical fluctuations in the body and causing it to not reflect the true pixel intensity value. Therefore noise reduction is needed in medical image processing. This research to apply denoising weighted bilateral filter and curvelet transform (WBFCT) on T2 weighted turbo spin echo (TSE) brain MRI images in non-cooperative patients. The aim of this research is to determine the differences in information on MRI brain images of T2 TSE sequences in non-cooperative patients between before and after the application of denoising WBFCT. This experimental research was carried out on MRI brain images of T2 TSE sequences in 19 non-cooperative patients. Then proceed with the application of denoising WBFCT on the image. Image assessment using 3 radiologists with data analysis using statistical processing and image post processing. The result shows that information on the brain MRI image of the T2 TSE sequence experienced differences in image information between before and after the application of denoising WBFCT (p-value <0.001), with an average mean square error (MSE) and peak signal-to-noise ratio (PSNR) of 387.995 and 70.5369 dB, where the image after denoising WBFCT had the most optimal image information. Finally, by denoising WBFCT can optimally produce information on MRI brain images of T2 TSE sequences in axial sections in non-cooperative patients.
Norm-Aware Margin Assignment for Person Re-Identification

Margin-based metric losses have shown great success in Person Re-identification and Face Verification. But most existing works adopt a fixed class-level margin regardless of the difference between each training sample. This paper proposes a Norm-Aware Margin Assignment (NAMA) scheme to dynamically adjust the weight of each sample during training. Combined with the existing margin-based classification losses, NAMA improves the robustness of feature embedding by assigning larger margins to more recognizable samples. NAMA is a fully trainable module that automatically models the correlation between the optimal margin and image quality during back-propagation without supervision. To stabilize the training and make the assigned margin more controllable, we introduce a margin re-balance mechanism to align the expectation of learned margins to a pre-defined value. Extensive experiments on three popular ReID benchmarks validate the effectiveness of our NAMA method. Code will be publicly available at: https://github.com/huangzongheng/NAMA.
Consumers’ Trust Mechanism and Trust Boundary on Humanizing Customer Service Chatbots in E-commerce

Humanizing customer service chatbots have sparked significant interest for companies across industries. These years have witnessed some controversy on trust issues of such booming application. Previous researches have proposed some antecedents of customer service chatbots adoption (e.g., anthropomorphic features, algorithm aversion, emotional state). However, consumers' trust mechanism and trust boundary on humanizing customer service chatbots are not clear. Hence, we pay attention to personalization and contextualization grounded on above antecedents of customer service, incorporating personal habit, task creativity and social presence to investigate trust mechanism and trust boundary. We propose a research model, in which personal habit and task creativity are captured as independent variables, trust in humanizing customer service chatbots as dependent variable, and social presence as moderating variable. Hypotheses are developed and between-subjects scenario experiments are conducted to test hypotheses. Results of analysis of covariance (ANCOVA) and moderating effect test show that there exists positive effect between personal habit and trust in humanizing customer service chatbots, giving insights on complementary and substitutive influences on the interaction of independent variables and social presence for trust boundary. This paper provides practical and theoretical implications for e-commerce practitioners to improve the collaboration performance of intelligent customer service and human customer service.
Lightweight and Dynamic Deblurring for IoT-Enabled Smart Cameras

Blurred images often significantly influence the stability of computer vision systems. With the development of the Internet of Things (IoT), a camera that leverages artificial intelligence and edge computing (hereafter referred to as an edge camera) can enhance the robustness of an IoT service. Although previous studies have used deep neural networks (DNNs) for image deblurring, existing image-deblurring techniques result in over-smoothing, even with a lightweight model design. In addition, previous deblurring studies have not considered the quality of the input image first; thus, a clear input image would be processed with unnecessary deblurring. Therefore, our study proposes a lightweight and dynamic border-enhancement deblurring deep network that can be operated with high performance on edge cameras, and the network can assess the quality of input images to determine whether deblurring is necessary. The experimental results show that our lightweight deblurring approach outperforms existing studies by up to 8%, given that the speed also improved by 33.78%. As for the dynamic deblurring approach, our results show that the image metrics can be slightly improved and that speed is improved by 294.5%.
Chosen-Ciphertext Attack Secure Public-Key Encryption with Keyword Search

As the use of cloud storage for various services increases, the amount of private personal information along with data stored in the cloud storage is also increasing. To remotely use the data stored on the cloud storage, the data to be stored needs to be encrypted for this reason. Since “searchable encryption” is enable to search on the encrypted data without any decryption, it is one of convenient solutions for secure data management. A public key encryption with keyword search (for short, PEKS) is one of searchable encryptions. Abdalla et al. firstly defined IND-CCA security for PEKS to enhance it’s security and proposed consistent IND-CCA secure PEKS based on the “robust” ANO-CCA secure identity-based encryption(IBE). In this paper, we propose two generic constructions of consistent IND-CCA secure PEKS combining (1) a hierarchical identity based encryption (for short, HIBE) and a signature scheme or (2) a HIBE, an encapsulation, and a message authentication code (for short, MAC) scheme. Our generic constructions identify that HIBE requires the security of a signature or a MAC as well as the weaker “ANO-CPA security (resp., IND-CPA security)” of HIBE than “ANO-CCA security (resp., IND-CCA security)” of IBE required in for achieving IND-CCA secure (resp., consistent) PEKS. Finally, we prove that our generic constructions satisfy IND-CCA security and consistency under the security models.
Hashtag-Based Tweet Expansion for Improved Topic Modeling

Topic modeling on tweets is known to experience under-specificity and data sparsity due to its character limitation. In earlier studies, researchers attempted to address this problem by either 1) tweet aggregation, where related tweets are combined into a single document or 2) tweet expansion with related text from external sources. The first approach faces the problem of losing the topic distribution in individual tweets. While finding a relevant text from the external source for a random tweet in the second approach is challenging for various reasons like differences in writing styles, multilingual content, and informal text. In contrast to adding context from external resources or combining related tweets into a pool, this study uses the internal vocabulary (hashtags) to counter under-specificity and sparsity in tweets. Earlier studies have indicated hashtags to be an important feature for representing the underlying context present in the tweet. Sequential models like Bi-directional Long Short Term Memory (BiLSTM) and Convolution Neural Network (CNN) over distributed representation of words have shown promising results in capturing semantic relationships between words of a tweet in the past. Motivated by the above, this article proposes a unified framework of hashtag-based tweet expansion exploiting text-based and network-based representation learning methods such as BiLSTM, BERT, and Graph Convolution Network (GCN). The hashtag-based expanded tweets using the proposed framework have significantly improved topic modeling performance compared to un-expanded (raw) tweets and hashtag-pooling-based approaches over two real-world tweet datasets of different nature. Furthermore, this article also studies the significance of hashtags in topic modeling performance by experimenting with different combination of word types such as hashtags, keywords, and user mentions.
Employing Contrastive Strategies for Multi-label Textual Emotion Recognition

Textual emotion recognition is an important part of the human-computer interaction field. Current methods of textual emotion recognition mainly use large-scale pre-trained models fine-tuning. However, these methods are not accurate enough in the semantic representation of sentences. Contrastive learning has been shown to optimize the representation of vectors in the feature space. Therefore, we introduce the contrastive strategies to the textual emotion recognition task. We propose two approaches: using self-supervised contrastive learning before fine-tuning the pre-trained model, and using contrastive training on the same inputs during fine-tuning. We experiment on two multi-label emotion classification datasets: Ren-CECps and NLPCC2018. The experimental results demonstrate that the latter contrastive approach effectively improves the accuracy of emotion recognition.
Sequence Alignment Ensemble With a Single Neural Network for Sequence Labeling

Sequence labeling, in which a class or label is assigned to each token in a given input order, is a fundamental task in natural language processing. Many advanced neural network architectures have recently been proposed to solve the sequential labeling problem affecting this task. By contrast, only a few approaches have been proposed to address the sequential ensemble problem. In this paper, we resolve the sequential ensemble problem by applying the sequential alignment method in a proposed ensemble framework. Specifically, we propose a simple but efficient ensemble candidate generation framework with which multiple heterogeneous systems can easily be prepared from a single neural sequence labeling network. To evaluate the proposed framework, experiments were conducted with part-of-speech (POS) tagging and dependency label prediction problems. The results indicate that the proposed framework achieved accuracy values that were higher by 0.19 and 0.33 than those achieved by the hard-voting method on the Penn-treebank POS-tagged and Universal dependency-tagged datasets, respectively.
Text Summarizer Using NLP

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Mobile Robot Path Planning Based on Angle-Guided Ant Colony Algorithm

Ant colony algorithm is easy to fall into local optimum and its convergent speed is slow in solving mobile robot path planning. Therefore, an ant colony algorithm based on angle guided is proposed in this paper to solve the problems. In the choice of nodes, integrate the angle factor into the heuristic information of the ant colony algorithm to guide the ants' search direction and improve the search efficiency. The pheromone differential updating is carried out for different quality paths and the pheromone chaotic disturbance updating mechanism is introduced, then the algorithm can make full use of the better path information and maintain a better global search ability. According to simulations, its global search is strong and it can range out of local optimum and it is fast convergence to the global optimum. The improved algorithm is feasible and effective.
RuMedBench: A Russian Medical Language Understanding Benchmark

The paper describes the open Russian medical language understanding benchmark covering several task types (classification, question answering, natural language inference, named entity recognition) on a number of novel text sets. Given the sensitive nature of the data in healthcare, such a benchmark partially closes the problem of Russian medical dataset absence. We prepare the unified format labeling, data split, and evaluation metrics for new tasks. The remaining tasks are from existing datasets with a few modifications. A single-number metric expresses a model's ability to cope with the benchmark. Moreover, we implement several baseline models, from simple ones to neural networks with transformer architecture, and release the code. Expectedly, the more advanced models yield better performance, but even a simple model is enough for a decent result in some tasks. Furthermore, for all tasks, we provide a human evaluation. Interestingly the models outperform humans in the large-scale classification tasks. However, the advantage of natural intelligence remains in the tasks requiring more knowledge and reasoning.
Workload Evaluations for Closed Captioners

Live captioning in broadcasting involves the translation from spoken words in broadcasted programming to text equivalents, speaker identification and some non-speech audio information in real-time where there is little or no opportunity for editing, or correction. Some of this live content can be too fast to type, and/or read, which then can translate into difficulties and barriers for viewers. Recently, paraphrasing has been permitted in order to attempt to mitigate some of these difficulties, but different cognitive elements must be recruited compared with verbatim translation. This research presents preliminary results of live captioner’s subjective mental workload (SMW) using the NASA TLX, and their experiences with paraphrasing. We hypothesize that the cognitive processes of paraphrasing increase SMW. Results indicate that live captioners experience high SMW particularly for fast-paced sports. Paraphrasing may contribute to higher perceived SMW for live captioning fast-paced sports, talk shows and weather.
The Influence of Active Projection Speckle Patterns on Underwater 3d Measurement Based on Binocular Stereo Vision

Due to the phenomena of refraction, absorption and scattering, underwater stereo vision faces more difficulties than in air. These phenomena will affect the accuracy and reliability of stereo matching and increase the difficulty of stereo matching. The method based on active speckle pattern is a more simple and reliable method to enhance the surface texture of underwater objects. A good-quality speckle pattern can provide more robust and accurate results for matching the corresponding points of the left and right images. This is a more important research problem in water than in air. In this paper, the row entropy of speckle pattern is adopted to quantify the quality of the projection speckle pattern suitable for underwater stereo vision, and the relevant experiment and analysis are carried out to prove the correctness and effectiveness of new parameters. Further, the influence of different projection speckle patterns on underwater 3D measurement is researched.
Face Mask Detection Using Deep Hybrid Network Architectures

As the world has been severely affected by Novel Coronavirus, scientists have been working hard to study this rapidly evolving virus, its long-term and short-term implications, and how to stop its spread. As newer variants of the virus are discovered, it has become even more important to enforce the various steps required to curb its spread. We can only fight this virus by wearing masks, using sanitizers, and social distancing. This paper proposes a hybrid masked face detection model for implementing the proper use of face masks. Our study focuses on combining machine learning models and Neural Networks. Even though various models have been proposed in the past for face mask detection, we tried to change the conventional machine learning methods by creating hybrid models like ResNet50 and VGG16 and combining classical machine learning models like SVM and Gradient Booster, and Neural Networks and comparing their performance. The Hybrid model architecture consisting of ResNet50 + SVM significantly outperformed the other models, returning an accuracy and precision of more than 97 and close to 100% each respectively.
Limitations

Artificial Intelligence is a popular concept amongst academics and business professionals these days. It is increasingly omnipresent in every aspect of our life, such as voice assistance, automated customer service and semi-automated driving experiences. Even with such advancements, it is important to recognize that AI is not capable of predicting our future exactly. Rather, we should view AI as a complementary tool which enables us to replace dated existing processes and improve our ways of living via automated learning. To start with, a comparison between Narrow & General AI and the interpretability of existing AI will be discussed. Additionally, the complex and non-trivial discussion between model generalization and overfitting will also be covered. We will also cover the importance of good data quality and provide a list of recommended but non-exhaustive best practices in data preparation. Finally, we end off by summarizing current developments in the issue of data privacy.
Object Detection in Maritime Scenarios Based on Swin-Transformer

Object detection is a favourite research technology in the field of maritime computer vision and current research mostly adopts region models based on convolutional neural networks (CNNs). Swin-transformer brings a new and effective approach, with its hierarchical structure and attention mechanism-based block, well compensating for the weakness of CNNs that cannot take into account global features. In this paper, we propose a method for applying the Swin-Transformer to maritime object detection, obtaining significantly improved results on the well-known Seaships dataset. Experiments demonstrate that Swin-Transformer, after pre-trained and fine-tuning, has a detection mean average precision of 96.61%, outperforming other CNNs. Furthermore, Swin-Transformer has demonstrated its capability to detect features of the irregular ship shapes in complex backgrounds, and the detection of general cargo ships and passenger ships is excellent.
Integrating Multiband Active and Passive Radar for Enhanced Situational Awareness

This article presents the data fusion results of integrating several multiband active and passive radar sensors for enhanced situational awareness. The two sensor technologies come with partially complementary properties, thus a suitable mix provides improved performance in a couple of situations of operational interest. The presented results have been obtained during a trial measurement campaign of the NATO Science and Technology Organization, which took place on 3–13 September 2019, in Poland. Held under the code name Active Passive Radar Trials—Ground-based, Airborne, Sea-borne (APART-GAS), this trial campaign was attended by over 70 participants from 10 countries. It covered many new predefined military scenarios tested in real time. This article's results verify and validate the Deployable Multiband Passive Active Radars (DMPAR) concept developed during previous work by various NATO research groups.