Reviewerly/exHarmony huggingface dataset:
Generalization in Reinforcement Learning by Soft Data Augmentation

Extensive efforts have been made to improve the generalization ability of Reinforcement Learning (RL) methods via domain randomization and data augmentation. However, as more factors of variation are introduced during training, optimization becomes increasingly challenging, and empirically may result in lower sample efficiency and unstable training. Instead of learning policies directly from augmented data, we propose SOft Data Augmentation (SODA), a method that decouples augmentation from policy learning. Specifically, SODA imposes a soft constraint on the encoder that aims to maximize the mutual information between latent representations of augmented and non-augmented data, while the RL optimization process uses strictly non-augmented data. Empirical evaluations are performed on diverse tasks from DeepMind Control suite as well as a robotic manipulation task, and we find SODA to significantly advance sample efficiency, generalization, and stability in training over state-of-the-art vision-based RL methods.<sup>1</sup>
A Literature Review of Using Machine Learning in Software Development Life Cycle Stages

Context: The software development industry is rapidly adopting machine learning for transitioning modern day software systems towards highly intelligent and self-learning systems. However, the full potential of machine learning for improving the software engineering life cycle itself is yet to be discovered, i.e., up to what extent machine learning can help reducing the effort/complexity of software engineering and improving the quality of resulting software systems. To date, no comprehensive study exists that explores the current state-of-the-art on the adoption of machine learning across software engineering life cycle stages. Objective: This article addresses the aforementioned problem and aims to present a state-of-the-art on the growing number of uses of machine learning in software engineering. Method: We conduct a systematic mapping study on applications of machine learning to software engineering following the standard guidelines and principles of empirical software engineering. Results: This study introduces a machine learning for software engineering (MLSE) taxonomy classifying the state-of-the-art machine learning techniques according to their applicability to various software engineering life cycle stages. Overall, 227 articles were rigorously selected and analyzed as a result of this study. Conclusion: From the selected articles, we explore a variety of aspects that should be helpful to academics and practitioners alike in understanding the potential of adopting machine learning techniques during software engineering projects.
IRKO: An Improved Runge-Kutta Optimization Algorithm for Global Optimization Problems

Optimization is a key technique for maximizing or minimizing functions and achieving optimal cost, gains, energy, mass, and so on. In order to solve optimization problems, metaheuristic algorithms are essential. Most of these techniques are influenced by collective knowledge and natural foraging. There is no such thing as the best or worst algorithm; instead, there are more effective algorithms for certain problems. Therefore, in this paper, a new improved variant of a recently proposed metaphorless Runge-Kutta Optimization (RKO) algorithm, called Improved Runge-Kutta Optimization (IRKO) algorithm, is suggested for solving optimization problems. The IRKO is formulated using the basic RKO and local escaping operator to enhance the diversification and intensification capability of the basic RKO version. The performance of the proposed IRKO algorithm is validated on 23 standard benchmark functions and three engineering constrained optimization problems. The outcomes of IRKO are compared with seven state-of-the-art algorithms, including the basic RKO algorithm. Compared to other algorithms, the recommended IRKO algorithm is superior in discovering the optimal results for all selected optimization problems. The runtime of IRKO is less than 0.5 s for most of the 23 benchmark problems and stands first for most of the selected problems, including real-world optimization problems.
Understanding Screen-Reader Users’ Experiences with Online Data Visualizations

Online data visualizations are widely used to communicate information from simple statistics to complex phenomena, supporting people in gaining important insights from data. However, due to the defining visual nature of data visualizations, extracting information from visualizations can be difficult or impossible for screen-reader users. To assess screen-reader users’ challenges with online data visualizations, we conducted two empirical studies: (1) A qualitative study with nine screen-reader users, and (2) a quantitative study with 36 screen-reader and 36 non-screen-reader users. Our results show that due to the inaccessibility of online data visualizations, screen-reader users extract information 61.48% less accurately and spend 210.96% more time interacting with online data visualizations compared to non-screen-reader users. Additionally, our findings show that online data visualizations are commonly indiscoverable to screen readers. In visualizations that are discoverable and comprehensible, screen-reader users suggested tabular and textual representation of data as techniques to improve the accessibility of online visualizations. Taken together, our results provide empirical evidence of the inequalities screen-readers users face in their interaction with online data visualizations.
Automated fact‐checking: A survey

Abstract As online false information continues to grow, automated fact‐checking has gained an increasing amount of attention in recent years. Researchers in the field of Natural Language Processing (NLP) have contributed to the task by building fact‐checking datasets, devising automated fact‐checking pipelines and proposing NLP methods to further research in the development of different components. This article reviews relevant research on automated fact‐checking covering both the claim detection and claim validation components.
Lite-HDSeg: LiDAR Semantic Segmentation Using Lite Harmonic Dense Convolutions

Autonomous driving vehicles and robotic systems rely on accurate perception of their surroundings. Scene understanding is one of the crucial components of perception modules. Among all available sensors, LiDARs are one of the essential sensing modalities of autonomous driving systems due to their active sensing nature with high resolution of sensor readings. Accurate and fast semantic segmentation methods are needed to fully utilize LiDAR sensors for scene understanding. In this paper, we present Lite-HDSeg, a novel real-time convolutional neural network for semantic segmentation of full 3D LiDAR point clouds. Lite-HDSeg can achieve the best accuracy vs. computational complexity trade-off in SemanticKITTI bench-mark and is designed on the basis of a new encoder-decoder architecture with light-weight harmonic dense convolutions as its core. Moreover, we introduce ICM, an improved global contextual module to capture multi-scale contextual features, and MCSPN, a multi-class Spatial Propagation Network to further refine the semantic boundaries. Our experimental results show that the proposed method outperforms state-of- the-art semantic segmentation approaches which can run real-time, thus is suitable for robotic and autonomous driving applications.
Time for Historicism in CSCW: An Invitation

This paper contributes to the development of an under-utilized area of focus for CSCW research and design: history. The design and evaluation of technology, as practiced in the field, has positioned CSCW as a largely forward-looking community. The enduring "presentism' and lack of historical view threatens to leave out a wealth of resources that can inspire design, support comparative analysis, and develop a deeper understanding of technology development and its social consequences. This paper argues that a historicist sensibility should inform the due diligence of all CSCW research, and we present connection points for the various ways in which historical research might more deeply inform CSCW, while offering a selection of historiographic challenges to sensitize CSCW scholars as we seek to better situate our collective work within both the present moment as well as ongoing temporal change.
IKEA Furniture Assembly Environment for Long-Horizon Complex Manipulation Tasks

The IKEA Furniture Assembly Environment is one of the first benchmarks for testing and accelerating the automation of long-horizon and hierarchical manipulation tasks. The environment is designed to advance reinforcement learning and imitation learning from simple toy tasks to complex tasks requiring both long-term planning and sophisticated low-level control. Our environment features 60 furniture models, 6 robots, photorealistic rendering, and domain randomization. We evaluate reinforcement learning and imitation learning methods on the proposed environment. Our experiments show furniture assembly is a challenging task due to its long horizon and sophisticated manipulation requirements, which provides ample opportunities for future research. The environment is publicly available at https://clvrai.com/furniture.
Hierarchical Planning for Long-Horizon Manipulation with Geometric and Symbolic Scene Graphs

We present a visually grounded hierarchical planning algorithm for long-horizon manipulation tasks. Our algorithm offers a joint framework of neuro-symbolic task planning and low-level motion generation conditioned on the specified goal. At the core of our approach is a two-level scene graph representation, namely geometric scene graph and symbolic scene graph. This hierarchical representation serves as a structured, object-centric abstraction of manipulation scenes. Our model uses graph neural networks to process these scene graphs for predicting high-level task plans and low-level motions. We demonstrate that our method scales to long-horizon tasks and generalizes well to novel task goals. We validate our method in a kitchen storage task in both physical simulation and the real world. Experiments show that our method achieves over 70% success rate and nearly 90% of subgoal completion rate on the real robot while being four orders of magnitude faster in computation time compared to standard search-based task-and-motion planner. <sup>1</sup>
A Survey on Blockchain in Robotics: Issues, Opportunities, Challenges and Future Directions

Robotics is the multi-disciplinary domain that is booming in today’s world, and expanding its roots deep into various fields of research, manufacturing industries, healthcare, and even in our day-to-day lives. Nevertheless, as with any other evolving technology, robotics face numerous challenges. In this context, lately, blockchain technology has been identified as a promising technology to resolve many of these issues such as identification of malicious/rogue nodes, malfunctioning/faults in automated processes, non-compliance to the agreed norms and privacy rules, security attacks on robotic systems, and non-transparency in performance monitoring and audits. In particular, blockchain with its features like decentrality, immutability, provenance, low operational cost, tight access control, and trustworthy operations, can offer significant improvements to new applications and use cases driven by robotics. Thus, the paper begins with exploring the key requirements and technical challenges encountered by robots in general. Next, it provides detailed overview of blockchain technology in a tutorial style. Then, the role of blockchain for different uses cases of robotics are surveyed. Furthermore, various technical challenges that need to be mitigated to harness full potential of blockchain for robotics are highlighted. Finally, the future research directions are presented that can pave the way ahead for advancements and profitable integration of blockchain in the realm of robotics.
The 1+5 Architectural Views Model in Designing Blockchain and IT System Integration Solutions

Service fulfillment for clients increasingly involves cooperation between information technology (IT) systems. Designing such solutions requires an architectural approach that ensures symmetry between the communicating parties. For the design of such systems, the author introduces the 1+5 architectural views model. The model contains three new architectural views. For business process modeling, it ensures the integrated processes view. Integration aspects cover two additional views: integrated services, and contracts. Moreover, new stereotypes and tagged values have been added to the unified modeling language (UML). The author has introduced two profiles: UML profile for integration flows, and UML profile for distributed ledger deployment. Communication between systems requires flows that arrange mediation mechanisms. The paper describes an integration flow diagram that extends a UML activity diagram. In the case of blockchain, the author has proposed the smart contract design pattern. The paper describes three case studies that have employed the model to design various solutions. The 1+5 model has proven to be well suited for designing both centralized integration environments with enterprise service bus (ESB) and distributed blockchain solutions with peer-to-peer (P2P) connections.
Identification of heavy metal pollution sources and its associated risk assessment in an industrial town using the K-means clustering technique

This study intends to (i) identify the potential sources of heavy metal (HM) pollution in an industrial town situated in the northwestern part of Iran and (ii) and assess whether the pollution levels discharged by HMs are significant. A suite of geochemical samples collected from the topsoil horizon of the study area were analyzed for HMs, namely Cr, Ni, Co, Cu, V, Zn, Mn, and Pb, to attain the objectives mentioned above. Compositional data analysis (CODA) techniques were employed for pinpointing the potential source of HM pollution. Initially, the centered log-ratio (clr) transformation was applied to the dataset containing raw values of chemically analyzed soil samples to address the data closure problem. Multifractal moving average interpolation was then applied to the clr-transformed data to model the spatial distribution of HMs in the study area. Maps of the spatial distribution of HMs in the study area were further classified by the concentration-area (C-A) fractal technique for identifying the potential hotspots of HM pollution in the area. It was followed by using the K-means clustering technique for classifying samples into distinct classes. This method was selected for geochemical pattern recognition owing to its simplicity and robustness in identifying subtle patterns embedded in geochemical data. The above hybrid methodology points to three anthropogenic sources discharging HM pollution into the area: leather factories, brickworks, and highways. Finally, pollution indices, namely the geo-accumulation index, pollution load index, and potential ecological risk, revealed that the elevated levels of Ni, Cr, and Co can pose serious impediments to the soil quality of the study area.
University Professor Training in Times of COVID-19: Analysis of Training Programs and Perception of Impact on Teaching Practices

During the confinement and post-confinement period, the continuous training centers at different Catalan universities adapted the lifelong learning of professors. The present study analyzes the education of lecturers from Catalan universities before and after the pandemic, as well as their perception of its impact. A documentary analysis was performed of the continuous training programs for professors. Posteriorly, an enquiry was made about the perception of impact of this training, through a descriptive and inferential analysis with the use of a questionnaire designed ad hoc. Results indicated an increase in training associated with institutional digital tools, online evaluation, and design of online courses. The efforts by the professors to implement the knowledge acquired is underlined, with a higher perception of impact observed in the areas of Social and Legal Sciences and Arts and Humanities. In addition, the main difficulties perceived were the lack of time for adapting their practice of teaching with the knowledge acquired, as well as the complexity of performing an online evaluation. It is concluded that there is a need for continuous training programs that help establish support networks and collaboration between professors for the improvement of teaching, and in which one of the priorities is online evaluation.
Proofs for Inner Pairing Products and Applications

We present a generalized inner product argument and demonstrate its applications to pairing-based languages. We apply our generalized argument to prove that an inner pairing product is correctly evaluated with respect to committed vectors of n source group elements. With a structured reference string (SRS), we achieve a logarithmic-time verifier whose work is dominated by \(6 \log n\) target group exponentiations. Proofs are of size \(6 \log n\) target group elements, computed using 6n pairings and 4n exponentiations in each source group.
Learning Selective Mutual Attention and Contrast for RGB-D Saliency Detection

How to effectively fuse cross-modal information is a key problem for RGB-D salient object detection. Early fusion and result fusion schemes fuse RGB and depth information at the input and output stages, respectively, and hence incur distribution gaps or information loss. Many models instead employ a feature fusion strategy, but they are limited by their use of low-order point-to-point fusion methods. In this paper, we propose a novel mutual attention model by fusing attention and context from different modalities. We use the non-local attention of one modality to propagate long-range contextual dependencies for the other, thus leveraging complementary attention cues to achieve high-order and trilinear cross-modal interaction. We also propose to induce contrast inference from the mutual attention and obtain a unified model. Considering that low-quality depth data may be detrimental to model performance, we further propose a selective attention to reweight the added depth cues. We embed the proposed modules in a two-stream CNN for RGB-D SOD. Experimental results demonstrate the effectiveness of our proposed model. Moreover, we also construct a new and challenging large-scale RGB-D SOD dataset of high-quality, which can promote both the training and evaluation of deep models.
MH UNet: A Multi-Scale Hierarchical Based Architecture for Medical Image Segmentation

UNet and its variations achieve state-of-the-art performances in medical image segmentation. In end-to-end learning, the training with high-resolution medical images achieves higher accuracy for medical image segmentation. However, the network depth, a massive number of parameters, and low receptive fields are issues in developing deep architecture. Moreover, the lack of multi-scale contextual information degrades the segmentation performance due to the different sizes and shapes of regions of interest. The extraction and aggregation of multi-scale features play an important role in improving medical image segmentation performance. This paper introduces the MH UNet, a multi-scale hierarchical-based architecture for medical image segmentation that addresses the challenges of heterogeneous organ segmentation. To reduce the training parameters and increase efficient gradient flow, we implement densely connected blocks. Residual-Inception blocks are used to obtain full contextual information. A hierarchical block is introduced between the encoder-decoder for acquiring and merging features to extract multi-scale information in the proposed architecture. We implement and validate our proposed architecture on four challenging MICCAI datasets. Our proposed approach achieves state-of-the-art performance on the BraTS 2018, 2019, and 2020 Magnetic Resonance Imaging (MRI) validation datasets. Our approach is 14.05 times lighter than the best method of BraTS 2018. In the meantime, our proposed approach has 2.2 times fewer training parameters than the top 3D approach on the ISLES 2018 Computed Tomographic Perfusion (CTP) testing dataset. MH UNet is available at https://github.com/parvezamu/MHUnet .
Digital Forensics Subdomains: The State of the Art and Future Directions

For reliable and relevant scientific evidence to be admitted in a court of law, it is important to apply digital forensic investigation techniques to corroborate a suspected potential security incident. Mainly, traditional digital forensics techniques have focused on computer desktops and servers. However, recent advances in digital media and platforms have seen an increased need for the application of digital forensic investigation techniques to other subdomains including small and mobile devices, databases, networks, cloud-based platforms, and the Internet of Things (IoT). To assist forensic investigators, conduct investigations within these subdomains, academic researchers have attempted to develop a number of investigative processes. However, many of these processes are domain-specific or describe domain-specific investigative tools. Hence, we hypothesize that the literature is littered with potentially overlapping and contradicting investigative process for conducting investigations within these subdomains. To investigate this hypothesis, a digital forensic model-orientated Systematic Literature Review (SLR) within the above digital forensic subdomains was undertaken. The purpose of the SLR was to identify the different and heterogeneous practices that have emerged within the specific subdomains. A key finding from the SLR is that there is a potential information overload and a high-degree of ambiguity among investigative processes in the above subdomains. The outcome of this study proposes a high-level abstract metamodel called The Digital Forensic Metamodel (DFM), which combines common processes, activities, techniques, and tasks for the above subdomains.
Albayzin 2018 Evaluation: The IberSpeech-RTVE Challenge on Speech Technologies for Spanish Broadcast Media

The IberSpeech-RTVE Challenge presented at IberSpeech 2018 is a new Albayzin evaluation series supported by the Spanish Thematic Network on Speech Technologies (Red Temática en Tecnologías del Habla (RTTH)). That series was focused on speech-to-text transcription, speaker diarization, and multimodal diarization of television programs. For this purpose, the Corporacion Radio Television Española (RTVE), the main public service broadcaster in Spain, and the RTVE Chair at the University of Zaragoza made more than 500 h of broadcast content and subtitles available for scientists. The dataset included about 20 programs of different kinds and topics produced and broadcast by RTVE between 2015 and 2018. The programs presented different challenges from the point of view of speech technologies such as: the diversity of Spanish accents, overlapping speech, spontaneous speech, acoustic variability, background noise, or specific vocabulary. This paper describes the database and the evaluation process and summarizes the results obtained.