Reviewerly/exHarmony huggingface dataset:
Practical hybrid confidentiality-based analytics framework with Intel SGX

Massive cloud infrastructure capabilities, including efficient, scalable, and elastic computing resources, have led to a widespread adoption of Internet of Things (IoT) cloud-enabled services. This involves giving complete control to cloud service providers (CSPs) of sensitive IoT data by moving data storage and processing in cloud. An efficient and lightweight advanced encryption standard (AES) cryptosystem can play a major role in protecting IoT data from exposure to CSPs by protecting the privacy of outsourced data. However, AES lacks computation capabilities, which is a critical factor that prevents individuals and organizations from taking full advantage of cloud computing services. When Intel software guard extensions (SGX) is used with AES cryptosystem, the developing framework can provide a practical solution to build a confidentiality-based data analytics framework for IoT-enabled applications in various domains. In this paper, a privacy-preserving data analytics framework is developed that relies on a hybrid-integrated approach, in which both software- and hardware-based solutions are applied to ensure confidentiality and process-sensitive outsourced data in the cloud environment.
A Fast and Simple Partially Oblivious PRF, with Applications

We build the first construction of a partially oblivious pseudorandom function (POPRF) that does not rely on bilinear pairings. Our construction can be viewed as combining elements of the 2HashDH OPRF of Jarecki, Kiayias, and Krawczyk with the Dodis-Yampolskiy PRF. We analyze our POPRF's security in the random oracle model via reduction to a new one-more gap strong Diffie-Hellman inversion assumption. The most significant technical challenge is establishing confidence in the new assumption, which requires new proof techniques that enable us to show that its hardness is implied by the q-DL assumption in the algebraic group model. Our new construction is as fast as the current, standards-track OPRF 2HashDH protocol, yet provides a new degree of flexibility useful in a variety of applications. We show how POPRFs can be used to prevent token hoarding attacks against Privacy Pass, reduce key management complexity in the OPAQUE password authenticated key exchange protocol, and ensure stronger security for password breach alerting services.
Objective Information Theory Exemplified in Air Traffic Control System

Information is the core of Air traffic control system (ATCS). In this paper, objective information theory is extended to depict, model and measure the information exemplified in ATCS. The sextuple model is presented with information ontology, state occurrence time, state set, carrier, reflection time, and reflection set. The metric system is given to quantitively measure the information with extensity, detailedness, continuity, richness, containability, delay, pervasiveness, authenticity, and adaptability. The results show that the proposed method is potential to find out actual indexes in ATCS for flight safety.
Ore-forming elements and their distribution of nanoparticles in the updraft from the Sanshandao concealed deposit, China

The search for concealed ore deposits has increasingly gained attention, due to the gradual depletion of surface resources over time. It is necessary to develop non-traditional geochemical theories and methods to locate concealed deposits. In this study, the morphological characteristics and chemical compositions of particles carried by the updraft of the Sanshandao deposit are observed through transmission electron microscopy. Results presented direct evidence that the composition of nanoparticles in the updraft is affected by the geological background, concealed ore bodies, and oxidation. Cl and Na in the study area are found to be higher than those in other deposits owing to the influence of seawater intrusion. Elements associated with ore-forming are contained in the updraft in the form of nanoparticles, which usually have the same distribution pattern and element assemblage in their particles. Oxygen is found to widely exist in the nanoparticles and matched well with the particle distribution. These combinations of elements from orebodies are not observed in the nanoparticles in the background area. Thus, we considered that nanoparticles associated with the orebodies are formed in the underground orebodies and migrate upward to the surface. Mapping can be used to understand the elemental composition and distribution of the nanoparticles. Based on the composition and distribution of nanoparticles, the elemental characteristics of the underground orebody can be inferred, and the minerals and the distribution range of the concealed orebodies can be predicted.
Optimization to Automated Phonetic Transcription Grading Tool (APTgt) – Automatic Exam Generator

The Automated Phonetic Transcription Grading Tool (APTgt) is an online exam system developed by Auburn University HCI group. This application aims to support faculty in communications disorders to improve their pedagogy and timely feedback for students. This article discusses an attempt to improve teacher's experience by providing an automated method for exam generation, which can significantly save time while creating exams. The exam entry is created with the AU IPA (International Phonetic Alphabet) keyboard and system that grades the exams upon student completion of the exam. The exam is composed of several linguistics words with their pronunciation. Students need to answer the questions by inputting the correct phonetic spells during the exam. The core part in the auto-exam generator is the classification module, which can classify the input words into different difficulty levels. In this paper, we proposed two classification algorithms in the classification module: Rule-based algorithm and Classification and Regression Trees (CART).
On Machine Learning Based Video QoE Estimation Across Different Networks

With Over-The-Top traffic being extensively encrypted end-to-end, network operators typically lack insight into the performance of these services, as perceived by the end users. Yet, such an insight is essential for employing QoE-aware network management and potential alleviation of problems that may originate in the network. There is a clear interest from network operators to find ways to estimate service performance in terms of Key Performance Indicators (KPIs) and Quality of Experience (QoE). Over the last years, machine-learning–based (ML) models have proven to be capable of inferring QoE/KPIs from patterns in encrypted network traffic. The particular focus has mostly been on adaptive video streaming services, considering their share of the global network traffic. Those ML–based models have typically been trained and tested on a single dataset collected under specific conditions only. Going beyond related work on the topic of QoE/KPI estimation, we collected two large datasets related to YouTube streaming using the same setup at two different locations in Europe and analyzed the extent to which the differences in network characteristics and location specifics influence the performance of such models. This is of interest, as applicability of the models across diverse networks would significantly reduce the needed extensiveness of data collection typically required for ML–based approaches. In this paper, we compare models trained and tested on a single dataset/location (network-specific), models trained on the merged dataset (general), and models trained on one dataset and tested on the other dataset (cross-tested). The results show that the performance of general models is comparable to that of network-specific models, but cross-tests exhibit a considerable reduction in performance. With the aim to understand and improve cross-network applicability of the models in the future, the paper also provides an investigation of underlying reasons for the performance degradation.
Adaptive Control Design for Multi-UAV Cooperative Lift Systems

Multi-unmanned aerial vehicle (UAV) cooperative lift systems use multiple UAVs to collectively lift and transport payloads. These systems have unique benefits over standard single-vehicle logistics paradigms in that they distribute lift capacity among several potentially inexpensive and man-portable aircraft, and furthermore provide redundancy to guard against vehicle failure. However, several challenges arise when multiple vehicles are tasked with coordinating stabilization and control tasks for an unknown payload, particularly when there is also uncertainty regarding relative vehicle placement and orientation. This paper presents a novel adaptive control framework for modular cooperative lift systems. The algorithm uses an extended Kalman filter to update system parameters within a control allocation scheme. Thrust vectoring is employed to ensure adequate yaw control authority, and null-space excitation is used to speed estimator convergence. The adaptive control approach is designed to execute a full payload transportation mission starting from rest on the ground. Extensive simulation studies and preliminary flight experiments evaluate robustness of the adaptive control scheme and explore tradeoffs with respect to several system parameters and levels of uncertainty. Overall, the proposed control and estimator scheme is shown to be highly effective in stabilizing multi-UAV payload systems under realistic uncertainty in vehicle configuration and payload parameters.
A Cross View Learning Approach for Skeleton-Based Action Recognition

With the prevalence of accessible multi-modal sensors and the maturity of pose estimation algorithms, skeleton-based action recognition has gradually become the mainstream of human action recognition (HAR). The key issue is to mine the correlations and dependencies between different joints and bones. In this paper, we propose a cross view learning approach. First, the static and dynamic representations of skeletons, from two different views (joints and bones), are calculated and aggregated respectively. Then, the integrated representations of these two views are used as parallel inputs to the cross view learning model, which mainly includes two blocks, namely a multi-scale learning block and a multi-view fusion block. The former is used to excavate the intra-view’s discriminative and comprehensive features, and the latter is utilized to capture the complementary representations of the inter-view. Finally, the fused representations are input to the classifier for action recognition. It has been experimentally proven that our proposed approach outperforms several state-of-the-art baseline methods and achieves a very competitive performance.
Lossless Compression of Satellite Images using a Versatile Hybrid Algorithm

Abstract The remote sensing images taken from various satellites have high resolution and occupy a lot of storage space. It, therefore, becomes difficult to send a greater number of images to the base station. Image compression becomes inevitable in such cases. Satellite image compression decreases repetition in representation of data so that the storage cost and transmission reduces and compensates for the limited on-board resources, in terms of large-scale memory and downlink bandwidth. The aim of this paper is to perform lossless image compression on satellite imagery using a hybrid (DWT-RLE) algorithm to obtain higher compression ratios. This compression is implemented using the software tool MATLAB.
Classification of Abusive Thai Language Content in Social Media Using Deep Learning

This paper presents binomial and multinomial models for Thai language abusive speech classification in social media. While previous similar research focused on using traditional machine learning models for binomial classification, we showed that deep learning models have better performance. Our binomial and multinomial models achieved F1 scores of 0.8510 and 0.9067, respectively. These scores were significantly better than the machine learning models' respective best F1 scores of 0.7452 and 0.8090. While the bidirectional LSTM performed well, the DistilBERT had higher accuracy and recall. Moreover, the recall was especially higher for the “figurative” class where certain words were more likely to have different meanings depending on context.
Blockchain-Based Access Control Approaches

In this paper, we study the existing blockchain consensus mechanisms that can be applied to access control scenarios and analyze advantages and disadvantages for each category. Besides, we divide access control procedures into policy formulation, storage, and request processing. For each procedure, we summarize existing methods and analyze possible problems. Finally, we give the potential future research directions. This paper provides a clear framework for access control, on which researchers can explore new types of blockchain-based access control solutions to solve the existing security and performance problems.
Operation and maintenance(O&amp;M) for data center: An intelligent anomaly detection approach

With the popularity of cloud services and big data in the Internet enterprise, the operation and maintenance (O&M) of data centers have become more critical. The key to O&M is to find anomalies through indicator data. However, the traditional O&M suffers from poor efficiency and low satisfaction, along with a low degree of intelligence. Therefore, introducing a machine learning based anomaly detection method in O&M can effectively improve real-time detection accuracy and make O&M more intelligent. Based on two typical O&M problems prevalent in Internet enterprises at present, stability detection and unattended release of cluster machines, this paper proposes an intelligent anomaly detection approach, called Ensemble learning on Partition Interval (ELPI). The main steps include dividing the data set into the stable interval and the unsteady interval. Then we establish an online/offline algorithm module and perform corresponding integrated learning for different interval characteristics to detect abnormal data. At last, we set up a self-feedback mechanism to dynamically adjust the module threshold. The results show that our method is more accurate and stable than traditional methods. Additionally, our method has been effectively applied to the anomaly detection of big clusters and app release.
Relational graph convolutional networks: a closer look

In this article, we describe a reproduction of the Relational Graph Convolutional Network (RGCN). Using our reproduction, we explain the intuition behind the model. Our reproduction results empirically validate the correctness of our implementations using benchmark Knowledge Graph datasets on node classification and link prediction tasks. Our explanation provides a friendly understanding of the different components of the RGCN for both users and researchers extending the RGCN approach. Furthermore, we introduce two new configurations of the RGCN that are more parameter efficient. The code and datasets are available at https://github.com/thiviyanT/torch-rgcn.
A Blockchain-Based Smart Contract Towards Developing Secured University Examination System

A smart contract (SC) is a digital negotiation protocol among two or more anonymous parties without the presence of any trusted intermediaries. It is a self-executing agreement in the form of computer code. Smart contracts run on the blockchain. Thus, the code and the agreement are stored on a distributed public database and cannot be changed. There is a broad range of prospective application scenarios for smart contracts in the digital economy, including financial services, management, healthcare, and the Internet of Things. Ethereum and Hyperledger, are two of the most widely used open-source advance cross-industry blockchain technologies. Major technical challenges such as security, privacy, correctness, and verifiability are among the issues that are yet to be matured and completely resolved for blockchain. In this work, a comprehensive survey on smart contracts is carried out. We also present a case study on a University examination system having a heterogeneous data structure. This deployment brings a comprehensive understanding of the smart contract framework and has been used to find and analyze the gaps in the state of the art in terms of smart contracts.
Less is More: Pursuing the Visual Turing Test with the Kuleshov Effect

The Turing test centers on the idea that if a computer could trick a human into believing that it was human, then the machine was deemed to be intelligent or indistinguishable from people. Designing a visual Turing test involves recognizing objects and their relationships on images and creating a method to derive new concepts from the visual information. Until now, the proposed visual tests heavily use natural language processing to conduct the questionnaire or storytelling. We deviate from the mainstream, and we propose to reframe the visual Turing test through the Kuleshov effect to avoid written or spoken language. The idea resides on elucidating a method that creates the concept of montage synthetically. Like the first days of cinema, we would like to convey messages with the interpretation of image shots that a machine could decipher while comparing it with those scored by humans. The first implementation of this new test uses images from a psychology study where the circumplex model is applied to rate each image. We consider five deep learning methodologies and eight optimizers, and through semiotics, we derive an emotional state in the computer. The results are promising since we confirm that this version of the visual Turing test is challenging as a new research avenue.
On Relationship of Multilayer Perceptrons and Piecewise Polynomial Approximators

The relationship between a multilayer perceptron (MLP) regressor and a piecewise polynomial approximator is investigated in this work. We propose an MLP construction method, including the choice of activation, the specification of neuron numbers and filter weights. Through the construction, a one-to-one correspondence between an MLP and a piecewise polynomial is established. Especially, we point out that the form of nonlinear activation is related to the polynomial order. Since the approximation capability of piecewise polynomials is well understood, our study sheds new light on the universal approximation capability of an MLP.
Controlled information transfer in continuous-time chiral quantum walks

Abstract In this paper we investigate properties of continuous time chiral quantum walks, which possess complex valued edge weights in the underlying graph structure, together with an initial Gaussian wavefunction spread over a number of vertices. We demonstrate that, for certain graph topology and phase matching conditions, we are able to direct the flow of probability amplitudes in a specific direction inside the graph network. We design a quantum walk graph analogue of an optical circulator which is a combination of a cycle and semi-infinite chain graphs. Excitations input into the circulator from a semi-infinite chain are routed in a directionally biased fashion to output to a different semi-infinite chain. We examine in detail a two port circulator graph which spatially separates excitations flowing back in forth between the two semi-finite chains to directionally occupy the top or bottom half of the cycle portion of the circulator. This setup can be used, for example, to detect non-Markovian processes, which leads to information and energy back-flow from the bath back into the system.
Security Vulnerabilities in Existing Security Mechanisms for IoMT and Potential Solutions for Mitigating Cyber-Attacks

The dynamic evolution of the Smart Medical Devices in Healthcare system has adopted an innovative transformation in simplifying the medical systems and controlling of diseases and improving the QoS (Quality of Service) for the Patients. However, The Smart Medical Devices are not immune to the security vulnerabilities, privacy breaches and physical threats. The MITM attacks (DDoS, Jamming, Node-Injection and Node-Hijacking) can cause serious threats to the Smart Medical Devices due to the insecure communication process and lack of anonymity in device connectivity through Heterogeneous Wireless Medical Sensor Networks (WMSNs). The main aim of the research is to identify major security vulnerabilities in Smart Medical Devices in Heterogeneous WMSNs. The Researcher aims to verify and analyse the existing IoMT (Internet of Medical Things) security protocols through security experiments for identifying the security flaws for preventing malicious Cyber-attacks in IoMT Devices. This research focuses on the critical security vulnerability analysis of the existing security protocols for IoMT Devices in 5G enabled WMSNs. The research also demonstrates security vulnerability analysis of IoMT devices and identify the major security flaws to tackle malicious cyber-attacks. The Research critically evaluates the performance of the current Cryptographic authentication protocols against cyber-attacks and recommend the potential solutions for mitigating security vulnerabilities in IoMT devices. The author concludes the research evaluating the research outcomes and presenting a brief discussion on advanced research directions, studies, and research works will be undertaken in future.
Image Watermarking Approach Using a Hybrid Domain Based on Performance Parameter Analysis

In today’s scenario, image watermarking has been an integral part in various multimedia applications. Watermarking is the approach for adding additional information to the existing image to protect the data from modification and to provide data integrity. Frequency transform domain techniques are complex and costly and degrade the quality of the image due to less embedding of bits. The proposed work utilize the original DCT method with some modifications and applies this method on frequency bands of DWT. Furthermore, the output is used in combination with a pixel modification method for embedding and extraction. The proposed outcome is the improvement of performance achieved in terms of time, imperceptibility, and robustness.
Vision-based Neural Scene Representations for Spacecraft

In advanced mission concepts with high levels of autonomy, spacecraft need to internally model the pose and shape of nearby orbiting objects. Recent works in neural scene representations show promising results for inferring generic three-dimensional scenes from optical images. Neural Radiance Fields (NeRF) have shown success in rendering highly specular surfaces using a large number of images and their pose. More recently, Generative Radiance Fields (GRAF) achieved full volumetric reconstruction of a scene from unposed images only, thanks to the use of an adversarial framework to train a NeRF. In this paper, we compare and evaluate the potential of NeRF and GRAF to render novel views and extract the 3D shape of two different spacecraft, the Soil Moisture and Ocean Salinity satellite of ESA’s Living Planet Programme and a generic cube sat. Considering the best performances of both models, we observe that NeRF has the ability to render more accurate images regarding the material specularity of the spacecraft and its pose. For its part, GRAF generates precise novel views with accurate details even when parts of the satellites are shadowed while having the significant advantage of not needing any information about the relative pose.