Reviewerly/exHarmony huggingface dataset:
Reflections from Practical Experiences of Managing Participatory Media Platforms for Development

The design of participatory media platforms can have many variations in terms of whether or not anonymous communication is allowed, what moderation policies are in use, the conversation model that is supported, etc. We use six case-studies of voice-based ICT4D participatory media platforms running in India to outline a typology of design axes that can be used to characterize these platforms. Such typologies can be useful for ICT4D practitioners to conceptualize suitable designs for their platforms in different contexts, and to reconfigure the platforms in case design changes are required. We also use this typology to discuss the design constraints and flexibilities allowed by Internet based social media platforms.
Towards Untrusted Social Video Verification to Combat Deepfakes via Face Geometry Consistency

Deepfakes can spread misinformation, defamation, and propaganda by faking videos of public speakers. We assume that future deepfakes will be visually indistinguishable from real video, and will also fool current deepfake detection methods. As such, we posit a social verification system that instead validates the truth of an event via a set of videos. To confirm which, if any, videos are being faked at any point in time, we check for consistent facial geometry across videos. We demonstrate that by comparing mouth movement across views using a combination of PCA and hierarchical clustering, we can detect a deepfake with subtle mouth manipulations out of a set of six videos at high accuracy. Using our new multi-view dataset of 25 speakers, we show that our performance gracefully decays as we increase the number of identically faked videos from different input views.
CT kernel conversions using convolutional neural net for super-resolution with simplified squeeze-and-excitation blocks and progressive learning among smooth and sharp kernels

Computed tomography (CT) volume sets reconstructed with different kernels are helping to increase diagnostic accuracy. However, several CT volumes reconstructed with different kernels are difficult to sustain, due to limited storage and maintenance issues. A CT kernel conversion method is proposed using convolutional neural networks (CNN). A total of 3289 CT images from ten patients (five men and five women; mean age, 63.0 ± 8.6 years) were obtained in May 2016 (Somatom Sensation 16, Siemens Medical Systems, Forchheim, Germany). These CT images were reconstructed with various kernels, including B10f (very smooth), B30f (medium smooth), B50f (medium sharp), and B70f (very sharp) kernels. Smooth kernel images were converted into sharp kernel images using super-resolution (SR) network with Squeeze-and-Excitation (SE) blocks and auxiliary losses, and vice versa. In this study, the single-conversion model and multi-conversion model were presented. In case of the single-conversion model, for the one corresponding output image (e.g., B10f to B70), SE-Residual blocks were stacked. For the multi-conversion model, to convert an image into several output images (e.g., B10f to B30f, B50f, and B70f, and vice versa), progressive learning (PL) was employed by calculating auxiliary losses in every four SE-Residual blocks. Through auxiliary losses, the model could learn mutual relationships between different kernel types. The conversion quality was evaluated by the root-mean-square-error (RMSE), structural similarity (SSIM) index and mutual information (MI) between original and converted images. The RMSE (SSIM index , MI) of the multi-conversion model was 4.541 ± 0.688 (0.998 ± 0.001 , 2.587 ± 0.137), 27.555 ± 5.876 (0.944 ± 0.021 , 1.735 ± 0.137), 72.327 ± 17.387 (0.815 ± 0.053 , 1.176 ± 0.096), 8.748 ± 1.798 (0.996 ± 0.002 , 2.464 ± 0.121), 9.470 ± 1.772 (0.994 ± 0.003 , 2.336 ± 0.133), and 9.184 ± 1.605 (0.994 ± 0.002 , 2.342 ± 0.138) in conversion between B10f–B30f, B10f–B50f, B10f–B70f, B70f–B50f, B70f–B30f, and B70f–B10f, respectively, which showed significantly better image quality than the conventional model. We proposed deep learning-based CT kernel conversion using SR network. By introducing simplified SE blocks and PL, the model performance was significantly improved.
Filtering Turkish Spam Using LSTM From Deep Learning Techniques

E-mails are used effectively by people or communities who want to do propaganda, advertisement, and phishing because of their ease of use and low cost. People or communities who want to achieve their goals send unnecessary and spam to the e-mail accounts they never knew. These mails cause serious financial and moral damages to internet users and also engage in internet traffic. Unsolicited e-mails (spam) are a method sent to the recipient without their consent and generally for malicious or promotional purposes. In this study, spam was detected with Keras deep learning library on the Turkish dataset. Turkish email dataset contains 800 e-mails, half of which are spam e-mails. With the deep learning algorithm long short term memory (LSTM), a 100% accuracy rate has been achieved in the Turkish e-mail dataset.
Bitstream Modification Attack on SNOW 3G

SNOW 3G is one of the core algorithms for confidentiality and integrity in several 3GPP wireless communication standards, including the new Next Generation (NG) 5G. It is believed to be resistant to classical cryptanalysis. In this paper, we show that SNOW 3G can be broken by a fault attack based on bitstream modification. By changing the content of some look-up tables in the bitstream, we reduce the non-linear state updating function of SNOW 3G to a linear one. As a result, it becomes possible to recover the key from a known plaintext-ciphertext pair. To our best knowledge, this is the first successful bitstream modification attack on SNOW 3G.
Scale Equivariant CNNs with Scale Steerable Filters

Convolution Neural Networks (CNNs), despite being one of the most successful image classification methods, are not robust to most geometric transformations (rotation, isotropic scaling) because of their structural constraints. Recently, scale steerable filters have been proposed to allow scale invariance in CNNs. Although these filters enhance the network performance in scaled image classification tasks, they cannot maintain the scale information across the network. In this paper, this problem is addressed. First, a CNN is built with the usage of scale steerable filters. Then, a scale equivariat network is acquired by adding a feature map to each layer so that the scale-related features are retained across the network. At last, by defining the cost function as the cross entropy, this solution is evaluated and the model parameters are updated. The results show that it improves the perfromance about 2% over other comparable methods of scale equivariance and scale invariance, when run on the FMNIST-scale dataset.
A Novel Resource Discovery Mechanism using Sine Cosine Optimization Algorithm in Cloud

Cloud computing, which is internet-based computing share cloud resources such as storage and network on demand. Resource Management plays a crucial role in cloud infrastructure. Management of cloud resources consists of discovering the suitable resources for processing the user request. Millions of users are using cloud resources. To process the request optimally submitted by the user, a novel resource discovery mechanism is required. In our work, sine cosine algorithm (SCA) based clustering is integrated with the resource discovery mechanism. The proposed clustering mechanism is compared with a well known-clustering algorithm called K-means and SCAK-Means. Metrics used for comparing SCAK-Means with K-Means are Intra Cluster similarity, Inter-Cluster similarity, the similarity of cloud resources, and convergence rate. The result shows that the proposed approach is optimal than the existing one for resource discovery.
Relative Position and Map Networks in Few-shot Learning for Image Classification

Few-shot learning is an important research topic in image classification, which aims to train robust classifiers to categorize images coming from new classes where only a few labeled samples are available. Recently, metric learning based methods have achieved promising performance, and in those methods a distance metric is learned to directly compare query images against training samples. In this work, we consider finer information from image feature maps and propose a new approach. Specifically, we newly develop Relative Position Network (RPN) based on the attention mechanism to compare different pairs of activation cells from each query and training images, which captures their intrinsic correspondences. Moreover, we introduce Relative Map Network (RMN) to learn a distance metric based on the attention maps obtained from RPN, which better measures the similarity between query and training images. Extensive experiments demonstrate the effectiveness of our proposed method. Our codes will be released at https://github.com/chrisyxue/RMN-RPN-for-FSL.
Pattern Recognition on Radar Images Using Augmentation

The text describes different models of convolutional neural networks (CNN) using to multiclass recognition of objects on radar images. It was suggested to use data augmentation methods because of limited size of source database. The main augmentation types are as follows: additive white Gaussian noise, blur, motion, shift, scale, grid distortion, brightness changing, flip, rotate and etc. A comparative analysis of recognition accuracy for the CNN based on simplest gradient descent (SGD) and adaptive moment estimation (ADAM) optimization techniques with different training duration time (number of epochs) and the neural network architecture is performed. After applied standard transformations to source images the size of source samples was increased. One of CNN provided excellent accuracy on the all images in dataset despite of its belonging to the training, validation or test sample. The developed CNN allows recognizing planes, roads and ships with quite good accuracy when the task condition is to recognize only one main pattern on the image.
Elastic Resource Provisioning Using Data Clustering in Cloud Service Platform

Currently, cloud computing has received great attention in commerce and scientific research due to its flexibility and strong data processing capability. However, in view of the fact that the types of tasks display an upward trend as the growth of service demands, and the different types of tasks arrive at the system without regularity. Moreover, the resources deployed in cloud are insufficiency to be flexibly provisioned in the face of obvious workload fluctuations. In this article, we present a method of elastic resource provisioning using date clustering in cloud service platform. The framework of proposed method consists of three core components: tasks clustering, the amount of tasks prediction in cluster, dynamic resource provisioning and scheduling. In workload classification, we propose a clustering ensemble method, which utilizes a novel distance decision-making method to obtain the final results. Our method can effectively partition the arriving tasks into several clusters based on similarity among tasks. For each cluster, we forecast the amount of tasks arriving at next moment by prediction model based on time-series to provide reference for the follow-up resource provisioning. Afterwards, an energy-saving resource provisioning method is designed to dynamically provide resources for tasks in each cluster to meet their performance requirements. We implement the experiments in Google cloud traces dataset and the results show that our method achieves 92.3, 91.2 percent, and 3679.2 kW<inline-formula><tex-math notation="LaTeX">$ \cdot$</tex-math><alternatives><mml:math><mml:mo>&#x00B7;</mml:mo></mml:math><inline-graphic xlink:href="zhu-ieq1-3002755.gif"/></alternatives></inline-formula>h respectively in terms of guarantee ratio, resource utilization and total energy consumption, which demonstrates the effectiveness of proposed method for dynamic resource provisioning.
Bringing intelligence to IoT Edge: Machine Learning based Smart City Image Classification using Microsoft Azure IoT and Custom Vision

Abstract Object detection, identification and classification techniques have seen many variants and improvements over past two decades. Together with Internet of Things (IoT) devices, improved computational algorithms and cloud support, real-time classification with low-cost devices has already been achieved. This paper discusses the real-time object detection and classification using Microsoft Custom Vision multi-class Machine Learning (ML) model operating at the Edge of IoT network. This paper further examines the use of virtual dockers or containers at the IoT edge devices for better security and isolation by decoupling physical hardware as well that supports multiple applications and services on a single hardware. The experiments are performed using emulated and simulated IoT devices on Microsoft Azure IoT platform for real-time object classification using Custom Vision Machine Learning (ML) models run directly from the edge device. The experimental results are further discussed to validate the model accuracy and its implementation in a future Smart City surveillance environment.
Image Colorization Using Generative Adversarial Networks and Transfer Learning

Automatic colorizing is one of the most interesting problems in computer graphics. During the colorization process, the gray one-dimensional images are converted to three-dimensional images with colored components. As a typical technique, Convolutional neural networks (CNNs) have been well studied and used for automatic coloring. In these networks, the information that is generalized over in the top layers is available in intermediate layers. Although the output of the last layer of CNNs is usually used in many applications, in this paper, we use a concept called "Hypercolumn" derived from neuroscience to exploit information at all levels to develop a fully automated image colorization system. There are not always millions of data available in the real world to train complex deep learning models. Therefore, the VGG19 model trained with the big data set of ImageNet is used as a pre-trained model in the generator network and the hypercolumn idea is implemented in it with DIV2K datasets. We train our model to predict each pixel’s color texture. The results obtained indicate that the proposed method is superior to competing models.
Participation through substituting and refusing

Asking participants to explore and critique prototype technologies by using them in the real-world settings of their communities is a common approach to democratizing design decisions. We report on a project that sought to seed a rural community radio station, and simultaneously test and refine an innovative radio technology. Living on a remote Irish island, community members saw value in the local radio contributing to the island's sustainability and oral history preservation. The novel, but not yet stable, prototype radio platform, however, did not enable them to produce content within the time constraints and quality requirements they considered vital to their goals. In response community members substituted the prototype platform with a more stable and familiar setup. We reflect on participants' refusal and argue that their technology substitution helped us not only to understand user and technical needs, but also meanings about participation that are integral to ensuring the genuine design democratisation.
Implementation of developmental education in the digital learning environment

The problem of the formation of a pedagogical basis for the control of training in a digital learning environment is considered. It is proposed to model psychological and pedagogical processes on the basis of developmental education. The choice of the theory of developmental education is due to the need to ensure high rates of individualization of the educational process. The research is aimed at the development of theoretical, methodological and applied solutions in the field of disruptive education..
The effect of presence in virtual reality video on handwashing intention

Virtual reality technology has been positioned as a useful tool for the promotion of public health. This study explored how exposure to a virtual reality handwashing promotion video influences users’ handwashing intention. Structural equation modeling analysis with data collected from college students in South Korea revealed that presence in the virtual reality environment was positively correlated with flow. We also found that flow played an important role in increasing levels of message acceptance and fear responses related to the content of the handwashing promotion video. Moreover, the greater the level of message acceptance of the handwashing promotion content, the more favorable users’ attitude toward handwashing. This, in turn, helped facilitate handwashing intention. The findings of the current study further our understanding of the psychological mechanism driving the impact of virtual reality as a health promotion tool for fostering people’s intention to engage in preventive health behavior.
Likelihood Maximization and Moment Matching in Low <scp>SNR</scp> Gaussian Mixture Models

Abstract We derive an asymptotic expansion for the log‐likelihood of Gaussian mixture models (GMMs) with equal covariance matrices in the low signal‐to‐noise regime. The expansion reveals an intimate connection between two types of algorithms for parameter estimation: the method of moments and likelihood optimizing algorithms such as Expectation‐Maximization (EM). We show that likelihood optimization in the low SNR regime reduces to a sequence of least squares optimization problems that match the moments of the estimate to the ground truth moments one by one. This connection is a stepping stone towards the analysis of EM and maximum likelihood estimation in a wide range of models. A motivating application for the study of low SNR mixture models is cryo‐electron microscopy data, which can be modeled as a GMM with algebraic constraints imposed on the mixture centers. We discuss the application of our expansion to algebraically constrained GMMs, among other example models of interest. © 2022 The Authors. Communications on Pure and Applied Mathematics published by Wiley Periodicals LLC.
Linearly equivalent S-boxes and the division property

Abstract Division property is a cryptanalysis method that proves to be very efficient on block ciphers. Computer-aided techniques such as MILP have been widely and successfully used to study various cryptanalysis techniques, and it especially led to many new results for the division property. Nonetheless, we claim that the previous techniques do not consider the full search space. We show that even if the previous techniques fail to find a distinguisher based on the division property over a given function, we can potentially find a relevant distinguisher over a linearly equivalent function. We show that the representation of the block cipher heavily influences the propagation of the division property, and exploiting this, we give an algorithm to efficiently search for such linear mappings. As a result, we exhibit a new distinguisher over 10 rounds of , while the previous best was over 9 rounds, and rule out such a distinguisher over more than 9 rounds of . We also give some insight about the construction of an S-box to strengthen a block cipher against our technique. We prove that using an S-box satisfying a certain criterion is optimal in term of resistance against classical division property. Accordingly, we exhibit stronger variants of and , improving the resistance against division property based distinguishers by 2 rounds.
A no-free-lunch theorem for multitask learning

Multitask learning and related areas such as multisource domain adaptation address modern settings where data sets from N related distributions {Pt} are to be combined toward improving performance on any single such distribution D. A perplexing fact remains in the evolving theory on the subject: while we would hope for performance bounds that account for the contribution from multiple tasks, the vast majority of analyses result in bounds that improve at best in the number n of samples per task, but most often do not improve in N. As such, it might seem at first that the distributional settings or aggregation procedures considered in such analyses might be somehow unfavorable; however, as we show, the picture happens to be more nuanced, with interestingly hard regimes that might appear otherwise favorable. In particular, we consider a seemingly favorable classification scenario where all tasks Pt share a common optimal classifier h∗, and which can be shown to admit a broad range of regimes with improved oracle rates in terms of N and n. Some of our main results are: ∙ We show that, even though such regimes admit minimax rates accounting for both n and N, no adaptive algorithm exists, that is, without access to distributional information, no algorithm can guarantee rates that improve with large N for n fixed. ∙ With a bit of additional information, namely, a ranking of tasks {Pt} according to their distance to a target D, a simple rank-based procedure can achieve near optimal aggregations of tasks’ data sets, despite a search space exponential in N. Interestingly, the optimal aggregation might exclude certain tasks, even though they all share the same h∗.
Designing interactive infographics to stimulate environmental awareness: an exploration with a University community

The Sustainable Development Goals (SDGs) are a universal call to action addressed not only to governments but also to businesses and civil society. In this scenario, Universities play a key role in achieving the SDGs and, also, in creating awareness about these global issues. As an example, the University of Bologna is carrying out several initiatives related to sustainability matters. Considering, in particular, the environmental dimension, the University undertook different strategies to reduce the waste of paper, embracing the dematerialization and paperless movements. To make these initiatives visible to the University community and to increase awareness about these issues, we designed a system that intends to provide data related to the implications of such initiatives in terms of paper waste avoided and benefits for the environment, to be exploited using different devices. In particular, this study focuses on the design and the evaluation of two infographics, conceived by two different groups of experts and researchers: one “animated” and one “aesthetic”. The evaluation intends to present the differences in terms of perceived design and informative dimensions, with the final aim to present insights on the design of infographics able to foster awareness, targeting specific communities.