Reviewerly/exHarmony huggingface dataset:
Learning One-Shot Imitation From Humans Without Humans

Humans can naturally learn to execute a new task by seeing it performed by other individuals once, and then reproduce it in a variety of configurations. Endowing robots with this ability of imitating humans from third person is a very immediate and natural way of teaching new tasks. Only recently, through meta-learning, there have been successful attempts to one-shot imitation learning from humans; however, these approaches require a lot of human resources to collect the data in the real world to train the robot. But is there a way to remove the need for real world human demonstrations during training? We show that with Task-Embedded Control Networks, we can infer control polices by embedding human demonstrations that can condition a control policy and achieve one-shot imitation learning. Importantly, we do not use a real human arm to supply demonstrations during training, but instead leverage domain randomisation in an application that has not been seen before: sim-to-real transfer on humans. Upon evaluating our approach on pushing and placing tasks in both simulation and in the real world, we show that in comparison to a system that was trained on real-world data we are able to achieve similar results by utilising only simulation data. Videos can be found here: https://sites.google.com/view/tecnets-humans.
SILCO: Show a Few Images, Localize the Common Object

Few-shot learning is a nascent research topic, motivated by the fact that traditional deep learning requires tremendous amounts of data. In this work, we propose a new task along this research direction, we call few-shot common-localization. Given a few weakly-supervised support images, we aim to localize the common object in the query image without any box annotation. This task differs from standard few-shot settings, since we aim to address the localization problem, rather than the global classification problem. To tackle this new problem, we propose a network that aims to get the most out of the support and query images. To that end, we introduce a spatial similarity module that searches the spatial commonality among the given images. We furthermore introduce a feature reweighting module to balance the influence of different support images through graph convolutional networks. To evaluate few-shot common-localization, we repurpose and reorganize the well-known Pascal VOC and MS-COCO datasets, as well as a video dataset from ImageNet VID. Experiments on the new settings for few-shot common-localization shows the importance of searching for spatial similarity and feature reweighting, outperforming baselines from related tasks.
UnOVOST: Unsupervised Offline Video Object Segmentation and Tracking

We address Unsupervised Video Object Segmentation (UVOS), the task of automatically generating accurate pixel masks for salient objects in a video sequence and of tracking these objects consistently through time, without any input about which objects should be tracked. Towards solving this task, we present UnOVOST (Unsupervised Offline Video Object Segmentation and Tracking) as a simple and generic algorithm which is able to track and segment a large variety of objects. This algorithm builds up tracks in a number stages, first grouping segments into short tracklets that are spatio-temporally consistent, before merging these tracklets into long-term consistent object tracks based on their visual similarity. In order to achieve this we introduce a novel tracklet-based Forest Path Cutting data association algorithm which builds up a decision forest of track hypotheses before cutting this forest into paths that form long-term consistent object tracks. When evaluating our approach on the DAVIS 2017 Unsupervised dataset we obtain state-of-the-art performance with a mean ℱ score of 67.9% on the val, 58% on the test-dev and 56.4% on the test-challenge benchmarks, obtaining first place in the DAVIS 2019 Unsupervised Video Object Segmentation Challenge. UnOVOST even performs competitively with many semi-supervised video object segmentation algorithms even though it is not given any input as to which objects should be tracked and segmented.
Looking Ahead: Anticipating Pedestrians Crossing with Future Frames Prediction

In this paper, we present an end-to-end future-prediction model that focuses on pedestrian safety. Specifically, our model uses previous video frames, recorded from the perspective of the vehicle, to predict if a pedestrian will cross in front of the vehicle. The long term goal of this work is to design a fully autonomous system that acts and reacts as a defensive human driver would - predicting future events and reacting to mitigate risk. We focus on pedestrian-vehicle interactions because of the high risk of harm to the pedestrian if their actions are miss-predicted. Our end-to-end model consists of two stages: the first stage is an encoder/decoder network that learns to predict future video frames. The second stage is a deep spatiotemporal network that utilizes the predicted frames of the first stage to predict the pedestrian's future action. Our system achieves state-of-the-art accuracy on the Joint Attention for Autonomous Driving (JAAD) dataset on both future frames prediction, with a pixel-wise prediction l <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> error of 1.12, and pedestrian behavior prediction with an average precision of 86.7.
Email phishing and signal detection: How persuasion principles and personality influence response patterns and accuracy

Phishing is a social engineering tactic where a malicious actor impersonates a trustworthy third party with the intention of tricking the user into divulging sensitive information. Previous social engineering research in a real-world setting has shown an interaction between the personality of the target and the persuasion principle used. This study investigated whether this interaction is present in the realm of email phishing. Additionally, a signal detection theory framework was used to evaluate how the various persuasion principles influence accuracy, sensitivity (d’), and response criterion placement. A personality inventory and an email identification task (phishing or legitimate) were used. These data support previous findings that high extroversion is predictive of increased susceptibility to phishing attacks. The various persuasions principles elicited diverse response criterions and sensitivities, though all investigated persuasion principles resulted in a liberal decision criterion, except one. These findings are interpreted and discussed.
The Role of Everyday Sounds in Advanced Dementia Care

The representation of sounds derived from everyday life can be beneficial for people with dementia by evoking memories and emotional responses. Despite this potential, integrating sound and sound-based interventions in care facilities has not received much research attention. In this paper, we present the findings from a field study that explored the responses of 19 people with advanced dementia to a selection of everyday sounds presented to them in a care home and the role of these responses in the care environment. To study this, we deployed Vita, a 'pillow-like' sound player, in two dementia care facilities for four weeks, during which observations were recorded. Afterwards, we conducted interviews with caregivers who used Vita in everyday care practice. Our findings reveal how everyday sounds provided by Vita stimulated meaningful conversation, playfulness, and connection between residents and caregivers. Furthermore, we propose design implications for integrating everyday sounds in dementia care.
Reversible data hiding for high dynamic range images using two-dimensional prediction-error histogram of the second time prediction

High dynamic range (HDR) images have become a wide-spread data format in multimedia communication due to its great expressiveness. In this paper, we present an efficient prediction-error histogram (PEH) based reversible data hiding (RDH) method for HDR images. To better exploit the correlation among encoded channels, a novel RDH scheme based on two-dimensional PEH of the second time prediction is designed. Firstly, the prediction-errors are calculated considering the encoding format of HDR images. Secondly, those pixels located in the complex regions are predicted for the second time by referring to the other channels. Thirdly, the two-dimensional PEH is generated to further optimize the embedding performance and maximize the embedding capacity (EC). Finally, an adaptive embedding strategy based on the two-dimensional PEH is employed to reduce the embedding distortion. With the second time prediction and the two-dimensional PEH strategy, our proposed method can greatly exploit the correlations among channels. Experimental results verify that the proposed method can significantly enlarge the embedding capacity and achieves an excellent embedding performance.
A Survey on Web Service QoS Prediction Methods

Nowadays, there are many Web services with similar functionality on the Internet. Users consider Quality of Service (QoS) of the services to select the best service from among them. The prediction of QoS values of the Web services and recommendations of the best service based on these values to the users is one of the major challenges in the web service area. Major studies in this field use collaboration filtering based methods for prediction. The paper introduced prediction methods and divided them into three main categories: memory-based methods, model-based methods, and Collaborative Filtering (CF) methods combined with other methods. In each category, some of the most famous studies were introduced, and then the problems and benefits of each category were reviewed. Finally, we have a discussion about these methods and propose suggestions for future works.
Blockchain in Healthcare

Blockchain, an immutable ledger or database shared by peers in a network, is comprised of records of events or transactions that are appended chronologically. Introduced via Bitcoin to the world, blockchain is increasingly being accepted and adopted in different industries and for diverse use cases. Among key industries, health care offers several significant opportunities for applying blockchain conceptualization. Chief areas for health care blockchain applications include electronic medical records management, pharmaceutical supply chain management, biomedical research and education, remote patient monitoring, health insurance claim processing, and health data analytics. Even so, applying blockchain concepts in health care is not without challenges, including interoperability, security-privacy, scalability-speed, and stakeholders' engagement issues. While these challenges may militate against blockchain applications in health care, there are possible countermeasures and implementation techniques, which if adhered to, can reasonably contain many aspects of such challenges.
Learning Management System (LMS) Based On Moodle To Improve Students Learning Activity

Abstract Learning Management System (LMS) is an application software that used to help in the online learning process. Moodle is one of the most popular LMS applications and is very appropriate for online learning study. Moodle has various features that are able to support student activities online. Some learning activities supported by moodle are (1) Videos; (2) Discussion forums; (3) Chat; (4) Materials; and (5) Quiz. This research is a quasi-experiment research that is research conducted to see the effect a treatment of the sample in this research is students. The aims of this research is to look at student learning activities that are learned in online using by LMS based Moodle applications.
Look More Into Occlusion: Realistic Face Frontalization and Recognition With BoostGAN

Many factors can affect face recognition, such as occlusion, pose, aging, and illumination. First and foremost are occlusion and large-pose problems, which may even lead to more than 10% accuracy degradation. Recently, generative adversarial net (GAN) and its variants have been proved to be effective in processing pose and occlusion. For the former, pose-invariant feature representation and face frontalization based on GAN models have been studied to solve the pose variation problem. For the latter, frontal face completion on occlusions based on GAN models have also been presented, which is much concerned with facial structure and realistic pixel details rather than identity preservation. However, synthesizing and recognizing the occluded but profile faces is still an understudied problem. Therefore, in this article, to address this problem, we contribute an efficient but effective solution on how to synthesize and recognize faces with large-pose variations and simultaneously corrupted regions (e.g., nose and eyes). Specifically, we propose a boosting GAN (BoostGAN) for occluded but profile face frontalization, deocclusion, and recognition, which has two aspects: 1) with the assumption that face occlusion is incomplete and partial, multiple images with patch occlusion are fed into our model for knowledge boosting, i.e., identity and texture information and 2) a new aggregation structure integrated with a deep encoder-decoder network for coarse face synthesis and a boosting network for fine face generation is carefully designed. Exhaustive experiments on benchmark data sets with regular and irregular occlusions demonstrate that the proposed model not only shows clear photorealistic images but also presents powerful recognition performance over state-of-the-art GAN models for occlusive but profile face recognition in both the controlled and uncontrolled environments. To the best of our knowledge, this article proposes to solve face synthesis and recognition under poses and occlusions for the first time.
Open-source tools and benchmarks for code-clone detection

A fragment of source code that is identical or similar to another is a code-clone. Code-clones make it difficult to maintain applications as they create multiple points within the code that bugs must be fixed, new rules enforced, or design decisions imposed. As applications grow larger and larger, the pervasiveness of code-clones likewise grows. To face the code-clone related issues, many tools and algorithms have been proposed to find and document code-clones within an application. In this paper, we present the historical trends in code-clone detection tools to show how we arrived at the current implementations. We then present our results from a systematic mapping study on current (2009-2019) code-clone detection tools with regards to technique, open-source nature, and language coverage. Lastly, we propose future directions for code-clone detection tools. This paper provides the essentials to understanding the code-clone detection process and the current state-of-art solutions.
P2B: Privacy Preserving Identity-Based Broadcast Proxy Re-Encryption

This paper proposes Privacy Preserving Identity-Based Broadcast Proxy Re-encryption (P2B) - a scheme to provide privacy preserving in identity-based broadcast proxy re-encryption. P2B uses Lagrange interpolation polynomial theorem to provide privacy to identities of the receiver group of broadcasted re-encrypted ciphertext. Proxy re-encryption is an efficient solution to securely share cloud data with receivers. For sharing data with a group of receivers, the sender needs to regenerate the re-encryption key for every receiver, which leads to an overhead on the sender side. To solve the issue, identity-based proxy re-encryption is extensible to identity-based broadcast proxy re-encryption. However, the later poses a privacy issue on the receiver side, as each receiver of the receiver group needs to know the other receiver's identity. We solve the problem using the Lagrange interpolation method. We prove that our scheme is secure against chosen plaintext attack using random oracle model and it successfully hides the identities of the receivers. Finally, we implement P2B and compare it with other existing systems. It is seen that P2B reduces the decryption time by 68% than recent existing Broadcast proxy re-encryption schemes and 98% than the existing privacy preserving schemes.
Segmentation of prostate and prostate zones using deep learning

Develop a deep-learning-based segmentation algorithm for prostate and its peripheral zone (PZ) that is reliable across multiple MRI vendors.This is a retrospective study. The dataset consisted of 550 MRIs (Siemens-330, General Electric[GE]-220). A multistream 3D convolutional neural network is used for automatic segmentation of the prostate and its PZ using T2-weighted (T2-w) MRI. Prostate and PZ were manually contoured on axial T2‑w. The network uses axial, coronal, and sagittal T2‑w series as input. The preprocessing of the input data includes bias correction, resampling, and image normalization. A dataset from two MRI vendors (Siemens and GE) is used to test the proposed network. Six different models were trained, three for the prostate and three for the PZ. Of the three, two were trained on data from each vendor separately, and a third (Combined) on the aggregate of the datasets. The Dice coefficient (DSC) is used to compare the manual and predicted segmentation.For prostate segmentation, the Combined model obtained DSCs of 0.893 ± 0.036 and 0.825 ± 0.112 (mean ± standard deviation) on Siemens and GE, respectively. For PZ, the best DSCs were from the Combined model: 0.811 ± 0.079 and 0.788 ± 0.093. While the Siemens model underperformed on the GE dataset and vice versa, the Combined model achieved robust performance on both datasets.The proposed network has a performance comparable to the interexpert variability for segmenting the prostate and its PZ. Combining images from different MRI vendors on the training of the network is of paramount importance for building a universal model for prostate and PZ segmentation.
Adoption of mobile applications for teaching-learning process in rural girls’ schools in India: an empirical study

Abstract The purpose of this study is to identify the factors that can impact the adoption of mobile apps for teaching-learning process focusing on the girls’ school in rural India. The hypotheses were proposed and a conceptual model has been developed. There is a survey work conducted to collect the data from different respondents using a convenience sampling method. The model has been validated statistically through PLS-SEM analysis covering feedbacks of 271 effective respondents. The study highlights the impact of different antecedents of the behavioural intention of the students of using mobile applications for teaching-learning process. The results also show that among other issues, price value has insignificant influence on the intention of the girl students of the rural India. During survey feedbacks have been obtained from the 271 respondents, which is meagre compared to vastness of the population and school of rural India. Only few predictors have been considered leaving possibilities of inclusion of other boundary conditions to enhance the explanative power more than that has been achieved in the proposed model with the explanative power of 81%. The model has provided laudable inputs to the educational policy makers and technology enablers and administrators to understand the impact of the mobile applications on the rural girls’ school of India and facilitate the development of m-learning. Very few studies been conducted to explore the impact of mobile applications on the school education of rural India especially focusing on the girls’ schools.
Progressive Cross-Camera Soft-Label Learning for Semi-Supervised Person Re-Identification

In this paper, we focus on the semi-supervised person re-identification (Re-ID) case, which only has the intra-camera (within-camera) labels but not inter-camera (cross-camera) labels. In real-world applications, these intra-camera labels can be readily captured by tracking algorithms or few manual annotations, when compared with cross-camera labels. In this case, it is very difficult to explore the relationships between cross-camera persons in the training stage due to the lack of cross-camera label information. To deal with this issue, we propose a novel Progressive Cross-camera Soft-label Learning (PCSL) framework for the semi-supervised person Re-ID task, which can generate cross-camera soft-labels and utilize them to optimize the network. Concretely, we calculate an affinity matrix based on person-level features and adapt them to produce the similarities between cross-camera persons (i.e., cross-camera soft-labels). To exploit these soft-labels to train the network, we investigate the weighted cross-entropy loss and the weighted triplet loss from the classification and discrimination perspectives, respectively. Particularly, the proposed framework alternately generates progressive cross-camera soft-labels and gradually improves feature representations in the whole learning course. Extensive experiments on five large-scale benchmark datasets show that PCSL significantly outperforms the state-of-the-art unsupervised methods that employ labeled source domains or the images generated by the GANs-based models. Furthermore, the proposed method even has a competitive performance with respect to deep supervised Re-ID methods.
AsgLDP: Collecting and Generating Decentralized Attributed Graphs With Local Differential Privacy

A large amount of valuable information resides in a decentralized attributed social graph, where each user locally maintains a limited view of the graph. However, there exists a conflicting requirement between publishing an attributed social graph and protecting the privacy of sensitive information contained in each user's local data. In this paper, we aim to collect and generate attributed social graphs in a decentralized manner while providing local differential privacy (LDP) for the collected data. Existing LDP-based synthetic graph generation methods either fail to preserve important graph properties (such as modularity and clustering coefficient) due to excessive noise injection or are unable to process attribute data, thus limiting their adoption and applicability. To overcome these weaknesses, we propose AsgLDP, a novel technique to generate privacy-preserving attributed graph data while satisfying LDP. AsgLDP preserves various graph properties through carefully designing the injected noise and estimating the joint distribution of attribute data. There are two key steps in AsgLDP: 1) collecting and generating graph data while satisfying LDP, and 2) optimizing the privacy-utility tradeoff of the generated data while preserving general graph properties such as the degree distribution, community structure and attribute distribution. Through theoretical analysis as well as experiments over 6 real-world datasets, we demonstrate the effectiveness of AsgLDP in preserving general graph properties such as degree distribution, community structure and attributed community search, while rigorously satisfying LDP. We also show that AsgLDP achieves a superior balance between utility and privacy as compared to the state-of-the-art approaches.