Reviewerly/exHarmony huggingface dataset:
A Multi-Modal Array of Interpretable Features to Evaluate Language and Speech Patterns in Different Neurological Disorders

Speech-based automatic approaches for evaluating neurological disorders (NDs) depend on feature extraction before the classification pipeline. It is preferable for these features to be interpretable to facilitate their development as diagnostic tools. This study focuses on the analysis of interpretable features obtained from the spoken responses of 88 subjects with NDs and controls (CN). Subjects with NDs have Alzheimer's disease (AD), Parkinson's disease (PD), or Parkinson's disease mimics (PDM). We configured three complementary sets of features related to cognition, speech, and language, and conducted a statistical analysis to examine which features differed between NDs and CN. Results suggested that features capturing response informativeness, reaction times, vocabulary richness, and syntactic complexity provided separability between AD and CN. Similarly, fundamental frequency variability helped differentiate PD from CN, while the number of salient informational units PDM from CN.
Towards perceptual image watermarking with robust texture measurement

Improving the perceptual quality of images is challenging for the robust watermarking method. To address this problem, most existing algorithms proposed to guide the watermark embedding based on just noticeable difference (JND) model, which mainly consists of corresponding contrast masking factor. However, existing metrics are mostly based on the intra-block texture energy, which cannot exactly describe the texture information and meet the needs of watermarking robustness. In this paper, we present a new texture metric, which incorporates the visual saliency based inter-block texture regularity with the robust discrete cosine transform (DCT) coefficients. The JND model is constructed as a modulation factor by using the proposed robust texture metric, which embeds the intra-block energy and inter-block regularity measurement to capture local and global features. Based on the proposed model, we introduce an adaptive quantization step for the watermarking framework. Both quantitative and qualitative results show that our scheme achieves better performance in comparison with several state-of-the-art schemes.
Mixture Domain Adaptation to Improve Semantic Segmentation in Real-World Surveillance

Various tasks encountered in real-world surveillance can be addressed by determining posteriors (e.g. by Bayesian inference or machine learning), based on which critical decisions must be taken. However, the surveillance domain (acquisition device, operating conditions, etc.) is often unknown, which prevents any possibility of scene-specific optimization. In this paper, we define a probabilistic framework and present a formal proof of an algorithm for the unsupervised many-to-infinity domain adaptation of posteriors. Our proposed algorithm is applicable when the probability measure associated with the target domain is a convex combination of the probability measures of the source domains. It makes use of source models and a domain discriminator model trained off-line to compute posteriors adapted on the fly to the target domain. Finally, we show the effectiveness of our algorithm for the task of semantic segmentation in real-world surveillance. The code is publicly available at https://github.com/rvandeghen/MDA.
A transformer–convolution model for enhanced session-based recommendation

Session-based recommendation aims to predict a user’s next action based on a series of anonymous sequences and plays an essential role in various online applications, such as e-commerce and music applications. Recently, transformer-based models have obtained results that are competitive with or even surpass those of recurrent neural networks, because of the good performance of transformer models in capturing long-distance dependencies. However, a transformer has a limited ability to mine local contextual information, which can be regarded as collective features. Researchers are seeking to address this limitation by augmenting the contextual transition to boost session representation learning. Accordingly, in this paper, we enhance the capabilities of a transformer in a session-based recommendation task by introducing convolutional neural networks (CNNs) at the stage of aggregating the item features with long- and short-distance dependencies. We first borrow a self-attention module from the classic transformer model to explore the long-distance dependencies. We next propose horizontal and vertical convolutions for enhancing the local collective information and then obtain a session representation by integrating the two types of features. Extensive experiments on real-world datasets show that our method outperforms those that rely on a transformer or a CNN alone.
Detection of acute promyelocytic leukemia in peripheral blood and bone marrow with annotation-free deep learning

While optical microscopy inspection of blood films and bone marrow aspirates by a hematologist is a crucial step in establishing diagnosis of acute leukemia, especially in low-resource settings where other diagnostic modalities are not available, the task remains time-consuming and prone to human inconsistencies. This has an impact especially in cases of Acute Promyelocytic Leukemia (APL) that require urgent treatment. Integration of automated computational hematopathology into clinical workflows can improve the throughput of these services and reduce cognitive human error. However, a major bottleneck in deploying such systems is a lack of sufficient cell morphological object-labels annotations to train deep learning models. We overcome this by leveraging patient diagnostic labels to train weakly-supervised models that detect different types of acute leukemia. We introduce a deep learning approach, Multiple Instance Learning for Leukocyte Identification (MILLIE), able to perform automated reliable analysis of blood films with minimal supervision. Without being trained to classify individual cells, MILLIE differentiates between acute lymphoblastic and myeloblastic leukemia in blood films. More importantly, MILLIE detects APL in blood films (AUC 0.94 ± 0.04) and in bone marrow aspirates (AUC 0.99 ± 0.01). MILLIE is a viable solution to augment the throughput of clinical pathways that require assessment of blood film microscopy.
Speedup deep learning models on GPU by taking advantage of efficient unstructured pruning and bit-width reduction

This work is focused on the UnSparse-Opt framework for the efficient unstructured pruning and quantisation of feedforward neural networks and the improvement of their efficiency on graphic processing units (GPU) by using a direct sparse algorithm. The Nvidia deep neural network (cuDnn) library is the most effective implementation of deep learning (DL) algorithms for GPUs. One of the most common techniques for improving the efficiency of Convolutional Neural Network (CNN) models is weight pruning and quantisation. There are two main types of pruning: structural and non-structural. The first enables much easier acceleration on many type of accelerators, but with structural it is difficult to achieve a sparsity level and accuracy as high as that obtained with the non-structural version. Non-structural pruning with retraining can generate the weight tensors up to ∼90% or more of sparsity in some deep CNN models. In this article, the pruning algorithm is presented which achieve high sparsity levels without drop in accuracy. In the next stage, the linear and non-linear quantisation is adapted for further reductions in time and memory footprint. Additionally, this work presents real CNN models pruned with high sparsities in which some subset of layers can have comparable or better efficiency than cuDnn by using a direct sparse method. Finally, it shows sparse CNN-based architectures with reduced precision which can be more efficient than CuDnn library.
Robust Texture-Aware Local Adaptive Image Watermarking With Perceptual Guarantee

Watermarking involves embedding a watermark in an image and later extracting it to prove the image’s copyright. In most cases, a complete image contains both smooth and textured regions. As a rule of thumb, the visual quality of an image with a watermark embedded in its textured regions is better than that of the same image with a watermark in smooth regions. This paper, by taking advantage of the fact, proposes a texture-aware local adaptive watermarking algorithm to maximize the watermark’s robustness while maintaining its imperceptibility. To identify textured regions in an image, we introduce the texture value, an efficient and proper metric of the richness of image texture. It combines the texture correlation of the AC coefficients, the luminance masking of the DC coefficient, and the distribution of image texture. A watermark is embedded adaptively into multiple non-overlapping textured regions of an image under the specified SSIM condition. Its adaptiveness comes from a novel texture-aware adaptive parameter model derived by multivariate regression analysis. Correct extraction of watermarks from multiple textured regions can be done by the cooperation of embedding and extraction strategies, with the assistance of RS-based watermark coding model. They allow for greater robustness, faster extraction, and adjustable watermark capacity. The simulation experiments on 100 images demonstrate that our proposed algorithm outperforms state-of-the-art algorithms with respect to imperceptibility, robustness, and adaptability.
Digitalization in Energy Production, Distribution, and Consumption: A Systematic Literature Review

For this study, we conducted a systematic review of the literature on digitalization in energy production, distribution, and consumption over a sufficiently long period in order to reveal the trends and particularities of this phenomenon at the sectoral level. For the systematic review of the literature, representative articles on the subject indexed in the Web of Science and Scopus databases were selected using the PRISMA 2020 flow diagram. As a result of the systematic review of the literature, a significant number of articles on the subject of digitalization in the energy sector were found—both over the entire period considered and especially in the last five years—indicating the magnitude of the digitalization process in this field. The impacts of digitalization in the energy production, distribution, and consumption sectors materialized in the aspects of health, safety, and environmental improvement; process improvements; and cost reductions. The most important technologies used in the digitalization process include data mining and machine learning, smart grid/smart metering/smart home, Internet of Things, cybersecurity, and automation solutions (e.g., robotics, drones, and distribution automation).
Energy Dispatching Based on an Improved PSO-ACO Algorithm

In order to improve the comprehensive performance of energy dispatching between different sites, the optimization research of particle swarm optimization (PSO) algorithm and ant colony optimization (ACO) algorithm is carried out. We proposed a new improved PSO-ACO algorithm based on the idea of hybrid algorithm to solve the problem of poor energy dispatching efficiency between sites. First, the multiobjective performance indicators were introduced to transform the sites’ energy dispatching problem into a multiobjective optimization problem. Second, the vitality factor was introduced into the PSO strategy to solve the local optimal problem, and in the PSO-ACO fusion strategy, the PSO routes were transformed into the ant colony enhancement pheromone to accelerate the accumulation speed of the ACO initial pheromone. Then, the angle guidance function was introduced into the state transition probability of the ACO strategy to improve the global search capability, and a high-quality pheromone update rule was proposed to improve the convergence speed of the algorithm. Finally, simulation experiments were carried out on the improved PSO-ACO algorithm, Min–Max Ant System (MMAS) algorithm, ACO algorithm, PSO algorithm, and PSO update algorithm in a variety of complex site scenarios. The simulation results show that the improved PSO-ACO algorithm can plan a site energy dispatching route with shorter route, less time-consuming, and higher security and realize the comprehensive and global optimization of energy dispatching.
New Cognitive Deep-Learning CAPTCHA

CAPTCHA (Completely Automated Public Turing test to tell Computers and Humans Apart), or HIP (Human Interactive Proof), has long been utilized to avoid bots manipulating web services. Over the years, various CAPTCHAs have been presented, primarily to enhance security and usability against new bots and cybercriminals carrying out destructive actions. Nevertheless, automated attacks supported by ML (Machine Learning), CNN (Convolutional Neural Network), and DNN (Deep Neural Network) have successfully broken all common conventional schemes, including text- and image-based CAPTCHAs. CNN/DNN have recently been shown to be extremely vulnerable to adversarial examples, which can consistently deceive neural networks by introducing noise that humans are incapable of detecting. In this study, the authors improve the security for CAPTCHA design by combining text-based, image-based, and cognitive CAPTCHA characteristics and applying adversarial examples and neural style transfer. Comprehend usability and security assessments are performed to evaluate the efficacy of the improvement in CAPTCHA. The results show that the proposed CAPTCHA outperforms standard CAPTCHAs in terms of security while remaining usable. Our work makes two major contributions: first, we show that the combination of deep learning and cognition can significantly improve the security of image-based and text-based CAPTCHAs; and second, we suggest a promising direction for designing CAPTCHAs with the concept of the proposed CAPTCHA.
A Deep Learning-Based Semantic Segmentation Model Using MCNN and Attention Layer for Human Activity Recognition

With the development of wearable devices such as smartwatches, several studies have been conducted on the recognition of various human activities. Various types of data are used, e.g., acceleration data collected using an inertial measurement unit sensor. Most scholars segmented the entire timeseries data with a fixed window size before performing recognition. However, this approach has limitations in performance because the execution time of the human activity is usually unknown. Therefore, there have been many attempts to solve this problem through the method of activity recognition by sliding the classification window along the time axis. In this study, we propose a method for classifying all frames rather than a window-based recognition method. For implementation, features extracted using multiple convolutional neural networks with different kernel sizes were fused and used. In addition, similar to the convolutional block attention module, an attention layer to each channel and spatial level is applied to improve the model recognition performance. To verify the performance of the proposed model and prove the effectiveness of the proposed method on human activity recognition, evaluation experiments were performed. For comparison, models using various basic deep learning modules and models, in which all frames were classified for recognizing a specific wave in electrocardiography data were applied. As a result, the proposed model reported the best F1-score (over 0.9) for all kinds of target activities compared to other deep learning-based recognition models. Further, for the improvement verification of the proposed CEF method, the proposed method was compared with three types of SW method. As a result, the proposed method reported the 0.154 higher F1-score than SW. In the case of the designed model, the F1-score was higher as much as 0.184.
A new Apache Spark-based framework for big data streaming forecasting in IoT networks

Analyzing time-dependent data acquired in a continuous flow is a major challenge for various fields, such as big data and machine learning. Being able to analyze a large volume of data from various sources, such as sensors, networks, and the internet, is essential for improving the efficiency of our society’s production processes. Additionally, this vast amount of data is collected dynamically in a continuous stream. The goal of this research is to provide a comprehensive framework for forecasting big data streams from Internet of Things networks and serve as a guide for designing and deploying other third-party solutions. Hence, a new framework for time series forecasting in a big data streaming scenario, using data collected from Internet of Things networks, is presented. This framework comprises of five main modules: Internet of Things network design and deployment, big data streaming architecture, stream data modeling method, big data forecasting method, and a comprehensive real-world application scenario, consisting of a physical Internet of Things network feeding the big data streaming architecture, being the linear regression the algorithm used for illustrative purposes. Comparison with other frameworks reveals that this is the first framework that incorporates and integrates all the aforementioned modules.
Mono Versus Multilingual BERT: A Case Study in Hindi and Marathi Named Entity Recognition

Named entity recognition (NER) is the process of recognizing and classifying important information (entities) in text. Proper nouns, such as a person’s name, an organization’s name, or a location’s name, are examples of entities. The NER is one of the important modules in applications like human resources, customer support, search engines, content classification, and academia. In this work, we consider NER for low-resource Indian languages like Hindi and Marathi. The transformer-based models have been widely used for NER tasks. We consider different variations of BERT like base-BERT, RoBERTa, and AlBERT and benchmark them on publicly available Hindi and Marathi NER datasets. We provide an exhaustive comparison of different monolingual and multilingual transformer-based models and establish simple baselines currently missing in the literature. We show that the monolingual MahaRoBERTa model performs the best for Marathi NER whereas the multilingual XLM-RoBERTa performs the best for Hindi NER. We also perform cross-language evaluation and present mixed observations.
Multi-Objective Robot Path Planning Using an Improved Hunter Prey Optimization Algorithm

This work considers the best path planning algorithm for a mobile robot that travels independently in an unknowable environment.To get around the limitations of unstable searches in the conventional Hunter-Prey Optimization Algorithm (HPO), the improved HPO optimization algorithm is used and introduces a new control parameter referred to as randomization adjustment in order to avoid stagnation and early convergence.The absence of the transfer parameter from exploration and exploitation is a significant flaw in the HPO algorithm, which results in an unstable search and additional time waste.Another new parameter called the changing parameter (CP) is used to address this flaw.It is used in an environment with erratic static and dynamic obstacles and a static and dynamic target.Finding a collision-free path that is also the objectively shortest path and the smoothest path can solve the pathplanning problem.The proposed algorithm attempts to mimic the real world by taking into account the actual size of the mobile robot, a kinematic model, and the robot's specifications.The proposed algorithm is evaluated by comparing it on 30 dimensions using 13 benchmark test functions.The performance of the proposed algorithm is evaluated against the results of five swarm optimization algorithms.According to the results of the standard deviation tests, the proposed algorithm performs the best in 92% of the 13 test functions.Furthermore, the average outcomes for three complex maps (10×10) m in size demonstrate the potency of this approach for robot paths from the starting point to the target.The average distance over ten runs for maps 1, 2, and 3 is 12.6436 meters, 12.4961 meters, and 17.6547 meters, respectively.It demonstrated how quickly and easily it could avoid both stationary and moving obstacles.
cleAR: an interoperable architecture for multi-user AR-based school curricula

Although there are some experiences that demonstrate the validity of the use of augmented reality in schools to help students understand and retain complex concepts, augmented reality has not been widely adopted in the education sector yet. This is in part because it is hard to use augmented reality applications in collaborative learning scenarios and to integrate them in the existing school curricula. In this work, we present an interoperable architecture that simplifies the creation of augmented reality applications, enables multi-user student collaboration and provides advanced mechanisms for data analysis and visualization. A review of the literature together with a survey answered by 47 primary and secondary school teachers allowed us to identify the design objectives of cleAR, an architecture for augmented reality-based collaborative educational applications. cleAR has been validated through the development of three proofs of concept. cleAR provides a more mature technological ecosystem that will foster the emergence of augmented reality applications for education and their inclusion in existing school programs.
Analysis of Intelligent Interfaces Based on Fuzzy Logic in Human-Computer Interaction

Human-Computer Interaction: It is the ability to use the computer interface (almost all software where we can visually click on buttons or menus and write text) that enables communication with the user and the computer. The human factor plays a big role in this field, as it does in every field. The most basic subject that instructional technologists work on is the design and development of educational products and programs that will support learners effectively, efficiently and satisfied and increase their performance. Compared to the past, significant changes have occurred in this regard, and information technologies have become more involved. Human Computer Interaction (HCI) is one of the most important tools of instructional technologists at this point. They can create more effective and satisfying learning environments by using it in their projects in the most effective way. In this article, Analysis of Intelligent Interfaces Based on Fuzzy Logic in Human-Computer Interaction has been applied.
Nanocavity-Integrated van der Waals Heterobilayers for Nano-excitonic Transistor

Optical computing with optical transistors has emerged as a possible solution to the exponentially growing computational workloads, yet an on-chip nano-optical modulation remains a challenge due to the intrinsically noninteracting nature of photons in addition to the diffraction limit. Here, we present an all-optical approach toward nano-excitonic transistors using an atomically thin WSe2/Mo0.5W0.5Se2 heterobilayer inside a plasmonic tip-based nanocavity. Through optical wavefront shaping, we selectively modulate tip-enhanced photoluminescence (TEPL) responses of intra- and interlayer excitons in a ∼25 nm2 area, demonstrating the enabling concept of an ultrathin 2-bit nano-excitonic transistor. We suggest a simple theoretical model describing the underlying adaptive TEPL modulation mechanism, which relies on the additional spatial degree of freedom provided by the presence of the plasmonic tip. Furthermore, we experimentally demonstrate a concept of a 2-trit nano-excitonic transistor, which can provide a technical basis for processing the massive amounts of data generated by emerging artificial intelligence technologies.