Reviewerly/exHarmony huggingface dataset:
The Participant Journey Map: Understanding the Design of Interactive Augmented Play Spaces

Augmented Play Spaces (APS) are (semi-) public environments where playful interaction is facilitated by enriching the existing environment with interactive technology. APS can potentially facilitate social interaction and physical activity in (semi-)public environments. In controlled settings APS show promising effects. However, people’s willingness to engage with APS in situ , depends on many factors that do not occur in aforementioned controlled settings (where participation is obvious). To be able to achieve and demonstrate the positive effects of APS when implemented in (semi-)public environments, it is important to gain more insight in how to motivate people to engage with them and better understand when and how those decisions can be influenced by certain (design) factors. The Participant Journey Map (PJM) was developed following multiple iterations. First, based on related work, and insights gained from previously developed and implemented APS, a concept of the PJM was developed. Next, to validate and refine the PJM, interviews with 6 experts with extensive experience with developing and implementing APS were conducted. The first part of these interviews focused on influential (design) factors for engaging people into APS. In the second part, experts were asked to provide feedback on the first concept of the PJM. Based on the insights from the expert interviews, the PJM was adjusted and refined. The Participant Journey Map consists of four layers: Phases, States, Transitions and Influential Factors. There are two overarching phases : ‘Onboarding’ and ‘Participation’ and 6 states a (potential) participant goes through when engaging with an APS: ‘Transit,’ ‘Awareness,’ ‘Interest,’ ‘Intention,’ ‘Participation,’ ‘Finishing.’ Transitions indicate movements between states. Influential factors are the factors that influence these transitions. The PJM supports directions for further research and the design and implementation of APS. It contributes to previous work by providing a detailed overview of a participant journey and the factors that influence motivation to engage with APS. Notable additions are the detailed overview of influential factors, the introduction of the states ‘Awareness,’ ‘Intention’ and ‘Finishing’ and the non-linear approach. This will support taking into account these often overlooked, key moments in future APS research and design projects. Additionally, suggestions for future research into the design of APS are given.
The impact of information security initiatives on supply chain robustness and performance: an empirical study

Purpose Information security is an essential element in all business activities. The damage to businesses from information security breaches has become pervasive. The scope of information security has widened as information has become a critical supply chain asset, making it more important to protect the organization’s data. Today’s global supply chains rely upon the speedy and robust dissemination of information among supply chain partners. Hence, processing of accurate supply chain information is quintessential to ensure the robustness and performance of supply chains. An effective information security management (ISM) is deemed to ensure the robustness of supply chains. The purpose of the paper is to examine the impact of information security initiatives on supply chain robustness and performance. Design/methodology/approach Based on extant literature, a research model was developed and validated using a questionnaire survey instrument administered among information systems/information technology managers. Data collected were analyzed using exploratory and confirmatory factor analysis. Further, to test the hypotheses and to fit the theoretical model, Structural equation modeling techniques were used. Findings Results of this study indicated that information security initiatives are positively associated with supply chain robustness and performance. These initiatives are likely to enhance the robustness and performance of the supply chains. Originality/value With the advancements in internet technologies and capabilities as well as considering the dynamic environment of supply chains, this study is relevant in terms of the capability that an organization needs to acquire with regards to ISM. Benefiting from the resource dependency theory, information security initiatives could be considered as a critical resource having an influence on the internal and external environment of supply chains.
SieveNet: Decoupling activation function neural network for privacy-preserving deep learning

Machine Learning as a Service (MLaaS) is increasingly popular but processing prediction requests will cause users to expose potentially sensitive information. Thanks to breakthrough in secure multi-party computation(MPC), privacy-preserving machine learning has attracted more attention. It allows outsourcing calculations to untrusted servers while maintaining data privacy. Some works proposed frameworks for secure prediction, but with high computation and communication overhead. This is the cost for MPC to deal with non-linear functions, and the activation functions that are widely used in neural networks are usually non-linear. We present a practical framework to perform privacy-preserving prediction. We first propose a new component as an alternative to the activation function so that in one forward propagation, the neural network can be regarded as a linear model. We call this component Sieve Layer and the corresponding network SieveNet. We then show how to use additive secret sharing and adversarial training to build privacy-preserving prediction framework based on SieveNet, and we report a comprehensive analysis of information leakage according to specific types of attacker. Finally, we evaluate our framework on MNIST, CIFAR-10 and CIFAR-100. The results show that the prediction time of our framework is in the same order of magnitude as the plaintext inference.
Efficient Lottery Ticket Finding: Less Data is More

The lottery ticket hypothesis (LTH) reveals the existence of winning tickets (sparse but critical subnetworks) for dense networks, that can be trained in isolation from random initialization to match the latter's accuracies. However, finding winning tickets requires burdensome computations in the train-prune-retrain process, especially on large-scale datasets (e.g., ImageNet), restricting their practical benefits. This paper explores a new perspective on finding lottery tickets more efficiently, by doing so only with a specially selected subset of data, called Pruning-Aware Critical set (PrAC set), rather than using the full training set. The concept of PrAC set was inspired by the recent observation, that deep networks have samples that are either hard to memorize during training, or easy to forget during pruning. A PrAC set is thus hypothesized to capture those most challenging and informative examples for the dense model. We observe that a high-quality winning ticket can be found with training and pruning the dense network on the very compact PrAC set, which can substantially save training iterations for the ticket finding process. Extensive experiments validate our proposal across diverse datasets and network architectures. Specifically, on CIFAR-10, CIFAR-100, and Tiny ImageNet, we locate effective PrAC sets at 35.32%~78.19% of their training set sizes. On top of them, we can obtain the same competitive winning tickets for the corresponding dense networks, yet saving up to 82.85%~92.77%, 63.54%~74.92%, and 76.14%~86.56% training iterations, respectively. Crucially, we show that a PrAC set found is reusable across different network architectures, which can amortize the extra cost of finding PrAC sets, yielding a practical regime for efficient lottery ticket finding.
Exploring Modeling as a Context to Support Content Integration for Chemistry and Earth Science

Rarely are scientific phenomena isolated to a single discipline such as biology, chemistry, physics, Earth science, or others. Rather, these phenomena span multiple disciplines and often require bringing together content and understandings from multiple disciplines to engage with or explain the target phenomena. This study uses a model-based explanations framework to explore high school students’ abilities to integrate science content from the disciplines of chemistry and Earth science within the context of generating models to explain a local phenomenon—the urban heat island effect. Students experienced a multiweek instructional unit as part of a larger integrated curriculum that brings together chemistry and Earth science content and features local phenomena to engage students in the scientific practice of modeling. Quantitative and qualitative findings from this study demonstrate students’ emerging proficiency with integrating science content, practices, and crosscutting concepts to explain a phenomenon. Students’ model-based explanations tended to include phenomenon-relevant components (e.g., physical features of the phenomenon) and sequences (e.g., cause–effect relationships), but tended to include relatively fewer explanatory mechanisms. Implications related to the utility of modeling as a practice-based venue for integrating content from diverse science disciplines are also discussed.
A Relational Aggregated Disjoint Multimedia Search Results Approach using Semantics

The web holds a vast collection of diverse multimedia information. Web search engines are a major gateway to access these resources. Multimedia objects, including images, video, and audio, are increasing on the world wide web. This increasing content heterogeneity is transforming user information needs from factual searches to boarder multimedia information exploration. At present, pertinent multimedia information is scattered in various places. Traditional search engines bridge the content heterogeneity via aggregation techniques. Yet, this aggregation is mostly partial or non-blended, which causes a loss of retrieved multimedia contents inter-relationships. We proposed an relational aggregation search approach for the disjoint multimedia search results using semantics. Our relational aggregation approach assembles the disjoint multimedia vertical snippets, fully blend, and re-rank them in a linearly ranked list based on their semantic similarity with respect to the user's query. This helps retain the inter-relationship between multimedia contents and enhance multimedia searches.
A Semantic Supervision Method for Abstractive Summarization

In recent years, many text summarization models based on pretraining methods have achieved very good results.However, in these text summarization models, semantic deviations are easy to occur between the original input representation and the representation that passed multi-layer encoder, which may result in inconsistencies between the generated summary and the source text content.The Bidirectional Encoder Representations from Transformers (BERT) improves the performance of many tasks in Natural Language Processing (NLP).Although BERT has a strong capability to encode context, it lacks the fine-grained semantic representation.To solve these two problems, we proposed a semantic supervision method based on Capsule Network.Firstly, we extracted the fine-grained semantic representation of the input and encoded result in BERT by Capsule Network.Secondly, we used the fine-grained semantic representation of the input to supervise the fine-grained semantic representation of the encoded result.Then we evaluated our model on a popular Chinese social media dataset (LCSTS), and the result showed that our model achieved higher ROUGE scores (including R-1, R-2), and our model outperformed baseline systems.Finally, we conducted a comparative study on the stability of the model, and the experimental results showed that our model was more stable.
Teaching English Through Online Learning (A Literature Review)

The aim of this study is to investigate the teaching strategy to be implemented on teaching English through online learning. The design of the present study is a literature review design. Related studies about strategies on teaching English through online learning were analyzed in order to be described in present study. The findings of this present study show that teaching through Learning Management System (LMS), video conference, instant messaging, and digital game could be beneficial strategies on teaching English through online learning. Each strategy has its own characteristics to be implemented in certain situation of online learning. Teachers could utilize various ICT applications which support the implementation of these strategies.
Steganalysis for Images Security Classification in Machine Learning Using SVM

Grouping is one of the most important errands for different applications, such as text order, tone recognition, image characterization, articulation of miniature cluster efficiency, protein function forecasts, and classification of details. A significant portion of the current controlled order modulation formats on traditional measurements that can give ideal results when the test size is maintained. Nonetheless, only minimal examples can be obtained through and through. A new learning technique, support vector machine (SVM), is used in this paper on different data (diabetes knowledge, heart data, satellite data, and shuttle data) that have two or multi-groups. SVM, an impressive computer technique created from observable learning, has achieved important achievements in some fields. Introduced in the mid-nineties, they inspired a blast of excitement for AI. The SVM establishments were founded by Vapnik and, due to numerous attractive highlights and promising precise execution, are gaining ubiquity in the field of AI. The SVM technique does not withstand information computational complexity constraints and minimal examples.
Time Optimal Trajectories for a Car-Like Mobile Robot

This article studies the time optimal paths of a car-like mobile robot navigating in an obstacle-free planar environment. The robot, with forward and backward speeds, is controlled by bounded acceleration and limited front-wheels steering rate. The article extends previous results that solved this problem for a simplified car-robot model controlled by bounded speed and limited heading rate. The simplified car-robot model forms a unicycle with coupled bounds on the control inputs, while the full car-robot model has independent control inputs. The problem is analyzed as an optimal control problem by the minimum principle and by singular control theory. While the simplified car-robot model gives six time optimal path primitives, the optimality conditions for the full car-robot model yield <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">twelve path primitives</i> that form the time optimal paths. Three primitives involve singular controls of the steering front wheels that asymptotically align the robot along a straight line motion. All path primitives are analytically characterized and representative examples are studied in order to demonstrate how the path primitives combine to form the time optimal paths.
Scene motion detection in imagery with anisoplanatic optical turbulence using a tilt-variance-based Gaussian mixture model

In long-range imaging applications, anisoplanatic atmospheric optical turbulence imparts spatially- and temporally varying blur and geometric distortions in acquired imagery. The ability to distinguish true scene motion from turbulence warping is important for many image-processing and analysis tasks. The authors present a scene-motion detection algorithm specifically designed to operate in the presence of anisoplanatic optical turbulence. The method models intensity fluctuations in each pixel with a Gaussian mixture model (GMM). The GMM uses knowledge of the turbulence tilt-variance statistics. We provide both quantitative and qualitative performance analyses and compare the proposed method to several state-of-the art algorithms. The image data are generated with an anisoplanatic numerical wave-propagation simulator that allows us to have motion truth. The subject technique outperforms the benchmark methods in our study.
Semantic Document Clustering using K-means algorithm and Ward's Method

Nowadays in the age of technology, textual documents are rapidly growing over the internet. Offline and online documents, websites, e-mails, social network and blog posts, are archived in electronic structured databases. It is very hard to maintain and reach these documents without acceptable ranking and provide demand clustering while there is classification without any details. This paper presents an approach based on semantic similarity for clustering documents using the NLTK dictionary. The procedure is done by defining synopses from IMDB and Wikipedia datasets, tokenizing and stemming them. Next, a vector space is constructed using TFIDF, and the clustering is done using the ward's method and K-mean algorithm. WordNet is also used to semantically cluster documents. The results are visualized and presented as an interactive website describing the relationship between all clusters. For each algorithm three scenarios are considered for the implementations: 1) without preprocessing, 2) preprocessing without stemming, and 3) preprocessing with stemming. The Silhouette metric and seven other metrics are used to measure the similarity with the five different datasets. Using the K-means algorithm, the best similarity ratio acquired from the Silhouette metric with (nltk-Reuters) dataset for all clusters, and the highest ratio is when k=10. Similarly, with Ward's algorithm, the highest similarity ratio of the Silhouette metric obtained using (IMDB and Wiki top 100 movies, and nltk-brown) datasets together for all clusters, and best similarity ratio is obtained when k=5 using the (IMDB and Wiki top 100 movies) dataset. The results are compared with the literature, and the outcome exposed that the Ward's method outperforms the results of K-means for small datasets.
Multi-View Facial Expression Recognition based o n Multitask Learning and Generative Adversarial Network

Facial expressions contain rich emotional information, which is an important method in human communication. At present, most of the researches on facial expression recognition is conducted on the frontal faces. However, in real life, the captu re device may capture facial expression data from various poses. Different from the existing techniques, in this paper, We propose a multitask deep learning method that uses the links between var ious poses and expressions to improve the accuracy of expression recognition. We use the adversarial network to supplement the e xpression information of the face for the head poses with a sever e lack of expression information. There are some advantages: Firstly, we use vgg16 to train the expression images of each deflecti on angle separately and find that the expression recognition acc uracy rate for small offset angle (-30 °, -15 °, 15°, 30 °) is larger t han 0 ° angle when the face angle is greater than 45 °, the accur acy of recognition decreases sharply. So we use multitask learnin g to jointly train these small offsets angle images and frontal ima ges, the multitask learning can learn the emotion-preserving repr esentations at various poses to predict the expression class label f rom the input face, and bring again to its recognition accuracy r ate. Secondly, for the poses of a severe lack of expression inform ation, we use TP-GAN to convert a large deflection pose image i nto a frontal face and supplement its expression information. Th e experimental results show that our proposed algorithm has a g ood recognition effect on facial expressions for all poses. Compa red with the most advanced expression recognition methods, this paper has also achieved the state-of-the-art recognition results.
ASFNet: Adaptive multiscale segmentation fusion network for real‐time semantic segmentation

Abstract Recently, the development of deep learning has facilitated continuous progress in the field of computer vision. Pixel‐level semantic segmentation serves as a fundamental task in computer vision. It achieves significant results by connecting wider and deeper backbone networks and building fine‐grained segmentation heads. However, applications such as self‐driving cars are more critical to the computational speed of the algorithms. The trade‐off between accuracy and real‐time performance of existing algorithms is still a challenging task. To address this challenge, this article proposes an adaptive multiscale segmentation fusion network to fuse multiscale contextual, which designs an adaptive multiscale segmentation fusion module based on an attention mechanism. Using segmentation fusion instead of feature fusion, the multiscale segmentation results are aggregated to obtain more precise segmentation results. The final results achieved 70.9% mIoU of accuracy in the Cityspace test set, processing images at 61 FPS when the input is 1024 × 2048. In addition, when adjusting the input size to 512 × 1024, the images are processed at 185 FPS.
Voice Email Based On SMTP For Physically Handicapped

In the present scenario, everybody needs communication technology to connect with each other. Communication technologies are significant these days for the betterment of social and personal interaction. The combination of technologies with the internet makes communication easy. However, the person who is physically challenged suffered a lot to utilize this technology due to visual and physical difficulties. There are many technologies advancements have come though it is not possible to use like normal users. This paper aims at creating an email system that helps even new users or physically impaired people to use the system for communication without any previous practices. There is no use of keywords, only with the help of mouse actions and voice conversion the email system works. The person who is not literate can also send emails as it is based on speech recognition and text to speech. The system is completely based on responsive voice interaction to utilize the technology easy and hassle free manner. The system is well designed to send the mails quickly. There are all the options available to send emails and perform all the functions for the email system.
Distributed Policy Synthesis of Multiagent Systems With Graph Temporal Logic Specifications

We study the distributed synthesis of policies for multiagent systems to perform <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">spatial–temporal</i> tasks. We formalize the synthesis problem as a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">factored</i> Markov decision process subject to <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">graph temporal logic</i> specifications. The transition function and the task of each agent are functions of the agent itself and its neighboring agents. In this work, we develop another distributed synthesis method, which improves the scalability and runtime by two orders of magnitude compared to our prior work. The synthesis method decomposes the problem into a set of smaller problems, one for each agent by leveraging the structure in the model, and the specifications. We show that the running time of the method is linear in the number of agents. The size of the problem for each agent is exponential only in the number of neighboring agents, which is typically much smaller than the number of agents. We demonstrate the applicability of the method in case studies on disease control, urban security, and search and rescue. The numerical examples show that the method scales to hundreds of agents with hundreds of states per agent and can also handle significantly larger state spaces than our prior work.