Reviewerly/exHarmony huggingface dataset:
Design and Testbed Implementation of Blind Parameter Estimated OFDM Receiver

Designing an intelligent or adaptive transceiver system is becoming a promising technology for upcoming generations of wireless communication systems due to its adaptivity, spectrum efficiency, and low-latency characteristics. However, there is no work available until now that characterizes and demonstrates complete adaptation in the physical layer for orthogonal frequency-division multiplexing (OFDM) systems. In this article, we propose and implement sequential blind parameter estimation methods for OFDM signals using radio frequency (RF) testbed setup in a realistic scenario. The estimations include the number of subcarriers, symbol duration, cyclic prefix, oversampling factor, symbol timing offset (STO), and carrier frequency offset (CFO). The proposed algorithms also include blind modulation classification for linearly modulated signals over a frequency-selective fading channel. The parameter estimation has been carried out through a cyclic cumulant process. The modulation formats are classified by using normalized fourth-order cumulant in the frequency domain. The STO and CFO are estimated by a proposed modified maximum likelihood algorithm. The performances of parameter estimations, modulation classification, and synchronization are measured through analytical, simulation, and measurement studies. The overall performance of the OFDM system is provided in terms of the received constellation diagram and bit error rate (BER) over an indoor propagation environment.
MISO- V: Misbehavior Detection for Collective Perception Services in Vehicular Communications

Recently, Collective Perception Messages (CPM) that carry additional information about the surrounding environment beyond Basic Safety Messages (BSM) or Cooperative Awareness Messages (CAM) have been proposed to increase the situational awareness for Connected and Automated Vehicles (CAV) in Intelligent Transportation Systems. However, blindly trusting perception information from neighbors that cannot be locally verified is dangerous given the safety impact that erroneous or malicious information might have. This paper addresses the data trust challenge of CPMs, proposing a misbehavior detection scheme called MISO- V (Multiple Independent Sources of Observations over V2X) that leverages the inherently overlapping nature of the perception observations from multiple vehicles to verify the semantic correctness of the V2X data and improve the data trust and robustness of V2X systems. CPM-enabled CAVs are implemented and MISO-V performance is evaluated in CARLA-based simulation tool, where falsified V2X packets presenting a ghost car are injected in a suburban T-junction scenario with other cars. The results show that MISO- V is very effective in detecting the ghost car attacks and removing the impact of such misbehavior from influencing the receiver and offers a conservative and sensible approach towards trustworthy Collective Perception Services for CAV s.
Novel Implementation of Audio Encryption Using Pseudorandom Byte Generator

In this paper, we present an algorithm for encrypting audio files based on the Ikeda map, a mathematical function of chaos theory. Detailed experimental, security and theoretical analysis is provided on the proposed algorithm using histogram analysis, using different measurements including the signal-to-noise ratio, the peak signal-to-noise ratio, the number of samples change rate and the correlation coefficient. The provided results show a highly secure and strong algorithm against different types of attacks.
Ecopedagogical literacy of a pandemic: Teaching to critically read the politics of COVID-19 with environmental issues

Politics of public pedagogies that systemically obscured, ignored, and/or flat-out lied about COVID-19 realities that led to, and worsened, the global pandemic coincides with education that falsely justifies environmental violence, unsustainability, and dominance of Nature. I discuss how ecopedagogy, grounded in the popular education models of Paulo Freire, are essential to critically compare and contrast COVID-19 aspects with environmental issues to construct effective environmental pedagogies for critical literacy (i.e., ecopedagogical literacy) to disrupt and "unlearn" ideologies that sustain and intensify unsustainable acts of environmental violence. The following three disruptions are argued as essential for socio-environmental justice and planetary sustainability: disrupting Northern, dominate epistemologies that instills world-Earth distancing (i.e., "us" humans (anthroposphere) from the rest of Nature); disrupting "development" defined within neoliberal and neocolonial framings, including being solely grounded upon epistemologies of the North, and disrupting academic scholarship and structures that lead to the first two aspects needing disruptions as mentioned.
Event Prediction Model Considering Time and Input Error Using Electronic Medical Records in the Intensive Care Unit: Retrospective Study

In the era of artificial intelligence, event prediction models are abundant. However, considering the limitation of the electronic medical record-based model, including the temporally skewed prediction and the record itself, these models could be delayed or could yield errors.In this study, we aim to develop multiple event prediction models in intensive care units to overcome their temporal skewness and evaluate their robustness against delayed and erroneous input.A total of 21,738 patients were included in the development cohort. Three events-death, sepsis, and acute kidney injury-were predicted. To overcome the temporal skewness, we developed three models for each event, which predicted the events in advance of three prespecified timepoints. Additionally, to evaluate the robustness against input error and delays, we added simulated errors and delayed input and calculated changes in the area under the receiver operating characteristic curve (AUROC) values.Most of the AUROC and area under the precision-recall curve values of each model were higher than those of the conventional scores, as well as other machine learning models previously used. In the error input experiment, except for our proposed model, an increase in the noise added to the model lowered the resulting AUROC value. However, the delayed input did not show the performance decreased in this experiment.For a prediction model that was applicable in the real world, we considered not only performance but also temporal skewness, delayed input, and input error.
Semi-Supervised Self-Training of Hate and Offensive Speech from Social Media

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Automating App Review Response Generation Based on Contextual Knowledge

User experience of mobile apps is an essential ingredient that can influence the user base and app revenue. To ensure good user experience and assist app development, several prior studies resort to analysis of app reviews, a type of repository that directly reflects user opinions about the apps. Accurately responding to the app reviews is one of the ways to relieve user concerns and thus improve user experience. However, the response quality of the existing method relies on the pre-extracted features from other tools, including manually labelled keywords and predicted review sentiment, which may hinder the generalizability and flexibility of the method. In this article, we propose a novel neural network approach, named CoRe, with the contextual knowledge naturally incorporated and without involving external tools. Specifically, CoRe integrates two types of contextual knowledge in the training corpus, including official app descriptions from app store and responses of the retrieved semantically similar reviews, for enhancing the relevance and accuracy of the generated review responses. Experiments on practical review data show that CoRe can outperform the state-of-the-art method by 12.36% in terms of BLEU-4, an accuracy metric that is widely used to evaluate text generation systems.
Bug Localization in Model-Based Systems in the Wild

The companies that have adopted the Model-Driven Engineering (MDE) paradigm have the advantage of working at a high level of abstraction. Nevertheless, they have the disadvantage of the lack of tools available to perform bug localization at the model level. In addition, in an MDE context, a bug can be related to different MDE artefacts, such as design-time models, model transformations, or run-time models. Starting the bug localization in the wrong place or with the wrong tool can lead to a result that is unsatisfactory. We evaluate how to apply the existing model-based approaches in order to mitigate the effect of starting the localization in the wrong place. We also take into account that software engineers can refine the results at different stages. In our evaluation, we compare different combinations of the application of bug localization approaches and human refinement. The combination of our approaches plus manual refinement obtains the best results. We performed a statistical analysis to provide evidence of the significance of the results. The conclusions obtained from this evaluation are: humans have to be involved at the right time in the process (or results can even get worse), and artefact-independence can be achieved without worsening the results.
Evaluating Unsupervised Text Embeddings on Software User Feedback

User feedback on software products has been shown to be useful for development and can be exceedingly abundant online.Many approaches have been developed to elicit requirements in different ways from this large volume of feedback, including the use of unsupervised clustering, underpinned by text embeddings.Methods for embedding text can vary significantly within the literature, highlighting the lack of a consensus as to which approaches are best able to cluster user feedback into requirements relevant groups.This work proposes a methodology for comparing text embeddings of user feedback using existing labelled datasets.Using 7 diverse datasets from the literature, we apply this methodology to evaluate both established text embedding techniques from the user feedback analysis literature (including topic modelling and word embeddings) as well as text embeddings from state of the art deep text embedding models.Results demonstrate that text embeddings produced by state of the art models, most notably the Universal Sentence Encoder (USE), group feedback with similar requirements relevant characteristics together better than other evaluated techniques across all seven datasets.These results can help researchers select appropriate embedding techniques when developing future unsupervised clustering approaches within user feedback analysis.
Inferring the Class Conditional Response Map for Weakly Supervised Semantic Segmentation

Image-level weakly supervised semantic segmentation (WSSS) relies on class activation maps (CAMs) for pseudo labels generation. As CAMs only highlight the most discriminative regions of objects, the generated pseudo labels are usually unsatisfactory to serve directly as supervision. To solve this, most existing approaches follow a multi-training pipeline to refine CAMs for better pseudo-labels, which includes: 1) re-training the classification model to generate CAMs; 2) post-processing CAMs to obtain pseudo labels; and 3) training a semantic segmentation model with the obtained pseudo labels. However, this multi-training pipeline requires complicated adjustment and additional time. To address this, we propose a class-conditional inference strategy and an activation aware mask refinement loss function to generate better pseudo labels without retraining the classifier. The class conditional inference-time approach is presented to separately and iteratively reveal the classification network’s hidden object activation to generate more complete response maps. Further, our activation aware mask refinement loss function introduces a novel way to exploit saliency maps during segmentation training and refine the foreground object masks without suppressing background objects. Our method achieves superior WSSS results without requiring re-training of the classifier. https://github.com/weixuansun/InferCam
Deep Unsupervised Active Learning via Matrix Sketching

Most existing unsupervised active learning methods aim at minimizing the data reconstruction loss by using the linear models to choose representative samples for manually labeling in an unsupervised setting. Thus these methods often fail in modelling data with complex non-linear structure. To address this issue, we propose a new deep unsupervised Active Learning method for classification tasks, inspired by the idea of Matrix Sketching, called ALMS. Specifically, ALMS leverages a deep auto-encoder to embed data into a latent space, and then describes all the embedded data with a small size sketch to summarize the major characteristics of the data. In contrast to previous approaches that reconstruct the whole data matrix for selecting the representative samples, ALMS aims to select a representative subset of samples to well approximate the sketch, which can preserve the major information of data meanwhile significantly reducing the number of network parameters. This makes our algorithm alleviate the issue of model overfitting and readily cope with large datasets. Actually, the sketch provides a type of self-supervised signal to guide the learning of the model. Moreover, we propose to construct an auxiliary self-supervised task by classifying real/fake samples, in order to further improve the representation ability of the encoder. We thoroughly evaluate the performance of ALMS on both single-label and multi-label classification tasks, and the results demonstrate its superior performance against the state-of-the-art methods. The code can be found at https://github.com/lrq99/ALMS.
Metrics for software requirements specification quality quantification

The initial and crucial phase of the automation and software development is identifying requirements and documenting them in an appropriate format that denotes software requirement specification (SRS). The quality and productivity at different phases of the software development depend on requirements specification. The quality of the end product of software development is proportionate to the quality of SRS. Hence, estimating the quality of the SRS is essential. Though the numerous metrics have been defined in contemporary research, the IEEE standard metrics are considered authentic to scale SRS quality. This manuscript endeavored to redefine the IEEE standard metrics’ measuring approaches to improvise SRS quality assessment. The experimental study exhibiting the significance and robustness of the proposed approach that compared to the contemporary contributions of the recent literature
Throttling Twitter

In March 2021, the Russian government started to throttle Twitter on a national level, marking the first ever use of large-scale, targeted throttling for censorship purposes. The slowdown was intended to pressure Twitter to comply with content removal requests from the Russian government.
Infinitary action logic with exponentiation

We introduce infinitary action logic with exponentiation—that is, the multiplicative-additive Lambek calculus extended with Kleene star and with a family of subexponential modalities, which allow some of the structural rules (contraction, weakening, permutation). The logic is presented in the form of an infinitary sequent calculus. We prove cut elimination and, in the case where at least one subexponential allows non-local contraction, establish exact complexity boundaries in two senses. First, we show that the derivability problem for this logic is Π11-complete. Second, we show that the closure ordinal of its derivability operator is ω1CK. In the case where no subexponential allows contraction, we show that complexity is the same as for infinitary action logic itself. Namely, the derivability problem in this case is Π10-complete and the closure ordinal is not greater than ωω.
Securing Connected Vehicle Applications with an Efficient Dual Cyber- Physical Blockchain Framework

While connected vehicle (CV) applications have the potential to revolutionize traditional transportation system, cyber and physical attacks on them may lead to disastrous consequences. In this work, we propose an efficient dual cyber-physical blockchain framework to build trust and secure communication for CV applications. Our approach incorporates blockchain technology and physical sensing capabilities of vehicles to quickly react to attacks in a large-scale vehicular network, with low resource overhead. We explore the application of our framework to three CV applications, i.e., highway merging, intelligent intersection management, and traffic network with route choices. Simulation results demonstrate the effectiveness of our blockchain-based framework in defending against spoofing attacks, bad mouthing attacks, and Sybil and voting attacks. We also provide analysis to show the timing and resource efficiency of our framework.
The <scp>fairness‐accuracy</scp> Pareto front

Abstract Algorithmic fairness seeks to identify and correct sources of bias in machine learning algorithms. Confoundingly, ensuring fairness often comes at the cost of accuracy. We provide formal tools in this work for reconciling this fundamental tension in algorithm fairness. Specifically, we put to use the concept of Pareto optimality from multiobjective optimization and seek the fairness‐accuracy Pareto front of a neural network classifier. We demonstrate that many existing algorithmic fairness methods are performing the so‐called linear scalarization scheme, which has severe limitations in recovering Pareto optimal solutions. We instead apply the Chebyshev scalarization scheme which is provably superior theoretically and no more computationally burdensome at recovering Pareto optimal solutions compared to the linear scheme.
Classification of Starling Image Using Artificial Neural Networks

research-article Share on Classification of Starling Image Using Artificial Neural Networks Author: Aviv Yuniar Rahman Universitas Widyagama Malang, Indonesia Universitas Widyagama Malang, IndonesiaView Profile Authors Info & Claims SIET '21: 6th International Conference on Sustainable Information Engineering and Technology 2021September 2021 Pages 309–314https://doi.org/10.1145/3479645.3479690Published:03 November 2021Publication History 0citation28DownloadsMetricsTotal Citations0Total Downloads28Last 12 Months12Last 6 weeks1 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my Alerts New Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Optimizing Sparse Matrix Multiplications for Graph Neural Networks

Graph neural networks (GNNs) are emerging as a powerful technique for modeling graph structures. Due to the sparsity of real-world graph data, GNN performance is limited by extensive sparse matrix multiplication (SpMM) operations involved in computation. While the right sparse matrix storage format varies across input data, existing deep learning frameworks employ a single, static storage format, leaving much room for improvement. This paper investigates how the choice of sparse matrix storage formats affect the GNN performance. We observe that choosing a suitable sparse matrix storage format can significantly improve the GNN training performance, but the right format depends on the input workloads and can change as the GNN iterates over the input graph. We then develop a predictive model to dynamically choose a sparse matrix storage format to be used by a GNN layer based on the input matrices. Our model is first trained offline using training matrix samples, and the trained model can be applied to any input matrix and GNN kernels with SpMM computation. We implement our approach on top of PyTorch and apply it to 5 representative GNN models running on a multi-core CPU using real-life and synthetic datasets. Experimental results show that our approach gives an average speedup of 1.17x (up to 3x) for GNN running time.
Fine-Grained Forward Secrecy: Allow-List/Deny-List Encryption and Applications

Forward secrecy is an important feature for modern cryptographic systems and is widely used in secure messaging such as Signal and WhatsApp as well as in common Internet protocols such as TLS, IPSec, or SSH. The benefit of forward secrecy is that the damage in case of key-leakage is mitigated. Forward-secret encryption schemes provide security of past ciphertexts even if a secret key leaks, which is interesting in settings where cryptographic keys often reside in memory for quite a long time and could be extracted by an adversary, e.g., in cloud computing. The recent concept of puncturable encryption (PE; Green and Miers, IEEE S&P'15) provides a versatile generalization of forward-secret encryption: it allows to puncture secret keys with respect to ciphertexts to prevent the future decryption of these ciphertexts.
Lightweight Blockchain Based on Storage Resource Optimization for Internet of Vehicles

Lightweight blockchain inherits the advantages of traditional blockchain, while avoiding excessive reliance on computing and storage resources, and has already been applied in IoV system. This paper combines the characteristics of IoV and node distribution, and proposes a lightweight blockchain architecture oriented to vehicles-road-cloud-server(V2R2CS) collaboration based on the existing lightweight blockchain architecture. Cooperating caching and Block Filtered Algorithm are used to allow road side units(RSUs) to cooperatively store and search blocks, which will further reduce the delay of blockchain query in IoV system. Finally, the simulation experiment proves the feasibility of the algorithm and the good real-time requirement.
Detecting Arabic Spam Reviews in Social Networks Based on Classification Algorithms

Reviews or comments that users leave on social media have great importance for companies and business entities. New product ideas can be evaluated based on customer reactions. However, this use of social media is complicated by those who post spam on social media in the form of reviews and comments. Designing methodologies to automatically detect and block social media spam is complicated by the fact that spammers continuously develop new ways to leave their spam comments. Researchers have proposed several methods to detect English spam reviews. However, few studies have been conducted to detect Arabic spam reviews. This article proposes a keyword-based method for detecting Arabic spam reviews. Keywords or Features are subsets of words from the original text that are labelled as important. A term's weight, Term Frequency–Inverse Document Frequency (TF-IDF) matrix, and filter methods (such as information gain, chi-squared, deviation, correlation, and uncertainty) have been used to extract keywords from Arabic text. The method proposed in this article detects Arabic spam in Facebook comments. The dataset consists of 3,000 Arabic comments extracted from Facebook pages. Four different machine learning algorithms are used in the detection process, including C4.5, kNN, SVM, and Naïve Bayes classifiers. The results show that the Decision Tree classifier outperforms the other classification algorithms, with a detection accuracy of 92.63%.