Reviewerly/exHarmony huggingface dataset:
Fault tolerance and noise immunity in freespace diffractive optical neural networks

Abstract Free-space diffractive optical networks are a class of trainable optical media that are currently being explored as a novel hardware platform for neural engines. The training phase of such systems is usually performed in a computer and the learned weights are then transferred onto optical hardware (‘ex-situ training’). Although this process of weight transfer has many practical advantages, it is often accompanied by performance degrading faults in the fabricated hardware. Being analog systems, these engines are also subject to performance degradation due to noises in the inputs and during optoelectronic conversion. Considering diffractive optical networks trained for image classification tasks on standard datasets, we numerically study the performance degradation arising out of weight faults and injected noises and methods to ameliorate these effects. Training regimens based on intentional fault and noise injection during the training phase are only found marginally successful at imparting fault tolerance or noise immunity. We propose an alternative training regimen using gradient based regularization terms in the training objective that are found to impart some degree of fault tolerance and noise immunity in comparison to injection based training regimen.
Client Scheduling in Wireless Federated Learning Based on Channel and Learning Qualities

Federated learning (FL) emerges as a distributed training method in the Internet of Things (IoT), allowing participating clients to use their local data to train local models and upload parameters for global model aggregation after every few local iterations, protecting data privacy and reducing communication overhead. Given the scarcity of wireless communication resources, in this letter, we propose a client scheduling strategy for a wireless FL network based on a joint quality of channel and learning. Finally, we compare the proposed scheduling method&#x2019;s performance with that of traditional methods considering the channel quality only. Experimental results show that our method can significantly improve training performance in terms of model accuracy and speed of convergence.
Feature distillation and accumulated selection for automated fraudulent publisher classification from user click data of online advertising

Purpose The problem of choosing the utmost useful features from hundreds of features from time-series user click data arises in online advertising toward fraudulent publisher's classification. Selecting feature subsets is a key issue in such classification tasks. Practically, the use of filter approaches is common; however, they neglect the correlations amid features. Conversely, wrapper approaches could not be applied due to their complexities. Moreover, in particular, existing feature selection methods could not handle such data, which is one of the major causes of instability of feature selection. Design/methodology/approach To overcome such issues, a majority voting-based hybrid feature selection method, namely feature distillation and accumulated selection (FDAS), is proposed to investigate the optimal subset of relevant features for analyzing the publisher's fraudulent conduct. FDAS works in two phases: (1) feature distillation, where significant features from standard filter and wrapper feature selection methods are obtained using majority voting; (2) accumulated selection, where we enumerated an accumulated evaluation of relevant feature subset to search for an optimal feature subset using effective machine learning (ML) models. Findings Empirical results prove enhanced classification performance with proposed features in average precision, recall, f1-score and AUC in publisher identification and classification. Originality/value The FDAS is evaluated on FDMA2012 user-click data and nine other benchmark datasets to gauge its generalizing characteristics, first, considering original features, second, with relevant feature subsets selected by feature selection (FS) methods, third, with optimal feature subset obtained by the proposed approach. ANOVA significance test is conducted to demonstrate significant differences between independent features.
Cognitive Benefits of Learning Additional Languages in Old Adulthood? Insights from an Intensive Longitudinal Intervention Study

Abstract Second language (L2) learning has been promoted as a promising intervention to stave off age-related cognitive decline. While previous studies based on mean trends showed inconclusive results, this study is the first to investigate nonlinear cognitive trajectories across a 30-week training period. German-speaking older participants (aged 64–75 years) enrolled for a Spanish course, strategy game training (active control) or movie screenings (passive control). We assessed cognitive performance in working memory, alertness, divided attention, and verbal fluency on a weekly basis. Trajectories were modeled using Generalized Additive Mixed Models to account for temporally limited transfer effects and intra-individual variation in cognitive performance. Our results provide no evidence of cognitive improvement differing between the Spanish and either of the control groups during any phase of the training period. We did, however, observe an effect of baseline cognition, such that individuals with low cognitive baselines increased their performance more in the L2 group than comparable individuals in the control groups. We discuss these findings against the backdrop of the cognitive training literature and Complex Dynamic Systems Theory.
Face Mask Detection using Convolutional Neural Network

During pandemic CoVID 19, people must use face masks in public areas to prevent and reducing the risk of transmission and spread of the virus. Computer Vision can help to monitor the use of face masks based on images captured via CCTV. Several public areas have installed CCTV that can monitor using masks, but too many people in the area would create problems. Face and side masked face detection is a challenge, given the removal of facial features such as the mouth and nose. A previous study built a mask detection system using Convolutional Neural Networks (CNN) based models, which produced high accuracy but was limited to the front face. This research proposed the CNN method to detect masks based on facial images taken from cameras in public areas. Images containing faces from CCTV are segmented, each faces first using the Retina Face. Experiments were carried out on a single face image in mask detection, resulting in an accuracy of 97.33%. These excellent results are not surprising given CNN's ability to recognize patterns. The most important thing is the segmentation of the face region from one image, which is then tested to produce an accuracy of 82.46%. We selected the best configuration from the two experiments, combined into a mask detection from an image containing multiple faces. The results also showed a significant effect between the face detection method and the learning rate value on the accuracy of the mask use detection system, with the best results of 79.45% using the RetinaFace face detection model.
Image-based Product Recommendation Method for E-commerce Applications Using Convolutional Neural Networks

Recommender systems (RS) are designed to eliminate the information overload problem in today's e-commerce platforms and other data-centric online services.They help users explore and exploit the system's information environment utilizing implicit and explicit data from internal e-commerce systems and user interactions.Today's product catalogues include pictures to provide visual detail at a glance.This approach can effectively convert potential buyers into customers.Since most e-commerce stores use product images to promote, arouse users' visual desires and encourage them to buy products, this paper develops an image-based RS using deep learning techniques.To perform the research, we use five convolutional neural network (CNN) models to extract the features of the products' images.Then, the system uses the features to calculate the similarity between images.The selected CNN models are VGG16, VGG19, ResNet50, Inception V3 and Xception.We also analysed four versions of the MovieLens dataset to demonstrate the accuracy improvement of the recommendations, including 100k, 1M, 10M and 20M.Results of the experiment showed a significant increase in accuracy compared with traditional approaches.Also, we express many related open issues including use of multiple images per item, different similarity metrics, other CNN models, and the hybridization of image-based and different RS techniques for future studies.This method also provides more accurate product recommendations on e-commerce platforms than traditional methods.
Video Captioning Based on Multi-layer Gated Recurrent Unit for Smartphones

Video captioning is the visual understanding process to generate grammatically and semantically meaningful descriptions that are of interest in the fields of computer vision (CV) and natural language processing (NLP).Recent advances in the computing power of the mobile platform have led to many video captioning applications that use CV and NLP techniques.These video captioning applications mainly depend on the encoder-decoder approach running with the internet connection, which employs convolutional neural networks (CNNs) on the encoder and recurrent neural networks (RNNs) on the decoder.However, this approach is not powerful enough to get accurate captioning results, and fast response due to online data transfer.In this paper, therefore, the encoder-decoder approach has been extended with a sequence-to-sequence model under a multi-layer gated recurrent unit (GRU) to generate a semantically more coherent caption.Visual information from image features of each video frame is extracted with ResNet-101 CNN in the encoder to feed the multi-layer GRU based decoder for caption generation.The proposed approach has been compared with the state-of-the-art approaches using experiments on the MSVD dataset under eight performance metrics.In addition, the proposed approach is embedded into our custom-designed Android application, called WeCap, capable of faster caption generation without an internet connection.
MPCN-RP: A Routing Protocol for Blockchain-Based Multi-Charge Payment Channel Networks

Blockchain-based cryptocurrencies are severely limited in transaction throughput and latency due to the need to seek consensus among all peers of the network. A promising solution to this issue is payment channels, which allow unlimited numbers of atomic and trust-free payments between two peers without exhausting the resources of the blockchain. A linked payment channel network enables payments between two peers without direct channels through a series of intermediate nodes that forward and charge for the transactions. However, the charging strategies of intermediate nodes vary with different payment channel networks. Existing works do not yet have a complete routing algorithm to provide the most economical path for users in a multi-charge payment channel network. In this work, we propose MPCN-RP, a general routing protocol for payment channel networks with multiple charges. Our extensive experimental results on both simulated and real payment channel networks show that MPCN-RP significantly outperforms the baseline algorithms in terms of time and fees.
Bayesian nonparametric change point detection for multivariate time series with missing observations

A time series is a commonly observed type of data, and it is analyzed in several ways in real applications. Within the possible analysis, change point detection is one of the crucial inferential targets for studying the behavior of a time series. We consider a multiple change point detection model for a multivariate time series. Among the possible approaches to perform multiple change point detection, we propose an extension to the multivariate case of one of the main state-of-the-art approaches, working in a Bayesian nonparametric framework. We combine a combinatorial prior distribution, which relies on a model-based clustering approach to detect the change points, with a multivariate kernel for time-dependent realizations in a general fashion. We further extend the model to the case of missing observations and derive opportune quantities to perform data imputation. Thereafter, we investigate the properties of the multivariate model with an extensive simulation study, and we apply the model to perform change point detection in two real data applications.
The Impact of a Hidden Object Game on English Vocabulary Learning and Motivation

&lt;p&gt;This paper investigates the impact of the classroom uses of a digital hidden object game in which students should match visual objects with their English vocabulary equivalents. The essential features of this game are similar to conventional classroom uses of pictures to help learn new English words, so this study investigates whether playing a hidden object game fosters English vocabulary learning and learning motivation. The 57 subjects studying at a junior high school (i.e. seventh-graders) in Taiwan were carefully assigned to form an experimental (N=28) and control group (N=29) and the only teaching variable between the two groups was controlled by the treatment with or without a hidden object game in class. It is revealed that compared with control group, the students in the hidden object game group significantly outperformed regarding vocabulary recognition. However, even though the experimental group also showed relatively higher learning motivation, there was no significant difference between two modes.&lt;/p&gt; &lt;p&gt;&amp;nbsp;&lt;/p&gt;
Data Augmentation Using CycleGAN for End-to-End Children ASR

Recent deep learning algorithms are known to perform better for Automatic Speech Recognition (ASR) of adult speakers, however, yet remains a challenge to recognize children's speech with the similar accuracy. Due to less availability of children's speech data to train the deep neural network, data augmentation is one of the key research areas for children ASR. In this paper, voice conversion-based data augmentation using CycleGAN is explored and performance comparison with and without data augmentation is presented. ASR experiments were performed using TLT school corpus. In our experiment, CycleGAN-based 200 hours of converted adult speech showed good performance improvement with the reduction of 5.58% WER compared to the baseline system. In addition, the combination of SpecAugment, speed perturbed, and CycleGAN converted adult speech showed the highest reduction of 7.44% WER compared to baseline system <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> .
Deep manifold feature fusion for classification of breast histopathology images

Histopathology image analysis is the gold standard for breast cancer diagnosis, yet the classification of breast histopathology images is challenging. Convolutional neural networks usually classify images based on deep abstract features only, ignoring the influence of structure information contained in low-level features, which limits the classification ability of deep models. To address the above problem, a breast histopathology image classification method based on the deep manifold fusion of multilayer features is proposed, LPMF2Net. By exploring the complementarity between features at different levels, the multilayer features were cross-cascaded and fused to enhance the cell structure characterization ability. Local preserving projection is applied for the fused features to reduce the interference of redundant information and optimize fusion performance. Moreover, the projection matrix was adaptively adjusted according to the local preserving regularization term to further optimize the model. The proposed LPMF2Net model was tested on the public dataset BreaKHis, and the experimental results (40×: 94.91%, 100×: 96.12%, 200×: 95.51%, 400×: 95.42%) proved its effectiveness.
Two or Three Weight Linear Codes From Non-Weakly Regular Bent Functions

Linear codes with few weights have applications in consumer electronics, communications, data storage systems, secret sharing, authentication codes, and association schemes. As a special class of linear codes, minimal linear codes have important applications in secret sharing and secure computation of data between two parties. The construction of minimal linear codes with new and desirable parameters is an interesting research topic in coding theory and cryptography. Recently, Mesnager et.al. stated that “constructing linear codes with good parameters from non-weakly regular bent functions is an interesting problem.” The goal of this paper is to construct linear codes with two or three weights from non-weakly regular bent functions over finite fields and analyze the minimality of the constructed linear codes. In doing so, we draw inspiration from a paper by Mesnager in which she constructed linear codes with small weights from weakly regular bent functions based on a generic construction method. First, we recall the definitions of the subsets <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$B_{+}(f)$ </tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$B_{-}(f)$ </tex-math></inline-formula> associated with a non-weakly regular bent function <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$f$ </tex-math></inline-formula> . Next, we construct two- or three-weight linear <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$p$ </tex-math></inline-formula> -ary codes on these sets using duals of the non-weakly regular bent functions that are also bent. We note that the constructed linear codes are minimal in almost all cases. Moreover, when <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$f$ </tex-math></inline-formula> is a non-weakly regular bent function in a certain subclass of Generalized Maiorana-McFarland bent functions, we determine the weight distributions of the corresponding linear codes. As far as we know, the construction of linear codes from non-weakly regular bent functions over finite fields is first studied in the literature by the second author in his dissertation.
Events, their names, and their synchronic structure

We present in this paper a novel ontological theory of events whose central tenet is the Aristotelian distinction between the object that changes and the actual subject of change, which is what we call an individual quality. While in the Kimian tradition events are individuated by a triple ⟨ o , P , t ⟩, where o is an object, P a property, and t an interval of time, for us the simplest events are qualitative changes, individuated by a triple ⟨ o , q , t ⟩, where q is an individual quality inhering in o or in one of its parts. Detaching the individuation of events from the property they exemplify results in a fine-grained theory that keeps metaphysics and semantics clearly separate, and lies between the multiplicative and the unitarian approaches. We discuss then the way language refers to events, observing that, in most cases, event descriptions refer to complex, cognitively relevant clusters of co-occurring qualitative changes, which exhibit a synchronic structure depending on the way they are described. Contra Bennett, who famously argued that the semantics of event names ultimately depends on “local context and unprincipled intuitions”, we show how the lexicon provides systematic principles for individuating such clusters and classifying them into kinds. Finally, we address some open challenges in the semantics of locative and manner modifiers.
Fast Startup Multicast Streaming on Operator IPTV Networks using HESP

Year after year, more video content is moving to adaptive video streaming systems using HTTP. Networks need to handle considerable load due to HTTP requiring unicast connections, and this load is often variable and unpredictable. As viewership grows, this load can lead to playback issues. Combined with other factors such as high latency and high startup delay, this often leads to poor quality of experience. We propose a solution where the High Efficiency Streaming Protocol (typically sent for unicast only) is used to send content over IP multicast. This solution can leverage existing IPTV networks to reduce overall bandwidth consumption while ensuring fast startup and low end-to-end latency. The latency of our solution is equal to that of HESP sent over unicast in most situations and even improves upon it in poor network environments. Although non-negligible bandwidth overhead is introduced to account for packet loss, transmitting content over IP multicast results in predictable and much more manageable network load while preserving the advantages of existing HTTP streaming protocols.
Regularizing the Deepsurv Network Using Projection Loss for Medical Risk Assessment

State-of-the-art deep survival prediction approaches expand network parameters to accommodate performance over a fine discretization of output time. For medical applications where data are limited, the regression-based Deepsurv approach is more advantageous because its continuous output design limits unnecessary network parameters. Despite the practical advantage, the typical network lacks control over the feature distribution causing the network to be more prone to noisy information and occasional poor prediction performance. We propose a novel projection loss as a regularizing objective to improve the time-to-event Deepsurv model. The loss formulation maximizes the lower bound of the multiple-correlation coefficient between the network’s features and the desired hazard value. Reducing the loss also theoretically lowers the upper bound on the likelihood of discordant pair and improves C-index performance. We observe superior performances and robustness of regularized Deepsurv over many state-of-the-art approaches in our experiments with five public medical datasets and two cross-cohort validation tasks.
Two families of optimal ternary cyclic codes with minimal distance four

In this paper, the two families of optimal ternary cyclic codes with parameters of [3m−1,3m−1−2m,4] are presented. Some conclusions proposed by Wang and Wu in 2016 are analyzed and refined, and the remaining problem in it is solved. In addition, a conjecture proposed by Ding and Helleseth in 2013 about a class of optimal ternary cyclic codes C(1,e) in which e=(3m−1−1)/2+3h+1 with parameters [3m−1,3m−2m−1,4] is analyzed, where 0≤h≤m−1.