Reviewerly/exHarmony huggingface dataset:
An Attribute-Based Searchable Encryption Scheme for Cloud-Assisted IIoT

The searchable encryption (SE) is a particular case of structured encryption, which has been intensively researched in the secure cloud storage system. By constructing a structured secure index, such as encrypted multimaps (EMMs), encrypted inverted index (EII), etc., SE can achieve efficient keyword search over the encrypted data set. However, existing SE constructions do not take search permissions into consideration, resulting in the lack of a mechanism of the data access control, which may not be suitable for Industrial Internet of Things (IIoT) applications, since an integrated industrial system contains all kinds of data with rigorous access permissions. In this article, we construct an attribute-based SE (ABSE) construction for a cloud-assisted IIoT application scenario. By designing the novel access policy-based structured secure index and the attribute-based search token, our construction achieves fine-grained keyword search privilege control over encrypted IIoT data as well as the same search complexity as the traditional SE. To the best of our knowledge, this is the first ABSE construction. We provide the correctness and security proofs for our construction. Experimental evaluation results in a real-world data set show the correctness and the practical search efficiency of the proposed ABSE.
Automatic Modulation Classification in Deep Learning

Due to the evolution and availability of vast amounts of data for transferring, receiving, and detection, the field of signal recognition and modulation classification has become vital in various fields and applications. Additionally, the classical approaches to machine learning (ML) no more can satisfy the current needs. Hence, this urged researchers to apply deep learning (DL) algorithms that have a very strong ability to train, learn, and automatically classify types of modulation categories. This paper focuses on three vital DL network algorithms, which are deep neural networks (DNN), convolutional neural networks (CNN), and deep belief networks (DBN). The mentioned algorithms are widely used in many applications for automatic modulation classification/recognition (AMC/AMR). Additionally, an empirical study is performed in this paper to compare a large number of different methods for the performance and recognition percentage of each considered technique.
Authentication, Authorization, and Selective Disclosure for IoT Data Sharing Using Verifiable Credentials and Zero-Knowledge Proofs

As IoT becomes omnipresent vast amounts of data are generated, which can be used for building innovative applications. However, interoperability issues and security concerns, prevent harvesting the full potentials of these data. In this paper we consider the use case of data generated by smart buildings. Buildings are becoming ever "smarter" by integrating IoT devices that improve comfort through sensing and automation. However, these devices and their data are usually siloed in specific applications or manufacturers, even though they can be valuable for various interested stakeholders who provide different types of "over the top" services, e.g., energy management. Most data sharing techniques follow an "all or nothing" approach, creating significant security and privacy threats, when even partially revealed, privacy-preserving, data subsets can fuel innovative applications. With these in mind we develop a platform that enables controlled, privacy-preserving sharing of data items. Our system innovates in two directions: Firstly, it provides a framework for allowing discovery and selective disclosure of IoT data without violating their integrity. Secondly, it provides a user-friendly, intuitive mechanisms allowing efficient, fine-grained access control over the shared data. Our solution leverages recent advances in the areas of Self-Sovereign Identities, Verifiable Credentials, and Zero-Knowledge Proofs, and it integrates them in a platform that combines the industry-standard authorization framework OAuth 2.0 and the Web of Things specifications.
Efficient Attribute-Based Signature With Collusion Resistance for Internet of Vehicles

Internet of Vehicles (IoV) realizes information interaction between vehicles and all networked entities, and the heterogeneous, openness, and diversity make it suffer from various security challenges such as authentication and access control problems. Attribute-based signature (ABS) is a promising cryptographic tool for addressing the above concerns, however, some existing ABS schemes take heavy computation overheads for signature verification. Though some server-aided ABS schemes are proposed to enhance performance, those schemes suffer from devastating collusion attacks. In addition, current signature-policy ABS schemes only support the single threshold access policy, which fails to meet various access control requirements of IoV. To address the above problems, we propose an efficient server-aided ABS with collusion resistance (SAABS-CR) for IoV. It utilizes server-aided computation technology to mitigate the computation burden of verifiers, while resisting perfectly the collusion attacks that include between signers and between the signer and the aided server. Moreover, SAABS-CR supports flexible expressions of access policy under the signature-policy framework by using the linear secret-sharing scheme (LSSS), and it is proved to be secure in the security model of strong unforgeability against chosen-message attacks (SU-CMA). Theoretical analyses and experimental evaluations show that SAABS-CR improves efficiency by reducing the time cost of signature generation at least 34.8% and user verification at least 15%.
Lightweight Flexible Group Authentication Utilizing Historical Collaboration Process Information

Existing device authentication techniques may suffer from heavy communication, computation, and storage overhead for identifying a growing number of devices in collaborations. This paper proposes a novel group authentication (GA) method for decentralized edge collaboration by exploiting the historical collaboration process information, i.e., the distributed learning parameters and results from the previous round of collaboration. Two strategies are developed to generate tokens locally at the edge devices’ side for mutual authentication, named random token generation (R-TG) and privacy-preserving token generation (PP-TG). Specifically, the R-TG strategy randomly selects several historical learning parameters as tokens, while the PP-TG strategy designs a one-way function to defend against privacy leakage by concealing the historical information. A GA protocol is proposed, where each device simultaneously authenticates the others in the same group by repeating the learning process using their tokens. If the process converges to an expected result, all the devices are authenticated as legitimate group members at once. The proposed scheme provides a lightweight flexible solution without pre-generating and distributing any keys/secrets operating on top of a standardized security protocol, and protects the collaboration continuously. The simulation results demonstrate the viability of our scheme and its superior performance compared to several benchmark schemes.
Memory Clustering Autoencoder Method for Human Action Anomaly Detection on Surveillance Camera Video

Unsupervised deep-learning methods with a deep clustering model are widely used to detect anomaly human actions obtained by surveillance camera due to their powerful image feature learning abilities. These methods aim to optimize a model through a clustering induction target to provide useful cluster assignment and usually use a forward propagation process: an autoencoder (AE) reconstructs an input sequence, a clustering model provides cluster allocation, and a scoring model evaluates distribution and provides scores for each sample. However, these methods have two main problems: one is that an AE is difficult to handle human posture when abnormal and normal actions occur in crowd scenes captured by a surveillance camera. The other is that network updating is interrupted by feature extraction and clustering in one epoch. To solve these problems, we design a deep memory clustering method based on graph convolution AE (MC-GCAE) to implement the real-time updating of pseudo-labels and network parameters. We also design a new loss function to express the similarity between the sample feature and the centroid feature in a memory storage module and to constrain the parameter update of a network. We evaluate the unsupervised method for three important and representative datasets mainly composed of surveillance videos and use the area under ROC curve (AUC) score as an experimental evaluation indicator.
Occlusion-Aware Graph Neural Networks for Skeleton Action Recognition

Due to its important applications in industry, the research of action recognition has attracted many attention. In particular, skeleton-based human action recognition methods have evolved to become more and more competitive. Nevertheless, occlusion is still a very challenging task in human action recognition as yet. Off-the-shelf works are usually based on complete skeleton data, few people consider action recognition in occlusion. For the sake of improving the recognition accuracy on occluded skeleton data, we put forward an occlusion-aware multistream fusion graph convolutional network (dubbed MSFGCN). Multiple streams are comprised in MSFGCN, and different occlusion cases can be disposed by different streams. Besides, in order to construct more discriminative features, multimodal features were extracted simultaneously, such as joint coordinates, relative coordinates, small-scale temporal differences, and large-scale temporal differences. In particular, it is the first time to take advantage of motion features at large and small scales at the same time, which helps to distinguish actions with different motion amplitudes. What is more, considering the different importance of different parts of the human body for different action recognition, the content adaptation operation is used to further optimize the recognition performance. Effective training strategies are also presented to further improve the performance of the model. Experimental results show that the proposed MSFGCN has a great advantage over other methods on occluded skeleton datasets. To showcase the effectiveness of different modules, extensive ablation experiments are performed on various skeleton action recognition datasets.
An effective approach for the nuclei segmentation from breast histopathological images using star-convex polygon

Early detection and patient-driven precise treatment can drastically reduce the elevation in the mortality rate due to breast tumours in women. Therefore, it is inevitable to develop an interpretable model, which detects and segments cancer cell nuclei. The main challenge to achieve this goal is the separation of the individual cell nucleus from the clustered overlapping nuclei without losing the pixel-level shape correspondence of each nucleus. The performance of the most common image processing conventional methods alone has not yielded the expected result for nuclei segmentation from the digitized tissue images either due to over-segmentation or under-segmentation. Here, this issue is addressed by developing an automatic nuclei segmentation algorithm which uses a deep learning-based approach. This paper introduces pixel-level analysis using the star convex polygon approach for segmenting the cell nuclei from the histopathological images. Analysis shows that this technique exemplifies a more accurate segmentation result with a validation IOU score of 87.56%. The true positive rate and the detection of pixel-level shape correspondence are also comparatively high for this method during the test phase.
Prediction of Software Effort by Using Non-Linear Power Regression for Heterogeneous Projects Based on Use case Points and Lines of code

Software cost estimation is necessary for all software organizations for contract negotiation. Numerous researchers have worked over the last four decades to provide estimation models, which can estimate efforts as accurately as they could, but the continuous change in the development models and use of new programming languages requires the development of new techniques for effort estimation. The size of the project must be estimated to determine the effort involved, which is rely on the programming languages and techniques employed for its development. Use case points, object points, lines of code, and story points are a few of the size matrices that are used for estimation using algorithmic models. There are several estimating techniques for the estimation of algorithmic models for various development approaches, but none of them offers a unified model for effort estimation for procedural and object-oriented projects. This work identifies common parameters for object-oriented and procedural-oriented programming languages. After the identification of common parameters, a combined model is developed for the estimation of effort for object-oriented and procedural-oriented programming language projects. The model's input is the size, which is measured in use case points and lines of source code. Power regression analysis of non-linear regression is applied to design the proposed model is designed using. The identified common parameters are multiplied with the mathematical equation achieved by power regression analysis to estimate the development effort for heterogeneous projects. The proposed work is assessed with the mean magnitude of relative error (MMRE). The error calculated with the proposed model is -0.16. The values of MMRE are further compared with the existing techniques such as linear regression, case-based reasoning, ensemble model, radial basis function neural network, and fuzzy analytic hierarchy process, and it was perceptible from the result that the proposed model delivers minimum error. The proposed work delivers the highest accuracy for software project estimation with the least amount of error.
VATEX2020: pLSTM framework for video captioning

Captioning a video involves condensing the video's information into text, which can be useful in video sentiment analysis, video-guided machine translation (VMT), visual question-answering and humanitarian aid. This paper discusses the details of the architecture of the pLSTM framework that is employed for the VATEX-2020 video captioning challenge. In this work, a sequential method is employed wherein to encode visual features a 3D convolutional neural network (C3D) is used. C3D was pretrained using the Sports-1M dataset. In the decoding phase, the input captions and visual features are fused separately in Long Short Term Memory networks (LSTM). The element-wise dot product is performed on the output of both LSTMs to get the final output. On both publicly available and private test data sets, our model achieves BLEU-4 scores of 0.20 and 0.22, respectively.
A Real-Time Deep Learning-based Smart Surveillance Using Fog Computing: A Complete Architecture

Fog computing offers low-latency and real-time big-data processing capabilities closer to the network edge. This particular benefit addresses the main bottleneck in a centralized cloud framework, which is, it cannot process latency-sensitive large video frames generated from the Internet of Things-based video surveillance cameras in real-time. Besides, the recent advancements in the computer vision field offer many state-of-the-art image processing capabilities that can be utilized for real-time surveillance data processing. Deploying those processing powers at several fog computing layers can bring novel solutions for computer vision-based real-time security solutions. This paper proposes a deep learning-based framework for smart video surveillance that can process the real-time frames on two consecutive fog layers, one for action recognition and the other for criminal threat-based response generation. The proposed architecture consists of three major modules. The first module is responsible for capturing surveillance videos by deploying RaspberryPi cameras in a distributed network. The second module is responsible for action recognition using a deep learning-based model installed inside NVIDIA Jetson Nano-devices placed on two fog layers. Finally, the security response is generated and broadcast to the law-enforcement agency. To evaluate the proposed model, experiments on semantic segmentation-based scene object recognition were run. The experimental results came up with a suitable recognition model that can be deployed in the fog layers of our proposed framework.
Personality Traits and Academic Motivation as Predictors of Attitudes towards Digital Educational Technologies among Russian University Students

In the modern era of digitalization, the development and implementation of digital educational technologies (DETs) have constantly been at the center of numerous discussions among teachers, psychologists, sociologists, physicians, etc. Moreover, the COVID-19 pandemic has simultaneously made DETs an integral part of contemporary social life around the world. However, both before and during the COVID-19 pandemic, little attention was paid to the study of the motivational and personality characteristics of university students, which would make it possible to predict their attitudes towards DETs and the effectiveness of their use in learning. The present study involved 173 Russian university students (61% - females) of various specialties (natural sciences, medicine and psychology), aged 17 to 26 years. Their attitudes towards DETs were measured according to The University Students’ Attitudes towards DET Questionnaire developed by the authors. To reveal their educational motivation, The Academic Motivation Scales by Gordeeva et al. were used. Their personality traits were identified using The NEO Five-Factor Inventory (adapted in Russian by Biryukov and Bodunov). For statistical analysis, the descriptive statistics methods, Mann - Whitney U test, and multiple regression analysis were used. The results of the study have shown that the scales of academic motivation have a greater impact on attitudes towards DETs among the university students as compared to personality traits. However, there is a specificity of these impacts in the students of different specialties, particularly in psychological students. The findings of this study indicate that taking into account such psychological factors as students’ academic motivation and personality traits can contribute to the optimal implementation of DETs in the educational process in blended and online formats.
Estimation of Confidence in the Dialogue based on Eye Gaze and Head Movement Information

In human-robot interaction, human mental states in dialogue have attracted attention to human-friendly robots that support educational use. Although estimating mental states using speech and visual information has been conducted, it is still challenging to estimate mental states more precisely in the educational scene. In this paper, we proposed a method to estimate human mental state based on participants’ eye gaze and head movement information. Estimated participants’ confidence levels in their answers to the miscellaneous knowledge question as a human mental state. The participants’ non-verbal information, such as eye gaze and head movements during dialog with a robot, were collected in our experiment using an eye-tracking device. Then we collect participants’ confidence levels and analyze the relationship between human mental state and non-verbal information. Furthermore, we also applied a machine learning technique to estimate participants’ confidence levels from extracted features of gaze and head movement information. As a result, the performance of a machine learning technique using gaze and head movements information achieved over 80 % accuracy in estimating confidence levels. Our research provides insight into developing a human-friendly robot considering human mental states in the dialogue.
Mitigation of Satellite Micro Vibrations on Remote Sensing Images

Recent advancements in satellite technologies, particularly in satellite image spatial resolution, have increased the importance of paying close attention to satellite vibration control capabilities. Anticipating, and mitigating satellite micro-vibrations caused by onboard internal disturbance sources is considered an impressive engineering challenge in both mechanical and electrical engineering branches, especially for users running highly sensitive optical sensor payloads with high standards of image quality requirements. This paper studies the different types of vibrations like jitter, linear and sinusoidal vibrations that cause many forms of degradations in remote sensing images acquired by satellite cameras in both mechanical and electrical engineering branches. The limitations of each approach are stated. Also, the two main solutions to soften the influence of satellite micro-vibrations on remote sensing image quality are introduced. A plan for overcoming the drawbacks of the mitigation techniques and how to deal with this problem in the future is introduced based on neural network techniques.
More is Less – Byte-quantized models are faster than bit-quantized models on the edge

Model quantization has been a popular approach to trade accuracy for speed during model serving, especially since new models are getting bigger and bigger. Traditionally, low-precision quantized models have better inference speed than their high-precision counterparts. However, the story may change with the advent of new machine learning instructions in modern processors. In this paper, we make a case for that using ARM processors. Our quantitative analysis shows that low-precision quantized models can be inferior in both accuracy and inference speed on modern processors. In response, we present, MiL, a new quantized neural network package. Operators in MiL are optimized for inferences based on the newly available machine learning instructions on the target platform. Experiments show that serving neural networks using PyTorch with MiL outperforms all state-of-the-art, including Riptide, TFLite with Ruy, and PyTorch with QNNPACK.
PMFL: Partial Meta-Federated Learning for heterogeneous tasks and its applications on real-world medical records

Federated machine learning is a versatile and flexible tool to utilize distributed data from different sources, especially when communication technology develops rapidly and an unprecedented amount of data could be collected on mobile devices nowadays. Federated learning method exploits not only the data but the computational power of all devices in the network to achieve more efficient model training. Nevertheless, while most traditional federated learning methods work well for homogeneous data and tasks, adapting the method to heterogeneous data and task distribution is challenging. This limitation has constrained the applications of federated learning in real-world contexts, especially in healthcare settings. Inspired by the fundamental idea of meta-learning, in this study we propose a new algorithm, which is an integration of federated learning and meta-learning, to tackle this issue. In addition, owing to the advantage of transfer learning for model generalization, we further improve our algorithm by introducing partial parameter sharing to balance global and local learning. We name this method partial meta-federated learning (PMFL). Finally, we apply the algorithms to two medical datasets. We show that our algorithm could obtain the fastest training speed and achieve the best performance when dealing with heterogeneous medical datasets.