Reviewerly/exHarmony huggingface dataset:
An Unseen Features-Enriched Lifelong Machine Learning Framework

The dialect of a machine learning model is comprised of the features encountered during training. Nonetheless, as time passes, a deployed machine learning model may encounter certain features for the first time. In conventional machine learning approaches, newly observed features are typically discarded during testing data sample preprocessing. In lifelong machine learning, newly observed features may have appeared in the feature space of previously learned tasks; consequently, the knowledge associated with those features present in the knowledge base is incorporated to handle these features. However, there may be some features that have yet to appear in the knowledge base; lifelong machine learning also discards such features. Features that were not seen before are called unseen features. In this paper, we propose an enhanced lifelong machine learning framework for handling unseen features during the testing phase that incorporates relative knowledge. To extract relative knowledge, we retrieve semantically similar features using a language model. In addition, semantically similar features are examined in the knowledge base, and the knowledge of those present in the knowledge base is incorporated in order to deal with unseen features. Experiments conducted on the Amazon review dataset indicate that the proposed method outperforms three baselines and is competitive with state-of-the-art methods.
An intelligent radar signal classification and deinterleaving method with unified residual recurrent neural network

Abstract The accuracy of radar emitter signal sorting nowadays deteriorates due to the high flexibility and complexity of modern radar pulse streams and the density of crowded electromagnetic environment. In modern radar signal sorting based on pulse repetition interval, conventional methods usually fail to achieve acceptable accuracy and lack stable performance for two main reasons: (1) Conventional methods require a large number of pulses in the stream, which is not practical in many applications. (2) These methods are sensitive to pulse loss and random noise pulses. These two reasons are the main problem that is addressed in this paper. Our proposed model is a machine learning architecture called Unified Residual Recurrent Neural Network (URRNN). In this architecture, residual neural network and recurrent neural network are combined and modified to alleviate the forementioned shortcomings of traditional approaches and enhance the model performance in both classification and deinterleaving tasks. This aim is achieved due to the fact that URRNN extracts both spatial and temporal features, which means more information about processed stream that is exploited to enhance model performance. Three different architectural combinations of URRNN, which show high accuracy and reasonable processing time, are built and trained. The structural and functional description are provided for each architecture. Simulation demonstrates high accuracy and reliable performance of the proposed methods in different circumstances. The results are compared with the results obtained by other conventional machine learning techniques.
Cross-Polarization-Extinction Enhancement and Spin-Orbit Coupling of Light for Quantum-Dot Cavity Quantum Electrodynamics Spectroscopy

Resonant laser spectroscopy is essential for the characterization, operation, and manipulation of single quantum systems such as semiconductor quantum dots. The separation of the weak resonance fluorescence from the excitation laser is key for high-quality single- and entangled photon sources. This is often achieved by cross-polarization laser extinction, which is limited by the quality of the optical elements. Recently, it was discovered that Fresnel-reflection birefringence in combination with single-mode filtering counteracting spin-orbit coupling effects enables a three-order of magnitude improvement of polarization extinction [PRX 11, 021007 (2021)]. Here, we first investigate multiple reflections and analyze beam reshaping, and observe that the single-reflection extinction enhancement is optimal. We then demonstrate this method for cross-polarization extinction enhancement for a resonantly excited semiconductor quantum dot in a birefringent optical micro cavity, and observe a 10x improvement of single-photon contrast.
Hate speech on social media: behaviour of Portuguese football fans on Facebook

This article examines how Facebook is shaping the nature of hate speech. The observation field is the Facebook pages of the 18 Portuguese football clubs of the Portuguese League – the main professional competition in the country. Data extraction was performed automatically via Facebook Graph API in the first three months of the 2020/2021 season. The analysed dataset has 5,192 publications and 276,231 fan comments, which registered over 5 million reactions (like, haha, love, sad, wow, etc.). The findings reveal that the volume of hate speech material on Facebook pages has a low incidence. In any case, the most significant comments are racists, xenophobes, and regional antagonisms. These cases are also the ones that have attracted the most reactions from fans.
Intent-Satisfaction Modeling: From Music to Video Streaming

Logged behavioral data is a common resource for enhancing the user experience on streaming platforms. In music streaming, Mehrotra et al. have shown how complementing behavioral data with user intent can help predict and explain user satisfaction. Do their findings extend to video streaming? Compared to music streaming, video streaming platforms provide relatively shallow catalogs. Finding the right content demands more active and conscious commitment from users than in the music streaming setting. Video streaming platforms, in particular, could thus benefit from a better understanding of user intents and satisfaction level. We replicate Mehrotra et al.’s study from music to video streaming and extend their modeling framework on two fronts: (i) improved modeling accuracy (random forests), and (ii) interpretability (Bayesian models). Like the original study, we find that user intent affects behavior and satisfaction itself, even if to a lesser degree, based on data analysis and modeling. By proposing a grouping of intents into decisive and explorative categories we highlight a tension: decisive video streamers are not as keen to interact with the user interface as exploration-seeking ones. Meanwhile, music streamers explore by listening. In this study, we find that in video streaming, unsatisfied users provide the main signal: intent influences satisfaction levels together with behavioral data, depending on our decisive vs. explorative grouping.
A High Throughput STR-based TRNG by Jitter Precise Quantization Superposing

With the rapid development of integrated circuits and the continuous progress of computing capability, higher demands have been placed on the security and speed of data encryption in security systems. As a basic hardware security primitive, the true random number generator (TRNG) plays an important role in the encryption system, which requires higher throughput and randomness with lower hardware overhead. However, the throughput of TRNG is related to the entropy source’s quality and the randomness extraction methodology. To quantify the randomness of the entropy source with higher efficiency and quality, we utilize the independent jitter of the self-timed ring (STR) to generate original entropy and propose a high throughput jitter-based TRNG which can extract random information at the pulse of oscillation signal by jitter precise quantization superposing and random oscillation sampling. The proposed TRNG has been implemented on Artix-7 and Virtex-6 FPGAs. The generated true random number successfully passes the NIST SP800-22 and NIST SP800-90B tests while also exhibiting a minimum entropy greater than 0.9947. The most prominent superiority of our proposed TRNG is that it achieves a high throughput of 330 Mbps with an ultra-low hardware overhead of only 35 LUTs and 12 DFFs.
Low-complexity Machine Learning Architecture for Hardware-aware True Random Number Generators Assessment and Continuous Monitoring

A new machine learning approach of assessment and monitoring of True Random Number Generators (TRNGs) is explored in order to create a new technical framework for entropy source development and validation, based on the direct estimation of the core source entropy, which is suitable for hardware-based lightweight cryptography. It consists of the integration between a pre-processing stage and an Artificial Neural Network (ANN). The pre-processing stage continuously generates stochastically patterned images from number sequences provided by TRNGs with assigned entropy thresholds. The ANN processes the images to determine whether the TRNG entropy remains higher than the assigned threshold, dynamically, during the sequence generation. A custom dataset, generated from a Markovian TRNG has been used to train, validate and test a very compact 3-layer ANN. The model achieves accuracy, precision, recall, and F-score all equal to 98.28% averaged on the test set. The low computational complexity of the ANN, favored by the effectiveness of the pre-processing stage, which, in turn, requires a simple architecture to be implemented, proves that the proposed solution can be effectively employed for a perspective resource-constrained hardware implementation.
Improved interval type-2 fuzzy K-means clustering based on adaptive iterative center with new defuzzification method

Interval Type-2 Fuzzy K-means (IT2FKM) is an efficient clustering algorithm, which mainly focuses on further describes the uncertainty of the data sets by introducing interval type-2 fuzzy membership function. Since the estimation center obtained by two membership functions is an interval type-1 fuzzy set, it is necessary to use the defuzzification method to obtain a crisp cluster center. The existing IT2FKM algorithms adopt the generalized centroid method (GC) in the process of type-reduction and defuzzification for iterative clustering center. But unfortunately, this method simply uses a single numerical value 0.5 as the type-reduction coefficient to balance and synthesize for the left value and the right value of an interval cluster center, while ignoring the distribution of the clusters. This defect makes IT2FKM fail to update the optimal cluster center when facing with imbalanced clusters, which affect the accuracy in some cases. To address this failing, in this paper, some adverse effects caused by generalized centroid method when facing with imbalanced clusters are analyzed in detail. Thereafter, an improved type-reduction method of iterative cluster centers for IT2FKM clustering is proposed considering the imbalanced clustering distributions. On the basis of which, an enhanced IT2FKM algorithm is further developed. The enhanced IT2FKM algorithm has an average classification accuracy of 97.5% when faced with four datasets with different degrees imbalance, which is about 3.25% higher than other clustering algorithms. The effectiveness and superiority of the proposed algorithm are further demonstrated by some experimental analysis of UCI standard datasets.
Parallel Framework for Memory-Efficient Computation of Image Descriptors for Megapixel Images

Image moments are image descriptors widely utilized in several image processing, pattern recognition, computer vision, and multimedia security applications. In the era of big data, the computation of image moments yields a huge memory demand, especially for large moment order and/or high-resolution images (i.e., megapixel images). The state-of-the-art moment computation methods successfully accelerate the image moment computation for digital images of a resolution smaller than 1K × 1K pixels. For digital images of higher resolutions, image moment computation is problematic. Researchers utilized GPU-based parallel processing to overcome this problem. In practice, the parallel computation of image moments using GPUs encounters the non-extended memory problem, which is the main challenge. This paper proposed a recurrent-based method for computing the Polar Complex Exponent Transform (PCET) moments of fractional orders. The proposed method utilized the symmetry of the image kernel to reduce kernel computation. In the proposed method, once a kernel value is computed in one quaternion, the other three corresponding values in the remaining three quaternions can be trivially computed. Moreover, the proposed method utilized recurrence equations to compute kernels. Thus, the required memory to store the pre-computed memory is saved. Finally, we implemented the proposed method on the GPU parallel architecture. The proposed method overcomes the memory limit due to saving the kernel's memory. The experiments show that the proposed parallel-friendly and memory-efficient method is superior to the state-of-the-art moment computation methods in memory consumption and runtimes. The proposed method computes the PCET moment of order 50 for an image of size 2K × 2K pixels in 3.5 seconds while the state-of-the-art method of comparison needs 7.0 seconds to process the same image, the memory requirements for the proposed method and the method of comparison for the were 67.0 MB and 3.4 GB, respectively. The method of comparison could not compute the image moment for any image with a resolution higher than 2K × 2K pixels. In contrast, the proposed method managed to compute the image moment up to 16K × 16K pixels image.
Korean named entity recognition based on language-specific features

Abstract In this paper, we propose a novel way of improving named entity recognition (NER) in the Korean language using its language-specific features. While the field of NER has been studied extensively in recent years, the mechanism of efficiently recognizing named entities (NEs) in Korean has hardly been explored. This is because the Korean language has distinct linguistic properties that present challenges for modeling. Therefore, an annotation scheme for Korean corpora by adopting the CoNLL-U format, which decomposes Korean words into morphemes and reduces the ambiguity of NEs in the original segmentation that may contain functional morphemes such as postpositions and particles, is proposed herein. We investigate how the NE tags are best represented in this morpheme-based scheme and implement an algorithm to convert word-based and syllable-based Korean corpora with NEs into the proposed morpheme-based format. Analyses of the results of traditional and neural models reveal that the proposed morpheme-based format is feasible, and the varied performances of the models under the influence of various additional language-specific features are demonstrated. Extrinsic conditions were also considered to observe the variance of the performances of the proposed models, given different types of data, including the original segmentation and different types of tagging formats.
High-order paired-ASPP for deep semantic segmentation networks

Current semantic segmentation models only exploit first-order information, while rarely exploring high-order semantics. However, traditional first-order statistics are insufficient to support a solid unanimous representation. In this paper, we propose High-Order Paired-ASPP (Atrous Spatial Pyramid Pooling) Network to exploit high-order statistics from various feature levels. The network first introduces a High-Order Representation module to extract the contextual high-order information from all stages of the backbone. They can provide more semantic clues and discriminative information than the first-order ones. Besides, a Paired-ASPP module is proposed to embed high-order statistics of the early stages into the last stage. It can further preserve the boundary-related and spatial context in the low-level features for final prediction. Our experiments show that the high-order semantics significantly boost the performance on confusing objects. Our method achieves competitive performance without bells and whistles on three benchmarks, i.e., Cityscapes, ADE20K and Pascal-Context.
Roots of certain polynomials over finite fields

We determine the roots in Fq3 of the polynomial X2qℓ+1+X+a for each positive integer ℓ and each a∈Fq, where q is a power of 2. We introduce a new approach for this type of question, and we obtain results which are more explicit than the previous results in this area. Our results resolve an open problem and a conjecture of Zheng, Kan, Zhang, Peng, and Li.
Fast 3D-graph convolutional networks for skeleton-based action recognition

Research on human action recognition based on skeletons has received much attention. But most of the research focuses on improving the model’s generalization ability, while ignoring significant efficiency issues. This leads to developing heavy models with poor scalability and cost-effectiveness in practical use. This paper, we investigate the under-studied but practically critical recognition model efficiency problem. To this end, we present a new Fast Recognition Distillation (FRD) model learning strategy. Specifically, FRD trains a lightweight recognition neural network structure that can be quickly executed at a low computational cost. It can be achieved by effectively disseminating the identification probability information of the teacher network to the lightweight network. We call the probability information of the teacher network as soft-target, and FRD can learn more potential information from soft-target. In addition, we also used a particular loss function for soft-target. Through the FRD network, while basically maintaining the recognition accuracy, we minimized the network structure. Extensive experiments on the two large-scale datasets, NTU-RGBD and Kinetics-Skeleton, demonstrate that our model (FRD) is more lightweight and refined than others. Therefore, our model FRD is efficient.
A transfer learning framework towards identifying behavioral changes of fraudulent publishers in pay-per-click model of online advertising for click fraud detection

The absence of a publicly available user-click dataset makes the task of fraudster identification particularly challenging to detect click fraud in online advertising. However, the task becomes more complicated with concept drift, where a publisher appears in distinct sets with the same pattern but differs in real status labels. Fraud-detection algorithms developed the actual status labels. The reliability of the predictions made by learning models needs to be examined to deal with the concept drift concerning the changing behaviour of fraudulent publishers without re-training the predictive model from scratch. However, the scarcity of pre-trained models aggravates the issue. Thus, we proposed a 1D-convolutional neural network-based fraudsters identification network (FINet) that deals with such challenges using a model trained on a correlated prediction modelling problem using transfer learning. To overcome the scarcity of pre-trained models, we designed a model FINet trained on the correlated dataset TalkingData Ad Tracking Fraud Detection (TDA) and utilized the weights of the trained model to learn a model on the FDMA2012 dataset better. This predicted modelling on a distinct but related problem is re-used for accelerating the training and improving the FINet's fraudster identification performance. The proposed FINet's behaviour is investigated based on eight optimizer algorithms in terms of Average Precision, Recall, F1-score, and AUC scores and performance generalization is verified by conducting experiments with existing state-of-art deep learning models. The implementation results are notably higher than the existing state-of-the-art deep learning models and exhibit effective performance towards fraudster's identification in detecting click fraud.
AN EGOCENTRIC LOGIC OF KNOWING HOW TO TELL THEM APART

Abstract Traditionally, the formulae in modal logic express properties of possible worlds. Prior introduced “egocentric” logics that capture properties of agents rather than of possible worlds. In such a setting, the article proposes the modality “know how to tell apart” and gives a complete logical system describing the interplay between this modality and the knowledge modality. An important contribution of this work is a new matrix-based technique for proving completeness theorems in an egocentric setting.
Secure, Accessible, Virtual Voting Infrastructure (SAVVI): Reducing Barriers for Disabled and Overseas Voters

We describe a way to deploy a secured ballot return for overseas, vision, or dexterity-impaired voters. SAVVI is a secure, accessible, virtual voting infrastructure. It uses multiple communication channels with synchronized multi–factor authentication, encryption, and hashing to preserve privacy, confidentiality, and vote integrity. Our usability goal is to supply voters easy access to a hardened system that will present secure instances of their familiar browser and email clients. A key to its viability is synchronizing authentication through two low-tech verifications such as phone calls. This strives to enhance security and usability for remote voting by mimicking best practices for in-person polling place procedures. Other more standard cryptographic measures include layered encryption and dynamically provisioning a secure virtual container–a virtual voting machine (VVM)-to process each ballot. In aggregate, the design of SAVVI seeks to allow remote voting while reducing difficulty for the voter, programming complexity for the election administrator, and material procurement for the voting authority.
Virtual and Augmented Reality in Mechanical Engineering Education

Nowadays innovative technologies are entering the educational process with the aim of learners easily understanding and learning the material provided. The technologies used and/or based on virtual and augmented reality in the educational process affects today’s digital generation and motivation of learners, giving them the opportunity to use their smartphones in the learning process. The aim of the paper is to show the main positive aspects of 3D Virtual Reality environments and 3D Augmented Reality Learning environments in mechanical engineering education. An interactive Mobile Apps for 3D Augmented Reality Learning environment and 3D Virtual Reality Learning environment for setting up and operating a variety of Grinding Devices for Cutting tool sharpening are presented. The Mobile Apps are using Mobile Virtual Glasses suitable for smartphones. The use of 3D Virtual learning and 3D Augmented learning reality in the training of Cutting Tools has been tested with students from the Faculty of Mechanical and Manufacturing Engineering of the University of Ruse. With the Mechanical engineering students, comparative research was conducted to determine the effectiveness of the learning and students’ experience level in a 3D Augmented and Virtual Reality Learning environment versus that of a real learning laboratory.