Reviewerly/exHarmony huggingface dataset:
Enriching API Documentation with Code Samples and Usage Scenarios from Crowd Knowledge

As one key resource to learn Application Programming Interfaces (APIs), a lot of API reference documentation lacks code samples with usage scenarios, thus heavily hindering developers from programming with APIs. Although researchers have investigated how to enrich API documentation with code samples from general code search engines, two main challenges remain to be resolved, including the quality challenge of acquiring high-quality code samples and the mapping challenge of matching code samples to usage scenarios. In this study, we propose a novel approach named ADECK towards enriching API documentation with code samples and corresponding usage scenarios by leveraging crowd knowledge from Stack Overflow, a popular technical Question and Answer (Q&A) website attracting millions of developers. Given an API related Q&A pair, a code sample in the answer is extensively evaluated by developers and targeted towards resolving the question under the specified usage scenario. Hence, ADECK can obtain high-quality code samples and map them to corresponding usage scenarios to address the above challenges. Extensive experiments on the Java SE and Android API documentation show that the number of code-sample-illustrated API types in the ADECK-enriched API documentation is 3.35 and 5.76 times as many as that in the raw API documentation. Meanwhile, the quality of code samples obtained by ADECK is better than that of code samples by the baseline approach eXoaDocs in terms of correctness, conciseness, and usability, e.g., the average correctness values of representative code samples obtained by ADECK and eXoaDocs are 4.26 and 3.28 on a 5-point scale in the enriched Java SE API documentation. In addition, an empirical study investigating the impacts of different types of API documentation on the productivity of developers shows that, compared against the raw and the eXoaDocs-enriched API documentation, the ADECK-enriched API documentation can help developers complete 23.81 and 14.29 percent more programming tasks and reduce the average completion time by 9.43 and 11.03 percent.
Acceleration of Online Recognition of 2D Sequences Using Deep Bidirectional LSTM and Dynamic Programming

In this work, the approach for online recognition of 2D sequences using deep bidirectional LSTM was proposed. One of the complex cases of online sequence recognition is handwritten mathematical expressions (HME). In spite of many achievements in this area, it is a still challenging task as, in addition to character segmentation and recognition, the tasks of structure, relations, and grammar analysis should be resolved. Such a combination of recognizers could lead to an increase in computational complexity for large expressions, which is unacceptable for on-device recognition in mobile applications. As end-to-end neural systems do not achieve plausible accuracy and recognition speed for on-device calculations so far, to overcome this problem we proposed a deep-learning solution that employs recurrent neural networks (RNNs) for structure and character recognition in combination with re-ordering and modified CYK algorithm for expression construction. Also, we explored a variety of structural and optimization enhancements to CYK algorithm that significantly improved the performance in terms of the recognition speed while the recognition accuracy remained at the same level. The ablation study for the introduced optimization techniques demonstrated significant improvement of recognition speed keeping the recognition accuracy comparable with the existing state-of-the-art approaches.
Frame-Based Elicitation of Mid-Air Gestures for a Smart Home Device Ecosystem

If mid-air interaction is to be implemented in smart home environments, then the user would have to exercise in-air gestures to address and manipulate multiple devices. This paper investigates a user-defined gesture vocabulary for basic control of a smart home device ecosystem, consisting of 7 devices and a total of 55 referents (commands for device) that can be grouped to 14 commands (that refer to more than one device). The elicitation study was conducted in a frame (general scenario) of use of all devices to support contextual relevance; also, the referents were presented with minimal affordances to minimize widget-specific proposals. In addition to computing agreement rates for all referents, we also computed the internal consistency of user proposals (single-user agreement for multiple commands). In all, 1047 gestures from 18 participants were recorded, analyzed, and paired with think-aloud data. The study reached to a mid-air gesture vocabulary for a smart-device ecosystem, which includes several gestures with very high, high and medium agreement rates. Furthermore, there was high consistency within most of the single-user gesture proposals, which reveals that each user developed and applied her/his own mental model about the whole set of interactions with the device ecosystem. Thus, we suggest that mid-air interaction support for smart homes should not only offer a built-in gesture set but also provide for functions of identification and definition of personalized gesture assignments to basic user commands.
Constraint-Based Optimized Human Skeleton Extraction from Single-Depth Camera

As a cutting-edge research topic in computer vision and graphics for decades, human skeleton extraction from single-depth camera remains challenging due to possibly occurring occlusions of different body parts, huge appearance variations, and sensor noise. In this paper, we propose to incorporate human skeleton length conservation and symmetry priors as well as temporal constraints to enhance the consistency and continuity for the estimated skeleton of a moving human body. Given an initial estimation of the skeleton joint positions provided per frame by the Kinect SDK or Nuitrack SDK, which do not follow the aforementioned priors and can prone to errors, our framework improves the accuracy of these pose estimates based on the length and symmetry constraints. In addition, our method is device-independent and can be integrated into skeleton extraction SDKs for refinement, allowing the detection of outliers within the initial joint location estimates and predicting new joint location estimates following the temporal observations. The experimental results demonstrate the effectiveness and robustness of our approach in several cases.
An Empirical Study of Fault Triggers in the Linux Operating System: An Evolutionary Perspective

This paper presents an empirical study of 5741 bug reports for the Linux kernel from an evolutionary perspective, with the aim of obtaining a deep understanding of bug characteristics in the Linux operating system. Bug classification is performed based on the fault triggering conditions, followed by an analysis of the proportions and evolution of the bug types as well as comparisons among versions, products, and repair locations. In addition, an analysis of regression bugs and the relationship between the types of bugs and the time needed to fix them are presented. Moreover, a procedure for the analysis of bug type characteristics based on complex network metrics is proposed, and four network metrics, i.e., degree, clustering coefficient, betweenness, and closeness, are utilized to further investigate the relationship between bug types and software metrics. In this paper, 22 interesting findings based on the empirical results are revealed, and guidance based on these findings is provided for developers and users.
The height Digital Image Correlation (hDIC) technique for the identification of triaxial surface deformations

This paper introduces the height digital image correlation (hDIC) technique for the identification of triaxial deformations. Conventional digital image correlation (DIC) uses pixel image intensity as the basis for the determination of in-plane displacement fields. We demonstrate the advantages of using the out-of-plane surface height and its variation during deformation to extract information about triaxial (in-plane and out-of-plane) displacement fields. Surface height maps can be obtained by optical profilometry or scanning probe microscopies, e.g. stylus profilometry, coordinate measurement machine (CMM), or atomic force microscope (AFM). The changes in height during deformation are sufficiently small to allow efficient correlation of in-plane displacements with sub-pixel accuracy, yet also provide information about out-of-plane displacements. In the present study, the contour map of surface height was created using digital dynamic focus (“deep focus”) optical microscope. The correlation between the reference and target maps to extract the displacement data was accomplished by a two-step correlation process. Initially, triaxial Cartesian coordinate data of reference and target data sets were cross-correlated at integer-pixel level sensitivity. This was followed by sub-pixel correlation using gradient descent method. As an example of technique application, Al alloy test specimens were subjected to large tensile deformations to failure. Good agreement was found between height digital image correlation (hDIC) analysis of displacements and strains and the reference material properties, with the evolution of both in-plane strains and out-of-plane displacements showing progressive localisation during post-critical deformation beyond the sample's ultimate tensile strength (UTS).
PneumAct

Virtual Reality Environments (VRE) create an immersive user experience through visual, aural, and haptic sensations. However, the latter is often limited to vibrotactile sensations that are not able to actively provide kinesthetic motion actuation. Further, such sensations do not cover natural representations of physical forces, for example, when lifting a weight. We present PneumAct, a jacket to enable pneumatically actuated kinesthetic movements of arm joints in VRE. It integrates two types of actuators inflated through compressed air: a Contraction Actuator and an Extension Actuator. We evaluate our PneumAct jacket through two user studies with a total of 32 participants: First, we perform a technical evaluation measuring the contraction and extension angles of different inflation patterns and inflation durations. Second, we evaluate PneumAct in three VRE scenarios comparing our system to traditional controller-based vibrotactile and a baseline without haptic feedback.
An Overview of Deep Learning Based Object Detection Techniques

Recent years have witnessed a boundless growth in the field of deep learning. With the preferment in the field of deep learning, the task of object detection has become more exciting and challenging. Object detection focuses on detecting the presence of entire objects within a given image. Deep learning based object detection techniques have shown an efficacy to learn the object features directly from the data. The paper mainly focuses on providing a survey on various state-of-the-art deep learning based object detection techniques. The work also concentrates on providing an extensive comparison regarding the opportunities and obstacles faced by different object detection techniques. The paper concludes by identifying the future golden scopes for research in these fields.
Influence of primacy, recency and peak effects on the game experience questionnaire

When a participant is asked to evaluate a stimulus, the judgment is based on the remembered experience, which might be different from the actual experience. This phenomenon happens according to the theory that some moments of an experience such as the beginning, peak and the end of the experience have more impact on the memory. These moments can be recalled with a higher probability than the other parts of the experience, and some minor bad moments of experience might be forgotten or forgiven due to the rest of the good experiences. This paper, using a subjective study and emulating an artificial delay on participants' gameplay investigates the influence of these serial-position effects on the Game Experience Questionnaire (GEQ). The result shows that GEQ does not suffer from either recency, primacy or peak effects. However, when users are asked about the controllability and responsiveness of the games, the recency effect exists. The paper also shows that GEQ has the forgiveness effect and participants forgive or may forget a bad experience if it coincides with a considerable duration of a good experience.
Cost-sensitive active learning through statistical methods

Clustering-based active learning splits the data into a number of blocks and queries the labels of most the representative instances. When the cost of labeling and misclassification are considered, we also face a key issue: How many labels should be queried for a given block. In this paper, we present theoretical and practical statistical methods to handle this issue. The theoretical statistical method calculates the optimal number of query labels for a predefined label distribution. Considering label distributions for different clustering qualities, we obtain three hypothetical models, namely Gaussian, Uniform, and V models. The practical statistical method calculates empirical label distribution of the cluster blocks. Considering four popular clustering algorithms, we use symmetry and curve fitting techniques on 30 datasets to obtain empirical distributions. Inspired by three-way decision, we design an algorithm called the cost-sensitive active learning through statistical methods (CATS). Experiments were performed on 12 binary-class datasets for both the distribution evaluation and learning task. The results of significance tests verify the effectiveness of CATS and its superior performance with respect to state-of-the-art cost-sensitive active learning algorithms.
Feeding the World with Data

Recent years have seen increased investment in data-driven farming through the use of sensors (hardware), algorithms (software), and networking technologies to guide decision making. By analyzing the discourse of 34 startup company websites, we identify four future visions promoted by data-driven farming startups: the vigilant farmer who controls all aspects of her farm through data; the efficient farmer who has optimized his farm operations to be profitable and sustainable; the enlightened farmer who achieves harmony with nature via data-driven insights; and the empowered farmer who asserts ownership of her farm's data, and uses it to benefit herself and her fellow farmers. We describe each of these visions and how startups propose to achieve them. We then consider some consequences of these visions; in particular, how they might affect power relations between the farmer and other stakeholders in agriculture--farm workers, nonhumans, and the technology providers themselves.
An Adaptive Online Scheme for Scheduling and Resource Enforcement in Storm

As more and more applications need to analyze unbounded data streams in a real-time manner, data stream processing platforms, such as Storm, have drawn the attention of many researchers, especially the scheduling problem. However, there are still many challenges unnoticed or unsolved. In this paper, we propose and implement an adaptive online scheme to solve three important challenges of scheduling. First, how to make a scaling decision in a real-time manner to handle the fluctuant load without congestion? Second, how to minimize the number of affected workers during rescheduling while satisfying the resource demand of each instance? We also point out that the stateful instances should not be placed on the same worker with stateless instances. Third, currently, the application performance cannot be guaranteed because of resource contention even if the computation platform implements an optimal scheduling algorithm. In this paper, we realize resource isolation using Cgroup, and then the performance interference caused by resource contention is mitigated. We implement our scheduling scheme and plug it into Storm, and our experiments demonstrate in some respects our scheme achieves better performance than the state-of-the-art solutions.
LeMo

Shared Virtual Environments (SVEs) have been extensively researched for education, entertainment, work, and training, yet there has been limited research on the creative aspects of collaboration in SVEs. This raises questions about how to design virtual working spaces to support collaborative creativity in SVEs. In this paper, we outline an SVE named LeMo, which allows two people to create music collaboratively. Then we present a study of LeMo, in which 42 users composed music together using three different virtual working space configurations. Results indicate that (i) two types of territory and working configurations emerged during collaborative composing (ii) when made available to them, personal working spaces were extensively used, and were considered to be essential to successful collaborative music making and (iii) a publicly visible personal working space was preferable to a publicly invisible one. Based on these findings, three corresponding design implications for Shared Virtual Environments focusing on supporting collaborative creativity are given.
Deep Visual Privacy Preserving for Internet of Robotic Things

In the past few years, visual information collection and transmission is increased significantly for various applications. Smart vehicles, service robotic platforms and surveillance cameras for the smart city applications are collecting a large amount of visual data. The preservation of the privacy of people presented in this data is an important factor in storage, processing, sharing and transmission of visual data across the Internet of Robotic Things (IoRT). In this paper, a novel anonymisation method for information security and privacy preservation in visual data in sharing layer of the Web of Robotic Things (WoRT) is proposed. The proposed framework uses deep neural network based semantic segmentation to preserve the privacy in video data base of the access level of the applications and users. The data is anonymised to the applications with lower level access but the applications with higher legal access level can analyze and annotated the complete data. The experimental results show that the proposed method while giving the required access to the authorities for legal applications of smart city surveillance, is capable of preserving the privacy of the people presented in the data.
Hierarchical Reinforcement Learning for Pedagogical Policy Induction

In interactive e-learning environments such as Intelligent Tutoring Systems, there are pedagogical decisions to make at two main levels of granularity: whole problems and single steps. Recent years have seen growing interest in data-driven techniques for such pedagogical decision making, which can dynamically tailor students' learning experiences. Most existing data-driven approaches, however, treat these pedagogical decisions equally, or independently, disregarding the long-term impact that tutor decisions may have across these two levels of granularity. In this paper, we propose and apply an offline, off-policy Gaussian Processes based Hierarchical Reinforcement Learning (HRL) framework to induce a hierarchical pedagogical policy that makes decisions at both problem and step levels. In an empirical classroom study with 180 students, our results show that the HRL policy is significantly more effective than a Deep Q-Network (DQN) induced policy and a random yet reasonable baseline policy.
Life-Tags

We introduce "Life-Tags," a wearable, smartglasses-based system for abstracting life in the form of clouds of tags and concepts automatically extracted from snapshots of the visual reality recorded by wearable video cameras. Life-Tags summarizes users' life experiences using word clouds, highlighting the "executive summary" of what the visual experience felt like for the smartglasses user during some period of time, such as a specific day, week, month, or the last hour. In this paper, we focus on (i) design criteria and principles of operation for Life-Tags, such as its first-person, eye-level perspective for recording life, passive logging mode, and privacy-oriented operation, as well as on (ii) technical and engineering aspects for implementing Life-Tags, such as the block architecture diagram highlighting devices, software modules, third-party services, and dataflows. We also conduct a technical evaluation of Life-Tags and report results from a controlled experiment that generated 21,600 full HD snapshots from six indoor and outdoor scenarios, representative of everyday life activities, such as walking, eating, traveling, etc., with a total of 180 minutes of recorded life to abstract with tag clouds. Our experimental results and Life-Tags prototype inform design and engineering of future life abstracting systems based on smartglasses and wearable video cameras to ensure effective generation of rich clouds of concepts, reflective of the visual experience of the smartglasses user.
Tectonics of the Northeastern border of the Parana Basin (Southeastern Brazil) revealed by lineament domain analysis

According to the classical paradigm of plate tectonics the cratonic area of the inner part of South America is considered tectonically stable. Nevertheless, the role of neotectonics on the shape of the Brazilian landforms has been demonstrated by several authors. In this work we perform a lineament domain analysis to explore the regional meaning of the sparse and local evidences of neotectonics within Southeast Brazil and frame them in a regional tectonic evolutionary model. Results from lineament analysis allowed finding out two main domains, NW-SE and NE-SW trending. These structural directions frame within an E-W strike-slip corridor characterized by a poly-phased tectonic history. A pre-Neogene left-lateral shear was followed by a right lateral movement whose activity is presently ruling the landform evolution of the region. The two identified structural trends from lineament analysis may represent the Cenozoic reactivation of ancient weakness zone and relate to the upper Cenozoic South Atlantic drifting and N to NW movement of the South America.
Blockchain and Public Record Keeping: Of Temples, Prisons, and the (Re)Configuration of Power

This paper discusses blockchain technology as a recordkeeping system, linking recordkeeping to power of authority, veneration (temples) and control (prisons) that configure and reconfigure social, economic and political relations. It discusses blockchain technology as being constructed as a mechanism to counter institutions and social actors that currently hold power, but whom are nowadays often viewed with mistrust. It explores claims for blockchain as a recordkeeping force of resistance to those powers. Using an archival theoretic analytic lens, the paper evaluates claims that blockchain technology can support the creation and preservation of trustworthy records able to serve as alternative sources of evidence of rights, entitlements and actions than that which is provided by centralized public repositories of records, such as archives and public registries. The analysis problematizes such claims, pointing to shortcomings in the design, implementation and governance of blockchain platforms that fall short of the ideal of trustworthy public recordkeeping, but suggesting that through unveiling these weaknesses, it may be possible for blockchain recordkeeping to address the ills of current, centralized approaches to public recordkeeping.