Reviewerly/exHarmony huggingface dataset:
SAT-Based Encodings for Optimal Decision Trees with Explicit Paths

Decision trees play an important role both in Machine Learning and Knowledge Representation. They are attractive due to their immediate interpretability. In the spirit of Occam’s razor, and interpretability, it is desirable to calculate the smallest tree. This, however, has proven to be a challenging task and greedy approaches are typically used to learn trees in practice. Nevertheless, recent work showed that by the use of SAT solvers one may calculate the optimal size tree for real-world benchmarks. This paper proposes a novel SAT-based encoding that explicitly models paths in the tree, which enables us to control the tree’s depth as well as size. At the level of individual SAT calls, we investigate splitting the search space into tree topologies. Our tool outperforms the existing implementation. But also, the experimental results show that minimizing the depth first and then minimizing the number of nodes enables solving a larger set of instances.
Multi-Dimension Convolutional Neural Network for Bug Localization

Software bugs remain frequent in the life cycle of software development and maintenance. Automatic localization of buggy source code files is critical for timely bug fixing and improving the efficiency of software quality assurance. Various bug localization techniques have been proposed using different dimensions of features. Recent studies have shown that different dimensions of features may play different roles in bug localization. Unfortunately, how to effectively merge these dimensions of features for improving bug localization has rarely been investigated. This article presents a Multi-Dimension Convolutional Neural Network (MD-CNN) model for bug localization automatically based on a bug report. Our approach has dual-novelty. First, we identify and extract five statistical dimensions of features. Second, we design a Convolutional Neural Network (CNN) model that takes our five statistical dimensions of features as the input and iteratively learns the complex and non-linear relationship between the features and the bug locations. The MD-CNN bug localization model is verified using six large-scale open source projects. The experimental results show that our MD-CNN outperforms the existing representative bug localization techniques in terms of the Mean Average Precision (MAP) and the number of bugs successfully localized in the top 1, 5, and 10 matched source code files.
The Disaster and Climate Change Artathon

Information systems increasingly shape our knowledge of crises such as disasters and climate change. While these tools improve our capacity to understand, prepare for, and mitigate such challenges, critical questions are being raised about how their design shapes public imagination of these problems and delimits potential solutions. Prior work in human-computer interaction (HCI) has pointed to art/science collaboration as one approach for helping to explore such questions. As an attempt to draw on this potential, our team designed and facilitated a 2-day "artathon" that brought together artists and scientists to create new works of art based on disaster and climate data. Reflecting on the artathon and its outcomes, we contribute two sets of findings. First, we articulate opportunities, suggested by the artwork, for expanding research and design in crisis informatics. Second, we offer suggestions for HCI researchers seeking to stage successful art/science collaborations or similar inter-disciplinary events.
Rapid Composition for Networked Devices: HappyBrackets

Abstract This article introduces an open-source Java-based programming environment for creative coding of agglomerative systems using Internet-of-Things (IoT) technologies. Our software originally focused on digital signal processing of audio—including synthesis, sampling, granular sample playback, and a suite of basic effects—but composers now use it to interface with sensors and peripherals through general-purpose input/output and external networked systems. This article examines and addresses the strategies required to integrate novel embedded musical interfaces and creative coding paradigms through an IoT infrastructure. These include: the use of advanced tooling features of a professional integrated development environment as a composition or performance interface rather than just as a compiler; techniques to create media works using features such as autodetection of sensors; seamless and serverless communication among devices on the network; and uploading, updating, and running of new compositions to the device without interruption. Furthermore, we examined the difficulties many novice programmers experience when learning to write code, and we developed strategies to address these difficulties without restricting the potential available in the coding environment. We also examined and developed methods to monitor and debug devices over the network, allowing artists and programmers to set and retrieve current variable values to or from these devices during the performance and composition stages. Finally, we describe three types of art work that demonstrate how the software, called HappyBrackets, is being used in live-coding and dance performances, in interactive sound installations, and as an advanced composition and performance tool for multimedia works.
Enabling Cost-Effective, SLO-Aware Machine Learning Inference Serving on Public Cloud

The remarkable advances of Machine Learning (ML) have spurred an increasing demand for ML- as-a-Service on public cloud: developers train and publish ML models as online services to provide low-latency inference for dynamic queries. The primary challenge of ML model serving is to meet the response-time Service-Level Objectives (SLOs) of inference workloads while minimizing serving cost. In this article, we proposes MArk (Model Ark), a general-purpose inference serving system, to tackle the dual challenge of SLO compliance and cost effectiveness. MArk employs three design choices tailored to inference workload. First, MArk dynamically batches requests and opportunistically serves them using expensive hardware accelerators (e.g., GPU) for improved performance-cost ratio. Second, instead of relying on feedback control scaling or over-provisioning to serve dynamic workload, which can be too slow or too expensive, MArk employs predictive autoscaling to hide the provisioning latency at low cost. Third, given the stateless nature of inference serving, MArk exploits the flexible, yet costly serverless instances to cover occasional load spikes that are hard to predict. We evaluated the performance of MArk using several state-of-the-art ML models trained in TensorFlow, MXNet, and Keras. Compared with the premier industrial ML serving platform SageMaker, MArk reduces the serving cost up to <inline-formula><tex-math notation="LaTeX">$7.8\times$</tex-math></inline-formula> while achieving even better latency performance.
Index Point Detection and Semantic Indexing of Videos—A Comparative Review

Mahrishi, Mehul Morwal, SudhaPrimarily used for fun and entertainment, videos are now a motivation behind social, commercial, and business activities. It is presumed that by 2025, about 75% of all Internet traffic will be of videos. In education, videos are a source of learning. Study Webs of Active Learning for Young Aspiring Minds (SWAYAM), National Programme on Technology Enhanced Learning (NPTEL), Massive Open Online Courses (MOOCs), Coursera, and many other similar platforms provide not only courseware but also beyond the curriculum contents apart from the conventional syllabi. Even at the junior level, Byju’s and similar educational portals are witnessing an explosive growth in video contents. Despite that we are now able to extract semantic features from images, video sequences and besides being ubiquitous in nature, video lectures have a limitation of smooth navigation between topics. Through this paper, we want to throw light on existing automated video indexing approaches and their prerequisites that are recently proposed. We tried to analyze them based on some existing measures.
Radial intersection count image: A clutter resistant 3D shape descriptor

A novel shape descriptor for cluttered scenes is presented, the Radial Intersection Count Image (RICI), and is shown to significantly outperform the classic Spin Image (SI) and 3D Shape Context (3DSC) in both uncluttered and, more significantly, cluttered scenes. It is also faster to compute and compare. The clutter resistance of the RICI is mainly due to the design of a novel distance function, capable of disregarding clutter to a great extent. As opposed to the SI and 3DSC, which both count point samples, the RICI uses intersection counts with the mesh surface, and is therefore noise-free. For efficient RICI construction, novel algorithms of general interest were developed. These include an efficient circle-triangle intersection algorithm and an algorithm for projecting a point into SI-like ($\alpha$, $\beta$) coordinates. The 'clutterbox experiment' is also introduced as a better way of evaluating descriptors' response to clutter. The SI, 3DSC, and RICI are evaluated in this framework and the advantage of the RICI is clearly demonstrated.
BNGBS: An efficient network boosting system with triple incremental learning capabilities for more nodes, samples, and classes

As an ensemble algorithm, network boosting enjoys a powerful classification ability but suffers from the tedious and time-consuming training process. To tackle the problem, in this paper, a broad network gradient boosting system (BNGBS) is developed by integrating gradient boosting machine with broad networks, in which the classification loss caused by a base broad network is learned and eliminated by followed networks in a cascade manner. The proposed system is constructed as an additive model and can be easily optimized by a greedy strategy instead of the tedious back-propagation algorithm, resulting in a more efficient learning process. Meanwhile, triple incremental learning capabilities including the increment of feature nodes, increment of input samples, and increment of target classes are designed. The proposed system can be efficiently updated and expanded based on the current status instead of being entirely retrained when the demands for more feature nodes, input samples, and target classes are proposed. The node-increment ability allows to add more feature nodes into the built system if the current structures are not effective for learning. The sample-increment ability is developed to allow the model to keep learning from the coming batch data. The class-increment ability is used to tackle the issue that the coming batch data may contain unseen categories. In comparison with existing popular machine learning methods, comprehensive results based on eight benchmark datasets illustrate the effectiveness of the proposed broad network gradient boosting system for the classification task.
Spatial augmented reality

Spatial Augmented Reality (sAR) as an assistive technology is a promising tool for experienced and novice industrial workers. Five industrial case studies in Dutch manufacturing companies are described to study the effects of sAR assistance on task completion time, learning speed, product quality, work load, technology acceptance and employability in manual assembly guidance and training. Although case study outcomes were rather positive and user acceptance was high, 2 out of 5 use case companies decided not to invest in this technology after the initial pilot project. The main barriers for implementation were concerns about the relatively high system costs, the initial instruction programming time and the required expertise to do so. Future system developments should improve the system's usability from a business process engineering perspective and thereby support zero programming of sAR systems and adaptive work instructions.
HAND-REHA

Hand-gesture recognition systems have recently gained more popularity. Moreover, there is a growing interest in building games for other purposes apart from entertainment, such as education and rehabilitation. This paper focuses on developing a novel game-based system for wrist rehabilitation, called HandReha. The idea is to automatically recognize pre-defined hand gestures using a web-camera, so to control an avatar in a three dimensional maze run game. The pre-defined gestures are picked from a pool of well-defined gestures suitable for wrist rehabilitation. Deep learning techniques were utilized to perform real-time hand gesture recognition from the images. To evaluate the performance of the developed wrist rehabilitation system, a preliminary study with 12 healthy participants was conducted. The results showed that the developed wrist rehabilitation system is intuitive and engages the user, which is crucial for rehabilitation purposes.
Learning Reward Function with Matching Network for Mapless Navigation

Deep reinforcement learning (DRL) has been successfully applied in mapless navigation. An important issue in DRL is to design a reward function for evaluating actions of agents. However, designing a robust and suitable reward function greatly depends on the designer's experience and intuition. To address this concern, we consider employing reward shaping from trajectories on similar navigation tasks without human supervision, and propose a general reward function based on matching network (MN). The MN-based reward function is able to gain the experience by pre-training through trajectories on different navigation tasks and accelerate the training speed of DRL in new tasks. The proposed reward function keeps the optimal strategy of DRL unchanged. The simulation results on two static maps show that the DRL converge with less iterations via the learned reward function than the state-of-the-art mapless navigation methods. The proposed method performs well in dynamic maps with partially moving obstacles. Even when test maps are different from training maps, the proposed strategy is able to complete the navigation tasks without additional training.
On the Shoulders of Giants

Pull-based development is a widely adopted paradigm for collaboration in distributed software development, attracting eyeballs from both academic and industry. To better study pull-based development model, this paper presents a new dataset containing 96 features collected from 11,230 projects and 3,347,937 pull requests. We describe the creation process and explain the features in details. To the best of our knowledge, our dataset is the most comprehensive and largest one toward a complete picture for pull-based development research.
The Influence of Game Character Appearance on Empathy and Immersion: Virtual Non-Robotic Versus Robotic Animals

Background. Empathic interactions with animated game characters can help improve user experience, increase immersion, and achieve better affective outcomes related to the use of the game. Method. We used a 2x2 between-participant design and a control condition to analyze the impact of the visual appearance of a virtual game character on empathy and immersion. The four experimental conditions of the game character appearance were: Natural (virtual animal) with expressiveness (emotional facial expressions), natural (virtual animal) with non-expressiveness (without emotional facial expressions), artificial (virtual robotic animal) with expressiveness (emotional facial expressions), and artificial (virtual robotic animal) with non-expressiveness (without emotional facial expressions). The control condition contained a baseline amorphous game character. 100 participants between 18 to 29 years old (M=22.47) were randomly assigned to one of five experimental groups. Participants originated from several countries: Aruba (1), China (1), Colombia (3), Finland (1), France (1), Germany (1), Greece (2), Iceland (1), India (1), Iran (1), Ireland (1), Italy (3), Jamaica (1), Latvia (1), Morocco (3), Netherlands (70), Poland (1), Romania (2), Spain (1), Thailand (1), Turkey (1), United States (1), and Vietnam (1). Results. We found that congruence in appearance and facial expressions of virtual animals (artificial + non-expressive and natural + expressive) leads to higher levels of self-reported situational empathy and immersion of players in a simulated environment compared to incongruent appearance and facial expressions. Conclusions. The results of this investigation showed an interaction effect between artificial/natural body appearance and facial expressiveness of a virtual character’s appearance. The evidence from this study suggests that the appearance of the virtual animal has an important influence on user experience.
A New Approach for Smoking Event Detection Using a Variational Autoencoder and Neural Decision Forest

Smoking is associated with cancer, cardiovascular disease and premature death and can cause severe fire hazards. To assist with smoking cessation, this paper presents a wireless body area network-based system consisting of two off-the-shelf devices, one smartphone and one smartwatch, to detect smoking events by mining the inertial sensor data from both devices. In the system, an end-to-end trainable unified model is implemented by combining a variational autoencoder with a random forest to classify the collected data into smoking and nonsmoking after data preprocessing. The variational autoencoder is adopted to learn the feature representation and deal with the class imbalance problem, and the stochastic decision forest is adopted to guide the global optimization of the parameter learning process. Extensive validation of our scheme is performed on a large dataset we collected, and our scheme yields quite promising results in terms of accuracy and efficiency.
DayClo

We introduce DayClo, an interactive clock visualizing schedule data for supporting users to reflect and self-track themselves in their daily lives. DayClo is designed by applying the form and time representation method of analog clocks. It shows the user's upcoming schedules through the sequential movement of two clock hands with hidden LEDs and a display. We deployed DayClo in eight participants' working spaces for a month to explore how the design and its interaction could support their reflections on their scheduling practices. The findings revealed that DayClo supported users' casual access to the schedule data and motivated self-tracking of their plans. Moreover, the moving clock hands revealed the empty time of their schedules and helped to draw new plans for their implicit goal. Our findings suggest new opportunities for designing everyday objects as a medium of delivering and fostering spontaneous interaction with personal data for self-reflection.
Feature standardisation and coefficient optimisation for effective symbolic regression

Symbolic regression is a common application of genetic programming where model structure and corresponding parameters are evolved in unison. In the majority of work exploring symbolic regression, features are used directly without acknowledgement of their relative scale or unit. This paper extends recent work on the importance of standardisation of features when conducting symbolic regression. Specifically, z-score standardisation of input features is applied to both inputs and response to ensure that evolution explores a model space with zero mean and unit variance. This paper demonstrates that standardisation allows a simpler function set to be used without increasing bias. Additionally, it is demonstrated that standardisation can significantly improve the performance of coefficient optimisation through gradient descent to produce accurate models. Through analysis of several benchmark data sets, we demonstrate that feature standardisation enables simple but effective approaches that are comparable in performance to the state-of-the-art in symbolic regression.
A novel centerline extraction algorithm for a laser stripe applied for turbine blade inspection

Line-laser 3D scanning is a novel and promising automation technique for turbine blade inspection. One key task encountered in line-laser 3D scanning is centerline extraction of the laser stripes. In some regions, there is asymmetry of the line profiles caused by high curvature, such as the leading and trailing edges of a blade. According to the asymmetric Gaussian distribution line profile, this paper presents a new algorithm to extract the centerline of a laser stripe based on sub-pixel boundary extraction and medial axis transformation. The candidate boundary points of the laser stripe are extracted by zero crossing. Based on Taylor expansion and interpolation, the gray gradient distribution of the boundary can be estimated accurately. Following this, the accurate boundary points are extracted by extreme values of the gray gradient, and the centerline is calculated by medial axis transformation of the accurate boundary points. The proposed method can effectively reduce errors caused by the asymmetry of line profiles in areas of high curvature. The effectiveness of this method is verified using simulation and measuring experiments. The experiment results show that the extraction error of the asymmetric Gaussian line profile is within ± 0.15 pixels, and the root mean square error in measurement accuracy experiments does not exceed 0.015 mm.
Mount Milligan alkalic porphyry Au--Cu deposit, British Columbia, Canada, and its AEM and AIP signatures: Implications for mineral exploration in covered terrains

Although alkalic porphyry Au--Cu deposits are much less common than their calc-alkalic counterparts, they represent attractive exploration targets both due to their occurrence as, locally concealed, clusters and their high Au contents. In this paper, the geology of the Mt. Milligan Au--Cu deposit, Canada, and its geophysical footprint on magnetic and AEM data is used as a case-study for the exploration and discovery of concealed alkalic porphyry Au--Cu deposits. The Cole-Cole parameters in the AEM data are extracted using an improved Airborne Induced Polarization (AIP) mapping algorithm. We propose that the subtle electromagnetic signatures of disseminated sulfides around the intrusive porphyry stocks at Mt. Milligan could be mapped by the mτ product, where m is AIP apparent chargeability, and τ the Cole-Cole time-constant, provided that the depths of the alteration zone does not exceed 75 m. The product is called Tau-Scaled Chargeability or TSC. In order to improve the exploration success in the search for concealed alkalic porphyry Au--Cu deposits, we propose to: (1) concentrate the exploration on known and well-endowed mineral districts; and (2) apply innovative digital data-processing techniques capable of extracting weak and subtle signatures from airborne geophysical data and, subsequently, to utilize DCIP ground surveys in the follow-up of anomalies, ideally in combination with enzyme-leach soil sampling.
Design principles for transforming making programs into online settings at public libraries

Purpose This paper aims to investigate design principles for transforming existing making communities of practice within public libraries into online knowledge-building communities to support youths, families with young children and adult members’ making and tinkering during COVID-19. Design/methodology/approach Building upon C4P and connected learning framework, the authors analyze existing literature and practitioner reports on informal learning projects related to making and STEM learning, family learning and online learning as well as emergent cases of innovative approaches in response to COVID-19 from public libraries, informal learning institutions and community groups. Findings The authors suggest 11 design principles around five areas: program design, facilitation, tools and materials, process documentation and sharing and feedback. Originality/value This work contributes to the information and learning sciences concerned with community engagement and knowledge creation by suggesting a design model to transform and sustain existing making communities of practice within public libraries into online knowledge-building communities during COVID-19.