Reviewerly/exHarmony huggingface dataset:
NormalFusion: Real-Time Acquisition of Surface Normals for High-Resolution RGB-D Scanning

Multiview shape-from-shading (SfS) has achieved high-detail geometry, but its computation is expensive for solving a multiview registration and an ill-posed inverse rendering problem. Therefore, it has been mainly used for offline methods. Volumetric fusion enables real-time scanning using a conventional RGB-D camera, but its geometry resolution has been limited by the grid resolution of the volumetric distance field and depth registration errors. In this paper, we propose a real-time scanning method that can acquire high-detail geometry by bridging volumetric fusion and multiview SfS in two steps. First, we pro-pose the first real-time acquisition of photometric normals stored in texture space to achieve high-detail geometry. We also introduce geometry-aware texture mapping, which progressively refines geometric registration between the texture space and the volumetric distance field by means of normal texture, achieving real-time multiview SfS. We demonstrate our scanning of high-detail geometry using an RGB-D cam-era at ∼20 fps. Results verify that the geometry quality of our method is strongly competitive with that of offline multi-view SfS methods.
Wide Receptive Field and Channel Attention Network for JPEG Compressed Image Deblurring

A motion blurred image stored in the joint photographic experts group (JPEG) image compression format contains both motion blur and JPEG artifacts. Therefore, it is very difficult to restore the original image from a blurred and JPEG-compressed image. To address this problem, this paper proposes two methods: a wide receptive field and channel attention network (WRCAN), and JPEG auto-encoder loss. First, the WRCAN utilizes a large receptive field and considers the interdependencies among channels of a feature map. Second, the proposed JPEG auto-encoder loss enables the WRCAN to learn prior knowledge of JPEG compression artifacts such that the proposed WRCAN can effectively restore the original image from JPEG-compressed images. The proposed methods are evaluated on the JPEG-compressed REDS dataset by participating in the NTIRE 2021 workshop challenges on Image Deblurring Track 2 JPEG artifacts. The WRCAN trained with the proposed loss ranked third with an output of 29.60dB on the REDS test set, indicating that the proposed methods provide state-of-the-art results. The source codes, model, and data are available at https://github.com/dhyeonlee/WRCAN-PyTorch.
RAID: Tool Support for Refactoring-Aware Code Reviews

Code review is a key development practice that contributes to improve software quality and to foster knowledge sharing among developers. However, code review usually takes time and demands detailed and time-consuming analysis of textual diffs. Particularly, detecting refactorings during code reviews is not a trivial task, since they are not explicitly represented in diffs. For example, a Move Function refactoring is represented by deleted (-) and added lines (+) of code which can be located in different and distant source code files. To tackle this problem, we introduce RAID, a refactoring-aware and intelligent diff tool. Besides proposing an architecture for RAID, we implemented a Chrome browser plug-in that supports our solution. Then, we conducted a field experiment with eight professional developers who used RAID for three months. We concluded that RAID can reduce the cognitive effort required for detecting and reviewing refactorings in textual diff. Besides documenting refactorings in diffs, RAID reduces the number of lines required for reviewing such operations. For example, the median number of lines to be reviewed decreases from 14.5 to 2 lines in the case of move refactorings and from 113 to 55 lines in the case of extractions.
Transfer of Hierarchical Reinforcement Learning Structures for Robotic Manipulation Tasks

While it is apparent that the transfer of knowledge between tasks is beneficial for training efficiency, the application of trained deep reinforcement learning agents to solve new tasks is not trivial. Especially when tasks are differently structured, retraining and fine tuning is not necessarily beneficial. Instead, it is often the most convenient approach to train a new agent from scratch. One potential solution for effectively reusing learned knowledge may be found in hierarchical reinforcement learning. In this paper we investigate the possibility of reusing low-level policies to improve training efficiency when learning manipulation tasks with an industrial robot. We consider four different scenarios and demonstrate for three of them an increased sample efficiency when training a high-level policy on top of pre-trained low-level skills. In the fourth scenario we uncover the reason for a failed transfer to be an ambitious higher hierarchy level enforcing a relearning of the low-level skills.
Performance analysis of dynamic optimization algorithms using relative error distance

Quantification of the performance of algorithms that solve dynamic optimization problems (DOPs) is challenging, since the fitness landscape changes over time. Popular performance measures for DOPs do not adequately account for ongoing fitness landscape scale changes, and often yield a confounded view of performance. Similarly, most popular measures do not allow for fair performance comparisons across multiple instances of the same problem type nor across different types of problems, since performance values are not normalized. Many measures also assume normally distributed input data values, while in reality the necessary conditions for data normality are often not satisfied. The majority of measures also fail to capture the notion of performance variance over time. This paper proposes a new performance measure for DOPs, namely the relative error distance. The measure shows how close to optimal an algorithm performs by considering the multi-dimensional distance between the vector comprising the normalized performance scores for specific algorithm iterations of interest, and the theoretical point of best possible performance. The new measure does not assume normally distributed performance data across fitness landscape changes, is resilient against fitness landscape scale changes, better incorporates performance variance across fitness landscape changes into a single scalar value, and allows easier algorithm comparisons using established nonparametric statistical methods.
Secretation: Toward a Decentralised Identity and Verifiable Credentials Based Scalable and Decentralised Secret Management Solution

Secrets such as passwords, encryption keys, and certificates are used to assist in protecting access to resources such as computing devices, customer data and other information. Unauthorised access to resources can cause significant disruption and/or disastrous consequences. Given the importance of protecting these secrets to the security and privacy of many software systems, many solutions have been proposed. These solutions take two main directions: either securely store the secret and implement an access control mechanism, or divide the secret into a set of shares and distribute them in different machines (such as the Shamir's secret sharing approach or multi-party computation MPC). However, apart from the MPC approach, they all share the same limitation: once the consumer receives the secret, it can be leaked and be used by any malicious actor. We believe that the secret management should not be centralised and that the secret should never be sent to the receiver. Therefore, in this paper we propose, Secretation, a new approach for managing the secrets in a decentralised way by leveraging decentralised identity concepts such as verifiable credential technologies, password-authenticated key exchange protocols and multi-party computation. The result is a more scalable and secure solution that significantly reduces the risk of leaking the secrets.
Transforming Conferences to an Online Format: Framework and Practices

End of March 2020 Statista reported on the impact of COVID-19 pandemic on formats of conferences, where only 35% of selected conferences moved to an online format, whereas 32% were either cancelled or postponed [1]. As conferences of any type aim at networking and knowledge exchange, cancellation or postponement of such events harm a collaboration among possible conference participants. An online format of conferences is not a new sensation but due to COVID-19 pandemic restrictions this becomes an emerging trend and must-have element in academia. Thus, in this paper best practices and success factors on online conferences development will be considered, based on which an agile framework of conference transformation will be proposed. The framework was further tested on two conferences organized by a German Higher Education Institution and a Chapter of the IEEE Society, and validated through surveys. Currently this framework is continuously used by the authors. Based on a positive feedback from last conferences, the framework could be further adopted by conference organisers, who may lack experience and resources in online events organisation.
Background Splitting: Finding Rare Classes in a Sea of Background

We focus on the problem of training deep image classification models for a small number of extremely rare categories. In this common, real-world scenario, almost all images belong to the background category in the dataset. We find that state-of-the-art approaches for training on imbalanced datasets do not produce accurate deep models in this regime. Our solution is to split the large, visually diverse background into many smaller, visually similar categories during training. We implement this idea by extending an image classification model with an additional auxiliary loss that learns to mimic the predictions of a pre-existing classification model on the training set. The auxiliary loss requires no additional human labels and regularizes feature learning in the shared network trunk by forcing the model to discriminate between auxiliary categories for all training set examples, including those belonging to the monolithic background of the main rare category classification task. To evaluate our method we contribute modified versions of the iNaturalist and Places365 datasets where only a small subset of rare category labels are available during training (all other images are labeled as background). By jointly learning to recognize both the selected rare categories and auxiliary categories, our approach yields models that perform 8.3 mAP points higher than state-of-the-art imbalanced learning baselines when 98.30% of the data is background, and up to 42.3 mAP points higher than fine-tuning baselines when 99.98% of the data is background.
Keywords Guided Method Name Generation

High quality method names are descriptive and readable, which are helpful for code development and maintenance. The majority of recent research suggest method names based on the text summarization approach. They take the token sequence and abstract syntax tree of the source code as input, and generate method names through a powerful neural network based model. However, the tokens composing the method name are closely related to the entity name within its method implementation. Actually, high proportions of the tokens in method name can be found in its corresponding method implementation, which makes it possible for incorporating these common shared token information to improve the performance of method naming task. Inspired by this key observation, we propose a two-stage keywords guided method name generation approach to suggest method names. Specifically, we decompose the method naming task into two subtasks, including keywords extraction task and method name generation task. For the keywords extraction task, we apply a graph neural network based model to extract the keywords from source code. For the method name generation task, we utilize the extracted keywords to guide the method name generation model. We apply a dual selective gate in encoder to control the information flow, and a dual attention mechanism in decoder to combine the semantics of input code sequence and keywords. Experiment results on an open source dataset demonstrate that keywords guidance can facilitate method naming task, which enables our model to outperform the competitive state-of-the-art models by margins of 1.5%-3.5% in ROUGE metrics. Especially when programs share one common token with method names, our approach improves the absolute ROUGE-1 score by 7.8%.
BERT-Based Ensemble Model for Statute Law Retrieval and Legal Information Entailment

The Competition on legal information extraction/entailment (COLIEE) is an international information processing and retrieval competition. As an aid to future participants as well as question designers, this article describes how to connect legal questions taken from past Japanese bar exams to relevant statutes (articles of the Japanese Civil Code, Task 3) and how to construct a Yes/No question answering system for legal queries (Task 4) incorporating background materials on Japanese law. We restructured the given data to a dataset which contains all possible combinations of queries and articles as continuous strings as our samples. In this way, the difficult pairing task has been turned into a simpler classification task and samples for training became sufficient in number. Next, we used three BERT-based models to solve binary questions in order to achieve stable performance. As a result, the model achieved an F2-score of 0.6587 in Task 3 (ranked 1st) and an accuracy of 0.6161 in Task 4.
Quality of Control in the Tavis–Cummings–Hubbard Model

In this paper, the Discrete Sources Method has been extended to describe the influence of the geometry asymmetry of a core-shell particle accounting for the effect of spatial dispersion inside the plasmonic metal shell. We found that varying the plasmonic shell thickness has more influence on the near field intensity distribution then on the average enhancement factor. Besides, we demonstrates that the effect of spatial dispersion can decrease the near field intensity up to 60% of its value and it provides a small blue shift.
Secure Login: A Blockchain based web application for Identity Access Management System

As the result of technological developments and the adaption of digital system, the importance of online data transfer has grown significantly. This has resulted for the users to depend on a third party, who provides the platform to transfer the asset safely and store their sensitive data. But, it may be dangerous if the third party is not trustworthy. Many network security issues like man in middle attack, session hijack, eavesdropping, etc. can breach the confidentiality of the transaction and the data stored, so we need a third party certification to avoid these risks. But the certification is a time consuming process and doesn't provide maximum security to the data. As a result there is a need of a system which can provide security to the data without any involvement of third party. Blockchain technology is meant to eliminate the sensitive issue of trust on the third party. It is a public ledger of the entire digital event that has been executed over the network. With today's world filled with information and data, it is very important for us to differentiate which information or data is authentic and which is fraudulent. That is exactly what we aim to develop and implement in the form of our research Authentication System using the Blockchain Technology, an authentication system that will allow users to create and manage a single, valid identity across a wide range of secured websites and applications.
Unaddressed challenges in persuasive dieting chatbots

Diet coaching is a behaviour change task which requires lots of interaction with patients. E-health apps gathered lots of interest in research and, recently, chatbots have been leveraged to address this task, with a focus on persuasion to motivate people towards behaviour change. In this paper, we take a look at current approaches in building persuasive dieting chatbots and expose a number of major unsolved challenges. We motivate them with evidence from previous work and show that current chatbots don't approach certain scenarios properly, hence limiting their communication and persuasion capabilities.
High-Performance Multi-party Computation for Binary Circuits Based on Oblivious Transfer

We present a unified view of the two-party and multi-party computation protocols based on oblivious transfer first outlined in Nielsen et al. (CRYPTO 2012) and Larraia et al. (CRYPTO 2014). We present a number of modifications and improvements to these earlier presentations, as well as full proofs of the entire protocol. Improvements include a unified pre-processing and online MAC methodology, mechanisms to pass between different MAC variants and fixing a minor bug in the protocol of Larraia et al. in relation to a selective failure attack. It also fixes a minor bug in Nielsen et al. resulting from using Jensen's inequality in the wrong direction in an analysis.
Chinese Text Classification Model Based On Bert And Capsule Network Structure

In view of the fact that natural language has strong contextual dependence on sentence structure, but the existing Chinese short text classification algorithms often have problems such as sparse features, irregular words and massive data, a new chinese news classification model based on BERT and capsule network structure is proposed. First, BERT's multi-layer bidirectional Transformer feature extractor based on attention mechanism is used to obtain a more global expression of feature relationships between words and sentences. Then input the obtained data into the capsule network layer to enhance the features and reduce the time cost to enhance the accuracy of data feature selection. Finally, text feature information with different weights is input into Softmax function layer for news classification. The experimental results on Sohu news data set show that the proposed feature fusion model has better classification performance than other models.
An Impact Evaluation Framework of Personalized News Aggregation and Recommendation Systems

There are mounting concerns that personalized recommenders contribute to population differentiation and polarization by creating “information cocoons” that insulate people from opposing views. Despite the vast amount of research devoting to design new algorithms to improve the performance of recommendation systems, the evaluation mostly focuses on recommendation quality. Little attention is paid to the social impact of recommendation systems. On the other hand, it is noted in the literature that the users of recommendation systems suffer from “partial information blindness”, i.e. only exposed to the like-minded content information cocoon or filter bubble. Therefore, in this paper, we propose an impact evaluation framework of recommendation systems, comprising of five constructs, namely, diversity, objectivity, information cocoon, affect cocoon, and intentionality. The evaluation is conducted in a “black-box” way based on the empirical interaction data between the recommender and its individual users. To enable the individual level evaluation, we propose a lightweight agent-based simulation to collect individual’s interaction data with recommenders in the complex real-world scenarios. Furthermore, we illustrate the effectiveness of the proposed methodology with a case study in Toutiao and Baidu News, which are the top two news aggregator apps in China. The results show that both applications demonstrate a periodic information cocoon of about 7-day time interval, the news recommended through these two apps bear distinct differences: Toutiao has better captured the user’s reading preference and the quality of the recommended articles, while Baidu News demonstrate a higher affect cocoon and intentionality in the recommended articles, with average perceived difficulty of the recommended articles 2.9% higher than Toutiao, and average perceived persuasiveness 9.6% higher than Toutiao.
ASID: Accessible Secure Unique Identification File Based Device Security in Next Generation Blockchains

In this paper, a new hardware security method is proposed to secure resource-constrained devices using blockchain technology. The new method is utilizing manufacturer parameters of a device, such as PUF, memory, firmware parameters, etc. to create a linked list of unique identification (UID) data, or an identity that could secure, monitor, and act accordingly to ensure device integrity and honesty.
Challenges in Developing Desktop Web Apps: a Study of Stack Overflow and GitHub

Software companies have an interest in reaching the maximum amount of potential customers while, at the same time, providing a frictionless experience. Desktop web app frameworks are promising in this respect, allowing developers and companies to reuse existing code and knowledge of web applications to create cross-platform apps integrated with native APIs. Despite their growing popularity, existing challenges in employing these technologies have not been documented, and it is hard for individuals and companies to weigh benefits and pros against drawbacks and cons.In this paper, we address this issue by investigating the challenges that developers frequently experience when adopting desktop web app frameworks. To achieve this goal, we mine and apply topic modeling techniques to a dataset of 10,822 Stack Overflow posts related to the development of desktop web applications. Analyzing the resulting topics, we found that: i) developers often experience issues regarding the build and deployment processes for multiple platforms; ii) reusing existing libraries and development tools in the context of desktop applications is often cumbersome; iii) it is hard to solve issues that arise when interacting with native APIs. Furthermore, we confirm our finding by providing evidence that the identified issues are also present in the issue reports of 453 open-source applications publicly hosted on GitHub.
Matching sensor ontologies through siamese neural networks without using reference alignment

Sensors have been growingly used in a variety of applications. The lack of semantic information of obtained sensor data will bring about the heterogeneity problem of sensor data in semantic, schema, and syntax levels. To solve the heterogeneity problem of sensor data, it is necessary to carry out the sensor ontology matching process to determine correspondences among heterogeneous sensor concepts. In this paper, we propose a Siamese Neural Network based Ontology Matching technique (SNN-OM) to align the sensor ontologies, which does not require the utilization of reference alignment to train the network model. In particular, a representative concepts extraction method is presented to enhance the model’s performance and reduce the time of the training process, and an alignment refining method is proposed to enhance the alignments’ quality by removing the logically conflict correspondences. The experimental results show that SNN-OM is capable of efficiently determining high-quality sensor ontology alignments.