Reviewerly/exHarmony huggingface dataset:
A Room Monitoring System Using Deep Learning and Perspective Correction Techniques

Studies on room monitoring have only focused on objects in a singular and uniform posture or low-density groups. Considering the wide use of convolutional neural networks for object detection, especially person detection, we use deep learning and perspective correction techniques to propose a room monitoring system that can detect persons with different motion states, high-density groups, and small-sized persons owing to the distance from the camera. This system uses consecutive frames from the monitoring camera as input images. Two approaches are used: perspective correction and person detection. First, perspective correction is used to transform an input image into a 2D top-view image. This allows users to observe the system more easily with different views (2D and 3D views). Second, the proposed person detection scheme combines the Mask region-based convolutional neural network (R-CNN) scheme and the tile technique for person detection, especially for detecting small-sized persons. All results are stored in a cloud database. Moreover, new person coordinates in 2D images are generated from the final bounding boxes and heat maps are created according to the 2D images; these enable users to examine the system quickly in different views. Additionally, a system prototype is developed to demonstrate the feasibility of the proposed system. Experimental results prove that our proposed system outperforms existing schemes in terms of accuracy, mean absolute error (MAE), and root mean squared error (RMSE).
Processual and Experiential Design in Wearable Music Workshopping

This paper describes a series of workshops in which dancers participated in the design of collectively-playable, computational, wearable musical instruments, exploring ensemble experience in improvised events. We describe the progressive phases of layering and computationally creative learning that occurred during our workshops with epistemically and culturally diverse groups of participants. Our approach is motivated by processual and experiential design, as well as a relational approach using collective sonic mappings. The media instruments we constructed during our workshops are based on trial-and-error development of signal processing symmetrizing action and perception, designed without pre-schematizing, abstract models of user intention or semantics. Hence we outline a pre-reflective, pre-individual, sub-semantic approach to relational media synthesis.
Inversion of soil heavy metals in Guanzhong area of Shaanxi based on VIS-NIR spectroscopy

Abstract This study takes soil Cr, Mn, Ni and Cu from the Guanzhong area as the research object. Using the ASD Fieldspec4 spectrometer to acquire soil spectra and perform different forms of data transformation, and partial least squares regression (PLSR) method was used to establish the calibration-validation model. The results indicated: (i) after the differentiation of the spectrum and the transformation of MSC, NOR, SNV, etc., the signal response strength is enhanced; (ii) The determination coefficients of Cr, Mn, Ni, and Cu are 0.7002, 0.7852, 0.6857, and 0.8036, respectively; (iii) The results show that the spectral predictive model have quickly obtain the distribution of heavy metals in the study area.
Two-stream deep sparse network for accurate and efficient image restoration

Deep convolutional neural network (CNN) has achieved great success in image restoration. However, previous methods ignore the complementarity between low-level and high-level features, thereby leading to limited image reconstruction quality. In this paper, we propose a two-stream sparse network (TSSN) to explicitly learn shallow and deep features to enforce their respective contribution to image restoration. The shallow stream learns shallow features (e.g., texture edges), and the deep stream learns deep features (e.g., salient semantics). In each stream, sparse residual block (SRB) is proposed to efficiently aggregate hierarchical features by constructing sparse connections among layers in the local block. Spatial-wise and channel-wise attention are used to fuse the shallow and deep stream which recalibrates features by weight assignment in both spatial and channel dimensions. A novel loss function called Softmax-L1 loss is proposed to increase penalties of pixels that have large L1 loss (i.e., hard pixels). TSSN is evaluated with three representative IR applications, i.e., single image super-resolution, image denoising and JPEG deblocking. Extensive experiments demonstrate that TSSN outperforms most of state-of-the-art methods on benchmark datasets on both quantitative metric and visual quality.
Invisiborders

We present the design, creation, and evaluation of a wearable technology for critical engagement with invisible borders in everyday civic life. Inspired by contestations over gerrymandering in the United States, Invisiborders engages wearers in embodied experiences of these borders, aiming to provoke reflection on the nature of political borders in daily life. We draw on DiSalvo's agonistic design and Kaye's evaluative techniques for experience-focused HCI in design and deployment of this proof of concept trial.
The Development Of Augmented Reality For Hardware Introduction For SDU Hamzanwadi Students

Abstract The study conducted on the Primary School (SDU) Hamzanwadi with the subject focused on the main informatics on introduction of hardware. Here, the teacher only explained by drawing on the blackboard without being equipped with learning media. The formulation of the problems were: How is the learning media design process in the form of Augmented Reality, What result from product validation both from media experts and material experts, What is the response of users, namely SDU students Hamzanwadi. Research and Development methods. Based on the results of the validation of media experts in the form of Augmented Reality hardware recognition got an average value of a percentage of 93.16 included in category 4 that is “very feasible”, the results of expert validation the material got an average value of a percentage of 94.5 included in category 4 that is “very feasible”. Based on the results of interviews conducted to 5 students, they found it that students enjoyed learning media products in the form of Augmented Reality. The results got show that there are positive responses got from students related to learning media products in the form of Augmented Reality.
Research on Intangible Cultural Heritage Protection Based on Augmented Reality Technology

Abstract With the development and progress of human beings, many forms of civilization are constantly innovating and evolving in their development. The people of our country should pay attention, use augmented reality technology, network computer equipment and scientific technology to simulate the physical objects and through superposition, the corresponding data information content generated in the virtual world to Show it in a way close to reality, so that people can perceive things in virtual reality and get a sensory experience that transcends reality.
Monocular Depth Prediction With Residual DenseASPP Network

Monocular depth estimation is an ill-posed problem because infinite 3D scenes can be projected to the same 2D scenes. Most recent methods focus on image-level information from deep convolutional neural networks, while training them may suffer from slow convergence and accuracy degeneration, especially for deeper network and more feature channels. Based on an encoder-decoder framework, we propose a novel Residual DenseASPP Network. In our Residual DenseASPP network, we define features as low/mid/high vision features and use two-kinds of skip connection to learn useful features with certain layers, where feature concentration in the dense block is used to generate more features in the same layer, and feature summation in the residual block is used to increase backward gradient. The experimental results show that high vision features require more channels by feature concentration, while low/mid vision features need better convergence by feature summation. Experiments show that our proposed approach achieves state-of-the-art performance on both NYUv2 and Make3D datasets.
A Comparative Study on Different Techniques of Sentimental Analysis

Sentimental Analysis is the technique of understanding the emotions embedded in the user response or text, i.e., part of datasets. There are various methods that can be employed to analyze the emotions. This understanding is helpful in the design and development of various products, opinions about fields like politics, entertainment, trade, etc. The main focus of this paper is to understand various techniques that can be implemented for sentimental analysis. The different methods we have considered for comparative analysis in this paper are machine learning techniques, Stanford NLP libraries, sentimental sentence dictionary based on various parameters.
Post-pandemic Science &amp; Technology Education

Much of the world is experiencing a crisis in which many ‘instructional packets’ (SARS-CoV-2 viruses) have commandeered ‘machinery’ of living beings to propagate themselves — regardless of surrounding harms their self-interested purposes may cause. Although they have, indeed, caused massive global disruption, crises linked to hegemonic actors are not uncommon. Capitalists, like viruses, conscript various living and nonliving entities to serve them and, in their persistent — and, generally, highly-successful — pursuit of profit, are said to be responsible for numerous social injustices and much environmental devastation, such as climate disruption and nuclear war (Ripple et al., 2020). Accordingly, like viral pandemics, many suggest that capitalism is a ‘pandemic’ and also must be eliminated — and, some would suggest, replaced with eco-socialist worlds. Capitalism seems, however, to be extremely resilient, often able to survive different crises and, sometimes, capable of emerging even stronger. In this vein, Naomi Klein (2007) suggests that capitalists and others have routinely exploited natural and anthropogenic disasters — using societal destabilization to further implement pro-capitalist policies, often at expense of well-being of many people (e.g., gig workers), societies (e.g., under surveillance) and environments (e.g., climate change). The CoViD-19 pandemic, however, may be a special kind of crisis — perhaps opening doors to more non-capitalist futures. Although enabling, for instance, more for-profit surveillance, it also may have disaggregated capitalist networks to the point of severe weakening and, in doing so, enlightened many people about pre-crisis neoliberal and populist infrastructures that may have contributed to this and other crises. Such conscientization may, in turn, have emboldened many to work for better futures. Given roles of science and technology (S&amp;T) in capitalist empowerment, a natural place for such transformation may be science and technology education. In this paper, a framework for S&amp;T education showing promise in this regard is described and defended. Nevertheless, those wishing societal transformation towards more eco-socialist futures need to engage multiple and diverse living, non-living and symbolic entities in ways that may generate networks supportive of such transformations.
Secure Fine-Grained Data Access Control Over Multiple Cloud Server Based Healthcare Applications

With the flourishing build out of the cloud computing, privacy and security worries of outsourcing data have been increasing to a great extent. Mobile cloud model aids for analyzing the information related with patients data and to give recommendations in healthcare applications. A fine-grained level access management of multi-server cloud information is very essential requirement accomplishing and for implementation of end users application, in mobile cloud computing. Present paper is to illustrate collective focus of fine grained access control of clouds based servers information besides a incontrovertibly mobile cloud users validation system of healthcare industry using ABE algorithm. We as a proposed system will use an auditing technique for fraud detection of data on cloud side. Initially distributed modular and distinguished construction of large-scale virtual mechanisms behavior is illustrated based on distributed monitoring agent and later on feasible, appropriate parallel trust computing system is presented for large data. Evaluation of analysis and results of the experiment confirms the practicability and efficiency of system proposed.
Designing Mobile Training Content: Challenges and Open Issues

Mobile training is an evolution of electronic training and is based on mobile learning technology, which is used to design mobile learning courses. Due to the widespread deployment of mobile devices and the need to remain current with developments in mobile technology, it is important to consider the design of appropriate mobile training content to increase learners' engagement in mobile learning courses. However, studies have emphasized the challenges in this area. Therefore, we conducted a systematic mapping study that offers an overview of the current literature in this domain based on a thorough search of the literature by using a process of selection that involves criteria for inclusion and exclusion, data extraction and synthesis strategies. Of the 194 journal articles identified in the initial search stage, 58 were selected as primary studies; they were published between 2009 and 2019. We applied a classification scheme to answer our research questions. Our study examines the current challenges in the design of mobile training content, identifies the key open issues, determines the trends in publication and emphasizes the most widely researched topics in recent years related to the design of mobile training content. Our study identifies the existing challenges and suggests further work on key open issues. Our study also suggests that, considering the major issues related to pedagogical challenges, the research focus should shift toward the design of attractive, interactive and motivating mobile content that is based on a theoretical framework for mobile training courses, and other technological and managerial challenges that can be addressed should be investigated in order to overcome the existing difficulties in the design of mobile training content and to provide better solutions for the continuity of this research domain.
Identifying faults in the building system based on model prediction and residuum analysis

The energy efficiency of the building HVAC systems can be improved when faults in the running system are known. To this day, there are no cost-efficient, automatic methods that detect faults of the building HVAC systems to a satisfactory degree. This study induces a new method for fault detection that can replace a graphical, user-subjective evaluation of a building data measured on site with an automatic, data-based approach. This method can be a step towards cost-effective monitoring. For this research, the data from a detailed simulation of a residential case study house was used to compare a faultless operation of a building with a faulty operation. We argue that one can detect faults by analysing the properties of residuals of the prediction to the actual data. A machine learning model and an ARX model predict the building operation, and the method employs various statistical tests such as the Sign Test, the Turning Point Test, the Box-Pierce Test and the Bartels-Rank Test. The results show that the amount of data, the type and density of system faults significantly affect the accuracy of the prediction of faults. It became apparent that the challenge is to find a decision rule for the best combination of statistical tests on residuals to predict a fault.
An Analysis of Natural Language Text Relating to Thai Criminal Law

This paper analyses Thailand's criminal law enforcement in chapter 1, Offenses causing death section category section 288 and 289 of title 10 offenses affecting life and body under the Thai Criminal Code. The first part of this paper is using criminal law domain knowledge and supreme court judgment results, to be the initial domain information and result is the rules that humans can understand. The second part of this paper is bringing training data set from the final judgment to train with deep learning methods. Due to the training set which have severe imbalances, the Synthetic Minority Over-Sampling TEchnique (SMOTE) [1] is used to solve this problem. Models are trained on the training set using unidirectional Long Short-Term Memory (LSTM) [2] networks and bidirectional Long Short-Term Memory (BiLSTM) [3] are type of Recurrent Neural Networks (RNN) [2]. The word embeddings of the dataset can be learned while training a deep neural network. BiLSTM average F1 score is higher than LSTM. Pre-trained word embeddings are then used to make the average F1 score higher than before. Finally, using models to predict online crime news, the highest average probability of each model is selected by using Soft Voting as input to the rules. The test results compared with the predictions of our methods with the opinion of the lawyer, corresponding 76%.
Designing for the Emotional Pregnancy

Pregnancy is a transformational journey towards parenthood. Bodily and mental changes are inevitable for the one who is pregnant, and many find this transformation to be challenging. This paper reimagines the emotional pregnancy within the context of a prenatal yoga setting. In doing so, it explores novel approaches to designing interactions in a supportive environment. We introduce an explorative study with three women at different stages in their pregnancy to suggest avenues for design research in support of emotional wellbeing during this time.
Digital Infrastructure Enabling Platforms for Health Information and Education in the Global South

This chapter aims at documenting how digital divide determinants are affecting the ability of digital infrastructure to be used as a platform for health information and education in Sub-Saharan Africa. More specifically, the chapter draws from case studies in Madagascar, Niger, Côte d’Ivoire, Senegal, and Tanzania, and we showcase the best praxis on how digital technologies are used to improve learning and health information usage. Some tested experiments have been successful but rarely scaled up, depriving part of the population—the poorest and those living in rural areas who “consume” few public resources—from opportunities to access improved education and health services. Along with the aforementioned challenges, we identify a need for the key performance indicators (KPIs) related to digital inclusion projects in Global South.
Multi-Carrier Information Hiding Algorithm Based on Angle Structure Descriptor

Aiming at the problem that the embedded capacity and security of the single-carrier information hiding algorithm can not be further improved because of the number of carriers, the carrier pre-processing and the embedding of secret information are combined with the image angle structure descriptor, and a multi-carrier information hiding algorithm based on the angle structure descriptor (ASD) is proposed.Firstly, the angle structure descriptors of the extracted image are used to classify the carrier set, and the image angle structure descriptor direction field coding is obtained.Secondly, the secret information is segmented according to the carrier classification number and the preprocessing such as scrambling and optimization is performed.Finally, based on the angle structure the coded data of the feature direction field performs information hiding on a plurality of types of carriers according to the information hiding rule.In particular, the ''judgment selection'' option is designed during secret information extraction to further improve the performance of the algorithm.The experimental analysis shows that the algorithm has good concealment, strong anti-analysis ability and robustness, and is suitable for applications such as large-capacity secret information covert communication with high security requirements.
28 Days Later

As mobile internet growth continues to bring New Internet Users (NIUs) online, technology has adapted to fit this user segment. User barriers like devices and connectivity have declined as mobile phone prices have become more affordable and infrastructure has continued to develop, connecting more communities globally. App development has also evolved to better suit users on low-cost Android devices. Lite apps have entered the space as a solution for users in constrained environments. While there are many benefits to lite app designs, their effectiveness is unclear for their likely target beneficiaries: NIUs coming online.
ELS: A Fast Parameter-Free Edition Algorithm With Natural Neighbors-Based Local Sets for <i>k</i> Nearest Neighbor

The instance reduction is one of the data preprocessing methods and aims to remove noises and (or) redundant instances from the training set. In the instance reduction, one of the most representative techniques is the edition method which can remove harmful instances from the training set to improve the prediction accuracy of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> nearest neighbor (KNN). Nevertheless, most of existing edition methods still have some drawbacks, such as the parameter dependency, high computational time and relatively low accuracy. To solve these problems, we present a new fast parameter-free edition method based on local sets with natural neighbors (ELS). In ELS, we define a new concept of local sets by introducing natural neighbors. ELS can use the local sets to keep more reasonable class boundaries and effectively filtering out noisy instances (including global outliers). The main advantages of ELS are that (a) it is parameter-free; (b) it can remove global outliers and noisy instances; (c) it is relatively fast. Experiments clearly verify that (a) ELS outperforms existing representative edition methods in improving the prediction accuracy of KNN; (b) ELS can improve the performance of the condensation method and hybrid method in terms of both accuracy and reduction; (c) ELS consumes relatively low running time.
Balancing Rates and Variance via Adaptive Batch-Size for Stochastic Optimization Problems

Stochastic gradient descent is a canonical tool for addressing stochastic optimization problems, which forms the bedrock of modern machine learning. In this work, we seek to balance the fact that attenuating step-size is required for exact convergence with the fact that constant step-size learns faster to a limiting error. To do so, rather than fixing the mini-batch and the step-size at the outset, we propose a strategy to allow parameters evolving adaptively. Specifically, the batch-size is set to be a piecewise-constant increasing sequence where the increase occurs when a suitable error criterion is satisfied. Moreover, the step-size is selected as that which yields the fastest convergence. The overall algorithm, two scale adaptive (TSA) scheme, is developed for both convex and non-convex problems. It inherits the exact convergence and more importantly, the optimal error decreasing rate and an overall computation reduction are achieved. Furthermore, we extended the TSA method to the generalized adaptive batching framework, which is a generic methodology modular to any stochastic algorithms pursuing a trade-off between convergence rates and stochastic variance. We evaluate the TSA method on the image classification problem on MNIST and CIFAR-10 datasets compared with standard SGD methods and existing adaptive batch-size methods, to corroborate theoretical findings.