Reviewerly/exHarmony huggingface dataset:
Effects of Nonlinearity and Network Architecture on the Performance of Supervised Neural Networks

The nonlinearity of activation functions used in deep learning models is crucial for the success of predictive models. Several simple nonlinear functions, including Rectified Linear Unit (ReLU) and Leaky-ReLU (L-ReLU) are commonly used in neural networks to impose the nonlinearity. In practice, these functions remarkably enhance the model accuracy. However, there is limited insight into the effects of nonlinearity in neural networks on their performance. Here, we investigate the performance of neural network models as a function of nonlinearity using ReLU and L-ReLU activation functions in the context of different model architectures and data domains. We use entropy as a measurement of the randomness, to quantify the effects of nonlinearity in different architecture shapes on the performance of neural networks. We show that the ReLU nonliearity is a better choice for activation function mostly when the network has sufficient number of parameters. However, we found that the image classification models with transfer learning seem to perform well with L-ReLU in fully connected layers. We show that the entropy of hidden layer outputs in neural networks can fairly represent the fluctuations in information loss as a function of nonlinearity. Furthermore, we investigate the entropy profile of shallow neural networks as a way of representing their hidden layer dynamics.
Interpreting fitness: self-tracking with fitness apps through a postphenomenology lens

Fitness apps on mobile devices are gaining popularity, as more people are engaging in self-tracking activities to record their status of fitness and exercise routines. These technologies also evolved from simply recording steps and offering exercise suggestions to an integrated lifestyle guide for physical wellbeing, thus exemplify a new era of "quantified self" in the context of health as individual responsibility. There is a considerable amount of literature in science, technology and society (STS) studies looking at this phenomenon from different perspectives, linking it with the sociology of self-surveillance and neoliberal regimes of health. However, the human-technology interface, through which the micro- (behavioral) and macro- (social) aspects converge, still calls for extensive examination. This paper approaches this topic from the postphenomenological perspective, in combination with empirical studies of design analysis and interviews of fitness apps, to reveal the human-technology link between the design elements and people's perception through the direct experiences and interpretations of technology. It argues that the intentionality of self-tracking fitness app designs mediates the human-technology relations by "guiding" people into a quantified knowledge regime. It shapes the perceptions of fitness and health with representations of meanings about a "good life" of individual success and management. This paper also gives a critique of current individual, performance-oriented fitness app designs and offers the possibility of seeking alternatives through the multistable nature of human-technology relations—how altering interpretation and meaning of the design with a cultural or social context could change the form of technological embodiment.
Image Co-Skeletonization via Co-Segmentation

Recent advances in the joint processing of a set of images have shown its advantages over individual processing. Unlike the existing works geared towards co-segmentation or co-localization, in this article, we explore a new joint processing topic: image co-skeletonization, which is defined as joint skeleton extraction of the foreground objects in an image collection. It is well known that object skeletonization in a single natural image is challenging, because there is hardly any prior knowledge available about the object present in the image. Therefore, we resort to the idea of image co-skeletonization, hoping that the commonness prior that exists across the semantically similar images can be leveraged to have such knowledge, similar to other joint processing problems such as co-segmentation. Moreover, earlier research has found that augmenting a skeletonization process with the object’s shape information is highly beneficial in capturing the image context. Having made these two observations, we propose a coupled framework for co-skeletonization and co-segmentation tasks to facilitate shape information discovery for our co-skeletonization process through the co-segmentation process. While image co-skeletonization is our primary goal, the co-segmentation process might also benefit, in turn, from exploiting skeleton outputs of the co-skeletonization process as central object seeds through such a coupled framework. As a result, both can benefit from each other synergistically. For evaluating image co-skeletonization results, we also construct a novel benchmark dataset by annotating nearly 1.8 K images and dividing them into 38 semantic categories. Although the proposed idea is essentially a weakly supervised method, it can also be employed in supervised and unsupervised scenarios. Extensive experiments demonstrate that the proposed method achieves promising results in all three scenarios.
IoT4Emergency

The increasing natural and man-induced disasters such as res, earthquakes, oods, hurricanes, overcrowding, or pandemic viruses endanger human lives. Hence, designing infrastructures to handle those possible crises has become an ever-increasing need. The Internet of Things (IoT) has changed our approach to safety systems by connecting sensors and providing real-time data to managers, rescuers, and endangered people. IoT systems can monitor and react to progressive disasters, people's movements and their behavioral patterns. The community faces challenges in using IoT for crises management: i) how to take advantage of technological advancements and deal with IoT resources installation issues? ii) what environmental contexts should be considered while designing IoT-based emergency handling systems? iii) how should system design comply with various levels of real-time requirements? This paper reports on the results of the First International Workshop on Internet of Things for Emergency Management (IoT4Emergency 2020), which speci cally focuses on challenges and envisioned solutions in using smart connected systems to handle disasters.
Repository Approaches to Improving the Quality of Shared Data and Code

Sharing data and code for reuse has become increasingly important in scientific work over the past decade. However, in practice, shared data and code may be unusable, or published results obtained from them may be irreproducible. Data repository features and services contribute significantly to the quality, longevity, and reusability of datasets. This paper presents a combination of original and secondary data analysis studies focusing on computational reproducibility, data curation, and gamified design elements that can be employed to indicate and improve the quality of shared data and code. The findings of these studies are sorted into three approaches that can be valuable to data repositories, archives, and other research dissemination platforms.
E-Governance: A Tendering Framework Using Blockchain With Active Participation of Citizens

E-governance, i.e., the use of information technology for government activities to provide services, exchange information, etc. are becoming increasingly popular. One such area of e-governance is e-tendering. While e-tendering makes the tendering process more efficient, a trust deficit remains between the citizens and the government, due to the centralized management of the whole tendering process. Several research works provide a decentralized solution to make the process of e-tendering more secure, transparent, and fair. However, in the present time, the government posts the problems/issues currently they are dealing with; and seems interested in taking the suggestions from the citizens to make the better solution for the problem. Thus, in this paper, a permissioned blockchain-based approach to provide a transparent and fair tendering system with citizens' active participation and tracking of funds is presented. In the proposed scheme, the citizens can see the tendering activities transparently and give ideas and suggestions to solve the government's posted problems and receive a due credit of that contribution is a fair manner.
Study on the Personalized Learning Model of Learner-Learning Resource Matching

With the development of service integration technology, online learning platforms have gathered a large number of learning resources, causing learners to get lost in a variety of course information and it is difficult to obtain learning resources that match their own needs. The proposal of personalized learning gives the problem a direction to solve. However, current personalized learning resource recommendation services facing problems such as excessive candidate resources, sparse history and cold starts. In addition, the learning resources provided also show problems of "difficult or easy, uneven quality". For this article researches the personalized learning recommendation model of learner-learning resource matching. The main content includes three parts: First, build a demand model based on learner registration information, learning behavior and other data. Second, analyze the access behavior of learning resources and assess their quality. Third, calculate the matching degree between learners and learning resources based on the demand model and the quality information of the learning resources, and recommend them.
An FPGA-Based On-Device Reinforcement Learning Approach using Online Sequential Learning

DQN (Deep Q-Network) is a method to perform Q-learning for reinforcement learning using deep neural networks. DQNS require a large buffer and batch processing for an experience replay and rely on a backpropagation based iterative optimization, making them difficult to be implemented on resource-limited edge devices. In this paper, we propose a lightweight on-device reinforcement learning approach for low-cost FPGA devices. It exploits a recently proposed neural-network based on-device learning approach that does not rely on the backpropagation method but uses OS-ELM (Online Sequential Extreme Learning Machine) based training algorithm. In addition, we propose a combination of L2 regularization and spectral normalization for the on-device reinforcement learning so that output values of the neural network can be fit into a certain range and the reinforcement learning becomes stable. The proposed reinforcement learning approach is designed for PYNQ-Z1 board as a low-cost FPGA platform. The evaluation results using OpenAI Gym demonstrate that the proposed algorithm and its FPGA implementation complete a CartPole-v0 task 29.77x and 89.40x faster than a conventional DQN-based approach when the number of hidden-layer nodes is 64.
Sparse non-negative transition subspace learning for image classification

Recently many variations of least squares regression (LSR) have been developed to address the problem of over-fitting that widely exists in the task of image classification. Among these methods, the most prevalent two means, such as label relaxation and graph manifold embedding, have been demonstrated to be highly effective. In this paper, we present a new strategy named sparse non-negative transition subspace learning (SN-TSL) based least squares regression algorithm which aims to avoid over-fitting by learning a transition subspace between the multifarious high-dimensional inputs and low-dimensional binary labels. Moreover, considering the final regression targets are sparse binary positive matrices, we use the l1-norm and the non-negativity constraint to enforce the transition subspace to be sparse and non-negative. The resulting subspace features can be viewed as intermediate representations between the inputs and labels. Because SN-TSL can simultaneously learn two projection matrices in one regression model and the dimensionality of the transition subspace can be set to any integer, SN-TSL has the potential to obtain more distinct projections for classification. It is also suitable for classification problems involving a small number of classes. Extensive experiments on public datasets have shown the proposed SN-TSL outperforms other state-of-the-art LSR based image classification methods.
Research on the Correlation between Image Enhancement and Underwater Object Detection

Underwater object detection has a wide range of application scenarios. Due to the characteristics of underwater imaging, the research focus of object detection based on underwater images differs from that of general object detection algorithms. The detailed quality of underwater images can be improved effectively by using the certain image enhancement algorithm, which is helpful for human recognition of underwater objects and object detection using traditional algorithms. In recent years, research on underwater object detection algorithms based on deep learning has gradually become popular, but the influence of image enhancement algorithms on object detection based on deep learning has not yet been systematically studied. In this paper, the correlation between image enhancement and underwater object detection based on deep learning is studied. First, three methods based on image dehazing, UWCNN, and FUNIE-GAN are selected to enhance the images of the URPC dataset, Then, SSD algorithm is adopted to train and evaluate the URPC dataset and the three enhanced dataset respectively, finally, a statistical analysis of the correlation between the changes of image quality parameter after image enhancement and the object detection accuracy is carried out. Experiments show that the average object detection accuracy on the image-enhanced dataset has been improved to a certain extent, but different image quality parameter changes have no obvious statistical correlations to the final detection accuracy. The small increase in the final object detection accuracy may be the result of a combination of several factors.
Outlier-Robust Clustering of Gaussians and Other Non-Spherical Mixtures

We give the first outlier-robust efficient algorithm for clustering a mixture of k statistically separated d - dimensional Gaussians ( k-GMMs). Concretely, our algorithm takes input an ε-corrupted sample from a k-GMM and outputs an approximate clustering that misclassifies at most k <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">O(k)</sup> (ε+η) fraction of the points whenever every pair of mixture components are separated by 1-exp(-poly(k/η)) in total variation distance. This is the statistically weakest possible notion of separation and allows, for e.g., clustering of mixtures with components with the same mean with covariances differing in a single unknown direction or separated in Frobenius distance. The running time of our algorithm is d <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">poly(k/η)</sup> . Such results were not known prior to our work, even for k=2. More generally, our algorithms succeed for mixtures of any distribution that satisfies two well-studied analytic assumptions - sum-of-squares certifiable hypercontractivity and anti-concentration. As an immediate corollary, they extend to clustering mixtures of arbitrary affine transforms of the uniform distribution on the d-dimensional unit sphere. Even the information theoretic clusterability of separated distributions satisfying our analytic assumptions was not known and is likely to be of independent interest. Our algorithms build on the recent flurry of work relying on certifiable anti-concentration first introduced in [1], [2]. Our techniques expand the sum-of-squares toolkit to show robust certifiability of TV-separated Gaussian clusters in data. This involves giving a low-degree sum-of-squares proof of statements that relate parameter (i.e. mean and covariances) distance to total variation distance by relying only on hypercontractivity and anti-concentration.
Next Generation Digital Forensic Investigation Model (NGDFIM) - Enhanced, Time Reducing and Comprehensive Framework

Abstract Rapid technological advancement can have a substantial impact on the process of digital forensic investigation and presents numerous challenges to the investigator. With these challenges, it is imperative to have a standard framework for the digital forensic investigation to be implemented within most incidents. This induces a great stride to formulate a nonspecific framework that may be applied to most digital investigation procedures. The Next Generation Digital Forensic Investigation Model (NGDFIM) formalizes the framework to facilitates the practitioners in the investigation process. This framework could potentially generate more evidence during the incidence response through on-site triage as compared to conventional investigations process. Moreover, the framework diminishes the analysis time and provides the suspect with privacy protection by incorporating custom content imaging.
Ship Detection with Sar Based on Yolo

Synthetic aperture radar (SAR) allows all-weather, day and night surveillance. Thus, it is of great significance for the ship detection and recognition. Because of the SAR special imaging mechanism, it is very difficult to extract the ship features with SAR image for the traditional target detection algorithm. In this paper, we proposed a approach which is composed of you only look once (YOLO) algorithm, sliding window detection strategy, and clustering algorithm. Firstly, the SAR images of GaoFen-3 and training dataset are gathered. Secondly, the experiments about the size of ship detection frame is carried out to find the optimum size of the frame for the training model. Thirdly, the ships are detected initially with YOLO v3 and fast region-based convolutional neural network (Fast-RCNN). Finally, the detected ships are clustered adaptively, and the experimental results of YOLO v3 and Fast-RCNN are compared and discussed at length. Our experimental results demonstrated that our method outperformed Fast-RCNN to detect the ships in the surface sea with low-resolution wide -band SAR images. Therefore, our approach is a robust method to detect the ships in the surface sea with SAR images.
Optical information hiding with non-mechanical ptychography encoding

Since the ptychographic encoding can transform the secret information into series of diffractive patterns optically, it achieves the high security for information hiding. However, traditional ptychographic encoding requires the mechanical movements of the illuminated probe or the hidden object, which accumulates the errors greatly reducing the decoding quality. The errors have been reduced with the way of the single-shot ptychographic encoding, while its reliability and the decoding quality are still unideal. To solve these problems, we propose a non-mechanical ptychography to hide optical information for the first time. The core of this method includes both the non-mechanical ptychographic encoding (NPE) via a spatial light modulator and the decoding algorithm constrained with the phase-only quick respond code (PQR). NPE completely eliminates the errors arising from the ptychographic scanning, and the PQR-constrained decoding algorithm enables the high-quality information extracted from even only a few embedded diffractive patterns. The optical experiments verify that the method can achieve satisfactory robustness performance with the high repeatability, real-time encoding speed and the excellent information fidelity.
Optical multiple information hiding via azimuth multiplexing

We propose an optical information hiding scheme based on azimuth multiplexing. Azimuth is first introduced into optical information security, which effectively improves the space utilization. Also, the security of the system is ensured in many aspects because of the combination of free-space optics and visual cryptography. In the hiding process, multiple secret messages are encoded by visual cryptography and hidden in a cascaded phase structure of different azimuthal states. When extracting, different information can be extracted by rotating pure phase masks to a specific azimuth. The simulations and analysis also demonstrate that the system is robust to common perturbations, such as noise and blocking.
News discourse and the dissemination of knowledge and perspective: From print and monomodal to digital and multisemiotic

The present paper focuses on the changes undergone by news discourse towards its current multisemiotic, digital nature. It is divided into three main sections; in the first one I will focus on how multimodal/multisemiotic developments have been gradually entrenched in news reporting, from 17th-century woodblock engravings in the early news texts to contemporary convergent journalism, with special reference to the increasing interconnection between verbal output and visuals. Secondly, I will illustrate the shift in topics and methodologies undergone by studies on news discourse particularly over the last few years, with attention to (a) news dissemination and consumer perception (b) the new language(s) of news. Finally, I will present and discuss a pilot case study of multimodality in news reporting, focussing on the coverage by The New York Times of the birth and development of the European Union between 1946 and 2009. The study will attempt to bring to the fore the role played by the dynamic interrelationship between image and verbal text in conveying the news stories and, possibly, the underlying perspective of the newspaper itself when informing readers on the steps leading to the shaping of the EU.