Reviewerly/exHarmony huggingface dataset:
A Hierarchical Control System for Autonomous Driving towards Urban Challenges

In recent years, the self-driving car technologies have been developed with many successful stories in both academia and industry. The challenge for autonomous vehicles is the requirement of operating accurately and robustly in the urban environment. This paper focuses on how to efficiently solve the hierarchical control system of a self-driving car into practice. This technique is composed of decision making, local path planning and control. An ego vehicle is navigated by global path planning with the aid of a High Definition map. Firstly, we propose the decision making for motion planning by applying a two-stage Finite State Machine to manipulate mission planning and control states. Furthermore, we implement a real-time hybrid A* algorithm with an occupancy grid map to find an efficient route for obstacle avoidance. Secondly, the local path planning is conducted to generate a safe and comfortable trajectory in unstructured scenarios. Herein, we solve an optimization problem with nonlinear constraints to optimize the sum of jerks for a smooth drive. In addition, controllers are designed by using the pure pursuit algorithm and the scheduled feedforward PI controller for lateral and longitudinal direction, respectively. The experimental results show that the proposed framework can operate efficiently in the urban scenario.
A Privacy Preserving Distributed Ledger Framework for Global Human Resource Record Management: The Blockchain Aspect

Blockchain is a technology used with the series of users in peer-to-peer transactions to utilize the usability properties of the immutable data records. The distributed nature of this technology has given the wide acceptance to its range of applications in various sectors. Seeing the prospect of this new technology, we have chosen the field of human resource management as these data needs to be privacy preserving and confidential along with significant research value. Distributed ledger approach is a novel idea in this field of work specifically for the application of human resource records management. We have used a privacy preserving framework that provides a transparent system for human resource record management. Wallets are generated with organization id and outputting with public-private key pair along with privacy parameter mapping with hash. Keys are used to provide confidentiality, integrity and authentication. Smart contract uses distributed but converged decision with privacy level classification. Performance of the proposed work has been measured based on time, memory consumptions, failure point identification and read-write latencies. The analysis of the results confirms the efficiency of the system.
Long-Term Video Question Answering via Multimodal Hierarchical Memory Attentive Networks

Long-term Video Question Answering plays an essential role in visual information retrieval, which aims at generating natural language answers to discretionary free-form questions about the referenced long-term video. Rather than remember the video as a sequence of visual content, humans have an innate cognitive ability to identify the critical moments related to the question at first glance, then tie together the specific evidence around these critical moments for further analysis and reasoning. Motivated by this intuition, we propose the multimodal hierarchical memory attentive networks with two heterogeneous memory subnetworks: the top guided memory network and the bottom enhanced multimodal memory attentive network. The top guided memory network serves as a shallow inference engine to pick relevant and informative moments of questions and obtain salient video content at a coarse-grained level. Subsequently, the bottom enhanced multimodal memory attentive network is designed as an in-depth reasoning engine to perform more accurate attention with cues from video bottom evidence in a fine-grained level to enhance question answering quality. We evaluate the proposed method on three publicly available video question answering benchmarks, namely ActivityNet-QA, MSRVTT-QA, and MSVD-QA. Experimental results demonstrate that the proposed approach significantly outperforms other state-of-the-art methods for long-term videos. Extensive ablation studies are carried out to explore the reasons behind the proposed model's effectiveness.
Multicriteria roof sorting for the integration of photovoltaic systems in urban environments

This work deals with the sorting of the suitability of the roofs for the integration of PV systems, implementable as a GIS (Geographical Information System). Such sorting can provide relevant information to help and guide urban actors further develop solar energy, and therefore contribute to the acceleration of the deployment of this source of energy in cities. Three scenarios of sorting are considered: the energetic, the economic and the multicriteria sorting. The energetic and economic sorting scenarios are actually similar to the great majority of the currently proposed sorting in publicly available solar GIS tools (solar cadastre), which consider only one attribute such as the solar irradiation or the payback period. Knowing the limits of these sorting scenarios, a multicriteria approach is adopted and takes into account more impactful attributes in the urban actors' choices. The ELECTRE TRI methodology is used for this sorting problem. Different decisional criteria related to energy, economy, historic buildings and heritage, the structure and superstructure states of the roof are identified and detailed. The method is then applied to a set of alternatives (roofs) in a district of Geneva. The sorting results show that the proposed multicriteria scenario better integrates the complexity of the urban environment and provides a more relevant information regarding the suitability of the roofs for PV integration.
Compositional GAN: Learning Image-Conditional Binary Composition

Generative Adversarial Networks can produce images of remarkable complexity and realism but are generally structured to sample from a single latent source ignoring the explicit spatial interaction between multiple entities that could be present in a scene. Capturing such complex interactions between different objects in the world, including their relative scaling, spatial layout, occlusion, or viewpoint transformation is a challenging problem. In this work, we propose a novel self-consistent Composition-by-Decomposition network to compose a pair of objects. Given object images from two distinct distributions, our model can generate a realistic composite image from their joint distribution following the texture and shape of the input objects. We evaluate our approach through qualitative experiments and user evaluations. Our results indicate that the learned model captures potential interactions between the two object domains, and generates realistic composed scenes at test time.
Α Benchmarking of IBM, Google and Wit Automatic Speech Recognition Systems

As the requirements for automatic speech recognition are continually increasing, the demand for accuracy and efficiency is also of particular interest. In this paper, we present most of the well-known Automated Speech Recognition systems (ASR), and we benchmark three of them, namely the IBM Watson, Google, and Wit, using the WER, Hper, and Rper error metrics. The experimental results show that Google’s automatic speech recognition performs better among the three systems. We intend to extend the benchmarking both to include most of the available Automated Speech Recognition systems and increase our test data.
MRAT: The Mixed Reality Analytics Toolkit

Significant tool support exists for the development of mixed reality (MR) applications; however, there is a lack of tools for analyzing MR experiences. We elicit requirements for future tools through interviews with 8 university research, instructional, and media teams using AR/VR in a variety of domains. While we find a common need for capturing how users perform tasks in MR, the primary differences were in terms of heuristics and metrics relevant to each project. Particularly in the early project stages, teams were uncertain about what data should, and even could, be collected with MR technologies. We designed the Mixed Reality Analytics Toolkit (MRAT) to instrument MR apps via visual editors without programming and enable rapid data collection and filtering for visualizations of MR user sessions. With MRAT, we contribute flexible interaction tracking and task definition concepts, an extensible set of heuristic techniques and metrics to measure task success, and visual inspection tools with in-situ visualizations in MR. Focusing on a multi-user, cross-device MR crisis simulation and triage training app as a case study, we then show the benefits of using MRAT, not only for user testing of MR apps, but also performance tuning throughout the design process.
XRDirector: A Role-Based Collaborative Immersive Authoring System

Immersive authoring is an increasingly popular technique to design AR/VR scenes because design and testing can be done concurrently. Most existing systems, however, are single-user and limited to either AR or VR, thus constrained in the interaction techniques. We present XRDirector, a role-based collaborative immersive authoring system that enables designers to freely express interactions using AR and VR devices as puppets to manipulate virtual objects in 3D physical space. In XRDirector, we adapt roles known from filmmaking to structure the authoring process and help coordinate multiple designers in immersive authoring tasks. We study how novice AR/VR creators can take advantage of the roles and modes in XRDirector to prototype complex scenes with animated 3D characters, light effects, and camera movements, and also simulate interactive system behavior in a Wizard of Oz style. XRDirector's design was informed by case studies around complex 3D movie scenes and AR/VR games, as well as workshops with novice AR/VR creators. We show that XRDirector makes it easier and faster to create AR/VR scenes without the need for coding, characterize the issues in coordinating designers between AR and VR, and identify the strengths and weaknesses of each role and mode to mitigate the issues.
Learning Recurrent 3D Attention for Video-Based Person Re-Identification

In this paper, we propose to learn recurrent 3D attention (A3D) for video-based person re-identification. Attention model plays a key role in both spatial and temporal domains for video representation. Most existing methods apply spatial attention model to extract feature from a single image and aggregate image features with attentive temporal pooling or RNN. However, the inherent consistencies and correlations between spatial and temporal clues are not leveraged. Our A3D method aims to utilize the joint constraints of temporal and spatial attentions to enhance the robustness of attention model. Towards this goal, we treat the pedestrian video as a unified 3D bin where the temporal domain is denoted as an additional dimension. Then we develop an attention agent to iteratively select the locations of the salient spatial-temporal parts in the 3D bin. In addition, we formulate our sequential 3D attention learning as a Markov Decision Process and train the representation network and attention detector with the policy gradient method in an end-to-end manner. We evaluate the proposed method on three challenging datasets including iLIDS-VID, PRID-2011 and the large-scale MARS dataset, and consistently improve the performance in comparison with the state-of-the-art methods.
Comparison Analysis of Machine Learning Techniques for Photovoltaic Prediction Using Weather Sensor Data

Over the past few years, solar power has significantly increased in popularity as a renewable energy. In the context of electricity generation, solar power offers clean and accessible energy, as it is not associated with global warming and pollution. The main challenge of solar power is its uncontrollable fluctuation since it is highly depending on other weather variables. Thus, forecasting energy generation is important for smart grid operators and solar electricity providers since they are required to ensure the power continuity in order to dispatch and properly prepare to store the energy. In this study, we propose an efficient comparison framework for forecasting the solar power that will be generated 36 h in advance from Yeongam solar power plant located in South Jeolla Province, South Korea. The results show a comparative analysis of the state-of-the-art techniques for solar power generation.
Embodied Axes: Tangible, Actuated Interaction for 3D Augmented Reality Data Spaces

We present Embodied Axes, a controller which supports selection operations for 3D imagery and data visualisations in Augmented Reality. The device is an embodied representation of a 3D data space -- each of its three orthogonal arms corresponds to a data axis or domain specific frame of reference. Each axis is composed of a pair of tangible, actuated range sliders for precise data selection, and rotary encoding knobs for additional parameter tuning or menu navigation. The motor actuated sliders support alignment to positions of significant values within the data, or coordination with other input: e.g., mid-air gestures in the data space, touch gestures on the surface below the data, or another Embodied Axes device supporting multi-user scenarios. We conducted expert enquiries in medical imaging which provided formative feedback on domain tasks and refinements to the design. Additionally, a controlled user study was performed and found that the Embodied Axes was overall more accurate than conventional tracked controllers for selection tasks.
BEMPAS: A Decentralized Employee Performance Assessment System Based on Blockchain for Smart City Governance

In recent years, the use of blockchain has gained attention in industrial interventions and research. Blockchain technologies can be particularly utilized in the domain of Smart Governance, which is a critical component of Smart Cities. Smart Governance aims to ensure smarter decision making, openness and collaborative participation of all entities involved as well as help minimize resource usage, reduce consumption and save cost. Smart Governance involves putting in place systems for monitoring and evaluating the performance of government workers. However, government workers lack trust in these systems because they are mostly centralized systems which are prone to single-point of failure problems and also data fed into these systems can be tampered with by the higher-ups or employers to favour individual workers. With these problems, there is a need for a decentralized system that can ensure trust, tamper-resistance, accountability, reliability, transparency and security. In this paper, we propose a decentralized employee evaluation system based on blockchain. We utilized a 3-chain model, namely; ID-Chain, Behavior Chain and Credit Chain fused to make a single unified chain. Our system also makes use of an automated game-based employee evaluation approach. Our system is based on Hyperledger Fabric as the blockchain platform and operating mechanism. Our Proof of Concept (PoC) results shows that in utilizing the Blockchain system, security concerns such as trust, privacy, accountability, among others, that are present in current employee performance evaluation systems are tackled and also in the adoption of the game-based automated system, our system can make an effective decision regarding employee performance. Our system achieves trust, transparency, security and accountability among government workers under a Smart City governing environment.
An asymmetric image encryption mechanism using QR decomposition in hybrid multi-resolution wavelet domain

In this paper, an asymmetric image encryption mechanism using QR decomposition technique in hybrid multi-resolution wavelet (HMW) domain is presented. QR decomposition technique utilized here makes the system asymmetric, as the obtained permutation matrix serves as ciphertext and the product of orthogonal and triangular matrices act as decryption keys. It also enables compression as the ciphertext obtained through QR decomposition is a sparse matrix which can be stored using CSR method. The HMW is obtained by utilizing existing orthogonal transforms: Walsh transform and fractional Fourier transform (frFT). Here the fractional orders of fractional Fourier transform (frFT) matrices are employed here to generate HMW, which function as supplementary keys. Therefore, they enhance the key space and strengthen the proposed system. The proposed encryption system exhibits non-linearity and enlarged key-space to dodge any brute-force attack. Computer-based simulations have been carried out on the grayscale images to verify and validate the proposed encryption mechanism. And the results obtained clearly demonstrate the robustness of the proposed mechanism against occlusion and noise attacks.
Effort-Aware semi-Supervised just-in-Time defect prediction

Software defect prediction is an important technique that can help practitioners allocate their quality assurance efforts. In recent years, just-in-time (JIT) defect prediction has attracted considerable interest, as it enables developers to identify risky changes at check-in time. Many studies have conducted research from supervised and unsupervised perspectives. A model that does not rely on label information would be preferred. However, the performance of unsupervised models proposed by previous studies in the classification scenario was unsatisfactory due to the lack of supervised information. Furthermore, most supervised models fail to outperform simple unsupervised models in the ranking scenario. To overcome this weakness, we conduct research from the semi-supervised perspective that only requires a small quantity of labeled data for training. In this paper, we propose a semi-supervised model for JIT defect prediction named Effort-Aware Tri-Training (EATT), which is an effort-aware method using a greedy strategy to rank changes. We compare EATT with the state-of-the-art supervised and unsupervised models with respect to different labeled rate. The experimental results on six open-source projects demonstrate that EATT outperforms existing supervised and unsupervised models for effort-aware JIT defect prediction, and has similar or superior performance in classifying defect-inducing changes. The results show that EATT can not only achieve high classification accuracy as supervised models, but also offer more practical value than other compared models from the perspective of the effort needed to review changes.
Domain Decluttering: Simplifying Images to Mitigate Synthetic-Real Domain Shift and Improve Depth Estimation

Leveraging synthetically rendered data offers great potential to improve monocular depth estimation and other geometric estimation tasks, but closing the synthetic-real domain gap is a non-trivial and important task. While much recent work has focused on unsupervised domain adaptation, we consider a more realistic scenario where a large amount of synthetic training data is supplemented by a small set of real images with ground-truth. In this setting, we find that existing domain translation approaches are difficult to train and offer little advantage over simple baselines that use a mix of real and synthetic data. A key failure mode is that real-world images contain novel objects and clutter not present in synthetic training. This high-level domain shift isn't handled by existing image translation models. Based on these observations, we develop an attention module that learns to identify and remove difficult out-of-domain regions in real images in order to improve depth prediction for a model trained primarily on synthetic data. We carry out extensive experiments to validate our attend-remove-complete approach (ARC) and find that it significantly outperforms state-of-the-art domain adaptation methods for depth prediction. Visualizing the removed regions provides interpretable insights into the synthetic-real domain gap.
Adaptation Strategies for Automated Machine Learning on Evolving Data

Automated Machine Learning (AutoML) systems have been shown to efficiently build good models for new datasets. However, it is often not clear how well they can adapt when the data evolves over time. The main goal of this study is to understand the effect of concept drift on the performance of AutoML methods, and which adaptation strategies can be employed to make them more robust to changes in the underlying data. To that end, we propose 6 concept drift adaptation strategies and evaluate their effectiveness on a variety of AutoML approaches for building machine learning pipelines, including Bayesian optimization, genetic programming, and random search with automated stacking. These are evaluated empirically on real-world and synthetic data streams with different types of concept drift. Based on this analysis, we propose ways to develop more sophisticated and robust AutoML techniques.
Real-Time Object Detection System with Multi-Path Neural Networks

Thanks to the recent advances in Deep Neural Networks (DNNs), DNN-based object detection systems become highly accurate and widely used in real-time environments such as autonomous vehicles, drones and security robots. Although the systems should detect objects within a certain time limit that can vary depending on their execution environments such as vehicle speeds, existing systems blindly execute the entire long-latency DNNs without reflecting the time-varying time limits, and thus they cannot guarantee real-time constraints. This work proposes a novel real-time object detection system that employs multipath neural networks based on a new worst-case execution time (WCET) model for DNNs on a GPU. This work designs the WCET model for a single DNN layer analyzing processor and memory contention on GPUs, and extends the WCET model to the end-to-end networks. This work also designs the multipath networks with three new operators such as skip, switch, and dynamic generate proposals that dynamically change their execution paths and the number of target objects. Finally, this work proposes a path decision model that chooses the optimal execution path at run-time reflecting dynamically changing environments and time constraints. Our detailed evaluation using widely-used driving datasets shows that the proposed real-time object detection system performs as good as a baseline object detection system without violating the time-varying time limits. Moreover, the WCET model predicts the worst-case execution latency of convolutional and group normalization layers with only 27% and 81% errors on average, respectively.