Reviewerly/exHarmony huggingface dataset:
Application of Computer in Mixed Reality Technology

Although mixed reality has broad prospects and huge market, it can not completely replace the traditional form of entity. The application of mixed reality technology is not to deny the traditional way of physical display, but to better play its function, improve the speed of information transmission, widen the width of information transmission, and enhance the accuracy and precision of information transmission. Both of them should show their respective advantages and complement each other in communication function and effect, and jointly realize the benign role of computer information transmission and communication. Focusing on the development trend of this field in recent years, this paper focuses on the human-computer interaction design and key technology characteristics in hybrid reality, analyses the application status of different interactive technologies in hybrid reality, and finally makes a summary.
Adaptive Prefetching at Network Edge to Improve DASH QoE

In recent years, Dynamic Adaptive Streaming over HTTP (DASH) has emerged as a popular adaptive video streaming technique to improve the bandwidth utilization. Moreover, DASH with segment-based flexibility also brings a potential benefit for the end user's Quality of Experience (QoE)1. Despite the tremendous popularity of technology like DASH, video delivery over the mobile networks is still facing some serious challenges.
Radically Simplifying Gated Recurrent Architectures Without Loss of Performance

Long Short-Term Memory (LSTM) units are a family of Recurrent Neural Network (RNN) architectures that have proven incredibly effective at learning from sequence data. They are also extremely complex, making them expensive to train and difficult to understand. A recent trend towards simplification has produced the Gated Recurrent Unit (GRU) and the Minimal Gated Unit (MGU), both of which perform as well as the LSTM (or better) on a variety of tasks. The MGU is one of the simplest gated recurrent architectures at the moment. Our study demonstrates that it is possible to radically simplify the MGU without significant loss of performance for some tasks and datasets. For the gun violence data used here, an extraordinarily simple Forget Gate (FG) architecture (as well as many other simplified architectures) performs just as well as an MGU on the given task. While more complex architectures such as the MGU, GRU, or LSTM may be needed in some situations, they are likely overkill for many real-world datasets, and the marginal performance benefit may come with a very large price tag.
The Growing Need for Cyberbiosecurity

Aim/Purpose: This paper describes the growing need for a new transdiscipline in cyberbiosecurity as well historical challenges associated with knowledge generation and integration among contributing disciplines. Background: Within the United States, there is an emerging call for cyberbiosecurity; however, cyberbiosecurity roles, practices and metrics have not been defined and federal agencies appear uncertain regarding how to proceed. Methodology: Scoping study. Contribution: This paper describes student research that is in progress. The research is aimed at providing a foundation for development of a cyberbiosecurity transdisciplinary knowledge framework. Findings: Key contributing disciplines such as safety and security have been slow to integrate; novel methods will be required to accelerate effective cyberbiosecurity. Recommendations for Practitioners and Researchers: Collaborate to form this new transdiscipline. Impact on Society: This research is intended to reduce stakeholder uncertainty and accelerate formation of cyberbiosecurity as an effective transdiscipline. Future Research: In-depth study that includes continued content review and analysis of knowledge artifacts and practices across contributing disciplines and engage-ment with stakeholders at different levels of government and industry to develop a cyberbiosecurity knowledge framework.
Speech Interface for Controlling Micro Air Vehicle

This paper focuses on the development of the speech interface for controlling a Micro Air Vehicle (MAV). A speech interface in such control applications will have two distinct modules. One is the Automatic Speech Recognition (ASR) module and the other is the Natural Language Processing (NLP) module. The ASR is developed using the models built using CMU Sphinx toolkit. The NLP scheme is proposed and developed using Natural Language Toolkit (NLTK). Understanding of the speech is very important in such kind of control applications. The NLP outcome is used to invoke the Ground Control Station (GCS) commands. The results are validated in a Flight Gear simulator using Mission Planner GCS configured for MAV.
Discovery of Band Order Dependencies.

We introduce band ODs to model the semantics ofattributes that are monotonically related with small variationswithout there being an intrinsic violation of semantics. Tomake band ODs relevant to real-world applications, we makethem less strict to holdapproximatelywith some exceptions andconditionallyon subsets of the data with a mix of ascendingand descending directions. Formulating integrity constraintsmanually requires domain expertise, is prone to human errors,and time consuming. Thus, we study the problem of automaticabcOD discovery. We propose an algorithm that utilizes thenotion of a longest monotonic band (LMB) to identify longestsubsequences of tuples that satisfy a band OD. We formulate theabcOD discovery problem as a constraint optimization problem,and devise a dynamic programming algorithm that determinesthe optimal solution in polynomial time (super-cubic complexity).To further optimize the performance over large datasets, weadapt the algorithm to consider pieces (contiguous sequencesof tuples) in a greedy fashion. This improves the performanceby orders-of-magnitude without sacrificing precision in practice.We show that for unidirectional abcODs, with only ascending ordescending orderings, our pieces-based algorithm is guaranteedto find the optimal solution. Finally, we perform a thoroughexperimental evaluation of our techniques over real-world andsynthetic datasets.
6D Object Pose Estimation using Few-Shot Instance Segmentation and 3D Matching

6D object pose estimation is an important but difficult computer vision task. It has many applications such as robotic manipulation and augmented reality. Although a large number of 6D object pose estimation methods have been developed, there are still many challenges, for example, background clutter, foreground occlusion, and lack of annotated training samples. To deal with these difficulties, a compact and effective algorithm for 6D pose estimation using RGB-D data under few-shot condition is presented in this paper. The proposed algorithm consists of two stages. The first stage is few-shot instance segmentation, which segments known objects from RGB image. The second stage is 3D matching, which recovers the poses of objects from cropped point clouds. Proposed segmentation method can achieve satisfactory performance using only few labeled samples. Comparison experiments on two challenging datasets are carried out, and the results demonstrate that the proposed method outperforms the state-of-the-art greatly. Recall scores obtained by the proposed method are 74.91 and 55.44%, while of the state-of-the-art are 61.87% and 44.92%, obtaining 13.04% and 10.52% improvement respectively.
Young people facing ICT: discourses around the use of social networks and their relationship with sociability in university students from Querétaro, Mexico.

This article shows the main results of an investigation on the interpretation that university students give to social networks in Queretaro, Mexico. With a mixed design of an exploratory-descriptive nature and starting from a discursive perspective, open-question questionnaires, semi-structured interviews and discussion groups were conducted with students from the different faculties of a public university. After analyzing the results, it can be seen that social networks, especially Facebook, are in permanent connection with the possibilities of face-to-face sociability.
Using Personalisation to improve User Experience in Public Display Systems with Mobile Interaction

Although public displays installations are easy to attract the attention of passers-by to specific content, they present an issue in targeting the right audience. We envision public displays installations working as platforms in which different interactive applications run with highly personalised content based on personal characteristics and interests of a person attending the public displays. Mobile devices can act as an important interaction tool that complements the public screen, providing a feeling of privacy, but they can also be used to get users' preferences in order to obtain their user profiles. This paper is focused on demonstrating how personalisation can be designed and applied to improve the user experience in public display systems, integrating mobile interaction to potentiate the approach. It describes a public display experiment deployed at a university campus and presents a user study composed of two phases: the system without personalisation and the system with personalisation.
LODeDeC: A Framework for Integration of Entity Relations from Knowledge Graphs

Large knowledge graphs (KGs), which are part of Linked Open Data (LOD) serve as the primary source for retrieving structured data in many Semantic Web applications. In order for machines to efficiently process the data for different data mining, entity linking and information retrieval tasks, it is always beneficial to have as many reliable facts from KGs as possible. But none of the KGs is complete on its own with respect to the number of relations describing an entity. Moreover, large KGs like DBpedia, YAGO and Wikidata appear similar in nature, but do not fully merge in terms relations of the entities from different domains. The complementary nature of different KGs can be utilized to expand the coverage of relations of the an entity. In order to achieve this, a framework for integration of entity information from different KGs using LOD, semantic similarity approaches and RDF reification is proposed in this paper.
Pengembangan Media Pembelajaran Akuntansi Berbasis Web Blog dalam Implementasi Kurikulum 2013 pada Mata Pelajaran Akuntansi Dasar Kelas X di SMK Muhammadiyah Delanggu

This study aims to determine (1) To find out student learning outcomes by using Web Blog learning media in class X Accounting students of SMK Muhammadiyah Delanggu. (2) To find out the feasibility of Web Blog learning media on economic learning in improving student learning outcomes in class X Accounting at SMK Muhammadiyah Delanggu. (3) To find out the application of Web Blog media learning on economic subjects of class X Accounting SMK Delanggu Muhammadiyah. (4) To find out the differences in student learning outcomes between groups that implement Web Blog media compared to groups that apply conventional based learning. The research method using the development model used is the Research and Development (R&D) method. Research and development methods are research methods used to produce certain products, and test the effectiveness of these products. This subject is intended for media experts, someone who is already an expert and experienced in the field of Web Blogs. The data analysis technique in this study is to use descriptive analysis techniques of quantitative analysis and different tests (t test). The results of the discussion showed that the learning process using the Web-based learning model Blog at SMK Muhammadiyah Delanggu was good but could not be said to be perfect. The level of eligibility of Web Blog products as an accounting learning media is known based on the assessment of media experts with a percentage of 85% with a very decent category. The use of Web Blogs as a learning medium can improve student learning outcomes.
Context-Aware Collaborative Ranking

Recommender systems (RS) are being used in a broad range of applications, from online shopping websites to music streaming platforms, which aim to provide users high-quality personalized services. Collaborative filtering (CF) is a promising technique to ensure the accuracy of a recommender system, which can be divided into specific tasks such as rating prediction and item ranking. However, there is a larger volume of published works studying the problem of rating prediction, rather than item ranking though it is recognized to be more appropriate for the final recommendation in a real application. On the other hand, many studies on item ranking devoted to leveraging implicit feedback are limited in performance improvements due to the uniformity of implicit feedback. Hence, in this paper, we focus on item ranking with informative explicit feedback, which is also called collaborative ranking. In particular, we propose a novel recommendation model termed context-aware collaborative ranking (CCR), which adopts a logistic loss function to measure the predicted error of ranking and exploits the inherent preference context derived from the explicit feedback. Moreover, we design an elegant strategy to distinguish between positive and negative samples used in the process of model training. Empirical studies on four real-world datasets clearly demonstrate that our CCR outperforms the state-of-the-art methods in terms of various ranking-oriented evaluation metrics.
You start it

This chapter presents an interview with Nick Tandavanitj, a member of the artist group Blast Theory. In the interview, Tandavanitj provides a framework for creating game/art works that produce hybrid play experiences for audiences. This framework includes developing works that have permeable boundaries, are pervasive, and allow a sense of presence/liveness. To demonstrate this, Tandavanitj describes the process of creating some of Blast Theory’s most prominent works, such as Kidnap (1998), Can You See Me Now (2001), and Operation Black Antler (2016). He additionally reflects on the design choices that Blast Theory has to make, so that their work will achieve its goal of prompting critical reflection about social and cultural issues. For instance, Blast Theory has to determine the audience’s role in a work, whether to permit or limit emergent behavior, and how to engage audiences. By discussing hybrid play as a designed experience, this interview suggests that hybridity is not a stable phenomenon but rather an ongoing process enabled by the artist, audiences, and multiple enfolded contexts.
A Hybrid Approach for Fast 3D Segmentation of Brain Tumor

Brain tumor segmentation and its study are tricky assignments of medical image processing due to complexity and variance of tumors however, forms a decisive factor for quantitative exploration of the spatial data in magnetic resonance imaging of human brain. In that mode, this modality of image has developed into a valuable investigative means in medicinal domain for detecting irregularity and discrepancy in human brain. The accuracy of segmentation method relies on its capability to discriminate different tissue, classes, discretely. Consequently there is an essential need to evaluate this capability prior to employing the segmentation method on medical images. In this paper, a semi-automatic segmentation technique is proposed to carry out the analysis and study of proficient pathologies of brain tumor of human brain. The task of segmentation is carried out integrating region growing with active contour methodologies. The evaluation of proposed methodology has been carried out on multislice image of MRI data and compared with other semi automatic and automatic techniques. It is observed by the experimental results that proposed system has the ability to accomplish fast segmentation and exact modeling of tumors in brain with a gratifying accuracy in order to support future treatment planning.
The Effect of the ‘Safety’ theme-based IT Convergence Education Program based on Technology Education on the Safety Awareness and Attitude Towards STEAM of Middle School Students

본 연구의 목적은 기술 교육에 기반한 ‘안전’ 주제 중심 IT 융합 교육이 중학생의 안전 의식과 융합인재교육(STEAM) 태도에 미치는 영향을 분석하는데 있다. 이 연구의 목적을 달성하기 위하여 중학교 자유학기활동 중 주제 선택 프로그램을 수강하는 29명에게 기술 교육 기반의 ‘안전’ 주제 중심 IT 융합 교육 프로그램을 적용하였다. 이 프로그램의 효과 검증을 위해 수업 전과 후에 안전 의식과 융합인재교육(STEAM) 태도를 검사하고 그 결과를 분석하였으며, 수업 후 만족도 설문과 학생 소감문을 통해 학생들의 반응을 분석하였다. 연구 결과는 다음과 같다. 첫째, 안전 주제 중심 IT 융합 교육이 중학생의 안전 의식에 긍정적인 변화를 주었으며 통계적으로 유의미한 차이를 나타냈다. 둘째, ‘안전’ 주제 중심 IT 융합 교육은 중학생의 융합인재교육(STEAM) 태도 전반에 걸쳐 긍정적인 영향을 미쳤으며 통계적으로 유의미한 향상 효과가 있었다. 셋째, 학생들은 안전 주제 중심 IT 융합 교육에 대해 기존 수업에 비해 학생 중심의 활동이 많았다는 의견이 많았다. 또한, 기계 요소를 이해하고 활용하는 것에 대해 어려움을 느꼈지만 큰 성취감을 느꼈으며 기계요소 및 안전에 대한 이해 및 관심이 크게 증가되었다는 응답이 많았다. 즉, 본 연구를 통해 기술 교육 기반의 ‘안전’ 주제 중심 IT 융합 교육이 중학생의 안전 의식 및 융합인재교육(STEAM) 태도에 긍정적인 영향을 미쳤음을 확인할 수 있었다. 이에 안전 교육이 강화된 2015 개정 교육과정에서 기술&#8228;가정과 기술의 세계 분야, 자유학기 활동, 방과 후 활동 등 다양한 수업에 활용될 수 있으리라 기대된다.In this study, it analysed the safety awareness and attitude towards STEAM of middle school students after conducting IT convergence education program focused on safety based on Technology education. The safety-themed IT convergence education program was conducted on 29 middle school students attending an elective theme program of the free semester system. To verify the effect of this program, attitude towards STEAM and safety awareness were assessed before and after the class, and the results were analysed. Students’ responses were collected through a survey and written reviews after the class. The findings of this research are as follows. First, the class that applied IT convergence education program with a focus on safety showed a statistically significant positive change on the safety awareness of students. Second, the IT convergence education program with safety as a theme showed a positive impact on students’overall attitude towards STEAM with statistical significance. Third, while participating in the IT convergence education program on safety, students felta strong sense of accomplishment despite the challenging theme, and valued activities integrating knowledge in diverse sectors. Furthermore, they said that they came to have a better understanding of and a strong interest in safety. This study confirmed the positive impact of safety-themed IT convergence education program on safety awareness and attitude of middle school students towards STEAM. As the 2015 revised national curriculum of Korea designed to cultivate creative convergence talents strengthened education on safety across all subjects, IT convergence education with a focus on safety, which was found to have a positive impact on the awareness and attitude of middle school students, needs to be more actively conducted at schools.
Towards Comprehensive Security Analysis of Hidden Services Using Binding Guard Relays

Tor Hidden Service is a widely used tool designed to protect the anonymity of both client and server. In order to prevent the predecessor attacks, Tor introduces the guard selection algorithms. While the long-term binding relation between hidden service and guard relay increases the cost of existing predecessor attacks, it also gives us a new perspective to analyze the security of hidden services. We utilize a novel method which can reveal guard relays for multiple hidden services. The method helps us to reveal guard relays for 13604 hidden services, and observe their binding relations for 7 months. Based on the binding relations, we conduct the first protocol-level measurement and family analysis of hidden services, and discover two types of families about hidden services, named onion family and onion-node family. Our measurement reveals 263 onion families in Tor network, and the analysis shows that onion addresses in these families tend to use common prefixes or meaningful prefixes. By analyzing the webpage of these hidden services, we surprisingly find a super onion family that contains 121 hidden services, most of which runs a fraudulent website of bitcoin. Additionally, we also discover 49 onion-node families which have abnormal binding relations between hidden services and their guard relays, including expire bindings, bridge bindings and middle node bindings.
THE IMAGE PROCESSING ALGORITHMS FOR BIOMETRIC IDENTIFICATION BY FINGERPRINTS

The quality and depth of content Web of Science offers to researchers, authors, publishers, and institutions sets it apart from other research databases. The inclusion of News of NAS RK. Series of geology and technical sciences in the Emerging Sources Citation Index demonstrates our dedication to providing the most relevant and influential content of geology and engineering sciences to our community.Қазақстан Республикасы Ұлттық ғылым академиясы "ҚР ҰҒА Хабарлары.Геология жəне техникалық ғылымдар сериясы" ғылыми журналының
Recovery of Forensic Artefacts from a Smart Home IoT Ecosystem

This paper reports an investigation into a modern smart-environment ecosystem comprising of multiple Internet of Things devices: Amazon Echo, Nest Indoor Camera and Philips Hue smart-bulb. As of yet, there is still little to no documentation, nor established methodology for the examination, acquisition and documentation of evidentiary artefacts from a smart-environment. Much of the research still remains individual to each device and does not incorporate the “melting pot” reality of most smart-environments. The methodology outlined in this paper was artefact-centric, and was purposely designed to facilitate the creation, discovery and documentation of network-native, cloud-native and device-native artefacts. Whilst not all aspects of the investigation were successful, a strong groundwork of documentation of the artefacts present on each of the smart-devices examined has been compiled, so as to inform and lay the foundations for future studies on this area of research.
Analysis of ERP Talent Requirements Based on Python

The training of ERP talent must be based on the analysis of talent requirements of human resource market. This article uses Python to crawl data from four mainstream recruitment websites, including Zhaopin, 51job, liepin, and BOSSzhipin. 4681 items of effective data highly related to ERP jobs are obtained after cleaning and standardizing the collected data. Through the analysis of the data, the distribution of ERP job in different cities, different industries, different positions and different enterprise scale are obtained. Through the analysis of salary and cities, positions, industries and working years, it is found that ERP average salary is higher than the average salary in the same city. Finally, this article find out the requirements for different ERP positions in business skills, IT skills and calibre through jieba of Python.
Детская библиотека в новом формате

The article considers the activities of the National Children’s Library Ion Creanga (Republic of Moldova). The author describes the new formats of work with children aimed to develop their interest in reading. Among them are access to books via QR code; public reading; contests and creative events; training in programming and robotics; etc. The author emphasizes the importance of innovative; creative and inventive approaches to work with children and adolescents
Exploration of Emotional Pattern Recognition and Emotional Model Construction Based on Big Data

Based on the brief introduction of the definition and correlation of big data and affective computing, this paper illustrates the methods of affective pattern recognition by taking face recognition, speech recognition, language recognition and bio-physiological recognition as examples, and then puts forward the viewpoint that the research hotspot of affective recognition under the background of big data has shifted from single mode to multi-mode fusion.