Reviewerly/exHarmony huggingface dataset:
Classifying issues into custom labels in GitBot

GitBots are bots in Git repositories to automate repetitive tasks that occur in software development, testing and maintenance. GitBots are expected to perform the repetitive tasks that are normally done by humans, such as feedback on issue reports and answers to questions. However, studies on GitBots for labeling issue reports fall short of replacing developers' labeling tasks. Developers still manually attach labels to issues. In this paper, we introduce an issue labeling bot classifying issue reports into custom labels that developers define by themselves so that our bot could attach labels in a similar way to human behavior.
Temporal View Synthesis of Dynamic Scenes through 3D Object Motion Estimation with Multi-Plane Images

The challenge of graphically rendering high frame-rate videos on low compute devices can be addressed through periodic prediction of future frames to enhance the user experience in virtual reality applications. This is studied through the problem of temporal view synthesis (TVS), where the goal is to predict the next frames of a video given the previous frames and the head poses of the previous and the next frames. In this work, we consider the TVS of dynamic scenes in which both the user and objects are moving. We design a framework that decouples the motion into user and object motion to effectively use the available user motion while predicting the next frames. We predict the motion of objects by isolating and estimating the 3D object motion in the past frames and then extrapolating it. We employ multi-plane images (MPI) as a 3D representation of the scenes and model the object motion as the 3D displacement between the corresponding points in the MPI representation. In order to handle the sparsity in MPIs while estimating the motion, we incorporate partial convolutions and masked correlation layers to estimate corresponding points. The predicted object motion is then integrated with the given user or camera motion to generate the next frame. Using a disocclusion infilling module, we synthesize the regions uncovered due to the camera and object motion. We develop a new synthetic dataset for TVS of dynamic scenes consisting of 800 videos at full HD resolution. We show through experiments on our dataset and the MPI Sintel dataset that our model outperforms all the competing methods in the literature.
A comparative study of different sentiment analysis classifiers for cybercrime detection on social media platforms

In the current scenario, social media has made it very easy to access and exploit the different types of data from various social media platforms, which are freely available to everyone to share their opinions openly. With this open access, the privacy and security of all social media users is a cause of concern and matters. Sentiment analysis plays an essential role in social media security as it is used by many applications domains like risk management, anomaly detection and disaster relief.The article proposes the sentiment analysis approaches and methods for social media defence and assessment. A study on the security challenges related to the user security breach, e-commerce websites, fake news, cyber-bullying, credibility on social media is presented in this paper. Here, the major challenge under consideration is phoney news detection on several social media platform. A brief outline of two classifiers, namely Multinomial Naïve Bayes and Passive-Aggressive Classifier, is presented initially. Then a comparison of two classifiers is conducted to analyze the techniques and methodologies on the Twitter dataset. The results from both the classifiers are discussed based on the performance metrics and the accuracy score. Finally, the article derives some important conclusion and future direction in this domain.
PAC-MAN: Multi-Relation Network in Social Community for Personalized Hashtag Recommendation

Previous personalized hashtag recommendations have been able to recommend suitable hashtags for a given microblog. Despite their performance improvement, we argue that three challenges remain unexplored. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">First</i> , prior studies capture user interests solely from user-hashtag interactions that are directly connected (i.e., first-order relations), making them unable to deal with multiple user behaviors, including user-user social and hashtag-hashtag co-occurrence, and also restrict relations from similar users that are indirectly connected (i.e., high-order relations). <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Second</i> , previous works personalize content at the microblog level, ignoring the personalized aspects that users have for each word in the microblog. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Third</i> , past studies capture correlations among hashtags in the same microblog from only the left-side correlations, restricting the right-side correlations. In this paper, we propose a novel integral model for personalized hashtag recommendation named PAC-MAN, which explores high-order multiple relations to model fruitful user and hashtag representation before fusing with word representation for word-level personalization and integrating with sequenceless hashtag correlation for the recommendation. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">First</i> , to derive fruitful user and hashtag representation from higher-order multiple relations, Multi-relational Attentive Network (MAN) applies GNN to jointly capture relations on three communities: (1) user-hashtag interaction; (2) user-user social; and (3) hashtag-hashtag co-occurrence. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Second</i> , to personalize content at a word level, Person-And-Content based BERT (PAC) extends BERT to input not only word representations from the microblog but also the fruitful user representation from MAN, allowing each word to be fused with user aspects. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Finally</i> , to capture sequenceless hashtag correlations, the fruitful hashtag representations from MAN that contain the hashtag’s community perspectives are inserted into BERT to integrate with the hashtag’s word-semantic perspectives, and a hashtag prediction task is then conducted under the mask concept, enabling hashtag correlations to be obtained from both left and right sides without sequence constraints. Extensive experiments on the Twitter dataset demonstrate that PAC-MAN consistently outperforms state-of-the-art methods, including neural network based and traditional graph based methods, over precision, recall, and F1-score metrics.
EvolvingBehavior: Towards Co-Creative Evolution of Behavior Trees for Game NPCs

To assist game developers in crafting game NPCs, we present EvolvingBehavior, a novel tool for genetic programming to evolve behavior trees in Unreal®Engine 4. In an initial evaluation, we compare evolved behavior to hand-crafted trees designed by our researchers, and to randomly-grown trees, in a 3D survival game. We find that EvolvingBehavior is capable of producing behavior approaching the designer’s goals in this context. Finally, we discuss implications and future avenues of exploration for co-creative game AI design tools, as well as challenges and difficulties in behavior tree evolution.
TB-ICT: A Trustworthy Blockchain-Enabled System for Indoor Contact Tracing in Epidemic Control

Recently, as a consequence of the coronavirus disease (COVID-19) pandemic, dependence on contact tracing (CT) models has significantly increased to prevent the spread of this highly contagious virus and be prepared for the potential future ones. Since the spreading probability of the novel coronavirus in indoor environments is much higher than that of the outdoors, there is an urgent and unmet quest to develop/design efficient, autonomous, trustworthy, and secure indoor CT solutions. Despite such an urgency, this field is still in its infancy. This article addresses this gap and proposes the trustworthy blockchain-enabled system for an indoor CT (TB-ICT) framework. The TB-ICT framework is proposed to protect privacy and integrity of the underlying CT data from unauthorized access. More specifically, it is a fully distributed and innovative blockchain platform exploiting the proposed dynamic Proof-of-Work (dPoW) credit-based consensus algorithm coupled with randomized hash window (W-Hash) and dynamic Proof-of-Credit (dPoC) mechanisms to differentiate between honest and dishonest nodes. The TB-ICT not only provides a decentralization in data replication but also quantifies the node’s behavior based on its underlying credit-based mechanism. For achieving a high localization performance, we capitalize on the availability of Internet of Things (IoT) indoor localization infrastructures, and develop a data-driven localization model based on bluetooth low-energy (BLE) sensor measurements. The simulation results show that the proposed TB-ICT prevents the COVID-19 from spreading by the implementation of a highly accurate CT model while improving the users’ privacy and security.
Research on cloud computing resource scheduling based on machine learning

In the cloud computing environment, concurrent training of multiple machine learning models will cause serious competition for shared cluster resources and affect the execution efficiency. Aiming at this problem, this paper proposes a cloud computing resource scheduling method for distributed machine learning. Based on historical monitoring data, a model between the number of iterations and model quality improvement is established, the impact of resource allocation on model quality improvement is predicted online, resource optimization scheduling strategies are formulated, and a resource scheduling framework is designed. Experimental results show that the proposed method can quickly adapt to the dynamic changes of tasks and loads and maximize the overall performance of multiple model training jobs.
Spatially-robust image style transfer for headshot portraits

For an input image, the image style transfer is to change the style of the input image accordingly without modifying its original content. Our empirical studies discover that, when the existing style transfer techniques are applied to headshot portraits, the noticeable distortions could be incurred due to the fact that the relative location of those facial components, such as lips, noses, and eyes, are sensitive to spatial differences. Based on the DPST (deep photo style transfer) method, a spatially-robust image style transfer algorithm for headshot portraits is proposed in this paper to improve the robustness of image style transfer. The proposed algorithm provides an improved solution for the problem that the transferred content could incur ghost shadows when segmented regions between the input image and the style image are significantly different. By maximizing the normalized cross-correlation of the channels of feature maps of corresponding segmented regions of the input image and the style image in a pre-trained convolutional neural network, we propose to apply affine transformations to the style image before it is used for the style transfer. Each segmented region takes the corresponding spatially transformed style image as style reference, making it adaptive to the spatial difference between the input and the style image and the incurred ghost shadow can be minimized or eliminated. The experimental results show that our proposed algorithm can obtain good results even when the semantic segmentation between two images have the large spatial differences. The new algorithm also displays the better robustness than the DPST algorithm and other benchmark.
Differentially Private Distributed Frequency Estimation

In order to remain competitive, Internet companies collect and analyse user data for the purpose of the improvement of user experiences. Frequency estimation is a widely used statistical tool, which could potentially conflict with the relevant privacy regulations. Privacy preserving analytic methods based on differential privacy have been proposed, which require either a large user base or a trusted server. Although the requirements for such solutions may not be a problem for larger companies, they may be unattainable for smaller organizations. To address this issue, we propose a distributed privacy-preserving sampling-based frequency estimation method which has high accuracy even in the scenario with a small number of users while not requiring any trusted server. This is achieved by combining multi-party computation and sampling techniques. We also provide a relation between its privacy guarantee, output accuracy, and the number of participants. Distinct from most existing methods, our methods achieve <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">centralized</i> differential privacy guarantee without the need of any trusted server. We established that, even for a small number of participants, our mechanisms can produce estimates with high accuracy and hence they provide smaller companies with more opportunity for growth through privacy-preserving statistical analysis. We further propose an architectural model to support weighted aggregation in order to achieve a higher accuracy estimate to cater for users with varying privacy requirements. Compared to the unweighted aggregation, our method provides a more accurate estimate. Extensive experiments are conducted to show the effectiveness of the proposed methods.
Artificial Psychologist: An intelligent virtual/robotic assistant based on a cognitive modeling framework

Applications of socially emotional artificial intelligence are demanded in many practical areas, including psychological counselling and psychotherapy. A person who is potentially in trouble and needs help has to overcome an internal barrier before deciding to see a psychiatrist. On the other hand, obtaining an anonymous online consultation or taking an online test is psychologically easy and acceptable for the many. At the same time, state-of-the-art solutions of this sort are limited. Here a concept of an intelligent system is proposed that should help to alleviate the problem. The system can be implemented as an embodied actor based on a virtual environment, VR/XR, or a robotic platform with social-emotional capabilities. The actor is controlled by a cognitive architecture with a possibility of its replacement by a deep neural network model. Interaction with the user is based on a multimodal human-computer interface. Possibilities that will be provided by this virtual psychologist system will enable conducting an initial screening, the results of which can later be used at the next session with a specialist, if chosen by the user. The key issue of ethics, privacy and anonymity is discussed. Systems of this sort could be used beyond psychological counseling and are expected to have an impact on the healthcare system in general.
Graph Classification via Graph Structure Learning

With the ability of representing structures and complex relationships between data, graph learning is widely applied in many fields. The problem of graph classification is important in graph analysis and learning. There are many popular graph classification methods based on substructures such as graph kernels or ones based on frequent subgraph mining. Graph kernels use handcraft features, hence it is so poor generalization. The process of frequent subgraph mining is NP-complete because we need to test isomorphism subgraph, so methods based on frequent subgraph mining are ineffective. To address this limitation, in this work, we proposed novel graph classification via graph structure learning, which automatically learns hidden representations of substructures. Inspired by doc2vec, a successful and efficient model in Natural Language Processing, graph embedding uses rooted subgraph and topological features to learn representations of graphs. Then, we can easily build a Machine Learning model to classify them. We demonstrate our method on several benchmark datasets in comparison with state-of-the-art baselines and show its advantages for classification tasks.
Boundaries, Extensions, and Challenges of Visualization for Humanities Data: Reflections on Three Cases

This paper discusses problems of visualizing humanities data of various forms, such as video data, archival data, and numeric-oriented social science data, with three distinct case studies. By describing the visualization practices and the issues that emerged from the process, this paper uses the three cases to each identify a pertinent question for reflection. More specifically, I reflect on the difficulty, thoughts, and considerations of choosing the most effective and sufficient forms of visualization to enhance the expression of specific cultural and humanities data in the projects. Discussions in this paper concern some questions, such as, how do the multi-modality of humanities and cultural data challenge the understanding, roles, and functions of visualizations, and more broadly, visual representations in humanities research? What do we lose of the original data by visualizing them in those projects? How to balance the benefits and disadvantages of visual technologies to display complex, unique, and often culturally saturated humanities datasets?
Frequentist Perspective on Robust Parameter Estimation Using the Ensemble Kalman Filter

Standard maximum likelihood or Bayesian approaches to parameter estimation for stochastic differential equations are not robust to perturbations in the continuous-in-time data. In this paper, we give a rather elementary explanation of this observation in the context of continuous-time parameter estimation using an ensemble Kalman filter. We employ the frequentist perspective to shed new light on three robust estimation techniques; namely subsampling the data, rough path corrections, and data filtering. We illustrate our findings through a simple numerical experiment.
Kinematics Modeling and Motions Analysis of Non-holonomic Mobile Robot

A study on kinematics modeling of non-holonomic mobile robot and the motion analysis is presented. The robot is a four-wheeled robot with two active wheels and two passive wheels. Each active wheel is driven by an independent DC motor. The robot has two degrees of freedom (2-DOF) of motions including translation and rotation. Kinematics modeling is carried out to obtain a mathematics model representing the robot motions. The resulted model is verified through analyzing the robot motion using calculus of parametric equations. A computer program is built based on the model for numeric simulation and visualization of the robot motions. Executing the program resulted in numerical data of the robot motion that was confirmed by an animation of the robot movement. The numerical data includes the position, orientation, linear and angular velocities of the robot, and the corresponding DC motors speed.
Candidate Trapdoor Claw-Free Functions from Group Actions with Applications to Quantum Protocols

Trapdoor Claw-free Functions (TCFs) are two-to-one trapdoor functions where it is computationally hard to find a claw, i.e., a colliding pair of inputs. TCFs have recently seen a surge of renewed interest due to new applications to quantum cryptography: as an example, TCFs enable a classical machine to verify that some quantum computation has been performed correctly. In this work, we propose a new family of (almost two-to-one) TCFs based on conjectured hard problems on isogeny-based group actions. This is the first candidate construction that is not based on lattice-related problems and the first scheme (from any plausible post-quantum assumption) with a deterministic evaluation algorithm. To demonstrate the usefulness of our construction, we show that our TCF family can be used to devise a computational test of a qubit, which is the basic building block used in the general verification of quantum computations.
Secure and Efficient k-Nearest Neighbor Query with Privacy-Preserving Authentication

Dealing with k-nearest neighbor (kNN) on untrusted cloud servers without revealing private information is an existing challenge. Although a large number of encryption and authentication techniques have been studied to guarantee data privacy and the integrity of results, there are defective in efficiency and security. This paper focuses on finding k-nearest neighbor (kNN) on encrypted data and verifying the query results. Firstly, this paper designs a novel index structure to support sub-linear computation. Based on it, we further propose a batch reading protocol for a faster read operator, by way of batch reading it is more suitable for large-scale kNN search. Secondly, all calculations are performed under ciphertext without clouds learning anything about the dataset, query and result, other indirect information such as access pattern privacy and intermediate result privacy also are guaranteed to resist the latest data recovery attacks. Moreover, this paper designs a verifiable strategy for secure kNN. Our verification process considers the privacy of authentication, which hides the confidential or unnecessary data in Verification Object using Paillier. This work integrates the index structure, verifiable structure and Paillier encryption to build secure and verifiable kNN scheme that gains strong privacy and low latency. Detailed experiments and analysis also are performed in this paper, and our schemes S-kQ and SV-kQ are an order of magnitude faster than the state-of-the-art work on real-world datasets.
Automatic laser steering for middle ear surgery

This paper deals with the control of a laser spot in the context of minimally invasive surgery of the middle ear, e.g., cholesteatoma removal. More precisely, our work is concerned with the exhaustive burring of residual infected cells after primary mechanical resection of the pathological tissues since the latter cannot guarantee the treatment of all the infected tissues, the remaining infected cells cause regeneration of the diseases in 20%-25 % of cases, which require a second surgery 12–18 months later. To tackle such a complex surgery, we have developed a robotic platform that consists of the combination of a macro-scale system (7 degrees of freedoms (DoFs) robotic arm) and a micro-scale flexible system (2 DoFs) which operates inside the middle ear cavity. To be able to treat the residual cholesteatoma regions, we proposed a method to generate optimal laser automatically scanning trajectories inside the areas and between them. The trajectories are tacked using an image-based control scheme. The proposed method and materials were validated experimentally using the lab-made robotic platform. The obtained results in terms of accuracy and behaviour meet the laser surgery requirements perfectly.