Reviewerly/exHarmony huggingface dataset:
Automatic Collection of Transcribed Speech for Low Resources Languages

Speech is a crucial for human communication and combined with the evolution of instant messaging in voice format as well as automated chatbots, its importance is greater. While the majority of speech technologies have achieved high accuracy, they fail when tested for accents that deviate from the “standard” of a language. This becomes more concerning for languages that lack on datasets and have scarce literature, like Brazilian Portuguese. Thus, this paper proposes a methodology to collect and release a speech dataset for Brazilian Portuguese. The method explores the availability of data and information in video platforms, and automatically extracts the audio from TEDx Talks.
Automatic Subtractive Clustering Algorithm Model (ASCAM) for Blind Identification of QAM Constellations

Blind System Identification (BSI) is a major pattern recognition task in many fields, including digital communication systems. Recently, many machine learning techniques have been applied to identify modulation types of signals solely by the receiver without prior knowledge of the transmission protocols. In this paper, we propose Automatic Subtractive Clustering Algorithm Model (ASCAM), a novel hybrid approach between subtractive clustering algorithm (SCA) and deep neural network for not only classifying the order of Quadrature Amplitude Modulation (QAM) from the corrupted baseband constellations, but also estimating the original symbols' locations in their constellations. For this model, a deep neural network based on parallel convolution is used in conjunction with a negative straight-through estimator to formulate this problem as a supervised task, regardless of the non-differentiability of SCA. Our simulation demonstrates that the model works well for QAMs with Rayleigh fading and additive white Gaussian noise (AWGN), and achieves better performance than other existing methods. It also generalizes its feature extraction well, as it can maintain its performance even when some symbols in the observed constellations are not present.
A Novel Kernel Extension for the Nearest Feature Line Classifier

The Nearest Feature Line (NFL) rule represents a widely applied variant of the Nearest Neighbor rule whose usefulness has been shown in many different contexts. However, even though the NFL rule has been extended in different directions, only one is based on kernels. In this paper we make one step forward, proposing a novel kernel extension of the NFL rule called Kernel Rectified NFL classifier. Our approach is based on tools and concepts coming from the Rectified Nearest Feature Line Segment (RNFLS) classifier, another extension of the NFL rule aimed at solving known problems of the original one, such as extrapolation and interpolation inaccuracies. In the paper, we present the method and an empirical evaluation based on 15 different datasets, which show that kernelization is a promising direction for extending the NFL family of classifiers.
Multi-Channel Gaussian Derivative Neural Networks for Crowd Analysis

This research shows the procedure to replace the image filtering for the counting of individuals carried out with a Gaussian filter kernel in order to obtain a density value (number of individuals) in a crowd, with Multi-Channel Gaussian Derivative Neural Networks. Gaussian operators, based in Scale-Space Theory, allows processing visual information in greater detail, especially in data sets for crowd counting with different scales, occlusion problems, or complex scenarios, which results in perfect candidates to be used as a primitive structure in a layer in deep neural network to significantly reduce the number of parameters in the model. Overall, the proposed mode achieves metrics comparable to high-level models, while using only approximately 10% of the parameters, which suggests a possible solution or future line of research for the study of urban congestion. In this way, Gaussian derivative neural network allows for more efficient processing of visual information and reduces the number of parameters required, making it an attractive option for crowd analysis in urban areas.
Towards Human Intentions Prediction

Accurately predicting human intentions from partially observed actions is critical in various applications. Although deep learning has shown promise in improving accuracy, most approaches ignore contextual information that impacts human behavior. This project aims to identify characteristics that best represent actions and evaluate deep learning architectures that incorporate contextual information in real time. Data from various sources will be considered, and the most predictive features and architectures will be identified. The models' performance will be evaluated using appropriate metrics such as mAP, mean Average Precision and mcAP, mean calibrated Average Precision [1].
DeepSIT: Deeply Supervised Framework for Image Translation on Breast Cancer Analysis

Image translation networks are deep learning models that can convert an image from one domain to another while preserving the semantic content. These networks are helpful in the medical field for noise reduction, reconstruction, and modality conversion. In this work, we propose DeepSIT, a deeply supervised framework for image translation. DeepSIT is a conditional generative adversarial network composed of a deeply supervised U-Net generator network and four PatchGAN discriminator networks. The generator performs the translation task while the discriminators judge the quality of the generated images. Unlike other works, the generator has four output layers located in the final and intermediate layers of the network. Each output layer generates a synthetic image, which is evaluated using a pixel-wise L1 loss function. Furthermore, the four discriminator networks receive a predicted image from an output layer to judge the quality of the translation at different scales. A promising application of image translation is the generation of immunohistochemical (IHC) images from Hematoxylin and Eosin (HE) images for breast cancer diagnosis. The proposed framework is evaluated in the latter tasks using the BCI Image Generation Grand Challenge dataset. DeepSIT achieves first place in the post-challenge leaderboard with an average of 0.545 SSIM and 18.037 PSNR in the test set.
Abnormal Scene Classification using Image Captioning Technique: A Landslide Case Study

Disaster occurs in various regions, which affects human life and activities. For instance, the one disaster we faced in the mountainous area was a landslide that could occur for various reasons, such as heavy precipitation, failure of slope stability, or earthquake. After this event, humans must evaluate the damage and the damaged area to recover it. Therefore, we must focus on autonomous damage detection to evaluate the damaged region and plan for the decision-maker for recovery. This research discusses a new technique for detecting dam-age in disaster situations, specifically landslides. The technique combines a transformer model for object detection and a vision encoder-decoder for image captioning. By using language to detect abnormal scenes rather than just visual features, the proposed method can accurately predict the extent of damage in the affected region. The results of this study show that the proposed method outperforms the traditional ResNet50 classification method in terms of accuracy, AUC, precision, recall, and F1 score. These findings suggest that the proposed technique could be an effective tool for evaluating the scope of damage after a disaster, which could help the recovery process.
Improving Drug Courts: A Preliminary Study

Since the early 1980s, specialized problem-solving courts known as drug courts emerged in the United States as a response to the backlog of drug and alcohol-related cases plaguing the U.S. criminal justice system. In a few decades, with the seeming success of the drug court in helping AOD defendants achieve sobriety while reducing recidivism, the drug court model has achieved international prominence as well. This paper discusses a pilot study which seeks to analyze the feasibility of connecting a website, drughelp.care, developed at the host institution of the co-authors, to the everyday operations of local drug courts. Talcott Parsons’ AGIL schema is utilized as a conceptual template for organizing our thinking about how the website could improve services to administrators and clients according to the unique functional elements of the drug court.
OWL: A data sharing scheme with controllable anonymity and integrity for group users

As our society becomes digital and communication technology develops, global collaboration is an inevitable trend, where data-sharing is a critical component of cooperation across organizations. Identity privacy and data integrity are vital issues in data-sharing. Existing works struggle to address these problems simultaneously, either privacy leaking or privacy abuse. In this work, we proposed OWL, a data-sharing scheme that (1) provides users on-demand anonymity and (2) allows users to verify data integrity while preserving anonymity. To achieve (1) OWL enables controllable anonymity that allows de-anonymity for the malicious while keeping anonymity to the honest providers based on traceable ring signature technology. To achieve (2), OWL designs a data integrity auditing scheme that uses vector commitment to verify data integrity without privacy leakage. Furthermore, OWL employs the blockchain to store immutable auxiliary information for the integrity and controllable anonymity. We also employ the state channel to resolve the performance bottleneck of blockchain and design methods to improve the usage of the state channel for group users. We prove that OWL achieves controllable anonymity and integrity. Finally, we implement the experiment to evaluate the efficiency of OWL.
Structural reasoning for image-based social relation recognition

Modern societies are composed of complex structures that emerge from the relationships between individuals, and the comprehension of these arrangements has the potential to become a powerful tool for intelligent systems. Current image-based social relation recognition methods isolate specific information from the input to capture essential aspects defining these relationships. However, this is an inaccurate approach since the interaction between all these parts form an intricate structure, which is as valuable as the data each piece carries individually. Consequently, capturing this implicit social structure is essential to achieve the high-level reasoning required to identify relationships adequately. In this work, we propose a novel approach to interpret relationships based on three distinct scopes considering individual, relative, and general information. Additionally, it also takes into account prior knowledge and data dependencies between all these different social perspectives. The Social Knowledge Graph (SKG) is proposed based on these concepts, producing a representation capable of replicating the original social structure. This unique representation is exploited with the Social Graph Network (SGN) by employing specific feature aggregation strategies according to the information embedded into the graph. The performance of the proposed method was evaluated in well-known benchmarks for social relation recognition, achieving a new state-of-the-art. Finally, a deep analysis of the methodology and its main concepts is conducted, delivering results that support our interpretation of social relationships.
Differentiating environmental scenarios to establish geochemical baseline values for heavy metals in soil: A case study of Hainan Island, China

Soil heavy metal distributions exhibit regional heterogeneity due to the complex characteristics of parent materials and soil formation processes, emphasizing the need for appropriate regional standards prior to assessing soil risks. This study focuses on Hainan Island and employs the Multi-purpose Regional Geochemical Survey dataset to establish heavy metal geochemical baseline and background values for soil using an iterative method. Geographical detector analysis reveals that parent materials are the primary factor influencing heavy metal distribution, followed by soil types and land use. Heavy metal geochemical baseline values are established for the island's three environments and administrative regions. Notably, a universal geochemical baseline value cannot adequately represent regional variations in heavy metal distribution, with parent materials playing a crucial role in various scenarios. Locally applicable values based on parent material are the most representative for Hainan Island. This study provides a reference framework for developing region-specific environmental baseline values for soil heavy metal assessments.
Critical Ising system testing of high-quality random number generators

Abstract Specialized hardware implemented on field programmable gate array (FPGA) is used to simulate critical 2D Ising lattices up to 4096 2 . Four mainstream, high-quality pseudorandom number generators (PRNGs) including Xorshift , Mersenne Twister , Xorwow , and ALFG are tested on this system, and three of them are found to misbehave with different degree of confidence levels. It is observed that PRNGs with quality issues tend to misbehave in critical Ising systems and the ones with bigger issues start to misbehave in smaller critical Ising systems and vice versa. The size at which a PRNG misbehaves is proposed as a measure of a PRNG’s quality.
Fast and accurate estimation of solar irradiation on building rooftops in Hong Kong: A machine learning-based parameterization approach

Harvesting solar energy on rooftops can be a promising solution for providing affordable energy. This requires accurately estimating spatio-temporal solar photovoltaic (PV) potential on urban surfaces. However, it is still a challenge to obtain a fast and accurate estimation of rooftop solar PV potential over large urban built-up areas. Thus, this study proposes a parametric-based method to estimate annual rooftop solar irradiation at a fine spatial resolution. Specifically, seven parameters (Digital Surface Model, Sky View Factor, shadow from buildings, shadow from terrain, building volume to façade ratio, slope, and aspect) are determined that having great importance in modeling rooftop solar irradiation. Three machine learning methods (Random Forest (RF), Gradient Boost Regression Tree (GBRT), and AdaBoost) trained by the selected parameters are cross-compared based on R2, Mean Absolute Error (MAE), and computation time. As a case study in Hong Kong, China, the RF outperformed GBRT and AdaBoost, with R2=0.77 and MAE=22.83kWh/m2/year. The time for training and prediction of rooftop solar irradiation is within 13 h, achieving a 99.32% reduction in time compared to the physical-based hemispherical viewshed algorithm. These results suggest that the proposed method can provide an accurate and fast estimation of rooftop solar irradiation for large datasets.
Towards real-time polarimetric de-scattering imaging: From thread framework to algorithm optimization and underwater demonstration

Underwater images acquired in scattering environments are generally of poor quality because of the attenuation and backscattering of light when it passes through water with scattering particles. Polarimetric de-scattering methods can be used to significantly enhance the imaging quality in such a so-called turbid water. However, due to the complexity of polarimetric de-scattering algorithms, it is hard to achieve real-time de-scattering output from a polarimetric camera. In this paper, in order to efficiently increase the computational efficiency, the polarimetric de-scattering algorithm is optimized and a multi-threading framework is developed that enables the algorithm to run in real-time on ordinary laptops for the same polarimetric camera. We demonstrate the imaging performance by using the underwater polarimetric de-scattering system we proposed. We analyze the algorithm under different scattering conditions and discuss its optimal parameters. We find that there is a significant increase in the number of SIFT (i.e., scale invariant feature transform) feature points extracted from the processed image compared to the original image, showing that our system has potential applications in pattern recognition, computer vision, etc.
Approximability and efficient algorithms for constrained fixed-horizon POMDPs with durative actions

Partially Observable Markov Decision Process (POMDP) is a fundamental model for probabilistic planning in stochastic domains. More recently, constrained POMDP and chance-constrained POMDP extend the model allowing constraints to be specified on some aspects of the policy in addition to the objective function. Despite their expressive power, these models assume all actions take a fixed duration, which poses a limitation in modeling real-world planning problems. In this work, we propose a unified model for durative POMDP and its constrained extensions. First, we convert these extensions into an Integer Linear Programming (ILP) formulation, which can be solved using existing solvers in the ILP literature. Second, a heuristic search approach is provided that can efficiently prune the search space, guided by solving successive partial ILP programs. Third, we give a theoretical analysis of the problem: unlike short-horizon POMDPs, with policies of a constant depth, which can be solved in polynomial time, the constrained extensions are NP-Hard even with a planning horizon of two and non-negative rewards. To alleviate that, we propose a Fully Polynomial Time Approximation Scheme (FPTAS) that computes (near) optimal deterministic policies in polynomial time. The FPTAS is among the best achievable in theory in terms of approximation ratio. Finally, evaluation results show that our approach is empirically superior to the state-of-the-art fixed-horizon chance-constrained POMDP solver.
A negative survey based privacy preservation method for topology of social networks

With the development of social platforms, the social network has aroused wide attention. Since social networks contain a lot of personal sensitive information, many privacy preservation methods have been designed for social networks to allay concerns about privacy disclosure of people. However, most of the existing methods disturb the social networks too much to ensure the utility of social networks. To this end, we propose a negative survey based privacy preservation method, called NetNS, to preserve the topology privacy of social networks, where a dedicated negative survey model is developed to disturb edges in social networks in order to preserve the topology privacy of them. The theoretical analysis indicates that the developed NetNS is efficient, and can resist two common graph structure attacks including friendship attack and subgraph attack, while empirical studies conducted on three real-world social networks show that compared to six existing privacy preservation algorithms tailored for the topology of social networks, the developed NetNS can provide disturbed social networks with better utility while achieving same privacy preservation level.
New Hybrid MCDM Approach for an Optimal Selection of Maintenance Strategies: Results of a Case Study

Summary Industrial systems are becoming more sophisticated, and their failure can result in significant losses for the company in terms of production loss, maintenance costs, fines, image loss, etc. Conventional approaches to modeling and evaluating the failure mechanisms of these systems do not consider certain important aspects, such as the interdependencies between failure modes (FMs) with information and data containing uncertainties as they are generally collected from experts’ judgments. These restrictions may lead to improper decision-making. The use of more advanced techniques to model and assess the interdependencies among components’ failures under uncertainties seems to be more than necessary to overcome these deficiencies. It is in this context that the proposed approach fits. It consists of proposing a hybrid multicriteria decision-aking (MCDM) approach that combines several techniques for a better selection of maintenance strategies. Using the failure mode and effects analysis (FMEA) technique, the potential FMs of components, along with their causes and effects, are identified. The relative importance (or weight) of these FMs is determined using the fuzzy simple additive weighing (FSAW) method based on how they affect the system’s goals. The causal relationships between FMs and their final weights are determined by the fuzzy cognitive maps (FCM) method and the nonlinear Hebbian learning and differential evolution (NHL-DE) algorithm. Finally, based on the final FM weights provided by the FCM, the simple additive weighing (SAW) method is used to select the optimal maintenance strategies. The results of applying the proposed approach to an operating compressor lubrication and sealing oil system demonstrate its importance and usefulness in assisting system operators to efficiently allocate the optimal maintenance strategies, considering the strong correlation between FMs and their effects on system performance while accounting for the uncertainties associated with experts’ judgments. These correlation effects have led to changes in the assigned weights of the selected FMs. Specifically, the FM related to the low output of the lube/seal oil pump, which was initially assigned a lower priority, and with the correlation effects has become the first critical FM. This shift in prioritization emphasizes the need to address this particular FM promptly. By focusing on addressing these high-priority FMs, maintenance efforts can be optimized to prevent or mitigate more severe consequences. Among the various maintenance strategies evaluated, it was determined that the combination of condition-based maintenance (CBM) and precision maintenance (PrM) yields the most favorable outcome in terms of mitigating the impact of accidental failures and undesired events on the selected system.
Speech Acts Analysis of “Spiderman No Way Home” Movie as A Medium of Learning Material

A movie is one of the beneficial teaching media. Its main function is for entertainment, but it is a good resource for language learning as well. The dialog in the movie provides material for learning language skills and language structure which can be learned for any element of language. In this research, the writer investigated the language structure focused on speech acts. The study aims to identify the types of speech acts delivered by the main actor in the Spider-man movie, which was used a learning material, along with their frequency. This research was conducted in descriptive qualitative method. The analysis of speech acts used Searle’s theory which divided speech acts into five classifications, commissive, declarative, directive, expressive, and representative. After analyzing, the writer found all types of speech acts in the conversations. Although, the amount utterances of each type are various. Directive (46%) is frequently used in the dialogue followed by expressive (30%) commissive (12%), declarative (6%), and representative (6%). Therefore, expressions such as command, order, request, thank, apology, etc., which are parts of the directive and expressive type, often appear in conversation, especially in action movies. Keywords: Speech act, learning material, movie