Reviewerly/exHarmony huggingface dataset:
FPGA Implementation of Keyword Spotting System Using Depthwise Separable Binarized and Ternarized Neural Networks

Keyword spotting (KWS) systems are used for human-machine communications in various applications. In many cases, KWS involves a combination of wake-up-word (WUW) recognition for device activation and voice command classification tasks. These tasks present a challenge for embedded systems due to the complexity of deep learning algorithms and the need for optimized networks for each application. In this paper, we propose a depthwise separable binarized/ternarized neural network (DS-BTNN) hardware accelerator capable of performing both WUW recognition and command classification on a single device. The design achieves significant area efficiency by redundantly utilizing bitwise operators in the computation of the binarized neural network (BNN) and ternary neural network (TNN). In a complementary metal-oxide semiconductor (CMOS) 40 nm process environment, the DS-BTNN accelerator demonstrated significant efficiency. Compared with a design approach where BNN and TNN were independently developed and subsequently integrated as two separate modules into the system, our method achieved a 49.3% area reduction while yielding an area of 0.558 mm2. The designed KWS system, which was implemented on a Xilinx UltraScale+ ZCU104 field-programmable gate array (FPGA) board, receives real-time data from the microphone, preprocesses them into a mel spectrogram, and uses this as input to the classifier. Depending on the order, the network operates as a BNN or a TNN for WUW recognition and command classification, respectively. Operating at 170 MHz, our system achieved 97.1% accuracy in BNN-based WUW recognition and 90.5% in TNN-based command classification.
Surface profile measurement using nonlinear optimization approach in diffraction phase microscopy

Nanoscale measurement of surface profile has important application in the field of non-destructive surface testing and evaluation. We propose a nonlinear optimization based phase retrieval approach in diffraction phase microscopy for nanoscale surface profile measurement. The proposed approach relies on minimizing an objective function derived from the interferogram signal and the application of total variation regularization. The performance of the method is demonstrated via numerical simulations for processing interferograms affected by sharp phase variations, noise and non-uniform amplitude fluctuations. In addition, the practical application of the method is validated for nanoscale surface topography by processing experimental interferograms obtained in diffraction phase microscopy.
Deep Learning-Enabled Zero-Touch Device Identification: Mitigating the Impact of Channel Variability Through MIMO Diversity

Deep learning-enabled device fingerprinting has proven efficient in enabling automated identification and authentication of transmitting devices. It does so by leveraging the transmitters' unique features that are inherent to hardware impairments caused during manufacturing to extract device-specific signatures that can be exploited to uniquely distinguish and separate between (identical) devices. Though shown to achieve promising performances, hardware fingerprinting approaches are known to suffer greatly when the training data and the testing data are generated under different channels conditions that often change when time and/or location changes. To the best of our knowledge, this work is the first to use MIMO diversity to mitigate the impact of channel variability and provide a channel-resilient device identification over flat fading channels. Specifically, we show that MIMO can increase the device classification accuracy by up to about 50 percent when model training and testing are done over the same channel and by up to about 70 percent when training and testing are done over different fading channels.
Image Forgery Detection

Images shared online have a high likelihood of being altered, and further global alterations like compression, resizing, or filtering mask the potential change. Many restrictions are placed on forgery detection systems by such manipulations. Image forgery detection is the fundamental solution to many issues, particularly social issues like those on Facebook and legal issues. The most frequent form of image fraud is called a copy-move forgery, where a portion of the original image is copied and pasted in a different spot within the same image. Because the duplicated portions' attributes are similar to those of the original image's components, this type of picture counterfeiting is simpler to carry out but more challenging to detect. The method for spotting copy-move forgeries described in this study is based on processing blocks into features and then extracting those features from the blocks' transforms. A Convolutional Neural Network (CNN) is another tool for detecting forgeries Serial pairings of convolution and pooling layers are employed to conduct feature extraction. Original and changed images are then categorised using transforms and without transformations. We use the CASIA2 dataset, which has 4795 images, of which 1701 are authentic and 3274 are forged. The accuracy of our proposed model is 97.7%. This improved the detection process's overall processing effectiveness and allowed it to fulfill real-time processing demands..
Semi-Supervised Implicit Neural Representation for Polarimetric ISAR Image Super-Resolution

Compared with the optical imaging system, polarimetric inverse synthetic aperture radar (ISAR) can work all-day and all-weather, which plays an important role in space surveillance. However, high-resolution (HR) ISAR images usually require large bandwidth and coherent integration angle, which is limited by the equipment’s physical conditions. In this vein, the super-resolution (SR) of ISAR images is of vital importance. At present, supervised learning methods are often used in image SR of computer vision. By constructing low-resolution (LR) and HR data pairs, the neural network can learn the mapping relationship between them. However, the low-frequency information in LR image data is less considered. In addition, to obtain different scales of SR reconstruction results, multiple network training repetitions are usually needed, which consumes time and hardware resources. Based on the idea of implicit neural representation, this paper constructs an implicit neural network representation framework for polarimetric ISAR image SR, which can obtain multiscale SR results through one training. A semi-supervised module is also constructed to make the network have the ability of supervised and unsupervised learning, which is conducive to mine and make better use of LR images. A polarimetric ISAR image SR dataset is constructed for satellite targets while four indexes are adopted for quantitative evaluation in global and local aspects. Experiments demonstrate that the proposed approach achieves better SR performance, where the PSNR index can be increased at least by 0.93dB.
Reinforcement learning-based hybrid differential evolution for global optimization of interplanetary trajectory design

The global optimization and design of interplanetary trajectories is one of the most important tasks in deep space exploration. Its search space is often characterized by multi-constraints, extreme non-linearity and sensitivity to initial conditions. To cope with these difficulties, an improved reinforcement learning-based hybrid differential evolution (DE) named RL_HDE is proposed in this paper. In RL_HDE, a novel multi-mutation strategy LSHADE based on an adaptive Q-Learning framework is proposed for global exploration. To further balance the global exploration and local exploitation of RL_HDE, a new parameter adaptive strategy based on Q-Learning is designed to control two trigger parameters. The performance of RL_HDE is verified by the well-known Global Trajectory Optimization Problems (GTOP), which are developed by Advanced Concepts Team of European Space Agency (ESA-ACT). A comparison of RL_HDE with three sets of algorithms, including ten state-of-the-art hybrid evolutionary algorithms, four interplanetary trajectory design algorithms, and seven reinforcement learning-based hybridizations. Experimental statistical results demonstrate that RL_HDE outperforms other competitors in terms of convergence efficiency and accuracy. RL_HDE has better performance for solving complex interplanetary trajectory design problems such as Cassini2 and Messenger-full.
Evaluating the Role of the Communication Skills of Engineering Students on Employability According to the Outcome-Based Education (OBE) Theory

With the advent of the digital age and Industry 4.0, the communication skills of engineering students has been exerting gradually increasing influence on the need for enhanced student employability. To address this issue, this study evaluates the influence of communication skills on the employability of engineering students from the aspects of verbal and non-verbal skills through the four key elements of Outcome-Based Education (OBE) theory. A total of 130 college engineering students and practitioners participated in the questionnaire survey, and the hypotheses were tested by regression analysis and a structural equation model (SEM). The results show that in the digital age, the verbal (p &lt; 0.01) and non-verbal (p &lt; 0.001) components of students’ communication skills play a significant role in the employability of engineering students. Meanwhile, the employer’s assessment simultaneously makes a significant contribution to the further improvement of students’ communication skills, which can help educators further cultivate and improve students’ communication skills through the assessment of their employability by employers. Therefore, this research has theoretical and practical significance, which not only enriches the basic theoretical knowledge, but also broadens the training methods of engineering students in the digital age, thus enhancing their employability.
WSDS-GAN: A weak-strong dual supervised learning method for underwater image enhancement

Underwater Image Enhancement (UIE) is a crucial preprocessing step for underwater vision tasks. Addressing the challenge of training supervised deep learning models on large, diverse datasets while learning the intrinsic degradation factors of underwater images is essential for improving model generalization performance. In this paper, we propose a Weak-Strong Dual Supervised Generative Adversarial Network (WSDS-GAN) for UIE. During the first weakly supervised learning phase, unpaired images, consisting of degraded underwater images and clear in-air images, are used to train the model with the goal of recovering color, brightness, and content. In the second strongly supervised learning phase, a limited number of paired images are fed into the model to further train the image detail recovery generator. Comprehensive experiments on public datasets and self-photographed images demonstrate the effectiveness of our proposed method over existing state-of-the-art methods, both qualitatively and quantitatively. Additionally, we show that our method significantly enhances image details to support subsequent underwater vision tasks.
Role Identification from Human Activity Videos using Recurrent Neural Networks

Recognizing the underlying roles in mutual activity is more informative. Role identification has the potential to improve wide range of applications, of activity recognition from safety and security to healthcare. In recent research, for role identification, work is done to identify roles by capturing the knowledge of body parts from an image. This work is complex and not sufficient to take input as English sentences and capture the sequencing and relationship between words. There is a need for simple work which could use recent technologies like Recurrent Neural Networks to capture the recurrent nature of sentences to identify roles. The contribution of this work is proposing a Computational Long Term Memory model where sentences are stored as features and given to a Recurrent Neural Network to identify roles. The appropriate dataset is not available for role identification using sentences. In this view, this work attempt to develop a new custom dataset. The proposed model is tested on accuracy using various Recurrent Neural Networks like Recurrent Neural Network (RNN), Long Short Term Memory (LSTM), Gated Recurrent Units (GRU) etc. The LSTM model gave effective accuracy of 60% on the small custom dataset.
A Machine-Learning-Inspired Opinion Extraction Mechanism for Classifying Customer Reviews on Social Media

Machine learning frameworks categorizing customer reviews on online products have significantly improved sales and product quality for major manufacturers. Manually scrutinizing extensive customer reviews is imprecise and time-consuming. Current product research techniques rely on text mining, neglecting audio, and image components, resulting in less productive outcomes for researchers and developers. AI-based machine learning frameworks that consider social media and online buyer reviews are essential for accurate recommendations in online e-commerce shops. This research paper proposes a novel machine-learning-based framework for categorizing customer reviews that uses a bag-of-features approach for feature extraction and a hybrid DNN framework for robust classification. We assess the performance of our machine learning framework using AliExpress and Amazon e-commerce product review data provided by customers, and we have achieved a classification accuracy of 91.5% with only 8.46% fallout. Moreover, when compared with state-of-the-art models, our proposed model shows superior performance in terms of sensitivity, specificity, precision, fallout, and accuracy.
Temporal-Weighted Bipartite Graph Model for Sparse Expert Recommendation in Community Question Answering

Community Question Answering (CQA) websites are valuable knowledge repositories where individuals exchange information by asking and answering questions. With an ever-increasing number of questions and high in-flow and out-flow of users in these communities, a key challenge is to design effective strategies for recommending experts for new questions. This requires robust approaches that facilitate modeling users' expertise given their changing interests and sparse historical data, at the same time being computationally less expensive for periodic updates. In this paper, we propose a simple graph diffusion-based expert recommendation model for CQA, that can outperform state-of-the-art convolutional neural network and transformers-based deep learning representatives and collaborative models. Our proposed method learns users' expertise in the context of both semantic and temporal information to capture their changing interests and activity levels with time. Experiments on six real-world datasets from the Stack Exchange network demonstrate that our approach outperforms competitive baseline methods. Further, experiments on cold-start users (users with a limited historical record) show our model achieves an average of 50% performance gain compared to the best baseline method.
Sustainability-oriented Recommender Systems

extended-abstract Share on Sustainability-oriented Recommender Systems Author: Pavel Merinov Free University of Bozen-Bolzano, Italy Free University of Bozen-Bolzano, Italy 0009-0009-5690-5718View Profile Authors Info & Claims UMAP '23: Proceedings of the 31st ACM Conference on User Modeling, Adaptation and PersonalizationJune 2023Pages 296–300https://doi.org/10.1145/3565472.3595617Published:19 June 2023Publication History 0citation40DownloadsMetricsTotal Citations0Total Downloads40Last 12 Months40Last 6 weeks40 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
The expression of hate speech against Afro-descendant, Roma, and LGBTQ+ communities in YouTube comments

Abstract This paper addresses the specificities of online hate speech against the Afro-descendant, Roma, and LGBTQ+ communities in Portugal. The research is based on the analysis of CO-HATE, a corpus composed of 20,590 YouTube comments, which were manually annotated following detailed guidelines created for that purpose. We applied methods from corpus linguistics to assess the prevalence of overt and covert hate speech, counter-speech, and offensive speech, considering different grounds of discrimination, and to investigate the main linguistic and rhetorical strategies underlying hatred messages. The research results highlight the importance of tackling covert hate speech, a recurring phenomenon often anchored in irony and fallacious argumentation, including the emotional appeal to fear and the implicit call to action. We believe this study will aid in advancing the analysis of online hate speech, while promoting the development of efficient automated detection models, specifically regarding the Portuguese language.
A synergy of the adaptive whale optimization algorithm and differential evolution for abrupt motion tracking

Aiming at the problem that conventional trackers are difficult to adapt to abrupt motion, a tracking algorithm based on a synergy of the adaptive whale optimization algorithm and differential evolution (AWOA-DE) is proposed in this paper. Firstly, the non-linear dynamic feedback strategy based on the one-fifth principle is employed to replace the linear mechanism, which is the variation of exploring step in the basic whale optimization algorithm (WOA), and an adaptive WOA (AWOA) is generated to improve the global optimization ability of the WOA. Secondly, AWOA adopts a random sampling strategy to generate new samples, which could ensure the diversity of solutions, but may lead to instability. By mixing AWOA and DE algorithms, the population is divided into two groups, which are updated by AWOA and DE algorithms respectively, to ensure the diversity of the populations and improve the optimization accuracy of the algorithm. Finally, a visual tracker based on AWOA-DE is proposed, which can deal with the problem of abrupt motion in tracking. In addition, the effectiveness of the hybrid optimization algorithm is evaluated using 23 benchmark functions. In the experiment of visual tracking, extensive experimental results show that the AWOA-DE tracker (AWOADET) has much stronger competitiveness compared to the WOA tracker (WOAT) and the other 10 state-of-the-art trackers, especially for abrupt motion tracking.
A Comparative Study on Satellite Image Analysis for Road Traffic Detection using YOLOv3-SPP, Keras RetinaNet and Full Convolutional Network

Satellite imagery has wide ranging applications that enable different fields including transportation. The authors would like to leverage this technology in terms of collecting traffic data. Traffic data is vital information in calibrating volume-delay functions to forecast demand to ensure efficient route plan outputs. This study aims to assess the readiness level of heavily trained object detection models in detecting vehicles occupying small pixels on open source satellite images. The results of the study show that the Full Convolutional Network outperforms YOLOv3-SPP and RetinaNet models with an accuracy of 92%, 81%, and 48% respectively. Considering other factors, the proponents have concluded as well that the YOLOv3-SPP model has the potential to surpass FCN given certain preconditions indicated in the recommendation section of the paper. Despite the poor performance of RetinaNet in this study, its capability was not discounted completely. In fact, RetinaNet architecture can be seen as more strategic than the YOLO family because it pays close attention to regions, which is a good fit for satellite images. Although it falls short on detecting as many vehicles as possible, it has a very high confidence level in its detection, so misclassification is almost not an issue for this model.
Automated Intervention of Blockchain in Human Resource Management

A requirement for any corporation to successfully embrace & execute the Fourth Industrial Revolution is the use of information technology (IT) in the area of human resource management (HRM) platforms (Industry 4.0). These techniques are required to provide an environment that is impartial, effective, transparent & secure. Blockchain a decentralized distributed ledger-based technology can make it easier to implement these requirements successfully. This study aims to determine the current state of application usage of blockchain technology in the area of human resource management. It also identifies potential opportunities related to the application utilization of blockchain technology in the realm of HRM along with awaited adoption challenges that may limit its application. Considering the suggested system to the current recruitment methods, there are clear advantages. So, the administration of human resources has also seen an extensive application of blockchain technology. The appliance potential of blockchain technology within HRM will be examined and analyzed in greater detail in this article.
Deploying of Artificial Intelligence and Blockchain in Domain of Non-Fungible Token

This study investigated how to resolve the conflict over profit distribution between the person who created the artificial intelligence and the owner of the Non-Fungible Token or the person who provided the creative input. A number of AI algorithms for suggesting search terms, finding the most important documents, ranking them, and visualising their content can be tested thanks to the verification. According to the review study, AI can reduce the time and cost associated with producing creative images and obtaining NFTs of those same images. This study placed special emphasis on the value of utilising blockchain technology in NFTs as well as the requirement for enhanced profit claiming.
Performance Analysis of Network Security System Using Bioinspired-Blockchain Technique for IP Networks

To mine a blockchain on IP Networks, one must do several tasks related to chain management, rule optimization, verification, and hash generation design. Various consensus model subsets may benefit from the various blockchain mining techniques proposed by researchers. Most of these techniques, however, are rather complicated, which slows down the mining process for large-scale blockchains. Overly simplistic models that include unnecessary redundancies are inefficient and have little practical use. To solve these issues and boost blockchain mining efficiency in large-scale deployments, the authors of this paper propose creating a novel hybrid bioinspired approach. The proposed IP Network model is adaptable to almost all consensus procedures and may be easily combined with dynamic consensus models with few alterations. After collecting performance and context-specific data from the underlying blockchains, the technique uses Genetic Algorithm (GA) that distributes these range sets among miner nodes that support trust, allowing for high-performance mining while maintaining a high degree of trust under actual application situations. The model was tested against Proof-of-Stake (PoS), Proof-of-Work (PoW), Proof-of-Trust (PoT), and Practical Byzantine Fault Tolerance (PBFT) based consensus algorithms to ensure its effectiveness in real-world scenarios. Mining latency, energy consumption, and computational complexity were used as metrics against which this performance was measured. This analysis revealed that the proposed model has the potential to decrease mining latency by 4.5%, energy usage by 3.9%, and compute complexity by 4.1% across a variety of consensus mechanisms, making it suitable for a number of real-time applications.