Reviewerly/exHarmony huggingface dataset:
Automatic Migration-Enabled Dynamic Resource Management for Containerized Workload

Containerized workloads are gaining traction due to microservices architecture adaptation in many fields, including healthcare, finance, Internet of Things, and smart cities. Modern data centers are containerized to facilitate this growing demand. Most of the existing resource allocation methods for data centers used efficient scheduling algorithms to place the containers using static computing resources. These static resource allocation techniques are not energy efficient and do not help maximize data center utilization. Dynamic resource allocation and a migration-enabled placement method can reduce energy utilization while improving the utilization of the available computing infrastructure. This article presents and evaluates a novel dynamic resource management system that uses active migrations to minimize energy utilization to serve containerized workloads and improve data center utilization. Our approach uses a deep learning method to estimate the job execution time and then employs an unsupervised learning method to identify similar jobs. Similar jobs are placed and migrated to achieve energy efficiency and better utilization of the available data center infrastructure. Our proposed system is evaluated and compared with the existing state-of-the-art baseline methods. The proposed solution reduces the energy consumption from <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\times 1.18$</tex-math></inline-formula> to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\times 2.35$</tex-math></inline-formula> compared to the baseline methods while maintaining similar performance.
Text-Based Sentiment Analysis Using Deep Learning Techniques

Today's world is the word of the internet and word of information. In this modern area of development, technology has contributed a lot to the global platform called Web. Reviews and emotions play a vital role in our day to day lives as they help in learning communication, decision making, product evaluation, election prediction. Artificial Intelligence (AI) is the branch of computer science that has worked on the analysis of the reviews as well as opinions generated by the people, and helps the media in order to cope with the situation. Currently to improve the marketing strategy and product advertisement traditional web-based survey methods have been replaced with the Sentiment Analysis which improves customer service. Therefore, various approaches such as machine learning, lexicon-based, hybrid, and other approaches were used to analyze these sentiments/opinions in the past. With the current advancements in deep neural networks, deep learning-based methods are becoming very popular due to their accuracy enhancement in recent times. Various methods like Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and Bi-LSTM (Bidirectional LSTM) are used for sentiment analysis. This work highlights different deep learning techniques used for text -based sentiment analysis for reviews generated by users.
Positive-Unlabeled Learning for Network Link Prediction

Link prediction is an important problem in network data mining, which is dedicated to predicting the potential relationship between nodes in the network. Normally, network link prediction based on supervised classification will be trained on a dataset consisting of a set of positive samples and a set of negative samples. However, well-labeled training datasets with positive and negative annotations are always inadequate in real-world scenarios, and the datasets contain a large number of unlabeled samples that may hinder the performance of the model. To address this problem, we propose a positive-unlabeled learning framework with network representation for network link prediction only using positive samples and unlabeled samples. We first learn representation vectors of nodes using a network representation method. Next, we concatenate representation vectors of node pairs and then feed them into different classifiers to predict whether the link exists or not. To alleviate data imbalance and enhance the prediction precision, we adopt three types of positive-unlabeled (PU) learning strategies to improve the prediction performance using traditional classifier estimation, bagging strategy and reliable negative sampling. We conduct experiments on three datasets to compare different PU learning methods and discuss their influence on the prediction results. The experimental results demonstrate that PU learning has a positive impact on predictive performances and the promotion effects vary with different network structures.
Elastic Information Bottleneck

Information bottleneck is an information-theoretic principle of representation learning that aims to learn a maximally compressed representation that preserves as much information about labels as possible. Under this principle, two different methods have been proposed, i.e., information bottleneck (IB) and deterministic information bottleneck (DIB), and have gained significant progress in explaining the representation mechanisms of deep learning algorithms. However, these theoretical and empirical successes are only valid with the assumption that training and test data are drawn from the same distribution, which is clearly not satisfied in many real-world applications. In this paper, we study their generalization abilities within a transfer learning scenario, where the target error could be decomposed into three components, i.e., source empirical error, source generalization gap (SG), and representation discrepancy (RD). Comparing IB and DIB on these terms, we prove that DIB’s SG bound is tighter than IB’s while DIB’s RD is larger than IB’s. Therefore, it is difficult to tell which one is better. To balance the trade-off between SG and the RD, we propose an elastic information bottleneck (EIB) to interpolate between the IB and DIB regularizers, which guarantees a Pareto frontier within the IB framework. Additionally, simulations and real data experiments show that EIB has the ability to achieve better domain adaptation results than IB and DIB, which validates the correctness of our theories.
An Automated Machine Learning Framework for Predictive Analytics in Quality Control

Developments in Machine Learning (ML) in the last years resulted in taking as granted their usage and their necessity clear in areas such as manufacturing and quality control. Such areas include case specific requirements and restrictions that require the human expert's knowledge and effort to apply the ML algorithms efficiently. This paper proposes a framework architecture that utilizes Automated Machine Learning (AutoML) to minimize human intervention while constructing and maintaining ML models for quality control. The data analyst gives the setting for multiple configurations while designing predictive quality models which are automatically optimized and maintained. Moreover, experiments are conducted to test the framework in both the performance of the prediction models and the time needed to construct the models.
Growing Knowledge Across Boundaries: Lessons from a Multi-Actor Design Project

It is widely acknowledged that projects that seek to address complex socio-technical problems benefit from a deliberate mix of individuals, whose complementary expertise and skills can be leveraged in service of the project goals. Three ways through which this team diversity can be achieved are through industry-academia collaborations, through combining various disciplines, and through the inclusion of individuals with different levels of proficiency in their field. This chapter takes a critical retrospective look at a recently concluded collaborative project, which integrated these dimensions of interest. Ostensibly, that project aimed to design devices for smart and connected cycling. However, in this chapter, it is taken as a case study for a post-project review by its team members. We provide a detailed description of this multi-actor team, the design brief, work process and outputs, alongside the findings from a reflective questionnaire. In doing so, we aim to contribute to informing best practices for future heterogeneous collaborative projects through the lessons we learned here.
Precise Quantitative Analysis of Binarized Neural Networks: A BDD-based Approach

As a new programming paradigm, neural-network-based machine learning has expanded its application to many real-world problems. Due to the black-box nature of neural networks, verifying and explaining their behavior are becoming increasingly important, especially when they are deployed in safety-critical applications. Existing verification work mostly focuses on qualitative verification, which asks whether there exists an input (in a specified region) for a neural network such that a property (e.g., local robustness) is violated. However, in many practical applications, such an (adversarial) input almost surely exists, which makes a qualitative answer less meaningful. In this work, we study a more interesting yet more challenging problem, i.e., quantitative verification of neural networks, which asks how often a property is satisfied or violated. We target binarized neural networks (BNNs), the 1-bit quantization of general neural networks. BNNs have attracted increasing attention in deep learning recently, as they can drastically reduce memory storage and execution time with bit-wise operations, which is crucial in recourse-constrained scenarios, e.g., embedded devices for Internet of Things. Toward quantitative verification of BNNs, we propose a novel algorithmic approach for encoding BNNs as Binary Decision Diagrams (BDDs), a widely studied model in formal verification and knowledge representation. By exploiting the internal structure of the BNNs, our encoding translates the input-output relation of blocks in BNNs to cardinality constraints, which are then encoded by BDDs. Based on the new BDD encoding, we develop a quantitative verification framework for BNNs where precise and comprehensive analysis of BNNs can be performed. To improve the scalability of BDD encoding, we also investigate parallelization strategies at various levels. We demonstrate applications of our framework by providing quantitative robustness verification and interpretability for BNNs. An extensive experimental evaluation confirms the effectiveness and efficiency of our approach.
Analysis of Language Characteristics of Multimedia English Based on Internet of Things

The emergence of the Internet of Things is the inevitable trend of the development of science and technology. There are great differences between Multimedia English based on the Internet of Things and conventional English. Only by deeply analyzing it in combination with the language characteristics of Multimedia English can Multimedia English based on the Internet of Things play its due role. This paper analyzes the language characteristics of Multimedia English under the Internet of Things and finds that from the perspective of vocabulary, the pure professional vocabulary in Multimedia English accounts for 25% of all English vocabulary, and the pure professional vocabulary and semiprofessional vocabulary account for 58% of the total vocabulary. This represents that Multimedia English has an extremely obvious professionalism, so we need to focus on the meaning of professional and semiprofessional words when using English; otherwise, it is easy to affect the actual use effect of Multimedia English with the Internet of Things as the core because of the polysemy of one word.
WoX+: A Meta-Model-Driven Approach to Mine User Habits and Provide Continuous Authentication in the Smart City

The literature is rich in techniques and methods to perform Continuous Authentication (CA) using biometric data, both physiological and behavioral. As a recent trend, less invasive methods such as the ones based on context-aware recognition allows the continuous identification of the user by retrieving device and app usage patterns. However, a still uncovered research topic is to extend the concepts of behavioral and context-aware biometric to take into account all the sensing data provided by the Internet of Things (IoT) and the smart city, in the shape of user habits. In this paper, we propose a meta-model-driven approach to mine user habits, by means of a combination of IoT data incoming from several sources such as smart mobility, smart metering, smart home, wearables and so on. Then, we use those habits to seamlessly authenticate users in real time all along the smart city when the same behavior occurs in different context and with different sensing technologies. Our model, which we called WoX+, allows the automatic extraction of user habits using a novel Artificial Intelligence (AI) technique focused on high-level concepts. The aim is to continuously authenticate the users using their habits as behavioral biometric, independently from the involved sensing hardware. To prove the effectiveness of WoX+ we organized a quantitative and qualitative evaluation in which 10 participants told us a spending habit they have involving the use of IoT. We chose the financial domain because it is ubiquitous, it is inherently multi-device, it is rich in time patterns, and most of all it requires a secure authentication. With the aim of extracting the requirement of such a system, we also asked the cohort how they expect WoX+ will use such habits to securely automatize payments and identify them in the smart city. We discovered that WoX+ satisfies most of the expected requirements, particularly in terms of unobtrusiveness of the solution, in contrast with the limitations observed in the existing studies. Finally, we used the responses given by the cohorts to generate synthetic data and train our novel AI block. Results show that the error in reconstructing the habits is acceptable: Mean Squared Error Percentage (MSEP) 0.04%.
An Adversarial Generative Network Designed for High-Resolution Monocular Depth Estimation from 2D HiRISE Images of Mars

In computer vision, stereoscopy allows the three-dimensional reconstruction of a scene using two 2D images taken from two slightly different points of view, to extract spatial information on the depth of the scene in the form of a map of disparities. In stereophotogrammetry, the disparity map is essential in extracting the digital terrain model (DTM) and thus obtaining a 3D spatial mapping, which is necessary for a better analysis of planetary surfaces. However, the entire reconstruction process performed with the stereo-matching algorithm can be time consuming and can generate many artifacts. Coupled with the lack of adequate stereo coverage, it can pose a significant obstacle to 3D planetary mapping. Recently, many deep learning architectures have been proposed for monocular depth estimation, which aspires to predict the third dimension given a single 2D image, with considerable advantages thanks to the simplification of the reconstruction problem, leading to a significant increase in interest in deep models for the generation of super-resolution images and DTM estimation. In this paper, we combine these last two concepts into a single end-to-end model and introduce a new generative adversarial network solution that estimates the DTM at 4× resolution from a single monocular image, called SRDiNet (super-resolution depth image network). Furthermore, we introduce a sub-network able to apply a refinement using interpolated input images to better enhance the fine details of the final product, and we demonstrate the effectiveness of its benefits through three different versions of the proposal: SRDiNet with GAN approach, SRDiNet without adversarial network, and SRDiNet without the refinement learned network plus GAN approach. The results of Oxia Planum (the landing site of the European Space Agency’s Rosalind Franklin ExoMars rover 2023) are reported, applying the best model along all Oxia Planum tiles and releasing a 3D product enhanced by 4×.
A Framework for the Blockchain and IoT-Based Supply Chain Management System

Supply chain management (SCM) applications are being developed using blockchain technologies (BCT). This work proposes a blockchain and IoT-based supply chain system (BITS) framework to help create permanent records using smart contracts that can be shared and acted across a product supply chain in the SCM in a decentralized fashion. BITS ensures improved trust, security, transparency, and traceability among the participants aided with BCT and IoT. A demonstration of the data flow and the conceptual illustration of BITS is presented. Algorithms for node authorization and verification based on threshold values are presented. The framework has been rigorously tested for performance considering increasing malicious nodes and over various performance evaluation determining factors (i.e., computational power, memory consumption, time delay, and stability analysis based on the block arrival rate). BITS was evaluated by comparing its performance with consensus algorithm peers (i.e., PoW, PoS, and DPoS). Simulation study reveals the efficient performance of BITS under various test conditions.
An Efficient Message Authentication Code Based on Modified MD5-384 Bits Hash Function and Quasigroup

Cryptographic hash functions and HMACs are used to achieve various security goals such as message integrity, message authentication, digital signatures, and entity authentication. This article proposes (1) a new hash function (QGMD5-384) and (2) an efficient message authentication code (QGMAC-384) based on a quasigroup. A quasigroup is a non-associative algebraic structure and its number grows exponentially with its order. Note that the existing hash functions and HMACs that use quasigroups are vulnerable to prefix and suffix attacks. The security of the proposed hash function is analyzed by comparing it with the MD5 and SHA-384. It is found that the proposed QGMD5-384 is more secure. Also, QGMAC-384 is analyzed against brute force and forgery attacks and it is found to be resistant to these attacks. The performance of the new schemes is compared with their counterparts, such as SHA-384 and HMAC-SHA-384. It is observed that QGMD5-384 and QGMAC-384 are slightly slower than MD5 and HMAC-MD5, respectively, but faster than both the SHA-384 and the HMAC-SHA-384.
A Secure Data Transfer Approach With an Efficient Key Management Over Cloud

The growth in the number of cloud users who transfer their health data have enhanced the importance of cloud technology's services and capabilities. However, transferring patient health data to the cloud leaves researchers with several concerns and obstacles in privacy, storage, access, key-formation, and management. The paper presents an efficient methodology for storing and accessing health information to and from the cloud. The symmetric key cryptography with the MD5 hash function is employed to enhance the framework's efficiency. The proposed method also provides secure data sharing and removes the burden of an exhaustive re-encryption computation. In the paper, two different keys are computed: one key for each legitimate user among a group and another key for the crypto-system, which is responsible to do all computations over the data. The method provides security against internal threats since only a single share of the key can be accessed. The efficiency of the model is measured by measuring the execution time for key formation, encryption, and decryption processes.
Reliable determination of SiO2 concentrations in sediments via sequential leaching and ICP-OES/MS analysis

Although one of the most abundant elements in bulk Earth, silicon has not received enough attention during routine elemental analysis by ICP-OES and ICP-MS as the standard methods cannot readily obtain Si due to its volatile SiF 4 removal during standard HF-HNO 3 digestion. This study developed a detailed calculation method for SiO 2 concentrations in a sequential analysis of sediment samples based on the mass balance principle. The prerequisite of our calculation is that (in)organic carbon is effectively removed through acid leaching and high-temperature ignition. In this study, powdered geologic reference materials were treated with a two-step sequential leaching method of i) 1 mol/L HCl to digest authigenic minerals such as carbonates followed by ii) ignition in a muffle furnace to remove remaining organic matter and a concentrated HF-HNO 3 high pressure-temperature digestion to attack the detrital silicate residues. Five reference materials, GSR4, GSR5, GSR6, GSD9, and GSD10, were selected to verify the trueness of our procedure. The SiO 2 contents of geostandards showed good agreement with reference values. The precisions of calculated SiO 2 values are better than 0.46 %; 1σ, n ≥ 3, and the accuracies are better than 2 %; 1 RSD, n ≥ 3, compared to the reference values. For other elements, the accuracy and precision of calculated results in bulk samples (i.e., the sum of leached and residual phases) are generally better than 15 %; 1 RSD, n ≥ 3, except for a few elements with extremely low concentrations. This strategy has the advantages of low sample consumption, high efficiency, and helps saving time and labour costs compared to separate sample preparation and analysis for SiO 2 and the other major/trace elements. • Obtaining SiO 2 content through HF-HNO 3 digestion • A method that sums up all of the element contents as oxides to 100 % applied • Element contents obtained in leachate, residue and bulk phases using an aliquot
Machine translation-based fine-grained comments generation for solidity smart contracts

As self-executing programs on blockchain platforms, smart contracts can build a trusted environment between multi-parties. However, participants who lack programming knowledge usually have difficulties understanding smart contracts by reading the source code. It brings them difficulties and risks when interacting with decentralized applications. We aim to translate the smart contract source code into natural language descriptions as fine-grained in-line comments to help people better understand, learn and operate smart contracts. We propose an automated translation approach for smart contracts written in Solidity, termed SolcTrans, based on an Syntax Tree (AST) and formal grammar. We have investigated representative Solidity smart contracts, identified the AST parsing paths and core attributes used for translation, and proposed corresponding translation templates for special statements. Then, we leveraged reinforcement learning to train a Probabilistic Context-Free Grammar-based syntax synthesizer used to generate comprehensible English sentences as comments. The experimental results show that SolcTrans outperforms four state-of-the-art neural machine translation models under currently available training data and is less affected by lengths of code snippets and translation outputs. We also conducted a human evaluation among 20 volunteers and asked them to score the generated comments. The results demonstrate that SolcTrans performs well on three metrics: Accuracy, Readability, and Instructiveness. Our approach produces high-quality fine-grained comments for smart contract source code under the small training dataset, which creates a paradigm for future studies.