Reviewerly/exHarmony huggingface dataset:
Phonetic Error Analysis Beyond Phone Error Rate

In this paper, we analyse the performance of the TIMIT-based phone recognition systems beyond the overall phone error rate (PER) metric.We consider three broad phonetic classes (BPCs): {affricate, diphthong, fricative, nasal, plosive, semi-vowel, vowel, silence}, {consonant, vowel, silence} and {voiced, unvoiced, silence} and, calculate the contribution of each phonetic class in terms of the substitution, deletion, insertion and PER.Furthermore, for each BPC we investigate the following: evolution of PER during training, effect of noise (NTIMIT), importance of different spectral subbands (1, 2, 4, and 8 kHz), usefulness of bidirectional vs unidirectional sequential modelling, transfer learning from WSJ and regularisation via monophones.In addition, we construct a confusion matrix for each BPC and analyse the confusions via dimensionality reduction to 2D at the input (acoustic features) and output (logits) levels of the acoustic model.We also compare the performance and confusion matrices of the BLSTM-based hybrid baseline system with those of the GMM-HMM based hybrid, Conformer and wav2vec 2.0 based end-to-end phone recognisers.Finally, the relationship of the unweighted and weighted PERs with the broad phonetic class priors is studied for both the hybrid and end-to-end systems.
Securely Aggregated Coded Matrix Inversion

Coded computing is a method for mitigating straggling workers in a centralized computing network, by using erasure-coding techniques. Federated learning is a decentralized model for training data distributed across client devices. In this work we propose approximating the inverse of an aggregated data matrix, where the data is generated by clients; similar to the federated learning paradigm, while also being resilient to stragglers. To do so, we propose a coded computing method based on gradient coding. We modify this method so that the coordinator does not access the local data at any point; while the clients access the aggregated matrix in order to complete their tasks. The network we consider is not centrally administrated, and the communications which take place are secure against potential eavesdroppers.
A Systematic Literature Review on Software Vulnerability Prediction Models

The prediction of software vulnerability requires crucial awareness during the software specification, design, development, and configuration to achieve less vulnerable and secure software. Software vulnerability prediction is the process of model development that can be beneficial for the early prediction of vulnerable components at various granularity levels such as file, class, and method. Machine learning and deep learning techniques are gaining popularity in developing vulnerability prediction models. This paper performs a systematic review of primary studies from 2000 to 2022 in the literature that used machine learning and deep learning techniques for software vulnerability prediction. In addition to this, the paper understands the concept of resampling methods to handle imbalanced dataset problems; summarizes the important hyperparameter optimization methods to tune hyperparameters; explains the types of features, data pre-processing techniques, dimensionality reduction, and feature selection techniques. Furthermore, encapsulating the comparison of ML/DL techniques and highlighting the best technique is performed. The paper identifies seventy-seven research studies that use thirty-two machine learning and five deep learning techniques. Additionally, it identifies five different feature types, data pre-processing methods, thirty-seven datasets, nine data balancing techniques, twenty-six performance measures, six hyperparameter optimization methods, and the ranges of hyperparameters. Finally, guidelines for researchers to increase the productivity of software vulnerability prediction models have been illustrated in the paper.
Automatic Modulation Classification: Convolutional Deep Learning Neural Networks Approaches

This study proposes robust convolutional neural network (CNN)-based automatic modulation classification (AMC) techniques. Traditional AMCs may be classified into two types: those that rely on ML (maximum likelihood-based AMCs) and those that rely on features. Numerous studies have been conducted on feature-based automatic modulation classification techniques. The current feature-based AMCs lack generalization capability and frequently target a small group of modulation techniques. The current paper develops three different CNN-based AMCs, each with a different classification layer (CL). The adopted classification layers are mean absolute error-based CL, a sum of squared errors-based CL, and crossentropy-based CL. The developed techniques can classify the received signals without feature extraction, where they can learn the features from the transmitted signals automatically during the offline training process, thus eliminating the necessity for feature extraction. A comparison study was done for the proposed CNN-based AMCs with three optimization algorithms at two signal-to-noise ratios. The proposed AMCs attain a true classification accuracy of up to 100% depending on the optimizer and loss function-base CL.
Sentiment Analysis Using Hybrid Model of Stacked Auto-Encoder-Based Feature Extraction and Long Short Term Memory-Based Classification Approach

The reviews of customer about a brand or product, movie reviews, and social media reviews, all can be analyzed through sentiment analysis. Sentiment analysis is used to identify the emotional tone of language in order to comprehend the attitudes, opinions, and feelings represented in online reviews. As for large data, it is a task that can take a lot of time and can be automated as the machine will learn through training and testing of data. Previously, various standard machine learning and deep learning models namely Recurrent Neural Network (RNN), Convolutional Neural Network (CNN), Long Short Term Memory (LSTM), Naïve Bayes (NB), Support Vector Machine (SVM), Gated Recurrent Unit (GRU) have been used. The key issue on which our research is focused on is that when text is provided to LSTM directly, it cannot adequately extract informative features from the text, leading to less accurate findings. The Stacked Auto-encoder’s softmax layer, when used directly to categorize the extracted features, is power-constrained and unable to do so accurately. A hybrid of the Stacked Auto-encoder and LSTM model has been proposed. SAE is used for relevant informative feature extraction. LSTM is used for further classification of sentiments based on extracted features. The proposed model has been evaluated on an IMDB dataset by splitting it in 5 different training testing ratios using performance evaluation metrics viz. confusion matrix, classification accuracy, precision, recall, sensitivity, specificity, and F1 score. The hybrid results perform best at a ratio of 90/10, and classifying sentiments with 87% accuracy. The proposed hybrid model’s accuracy is better as compared to standard models namely RNN, CNN, LSTM, NB, SVM, and GRU.
Exploiting Unfairness with Meta-set Learning for Chronological Age Estimation

Facial age estimation aims to rank the face aging data by taking in the correlation among age categories. Conventional age estimation models are trained based on assumed high-quality training annotations in a totally-supervised manner. However, noisy data in a sparse distribution collected from unconstrained environment usually account for the corruption of produced gradients and ordinal relationships, which may fail to fairly describe the correlated face aging data. In this paper, we propose a meta-set learning (MSL) approach for exploiting the unfairness of face aging datasets, thus achieving unbiased age classification in unconstrained conditions. To address this, we elaborately create an unfairness filtration network under the meta-learning paradigm, which exceeds a reliable margin-reweighting initialization suffering from class variance, simultaneously exploiting the meta-reweighting intervention to minimize the training bias caused by class imbalance. Moreover, our proposed model leverages the learned instance-level margin between logits and develops a unimodal constrained logits loss, further surviving age regression models from unfairness. Experimental results on multiple in-the-wild datasets demonstrate that our proposed method achieves superior results compared to existing state-of-the-art methods.
PSO-K2PC: Bayesian structure learning using optimized K2 algorithm for parents-children detection

Bayesian networks, revered for their adeptness in modeling uncertainty and predicting outcomes, encounter a formidable hurdle during the structure learning phase – an NP-hard problem, posing insurmountable computational challenges for large networks. To surmount this barrier and advance the field, we propose an innovative optimization of the K2PC algorithm for Bayesian network structure learning. Derived from the popular K2 algorithm, our novel optimization ingeniously tackles K2PC's vulnerability to predetermined node order. Leveraging the power of a particle swarm optimization algorithm, we adeptly seek the optimal node ordering, yielding exceptional results. Through rigorous evaluations on benchmark networks, our proposed method surpasses prior approaches in structure difference and accuracy, affirming its potential as a promising avenue for Bayesian network structure learning in large, complex networks. We posit that our novel approach constitutes an important advance in the field of Bayesian network structure learning, with the potential to stimulate additional progress through further scientific investigation.
Future Directions

chapter Share on Future Directions Author: Manish Raghavan Massachusetts Institute of Technology, Sloan School of Management and Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology, Sloan School of Management and Department of Electrical Engineering and Computer ScienceView Profile Authors Info & Claims The Societal Impacts of Algorithmic Decision-MakingSeptember 2023https://doi.org/10.1145/3603195.3603205Published:08 September 2023Publication History 0citation0DownloadsMetricsTotal Citations0Total Downloads0Last 12 Months0Last 6 weeks0 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 AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Turkish sign language recognition using fuzzy logic asisted ELM and CNN methods

This work aimed to develop a data glove for the real-time translation of Turkish sign language. In addition, a novel Fuzzy Logic Assisted ELM method (FLA-ELM) for hand gesture classification is proposed. In order to acquire motion information from the gloves, 12 flexibility sensors, two inertial sensors, and 10 Hall sensors were employed. The NVIDIA Jetson Nano, a small pocketable minicomputer, was used to run the recognition software. A total of 34 signal information was gathered from the sensors, and feature matrices were generated in the form of time series for each word. In addition, an algorithm based on Euclidean distance has been developed to detect end-points between adjacent words in a sentence. In addition to the proposed method, CNN and classical ANN methods, whose model was created by us, were used in sign language recognition experiments, and the results were compared. For each classified word, samples were collected from 25 different signers, and 3000 sample data were obtained for 120 words. Furthermore, the dataset’s size was reduced using PCA, and the results of the newly created datasets were compared to the reference results. In the performance tests, single words and three-word sentences were translated with an accuracy of up to 96.8% and a minimum 2.4 ms processing time.
Devising a method for rapid data retrieval using explorers for blockchain analysis

The object of this study is blockchain explorers and their usefulness in efficiently gathering data for blockchain network analysis. The process of blockchain analysis typically involves deploying and synchronizing a blockchain node, which requires significant computational resources and time for synchronization. Analyzing multiple blockchain networks simultaneously demands substantial effort and requires even greater costs. The developed method involves utilizing publicly accessible blockchain explorers, which allows for rapid data retrieval with minimal computational resources for further analysis. Additionally, obtaining supplementary information from blockchain explorers provides valuable details that may be inaccessible using traditional data retrieval methods. The efficiency of the proposed method was verified through the development of a prototype system. Data was collected for 14 specified blockchain networks to analyze smart contracts within these networks. Information about accounts (including balance statistics) was gathered, smart contracts were identified among the accounts, data on existing tokens owned by smart contracts was obtained, and bytecode and source code (where available) of contracts were collected and decompiled. The process took nearly 24 hours on a cloud computing machine with minimal configuration. Based on the collected data, an example smart contract was analyzed to demonstrate the completeness of the process. The results of this research minimize computational resource expenses and allow for a simplified and rapid data gathering process without manual configuration, enabling researchers and analysts to concentrate on subsequent stages of analysis
Enhanced Detection of Suspicious Breast Lesions: A Comparative Study of Full-Field Digital Mammography and Automated Breast Ultrasound in 117 Patients with Core Needle Biopsy

BACKGROUND This retrospective study from a single center aimed to compare the performance of full-field digital mammography (FFDM) vs automated breast ultrasound (ABUS) in the identification and characterization of suspicious breast lesions in 117 patients who underwent core-needle biopsy (CNB) of the breast. MATERIAL AND METHODS The study involved a group of 301 women. Every patient underwent FFDM followed by ABUS, which were assessed in concordance with BI-RADS (Breast Imaging Reporting and Data System) classification. RESULTS No focal lesions were found in 168 patients. In 133 patients, 117 histopathologically verified focal lesions were found. Among them, 78% appeared to be malignant and 22% benign. ABUS detected 246 focal lesions, including 115 classified as BI-RADS 4 or 5 and submitted to verification, while FFDM revealed 122 lesions, including 75 submitted to verification. The analysis revealed that combined application of both methods caused sensitivity to increase to 100, and improved accuracy improvement. Margin assessments in these examinations are consistent (P<0.00), the lesion's margin type with both methods depends on its malignant or benign character (P<0.03), lesion margins distribution on ABUS depends on estrogen receptor presence (P=0.033), and there was significant correlation between malignant character of the lesion and retraction phenomenon sign (P=0.033). ABUS obtained higher compliance between the size of the lesion in histopathology compared to FFDM (P>0.05). CONCLUSIONS The results shows that ABUS is comparable to FFDM, and even outperforms it in a few of the analyzed categories, suggesting that the combination of these 2 methods may have an important role in breast cancer detection.
The State of Streaming Video Content at Academic Libraries

This paper provides a literature review of academic library collection activities related to the provision of streaming video content in support of higher education curricula. It reviews the budgetary, collection management, licensing, technology, and acquisition processes and associated challenges that the provision of streaming video content poses for academic libraries in offering this much needed content to their patron base. The authors present a recent history of developing academic video collections, the transition to and increased demand for the streaming video format, and the evaluations required of funding models and vendor supply models to make the availability of streaming video content sustainable in the academic environment. The article also reviews current and future research and planned collaboration which hold great promise in addressing the current challenges academic libraries face in offering streaming video content to their patrons.
A Novel Data Management Scheme in Cloud for Micromachines

In cyber-physical systems (CPS), micromachines are typically deployed across a wide range of applications, including smart industry, smart healthcare, and smart cities. Providing on-premises resources for the storage and processing of huge data collected by such CPS applications is crucial. The cloud provides scalable storage and computation resources, typically through a cluster of virtual machines (VMs) with big data tools such as Hadoop MapReduce. In such a distributed environment, job latency and makespan are highly affected by excessive non-local executions due to various heterogeneities (hardware, VM, performance, and workload level). Existing approaches handle one or more of these heterogeneities; however, they do not account for the varying performance of storage disks. In this paper, we propose a prediction-based method for placing data blocks in virtual clusters to minimize the number of non-local executions. This is accomplished by applying a linear regression algorithm to determine the performance of disk storage on each physical machine hosting a virtual cluster. This allows us to place data blocks and execute map tasks where the data blocks are located. Furthermore, map tasks are scheduled based on VM performance to reduce job latency and makespan. We simulated our ideas and compared them with the existing schedulers in the Hadoop framework. The results show that the proposed method improves MapReduce performance in terms of job latency and makespan by minimizing non-local executions compared to other methods taken for evaluation.
A Fusion Underwater Salient Object Detection Based on Multi-Scale Saliency and Spatial Optimization

Underwater images contain abundant information, but many challenges remain for underwater object detection tasks. Various salient object detection methods may encounter low detection precision, and the segmented map has an incomplete region of the target object. To deal with blurry underwater scenes and vague detection problems, a novel fusion underwater salient object detection algorithm (FUSOD) is proposed based on multi-scale saliency and spatial optimization. Firstly, an improved underwater color restoration was utilized to restore the color information for afterward color contrast saliency calculation. Secondly, a more accurate multi-scale fusion saliency map was obtained by fully considering both the global and local feature contrast information. Finally, the fusion saliency was optimized by the proposed spatial optimization method to enhance the spatial coherence. The proposed FUSOD algorithm may process turbid and complex underwater scenes and preserve a complete structure of the target object. Experimental results on the USOD dataset show that the proposed FUSOD algorithm can segment the salient object with a comparatively higher detection precision than the other traditional state-of-the-art algorithms. An ablation experiment showed that the proposed spatial optimization method increases the detection precision by 0.0325 scores in the F-Measure.
An Ontology-Based Framework for Geospatial Integration and Querying of Raster Data Cube Using Virtual Knowledge Graphs

The integration of the raster data cube alongside another form of geospatial data (e.g., vector data) raises considerable challenges when it comes to managing and representing it using knowledge graphs. Such integration can play an invaluable role in handling the heterogeneity of geospatial data and linking the raster data cube to semantic technology standards. Many recent approaches have been attempted to address this issue, but they often lack robust formal elaboration or solely concentrate on integrating raster data cubes without considering the inclusion of semantic spatial entities along with their spatial relationships. This may constitute a major shortcoming when it comes to performing advanced geospatial queries and semantically enriching geospatial models. In this paper, we propose a framework that can enable such semantic integration and advanced querying of raster data cubes based on the virtual knowledge graph (VKG) paradigm. This framework defines a semantic representation model for raster data cubes that extends the GeoSPARQL ontology. With such a model, we can combine the semantics of raster data cubes with features-based models that involve geometries as well as spatial and topological relationships. This could allow us to formulate spatiotemporal queries using SPARQL in a natural way by using ontological concepts at an appropriate level of abstraction. We propose an implementation of the proposed framework based on a VKG system architecture. In addition, we perform an experimental evaluation to compare our framework with other existing systems in terms of performance and scalability. Finally, we show the potential and the limitations of our implementation and we discuss several possible future works.
Spatial Overlay Analysis of Geochemical Singularity Index α-Value of Porphyry Cu Deposit in Gangdese Metallogenic Belt, Tibet, Western China

The statistical modeling with ILR-RPCA-back CLR has two problems when dealing with the closure effect of geochemical data. Firstly, after performing isometric logratio (ilr) transformation, robust principal component analysis (RPCA) is employed for processing. The double-plot diagram illustrates that the element sequence transformation occurs in the first and second principal components, while the unique principal component remains unattainable. Secondly, by transforming both the score and load into the centered logratio (CLR) space using the U matrix, it is possible to obtain a score result that corresponds to the original order of elements according to the CLR = ILR·U formula. However, for obtaining a load result that corresponds to the original order of elements, an alternative formula “CLR = UT·ILR” must be used instead. In order to determine the optimal element assemblage for porphyry copper deposits, this study conducted statistical analysis on mineral assemblages from discovered deposits in the Gangdese metallogenic belt and identified Cu, Mo, Au, Ag, W, and Bi as key elements associated with porphyry copper deposits. Subsequently, by analyzing the singularities of the composite elements, the spatial overlay of the combined element is carried out, and concentration-area (C-A) fractal filtering is applied to identify the anomaly and background areas. To facilitate comparison, we conducted an analysis of various mineral and ore deposit types, revealing the following findings: (1) Combination elements exhibit superior recognition capability than single elements in porphyry copper deposits; (2) Skarn-type copper deposits unrelated to porphyry show a high degree of dissimilarity compared to those related to porphyry; (3) this method offers advantages over the single element method in evaluating porphyry gold deposits by reducing anomaly levels and initial investment during the evaluation stage for porphyry copper anomalies; (4) However, this method has limited ability in distinguishing between porphyry copper and molybdenum deposits.