Reviewerly/exHarmony huggingface dataset:
A Survey on Research of Code Comment Auto Generation

Abstract A code comment is an explanation of the code. As an important part of software development process, code comment plays an important role in software maintenance, software reuse and other fields. Automatic generation of code comment aims to study the automatic method of comment generation so as to improve the efficiency of software development. This paper summarizes the related concepts and researches of code comment, discusses the automatic generation technology of code comment and the evaluation of comment quality, introduces the application of code comment generation in the software domain, and finally analyzes the challenges of automatic generation of code comment, and looks forward to the future research in this field.
Using IFTTT to Express and Enforce UCON Obligations

If This Then That (IFTTT) is a free and widely used web-based platform where it is possible to create applet chains (Applets) of simple conditional statements that combine different web and smart services. In this paper we propose a methodology to express Usage Control (UCON) obligations in such a way that they can contain valid data in order to trigger such applet chains. The obligations that follow the response of access requests coming from UCON, become a trigger to the IFTTT platform and this enables a more abstract and non application specific mixture of them without each one losing their abstract structure. We will present the architecture and workflow of our approach, also together with a couple of use cases and the evaluation of an implementation of UCON together with a real IFTTT Applet.
Knowledge Processing Method with Calculated Functors

Intellectual inference methods are among of the convenient tools for solving certain classes of knowledge processing tasks. One of the current areas in which the application of logical inference methods and engines can lead to new results is the field of cyber-physical systems that has been actively developing in recent years, including the control of unmanned vehicles and aircrafts, intelligent mechatronics and robotics. But this requires the operations of processing numerical information to enter into the logical conclusion procedure. The high-performance method of parallel output based on the disjunct (clauses) division is selected as the basic method of logical inference. To implement arithmetic operations, this method is proposed to be supplemented with a special mechanism of calculated functors. The developed modified inference method differs from the known methods by a number of important advantages. Firstly, it will significantly expand the use of artificial intelligence methods in cyber-physical systems. Secondly, inferences and arithmetic operations can be performed in parallel. And thirdly, it is an opportunity to use for the arithmetic calculations the available special processors of logical inference on the FPGA for autonomous intelligent systems for various purposes.
UNIC: A fast nonparametric clustering

Clustering is among the tools for exploring, analyzing, and deriving information from data. In the case of large data sets, the real burden to the application of clustering algorithms can be their complexity and demand of control parameters. We present a new fast nonparametric clustering algorithm, UNIC, to address these challenges. To identify clusters, the algorithm evaluates the distances between selected points and other points in the set. While assessing these distances, it employs methods of robust statistics to identify the cluster borders. The performance of the proposed algorithm is assessed in an experimental study and compared with several existing clustering methods over a variety of benchmark data sets.
Head-up Displays for Augmented Reality Applications in Racing Vehicles: a Feasibility Study

This paper studies the applicability of Augmented Reality Head-Up Displays (HUD) to car racing applications. HUD have been used for years in avionics to effectively represent environmental cues to the pilot without the need to look at the instruments. Recent advances in technology claim that similar systems can be developed for cars. HUD have been available for cars for some time, but have been exploited only as virtual dashboards. This paper looks into the potential benefits and limitations of truly Augmented Reality for car racing. In particular, the objective is to highlight the curbs of the track so to help the driver plan the best trajectory. We develop the algorithms needed to detect the curb and project its position on the HUD. We show that, in order to provide a precise projection of the cue, the attitude of the vehicle needs to be estimated and the information from a camera needs to be merged with a map of the racing track. We test the approach on an experimental vehicle rigged to emulate a HUD. The results indicate that, while the algorithms can be designed to be robust and precise, they require a very large Field of View.
Pyramid Context Contrast for Semantic Segmentation

Semantic segmentation plays a critical role in image understanding. Recently, Fully Convolutional Network (FCN)-based models have made significant progress in semantic segmentation. However, achieving the full utilization of contextual information and recovery of lost spatial details remains a huge challenge. In this paper, we present a semantic segmentation model based on pyramid context contrast and a subpixel-aware dense decoder. We propose first using the pyramid context contrast to exploit the capability of contextual information by aggregating multi-scale foreground representations in different background regions via the pyramid context contrast module. Then, we add a subpixel-aware dense decoder architecture to reuse features extracted from different decoder levels by pixel shuffle, which can reasonably resolve resolution inconsistency between feature maps. Next, we refine the boundary by utilizing spatial visual information about low-level features via a boundary refinement branch with addition of auxiliary supervision. The presented model was evaluated using the PASCAL VOC 2012 semantic segmentation benchmark and achieved a performance of 86.9%, demonstrating that the proposed model achieves considerable improvement over most state-of-the-art models.
Pedestrian segmentation based on a spatio-temporally consistent graph-cut with optimal transport

Abstract We address a method of pedestrian segmentation in a video in a spatio-temporally consistent way. For this purpose, given a bounding box sequence of each pedestrian obtained by a conventional pedestrian detector and tracker, we construct a spatio-temporal graph on a video and segment each pedestrian on the basis of a well-established graph-cut segmentation framework. More specifically, we consider three terms as an energy function for the graph-cut segmentation: (1) a data term, (2) a spatial pairwise term, and (3) a temporal pairwise term. To maintain better temporal consistency of segmentation even under relatively large motions, we introduce a transportation minimization framework that provides a temporal correspondence. Moreover, we introduce the edge-sticky superpixel to maintain the spatial consistency of object boundaries. In experiments, we demonstrate that the proposed method improves segmentation accuracy indices, such as the average and weighted intersection of union on TUD datasets and the PETS2009 dataset at both the instance level and semantic level.
Augmented Reality Exposure Therapy with Tactile Feedback for Small Animal Phobia : Hardware Concept and User Study Design

Exposure therapy in Augmented Reality has proven its effectiveness in the treatment of small animal phobia. However, the conventional Augmented Reality Exposure Therapy confronts the user with visual stimulation only. The proposed concept called ARA aims to enhance the sense of realism by adding vibrotactile feedback to Augmented Reality Exposure Therapy. Thus, the user sees and feels the small animal, e.g., a spider crawling up his/her arm. The system includes a Head-Mounted Display and an arm-sleeve with small vibration motors, both worn by the user. A web application is used to control and monitor the therapy. ARA is a prototypical development intended as a proof of concept. An evaluation to assess whether Augmented Reality Exposure Therapy with tactile feedback performs better than a standalone Augmented Reality Exposure Therapy application is planned but has not been conducted yet.
Conceptual Abstraction of Attack Graphs - A Use Case of securiCAD

Attack graphs quickly become large and challenging to understand and overview. As a means to ease this burden this paper presents an approach to introduce conceptual hierarchies of attack graphs. In this approach several attack steps are aggregated into abstract attack steps that can be given more comprehensive names. With such abstract attack graphs, it is possible to drill down, in several steps, to gain more granularity, and to move back up. The approach has been applied to the attack graphs generated by the cyber threat modeling tool securiCAD.
Adversarial Network Algorithm Benchmarking

Most research papers should have one thing in common: a clear and expressive evaluation of proposed solutions to problems. However, evaluating solutions is interestingly a challenging task: when using human-constructed examples or real-world data, it is difficult to assess to which degree the data represents the input spectrum also of future demands. Moreover, evaluations which fail to show generalization might hide algorithm weak-spots, which could eventually lead to reliability and security issues later on. To solve this problem we propose Toxin, a framework for automated, data-driven benchmarking of, e.g., network algorithms. In a first proof-of-concept implementation, we use Toxin to generate challenging traffic datasets for a data center networking use case.
The van hiele levels of geometric of students in first semester reviewed from gender

Abstract This research is an exploratory study that aims to examine the geometry abilities of students based on gender. Participants numbered 90 people. The number of female students is 72 people and male students are 18 people. Participants are students of biology education programs who attend basic mathematics lectures in the first semester at one of the universities in Indonesia. The instrument of the geometry thinking level used test from CDASSG project. The results showed that from 18 male students as many as 4 people were at the level of pre-visualization, 2 were at level 0, and 12 were at level 1. Whereas of 72 female students, 13 were at pre-visualization level, 21 were at level 0, 34 people are at level 1, and 4 people at level 2. Based on these results there is a difference between male and female students, namely some female students reach level 2, but not male students.
A Method of Small Face Detection Based on CNN

Under the existing technology, due to the limitation of some scenes, image data will have illumination changes, blurring, occlusion, low resolution and other issues. These problems have brought great challenges to face detection. At present, many algorithm models can recognize face detection well under the condition of positive and high resolution. However, most of the faces in real scenes are lateral and have low resolution. For this kind of face detection, the existing algorithm models will face the problems of accuracy and real-time performance. In this paper, various models of face detection algorithms are deeply studied and analyzed. Combined with the accuracy and speed of the algorithm model, this paper designs a face detection algorithm model based on MTCNN (Multi-task Convolution Neural Network) network model. The algorithm is tested on the WiderFace. WiderFace is the most commonly used dataset in the field of face detection. The result shows that the algorithm is superior to other algorithms in the accuracy and speed of face detection.
Address negotiations in Dutch emails

This chapter presents a qualitative analysis of address forms in a small corpus of Dutch emails/email chains, in particular their strategic use in moving from a more formal, distant relation between correspondents to a more familiar one. In addition to pronominal address forms, it also considers other linguistic features, including salutations and signing off formulae, and style. Adopting a discursive approach, the chapter identifies a number of different strategies in negotiating address, including avoidance of address forms and the use of the plural familiar pronoun jullie as a transitional form between formal and familiar address. The chapter makes an original contribution to the (currently rather limited) research on address in computer-mediated communication (CMC) and comments on the hybridity of email.
Virtual Immersive Educational Systems: Early Results and Lessons Learned

Higher education is embracing a digital transformation in a relatively slow adoption rate, with few fragmented solutions to portray the capabilities of new immersive technologies like Virtual Reality (VR). One may argue that deployment costs and substantial levels of design knowledge might be critical stagnation factors to create effective Virtual Immersive Educational (VIE) systems. We attempt to address these impediments in a cost-effective and user-friendly VIE system. In this paper, we report in brief the main elements of this design and initial result and lessons learned.
Chinese Key-Image Learning: An App Designed with Handwriting Evaluation and Instant Feedback to Support Chinese Character Learning

The purpose of this study is to develop and evaluate a mobile-assisted Chinese language learning app that introduces hundreds of fundamental Chinese characters with an innovative method, namely the Key-Image Mnemonics (KIM). This method is developed based on cognitive and learning theories (i.e., the Elaboration Theory, the Dual Coding Theory, and the Key-Word Method) and the effectiveness of this method in Chinese-as-foreign/second-language (CFL/CSL) learning is strongly supported by laboratory and classroom studies. In this paper, mobile technologies were applied to enhance the effect of this Key-Image Mnemonics. Firstly, we reviewed background research in the fields of mobile learning and character learning of CSL/CFL learners. Second, we presented the framework and development of the app, Chinese Key-Image Learning, which aims to support autonomous learning of CFL/CSL learners. Specifically, this app provides handwriting practices with instant feedback, including handwriting demonstration, evaluation, and error correction. Finally, we conducted a focus group to gather feedback from Chinese teachers and students to understand their perceived usefulness, perceived ease of use, and perceived enjoyment after using this app. The evaluation included both quantitative and qualitative aspects. Implications for using this app in supporting character learning of CFL/CSL learners as well as our future work are discussed.
A Versatile 3D Face Reconstruction from Multiple Images for Face Shape Classification

In this paper we present a 3D facial reconstruction algorithm for facial shape classification. We propose a reconstruction method that preserves facial ratios independent on the captured poses allowing thus a precise facial shape classification/determination. We use an Active Appearance Model on a set of facial image captures with different poses on which we auto-calibrate the camera parameters (intrinsic and extrinsic). We then fit/morph the statistical model to a set of facial landmarks in order to obtain an accurate 3D face reconstruction. We show the obtained results and compare them with other approaches in the literature.
A Perspective Study Towards Biometric-based Rider Authentication Schemes For Driverless Taxis

Driverless vehicles exploit Artificial Intelligence (AI) to offer rides to their users with minimal or no human input. Driverless vehicles as taxis are indeed evolving an entirely new transportation concept, Transportation-As-A-Service (TAAS), steered by well-proven client-server infrastructures. Typically, the servers, managed by multinational companies (e.g., Waymo), operate driverless taxis for public transportation, whereas the clients are simply the smartphone-based applications that can be used by customers for one-time registration and booking rides with driverless taxis anytime anywhere. In this paper, we perform a perspective study towards secure and usable biometric-based rider authentication schemes for driverless taxis. Our study is complemented by an online survey that comprises 75 responses from participants, worldwide. We published the survey on social media platforms like Linkedin, Facebook, Twitter, and various public transport users groups to collect public poll for rider authentication for driverless taxis. Approximately, 90% of the overall participants in our survey either strongly agreed or agreed with the necessity to deploy biometric-based rider authentication for driverless taxis. Then, considering the survey's results, we proposed the design of a possible solution for riders biometric authentication.
Background Subtraction Based on Encoder-Decoder Structured CNN

Background subtraction is commonly adopted for detecting moving objects in image sequence. It is an important and fundamental computer vision task and has a wide range of applications. We propose a background subtraction framework with deep learning model. Pixels are labeled as background or foreground by an Encoder-Decoder Structured Convolutional Neural Network (CNN). The encoder part produces a high-level feature vector. Then, the decoder part uses the feature vector to generate a binary segmentation map, which can be used to identify moving objects. The background model is generated from the image sequence. Each frame of the image sequence and the background model are input to the CNN for pixel classification. Background subtraction result can be erroneous as videos may be captured in various complex scenes. The background model must be updated. Therefore, we propose a feedback scheme to perform the pixelwise background model updating. For the training of the CNN, the input images and the corresponding ground truths are drawn from the benchmark dataset Change Detection 2014. The results show that our proposed architecture outperforms many well-known traditional and deep learning background subtraction algorithms.
Towards cognitive systems for assisted cooperative processes of goal finding and strategy change

In the future, people and intelligent technical systems will cooperate in situations in which the goals, means, or actions are not completely prespecified, but develop in the course of a process that entails also the finding of new goals and strategies. This position paper presents an account of how these processes can be described such that they enable support of human decision-making and action coordination in such open, under-specified scenarios. We discuss how technical cognitive systems can assist these processes by bringing together techniques for multi-modal processing, information retrieval, situated action planning and autonomous action generation, with novel capabilities of recognizing and anticipating task-related (cognitive-intentional, procedural, affective) states of the actors, and for cooperative goal refinement and action coordination among the actors. A foremost requirement is to automatically provide markers for the indication of necessary strategy changes that reconFigure the space of actions, where it is to be expected that such strategy changes may require explanation and mediation. To that end, cognitive systems and robots must be endowed with new kinds of models of (explicit or implicit) cooperative processes.
Special Issue of the Elsevier IoT Journal on Blockchain Applications in IoT Environments

There has been an emergence in big data analytics since it plays a crucial role within the IoT sector as data is augmenting at a great depth and scale, but considerable uncertainty still holds regarding how firms have to employ big data analytics for the capitalization of the IoT. The research paper argues the importance of presenting a more refined interpretation of how close big data analytics and the IoT are because they always tend to be related through an economical and technological perspective. The central core principle assertions have been made. First, there is a need for the clarification of various elements, shaping and configuring several discourses within the IoT. It is realized that the IoT has been segmented to be a complex, multi-scale, technological, and a multi-level data infrastructure which tends to be uncertain and emergent. Secondly, the primary characteristics of the IoT tend to be intimidating authority capacities focusing on big data analytics. Third, the effect of the IoT via big data analytics used in the perfection of sustainable futures has emerged several queries about the role of innovation and research.
CNN-based Camera Model Identification Using Image Noise in Frequency Domain

Camera model identification has been studied extensively within digital image forensics as a deterrence to secret photography and image forgery. The feasibility of convolutional neural networks (CNNs) has been proven for an image classification algorithm. CNN-based algorithms have been proposed for camera model classification, and they focus on training with image/noise in a spatial domain. However, because the periodic characteristics of image noise are one of the essential types of information for model classification, it is more efficient to train CNN models with image noise in a frequency domain. In this paper, we propose a CNN-based approach for camera model classification that trains a CNN model with high-frequency components of images in the frequency domain. In the experiments, we evaluated the accuracy of camera model/brand classification using a Dresden image dataset. We achieved 97.35% and 99.32% accuracy, respectively, for 14-model classification of 256×256 image patches and full images. Using this approach, our results indicated a 1.84% and 1.35% improvement, respectively, compared with a state-of-the-art method. We also achieved 100% accuracy for 10-brand classification.
Perceived Actors and Factors for Sustaining M-learning in Higher Education: A South African Students Perspective

This paper reveals how a multiple case study approach, following an interpretive philosophy, may be adopted to profoundly understand factors and actors for sustaining m-learning adoption and usage within higher educational institutions from students perspective. Therefore, the use of Actor Network Theory (ANT) encapsulated unambiguous findings which might not have been possible to achieve, extract or uncover had traditional adoption, acceptance and use theoretical frameworks been adopted. This study found that in order to sustain m-learning adoption and usages in higher education, there must be a collaboration between students, lecturers and other departments responsible for providing learning with technology. The paper also evident that visible top management support as well as visible impact of mobile learning (m-learning) usage, compliance to institutional m-learning guidelines and methodology, continuous upgrade of Learning Management Systems (LMS) and availability of technology are some of the actors and factors to be considered for sustaining m-learning in higher education. These findings of which were extracted from thirty (30) students in South African three higher educational institutions, using open-ended qualitative questionnaires, could assist higher educational institutions to be in better position to assess their level of m-learning adoption and usage to establish its sustainability.