Reviewerly/exHarmony huggingface dataset:
Permissioned Blockchain-based Security for IIoT

Industrial Internet of Things (IIoT) is one of the most trending topics in the present world. It has numerous benefits and its potentiality is unfolding gradually. The implementation of IIoT in mission and safety-critical systems has shown its significance. Since it deals with important and sensitive information, an extensive study is required to address the susceptibility of it towards security issues. Therefore, there have been many proposals to use certificateless signature scheme, machine learning approaches, public key encryption as well as blockchain to improve the security of IIoT. It is important to note that blockchain is playing a significant role in the IIoT technology where the important characteristics of blockchain, immutability, decentralization, tamper-proof, have made a profound impact on the security vulnerabilities of IIoT. Taking this into account, this paper proposes a permissioned blockchain for IIoT that addresses and guarantees to ensure and improve the security vulnerabilities and susceptibility of IIoT towards cyber threats. The permissioned blockchain enabled IIoT ensures a secure medium for device communication, data sharing, and access control. This paper also discusses the security issues of IIoT and presents a comprehensive analysis of some of the proposed blockchain based solutions to improve IIoT security challenges. It addresses the benefits of a permissioned blockchain enabled IIoT over a public blockchain as well as presents a future direction for the upcoming integration of blockchain with IIoT and other industries.
Rapid assessment of building collapse based on sequential dynamic fusion of multi-source disaster information from news media

News media provides time and data guarantee for rapid post-disaster assessment. However, disaster information from news media often suffers from semantic ambiguity and conflicting content, and disaster information changes over time. These characteristics of disaster information seriously affect its application in disaster assessment. How to obtain timely reasonable quantitative disaster assessment results based on this kind of disaster information is the key to making full use of media information, but existing studies rarely have addressed the issue. This paper proposes a novel method to assess the percentage of buildings collapse by leveraging timely disaster information with geographical positions. The method includes four parts. First, based on preprocessing of disaster information, the cloud model is used to quantitatively express fuzzy disaster information and construct basic probability assignments (BPAs) for different ranges of buildings collapse. Second, the conflict and ambiguity of each item of disaster information is analyzed, and its credibility is measured. Based on this, the BPAs are modified to reduce conflicting disaster information. Third, multiple items of disaster information are fused by using Dempster–Shafer theory to obtain buildings damage assessment results. Finally, the assessment results of the previous stage are dynamically updated with new information. The method is applied to Ya'an earthquake on April 20, 2013 and Yi'bin earthquake on June 17, 2019. The assessment results are analyzed and compared with those obtained by other methods, and it is found that the method can obtain reliable results in a short time. This study can provide a reference for timely emergency decision-making.
CNN based Hate-o-Meter: A Hate Speech Detecting Tool

Hate Speech is a widespread problem that degrades a person or people based on their race, religion, gender or disability. This research work proposes a tool to raise awareness on the persistent hate speech in blogs, online-forums, and newspapers. The primary aim of this research work is to highlight the content that promotes violence or hatred against individuals or groups based on religion, gender, ethnicity or disability. A convolutional neural network architecture is used along with the natural language processing techniques. Using this algorithm, the tool identifies the percentage of hate and displays the bias of the statements. To host the proposed model, flask API and heroku platform is used. The proposed tool has the ability to detect hate speech with 80.15 percent accuracy and f1-score of 80.35 percent. The tool is made free and available for demo use to the public.
An empirical study of performance using Clone &amp; Own and Software Product Lines in an industrial context

Clone and Own (CaO) is a widespread approach to generate new software products from existing software products by adding small changes. The Software Product Line (SPL) approach addresses the development of families of products with similar features, moving away from the production of isolated products. Despite the popularity of both approaches, no experiment has yet compared them directly. The goal of this paper is to know the different performances of software engineers in the software products development process using two different approaches (SPL and CaO). We conducted an experiment in the induction hobs software environment with software engineers. This experiment is a single factor experiment where the factor is the approach that is used to develop software products, with two treatments: (SPL or CaO). We compared the results obtained by the software engineers when they develop software products related to effectiveness, efficiency, and satisfaction. The findings show that: (1) the SPL approach is more efficient even though the number of checking actions required by this approach is greater than the number required by the CaO approach; (2) the SPL approach offers more possibilities than software engineers need to perform their daily tasks; and (3) software engineers require better search capabilities in the CaO approach. The possible explanations for these results are presented in the paper. The results show that there are significant differences in effectiveness, efficiency, and satisfaction, with the SPL approach yielding the best results.
Visual servoing-based camera control for the da Vinci Surgical System

Minimally Invasive Surgery (MIS)-which is a very beneficial technique to the patient but can be challenging to the surgeon-includes endoscopic camera handling by an assistant (traditional MIS) or a robotic arm under the control of the operator (Robot-Assisted MIS, RAMIS). Since in the case of RAMIS the endoscopic image is the sole sensory input, it is essential to keep the surgical tools in the field-of-view of the camera for patient safety reasons. Based on the endoscopic images, the movement of the endoscope holder arm can be automated by visual servoing techniques, which can reduce the risk of medical error. In this paper, we propose a marker-based visual servoing technique for automated camera positioning in the case of RAMIS. The method was validated on the research-enhanced da Vinci Surgical System. The implemented method is available at: https://github.com/ABC-iRobotics/irob-saf/tree/visua1_servoing.
Enforcing situation-aware access control to build malware-resilient file systems

Traditional non-semantic file systems are not sufficient in protecting file systems against attacks, either caused by ransomware attacks or software-related defects. Furthermore, outbreaks of new malware often cannot provide a large quantity of training samples for machine-learning-based approaches to counter malware campaigns. The malware defense system should aim to achieve the best balance between early detection and detection accuracy. In this paper, we present a situation-aware access control framework to work with existing file systems as a stackable add-on. Our framework enables the access control decision making to be deferred when required, to observe the consequence of such an access request to the file system and to roll back changes if required. As an application against ransomware attacks, it can be applied to preserve file content integrity, by enforcing that all binary files written to the file system have consistent internal file structures with the declared file types, and rolling back changes that violate such constraints. We envision our access control framework to complement existing operating system access control frameworks, to significantly reduce the dimension of data required for machine learning, and to build extra resilience into the operating systems against damages caused by either malware or software defects. We demonstrate the practicality of our framework through a prototype testing, capturing relevant ransomware situations. The experimental results along with a large ransomware dataset show that our framework can be effectively applied in practice.
Consensus Mechanism of IoT Based on Blockchain Technology

Applying blockchain technology to the Internet of Things (IoT) remains a huge challenge. To meet the actual needs of IoT, a lightweight and high-throughput consensus mechanism, combined with blockchain technology, is proposed in this study. Blockchain nodes use the Diffie–Hellman algorithm for key negotiation. Sensors and blockchain nodes can use the shared key to generate HMAC (Hash-based Message Authentication Code) signatures for sensor-aware transactions and use the Verifiable Random Function to implement block nodes. Offline fast election, which is the node that wins the election, becomes the block node. Machine learning methods are also introduced to identify or remove outliers in the sensor data before such data are uploaded to the chain. Experimental results show that the system throughput synchronously increases as the test load increases. Moreover, when the test load is 800 tps, the system throughput reaches the maximum, close to 600 tps. When the test load exceeds 800 tps, the actual system throughput starts to drop, and approximately 90% of transactions have a delay time within 5000 ms. This method can be used in a lightweight IoT system.
Generalized fuzzy logic based performance prediction in data mining

In recent days, the single and multiple economies depend upon the human capital to build a valuable service. The individual employee level is important to process and maintain the whole organization. Consequently, performance management is needed at each employee level and the business level to implement a system in order to measure the employee performance and provide growth based on the performance. In data mining applications, the knowledge discovery of interest in Human Resources Management (HRM) is applicable. To extract the knowledge significant data mining classification techniques were used. The scope of this work compares the predictive analyzing of theC4.5 algorithm, Naive Bayes and Fuzzy logics are made by comparing its accuracy. This paper proposed a framework to help human resource to monitor the employee performance. The exact accuracy of the proposed framework found to be more efficient in terms of the accurately predicting the outcome of the employee.
Unpaired Image Enhancement with Quality-Attention Generative Adversarial Network

In this work, we aim to learn an unpaired image enhancement model, which can enrich low-quality images with the characteristics of high-quality images provided by users. We propose a quality attention generative adversarial network (QAGAN) trained on unpaired data based on the bidirectional Generative Adversarial Network (GAN) embedded with a quality attention module (QAM). The key novelty of the proposed QAGAN lies in the injected QAM for the generator such that it learns domain-relevant quality attention directly from the two domains. More specifically, the proposed QAM allows the generator to effectively select semantic-related characteristics from the spatial-wise and adaptively incorporate style-related attributes from the channel-wise, respectively. Therefore, in our proposed QAGAN, not only discriminators but also the generator can directly access both domains which significantly facilitate the generator to learn the mapping function. Extensive experimental results show that, compared with the state-of-the-art methods based on unpaired learning, our proposed method achieves better performance in both objective and subjective evaluations.
A Comprehensive Survey on Passive Video Forgery Detection Techniques

In the ongoing year, video falsification identification is a significant issue in video criminology. Unapproved changes in video outline causing debasement of genuineness and uprightness of inventiveness. With the progression in innovation, video preparing apparatuses and procedures are accessible for modifying the recordings for falsification. The adjustment or changes in current video is imperative to identify, since this video can be utilized in the validation procedure. Video authentication thus required to be checked. There are various ways by which video can be tempered, for example, frame insertion, deletion, duplication, copy and move, splicing and so on. This article presents forgery detection techniques like inter-frame forgery, intra-frame forgery & compression-based forgery detection that can be used for video tampering detection. Thorough analysis of newly developed techniques, passive video forgery detection is helpful for finding the problems and getting out new opportunities in the area of passive video forgery detection.
Disentangled Representation Learning for Multiple Attributes Preserving Face Deidentification

Face is one of the most attractive sensitive information in visual shared data. It is an urgent task to design an effective face deidentification method to achieve a balance between facial privacy protection and data utilities when sharing data. Most of the previous methods for face deidentification rely on attribute supervision to preserve a certain kind of identity-independent utility but lose the other identity-independent data utilities. In this article, we mainly propose a novel disentangled representation learning architecture for multiple attributes preserving face deidentification called replacing and restoring variational autoencoders (R2VAEs). The R2VAEs disentangle the identity-related factors and the identity-independent factors so that the identity-related information can be obfuscated, while they do not change the identity-independent attribute information. Moreover, to improve the details of the facial region and make the deidentified face blends into the image scene seamlessly, the image inpainting network is employed to fill in the original facial region by using the deidentified face as a priori. Experimental results demonstrate that the proposed method effectively deidentifies face while maximizing the preservation of the identity-independent information, which ensures the semantic integrity and visual quality of shared images.
A Lightweight Design for Serverless Function as a Service

FaaS (Function as a Service) allows developers to upload and execute code in the cloud without managing servers. FaaS offerings from leading public cloud providers are based on system microVM or application container technologies such as Firecracker or Docker. In this paper, we demonstrate that lightweight high-level runtimes, such as WebAssembly, could offer performance and scaling advantages over existing solutions, and could enable finely-grained pay-as-you-use business models. We compared widely used performance benchmarks between Docker native and WebAssembly implementations of the same algorithms. We also discuss the barriers for WebAssembly adoption in serverless computing, such as the lack of tooling support.
K-armed Bandit based Multi-Modal Network Architecture Search for Visual Question Answering

In this paper, we propose a cross-modal network architecture search (NAS) algorithm for VQA, termed as k-Armed Bandit based NAS (KAB-NAS). KAB-NAS regards the design of each layer as a k-armed bandit problem and updates the preference of each candidate via numerous samplings in a single-shot search framework. To establish an effective search space, we further propose a new architecture termed Automatic Graph Attention Network (AGAN), and extend the popular self-attention layer with three graph structures, denoted as dense-graph, co-graph and separate-graph.These graph layers are used to form the direction of information propagation in the graph network, and their optimal combinations are searched by KAB-NAS. To evaluate KAB-NAS and AGAN, we conduct extensive experiments on two VQA benchmark datasets, i.e., VQA2.0 and GQA, and also test AGAN with the popular BERT-style pre-training. The experimental results show that with the help of KAB-NAS, AGAN can achieve the state-of-the-art performance on both benchmark datasets with much fewer parameters and computations.
Dual-Gradients Localization Framework for Weakly Supervised Object Localization

Weakly Supervised Object Localization (WSOL) aims to learn object locations in a given image while only using image-level annotations. For highlighting the whole object regions instead of the discriminative parts, previous works often attempt to train classification model for both classification and localization tasks. However, it is hard to achieve a good tradeoff between the two tasks, if only classification labels are employed for training on a single classification model. In addition, all of recent works just perform localization based on the last convolutional layer of classification model, ignoring the localization ability of other layers. In this work, we propose an offline framework to achieve precise localization on any convolutional layer of a classification model by exploiting two kinds of gradients, called Dual-Gradients Localization (DGL) framework. DGL framework is developed based on two branches: 1) Pixel-level Class Selection, leveraging gradients of the target class to identify the correlation ratio of pixels to the target class within any convolutional feature maps, and 2) Class-aware Enhanced Maps, utilizing gradients of classification loss function to mine entire target object regions, which would not damage classification performance. Extensive experiments on public ILSVRC and CUB-200-2011 datasets show the effectiveness of the proposed DGL framework. Especially, our DGL obtains a new state-of-the-art Top-1 localization error of 43.55% on the ILSVRC benchmark.
Look, Read and Feel: Benchmarking Ads Understanding with Multimodal Multitask Learning

Given the massive market of advertising and the sharply increasing online multimedia content (such as videos), it is now fashionable to promote advertisements (ads) together with the multimedia content. However, manually finding relevant ads to match the provided content is labor-intensive, and hence some automatic advertising techniques are developed. Since ads are usually hard to understand only according to its visual appearance due to the contained visual metaphor, some other modalities, such as the contained texts, should be exploited for understanding. To further improve user experience, it is necessary to understand both the ads' topic and sentiment. This motivates us to develop a novel deep multimodal multitask framework that integrates multiple modalities to achieve effective topic and sentiment prediction simultaneously for ads understanding. In particular, in our framework termed Deep$M^2$Ad, we first extract multimodal information from ads and learn high-level and comparable representations. The visual metaphor of the ad is decoded in an unsupervised manner. The obtained representations are then fed into the proposed hierarchical multimodal attention modules to learn task-specific representations for final prediction. A multitask loss function is also designed to jointly train both the topic and sentiment prediction models in an end-to-end manner, where bottom-layer parameters are shared to alleviate over-fitting. We conduct extensive experiments on a large-scale advertisement dataset and achieve state-of-the-art performance for both prediction tasks. The obtained results could be utilized as a benchmark for ads understanding.
Self-Paced Video Data Augmentation by Generative Adversarial Networks with Insufficient Samples

An effective video classification method by means of a small number of samples is urgently needed. The deficiency of samples could be alleviated by generating samples through generative adversarial networks (GANs). However, the generation of videos in a typical category remains underexplored because the complex actions and the changeable viewpoints are difficult to simulate. Thus, applying GANs to perform video augmentation is difficult. In this study, we propose a generative data augmentation method for video classification using dynamic images. The dynamic image compresses the motion information of a video into a still image, removing the interference factors such as the background. Thus, utilizing the GANs to augment dynamic images can keep the categorical motion information and save memory compared with generating videos. To deal with the uneven quality of generated images, we propose a self-paced selection method to automatically select high-quality generated samples for training. These selected dynamic images are used to enhance the features, attain regularization, and finally achieve video augmentation. Our method is verified on two benchmark datasets, namely, HMDB51 and UCF101. Experimental results show that the method remarkably improves the accuracy of video classification under the circumstance of sample insufficiency and sample imbalance.
ChefGAN

Although significant progress has been made in generating images from the text by using generative adversarial networks (GANs), it is still challenging to deal with long text, which contains complex semantic information like recipes. This paper focuses on generating images with high visual realism and semantic consistency from the complex text of recipes. To achieve this, we propose a GANs based method termed ChefGAN. The critical concept of ChefGAN is that a joint image-recipe embedding model is used before the generation task to provide high-quality representations of recipes, and it acts as an extra regularization during the generation to improve semantic consistency. Two modules are designed for this image text embedding module (ITEM) and a cascaded image generation module (CIGM). The generation process is carried out in 3 steps: (1) Two encoders in ITEM are trained simultaneously to generate similar representations for each image-recipe pair. (2) CIGM generates images according to the representations from ITEM's text encoder. (3) The generated image is fed into ITEM's image encoder to calculate the similarity with the given recipe. This process can provide additional regularization effect other than the impact of a discriminator. To facilitate convergence, we applied a two-stage training strategy, which generates an image with low resolution and then one with high resolution in the CIGM module. Compared with other representative state-of-the-art methods, ChefGAN demonstrates better performance both in visual realism and semantic consistency.