Reviewerly/exHarmony huggingface dataset:
Seed-free Graph De-anonymiztiation with Adversarial Learning

The huge amount of graph data are published and shared for research and business purposes, which brings great benefit for our society. However, user privacy is badly undermined even though user identity can be anonymized. Graph de-anonymization to identify nodes from an anonymized graph is widely adopted to evaluate users' privacy risks. Most existing de-anonymization methods which are heavily reliant on side information (e.g., seeds, user profiles, community labels) are unrealistic due to the difficulty of collecting this side information. A few graph de-anonymization methods only using structural information, called seed-free methods, have been proposed recently, which mainly take advantage of the local and manual features of nodes while overlooking the global structural information of the graph for de-anonymization.
Strategies for Enhancing Training and Privacy in Blockchain Enabled Federated Learning

Several recent advances in Federated Learning have made it possible for researchers to train their models on private data present on contributing devices without compromising their privacy. In this paradigm, each contributor’s local updates are aggregated and averaged to update the global model. In this paper, we introduce a secure and decentralized training for distributed data. In order to develop an efficient decentralized system, blockchain technology is introduced via Ethereum, which enables us to create a value-driven incentive mechanism. This is done to encourage the contributors to positively affect the learning of the global model. We provide an enhanced security mechanism by implementing differential privacy and homomorphic encryption. The performance of the global model has been significantly boosted by implementing Elastic Weight Consolidation, which prevents Catastrophic forgetting, a scenario where the model learns only on new data and forgets its previous learnings. It proves essential in distributed training since the model is being trained on a spectrum of data, often present in clusters on each contributor’s device. We introduce an innovative way of using hyperparameter optimization in federated learning with the help of Hyperopt and deposit based reward mechanism. Experiments verify the capability of the novel strategies incorporated in our system.
Multimodal Knowledge Graph for Deep Learning Papers and Code

Keeping up with the rapid growth of Deep Learning (DL) research is a daunting task. While existing scientific literature search systems provide text search capabilities and can identify similar papers, gaining an in-depth understanding of a new approach or an application is much more complicated. Many publications leverage multiple modalities to convey their findings and spread their ideas - they include pseudocode, tables, images and diagrams in addition to text, and often make publicly accessible their implementations. It is important to be able to represent and query them as well. We utilize RDF Knowledge graphs (KGs) to represent multimodal information and enable expressive querying over modalities. In our demo we present an approach for extracting KGs from different modalities, namely text, architecture images and source code. We show how graph queries can be used to get insights into different facets (modalities) of a paper, and its associated code implementation. Our innovation lies in the multimodal nature of the KG we create. While our work is of direct interest to DL researchers and practitioners, our approaches can also be leveraged in other scientific domains.
Enhancing Privacy via Hierarchical Federated Learning

Federated learning suffers from several privacy-related issues that expose the participants to various threats. A number of these issues are aggravated by the centralized architecture of federated learning. In this paper, we discuss applying federated learning on a hierarchical architecture as a potential solution. We introduce the opportunities for more flexible decentralized control over the training process and its impact on the participants' privacy. Furthermore, we investigate possibilities to enhance the efficiency and effectiveness of defense and verification methods.
A Comparative Study on Convolutional Neural Network Based Face Recognition

This paper presents a comparative study to recognize faces from a customized dataset of 10 identities of different celebrities using Convolutional Neural Network based models such as AlexNet, VGG16, VGG19 and MobileNet. These pre-trained models previously trained on ImageNet dataset are used with the application of Transfer Learning and Fine Tuning. For our experiment we used Keras API with TensorFlow backend written in Python. The performance analysis includes training, validation, and testing on different images created from original dataset. The validation accuracy of VGG19 model is found better than the other three but MobileNet model showed better test accuracy.
Deep learning-based compressed image artifacts reduction based on multi-scale image fusion

One of the visually noticeable compression artifacts in block-based image/video compression platforms is called blocking artifact. Several post-processing methods were presented to reduce such kind of artifacts. However, most methods in the literature often induce visibly blurring artifacts. The paper presents a deep network to eliminate image compression artifacts (usually denoted by image deblocking) based on image fusion in multi-scale manner. Recent deep learning-based related methods usually learn deep models using a loss function in per-pixel manner based on explicit image priors in order to directly produce clean images. In place of existing deep learning-guided approaches, the problem is reformulated in this paper to the learning of the residuals (or artifacts) between the received images and their corresponding clean images (ground truths). In the presented deep framework, an input image is first down-sampled while naturally reducing the blocking artifacts. Then, our multi-scale image fusion model is used for fusing the different down-scaled versions (of less artifacts) with the input image (with severer artifacts) to estimate the blocking artifacts. Then, by deducting the estimated artifacts from the input image, the blocking artifacts can be significantly eliminated and most original image details are preserved simultaneously. The presented method is well applicable to any vision-based computer systems with digital visual codec embedded.
Small-Step Live Programming by Example

Live programming is a paradigm in which the programming environment continually displays runtime values. Program synthesis is a technique that can generate programs or program snippets from examples. \deltextThis paper presents a new programming paradigm called Synthesis-Aided Live Programming that combines these two prior ideas in a synergistic way. When using Synthesis-Aided Live Programming, programmers can change the runtime values displayed by the live \addtextPrevious works that combine the two have taken a holistic approach to the way examples describe the behavior of functions and programs. This paper presents a new programming paradigm called Small-Step Live Programming by Example that lets the user apply Programming by Example locally. When using Small-Step Live Programming by Example, programmers can change the runtime values displayed by the live visualization to generate local program snippets. % Live programming and program % synthesis work perfectly together because the live programming environment % reifies values, which makes it easy for programmers to provide the examples % needed by the synthesizer. We implemented this new paradigm in a tool called \toolname, and performed a user study on $13$ programmers. Our study finds that Small-Step Live Programming by Example with \toolname helps users solve harder problems faster, and that for certain types of queries, users prefer it to searching the web. Additionally, we identify the \usersynthgap, in which users' mental models of the tool do not match its ability, and needs to be taken into account in the design of future synthesis tools.
Fall Detection for Elderly People using Machine Learning

Health is the major worrisome point whose impalpability increases with increase in the age. Thus, taking care of elders is very important responsibility. In such scenario, technology is helping people by providing living assistance. One of the major causes of health degradation or death of elders is `fall'. In this paper, a fall detection system is proposed based on machine learning. The system detects falls by classifying different activities into fall and non-fall actions and alert the relative or care taker of the elderly person in case of emergency. The dataset SisFall with variety of activities of multiple participants is used to calculate features. Machine learning algorithms SVM and decision tree are used to detect the falls on the basis of calculated features. The system acquires accuracy up to 96% by using decision tree algorithm.
Spatial forecasting of solar radiation using ARIMA model

This paper is an attempt to forecast monthly solar radiation using remote sensing data on a region. Seasonal ARIMA (SARIMA) models are used for simulating and forecasting time series of insolation data from NASA's POWER (Prediction of Worldwide Energy Resources) data archive. Remotely sensed modelled insolation data for 34 years (i.e. January 1984 to December 2017) has been retrieved and analysed for forecasting. Monthly average insolation forecasts of the region around India's capital Delhi have been generated for next four years (i.e. January 2018 to December 2021) and presented in the form of contours obtained using marching square algorithm. The overall accuracy of forecasts in terms of R2 (0.9293), Root Mean Square Error (0.3529), Mean Absolute Error (0.2659) and Mean Absolute Percentage Error (6.556) was obtained. The ARIMA model forecasted the maximum insolation values in the months of May (6.52–6.76 KwH/m2/day in year 2018, 6.56–6.8 KwH/m2/day in 2019, 6.6–6.8 KwH/m2/day in 2020 and 6.6–6.84 KwH/m2/day in 2021) and Minimum in the months of January and December (3.2–3.7 KwH/m2/day in January 2018, 3.28–3.52 KwH/m2/day in December 2018). Insolation contours were analysed for identification of potential regions receiving maximum insolation as well as high average annual values of insolation for implementing efficient solar power generation projects. Parts of Haryana and Rajasthan region in study area were found most suitable for such projects.
Biomedical signals reconstruction and zero-watermarking using separable fractional order Charlier–Krawtchouk transformation and Sine Cosine Algorithm

In this paper, we present a novel discrete orthogonal transformation called the separable Charlier-Krawtchouk transformation of fractional order (FrCKTs). This bivariate transformation is defined from the monovariable Charlier and Krawtchouk polynomials of fractional orders. Based on FrCKTs and the Sine Cosine Algorithm (SCA), a new method for the optimal reconstruction of large-size biomedical signals (bio-signals) is proposed. In addition, we propose a new technique for the zero watermarking of bio-signals for the protection of intellectual property rights. The results of simulations and comparisons clearly show the effectiveness of the proposed applications in the field of bio-signals.
Deep reinforcement learning for quadrotor path following with adaptive velocity

This paper proposes a solution for the path following problem of a quadrotor vehicle based on deep reinforcement learning theory. Three different approaches implementing the Deep Deterministic Policy Gradient algorithm are presented. Each approach emerges as an improved version of the preceding one. The first approach uses only instantaneous information of the path for solving the problem. The second approach includes a structure that allows the agent to anticipate to the curves. The third agent is capable to compute the optimal velocity according to the path’s shape. A training framework that combines the tensorflow-python environment with Gazebo-ROS using the RotorS simulator is built. The three agents are tested in RotorS and experimentally with the Asctec Hummingbird quadrotor. Experimental results prove the validity of the agents, which are able to achieve a generalized solution for the path following problem.
Static Hand Gesture Recognition for American Sign Language using Deep Convolutional Neural Network

One of the complicated issues of computer vision is the recognition of any sign languages. The deaf and the dumb people use these sign languages to communicate. In the area of deep learning at recent progress there are numerous applications that neural networks can have for interpreting sign languages. The recognition of American Sign Language static images employing a capable artificial intelligence tool, Convolutional Neural Network has been proposed in this paper. ASL dataset of 1815 images of 26 English alphabets has been used to train and validate our model. Validation accuracy has been found 94.34% which is better than many existing methods.
Towards Target-Driven Visual Navigation in Indoor Scenes via Generative Imitation Learning

We present a target-driven navigation system to improve mapless visual navigation in indoor scenes. Our method takes a multi-view observation of a robot and a target image as inputs at each time step to provide a sequence of actions that move the robot to the target without relying on odometry or GPS at runtime. The system is learned by optimizing a combinational objective encompassing three key designs. First, we propose that an agent conceives the next observation before making an action decision. This is achieved by learning a variational generative module from expert demonstrations. We then propose predicting static collision in advance, as an auxiliary task to improve safety during navigation. Moreover, to alleviate the training data imbalance problem of termination action prediction, we also introduce a target checking module to differentiate from augmenting navigation policy with a termination action. The three proposed designs all contribute to the improved training data efficiency, static collision avoidance, and navigation generalization performance, resulting in a novel target-driven mapless navigation system. Through experiments on a TurtleBot, we provide evidence that our model can be integrated into a robotic system and navigate in the real world. Videos and models can be found in the supplementary material.
Towards Physically Interactive Virtual Environments: Reactive Alignment with Redirected Walking

Interactions with the physical environment, such as passive haptic feedback, have been previously shown to provide richer and more immersive virtual reality experiences. A strict correspondence between the virtual and real world coordinate systems is a staple requirement for physical interaction. However, many of the commonly employed VR locomotion techniques allow for, or even require, this relationship to change as the experience progresses. The outcome is that experience designers frequently have to choose between flexible locomotion or physical interactivity, as the two are often mutually exclusive. To address this limitation, this paper introduces reactive environmental alignment, a novel framework that leverages redirected walking techniques to achieve a desired configuration of the virtual and real world coordinate systems. This approach can transition the system from a misaligned state to an aligned state, thereby enabling the user to interact with physical proxy objects or passive haptic surfaces. Simulation-based experiments demonstrate the effectiveness of reactive alignment and provide insight into the mechanics and potential applications of the proposed algorithm. In the future, reactive environmental alignment can enhance the interactivity of virtual reality systems and inform new research vectors that combine redirected walking and passive haptics.
A Secure and Efficient ECC-Based Scheme for Edge Computing and Internet of Things

Recent growth in the Internet of Things (IoT) has raised security concerns over the confidentiality of data exchanged between IoT devices and the edge. Many IoT systems adopt asymmetric cryptography to secure their data and communications. A drawback of asymmetric cryptography is the sizeable computation and space requirements. However, elliptic curve cryptography (ECC) is widely used in constrained environments for asymmetric cryptography due its superiority in generating a powerful encryption mechanism with small key sizes. ECC increases device performance and lowers power consumption, meaning it is suitable for diverse applications ranging from the IoT to wireless sensor network (WSN) devices. To ensure the confidentiality and security of data and communications, it is necessary to implement ECC robustly. A special area of focus in this regard is the mapping phase. This study’s objective was to propose a tested and trusted scheme that offers authenticated encryption (AE) via enhancing the mapping phase of a plain text to an elliptic curve to resist several encryption attacks such as Chosen Plaintext Attack (CPA) and Chosen Ciphertext Attack (CCA). The proposed scheme also undertakes evaluation and analysis related to security requirements for specific encryption attributes. Finally, results from a comparison of the proposed scheme and other schemes are presented, evaluating each one’s security characteristics and performance measurements. Our scheme is efficient in a way that makes so suitable to the IoT, and in particular to the Industrial IoT and the new Urbanization where the demands for services are huge.
Enabling Deep Residual Networks for Weakly Supervised Object Detection

Weakly supervised object detection (WSOD) has attracted extensive research attention due to its great flexibility of exploiting large-scale image-level annotation for detector training. Whilst deep residual networks such as ResNet and DenseNet have become the standard backbones for many computer vision tasks, the cutting-edge WSOD methods still rely on plain networks, e.g., VGG, as backbones. It is indeed not trivial to employ deep residual networks for WSOD, which even shows significant deterioration of detection accuracy and non-convergence. In this paper, we discover the intrinsic root with sophisticated analysis and propose a sequence of design principles to take full advantages of deep residual learning for WSOD from the perspectives of adding redundancy, improving robustness and aligning features. First, a redundant adaptation neck is key for effective object instance localization and discriminative feature learning. Second, small-kernel convolutions and MaxPool down-samplings help improve the robustness of information flow, which gives finer object boundaries and make the detector more sensitivity to small objects. Third, dilated convolution is essential to align the proposal features and exploit diverse local information by extracting high-resolution feature maps. Extensive experiments show that the proposed principles enable deep residual networks to establishes new state-of-the-arts on PASCAL VOC and MS COCO.
Assessing Preknowledge Cheating via Innovative Measures: A Multiple-Group Analysis of Jointly Modeling Item Responses, Response Times, and Visual Fixation Counts

Many approaches have been proposed to jointly analyze item responses and response times to understand behavioral differences between normally and aberrantly behaved test-takers. Biometric information, such as data from eye trackers, can be used to better identify these deviant testing behaviors in addition to more conventional data types. Given this context, this study demonstrates the application of a new method for multiple-group analysis that concurrently models item responses, response times, and visual fixation counts collected from an eye-tracker. It is hypothesized that differences in behavioral patterns between normally behaved test-takers and those who have different levels of preknowledge about the test items will manifest in latent characteristics of the different data types. A Bayesian estimation scheme is used to fit the proposed model to experimental data and the results are discussed.
A Socially-Aware Conversational Recommender System for Personalized Recipe Recommendations

One potential solution to help people change their eating behavior is to develop conversational systems able to recommend healthy recipes. Beyond the intrinsic quality of the recommendations themselves, various factors might also influence users? perception of a recommendation. Two of these factors are the conversational skills of the system and users' interaction modality. In this paper, we present Cora, a conversational system that recommends recipes aligned with its users? eating habits and current preferences. Users can interact with Cora in two different ways. They can select predefined answers by clicking on buttons to talk to Cora or write text in natural language. On the other hand, Cora can engage users through a social dialogue, or go straight to the point. We conduct an experiment to evaluate the impact of Cora's conversational skills and users' interaction mode on users' perception and intention to cook the recommended recipes. Our results show that a conversational recommendation system that engages its users through a rapport-building dialogue improves users' perception of the interaction as well as their perception of the system.
Aspect Based Sentiment Analysis with Self-Attention and Gated Convolutional Networks

Aspect based sentiment analysis (ABSA) is a fine-grained sentiment analysis task, whose main goal is to identify the sentiment polarity of an aspect in a sentence. A sentence may contain many different aspects, each of which may have different sentiment polarities. Based on the current researches in this area, ABSA can be divided into two subtasks: aspect-category sentiment analysis (ACSA) and aspect-term sentiment analysis (ATSA). In the past, more commonly used method is to adopt the time serial algorithm such as Long Short-Term Memory (LSTM) or Recurrent Neural Network (RNN), which usually needs more training time and has complex structures. Moreover, many previous models lack the abilities to effectively learn the internal structure features of sentences. For ABSA, sometimes the sentence structure may significantly affect the final classification results. However, we found the excellent performance of self-attention algorithm and gating mechanism in some other related researches. Therefore, to solve the problems above, we build a new model based on gating mechanism, combined with convolutional neural networks (CNN) and self-attention mechanism. First, we use self-attention to extract the structural feature of the input, and integrate it with the features of the original sentence extracted by CNN. On such basis, we further combine the aspect-category or aspect-term of the input sentence to form the final sentiment feature. Experiments on SemEval datasets show the performance of our models and the effectiveness of the model is proved.