Reviewerly/exHarmony huggingface dataset:
APPLICATION OF RSA AND LSB IN SECURITY OF MESSAGES ON IMAGERY

In this study is to discuss cryptography and steganography where the function is to insert a message or text into an image with JPG extension, the text to be inserted into the image has been encrypted using the RSA method so that the file is safer to be inserted into images, messages that are inserted into a blue image, this application aims to secure a message that you want to save, this application is made made using Android Studio and can be run on a mobile phone.
Digital Social Futures Research

Social research is almost inevitably digital: in its subject matter because the digital, social and material dimensions of our worlds and lives are now inseparably entangled; and in its methods as our research techniques and encounters are, even if indirectly, implicated with digital technologies, platforms and practices. Social scientific renderings of digital technologies and media and everyday life propose a range of discipline-specific ways of understanding this relationship between the online/offline and digital/material, and a large and growing literature about digital methods and practice for research and its dissemination. The new challenge is to advance from this strong base of critical research and scholarship within the social sciences and humanities, in two ways. First towards interdisciplinary interventions that will bring theory, methods and concepts into dialogue with technology and design disciplines, and policy and industry agendas; and second to engage with emerging digital technologies and communication, including Artificial Intelligence (AI), Machine Learning, and Automated Decision Making (ADM), and the new socialities, everyday life practices and business models associated with these technological possibilities.
An Automatic Reading Recognition Method for Pointer Spring Tube Pressure Instrument

In order to realize automatic reading recognition of spring tube pressure gauge pointer instrument, a method based on threshold segmentation, Hough circle detection, morphology and pixel normalization is proposed. Aiming at the special characteristics of this kind of instrument, this method only relies on the image information of the instrument to detect, and the experimental accuracy is high.
CoAT

During the dataset creation process for activity and context recognition research, manual annotation of ground truth events can be a time-consuming and error-prone task. In the typical use case, one or more annotators have to go over the videos recorded during the experiments and label what happens at what time of the experiment. In this paper, we introduce a new open source, web-based tool to assist researchers to create event annotations easily and to leverage group work by supporting intuitive collaboration features. We provide an overview of the main technical components and their respective technologies used to realize the tool. The first version of the tool with the core features for video annotation is implemented and publicly available. By using containerized services, the deployment involves only a small number of steps and dependencies. We point out some possible direction for future development and customization options for using the tool in other annotation tasks.
Formalizing Object-Ontological Mapping Using F-logic

Ontologies can represent a significant asset of domain-specific information systems, written predominantly using the object-oriented paradigm. However, to be able to work with ontological data in this paradigm, a mapping must ensure transformation between the ontology and the object world. While many software libraries provide such a mapping, they lack standardization or formal guarantees of its semantics. In this paper, we provide a formalism for mapping ontologies between description logics and F-logic, a formal language for representing structural aspects of object-oriented programming languages. This formalism allows to precisely specify the semantics of the object-ontological mapping and thus ensure a predictable shape and behavior of the object model.
Adaptive scheduling for adaptive sampling in pos taggers construction

We introduce an adaptive scheduling for adaptive sampling as a novel way of machine learning in the construction of part-of-speech taggers. The goal is to speed up the training on large data sets, without significant loss of performance with regard to an optimal configuration. In contrast to previous methods using a random, fixed or regularly rising spacing between the instances, ours analyzes the shape of the learning curve geometrically in conjunction with a functional model to increase or decrease it at any time. The algorithm proves to be formally correct regarding our working hypotheses. Namely, given a case, the following one is the nearest ensuring a net gain of learning ability from the former, it being possible to modulate the level of requirement for this condition. We also improve the robustness of sampling by paying greater attention to those regions of the training data base subject to a temporary inflation in performance, thus preventing the learning from stopping prematurely. The proposal has been evaluated on the basis of its reliability to identify the convergence of models, corroborating our expectations. While a concrete halting condition is used for testing, users can choose any condition whatsoever to suit their own specific needs.
Nutzung eines Smartphones als virtuelle Sonde im medizinischen Ultraschalltraining

Six Degrees of Freedom (6DoF) controllers are usually expensive and/or complex to set up. A smartphone as 6DoF controller could be a cost-effective and simple alternative. Within the scope of this research, the application context of medical ultrasound training is addressed, in which the smartphone could be used to control a virtual probe. For this purpose, the difference in rotation and position tracking has been examined between an HTC Vive Tracker as an established reference system and a smartphone using ARCore. Additionally, the usability of the smartphone for controlling a virtual ultrasound probe has been qualitatively evaluated by nine subjects using a simplified test system. ARCore's 6DoF tracking is generally less reliable and accurate than the HTC Vive tracker. However, both the characteristics of the measured tracking errors and the qualitative evaluation show that an ARCore-based 6DoF tracking can be used as a simple and acceptable solution for controlling a virtual ultrasound probe.
Training artificial neurons: an introduction to machine learning

Machine Learning is a rapidly growing field that has the power to change the way many tasks are performed. This tutorial serves as a basic introduction to Machine Learning for those who are unfamiliar with the topic. To fully understand the neural networks that make up the main stream of Machine Learning, the neural functions in biology are first discussed, followed by the translation of these concepts to artificial neurons as an example of biomimicry. Furthermore, basic knowledge and skills are given to analyze different neural networks. Finally, applications of this technology to medical imaging are mentioned, along with important improvements on the generic networks, including ResNet, GAN, LTSM, and quadratic networks.
Research Status and Prospect of Face Expression Recognition

Facial expression is an important feedback of human feelings, and has always been the focus of human-computer interaction research. In recent years, with the rapid rise of artificial intelligence technology and the dramatic improvement of computing technology, the method of facial expression recognition has changed greatly, from the traditional machine learning method of image preprocessing, feature extraction and classification into classifier to the deep learning method based on large data cloud computing, and the recognition rate of facial expression has been remarkable. Raise. This paper introduces the common facial expression database, facial expression recognition based on classical machine learning method and popular deep learning method. Finally, the research of facial expression recognition is summarized and prospected.
Human Behavior Recognition Based on IC3D

Aiming at the low accuracy of human behavior in videos, this paper proposes a method based on IC3D to identify human behavior in 3D-CNN neural network. A continuous 7-frame video images as input, using the gray level of video image, x-directional gradient, y-direction gradient, x-directional optical flow, y-directional optical flow, multichannel processing, after the 3D convolution and 3D pooling to extract the feature information with space and time dimensions. At the end of the network structure, the average pooling is processed to replace a layer of fully connected layer in the network structure, and the whole network is made regularization to prevent over-fitting, also because of the reduction of a layer of fully connected layer, so that the parameters required for training model are reduced greatly and the algorithm runs faster. Finally the action classification results are gotten through the fully connection layer and softmax classifier. Compared with the C3D, C3D+SVM and C3D+IDT+SVM three algorithms in UCF101 dataset, it is proved that the IC3D network framework has higher recognition accuracy and faster operation speed in the video human behavior recognition problem.
Fast White-Box Implementations of Dedicated Ciphers on the ARMv8 Architecture

Dedicated white-box ciphers concern the design of algorithms that withstand secret (key) extraction while executing in an insecure, fully explorable environment. This work presents strategies to efficiently implement on software three families of dedicated white-box ciphers targeted towards the ARMv8 architecture with NEON vector instructions. We report results of our white box implementations for the dedicated ciphers SPACE, WEM and SPNBox on four different ARMv8 CPU cores. In most cases, our optimized implementations improve the performance when compared with the best known implementations. For the cipher SPNbox-16 we propose a faster method for its matrix multiplication layer and discuss the impact on performance.
The Influence of User Openness on Acceptance and UX of Smart Speakers

Smart speakers have recently gained much interest and are expected to get even more attention in the near future. However, not everybody has positive attitudes towards this new technology. In this work, we investigate whether or not the acceptance of smart speakers is dependent on their general "openness" as one personality dimension. We compared "open" persons (early adopter) with non-open persons (laggard) in a Wizard-of-Oz study setting and let our subjects interact with a smart speaker-replica (with both natural and computer-generated voice feedback). As baseline condition, subjects had to carry-out tasks in a traditional way, i. e., without the help of a smart assistant. Our study could not reveal significant effects regarding "openness" and acceptance of smart speakers, but we identified that the design of smart speakers need to be enhanced anyway to achieve a higher acceptance in society.
Classification of Phonological Changes Reflected in Text: Toward a Characterization of Written Utterances

「こりゃひでえ」（元の形：「これはひどい」）のような音変化表現は，対話エージェントの発話や小説のセリフの自動生成において，話者であるキャラクタを特徴付けるための強力な手段となると考えられる．音変化表現を発話のキャラクタ付けに利用するために，本研究では，(i) キャラクタの発話に現れる音変化表現を収集し，(ii) それらを基に，音変化表現を人為的に発生させるための知識を整理した．具体的には，収集した音変化表現を現象と生起環境の観点で分類し，137 種類のパターンとして整理した．そして，これらのパターンが小説やコミックで用いられる音変化表現の 80% 以上をカバーすることを確認した．さらに，(iii) 音変化表現がキャラクタらしさを特徴付ける手段になるという仮説を検証するために，小説やコミックにおける発話文の話者（キャラクタ）を推定する実験を行い，音変化表現のパターンの情報を利用することで，推定性能が向上するキャラクタが存在することを確認した．
Bayesian Optimization of Neural Architectures for Human Activity Recognition

Design of neural architectures is a critical aspect in deep-learning based methods. In this chapter, we explore the suitability of different neural architectures for the recognition of mobility-related human activities. Neural architecture search (NAS) is getting a lot of attention in the machine learning community and improves deep learning models’ performances in many tasks like language modeling and image recognition. Deep learning techniques were successfully applied to human activity recognition (HAR). However, the design of competitive architectures remains cumbersome, time-consuming, and rely strongly on domain expertise. To address this, we propose a large-scale systematic experimental setup in order to design and evaluate neural architectures for HAR applications. Specifically, we use a Bayesian optimization (BO) procedure based on a Gaussian process surrogate model in order to tune architectures’ hyper-parameters. We train and evaluate more than 600 different architectures which are then analyzed via the functional ANalysis Of VAriance (fANOVA) framework to assess hyper-parameters relevance. We experiment our approach on the Sussex-Huawei Locomotion and Transportation (SHL) dataset, a highly versatile, sensor-rich and precisely annotated dataset of human locomotion modes.
Segmentation of Microcalcifications in Mammograms: A comparative Study

Breast cancer is the most common type of cancer among women and it is the major cause of female cancer-related deaths worldwide. Microcalcifications, which are small crystals of calcium apatites, are considered the first sign of breast cancer in more than half of all breast cancer cases. Since these calcium apatites are very small and may be easily overlooked by the radiologists, automatic image processing systems can help radiologists in early diagnosis of breast cancer. In these systems, detection and segmentation of breast microcalcifications from the background tissue are important steps. The purpose of this work is to evaluate the performance of several breast microcalcifications segmentation techniques and select the best technique using a multicriteria decision making approach. The approaches were applied on 630 mammograms from the Digital Database for Screening Mammography. Of the 630 images, 315 images correspond to malignant cases and 315 correspond to benign cases. The efficiency of the considered techniques was evaluated by using five metrics, namely a similarity index, the extra overlap fraction, sensitivity, specificity, and accuracy.
Fast Nonlocal Diffusion by Stacking Local Filters for Image Denoising

Many image denoising methods achieve state-of-the-art performance with little consideration of computation efficiency. With the popularity of high-definition imaging devices, these denoising methods do not scale well to the high-definition images. Therefore, a fast image denoising method is proposed to meet the demand of processing high-definition images. Based on the analysis of the distribution of the distance $$dist_{rs}$$ between the similar patches and their reference patches from a semantic aspect, the large $$dist_{rs}$$ s was found to occur while their contribution to the overall distribution was small. Therefore, the nonlocal filters was replaced in trainable non-local reaction diffusion (TNLRD) with local filters. For image with $$4096 \times 4096$$ resolution, the proposed method runs about 6 times faster than TNLRD via a single-thread CPU implementation. And the GPU implementation of the proposed method is about 10 times faster than the CPU implementation. Furthermore, the proposed model achieves competing denoising performance compared with TNLRD in terms of PSNR and SSIM.
Formalising Process Assessment and Capability Determination: An Ontology Approach

Determining the extent to which software development process achieves its purpose is a major concern in software engineering. Where as software process prescribes the actions, software developers should undertake to produce quality software products within budget and time frame, process assessment on the other hand, is designed to evaluate the performance and capability of the undertaken software process to achieve business objectives. Process assessment models such as ISO/IEC 15504-5 rely on conventional methods of evidence collection to rate process attributes and determine the capability level of the assessed process. Conventional methods are however, subjective, time consuming and prone to errors. In earlier work, we developed a formalisation approach for the process dimension of process assessment model (PAM). In the current paper, we extend our formalisation approach to the process capability dimension of PAM using a semantically computable formalism to enable automated process attribute rating and capability determination. We demonstrate and evaluate the functionality of our approach using capability level two for software requirements analysis (SRA) process.
AMAP

Recently, Visual Programming Languages (VPLs) have been applied to a wide variety of domains ranging, usually for users lacking traditional programming skills. However, there have not been yet any concrete attempts to harness VPLs in the domain of Document Image Analysis (DIA), an applied-research field that tends to mainly cater to the needs of non-programmers. In this context, we introduce AMAP, a VPL-based system to empower non-programming users of DIA methods. We expound the motivation behind the system and briefly outline its design and development along with the architecture. We focus on showcasing several use cases that will demonstrate the system's ability to cope with different DIA scenarios.
Sample Size Planning and Questionnaire Length in Website-Evaluations

When planning studies to evaluate websites practitioners need to answer two central questions: (1) Which instrument should be used? (2) How many users should be queried? While the answer to the first question depends on the goal of the specific evaluation, the second question can also be answered in relation to fundamental psychometric relationships. The present study explores the relationship between the reliability of the questionnaire, and the required sample size. Based on questionnaires to assess website-content, we determine how many user have to be sampled to achieve a specific precision. Based on these results we present guidelines, that practitioners can use when planning their evaluation study.
IRF

Recent research focuses beyond recommendation accuracy, towards human factors that influence the acceptance of recommendations, such as user satisfaction, trust, transparency and sense of control. We present a generic interactive recommender framework that can add interaction functionalities to non-interactive recommender systems. We take advantage of dialogue systems to interact with the user and we design a middleware layer to provide the interaction functions, such as providing explanations for the recommendations, managing users' preferences learnt from dialogue, preference elicitation and refining recommendations based on learnt preferences.
Microservice-oriented Product Line Architecture Design

Microservice has been successfully employed in software industry [1, 11], as they provide modularization and easy management of small and autonomous services, high availability, scalability and short time-to-market. A recent study on microservices shows that most studies generate specific solutions, which emphasize the need for fundamental research, proposals of reusable practices and works that focus on providing information to ease communication between architects and stakeholders [11] -- software architecture can be a powerful tool for this regard. The definition and documentation of the software product line architecture (PLA) is an important activity, especially for inception and extraction of microservice-oriented PLA, because they involve decisions about how to design customizable microservices, how to arrange the communication between microsservices and APIs, etc. In this work, it is proposed a metamodel for the specification of microsserve-oriented PLA design in order to assist the developer in carrying out such an activity. The proposed metamodel was validated in an exploratory study, in which a new PLA was designed through the instantiation of the proposed metamodel and a product was configured from the designed PLA. Both the metamodel and its instantiation were evaluated in a survey involving software developers. The results indicate that the metamodel addresses the structural needs of microservice-oriented architectures. The main contributions of this work are (i) to assist the software product line developers in the specification and documentation of microservice-oriented PLA design and (ii) the lessons about the industrial practice learned from the surveys that are useful to enhance the proposed metamodel.
MREAK: Morphological Retina Keypoint Descriptor

A variety of computer vision applications depend on the efficiency of image matching algorithms used. Various descriptors are designed to detect and match features in images. Deployment of this algorithms in mobile applications creates a need for low computation time. Binary descriptors requires less computation time than float-point based descriptors because of the intensity comparison between pairs of sample points and comparing after creating a binary string. In order to decrease time complexity, quality of keypoints matched is often compromised. We propose a keypoint descriptor named Morphological Retina Keypoint Descriptor (MREAK) inspired by the function of human pupil which dilates and constricts responding to the amount of light. By using morphological operators of opening and closing and modifying the retinal sampling pattern accordingly, an increase in the number of accurately matched keypoints is observed. Our results show that matched keypoints are more efficient than FREAK descriptor and requires low computation time than various descriptors like SIFT, BRISK and SURF.
Öğretmen Adaylarının Mobil Öğrenmeye İlişkin Tutumları

Accessing qualified information has an important place in the efficiency of education and training.In order to support the learning that takes place in the individual, there are changes in the means of information transferred from the outside.It is important to determine the attitudes of prospective teachers towards the new learning environment which is used to transfer information in education through devices such as smart phones and tablets.The purpose of this study is to investigate the attitudes of prospective teachers towards mobile learning.Being one of the quantitative research, the Cross-Sectional Survey design was used in the study.Data in the study were collected using the M-learning attitude scale.The data for the study were obtained on a volunteer basis from students in six different departments attending the education faculty of a state university.The results of the study showed that prospective teachers' attitudes towards mobile learning were positive.Based on these results, it is understood that mobile learning tools may be included in the educational activities to be planned for primary and secondary education in the future.
Augmenting Musical Performance

This personal take describes the motivation, development and eventual commercial release of TouchKeys, an augmented musical keyboard which measures the motion of the player’s fingers on the key surfaces. Digital musical instruments typically lack the mechanical constraints of their acoustic counterparts. Instead, the major obstacle facing novel digital instruments is the time it takes a performer to learn them. TouchKeys preserves the familiar action and layout of the keyboard while adding new techniques such as vibrato and pitch bends, aiming to connect to the expertise of trained keyboardists while providing a gentler learning curve compared to other novel instruments.