Reviewerly/exHarmony huggingface dataset:
Cognitive plausibility in voice-based AI health counselors

Voice-based personal assistants using artificial intelligence (AI) have been widely adopted and used in home-based settings. Their success has created considerable interest for its use in healthcare applications; one area of prolific growth in AI is that of voice-based virtual counselors for mental health and well-being. However, in spite of its promise, building realistic virtual counselors to achieve higher-order maturity levels beyond task-based interactions presents considerable conceptual and pragmatic challenges. We describe one such conceptual challenge-cognitive plausibility, defined as the ability of virtual counselors to emulate the human cognitive system by simulating how a skill or function is accomplished. An important cognitive plausibility consideration for voice-based agents is its ability to engage in meaningful and seamless interactive communication. Drawing on a broad interdisciplinary research literature and based on our experiences with developing two voice-based (voice-only) prototypes that are in the early phases of testing, we articulate two conceptual considerations for their design and use-conceptualizing voice-based virtual counselors as communicative agents and establishing virtual co-presence. We discuss why these conceptual considerations are important and how it can lead to the development of voice-based counselors for real-world use.
Towards Photographic Image Manipulation with Balanced Growing of Generative Autoencoders

We present a generative autoencoder that provides fast encoding, faithful reconstructions (e.g. retaining the identity of a face), sharp generated/reconstructed samples in high resolutions, and a well-structured latent space that supports semantic manipulation of the inputs. There are no current autoencoder or GAN models that satisfactorily achieve all of these. We build on the progressively growing autoencoder model PIONEER , for which we completely alter the training dynamics based on a careful analysis of recently introduced normalization schemes. We show significantly improved visual and quantitative results for face identity conservation in CELEBA-HQ. Our model achieves state-of-the-art disentanglement of latent space, both quantitatively and via realistic image attribute manipulations. On the LSUN Bedrooms dataset, we improve the disentanglement performance of the vanilla PIONEER, despite having a simpler model. Overall, our results indicate that the PIONEER networks provide a way towards photorealistic face manipulation.
Fuzzy clustering‐based image segmentation techniques used to segment <scp>magnetic resonance imaging</scp>/<scp>computed tomography</scp> scan brain <scp>tissues: Comparative</scp> analysis

Abstract Medical images are obtained with computer‐aided diagnosis using electronic devices such as CT scanners and MRI machines. The captured computed tomography (CT)/magnetic resonance imaging (MRI) images typically have limited spatial resolution, low contrast, noise and nonuniform variability in intensity due to environmental effects. Therefore, the distinctions of the objects are blurred, distorted and the meanings of the objects are not quite precise. Fuzzy sets and fuzzy logic are best suited for addressing vagueness and ambiguity. Fuzzy clustering technique has been commonly used for segmentation of images throughout the last decade. This study presents a comparative study of 14 fuzzy‐clustered image segmentation algorithms used in the CT scan and MRI brain image segments. This study used 17 data sets including 4 synthetic data sets, namely, Bensaid, Diamond, Square, and its noisy version, 5 real‐world digital images, and 8 CT scan/MRI brain images to analyze the algorithms. Ground truth images are used for qualitative analysis. Apart from the qualitative analysis, the study also quantitatively evaluated the methods using three validity metrics, namely, partition coefficient, partition entropy, and Fukuyama‐Sugeno. After a thorough and careful review of the results, it is observed that extension of the fuzzy C‐means (EFCM) outperformed every other image segmentation algorithm, even in a noisy environment, followed by kernel‐based FCM σ , the output of which is also very good after EFCM.
Dispersion-Managed Fiber Echo State Network Analogue With High (Including THz) Bandwidth

We propose a design for high (including THz) bandwidth neuromorphic signal processing based on fiber echo state network analogue and demonstrate through numerical modeling its efficiency for distortion mitigation in optical communications with high-order (including 1024-QAM) formats. This is achieved via all-optical implementation of masking and neural functionality by utilizing dispersion and nonlinear properties of the fiber. The design is flexible and format-transparent making it relevant for future communication systems. The model is simple to implement, as it requires only two attenuators and pumps in addition to a traditional nonlinear optical loop mirror (NOLM), and offers a vast range of optimization parameters.
EM-Net: Centerline-Aware Mitochondria Segmentation in EM Images Via Hierarchical View-Ensemble Convolutional Network

Although deep fully convolutional networks have achieved astonishing performance for mitochondria segmentation from electron microscopy (EM) images, they still produce coarse segmentations with discontinuities and false positives. Besides, the need for labor intensive annotations of large 3D volumes and huge memory overhead by 3D models are also major limitations. To address these problems, we introduce a multi-task network named EM-Net, which includes an auxiliary centerline detection task to account for shape information of mitochondria represented by centerline. Therefore, the centerline detection sub-network is able to enhance the accuracy and robustness of segmentation task, especially when only a small set of annotated data are available. To achieve a light-weight 3D network, we introduce a novel hierarchical view-ensemble convolution module to reduce number of parameters, and facilitate multi-view information aggregation. Validations on public benchmark showed state-of-the-art performance by EM-Net. Even with significantly reduced training data, our method still showed quite promising results.
On the rates of convergence of parallelized averaged stochastic gradient algorithms

The growing interest for high-dimensional and functional data analysis led in the last decade to important research developing a consequent amount of techniques. Parallelized algorithms, which consist of distributing and treat the data into different machines, for example, are a good answer to deal with large samples taking values in high-dimensional spaces. We introduce here a parallelized averaged stochastic gradient algorithm, which enables to treat efficiently and recursively the data, and so, without taking care if the distribution of the data into the machines is uniform. The rate of convergence in quadratic mean, as well as the asymptotic normality of the parallelized estimates are given, for strongly and locally strongly convex objectives.
Flexible Distribution-Based Regression Models for Count Data: Application to Medical Diagnosis

Data mining techniques have been successfully utilized in different applications of significant fields, including medical research. With the wealth of data available within the health-care systems, there is a lack of practical analysis tools to discover hidden relationships and trends in data. The complexity of medical data that is unfavorable for most models is a considerable challenge in prediction. The ability of a model to perform accurately and efficiently in disease diagnosis is extremely significant. Thus, the model must be selected to fit the data better, such that the learning from previous data is most efficient, and the diagnosis of the disease is highly accurate. This work is motivated by the limited number of regression analysis tools for multivariate counts in the literature. We propose two regression models for count data based on flexible distributions, namely, the multinomial Beta-Liouville and multinomial scaled Dirichlet, and evaluated the proposed models in the problem of disease diagnosis. The performance is evaluated based on the accuracy of the prediction which depends on the nature and complexity of the dataset. Our results show the efficiency of the two proposed regression models where the prediction performance of both models is competitive to other previously used regression models for count data and to the best results in the literature.
Optical Signal Phase Reconstruction Based on Temporal Transport-of-Intensity Equation

A non-iterative reconstruction scheme of phase modulated optical signals using dispersive media in direct-detection receiver is described. The phase retrieval is achieved by solving the temporal transport-of-intensity equation (TIE). The TIE is solved by the use of Fast Fourier Transform algorithm. Required carrier power added to the signal is examined and it is shown that carrier power larger by 2 to 3 dB than that needed in the Kramers-Kronig receiver. The carrier can be located inside the signal spectrum, lifting the condition that the signal needs to be single-sideband with tightly confined spectrum. The noise immunity in solving the TIE, which will be an issue in practical usage of the scheme, is also discussed.
On The Scalability of Blockchain Systems

Blockchain, as a promising solution to develop secure distributed ledgers, has drawn a huge attention over the last decade. By introducing a pseudonymous payment model with no central authority, blockchain marked the new generation of online payment systems, known as Cryptocurrencies. For most of the existing cryptocurrencies, scalability has become a challenging problem. When dealing with an ever increasing number of users, miners, and transactions, the technology is unable to scale and provide the same performance as centralised systems (e.g. centralised payment systems).Without addressing this fundamental scalability problem, such a promising technology may not be able to be adopted in mainstream. This paper provides an attempt to analyse the scalability of existing blockchain protocols and look at the major factors affecting scalability, namely throughput and latency. We also describe the HTNZ protocol, a new approach to improve the scalability of Satoshi Nakamoto's model [1], validated by experimental results. HTNZ introduces two new components, namely, sideBlock and helper. SideBlock has a slightly different structure of block and increases the number of transactions that can be processed per each interval.
ROI-Based Compression Strategy of 3D MRI Brain Datasets for Wireless Communications

Because of the large amount of medical imaging data, the transmission process becomes complicated in telemedicine applications. Thus, in order to adapt the data bit streams to the constraints related to the limitation of the bandwidths a reduction of the size of the data by compression of the images is essential. Despite the improvements in the field of compression, the transmission itself can also introduce errors. For this reason, it is important to develop an adequate strategy which will help reduce this volume of data without having to introduce some distortion and resist the errors introduced by the channel noise during transmission. Thus, in this paper, we propose a ROI-based coding strategy and unequal bit stream protection to meet this dual constraint. The proposed ROI-based compression strategy with unequal bit stream protection is composed of three parts: the first one allows the extraction of the ROI region, the second one consists of a ROI-based coding and the third one allows an unequal protection of the ROI bit stream. First, the Regions Of Interest (ROI) are extracted by hierarchical segmentation of these regions according to a segmentation method based on the technique of Marker-based-watershed combined with the technique of active contours by level set. The resulting regions are selectively encoded by a 3D coder based on a shape adaptive discrete wavelet transform 3D-BISK, where the compression ratio of each region depends on its relevance in diagnosis. These obtained regions of interest are protected with an error-correcting code of Reed-Solomon type with a code rate that varies according to the relevance of the region by an unequal protection strategy (UEP). The performance of the proposed compression scheme is evaluated in several ways. First, tests are performed to study the impact of errors on the different bit streams. In the first place, these tests are carried out in order to study the effect of the variation of the compression rates on the different bit streams. Secondly, different Reed Solomon error-correcting codes of different code rates are tested at different compression rates on a BSC channel. Finally, the performances of this coding strategy are compared with those of SPIHT 3D in the case of transmission on a BSC channel. The obtained results show that the proposed method is quite efficient in transmission time reduction. Therefore, our proposed scheme will reduce the volume of data without having to introduce some distortion and resist the errors introduced by the channel noise in the case of telemedicine.
Fashion Landmark Detection and Category Classification for Robotics

The following topics are dealt with: mobile robots; learning (artificial intelligence); robot vision; path planning; multi-robot systems; robot programming; image classification; object detection; medical robotics; image colour analysis.
Breast Lesion Segmentation in Ultrasound Images with Limited Annotated Data

Ultrasound (US) is one of the most commonly used imaging modalities in both diagnosis and surgical interventions due to its low-cost, safety, and non-invasive characteristic. US image segmentation is currently a unique challenge because of the presence of speckle noise. As manual segmentation requires considerable efforts and time, the development of automatic segmentation algorithms has attracted researchers' attention. Although recent methodologies based on convolutional neural networks have shown promising performances, their success relies on the availability of a large number of training data, which is prohibitively difficult for many applications. Therefore, in this study we propose the use of simulated US images and natural images as auxiliary datasets in order to pre-train our segmentation network, and then to fine-tune with limited in vivo data. We show that with as little as 19 in vivo images, fine-tuning the pre-trained network improves the dice score by 21% compared to training from scratch. We also demonstrate that if same number of natural and simulation US images is available, pre-training on simulation data is preferable.
Depth-bounded Belief functions

This paper introduces and investigates Depth-bounded Belief functions, a logic-based representation of quantified uncertainty. Depth-bounded Belief functions are based on the framework of Depth-bounded Boolean logics [4], which provide a hierarchy of approximations to classical logic. Similarly, Depth-bounded Belief functions give rise to a hierarchy of increasingly tighter lower and upper bounds over classical measures of uncertainty. This has the rather welcome consequence that “higher logical abilities” lead to sharper uncertainty quantification. In particular, our main results identify the conditions under which Dempster-Shafer Belief functions and probability functions can be represented as a limit of a suitable sequence of Depth-bounded Belief functions.
Applying AR-based infographics to enhance learning of the heart and cardiac cycle in biology class

This study is based on assessing the potential of augmented reality (AR) and infographics in science learning, emphasizing the synergistic effect on improving learning. The study investigates the impact of the combination of AR and infographic design on learning biology. Seventy-five 10th grade male students participated in this study, which employed a pre-test and post-test experimental design. After administering the pre-test, participants were randomly assigned to three groups; the first group used static infographics, the second group used infographics combined with AR, and the third group learned in an AR-based environment. While the results showed no significant difference between the mean of learning scores in the experimental Group 1 (static infographics) and the experimental Group 3 (AR), there was a significant difference between the experimental Group 2 (infographic method with AR combination) and other groups. According to the results, the infographic with AR had significant effects on learning. Some of the reasons for the superiority of this method compared to other methods used in other groups are as follows: visualizing the data, infographic illustration with 3D AR representation, the use of organizer images and, at the same time, reducing the complexity of AR with the help of infographic visualisation.
High-Precision Portrait Classification Based on MTCNN and Its Application on Similarity Judgement

Abstract Portrait classification is a complex course including at least face detection, recognization and compare each of which contains multi-tasks, facing plenty of various challenging questions due to askew poses, illuminations, occlusions, image blurring and small scale face in the pictures. Though deep learning methods, such as Convolutional Neural Network (CNN) family and You Only Look Once (YOLO) series, had boomed a large number of areas on object detection and accelerated the solving of these difficulties on image processing, they are not specially designed for the image classification and may require a great deal of resource, expensive computation and taxing annotation. In 2016, an innovative face detection model named Multi-task convolutional neural network (MTCNN) arose and triggered viral and wide spread. Its high efficient and accurate performance on both face detection and face alignment tasks, real time effect based on lightweight CNN as well as effective conducting online hard sample mining, all contribute to significant improvement to the challenges above. This paper introduces the MTCNN algorithm and applies it to the similarity judgement with two industrial real problems together with FaceNet model. In addition, some effective practical methods on increase precision of classification are also proposed to gain better effect.
Self-directed Multimodal Learning to Support Demiurgic Access

This chapter provides a framework—based on the theoretical background of self-directed multimodal demiurgic learning—that supports students to be able to act as content creators within higher education. This critical literature review shows how self-directed learning and multimodality provide affordances towards extending formal and epistemological access in order for students to take charge of their own learning and play a prominent role in generating new content. This chapter makes recommendations through a wider framework and description of a learning cycle towards enabling demiurgic access through self-directed multimodal learning within a South African university context.
Breast Cancer Image Classification based on CNN and Bit-Plane slicing

In this paper we propose a CNN classifier base on image bit-plane slicing. The purpose is to improve recognition accuracy when we apply it to breast cancer images classification. Each texture image is decomposed into eight bit-plane images. Different bit-planes provide different levels and detail of image texture feature. At the same time, we have also tested feature classification performance by each bit-plane respectively and the fusion of all bit-planes. CNN classifier is used for classification and recognition. The simulation results on the breast cancer image datasets show that the proposed method on some bit-plane can greatly improve recognition rate and promote classification performance.
Super-Resolution of Thermal Images Using an Automatic Total Variation Based Method

The relatively poor spatial resolution of thermal images is a limitation for many thermal remote sensing applications. A possible solution to mitigate this problem is super-resolution, which should preserve the radiometric content of the original data and should be applied to both the cases where a single image or multiple images of the target surface are available. In this perspective, we propose a new super-resolution algorithm, which can handle either single or multiple images. It is based on a total variation regularization approach and implements a fully automated choice of all the parameters, without any training dataset nor a priori information. Through simulations, the accuracy of the generated super-resolution images was assessed, in terms of both global statistical indicators and analysis of temperature errors at hot and cold spots. The algorithm was tested and applied to aerial and terrestrial thermal images. Results and comparisons with state-of-the-art methods confirmed an excellent compromise between the quality of the high-resolution images obtained and the required computational time.
Development of a mini-tensile approach for sheet metal testing using Digital Image Correlation

The miniature uniaxial tensile testing is a simple yet powerful method to characterise the behaviour of a material. Based on this technique, an experimental system was developed aiming the determination of sheet metal mechanical properties, using the Digital Image Correlation (DIC) technique. A tensile-compression loading device was designed with a maximum load capacity of 2.5 kN. The device includes custom-made clamps that guarantee the alignment of the sample with respect to the loading axis. To evaluate the aptitude of miniature samples and the use of Digital Image Correlation (DIC) in this type of scale, some tensile tests were performed using different types of miniaturised geometries and the results were compared with a macro specimen reference. Additionaly, some cyclic tests were also performed, in order to evaluate the possibilities of developed prototype on the tensioncompression loading paths.
The Google Lens analyzing quality: an analysis of the possibility to use in the educational process

Biology is a fairly complicated initial subject because it involves knowledge of biodiversity. Google Lens is a unique, mobile software that allows you to recognition species and genus of the plant student looking for. The article devoted to the analysis of the efficiency of the functioning of the Google Lens related to botanical objects. In order to perform the analysis, botanical objects were classified by type of the plant (grass, tree, bush) and by part of the plant (stem, flower, fruit) which is represented on the analyzed photo. It was shown that Google Lens correctly identified plant species in 92.6% cases. This is a quite high result, which allows recommending this program using during the teaching. The greatest accuracy of Google Lens was observed under analyzing trees and plants stems. The worst accuracy was characterized to Google Lens results of fruits and stems of the bushes recognizing. However, the accuracy was still high and Google Lens can help to provide the researches even in those cases. Google Lens wasn’t able to analyze the local endemic Ukrainian flora. It has been shown that the recognition efficiency depends more on the resolution of the photo than on the physical characteristics of the camera through which they are made. In the article shown the possibility of using the Google Lens in the educational process is a simple way to include principles of STEM-education and “New Ukrainian school” in classes.