Reviewerly/exHarmony huggingface dataset:
HARTH: A Human Activity Recognition Dataset for Machine Learning

Existing accelerometer-based human activity recognition (HAR) benchmark datasets that were recorded during free living suffer from non-fixed sensor placement, the usage of only one sensor, and unreliable annotations. We make two contributions in this work. First, we present the publicly available Human Activity Recognition Trondheim dataset (HARTH). Twenty-two participants were recorded for 90 to 120 min during their regular working hours using two three-axial accelerometers, attached to the thigh and lower back, and a chest-mounted camera. Experts annotated the data independently using the camera's video signal and achieved high inter-rater agreement (Fleiss' Kappa =0.96). They labeled twelve activities. The second contribution of this paper is the training of seven different baseline machine learning models for HAR on our dataset. We used a support vector machine, k-nearest neighbor, random forest, extreme gradient boost, convolutional neural network, bidirectional long short-term memory, and convolutional neural network with multi-resolution blocks. The support vector machine achieved the best results with an F1-score of 0.81 (standard deviation: ±0.18), recall of 0.85±0.13, and precision of 0.79±0.22 in a leave-one-subject-out cross-validation. Our highly professional recordings and annotations provide a promising benchmark dataset for researchers to develop innovative machine learning approaches for precise HAR in free living.
DeepLesionBrain: Towards a broader deep-learning generalization for multiple sclerosis lesion segmentation

Recently, segmentation methods based on Convolutional Neural Networks (CNNs) showed promising performance in automatic Multiple Sclerosis (MS) lesions segmentation. These techniques have even outperformed human experts in controlled evaluation conditions such as Longitudinal MS Lesion Segmentation Challenge (ISBI Challenge). However, state-of-the-art approaches trained to perform well on highly-controlled datasets fail to generalize on clinical data from unseen datasets. Instead of proposing another improvement of the segmentation accuracy, we propose a novel method robust to domain shift and performing well on unseen datasets, called DeepLesionBrain (DLB). This generalization property results from three main contributions. First, DLB is based on a large group of compact 3D CNNs. This spatially distributed strategy aims to produce a robust prediction despite the risk of generalization failure of some individual networks. Second, we propose a hierarchical specialization learning (HSL) by pre-training a generic network over the whole brain, before using its weights as initialization to locally specialized networks. By this end, DLB learns both generic features extracted at global image level and specific features extracted at local image level. Finally, DLB includes a new image quality data augmentation to reduce dependency to training data specificity (e.g., acquisition protocol). DLB generalization was validated in cross-dataset experiments on MSSEG'16, ISBI challenge, and in-house datasets. During experiments, DLB showed higher segmentation accuracy, better segmentation consistency and greater generalization performance compared to state-of-the-art methods. Therefore, DLB offers a robust framework well-suited for clinical practice.
InP photonic integrated multi-layer neural networks: Architecture and performance analysis

We demonstrate the use of a wavelength converter, based on cross-gain modulation in a semiconductor optical amplifier (SOA), as a nonlinear function co-integrated within an all-optical neuron realized with SOA and wavelength-division multiplexing technology. We investigate the impact of fully monolithically integrated linear and nonlinear functions on the all-optical neuron output with respect to the number of synapses/neuron and data rate. Results suggest that the number of inputs can scale up to 64 while guaranteeing a large input power dynamic range of 36 dB with neglectable error introduction. We also investigate the performance of its nonlinear transfer function by tuning the total input power and data rate: The monolithically integrated neuron performs about 10% better in accuracy than the corresponding hybrid device for the same data rate. These all-optical neurons are then used to simulate a 64:64:10 two-layer photonic deep neural network for handwritten digit classification, which shows an 89.5% best-case accuracy at 10 GS/s. Moreover, we analyze the energy consumption for synaptic operation, considering the full end-to-end system, which includes the transceivers, the optical neural network, and the electrical control part. This investigation shows that when the number of synapses/neuron is &amp;gt;18, the energy per operation is &amp;lt;20 pJ (6 times higher than when considering only the optical engine). The computation speed of this two-layer all-optical neural network system is 47 TMAC/s, 2.5 times faster than state-of-the-art graphics processing units, while the energy efficiency is 12 pJ/MAC, 2 times better. This result underlines the importance of scaling photonic integrated neural networks on chip.
A Plug-and-Play Method for Controlled Text Generation

Large pre-trained language models have repeatedly shown their ability to produce fluent text.Yet even when starting from a prompt, generation can continue in many plausible directions.Current decoding methods with the goal of controlling generation, e.g., to ensure specific words are included, either require additional models or fine-tuning, or work poorly when the task at hand is semantically unconstrained, e.g., story generation.In this work, we present a plug-and-play decoding method for controlled language generation that is so simple and intuitive, it can be described in a single sentence: given a topic or keyword, we add a shift to the probability distribution over our vocabulary towards semantically similar words.We show how annealing this distribution can be used to impose hard constraints on language generation, something no other plug-and-play method is currently able to do with SOTA language generators.Despite the simplicity of this approach, we see it works incredibly well in practice: decoding from GPT-2 leads to diverse and fluent sentences while guaranteeing the appearance of given guide words.We perform two user studies, revealing that (1) our method outperforms competing methods in human evaluations; and(2) forcing the guide words to appear in the generated text has no impact on the fluency of the generated text. 1
Sustainable Modular Debiasing of Language Models

Unfair stereotypical biases (e.g., gender, racial, or religious biases) encoded in modern pretrained language models (PLMs) have negative ethical implications for widespread adoption of state-of-the-art language technology.To remedy for this, a wide range of debiasing techniques have recently been introduced to remove such stereotypical biases from PLMs.Existing debiasing methods, however, directly modify all of the PLMs parameters, which -besides being computationally expensivecomes with the inherent risk of (catastrophic) forgetting of useful language knowledge acquired in pretraining.In this work, we propose a more sustainable modular debiasing approach based on dedicated debiasing adapters, dubbed ADELE.Concretely, we (1) inject adapter modules into the original PLM layers and (2) update only the adapters (i.e., we keep the original PLM parameters frozen) via language modeling training on a counterfactually augmented corpus.We showcase ADELE in gender debiasing of BERT: our extensive evaluation, encompassing three intrinsic and two extrinsic bias measures, renders ADELE very effective in bias mitigation.We further show that -due to its modular nature -ADELE, coupled with task adapters, retains fairness even after large-scale downstream training.Finally, by means of multilingual BERT, we successfully transfer ADELE to six target languages.
Smish: A Novel Activation Function for Deep Learning Methods

Activation functions are crucial in deep learning networks, given that the nonlinear ability of activation functions endows deep neural networks with real artificial intelligence. Nonlinear nonmonotonic activation functions, such as rectified linear units, Tan hyperbolic (tanh), Sigmoid, Swish, Mish, and Logish, perform well in deep learning models; however, only a few of them are widely used in mostly all applications due to their existing inconsistencies. Inspired by the MB-C-BSIF method, this study proposes Smish, a novel nonlinear activation function, expressed as f(x)=x·tanh[ln(1+sigmoid(x))], which could overcome other activation functions with good properties. Logarithmic operations are first used to reduce the range of sigmoid(x). The value is then calculated using the tanh operator. Inputs are ultimately used to multiply the previous value, thus exhibiting negative output regularization. Experiments show that Smish tends to operate more efficiently than Logish, Mish, and other activation functions on EfficientNet models with open datasets. Moreover, we evaluated the performance of Smish in various deep learning models and the parameters of its function f(x)=αx·tanh[ln(1+sigmoid(βx))], and where α = 1 and β = 1, Smish was found to exhibit the highest accuracy. The experimental results show that with Smish, the EfficientNetB3 network exhibits a Top-1 accuracy of 84.1% on the CIFAR-10 dataset; the EfficientNetB5 network has a Top-1 accuracy of 99.89% on the MNIST dataset; and the EfficientnetB7 network has a Top-1 accuracy of 91.14% on the SVHN dataset. These values are superior to those obtained using other state-of-the-art activation functions, which shows that Smish is more suitable for complex deep learning models.
SVC-onGoing: Signature verification competition

This article presents SVC-onGoing1, an on-going competition for on-line signature verification where researchers can easily benchmark their systems against the state of the art in an open common platform using large-scale public databases, such as DeepSignDB2 and SVC2021_EvalDB3, and standard experimental protocols. SVC-onGoing is based on the ICDAR 2021 Competition on On-Line Signature Verification (SVC 2021), which has been extended to allow participants anytime. The goal of SVC-onGoing is to evaluate the limits of on-line signature verification systems on popular scenarios (office/mobile) and writing inputs (stylus/finger) through large-scale public databases. Three different tasks are considered in the competition, simulating realistic scenarios as both random and skilled forgeries are simultaneously considered on each task. The results obtained in SVC-onGoing prove the high potential of deep learning methods in comparison with traditional methods. In particular, the best signature verification system has obtained Equal Error Rate (EER) values of 3.33% (Task 1), 7.41% (Task 2), and 6.04% (Task 3). Future studies in the field should be oriented to improve the performance of signature verification systems on the challenging mobile scenarios of SVC-onGoing in which several mobile devices and the finger are used during the signature acquisition.
Deep Learning-Based Small Object Detection and Classification Model for Garbage Waste Management in Smart Cities and IoT Environment

In recent years, object detection has gained significant interest and is considered a challenging problem in computer vision. Object detection is mainly employed for several applications, such as instance segmentation, object tracking, image captioning, healthcare, etc. Recent studies have reported that deep learning (DL) models can be employed for effective object detection compared to traditional methods. The rapid urbanization of smart cities necessitates the design of intelligent and automated waste management techniques for effective recycling of waste. In this view, this study develops a novel deep learning-based small object detection and classification model for garbage waste management (DLSODC-GWM) technique. The proposed DLSODC-GWM technique mainly focuses on detecting and classifying small garbage waste objects to assist intelligent waste management systems. The DLSODC-GWM technique follows two major processes, namely, object detection and classification. For object detection, an arithmetic optimization algorithm (AOA) with an improved RefineDet (IRD) model is applied, where the hyperparameters of the IRD model are optimally chosen by the AOA. Secondly, the functional link neural network (FLNN) technique was applied for the classification of waste objects into multiple classes. The design of IRD for waste classification and AOA-based hyperparameter tuning demonstrates the novelty of the work. The performance validation of the DLSODC-GWM technique is performed using benchmark datasets, and the experimental results show the promising performance of the DLSODC-GWM method on existing approaches with a maximum accuy of 98.61%.
Regulating watermarking semi-authentication of multimedia audio via counting-based secret sharing

ÖzWatermarking is the process of embedding specific data to prove ownership copyright authentication.It is needed whenever media-files are used without proper permission is granted for authentication accuracy.The current watermarking challenge comes from the ownership proof especially as slight tampering occurs on the audio multimedia file which injure the watermarking causing difficulty in its copyright proof.This paper proposes utilizing counting-based secret sharing strategy to allow validation of ownership correctness watermarking even if some of the multimedia audio-file is interfered, as semi-authentication.The research testing run experimentations showed interesting features although this work is still in its early stage. The authentication verification researched secrecy on the audio's media remarked data altered as on LSB 3 models (1-LSB, 2-LSB, 3-LSB) testing hiding capacity capability as well as PSNR security. Its promising investigation revealed complete data dependency consequences showing real attractive contribution opportunities to be remarked.Filigran, sahiplik telif hakkı kimlik doğrulamasını kanıtlamak için belirli verileri gömme işlemidir.Kimlik doğrulama doğruluğu için uygun izin olmadan medya dosyalarını kullanırken gereklidir.Mevcut filigran sorunu, esas olarak sahiplik kanıtından kaynaklanmaktadır, çünkü ses multimedya dosyasında filigrana zarar veren ve telif hakkı kanıtında zorluklara neden olan hafif bir kurcalama meydana gelir.Bu makale, multimedya ses dosyasının bir kısmı kurcalanmış olsa bile, yarı kimlik doğrulama olarak, sahiplik özgünlük damgasının
Do Large Language Models Understand Us?

Abstract Large language models (LLMs) represent a major advance in artificial intelligence and, in particular, toward the goal of human-like artificial general intelligence. It is sometimes claimed, though, that machine learning is “just statistics,” hence that, in this grander ambition, progress in AI is illusory. Here I take the contrary view that LLMs have a great deal to teach us about the nature of language, understanding, intelligence, sociality, and personhood. Specifically: statistics do amount to understanding, in any falsifiable sense. Furthermore, much of what we consider intelligence is inherently dialogic, hence social; it requires a theory of mind. Complex sequence learning and social interaction may be a sufficient basis for general intelligence, including theory of mind and consciousness. Since the interior state of another being can only be understood through interaction, no objective answer is possible to the question of when an “it” becomes a “who,” but for many people, neural nets running on computers are likely to cross this threshold in the very near future.
The Impact of Data processing and Ensemble on Breast Cancer Detection Using Deep Learning

According to the World Health Organization, cancer is the second leading cause of mortality. Breast cancer is the most prevalent cancer diagnosed in women around the world. Breast cancer diagnostics range from mammograms to CT scans and ultrasounds, but a biopsy is the only way to know for sure if the suspicious cells detected in the breast are cancerous or not. This paper’s main contribution is multi-fold. First, it proposes a deep learning approach to detect breast cancer from biopsy microscopy images. Deep convolution nets of various types are used. Second, the paper examines the effects of different data preprocessing techniques on the performance of deep learning models. Third, the paper introduces an ensemble method for aggregating the best models in order to improve performance. The experimental results revealed that Densenet169, Resnet50, and Resnet101 are the three best models achieving accuracy scores of 62%, 68%, and 85%, respectively. without data preprocessing. With the help of data augmentation and segmentation, the accuracy of these models increased by 20%, 17%, and 6%, respectively. Additionally, the ensemble learning technique improves the accuracy of the models even further. The results show that the best accuracy achieved is 92.5%.
UNKs Everywhere: Adapting Multilingual Language Models to New Scripts

Massively multilingual language models such as multilingual BERT offer state-of-the-art cross-lingual transfer performance on a range of NLP tasks.However, due to limited capacity and large differences in pretraining data sizes, there is a profound performance gap between resource-rich and resource-poor target languages.The ultimate challenge is dealing with under-resourced languages not covered at all by the models and written in scripts unseen during pretraining.In this work, we propose a series of novel data-efficient methods that enable quick and effective adaptation of pretrained multilingual models to such lowresource languages and unseen scripts.Relying on matrix factorization, our methods capitalize on the existing latent knowledge about multiple languages already available in the pretrained model's embedding matrix.Furthermore, we show that learning of the new dedicated embedding matrix in the target language can be improved by leveraging a small number of vocabulary items (i.e., the so-called lexically overlapping tokens) shared between mBERT's and target language vocabulary.Our adaptation techniques offer substantial performance gains for languages with unseen scripts.We also demonstrate that they can yield improvements for low-resource languages written in scripts covered by the pretrained model.
On Parameter Estimation Approaches for Predicting Disease Transmission Through Optimization, Deep Learning and Statistical Inference Methods

In this paper, we consider compartmental disease transmission models and discuss the importance of determining model parameters that provide an insight into disease transmission and prevalence. After a brief review and comparison of the performance of some heuristic approaches, the paper introduces three approaches including an optimization approach, a physics informed deep learning and a statistical inference method to estimate parameters and analyse disease transmission. The deep learning framework utilizes the hidden physics of infectious diseases and infer the latent quantities of interest in the model by approximating them using deep neural networks. The performance of the deep learning method is validated against representative small and big data sets corresponding to a well-known benchmark example and the results indicate that deep learning is a viable candidate to determine model parameters. The paper also presents the need for researchers to understand different types of dependencies exhibited in a typical data set and discovering the most appropriate approaches for statistical inference. Specifically, in this work we apply a time-series inferential method with a variety of statistical models. Our results indicate the efficiency and importance of statistical inference methods for researchers to understand and analyse time-series data to make confident predictions.
LiveDeep: Online Viewport Prediction for Live Virtual Reality Streaming Using Lifelong Deep Learning

Live virtual reality (VR) streaming has become a popular and trending video application in the consumer market providing users with 360-degree, immersive viewing experiences. To provide premium quality of experience, VR streaming faces unique challenges due to the significantly increased bandwidth consumption. To address the bandwidth challenge, VR video viewport prediction has been proposed as a viable solution, which predicts and streams only the user’s viewport of interest with high quality to the VR device. However, most of the existing viewport prediction approaches target only the video-on-demand (VOD) use cases, requiring offline processing of the historical video and/or user data that are not available in the live streaming scenario. In this work, we develop a novel viewport prediction approach for live VR streaming, which only requires video content and user data in the current viewing session. To address the challenges of insufficient training data and real-time processing, we propose a live VR-specific deep learning mechanism, namely LiveDeep, to create the online viewport prediction model and conduct real-time inference. LiveDeep employs a hybrid approach to address the unique challenges in live VR streaming, involving (1) an alternate online data collection, labeling, training, and inference schedule with controlled feedback loop to accommodate for the sparse training data; and (2) a mixture of hybrid neural network models to accommodate for the inaccuracy caused by a single model. We evaluate LiveDeep using 48 users and 14 VR videos of various types obtained from a public VR user head movement dataset. The results indicate around 90% prediction accuracy, around 40% bandwidth savings, and premium processing time, which meets the bandwidth and real-time requirements of live VR streaming.
Quantifying Power in Silicon Photonic Neural Networks

Due to challenging efficiency limits facing conventional and unconventional electronic architectures, information processors based on photonics have attracted renewed interest. Research communities have yet to settle on definitive techniques to describe the performance of this class of information processors. Photonic systems are different from electronic ones, so the existing concepts of computer performance measurement cannot necessarily apply. In this manuscript, we attempt to quantify the power use of photonic neural networks with state-of-the-art and future hardware. We derive scaling laws, physical limits, and new platform performance metrics. We find that overall performance is regime-like, which means that energy efficiency characteristics of a photonic processor can be completely described by no less than seven performance numbers. The introduction of these analytical strategies provides a much needed foundation for quantitative roadmapping and commercial value assignment for silicon photonic neural networks.