Reviewerly/exHarmony huggingface dataset:
A Global Path Planning Algorithm for Robots Using Reinforcement Learning

Path planning is the key technology for autonomous mobile robots. In view of the shortage of paths found by traditional best first search (BFS) and rapidly-exploring random trees (RRT) algorithm which are not short and smooth enough for robot navigation, a new global planning algorithm combined with reinforcement learning is presented for robots. In our algorithm, a path graph is established firstly, in which the paths collided with the obstacles are removed directly. Then a collision-free path will be found by Q-Learning from starting point to the goal. The experiment results illustrate that it can generate shorter and smoother paths, compared with the BFS and RRT algorithm.
Future tense: Exploring dissonance in young people’s images of the future through design futures methods

The lack of progress in the face of complex sustainability challenges has in part been attributed to a lack of imagination, rather than awareness. Nurturing and surfacing pluralistic alternative futures, as well imagining the pathways that might get us there, are key processes in bridging this imagination gap. The emerging field of design futures provides methods and tools to develop narratives as well as tangible artefacts depicting products, services and experiences set within alternative futures. These methods build on long-established interdisciplinary inquiries into how and why people develop images of the futures. The present research aims to explore young people's images of the future (set in 2068 and 2038) through design futures methods. This paper presents the outcomes of a series of workshops in which over 70 young people (aged 16–17) imagined a series of alternative futures and developed artefacts that support the pathways towards these futures. The results reveal that while design futures methods are effective in developing and interrogating collective future imaginaries, deeper challenges rooted in the homogeneity of dominant Western imaginaries and the hyper–individualistic turn of late–modernity remain.
Intrinsically-typed definitional interpreters for linear, session-typed languages

An intrinsically-typed definitional interpreter is a concise specification of dynamic semantics, that is executable and type safe by construction. Unfortunately, scaling intrinsically-typed definitional interpreters to more complicated object languages often results in definitions that are cluttered with manual proof work. For linearly-typed languages (including session-typed languages) one has to prove that the interpreter, as well as all the operations on semantic components, treat values linearly. We present new methods and tools that make it possible to implement intrinsically-typed definitional interpreters for linearly-typed languages in a way that hides the majority of the manual proof work. Inspired by separation logic, we develop reusable and composable abstractions for programming with linear operations using dependent types. Using these abstractions, we define interpreters for linear lambda calculi with strong references, concurrency, and session-typed communication in Agda.
A Dynamic Look-Ahead Heuristic for the Qubit Mapping Problem of NISQ Computers

In the past few years, several quantum computers realized by the noisy intermediate-scale quantum (NISQ) technology have been released. However, there exists a significant limitation to using such computers, i.e., the connectivity constraint between physical qubits. To perform a 2-qubit quantum operation on such NISQ computers, its two logical qubits have to be mapped to a pair of physical qubits that satisfy the connectivity constraint. This mapping procedure requires additional operations to be introduced into the original quantum circuit, reducing its fidelity. Therefore, it is of great significance to design an algorithm that is able to complete the mapping task with minimal additional operations. In this article, we propose an efficient algorithm to solve this problem. Our algorithm consists of two core components, an expansion-from-center scheme to determine the initial mapping and a SWAP-based heuristic search algorithm to update the mapping. We introduce the maximum consecutive positive effect of a SWAP operation as the heuristic cost function, allowing our search algorithm to look ahead dynamically. Our algorithm is evaluated on IBM Q 20. The experimental results show that our algorithm can complete the mapping task in a very short time even for large-scale benchmarks with hundreds of thousands of operations, and outperforms the state-of-the-art in terms of the number of additional operations for most benchmarks considered.
Forecasting Time-to-Collision from Monocular Video: Feasibility, Dataset, and Challenges

We explore the possibility of using a single monocular camera to forecast the time to collision between a suitcase-shaped robot being pushed by its user and other nearby pedestrians. We develop a purely image-based deep learning approach that directly estimates the time to collision without the need of relying on explicit geometric depth estimates or velocity information to predict future collisions. While previous work has focused on detecting immediate collision in the context of navigating Unmanned Aerial Vehicles, the detection was limited to a binary variable (i.e., collision or no collision). We propose a more fine-grained approach to collision forecasting by predicting the exact time to collision in terms of milliseconds, which is more helpful for collision avoidance in the context of dynamic path planning. To evaluate our method, we have collected a novel dataset of over 13,000 indoor video segments each showing a trajectory of at least one person ending in a close proximity (a near collision) with the camera mounted on a mobile suitcase-shaped platform. Using this dataset, we do extensive experimentation on different temporal windows as input using an exhaustive list of state-of-the-art convolutional neural networks (CNNs). Our results show that our proposed multi-stream CNN is the best model for predicting time to near-collision. The average prediction error of our time to near-collision is 0.75 seconds across the test videos. The project webpage can be found at https://aashi7.github.io/NearCollision.html.
A Three-Tier Framework for Understanding Disruption Trajectories for Blockchain in the Electricity Industry

Ever since the invention of Bitcoin by the pseudonymous Satashi Nakamoto, cryptocurrency has provoked debate in banking and finance sectors, and is sometimes considered a potential successor to fiat currency. Blockchain, the new technology underpinning decentralised and immutable databases, has seen much discussion as a potentially game-changing development. Although many industries are exploring its value, the technology has thus far made only minor impacts. A rapidly expanding base of research has emerged on blockchain's role as a potential disruptor in the electrical energy industry. However, it may be difficult to distinguish hype from more imminently plausible impacts. This paper attempts to serve as a guide for engineering managers wishing to make sense of blockchain's potential in electricity. This is accomplished by formulating a novel blockchain industry disruption framework, which exists across three tiers. These tiers extend from ideas with the least effect on an industry to total revolutionary concepts that could completely transform an industry. This taxonomy is constructed by examining existing research into disruption hierarchies and blockchain classification methods. Through the lens of this taxonomy, a literature review is performed on blockchain's role in energy to draw out themes and ideas characterising each tier. The potential likelihood of real-world application of various ideas are discussed, considering how established industries may be affected or disrupted. The authors provide some conjecture here. Finally, courses of action are suggested for those whose sector may be affected by blockchain.
Researching AI Legibility through Design

Everyday interactions with computers are increasingly likely to involve elements of Artificial Intelligence (AI). Encompassing a broad spectrum of technologies and applications, AI poses many challenges for HCI and design. One such challenge is the need to make AI's role in a given system legible to the user in a meaningful way. In this paper we employ a Research through Design (RtD) approach to explore how this might be achieved. Building on contemporary concerns and a thorough exploration of related research, our RtD process reflects on designing imagery intended to help increase AI legibility for users. The paper makes three contributions. First, we thoroughly explore prior research in order to critically unpack the AI legibility problem space. Second, we respond with design proposals whose aim is to enhance the legibility, to users, of systems using AI. Third, we explore the role of design-led enquiry as a tool for critically exploring the intersection between HCI and AI research.
DRCGR: Deep Reinforcement Learning Framework Incorporating CNN and GAN-Based for Interactive Recommendation

Recently, the application of deep reinforcement learning into the field of session-based interactive recommendation has attracted great attention from researchers. However, despite that some interactive recommendation models based on deep reinforcement learning have been proposed, they still suffer to the following limitations: (1) these works ignore the skip behaviors of sequential patterns in users' clicking behavior; (2) these works fail to incorporate positive feedback and negative feedback into the proposed deep reinforcement recommender system when the positive feedback is sparse. Therefore, to solve the problems mentioned above, a novel Deep Q-Network based recommendation framework incorporating CNN and GAN-based models is proposed to acquire robust performance, named DRCGR. Specifically, in DRCGR, a CNN model is used to capture the sequential features for positive feedback. Then, an adversarial training is adopted to learn optimal negative feedback representations Then, positive/negative representations are fed into DQN simultaneously, which are conducive to generating better action-value function The experimental results based on real-world e-commerce data demonstrate our framework's superiority over some state-of-the-art recommendation models.
Similarity-Aware and Variational Deep Adversarial Learning for Robust Facial Age Estimation

In this paper, we propose a similarity-aware deep adversarial learning (SADAL) approach for facial age estimation. Instead of making full access to the limited training samples which likely leads to bias age prediction, our SADAL aims to seek batches of unobserved hard-negative samples based on existing training samples, which typically reinforces the discriminativeness of the learned feature representation for facial ages. Motivated by the fact that age labels are usually correlated in real-world scenarios, we carefully develop a similarity-aware function to well measure the distance of each face pair based on the age value gaps. Consequently, the age-difference information is exploited in the synthetic feature space for robust age estimation. During the learning process, we jointly optimize both procedures of generating hard negatives and learning discriminative age ranker via a sequence of adversarial-game iterations. Another major issue lies on that existing methods only enforce the indiscriminativeness within each class, which is probably trapped into model overfitting and thus the generation capacity is limited particularly on unseen age classes with many individuals. To circumvent this problem, we propose a variational deep adversarial learning (VDAL) paradigm, which learns to encode each face sample in two factorized parts, i.e., the intra-class variance distribution and the intra-class invariant class center. Moreover, our VDAL principally optimizes the variational confidence lower bound on the variational factorized feature representation. To better enhance the discriminativeness of the age representation, our VDAL further learns to encode the ordinal relationship among age labels in the reconstructed subspace. Experimental results on folds of widely-evaluated benchmarking datasets demonstrate that our approach achieves promising performance in contrast to most state-of-the-art age estimation methods.
Copy-Move Forgery Detection using Residuals and Convolutional Neural Network Framework: A Novel Approach

With the sudden advancement in digital image processing, there has been a huge upsurge in the creation of doctored or tampered images with the successful aid of softwares like GNU Gimp and Adobe Photoshop. These manipulated images have become a serious cause of concern, especially in the news, politics and the entertainment sector. Therefore, there is an alarming requirement for a robust image tampering detection system which can distinguish between authentic and tampered images. Common image tampering techniques include copy-move forgery, seam carving, splicing and re-compress. Amongst these techniques, copy-move forgery detection (CMFD) and splicing are dominating the research field due to their complexity stratum and difficulty in detection. In this work, we focus on proposing an efficient splicing detection and CMFD pipeline architecture that focuses on detecting the traces left by various post-processing operations of Splicing and copy-move forgery that are JPEG Compression, noise adding, blurring, contrast adjustment, etc. We use second difference of median filter (SDMFR) on the image as one of the residual and the Laplacian filter residual (LFR) together to suppress image content and focus only on the traces of the tampering operations. The proposed method achieves higher accuracy of 95.97% on the CoMoFoD dataset and 94.26% on the BOSSBase dataset.
Adaptive Function Launching Acceleration in Serverless Computing Platforms

Serverless computing has emerged as a new compelling paradigm for the deployment of applications and services, which enables developers to focus more on business logic rather than on infrastructure. Serverless computing platform enables the function container scales to zero, which results in a serious problem called cold start. Cold start severely affects the responsiveness of serverless computing platform and limits the use and adoption of serverless computing by a broader range of applications. The traditional strategies reduce the cold start latency at the expense of resources. How to simultaneously minimize the cold start latency and reduce the resources consumption of strategy implementation is a challenging problem. In this paper, we firstly propose an Adaptive Warm-Up Strategy (AWUS) to predict the function invoking time and warm up the functions, thus reducing the cold start latency. We use the function chain model to improve the AWUS. We adopt a fine-grained regression method to predict non-first functions in the function chain more accurately. Secondly, we propose an Adaptive Container Pool Scaling Strategy (ACPSS) to reduce the function launching time. We dynamically adjust the capacity of the container pool to reduce the resources waste. The AWUS and ACPSS work together to reduce the cold start latency and the resources waste. Finally, we implement a serverless computing platform and conduct extensive experiments to evaluate our strategy. The evaluation results demonstrate the effectiveness of our strategies.
Counting Vehicles with Deep Learning in Onboard UAV Imagery

The integration of mobile and ubiquitous computing with deep learning methods is a promising emerging trend that aims at moving the processing task closer to the data source rather than bringing the data to a central node. The advantages of this approach range from bandwidth reduction, high scalability, to high reliability, just to name a few. In this paper, we propose a real-time deep learning approach to automatically detect and count vehicles in videos taken from a UAV (Unmanned Aerial Vehicle). Our solution relies on a convolutional neural network-based model fine-tuned to the specific domain of applications that is able to precisely localize instances of the vehicles using a regression approach, straight from image pixels to bounding box coordinates, reasoning globally about the image when making predictions and implicitly encoding contextual information. A comprehensive experimental evaluation on real-world datasets shows that our approach results in state-of-the-art performances. Furthermore, our solution achieves real-time performances by running at a speed of 4 Frames Per Second on an NVIDIA Jetson TX2 board, showing the potentiality of this approach for real-time processing in UAVs.
Patch Aggregator for Scene Text Script Identification

Script identification in the wild is of great importance in a multi-lingual robust-reading system. The scripts deriving from the same language family share a large set of characters, which makes script identification a fine-grained classification problem. Most existing methods make efforts to learn a single representation that combines the local features by making a weighted average or other clustering methods, which may reduce the discriminatory power of some important parts in each script for the interference of redundant features. In this paper, we present a novel module named Patch Aggregator (PA), which learns a more discriminative representation for script identification by taking into account the prediction scores of local patches. Specifically, we design a CNN-based method consisting of a standard CNN classifier and a PA module. Experiments demonstrate that the proposed PA module brings significant performance improvements over the baseline CNN model, achieving the state-of-the-art results on three benchmark datasets for script identification: SIW-13, CVSI 2015 and RRC-MLT 2017.
Deposit-scale structural architecture of the Sigma-Lamaque gold deposit, Canada—insights from a newly proposed 3D method for assessing structural controls from drill hole data

Abstract Deposit-scale structural analysis is a practical method of structurally analyzing 3D drilling data. This interpretation method uses readily available distributed 3D data at the most relevant scale for mining and exploration. This method does not depend on collecting traditional structural data from core, such as orientation of planar and linear data, but relies on the premise that grade distributions sampled from drilling provide insight into the structural control of the deposit, with the interpretation of structural geometry done using a computer 3D viewer. This method is applied to the well-studied Archean Sigma-Lamaque gold deposit, which features late-orogenic vein mineralization, and which is controlled by three orders of structural features. The first-order control is the easterly plunging pipe-like conduit parallel to F 2 fold plunge defined by the intersection of bedding (S 0 ) and foliation (S 2 ) (~ 1.5 km radius × 3 km down-plunge). The second-order controls, nested within the first-order, are the younger subvertical Late Diorite plugs that intruded vertically into the host rocks along the established S 2 foliation anisotropy (1250:400:150 m dimension for Main Plug). The third-order control, laterally limited by first- and second-order structural controls, is the well-documented gold-bearing vein sets (1–10 m thick and up to 1.5 km in extent) that developed in the Late Diorites and in the older folded volcanogenic stratigraphy and diorites. It is proposed that the first-order control defined by the moderately plunging F 2 fold is the main fluid pathway resulting from structural permeability formed during earlier folding. The second-order Late Diorite plugs and other competent lithological units, which intersected and fractured during late-stage D 2 compression, served as chemically reactive sites causing gold to precipitate along this first-order pathway. It is predicted that intrusions that do not intersect the first-order structural conduits are less likely to be mineralized. Greenfield orogenic-gold exploration requires that the first-order controls are identified, and the geometry of hydrothermal alteration is best understood in the context of the structural architecture of the host rocks.
The Impact of Domain Randomization on Object Detection: A Case Study on Parametric Shapes and Synthetic Textures

Recent advances in deep learning-based object detection techniques have revolutionized their applicability in several fields. However, since these methods rely on unwieldy and large amounts of data, a common practice is to download models pre-trained on standard datasets and fine-tune them for specific application domains with a small set of domain-relevant images. In this work, we show that using synthetic datasets that are not necessarily photo-realistic can be a better alternative to simply fine-tune pre-trained networks. Specifically, our results show an impressive 25%improvement in the mAP metric over a fine-tuning baseline when only about 200 labelled images are available to train. Finally, an ablation study of our results is presented to delineate the individual contribution of different components in the randomization pipeline.
A Systematic Literature Review on Virtual Reality for Learning

Learning can be performed in various ways and can utilize different technologies. This paper presents a review of current and previous research to understand the use of virtual reality technology for learning. This paper used a systematic literature review (SLR) as a method. Research question (RQ) was determined in the first step. The query for searching the previous research on popular database journals was generated from previously created RQ. Popular journals included IEEE Xplore, ScienceDirect, SpringerLink, Scopus, and ACM Digital Library. Thirty-two related articles were produced from the search, then reviewed. The study concluded that there were four purposes of using virtual reality for learning, two types of devices used, and two types of user experiences.
Page Segmentation using a Convolutional Neural Network with Trainable Co-Occurrence Features

In document analysis, page segmentation is a fundamental task that divides a document image into semantic regions. In addition to local features, such as pixel-wise information, co-occurrence features are also useful for extracting texture-like periodic information for accurate segmentation. However, existing convolutional neural network (CNN)-based methods do not have any mechanisms that explicitly extract co-occurrence features. In this paper, we propose a method for page segmentation using a CNN with trainable multiplication layers (TMLs). The TML is specialized for extracting co-occurrences from feature maps, thereby supporting the detection of objects with similar textures and periodicities. This property is also considered to be effective for document image analysis because of regularity in text line structures, tables, etc. In the experiment, we achieved promising performance on a pixel-wise page segmentation task by combining TMLs with U-Net. The results demonstrate that TMLs can improve performance compared to the original U-Net. The results also demonstrate that TMLs are helpful for detecting regions with periodically repeating features, such as tables and main text.