Reviewerly/exHarmony huggingface dataset:
Tabletop Roleplaying Games as Procedural Content Generators

Tabletop roleplaying games (TTRPGs) and procedural content generators can both be understood as systems of rules for producing content. In this paper, we argue that TTRPG design can usefully be viewed as procedural content generator design. We present several case studies linking key concepts from PCG research – including possibility spaces, expressive range analysis, and generative pipelines – to key concepts in TTRPG design. We then discuss the implications of these relationships and suggest directions for future work uniting research in TTRPGs and PCG.
Pedestrian detection using quaternion histograms of oriented gradients

In recent years, pedestrian detection has attracted more attention in many practical applications. In this paper, a novel pedestrian detection method using quaternion histograms of oriented gradients (QHOG) was proposed, which is integrated the advantages of both the quaternionic representation and HOG feature of a color image. Firstly, the quaternionic representation was performed on the color image in the sliding window, and then the histograms of oriented gradients (HOG) feature was extracted over the quaternionic representation map. After that, the QHOG histogram was constructed to represent the sliding window. Two groups of experiments are performed on two popular pedestrian datasets, INRIA dataset and Daimler Chrysler (DC) dataset, respectively. The experimental results show that our proposed QHOG detector performs better than the HOG detector, HOG-LBP detector and MWLD detector.
CNN Assisted Hybrid Algorithm for Medical Images Segmentation

In this report we focus on a hybrid method based on a convolutional neural network (CNN) for histological image segmentation. We propose a CNN assisted interactive segmentation tool with weakly-supervised learning to accelerate the process of manual image annotation. The core of our annotation approach is a classical KNN classifier that uses parameters predicted by CNN. User annotates an image with scribbles of two types corresponding to glands and non-glands histological structures. Next the model performs label propagation to all unlabeled pixels providing user a fully annotated image build from his scribbled-based input. User can interact with the annotation tool and add new scribbles to correct the result. The algorithm allows to reduce one image annotation time from 150 to 25-30 minutes for PATH-DT-MSU dataset that can potentially seriously increase the number of fully annotated histological images which is necessary for the development of diagnostic algorithms.
How junior developers deal with their technical debt?

Technical debt is a metaphor that measures the additional effort needed to continue to add more features in a software due to its inherent decrease in code quality. Most software systems suffer from technical debt at some point so that dedicated tools and metrics have been developed to monitor such debt. Alongside tools, appropriate engineering practices must be put in place by the development team to keep that debt at an acceptable level. In this empirical study, we observed and surveyed Scrum development teams composed of experienced students in order to understand their quality-related processes on a year-long academic project. We found that (1) students do use static analysis tools of many forms, but their actual usage is limited due to time pressure; (2) retrospective and non-constraining feedback on code quality has little to no effect, even when given regularly during the course of the project; and (3) junior developers value composite quality indicators (e.g., maintainability, reliability in SonarQube), even if they do not fully understand their meaning. From our findings, we propose a series of recommendations, both technical and methodological, on how to train junior developers to understand and manage technical debt.
Deep Pyramid Convolutional Neural Network Integrated with Self-attention Mechanism and Highway Network for Text Classification

Abstract Text classification is one of the basic tasks of natural language processing. In recent years, deep learning has been widely used in text classification tasks. The representative one is the convolutional neural network. DPCNN is a deep convolutional neural network text classification model that can obtain long-distance text information, but it focuses on the extraction of global features and ignores the extraction of local features of the text. Some local feature information is very important and plays an important role in text classification tasks. Therefore, in this paper, a self-attention mechanism is introduced to extract local features of text based on the lack of extracting local features of DPCNN. In addition, although the deep convolutional neural network can extract deeper features, it is easy to cause the problem of gradient disappearance during training. Therefore, the highway network is introduced to prevent the problem of gradient disappearance caused by network training and improve the performance of the model. Experimental results show that the proposed model is better than a single DPCNN model, which further improves the accuracy of text classification tasks.
Precfix

Patch recommendation is the process of identifying errors in software systems and suggesting suitable fixes for them. Patch recommendation can significantly improve developer productivity by reducing both the debugging and repairing time. Existing techniques usually rely on complete test suites and detailed debugging reports, which are often absent in practical industrial settings. In this paper, we propose Precfix, a pragmatic approach targeting large-scale industrial codebase and making recommendations based on previously observed debugging activities. Precfix collects defect-patch pairs from development histories, performs clustering, and extracts generic reusable patching patterns as recommendations. We conducted experimental study on an industrial codebase with 10K projects involving diverse defect patterns. We managed to extract 3K templates of defect-patch pairs, which have been successfully applied to the entire codebase. Our approach is able to make recommendations within milliseconds and achieves a false positive rate of 22% confirmed by manual review. The majority (10/12) of the interviewed developers appreciated Precfix, which has been rolled out to Alibaba to support various critical businesses.
Probabilistic Effect Prediction through Semantic Augmentation and Physical Simulation

Nowadays, robots are mechanically able to perform highly demanding tasks, where AI-based planning methods are used to schedule a sequence of actions that result in the desired effect. However, it is not always possible to know the exact outcome of an action in advance, as failure situations may occur at any time. To enhance failure tolerance, we propose to predict the effects of robot actions by augmenting collected experience with semantic knowledge and leveraging realistic physics simulations. That is, we consider semantic similarity of actions in order to predict outcome probabilities for previously unknown tasks. Furthermore, physical simulation is used to gather simulated experience that makes the approach robust even in extreme cases. We show how this concept is used to predict action success probabilities and how this information can be exploited throughout future planning trials. The concept is evaluated in a series of real world experiments conducted with the humanoid robot Rollin' Justin.
Replay attack detection using variable-frequency resolution phase and magnitude features

Replay attacks pose the most severe threat to automatic speaker verification systems among various spoofing attacks. In this paper, we propose a novel feature extraction method that leverages both the phase-based and magnitude-based features. The proposed method fully utilizes the subband information and the complementary information from the phase and magnitude spectra. First, we conduct a discriminative performance analysis on full frequency bands via the F-ratio method. Then, variable-frequency resolution features are extracted via several techniques to capture highly discriminative information on frequency bands. Finally, complementary information from the phase and magnitude domains are fused to achieve higher performance. The results on the ASVspoof 2017 database demonstrate that our proposed frequency adaptive features attain relative error reduction rates of 83.4% and 62.3% on the development and evaluation datasets, respectively, compared to the baseline method.
Anomalous Motion Detection On Highway Using Deep Learning

Research in visual anomaly detection draws much interest due to its applications in surveillance. Common datasets for evaluation are constructed using a stationary camera overlooking a region of interest. Previous research has shown promising results in detecting spatial as well as temporal anomalies in these settings. The advent of self-driving cars provides an opportunity to apply visual anomaly detection in a more dynamic application yet no dataset exists in this type of environment. This paper presents a new anomaly detection dataset the Highway Traffic Anomaly (HTA) dataset- for the problem of detecting anomalous traffic patterns from dash cam videos of vehicles on highways. We evaluate state-of-the-art deep learning anomaly detection models and propose novel variations to these methods. Our results show that state-of-the-art models built for settings with a stationary camera do not translate well to a more dynamic environment. The proposed variations to these SoTA methods show promising results on the new HTA dataset.
Contrasts and similarities between two audio research communities in evaluating auditory artefacts

The design of auditory artefacts has been establishing its practice as a scientific area for more than 20 years, with a crucial element in this process being how to properly evaluate acoustic outputs. In this paper, we sought to map the evaluation methods applied in a general search inside two main audio-focused conferences: Audio Mostly and the International Conference on Auditory Display (ICAD). Revisiting last year's editions, as well as a keyword-based search in the last ten years, we attempted to gather and classify each evaluation method according to the level of user involvement, their role, and the authors intentions in using each method. We propose an initial mapping for this gathering, in a framework of evaluation approaches which can reinforce and expand current practices in the creation of auditory artefacts.
PGD-UNet: A Position-Guided Deformable Network for Simultaneous Segmentation of Organs and Tumors

Precise segmentation of organs and tumors plays a crucial role in clinical applications. It is a challenging task due to the irregular shapes and various sizes of organs and tumors as well as the significant class imbalance between the anatomy of interest (AOI) and the background region. In addition, in most situation tumors and normal organs often overlap in medical images, but current approaches fail to delineate both tumors and organs accurately. To tackle such challenges, we propose a position-guided deformable UNet, namely PGD-UNet, which exploits the spatial deformation capabilities of deformable convolution to deal with the geometric transformation of both organs and tumors. Position information is explicitly encoded into the network to enhance the capabilities of deformation. Meanwhile, we introduce a new pooling module to preserve position information lost in conventional max-pooling operation. Besides, due to unclear boundaries between different structures as well as the subjectivity of annotations, labels are not necessarily accurate for medical image segmentation tasks. It may cause the overfitting of the trained network due to label noise. To address this issue, we formulate a novel loss function to suppress the influence of potential label noise on the training process. Our method was evaluated on two challenging segmentation tasks and achieved very promising segmentation accuracy in both tasks.
Semantic-driven watermarking of relational textual databases

In relational database watermarking, the semantic consistency between the original database and the distorted one is a challenging issue which is disregarded by most watermarking proposals, due to the well-known assumption for which a small amount of errors in the watermarked database is tolerable. We propose a semantic-driven watermarking approach of relational textual databases, which marks multi-word textual attributes, exploiting the synonym substitution technique for text watermarking together with notions in semantic similarity analysis, and dealing with the semantic perturbations provoked by the watermark embedding. We show the effectiveness of our approach through an experimental evaluation, highlighting the resulting capacity, robustness and imperceptibility watermarking requirements. We also prove the resilience of our approach with respect to the random synonym substitution attack.
The Supatlantique Scanned Documents Database for Digital Image Forensics Purposes

The ease of use and the capabilities of image editing tools has raised the challenges in the emerging field of digital image forensics related to scanned documents. Unfortunately, the universality of current methods and their applicability in real world scenarios have not been proven yet due to the absence of a standardized image database. In this paper, we introduce a novel test collection of more than 4500 images annotated with respect to each scanner as a useful tool for forensics in-vestigators to test and compare scanner-based forensic techniques. It is an image database that contains document of various content scanned with more than one resolution with 11 different scanner instances of widely known brands. This selection is based on our latest work on the identification of scanners at the origin of digitized documents and is adapted to fit any source scanner identification technique. The SUPATLANTIQUE database is available for free to the research community and is intended to become a useful and a reference resource for researchers in this field.
Audiovisual cognitive architecture for autonomous learning of face localisation by a Humanoid Robot

Newborn infants are naturally attracted to human faces, a crucial source of information for social interaction. In robotics, acquisition of such information is crucial and social robots should also learn to exhibit such social skill. Deep learning algorithms are valid candidates to address the problem of face localisation. However, a major drawback of these methods is the large amount of data and human supervision needed in the training procedure. In this work, we propose a cognitive architecture to address autonomous learning from raw sensory signals without supervision. We demonstrate the success of our cognitive framework for the task of face localisation. The proposed cognitive architecture builds on existing work and uses audiovisual attention and a proactive stereo vision mechanism to autonomously direct a robot’s attentive focus towards human faces. The gathered information is used to incrementally generate a dataset that can be used to train a state-of-the-art deep network. The learning system imitates the typical learning process of infants and enhances the learning generalization process by leveraging on the interaction experience with people. The integration of HRI with machine learning, inspired by early development in humans, constitutes an innovative approach for improving autonomous learning in robots.
Attention Selective Network For Face Synthesis And Pose-Invariant Face Recognition

Face recognition algorithms have improved significantly in recent years since the introduction of deep learning and the availability of large training datasets. However, their performance is still inadequate when the face pose varies as pose variation can dramatically increase intra-person variability. This work proposes a novel generative adversarial architecture called the Attention Selective Network (ASN) to address the problem of pose-invariant face recognition. The ASN introduces an efficient attention mechanism and a multi-part loss function to generate realistic-looking frontal face images from other face poses that can be used for recognizing faces under various poses. Thanks to the high quality of the synthesized images, the ASN achieves superior performance in terms of recognition rates compared to the state-of-the-art supervised methods.
Ethical Mining

Research in Mining Software Repositories (MSR) is research involving human subjects, as the repositories usually contain data about developers' interactions with the repositories. Therefore, any research in the area needs to consider the ethics implications of the intended activity before starting. This paper presents a discussion of the ethics implications of MSR research, using the mining challenges from the years 2010 to 2019 as a case study to identify the kinds of data used. It highlights problems that one may encounter in creating such datasets, and discusses ethics challenges that may be encountered when using existing datasets, based on a contemporary research ethics framework. We suggest that the MSR community should increase awareness of ethics issues by openly discussing ethics considerations in published articles.
GIS-Based Automatic Flight Planning of Camera-Equipped UAVs for Fire Emergency Response

Emerging technologies such as camera-equipped unmanned aerial vehicles (UAVs) are increasingly being applied in building fire rescue to provide real-time visualization and 3D reconstruction of the entire fireground. However, flight planning of camera-equipped UAVs is usually a manual process, which is not sufficient to fulfill the needs of emergency management. This research proposes a Geographic Information System (GIS)-based approach to automatic flight planning of camera-equipped UAVs for building fire emergency response. In this research, Haversine formula and lawn mowing patterns are employed to automate flight planning based on geometrical and spatial information from GIS. The resulting flight mission satisfies the requirements of the 3D reconstruction purposes of the fireground, in consideration of flight execution safety and visibility of camera frames. The proposed approach is implemented within a GIS environment. A case study is used to demonstrate the effectiveness of the proposed approach. The result shows that the flight mission can be generated in a timely manner for fire emergency response.
FT-VMP: Fault-Tolerant Virtual Machine Placement in Cloud Data Centers

Virtual machine (VM) replication is an effective technique in cloud data centers to achieve fault-tolerance, load-balance, and quick-responsiveness to user requests. In this paper we study a new fault-tolerant VM placement problem referred to as FT-VMP. Given that different VM has different fault-tolerance requirement (i.e., difference VM requires different number of replica copies) and compatibility requirement (i.e., some VMs and their replicas cannot be placed into some physical machines (PMs) due to software or platform incompatibility), FT-VMP studies how to place VM replica copies inside cloud data centers in order to minimize the number of PMs storing VM replicas, under the constraints that i) for fault-tolerant purpose, replica copies of the same VM cannot be placed inside the same PM and ii) each PM has a limited amount of storage capacity. We first prove that FT-VMP is NP-hard. We then design an integer linear programming (ILP)-based algorithm to solve it optimally. As ILP takes time to compute thus is not suitable for large scale cloud data centers, we design a suite of efficient and scalable heuristic fault-tolerant VM placement algorithms. We show that a) ILP-based algorithm outperforms the state-of-the-art VM replica placement in a wide range of network dynamics and b) that all our fault-tolerant VM placement algorithms are able to turn off significant number of PMs to save energy in cloud data centers. In particular, we show that our algorithms can consolidate (i.e., turn off) around 100 PMs in a small data center of 256 PMs and 700 PMs in a large data center of 1028PMs.
Headbang: Using Head Gestures to Trigger Discrete Actions on Mobile Devices

We present Headbang, an interaction technique that enriches touch input on handheld devices through slight head movement gestures. This way, users can easily execute shortcuts, like Copy, Paste, or Share, to on-screen targets while touching them. Headbang utilizes the capabilities of commodity smartphones to track the user’s head with their front facing cameras. We evaluated Headbang in two studies and show that the system can be reliably used while sitting and walking and offers a similar accuracy and speed as touch interaction.
MResearch on Ethereum Private Blockchain Multi-nodes Platform

With the gradual development of blockchain technology and the decentralized demand of the Internet market, the programmable financial system with blockchain technology has been proposed. However, the uncertainty of smart contract in Ethereum application layer leads to fatal problems on Ethereum network, which affects the efficiency and usages. Moreover, most schools or enterprises have not mastered blockchain technology, so it is difficult to carry out experiments of Ethereum, such as the simple account transfer transaction and deployment of smart contract. The paper is to research the Ethereum private blockchain network locally, and provide an experimental platform, which can conduct operations of Ethereum account and block information among multiple nodes. Through the research on private blockchain multi-nodes platform, a test environment for some school lessons or enterprise projects is provided. Then we implement and test of smart contract applications on Ethereum to ensure the overall security of Ethereum network.
Enhancing Privacy in Robotics via Judicious Sensor Selection

Roboticists are grappling with how to address privacy in robot design at a time when regulatory frameworks around the world increasingly require systems to be engineered to preserve and protect privacy. This paper surveys the top robotics journals and conferences over the past four decades to identify contributions with respect to privacy in robot design. Our survey revealed that less than half of one percent of the ~89,120 papers in our study even mention the word privacy. Herein, we propose privacy preserving approaches for roboticists to employ in robot design, including, assessing a robot's purpose and environment; ensuring privacy by design by selecting sensors that do not collect information that is not essential to the core objectives of that robot; embracing both privacy and performance as fundamental design challenges to be addressed early in the robot lifecycle; and performing privacy impact assessments.
Motion Reasoning for Goal-Based Imitation Learning

We address goal-based imitation learning, where the aim is to output the symbolic goal from a third-person video demonstration. This enables the robot to plan for execution and reproduce the same goal in a completely different environment. The key challenge is that the goal of a video demonstration is often ambiguous at the level of semantic actions. The human demonstrators might unintentionally achieve certain subgoals in the demonstrations with their actions. Our main contribution is to propose a motion reasoning framework that combines task and motion planning to disambiguate the true intention of the demonstrator in the video demonstration. This allows us to recognize the goals that cannot be disambiguated by previous action-based approaches. We evaluate our approach on a new dataset of 96 video demonstrations in a mockup kitchen environment. We show that our motion reasoning plays an important role in recognizing the actual goal of the demonstrator and improves the success rate by over 20%. We further show that by using the automatically inferred goal from the video demonstration, our robot is able to reproduce the same task in a real kitchen environment.