Reviewerly/exHarmony huggingface dataset:
Learning through interactive artifacts: Personal fabrication using electrochromic displays to remember Atari women programmers

In recent years makerspaces have gained traction as an environment where makers and tinkerers can freely create artefacts with digital fabrication tools. They are particularly suited for introducing new fabrication techniques because these spaces support hands-on experiences. Electrochromic displays are one such technology that has become possible to fabricate using new techniques and off-the-shelf tools which lends itself to be used in a workshop setting. Leveraging this development, we facilitated a makerspace workshop that introduced participants to this new technology. To limit the scope of the workshop outcome we used the little known history of female developers of video games (Atari) from the 1970s and 1980s as a design framing. The participants (undergraduates, 16 female, 2 male, aged 19–21 years) explored the Atari women’s role in development and through this exploration they created artifacts using novel electrochromic displays as designed responses. Throughout the workshop participants answered daily questionnaires and kept records of their progress. Our analysis of the questionnaires and the resulting projects suggests that having a relatable and meaningful context increases both motivation and engagement of the participants. We discuss the extrinsic motivations that enhance engagement, and provide suggestions for introducing new technologies in the makerspace context.
Robust location-secured high-definition image watermarking based on key-point detection and deep learning

In this paper, we propose a blind high-definition image watermarking scheme named multiscale-fusion dilated ResNet-based high-definition watermarking (MDResNet-HDWM), which is based on a key-point detection and deep learning framework. The proposed watermarking scheme embeds watermark in multiple non-overlapping regions, whose locations are secured with a private key referring to several dominant key points. To achieve scale-invariant watermark embedding, regions are first determined in a normalized image copy and then mapped back to its origin. The watermarks are embedded in the central part of the region in such a way that the watermarks will always be located inside the regions even if the image is geometrically transformed. The watermarks are embedded and extracted with MDResNet, which is trained with a curriculum learning strategy that makes it be robust to signal processing operations and geometric transforms. Experimental results demonstrate that the proposed MDResNet-HDWM achieves good performance and is robust to both common signal operations and geometric attacks.
Forecasting Multivariate Chaotic Processes with Precedent Analysis

Predicting the state of a dynamic system influenced by a chaotic immersion environment is an extremely difficult task, in which the direct use of statistical extrapolation computational schemes is infeasible. This paper considers a version of precedent forecasting in which we use the aftereffects of retrospective observation segments that are similar to the current situation as a forecast. Furthermore, we employ the presence of relatively stable correlations between the parameters of the immersion environment as a regularizing factor. We pay special attention to the choice of similarity measures or distances used to find analog windows in arrays of retrospective multidimensional observations.
Development and Effect of Virtual Reality Practice Program for Improving Practical Competency of Caregivers Specializing in Dementia

The number of dementia patients in Korea is increasing with the increase in the elderly population. Accordingly, the importance of the role of the caregivers, who are the main care worker other than the family, is increasing. Therefore, in this study, a virtual reality practice program was developed to enhance the practical competency of caregivers who take care of dementia patients, and the effects were analyzed. The caregiver said that among the mental behaviors of dementia patients, aggression and delusion were the most difficult. Based on this information, a practice program was developed by realizing a case of a male dementia patient who expressed refusal to bathing help as an aggressive behavior due to delusion in virtual reality, and the effect of the virtual reality practice program was analyzed for five caregivers. As a result, 'interest in new teaching methods', 'improving concentration of practical education based on real cases', and 'increasing confidence in caring for dementia patients' were found. As this study is a pilot test, it is necessary to repeat the study with more subjects in the future, and to develop virtual reality implementation cases for various mental and behavioral symptoms.
NLP-Fast: A Fast, Scalable, and Flexible System to Accelerate Large-Scale Heterogeneous NLP Models

Emerging natural language processing (NLP) models have become more complex and bigger to provide more sophisticated NLP services. Accordingly, there is also a strong demand for scalable and flexible computer infrastructure to support these large-scale, complex, and diverse NLP models. However, existing proposals cannot provide enough scalability and flexibility as they neither identify nor optimize a wide spectrum of performance-critical operations appearing in recent NLP models and only focus on optimizing specific operations. In this paper, we propose NLP-Fast, a novel system solution to accelerate a wide spectrum of large-scale NLP models. NLP-Fast mainly consists of two parts: (1) NLP-Perf: an in-depth performance analysis tool to identify critical operations in emerging NLP models and (2) NLP-Opt: three end-to-end optimization techniques to accelerate the identified performance-critical operations on various hardware platforms (e.g., CPU, GPU, FPGA). In this way, NLP-Fast can accelerate various types of NLP models on different hardware platforms by identifying their critical operations through NLP-Perf and applying the NLP-Opt's holistic optimizations. We evaluate NLP-Fast on CPU, GPU, and FPGA, and the overall throughputs are increased by up to 2.92×, 1.59×, and 4.47× over each platform's baseline. We release NLP-Fast to the community so that users are easily able to conduct the NLP-Fast's analysis and apply NLP-Fast's optimizations for their own NLP applications.
Cyber Risk Quantification: Investigating the Role of Cyber Value at Risk

The aim of this paper is to deepen the application of value at risk in the cyber domain, with particular attention to its potential role in security investment valuation. Cyber risk is a fundamental component of the overall risk faced by any organization. In order to plan the size of security investments and to estimate the consequent risk reduction, managers strongly need to quantify it. Accordingly, they can decide about the possibility of sharing residual risk with a third party, such as an insurance company. Recently, cyber risk management techniques are including some risk quantile-based measures that are widely employed in the financial domain. They refer to value at risk that, in the cyber context, takes the name of cyber value at risk (Cy-VaR). In this paper, the main features and challenging issues of Cy-VaR are examined. The possible use of this risk measure in supporting investment decisions in cyber context is discussed, and new risk-based security metrics are proposed. Some simple examples are given to show their potential.
Detection of entanglement during pure dephasing evolutions for systems and environments of any size

We generalize the scheme for detection of qubit-environment entanglement to qudit-environment systems. This is of relevance for many-qubit systems and the quantification of the operation of quantum algorithms under the influence of external noise, since only decoherence that is not entangling in its nature can be effectively described by quantum channels and similar methods in more complicated scenarios. The generalization involves an increase in the class of entangled states which are not detected by the scheme, but the type of entanglement which cannot be detected is also least likely to qualitatively influence decoherence. We exemplify the operation of the scheme on a realistically modeled nitrogen-vacancy-center spin qutrit interacting with an environment of nuclear spins.
An Optimized Deep Learning Model for Emotion Classification in Tweets

The task of automatically analyzing sentiments from a tweet has more use now than ever due to the spectrum of emotions expressed from national leaders to the average man.Analyzing this data can be critical for any organization.Sentiments are often expressed with different intensity and topics which can provide great insight into how something affects society.Sentiment analysis in Twitter mitigates the various issues of analyzing the tweets in terms of views expressed and several approaches have already been proposed for sentiment analysis in twitter.Resources used for analyzing tweet emotions are also briefly presented in literature survey section.In this paper, hybrid combination of different model's LSTM-CNN have been proposed where LSTM is Long Short Term Memory and CNN represents Convolutional Neural Network.Furthermore, the main contribution of our work is to compare various deep learning and machine learning models and categorization based on the techniques used.The main drawback of LSTM is that it's a timeconsuming process whereas CNN do not express content information in an accurate way, thus our proposed hybrid technique improves the precision rate and helps in achieving better results.Initial step of our mentioned technique is to preprocess the data in order to remove stop words and unnecessary data to improve the efficiency in terms of time and accuracy also it shows optimal results when it is compared with predefined approaches.
Rate limits in quantum networks with lossy repeaters

The derivation of ultimate limits to communication over certain quantum repeater networks have provided extremely valuable benchmarks for assessing near-term quantum communication protocols. However, these bounds are usually derived in the limit of ideal devices and leave questions about the performance of practical implementations unanswered. To address this challenge, we quantify how the presence of loss in repeater stations affect the maximum attainable rates for quantum communication over linear repeater chains and more complex quantum networks. Extending the framework of node splitting, we model the loss introduced at the repeater stations and then prove the corresponding limits. In the linear chain scenario we show that, by increasing the number of repeater stations, the maximum rate cannot overcome a quantity, which solely depends on the loss of a single station. We introduce a way of adapting the standard machinery for obtaining bounds to this realistic scenario. The difference is that whilst ultimate limits for any strategy can be derived given a fixed channel, when the repeaters introduce additional decoherence, then the effective overall channel is itself a function of the chosen repeater strategy (e.g., one-way versus two-way classical communication). Classes of repeater strategies can be analysed using additional modeling and the subsequent bounds can be interpreted as the optimal rate within that class.
Real-Time Emotion Recognition from Facial Expressions Using Convolutional Neural Network with Fer2013 Dataset

Over the most recent couple of years, facial detection and emotion recognition research has been widely considered. In many human–computer interactions, face-communication methods are used, such as facial expressions, eye movement and gestures, which are commonly used among them because they convey the individual emotional states and sentiments. The ability to recognize facial expressions in real time is automatically enabling fictitious applications in human and computer interactivity and different other regions like security, customer satisfaction recognition, etc. This paper provides a brisk measure to coordinate whether the CNN engineering model performs better if it uses only the raw pixels of image data for training or whether it is better to give the CNN some additional details, such as face landmarks and HOG characteristics. The faces are first detected using the LBP classifier; at that point, we extricate the face landmarks utilizing dlib. We additionally extricated the HOG features, and finally, we trained the CNN model with the raw pixel image data with the additional information. In support of this work, we used the FER-2013 Dataset collected from Kaggle. We obtained an accuracy of 75.1 and 59.1% when we trained the model with and without feature extraction, respectively. The results show that the additional information helps CNN to carry out better.
A Novel Approach to Generate Type-1 Fuzzy Triangular and Trapezoidal Membership Functions to Improve the Classification Accuracy

Fuzzy logic is an approach that reflects human thinking and decision making by handling uncertainty and vagueness using fuzzy membership functions. When a human is engaged in the design of a fuzzy system, symmetric properties are naturally preferred. Fuzzy c-means clustering is a clustering algorithm that can cluster datasets to produce membership matrix and cluster centers, which results in generating type-1 fuzzy membership functions. However, fuzzy c-means algorithm has a limitation of producing only a single membership function type, Gaussian MF. Generation of multiple fuzzy membership functions is of immense importance as it provides more efficient and optimal solutions to a problem. Therefore, an approach to generate multiple type-1 fuzzy membership functions through fuzzy c-means is required for the optimal and improved results of classification datasets. Hence, to overcome the limitation of the fuzzy c-means algorithm, an approach for the generation of type-1 fuzzy triangular and trapezoidal membership function through fuzzy c-means is considered in this study. The approach is used to calculate and enhance the accuracy of classification datasets called iris, banknote authentication, blood transfusion, and Haberman’s survival. The proposed approach of generating MFs using FCM produce asymmetric MFs, whose results are compared with the MFs produced from grid partitioning (GP), which are symmetric MFs. The results show that the proposed approach of generating type-1 fuzzy membership function through fuzzy c-means is effective and can be adopted.
M2SPL: Generative multiview features with adaptive meta-self-paced sampling for class-imbalance learning

Class-imbalance learning is an important research area and draws continued attention in various real-world applications for many years. Undersampling is a key method of class-imbalance learning in order to obtain a balanced class distribution, but it may discard potentially crucial samples and may be influenced by outliers or noises in imbalanced data. Multiview learning methods have shown that models trained on different views can help each other to improve their performances and robustness, but the existing imbalance learning approaches rely only on single-view samples. In this paper, we propose a multiview feature imbalance sampling method via meta self-paced learning (M2SPL) to effectively choose high-quality samples and separate adjacent features to improve the robustness of the trained model. There are two advantages of our proposed method: (1) An adaptive reweight generation process acts as a pivotal part in our M2SPL. The adaptive density-based reweight samples learning mechanism considers noisy and intractable samples to improve the robustness of model. (2) The multiview feature learning can avoid the large value of the loss function to learn a robust model from original data, and can enhance the discrimination capability of the model. Comparison with the existing sampling approaches shows that our proposed M2SPL approach significantly improves the classification performance, with increases in the F1-score and G-mean of 15.4% and 12.5%, respectively, on average. Finally, our experimental results pass the Friedman and Holm tests, indicating that our model has a significant improvement over existing methods.
THE HUMAN FACTOR CAPABILITIES IN SECURITY OPERATION CENTER (SOC)

The human factor is considered the weakest link in cybersecurity and inside the Security Operation Centers (SOC) and it represents the most important component at the same time. Human factor capabilities and challenges attracted the attention of researchers to address how these challenges can be reduced or mitigated. However, these research papers do not consider the complexity, unpredictability, interdependent and evolving nature of the SOC systems. This study aims to explore the human capabilities and weaknesses inside the Security Operation Centre. To this end, we employed survey bases questionaries alongside the daily observation of SOC analysts and interviews with SOC experts. Forty SOC analysts and five experts conducted the survey. The finding of this study will help SOC managers and SOC designers better understand the challenges faced by the SOC analysts and take into account the interdependent and evolving nature of the Security Operation Centers.
Semantic Scene Manipulation Based on 3D Spatial Object Relations and Language Instructions

Robot understanding of spatial object relations is key for a symbiotic human-robot interaction. Understanding the meaning of such relations between objects in a current scene and target relations specified in natural language commands is essential for the generation of robot manipulation action goals to change the scene by relocating objects relative to each other to fulfill the desired spatial relations. This ability requires a representation of spatial relations, which maps spatial relation symbols extracted from language instructions to subsymbolic object goal locations in the world. We present a generative model of static and dynamic 3D spatial relations between multiple reference objects. The model is based on a parametric probability distribution defined in cylindrical coordinates and is learned from examples provided by humans manipulating a scene in the real world. We demonstrate the ability of our representation to generate suitable object goal positions for a pick-and-place task on a humanoid robot, where object relations specified in natural language commands are extracted, object goal positions are determined and used for parametrizing the actions needed to transfer a given scene into a new one that fulfills the specified relations.
Domain Generalization Via Encoding and Resampling in a Unified Latent Space

Domain generalization aims to generalize a network trained on multiple domains to unknown yet related domains. Operating under the assumption that invariant information generalizes well to unknown domains, previous work has aimed to minimize the discrepancies amongst distributions across given domains. However, without prior regularization of feature distributions, the network in practice overfits the invariant information in the given domains. Moreover, if there are insufficient samples in given domains, then domain generalizability is limited, as diverse domain variations are not captured. To address these two drawbacks, we propose to explicitly map features in known and unknown domains onto latent space in a fixed Gaussian mixture distribution by variational coding. As a result, features in different classes follow Gaussian distributions with different mean values. The predefined latent space narrows discrepancies between known and unknown domains and effectively separates samples into different classes. Moreover, we propose to perturb sample features with gradients from the distribution regularized loss. This perturbation generates samples beyond but near the latent space of prior distributions, which has a profound impact on domain variations. Experiments and visualizations demonstrate the effectiveness of our proposed method.
AFD-Net: Adaptive Fully-Dual Network for Few-Shot Object Detection

Few-shot object detection (FSOD) aims at learning a detector that can fast adapt to previously unseen objects with scarce annotated examples. Existing methods solve this problem by performing subtasks of classification and localization utilizing a shared component in the detector, yet few of them take the distinct preferences towards feature embedding of two subtasks into consideration. In this paper, we carefully analyze the characteristics of FSOD, and present that a few-shot detector should consider the explicit decomposition of two subtasks, as well as leveraging information from both of them to enhance feature representations. To the end, we propose a simple yet effective Adaptive Fully-Dual Network (AFD-Net). Specifically, we extend Faster R-CNN by introducing Dual Query Encoder and Dual Attention Generator for separate feature extraction, and Dual Aggregator for separate model reweighting. In this way, separate state estimation is achieved by the R-CNN detector. Furthermore, we introduce Adaptive Fusion Mechanism to guide the design of encoders for efficient feature fusion in the specific subtask. Extensive experiments on PASCAL VOC and MS COCO show that our approach achieves state-of-the-art performance by a large margin, demonstrating its effectiveness and generalization ability.
Privacy Preserving Parallel Clustering Based Anonymization for Big Data Using MapReduce Framework

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Keyframe Extraction from Motion Capture Sequences with Graph based Deep Reinforcement Learning

Animation production workflows centred around motion capture techniques often require animators to edit the motion for various artistic and technical reasons. This process generally uses a set of keyframes. Unsupervised keyframe selection methods for motion capture sequences are highly demanded to reduce the laborious annotations. However, most existing methods are optimization-based, which cause the issues of flexibility and efficiency and eventually constrains the interactions and controls with animators. To address these limitations, we propose a novel graph based deep reinforcement learning method for efficient unsupervised keyframe selection. First, a reward function is devised in terms of reconstruction difference by comparing the original sequence and the interpolated sequence produced by the keyframes. The reward complies with the requirements of the animation pipeline satisfying: 1) incremental reward to evaluate the interpolated keyframes immediately; 2) order insensitivity for consistent evaluation; and 3) non-diminishing return for comparable rewards between optimal and sub-optimal solutions. Then by representing each skeleton frame as a graph, a graph-based deep agent is guided to heuristically select keyframes to maximize the reward. During the inference it is no longer necessary to estimate the reconstruction difference, and the evaluation time can be reduced significantly. The experimental results on the CMU Mocap dataset demonstrate that our proposed method is able to select keyframes at a high efficiency without clearly compromising the quality in comparison with the state-of-the-art methods.