Reviewerly/exHarmony huggingface dataset:
Covertly communicated hate speech: A corpus-assisted pragmatic study

While online hate speech is a perennial problem of modern times, there is still a lack of academic research on the topic. This could be attributed to the fact that hate speech does not always have clearly distinguishable linguistic features. As such, hate speech could well be communicated covertly. Consequently, and in order to better understand the phenomenon of ‘covertly communicated’ hate speech, one would need to first investigate the overarching thematic and discursive patterns in which hate speech is rooted. This study is a step in this direction. Assisted by a corpus-informed pragmatic analysis of hate language on Instagram, it focuses on hate language directed at Afghan immigrants in Iran, a rarely explored context involving one of the world's most widespread population movements and displacement patterns. The findings of the study lay bare the fact that, in the corpus under investigation, overt expressions of hate were few and far between, to the extent that they did not have meaningful salience in the data. In this respect, the study indicates how a number of recurring discursive patterns provide the foundation on which covert ways of expressing hate are based.
Domain adaptive object detection with model-agnostic knowledge transferring

The development of deep learning techniques has greatly benefited CNN-based object detectors, leading to unprecedented progress in recent years. However, the distribution variance between training and testing domains causes significant performance degradation. Labeling data for new scenarios is costly and time-consuming, so most existing domain adaptation methods perform feature alignment through adversarial training. While this can improve the accuracy of detectors in unlabeled target domains, the unconstrained domain alignment also negatively transfers the feature distribution, which compromises the recognition ability of the model. To address this problem, we propose the Knowledge Transfer Network (KTNet), which consists of object intrinsic knowledge mining and category relational knowledge constraint modules. Specifically, a binary classifier shared by the source and target domains is designed to extract common attribute knowledge of objects, which can align foreground and background features from different data domains adaptively. Then, we construct relational knowledge graphs to explicitly constrain the category correlations in the source, target, and cross-domain settings. These two modules guide the detector to learn object-related and domain-invariant representations, enabling the proposed KTNet to perform well in four commonly-used cross-domain scenarios. Furthermore, the ablation experiments show that our method is scalable to more complex backbone networks and different detection architectures.
Techno-economic assessment of photovoltaic plants considering high temporal resolution and non-linear dynamics of battery storage

The addition of energy storage systems for photovoltaic plants in order to provide reliability and flexibility in their dispatch is a very active line of research, as it studies plant design configurations and different dispatch strategies to improve the coupling of variable renewable solar generation with conventional hydro/thermal electric grids to enable large-scale participation of renewables in these systems. Nevertheless, battery degradation is a critical parameter to assess the plant's lifetime performance as it depends upon the battery's operational principles, design and usage pattern, which coupled with their inherent non-linearities makes them a potential source of uncertainty in assessments, should the involved phenomena be overly simplified. In this work, we explore different photovoltaic plant configurations with batteries operating under a dispatch optimization algorithm that considers operational power forecasts, market pricing information and a series of constraints to improve their coupling with electric networks. The battery system is modelled considering the electrical, thermal and degradation phenomena occurring during its life cycle, as well as considering a variable charge/discharge efficiency and it's simulated at high temporal resolution to account for any sub-hourly non-linear dynamics. The results show that considering time steps over 30 min for battery simulation during dispatch can lead to noticeable differences in key performance indicators when compared to one-minute cases and it was found that 10-minutes offered the best compromise in accuracy and computational cost. Additionally, it was found that, depending on their design, effective yearly degradation rates for batteries are highly variable and can reach values that deviate substantially from commonly-used constant assumptions, demonstrating the value of degradation modeling for lifecycle assessment. Finally, it was observed that plant configurations suited for specific operational modes such as smoothing and peak shaving could be obtained by analyzing their dispatch characteristics under the dispatch optimization algorithm, instead of following the usual approach of setting the operational mode and tuning the plant for it. This finding suggests that a design philosophy that adapts the plant's design to the dynamic the market's dynamic could be highly suitable for electric systems in which no central agent determines the expansion of the system.
Machine Learning Application for News Text Classification

News article are the source of historic information that can be accessed whenever required. Today in modern world, Internet is also doing the same task and full of online news on multiple topics which also contains some irrelevant or unhelpful information. People are living a very busy and hectic life and they does not have enough time to read the complete news article. They want to read information as per their area of interest. Therefore, it becomes important to process online news content and classify them into various categories like sports, entertainment, Business, Finance etc. so that it become easily and quicky accessible to the user. This paper is an attempt in the direction of classifying the news text into different sections using Machine Learning algorithms. Experiment is performed on an online dataset taken from Kaggle. This dataset was having around 210K news headlines from 2012 to 2022. Four different classification algorithms (Random Forest, Decision Tree, K-Neighbor Classifier and Gaussian NB) are applied on the dataset to find the results.
Nearly-lossless-to-lossy medical image compression by the optimized JPEGXT and JPEG algorithms through the anatomical regions of interest

Currently, plenty of image data is generated that complicate storage and image transmission. Great efforts have been attempted on how to increase compression ratio (CR) without loss of critical diagnostic information. In this study, we designed two optimized JPEGXT (JPEGXT_OPT) and JPEG (JPEG_OPT) approaches by amplifying discrete cosine transform coefficients and using the entire anatomical region as ROI (region of interest). We found that ROI percentages have a great impact CR: smaller ROI percentages (10–30 %) could obtain a larger CR. Under the near-lossless compression, JPEGXT_OPT could have CRs up to 4.0 under small ROI percentages (10–30 %), while only ∼ 1.2 for large ROI percentages (90–100 %). JPEG_OPT could obtain a much higher CR: up to over 20.0 for both CT and MRI images under small ROI percentages (10 %-30 %), and over 10.0 in CT and 5.0 in MRI under large ROI percentages (90 %). Both of them show a compression efficiency than the DICOM-recommended JPEGXT and JEPG_2000. From the distortion analysis, MSSIM (Multiscale Structural Similarity) and PRD (percent ratio of distortion) indicate our methods have a less image distortion than DICOM-recommended JPEGXT (PDR > 20 %) and JPEG_2000: approximately 3.0 % PRD were seen under JPEG_OPT, while<0.15 % PRD were observed under JPEGXT_OPT. MSSIM > 0.98 was found in JPEG_OPT, which the reconstructed images have almost no changes in luminance, contrast, and structure, and this was confirmed by low PRD (about 3.0 %). Overall, our two methods could provide a high compression ratio of medical images without significant loss of important diagnostic information in reconstructed images.
Feature Aggregation via Attention Mechanism for Visible-Thermal Person Re-Identification

Visible-thermal person re-identification (VT-ReID) is an image retrieval task that aims at matching the target pedestrian across the visible and thermal modalities. However, intra-class variations and cross-modality discrepancy degrade the performance of VT-ReID. Recent methods focus on extracting discriminative local features of each modality to alleviate the intra-class variations and cross-modality discrepancy, but these methods ignore semantic relations between the local features of two modalities, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e.</i> , the spatial relations and channel relations. In this paper, we proposed a feature aggregation module (FAM) to enhance the correlation between local features including spatial dependencies and channel dependencies. Furthermore, FAM implements cross-modality feature aggregation on the enhanced features to reduce the cross-modality discrepancy. Moreover, we also proposed near neighbor cross-modality loss (NNCLoss) to mine feature consistency between modalities by constructing a cross-modality near neighbor set, which facilitates feature alignment between two modalities. Extensive experiments on two datasets demonstrate the superior performance of our approach over the existing state-of-the-arts.
Scalable and programmable phononic network with trapped ions

Controllable bosonic systems can provide post-classical computational power with sub-universal quantum computational capability. A network that consists of a number of bosons evolving through beam-splitters and phase-shifters between different modes, has been proposed and applied to demonstrate quantum advantages. While the network has been implemented mostly in optical systems with photons, recently alternative realizations have been explored, where major limitations in photonic systems such as photon loss, and probabilistic manipulation can be addressed. Phonons, the quantized excitations of vibrational modes, of trapped ions can be a promising candidate to realize the bosonic network. Here, we experimentally demonstrate a minimal-loss phononic network that can be programmed and in which any phononic states are deterministically prepared and detected. We realize the network with up to four collective-vibrational modes, which can be straightforwardly extended to reveal quantum advantage. We benchmark the performance of the network with an exemplary algorithm of tomography for arbitrary multi-mode states with a fixed total phonon number. We obtain reconstruction fidelities of 94.5 $\pm$ 1.95 % and 93.4 $\pm$ 3.15 % for single-phonon and two-phonon states, respectively. Our experiment demonstrates a clear and novel pathway to scale up a phononic network for various quantum information processing beyond the limitations of classical and other quantum systems.
A Multiscale Approach to Deep Blind Image Quality Assessment

Faithful measurement of perceptual quality is of significant importance to various multimedia applications. By fully utilizing reference images, full-reference image quality assessment (FR-IQA) methods usually achieves better prediction performance. On the other hand, no-reference image quality assessment (NR-IQA), also known as blind image quality assessment (BIQA), which does not consider the reference image, makes it a challenging but important task. Previous NR-IQA methods have focused on spatial measures at the expense of information in the available frequency bands. In this paper, we present a multiscale deep blind image quality assessment method (BIQA, M.D.) with spatial optimal-scale filtering analysis. Motivated by the multi-channel behavior of the human visual system and contrast sensitivity function, we decompose an image into a number of spatial frequency bands by multiscale filtering and extract features for mapping an image to its subjective quality score by applying convolutional neural network. Experimental results show that BIQA, M.D. compares well with existing NR-IQA methods and generalizes well across datasets.
Query Performance Prediction for Neural IR: Are We There Yet?

Evaluation in Information Retrieval (IR) relies on post-hoc empirical procedures, which are time-consuming and expensive operations. To alleviate this, Query Performance Prediction (QPP) models have been developed to estimate the performance of a system without the need for human-made relevance judgements. Such models, usually relying on lexical features from queries and corpora, have been applied to traditional sparse IR methods – with various degrees of success. With the advent of neural IR and large Pre-trained Language Models, the retrieval paradigm has significantly shifted towards more semantic signals. In this work, we study and analyze to what extent current QPP models can predict the performance of such systems. Our experiments consider seven traditional bag-of-words and seven BERT-based IR approaches, as well as nineteen state-of-the-art QPPs evaluated on two collections, Deep Learning ’19 and Robust ’04. Our findings show that QPPs perform statistically significantly worse on neural IR systems. In settings where semantic signals are prominent (e.g., passage retrieval), their performance on neural models drops by as much as 10% compared to bag-of-words approaches. On top of that, in lexical-oriented scenarios, QPPs fail to predict performance for neural IR systems on those queries where they differ from traditional approaches the most.
Impact of Proof of Work (PoW)-Based Blockchain Applications on the Environment: A Systematic Review and Research Agenda

Blockchain technology is being looked at to solve numerous real-world problems that demand transparency by meeting sustainable goals. Do we ponder whether this technology is a boon or a bane for the environment? This paper analyses blockchain’s dominant consensus method, Proof-of-Work (PoW), which consumes more energy than Malaysia and Sweden and further deteriorates the environment through carbon emissions. This study is the first systematic evaluation of PoW consensus-based blockchain applications’ environmental consequences. We found 11 significant Theories, 6 Contexts, and 26 Methodologies (TCM) in 60 reviewed articles. We propose an Antecedents, Drivers, and Outcomes (ADO) model, which depicts that marginal profits drive high energy consumption and carbon emissions, with non-renewable energy proportionally responsible for carbon emissions. The article distinctively uses an integrated TCM-ADO framework for literature synthesis and the PESTLE framework for reporting future research areas. This is the first study to use the following four frameworks: PRISMA; TCM; ADO; and PESTLE for systematic literature review. Profit is identified as one of the most significant drivers of energy consumption and further carbon emissions. The article proposes 65 future research areas and makes theoretical contributions to the literature that may interest academicians, practitioners, and social stakeholders.
Resilience towarded Digital Twins to improve the adaptability of transportation systems

This work aims to investigate the role of the resilience of Digital Twins on the applicability of the transportation system. A literature study is conducted to review the current status of research on transportation systems and Digital Twins. It is found that the current research on Digital Twins technology has achieved different degrees of success in different aspects of transportation systems. Yet, the system performance of Digital Twins has to be optimized. First, the application of Digital Twins in intelligent transportation systems is analyzed. Then, how the changes in traveler behavior patterns reflect the extent to which the traffic network is affected by uncertain events is analyzed from the traveler's perspective. Finally, an Internet of Vehicles (IoV) system based on Digital Twins and blockchain is established to solve the data redundancy and high computational volume problems of in-vehicle data sharing common in the IoV system. Moreover, the performance of the twin system is optimized by proposing a multi-intelligence body algorithm based on local perception, and a case validation is performed. The results demonstrate that the adaptability of the transportation system to uncertain events and its response and recovery measures taken are reflected to some extent in the traveler behavior model. Besides, data sharing between vehicles and infrastructure in the transportation network can be well solved by Digital Twins Blockchain. The locally-aware multi-intelligent body algorithm saves more than 50% communication overhead and improves operational efficiency by nearly 20% over traditional algorithms by increasing intelligent body infrastructure units. It is adequately suited for large-scale vehicle traffic twins. It can be seen that improving the resilience of Digital Twins is a very obvious change in the adaptability of the traffic system.
Color Image Encryption Algorithm Based on a Chaotic Model Using the Modular Discrete Derivative and Langton’s Ant

In this work, a color image encryption and decryption algorithm for digital images is presented. It is based on the modular discrete derivative (MDD), a novel technique to encrypt images and efficiently hide visual information. In addition, Langton’s ant, which is a two-dimensional universal Turing machine with a high key space, is used. Moreover, a deterministic noise technique that adds security to the MDD is utilized. The proposed hybrid scheme exploits the advantages of MDD and Langton’s ant, generating a very secure and reliable encryption algorithm. In this proposal, if the key is known, the original image is recovered without loss. The method has demonstrated high performance through various tests, including statistical analysis (histograms and correlation distributions), entropy, texture analysis, encryption quality, key space assessment, key sensitivity analysis, and robustness to differential attack. The proposed method highlights obtaining chi-square values between 233.951 and 281.687, entropy values between 7.9999225223 and 7.9999355791, PSNR values (in the original and encrypted images) between 8.134 and 9.957, the number of pixel change rate (NPCR) values between 99.60851796% and 99.61054611%, unified average changing intensity (UACI) values between 33.44672377% and 33.47430379%, and a vast range of possible keys &gt;5.8459×1072. On the other hand, an analysis of the sensitivity of the key shows that slight changes to the key do not generate any additional information to decrypt the image. In addition, the proposed method shows a competitive performance against recent works found in the literature.
Realizing quantum gates with optically addressable <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mmultiscripts><mml:mi>Yb</mml:mi><mml:none /><mml:mo>+</mml:mo><mml:mprescripts /><mml:none /><mml:mn>171</mml:mn></mml:mmultiscripts></mml:math> ion qudits

The use of multilevel information carriers, also known as qudits, is a promising path for exploring scalability of quantum computing devices. Here we present a proof-of-principle realization of a quantum processor register that uses optically addressed $^{171}\mathrm{Yb}^{+}$ ion qudits in a linear trap. The rich level structure of $^{171}\mathrm{Yb}^{+}$ ions allows using the Zeeman sublevels of the quadrupole clock transition at 435.5 nm for efficient and robust qudit encoding. We demonstrate the realization of the universal set of gates consisting of single-qudit rotations and a two-qudit M\o{}lmer-S\o{}rensen operation with a two-ququart system, which is formally equivalent to a universal gate-based four-qubit processor. Our results paves a way toward further studies of more efficient implementations of quantum algorithms with trapped-ion-based processors and, specifically, exploring properties of $^{171}\mathrm{Yb}^{+}$ ion qudits.
Blockchain Assisted Data Edge Verification With Consensus Algorithm for Machine Learning Assisted IoT

Internet of Things (IoT) devices are becoming increasingly ubiquitous in daily life. They are utilized in various sectors like healthcare, manufacturing, and transportation. The main challenges related to IoT devices are the potential for faults to occur and their reliability. In classical IoT fault detection, the client device must upload raw information to the central server for the training model, which can reveal sensitive business information. Blockchain (BC) technology and a fault detection algorithm are applied to overcome these challenges. Generally, the fusion of BC technology and fault detection algorithms can give a secure and more reliable IoT ecosystem. Therefore, this study develops a new Blockchain Assisted Data Edge Verification with Consensus Algorithm for Machine Learning (BDEV-CAML) technique for IoT Fault Detection purposes. The presented BDEV-CAML technique integrates the benefits of blockchain, IoT, and ML models to enhance the IoT network’s trustworthiness, efficacy, and security. In BC technology, IoT devices that possess a significant level of decentralized decision-making capability can attain a consensus on the efficiency of intrablock transactions. For fault detection in the IoT network, the deep directional gated recurrent unit (DBiGRU) model is used. Finally, the African vulture optimization algorithm (AVOA) technique is utilized for the optimal hyperparameter tuning of the DBiGRU model, which helps in improving the fault detection rate. A detailed set of experiments were carried out to highlight the enhanced performance of the BDEV-CAML algorithm. The comprehensive experimental results stated the improved performance of the BDEV-CAML technique over other existing models with maximum accuracy of 99.6%.
Robust Multi‐Party Semi‐Quantum Private Comparison Protocols with Decoherence‐Free States against Collective Noises

Abstract Based on the decoherence‐free states, two multi‐party semi‐quantum private comparison protocols are proposed to counteract collective noises. One can resist the collective‐dephasing noise well, and the other can resist the collective‐rotation noise. With the assistance of a semi‐honest third party (TP), multiple classical participants with restricted quantum abilities can compare their secret information by performing the proposed protocols once. It is manifested that the proposed protocols can resist both external attacks and internal attacks. Additionally, the operations in the multi‐party semi‐quantum private comparison protocols are simulated on the IBM Quantum Experience.