Reviewerly/exHarmony huggingface dataset:
Protecting the Intellectual Property of Speaker Recognition Model by Black-Box Watermarking in the Frequency Domain

Benefiting from the rapid development of computer hardware and big data, deep neural networks (DNNs) have been widely applied in commercial speaker recognition systems, achieving a kind of symmetry between “machine-learning-as-a-service” providers and consumers. However, this symmetry is threatened by attackers whose goal is to illegally steal and use the service. It is necessary to protect these DNN models from symmetry breaking, i.e., intellectual property (IP) infringement, which motivated the authors to present a black-box watermarking method for IP protection of the speaker recognition model in this paper. The proposed method enables verification of the ownership of the target marked model by querying the model with a set of carefully crafted trigger audio samples, without knowing the internal details of the model. To achieve this goal, the proposed method marks the host model by training it with normal audio samples and carefully crafted trigger audio samples. The trigger audio samples are constructed by adding a trigger signal in the frequency domain of normal audio samples, which enables the trigger audio samples to not only resist against malicious attack but also avoid introducing noticeable distortion. In order to not impair the performance of the speaker recognition model on its original task, a new label is assigned to all the trigger audio samples. The experimental results show that the proposed black-box DNN watermarking method can not only reliably protect the intellectual property of the speaker recognition model but also maintain the performance of the speaker recognition model on its original task, which verifies the superiority and maintains the symmetry between “machine-learning-as-a-service” providers and consumers.
BiLSTM Network-Based Approach for Solar Irradiance Forecasting in Continental Climate Zones

Recent research on solar irradiance forecasting has attracted considerable attention, as governments worldwide are displaying a keenness to harness green energy. The goal of this study is to build forecasting methods using deep learning (DL) approach to estimate daily solar irradiance in three sites in Kuwait over 12 years (2008–2020). Solar irradiance data are used to extract and understand the symmetrical hidden data pattern and correlations, which are then used to predict future solar irradiance. A DL model based on the attention mechanism applied to bidirectional long short-term memory (BiLSTM) is developed for accurate solar irradiation forecasting. The proposed model is designed for two different conditions (sunny and cloudy days) to ensure greater accuracy in different weather scenarios. Simulation results are presented which depict that the attention based BiLSTM model outperforms the other deep learning networks in the prediction analysis of solar irradiance. The attention based BiLSTM model was able to predict variations in solar irradiance over short intervals in continental climate zones (Kuwait) more efficiently with an RMSE of 4.24 and 20.95 for sunny and cloudy days, respectively.
Immersive Technology Implementation in the Construction Industry: Modeling Paths of Risk

The purposes of this paper are to identify risk factors impacting the successful implementation of immersive reality technology (ImT) in the construction industry, analyze these risk factors (impact and probability), assess the relationships among different categories of risk factors, and provide recommendations to improve ImT implementation. A literature review, a pilot test based on expert interviews, and a questionnaire survey were used. First, the risk factors of ImT applications were identified by consulting the relevant literature on virtual reality, mixed reality, and augmented reality; these were subsequently grouped into five categories—technology, operation, individual/worker, investment, and external. Next, a questionnaire survey was designed and distributed to relevant construction practitioners in South Africa (usable response = 175). Twenty-one ImT implementation risk factors were identified, and risk criticality scores ranged from 2.02 to 3.18. High investment cost, the need for extensive worker training, and the possible introduction of new risks for workers were rated as significant risks. The present study confirmed three statistically significant hypothesized risk paths—namely, those between external issues and individual/worker’s concerns, between external issues and investment limitations, and between individual/worker’s concerns and technology concerns. The present study contributes to the literature regarding the adoption of construction technology by providing a list of critical risk factors that could be used to develop models and tools for assessing ImT adoption and guide practitioners involved in integrating ImTs.
Unifying Obstacle Detection, Recognition, and Fusion Based on the Polarization Color Stereo Camera and LiDAR for the ADAS

The perception module plays an important role in vehicles equipped with advanced driver-assistance systems (ADAS). This paper presents a multi-sensor data fusion system based on the polarization color stereo camera and the forward-looking light detection and ranging (LiDAR), which achieves the multiple target detection, recognition, and data fusion. The You Only Look Once v4 (YOLOv4) network is utilized to achieve object detection and recognition on the color images. The depth images are obtained from the rectified left and right images based on the principle of the epipolar constraints, then the obstacles are detected from the depth images using the MeanShift algorithm. The pixel-level polarization images are extracted from the raw polarization-grey images, then the water hazards are detected successfully. The PointPillars network is employed to detect the objects from the point cloud. The calibration and synchronization between the sensors are accomplished. The experiment results show that the data fusion enriches the detection results, provides high-dimensional perceptual information and extends the effective detection range. Meanwhile, the detection results are stable under diverse range and illumination conditions.
Towards Enhancing Traffic Sign Recognition through Sliding Windows

Automatic Traffic Sign Detection and Recognition (TSDR) provides drivers with critical information on traffic signs, and it constitutes an enabling condition for autonomous driving. Misclassifying even a single sign may constitute a severe hazard, which negatively impacts the environment, infrastructures, and human lives. Therefore, a reliable TSDR mechanism is essential to attain a safe circulation of road vehicles. Traffic Sign Recognition (TSR) techniques that use Machine Learning (ML) algorithms have been proposed, but no agreement on a preferred ML algorithm nor perfect classification capabilities were always achieved by any existing solutions. Consequently, our study employs ML-based classifiers to build a TSR system that analyzes a sliding window of frames sampled by sensors on a vehicle. Such TSR processes the most recent frame and past frames sampled by sensors through (i) Long Short-Term Memory (LSTM) networks and (ii) Stacking Meta-Learners, which allow for efficiently combining base-learning classification episodes into a unified and improved meta-level classification. Experimental results by using publicly available datasets show that Stacking Meta-Learners dramatically reduce misclassifications of signs and achieved perfect classification on all three considered datasets. This shows the potential of our novel approach based on sliding windows to be used as an efficient solution for TSR.
Optimal vocabulary selection approaches for privacy-preserving deep NLP model training for information extraction and cancer epidemiology

With the use of artificial intelligence and machine learning techniques for biomedical informatics, security and privacy concerns over the data and subject identities have also become an important issue and essential research topic. Without intentional safeguards, machine learning models may find patterns and features to improve task performance that are associated with private personal information.The privacy vulnerability of deep learning models for information extraction from medical textural contents needs to be quantified since the models are exposed to private health information and personally identifiable information. The objective of the study is to quantify the privacy vulnerability of the deep learning models for natural language processing and explore a proper way of securing patients' information to mitigate confidentiality breaches.The target model is the multitask convolutional neural network for information extraction from cancer pathology reports, where the data for training the model are from multiple state population-based cancer registries. This study proposes the following schemes to collect vocabularies from the cancer pathology reports; (a) words appearing in multiple registries, and (b) words that have higher mutual information. We performed membership inference attacks on the models in high-performance computing environments.The comparison outcomes suggest that the proposed vocabulary selection methods resulted in lower privacy vulnerability while maintaining the same level of clinical task performance.
Joint Optimization of Depth and Ego-Motion for Intelligent Autonomous Vehicles

The three-dimensional (3D) perception of autonomous vehicles is crucial for localization and analysis of the driving environment, while it involves massive computing resources for deep learning, which can't be provided by vehiclemounted devices.This requires the use of seamless, reliable, and efficient massive connections provided by the 6G network for computing in the cloud.In this paper, we propose a novel deep learning framework with 6G enabled transport system for joint optimization of depth and ego-motion estimation, which is an important task in 3D perception for autonomous driving.A novel loss based on feature map and quadtree is proposed, which uses feature value loss with quadtree coding instead of photometric loss to merge the feature information at the texture-less region.Besides, we also propose a novel multi-level V-shaped residual network to estimate the depths of the image, which combines the advantages of V-shaped network and residual network, and solves the problem of poor feature extraction results that may be caused by the simple fusion of low-level and high-level features.Lastly, to alleviate the influence of image noise on pose estimation, we propose a number of parallel sub-networks that use RGB image and its feature map as the input of the network.Experimental results show that our method significantly improves the quality of the depth map and the localization accuracy and achieves the state-of-the-art performance.
A New Chaotic Image Encryption Algorithm Based on Transversals in a Latin Square

In this paper, a new combinatorial structure is introduced for image encryption, which has an excellent encryption effect on security and efficiency. An n-transversal in a Latin square has the function of classifying all the matrix’s positions, and it can provide a pair of orthogonal Latin squares. Employing an n-transversal of a Latin square, we can permutate all the pixels of an image group by group for the first time, then use two Latin squares for auxiliary diffusion based on a chaotic sequence, and finally, make use of a pair of orthogonal Latin squares to perform the second scrambling. The whole encryption process is “scrambling–diffusion–scrambling”. The experimental results indicated that this algorithm passed various tests and achieved a secure and fast encryption effect, which outperformed many of the latest papers. The final information entropy was very close to 8, and the correlation coefficient was approximately 0. All these tests verified the robustness and practicability of the proposed algorithm.
UCA-EHAR: A Dataset for Human Activity Recognition with Embedded AI on Smart Glasses

Human activity recognition can help in elderly care by monitoring the physical activities of a subject and identifying a degradation in physical abilities. Vision-based approaches require setting up cameras in the environment, while most body-worn sensor approaches can be a burden on the elderly due to the need of wearing additional devices. Another solution consists in using smart glasses, a much less intrusive device that also leverages the fact that the elderly often already wear glasses. In this article, we propose UCA-EHAR, a novel dataset for human activity recognition using smart glasses. UCA-EHAR addresses the lack of usable data from smart glasses for human activity recognition purpose. The data are collected from a gyroscope, an accelerometer and a barometer embedded onto smart glasses with 20 subjects performing 8 different activities (STANDING, SITTING, WALKING, LYING, WALKING_DOWNSTAIRS, WALKING_UPSTAIRS, RUNNING, and DRINKING). Results of the classification task are provided using a residual neural network. Additionally, the neural network is quantized and deployed on the smart glasses using the open-source MicroAI framework in order to provide a live human activity recognition application based on our dataset. Power consumption is also analysed when performing live inference on the smart glasses’ microcontroller.
Navigating learner data in translator and interpreter training

Abstract The development of technology, in particular, innovations in natural language processing and means to explore big data, has influenced different aspects in the training of translators and interpreters. This paper investigates how learner corpora and their research contribute to the teaching and learning of translation and interpreting. It starts with a review of the evolvement of learner corpora in translator and interpreter training. Drawing on data from the Chinese/English Translation and Interpreting Learner Corpus (CETILC), a learner corpus developed for the study of lexical cohesion, the paper introduces three case studies to illustrate the possibilities of exploring learner data through human annotation, machine-facilitated human annotation, and finally human-supervised/edited machine annotation. The findings of the case studies suggest the complexity of learner language and its intricate relationships with various factors concerning the learner, text, and task. The paper ends with a discussion of the great potentials of purposely made learner corpora such as the CETILC in translator and interpreter training, as well as the application of learner corpora in (semi-) automatic processing of learner texts.
Arabic Knowledge Graph Construction: A close look in the present and into the future

With the widespread growth of data on the Web, the need for efficient methods to get and arrange valuable information from these big noisy data is increased. The knowledge graph (KG) is a way to represent and organize the data in a more efficient and easy way to modify, use, and understand. Recently, KG has become a new hotspot topic in academic and business research; it is used in many applications such as intelligent question-answering (QA), recommender systems, map navigation, etc. There has been a tendency to construct KG in different languages such as English, Chinese, Persian, or Arabic. Constructing KG faces many challenges and obstacles, especially constructing Arabic Knowledge Graph (AKG) due to the sparse Arabic data in online encyclopedias and academic research, as well as the lack of tools that can process the proprietary nature of the Arabic language effectively, besides other challenges. This research aims to review and discuss KG construction best practices (systems, phases, problems, and challenges) with highlighting the Arabic perspective. Besides, it elaborates a classification of the AKG challenges and investigates the potential solutions and opportunities that might define the key future research directions of constructing AKG.
Can Rationalization Improve Robustness?

A growing line of work has investigated the development of neural NLP models that can produce rationales-subsets of input that can explain their model predictions.In this paper, we ask whether such rationale models can provide robustness to adversarial attacks in addition to their interpretable nature.Since these models need to first generate rationales ("rationalizer") before making predictions ("predictor"), they have the potential to ignore noise or adversarially added text by simply masking it out of the generated rationale.To this end, we systematically generate various types of 'AddText' attacks for both token and sentence-level rationalization tasks and perform an extensive empirical evaluation of state-of-the-art rationale models across five different tasks.Our experiments reveal that rationale models show promise in improving robustness but struggle in certain scenarios-e.g., when the rationalizer is sensitive to position bias or lexical choices of the attack text.Further, leveraging human rationales as supervision does not always translate to better performance.Our study is a first step towards exploring the interplay between interpretability and robustness in the rationalize-then-predict framework. 1
Research of coal and gangue identification and positioning method at mobile device

A method of identifying and positioning coal and gangue on mobile devices is provided to address the difficulties of high complexity and difficult deployment of existing machine vision algorithms. Combining CSPDarknet53 and the GhostNet embedded with efficient channel domain attention to build a lightweight feature extraction network and using the Meta-ACON activation function to adapt the network’s nonlinearity at each layer. The detection head’s expression capability is enhanced using a decoupled head. The actual position coordinate relationship equation of coal and gangue is deduced by borrowing the center point object box positioning principle. Build an experimental setup, collect and construct coal and gangue datasets, and use Mixup data augmentation to improve the network’s ability to position coal and gangue for stacking cases. The models are trained, and tested for different lighting conditions, sizes, and stacking cases, then deployed on the mobile device. The results show that the model has the highest detection accuracy and can correctly identify and position both coal and gangue under complex conditions with a high confidence value. The FLOPs is 76.83% lower compared to the original network YOLOv5s. The mAP reaches 0.996 and the FPS is 40.11. The inference time on the mobile device is reduced to 228 ms, which basically meets the speed requirement of coal gangue identification and positioning.
Giallar: push-button verification for the qiskit Quantum compiler

This paper presents Giallar, a fully-automated verification toolkit for quantum compilers. Giallar requires no manual specifications, invariants, or proofs, and can automatically verify that a compiler pass preserves the semantics of quantum circuits. To deal with unbounded loops in quantum compilers, Giallar abstracts three loop templates, whose loop invariants can be automatically inferred. To efficiently check the equivalence of arbitrary input and output circuits that have complicated matrix semantics representation, Giallar introduces a symbolic representation for quantum circuits and a set of rewrite rules for showing the equivalence of symbolic quantum circuits. With Giallar, we implemented and verified 44 (out of 56) compiler passes in 13 versions of the Qiskit compiler, the open-source quantum compiler standard, during which three bugs were detected in and confirmed by Qiskit. Our evaluation shows that most of Qiskit compiler passes can be automatically verified in seconds and verification imposes only a modest overhead to compilation performance.
Remote sensing of solar surface radiation – a reflection of concepts, applications and input data based on experience with the effective cloud albedo

Abstract. Accurate solar surface irradiance (SSI) data are a prerequisite for efficient planning and operation of solar energy systems. Respective data are also essential for climate monitoring and analysis. Satellite-based SSI has grown in importance over the last few decades. However, a retrieval method is needed to relate the measured radiances at the satellite to the solar surface irradiance. In a widespread classical approach, these radiances are used directly to derive the effective cloud albedo (CAL) as basis for the estimation of the solar surface irradiance. This approach was already introduced and discussed in the early 1980s. Various approaches are briefly discussed and analysed, including an overview of open questions and opportunities for improvement. Special emphasis is placed on the reflection of fundamental physical laws and atmospheric measurement techniques. In addition, atmospheric input data and key applications are briefly discussed. It is concluded that the well-established observation-based CAL approach is still an excellent choice for the retrieval of the cloud transmission. The coupling with lookup-table-based clear-sky models enables the estimation of solar surface irradiance with high accuracy and homogeneity. This could explain why, despite its age, the direct CAL approach is still used by key players in energy meteorology and the climate community. For the clear-sky input data, it is recommended to use ECMWF forecast and reanalysis data.
Cu–Au exploration target generation in the eastern Carajás Mineral Province using random forest and multi-class index overlay mapping

The Carajás Mineral Province (CMP) located in the southeast of the Amazon craton, northern Brazil is one of the most important provinces of the world hosting a high number of large-tonnage Cu–Au deposits. This potential is due to overprinted mineralization events dated from the Neoarchean to Paleoproterozoic, including VMS, IOCG, and Cu–Au granite-related systems. The discovery of a new mineral deposit is linked to direct detection methods that have an elevated cost. Mineral potential modeling is a multi-source geoscience data that can help outline high prospective areas, containing new mineral exploration targets in the order to reduce the exploration risk. This work employed one knowledge-based method, multi-class index overlay, and one machine-learning algorithm, random forest, to produce two mineral potential maps in a district scale for Cu–Au deposits in the easternmost CMP (Parauapebas area) using a mineral system-based approach. This approach consists of translating the critical ore-forming processes of the mineral system components (source of fluids, metals, and ligands, source of energy, pathways, ore deposition gradient) into spatial data (evidence maps). For this study, ten evidence maps were created using the Spatial Data Analyst in GIS, then for the multi-class index overlay, each evidence map received a weight according to its relevance in the modeling, and integrated through the Image Analyst Tools. For the random forest algorithm, meanwhile, the model was trained four times using the Orange Data Mining software. The performance of the models was measured through the classification of accuracy, ROC curve, AUC values, predictive and success-rate curves. Both models achieve good performance with classification accuracy and AUC values of 80% and 0,805 for the multi-class index overlay and values of 76,4% and 0,812 for the random forest. Although the models have similar performance, the analysis of the curves showed that the random forest model was able to predict 82% of the locations of the known deposits, selecting about 25% of the total study area, while the multi-class index overlay predicted 72% of the location of the known deposits over 35% of the study area. Furthermore, both models predicted less than 10% of the study area as highly prospective helping prioritize areas for research and follow-up copper and gold exploration activities.