Reviewerly/exHarmony huggingface dataset:
Mobile Supported Interaction Modeling to Find Engagement of Acolyte in Live Classroom

The affordable small devices have changed people’s lives in the last decades exponentially, especially mobile phones because people are now moving with mobile devices in a pervasive manner. These changes influenced human attention in positive and negative both manner. This work, try to explore how these small devices can affect people’s attention in the live classroom. Mobile can be used as a device to identify how mobile usage effects the level of engagement during the live classroom. During learning and listening activity maintaining the concentration of acolyte through mobile devices has become a vital issue. Research on maintaining attentiveness during learning can offer new insights into the selection of m-learning topics. This study used a quasi-experimental design to investigate the concentration and understanding of knowledge of the student in the traditional classroom as well as the classroom where everyone used mobile phones for learning purposes. Ninety numbers of undergraduate engineering students participated in the study who were divided into an equal number of unbiased groups to be treated as a control and treatment group. After performing an independent sample t-test, results are obtained like: correlations existed between the interest and attention also in between attention and success in the mobile learning concept. Research findings entail the correlation between the student’s score and engagement which can be identified using mobile devices can give a formative influence on the teacher and student to identify the time gap among students in understanding coursework that can stair the teacher to drive for a better class. The limitation of the study also identified in this work.
A General Approach to Uniformly Handle Different String Metrics Based on Heterogeneous Alphabets

In the last few years, we have assisted in a great increase of the usage of strings in the most disparate areas. In the meantime, the development of the Internet has brought the necessity of managing strings from very different contexts and possibly using different alphabets. This issue is not addressed by the numerous string comparison metrics previously proposed in the literature. In this paper, we aim at providing a contribution in this context. In fact, first we propose an approach to measure the similarity of strings based on different alphabets. Then we show that our approach can be specifically adapted to several classic string comparison metrics and that each specialization can lead to addressing completely different issues.
Smart Beaker Based on Multimodal Fusion and Intentional Understanding

In the current simulation experiment system, the experimental design of single mode is less interactive and less accurate. In order to solve this problem, this paper proposes an experimental interaction kit based on sound and sensor, and designs a multi-modal fusion and intent understanding algorithm. Firstly, the method of multi-sensor signal extraction and speech feature extraction is introduced. Then, based on the results obtained by the two methods, an algorithm based on decision-level fusion is studied, which solves the problem of perception of user's operation intention in virtual chemistry experiments. Finally, the usability of the multimodal intent understanding algorithm proposed in this paper is verified by designing a complete chemical experiment system. Experiments show that the multi-modal intent understanding algorithm based on sensor and speech input is due to a single modality in terms of interactivity and accuracy, and the physical interaction suite designed in this paper greatly improves the intelligence and interactivity of the system.
AMPL: aspect multiple product lines

The Software Product Line approach is well-known for the advantages it brings to the software-development field. However, single SPLs had been found to be inadequate in managing the variability of large scale and complex software-intensive systems. For this reason, although the Multiple Software Product Lines have been proposed as another solution to tame the complexity of SPLs, the adoption of an MPL has given rise to several challenges such as facilitating the reuse between SPLs, the complexity of the model as well as distributed-derivation. In this paper, we have presented a new methodology for MPLs engineering, which is called the AMPL. Our approach had helped the reuse management of MPLs through the two key concepts of separation of concerns and partial derivation. The various steps of the AMPL approach are illustrated in this paper and were validated in the context of an e-Government MPL. Apart from the systemization of reuse within the MPLs as well as in the structuring of the MPL model, our approach on the early integration of the SPLs during the development process had also been proven to be effective in the prevention of the distributed-derivation issue.
Librarianship and beyond: The twenty-year evolution of an interdisciplinary curriculum

This paper describes the 20-year evolution of an information studies program from one primarily focused on librarianship, to one that now encompasses a range of interdisciplinary topics including knowledge management, user-experience design, data science, and bioinformatics, among others. The progr am revisions, course development, and faculty renewal were guided by the School’s strategic plan to centre its teaching and research on the focus of the interaction between people and information, supported by technology. Over twenty years, the proportion of interdisciplinary courses increased from less than 10% to almost half. There were a number of challenges throughout the process, including finding the right balance within the program, maintaining accreditation, and working with limited resources. Yet, the outcomes have been well worth the effort, and we suggest best practices based on our lessons learned.
Low-Complexity Flow Scheduling for Commodity Switches in Data Center Networks

Recently proposed approaches to minimize the Flow Completion Time (FCT) in data centers do not require any a-priory information about the flow size, thus appear to be both practical and efficient. These solutions are based on a system of multiple priority queues (PQs) at both the servers and the switches and they may require to solve a complex algorithm to optimally split the traffic across the different PQs. However, the actual availability of priority queues at the switches is typically limited, thus restricting the applicability of these approaches. In our work we propose a novel approach, NOS2, which requires only 2 PQs at the switches while maintaining multiple PQs at the servers, and leverages a central controller that optimally coordinates the traffic split among the different priority levels. We show by simulation that NOS2 is able to achieve performance close to state-of-art solutions with significantly smaller implementation complexity. Thus, NOS2 is expected to provide a better trade- off between performance and implementation complexity.
Spelling normalisation of Late Modern English

To be able to profit from natural language processing (NLP) tools for analysing historical text, an important step is spelling normalisation. We first compare and second combine two different approaches: on the one hand VARD, a rule-based system which is based on dictionary lookup and rules with non-probabilistic but trainable weights; on the other hand a language-independent approach to spelling normalisation based on statistical machine translation (SMT) techniques. The rule-based system reaches the best accuracy, up to 94% precision at 74% recall, while the SMT system improves each tested period. We obtain the best system by combining both approaches. Re-training VARD on specific time-periods and domains is beneficial, and both systems benefit from a language sequence model using collocation strength.
Classification System with Capability to Reject Unknowns

In this paper, we propose a novel method for object classification with capability to reject unknown inputs. In the real world application such as an image-recognition-based checkout system, it is crucial to reject unknown inputs while correctly classifying registered objects. Conventional deep-learning-based classification systems with softmax output suffer from overconfident score on unknown objects. We tackled the problem by the following two approaches. First, we incorporated a metric-learning-based method proposed for face verification into object classification. Second, we utilize available unregistered objects (known unknowns) in the training phase by proposing a novel “Margined Unknown Loss”. In the experiment, we showed the effectiveness of the proposed method by confirming that it outperformed conventional softmax-based approaches which also use the known unknowns, on two datasets, MNIST dataset and a retail product dataset, in terms of Recall at a low false positive rate.
KSR‐BOF: a new and exemplified method (as KSRs method) for image classification

Image classification is very important in pattern recognition and computer vision, where, for integrating final representation, feature pooling methods of the max-pooling, sum-pooling and average-pooling have been widely used. In this study, the authors propose a new method called K-strongest responses (KSRs) on the dictionary atoms for integrating the coding coefficients to generate the final representation that is compared with the previous pooling methods, produces better performance for the image classification task. On the basis of the KSR method, to improve classification accuracy and generate more compact and discriminative final representation, a new framework consisting of two-part KSR and bag-of-features is proposed. To evaluate the performance of the proposed method and framework, they apply it to locality-constrained linear coding, linear distance coding and sparse coding by using two datasets from benchmarks of scene classification: 19-class satellite scene and UC Merced Land. The results show that the coding coefficients integrated by their method and framework are more discriminative than other methods.
A Test-Driven Approach to Improving Student Contributions to Open-Source Projects

Test-driven development (TDD) promises to help students write high-quality code with fewer defects. Although many studies of TDD usage have been conducted in entry-level computer science courses, few have looked at more advanced students doing projects of larger scope, such as contributing to open-source software (OSS). To test the performance of the test-driven approach on OSS-based course projects, we conducted a quasi-experimental controlled study, which lasted for more than one month. Thirty-five masters students participated in our study. They worked on course projects in teams, half of which were assigned to the TDD group (using a test-driven approach), and the rest of which were assigned to the non-TDD group (using the traditional test-last approach). We found that students in the TDD group were able to apply test-driven techniques pragmatically-spending more than 20% of their time on average complying with the test-driven process-throughout the whole project. There were no major differences in the quality of source-code modifications and newly added tests between the TDD group and the non-TDD group; however, the TDD group wrote more tests and achieved significantly higher (12% more) statement coverage.
A Group Identification Protocol with Leakage Resilience of Secret Sharing Scheme

Secret sharing has been study for many years and has had a number of real-word applications. There are several methods to construct the secret-sharing schemes. One of them is based on coding theory. In this work, we construct a secret-sharing scheme that realizes an access structure by using linear codes, in which any element of the access structure can reconstruct the secret key. We prove that our scheme is a multiprover zero-knowledge proof system in the random oracle model, which shows that a passive adversary gains no information about the secret key. Our scheme is also a leakage-resilient secret-sharing scheme (LRSS) in the bounded-leakage model, which remain provably secure even if the adversary learns a bounded amount of leakage information about their secret key. As an application, we propose a new group identification protocol (GID-scheme) from our LRSS. We prove that our GID-scheme is a leakage-resilient scheme. In our leakage-resilient GID-scheme, the verifier believes the validity of qualified group members and tolerates l bits of adversarial leakage in the distribution protocol, whereas for unqualified group members, the verifier cannot believe their valid identifications in the proof protocol.
Digital Music Course Based on Computer Application Technology

This paper carries out the design and development of digital music teaching assistant system based on computer application technology. The system analyzes students’ video, audio, physiology and other related information in detail, and stores the specific training status to the database in an all-round way to test the real-time changes of skill level. It has been proved by practice that this system can not only provide more visual information and all parameters for teaching and training, but also transform the theory and method of skills into vivid image data information. It can also significantly improve the rationality of music skill training and the efficiency of skill learning, which is worth promoting and applying.
Automating web Table Columns to Knowledge Base Mapping using Translation Embedding

Extracting semantic information from web tables is a heavily researched area in natural language processing (NLP) and knowledge base augmentation. Identifying the kind of information each table column contains makes it easy for computers to read, understand and use the information in a table. Mapping the columns to concepts from an ontology solves this issue. This paper introduces Col2Pedia, a novel supervised learning based approach to map web table columns to concepts from a knowledge base, automatically. Col2Pedia leverages the behaviour of columns of a table when taken as a pair to infer mappings for these columns with high precision and recall.
Timing is Everything: Identifying Diverse Interaction Dynamics in Scenario and Non-Scenario Meetings

In this paper we explore the use of temporal patterns to define interaction dynamics between different kinds of meetings. Meetings occur on a daily basis and include different behavioral dynamics between participants, such as floor shifts and intense dialog. These dynamics can tell a story of the meeting and provide insight into how participants interact. We focus our investigation on defining diversity metrics to compare the interaction dynamics of scenario and non-scenario meetings. These metrics may be able to provide insight into the similarities and differences between scenario and non-scenario meetings. We observe that certain interaction dynamics can be identified through temporal patterns of speech intervals, i.e., when a participant is talking. We apply the principles of Parallel Episodes in identifying moments of speech overlap, e.g., interaction "bursts", and introduce Situated Data Mining, an approach for identifying repeated behavior patterns based on situated context. Applying these algorithms provides an overview of certain meeting dynamics and defines metrics for meeting comparison and diversity of interaction. We tested on a subset of the AMI corpus and developed three diversity metrics to describe similarities and differences between meetings. These metrics also present the researcher with an overview of interaction dynamics and presents points-of-interest for analysis.
Residual Net for Car Detection with spatial transformation

In spite of the immense success of deep neural networks for classification tasks, it's challenging to use them to extend the usage for industrial applications. The struggle comes from the nature of variation in data sources and the distribution of data. For image classification and detection schemes, it's significant to design models that are less prone to transformation shifts. In this work, we propose LadonNet, a CNN which trains with multiple spatial transformations of the input instance. The model is extended to work with a Residual CNN model on training samples and generated augmented samples. The model was compared with other varieties of residual architectures and showed competitive performance. In the future, the model can be extended with attention to better visualize the strongest features.
Background of the Revision of the Secondary School Engineering Curriculum in the Context of the Society 4.0

The study responds to the characteristics of Society 4.0, which links industry, services, science, research, innovation and new digital technologies. Education 4.0 must ensure the preparation for the digital age and the expansion of digital skills across generations while increasing the use of digital technology in school and in-service training. Digitization, robotics and the potency of artificial intelligence also significantly influence engineering production, which requires a different qualification of workers. European Qualifications Frameworks are a suitable tool for revising engineering study. The revision must be based on trends in engineering, analysis of the educational needs of this sector, current educational concept and knowledge of the specifics of the current young generation. The revised curriculum should integrate professional competences with key competences and transversal (cross-curricular) skills, allowing school leavers to enter the production or production practice and excellent students a higher level of engineering education. The authors of the study deal with the research grant of the Technological Agency of the Czech Republic entitled “Education in engineering fields and its optimisation for the labour market”, which will be one of the research starting points for the commenced review of the curriculum. Research uses a number of empirical methods to identify unnecessary parts of the curriculum and to identify new components in terms of learning goals and learning outcomes in both the vocational and general education fields. Research findings will be compared with foreign results and discussed with engineering companies as purchasers of graduates and educators at secondary engineering schools to meet the needs of the emerging Industry 4.0 and the real educational potential of schools. The study presents the results of analyses of trends in the engineering industry, its human resources needs and requirements for the professional profile of workers in this sector, especially in the Czech Republic as an industrialized country.
Classification of Websites Based on the Content and Features of Sites in Onion Space

This paper describes a method for classifying onion sites. According to the results of the research, the most spread model of site in onion space is built. To create such a model, a specially trained neural network is used. The classification of neural network is based on five different categories such as using authentication system, corporate email, readable URL, feedback and type of onion-site. The statistics of the most spread types of websites in Dark Net are given.
Static Load Balancing Algorithms in Cloud Computing: Challenges and Solutions

Cloud computing offers paid hosting and Internet services. It has now become a preferred method for scalable network communication and computing because of its many attractive features such as high availability, scalability, fault tolerance, management simplicity and low costs. Due to the strong demand for cloud computing, efficient load balancing is essential to ensure a uniform distribution of IT tasks on servers to avoid bottlenecks. The purpose of this evaluation document is to focus on existing issues of cloud computing, including balancing cloud loads through static algorithms and fetching gaps to create feasible and efficient static load balancing in cloud. We believe that the ideas presented as a new solution will enable researchers to redefine better algorithms for better features and better experiences in cloud systems. This can help businesses that cannot be affordable for an infrastructure that supports complex and load balancing algorithms.
The experience of organizing blended learning at a university (using pedagogical disciplines as an example)

The article presents the experience of organizing blended learning in pedagogical disciplines based on social networks. It is concluded that while viewing educational content, students mainly use mobile devices. It is noted that the use of blended learning activates students, leads to an increase in independent activity in learning. The authors note the high potential for the use of video in student learning. Blended learning helps to involve students in the pedagogical process, to simulate situations in which students can demonstrate their professional competencies.
The predicative adjectiveand its propositional arguments

Abstract The purpose of this paper is to classify Italian adjectives that have the predicative function and accept a complement clause ( Ch F ) or an infinitive ( V-inf ) in N 0 or N 1 . For the formation of the word list, we have used electronic dictionaries from the “DELA” system in use in the Department of Communication Sciences at the University of Salerno (now Department of Political and Communication Sciences) and De Mauro Dictionaries ( 2000a and 2000b ). 34,132 adjectives of the Nooj list have been examined. The acquired data have been subjected to further verifications in order to obtain the adjectives responding to the basic theoretical hypothesis. The adjectives have been classified according to the principles of Lexicon-Grammar methodology as developed by Maurice Gross ( 1975 , 1981 and further). The result was a list of 1,732 entries relating to 1,619 adjectives classified in 10 classes.
Identification of Threats and Vulnerabilities in Public Cloud-Based Apache Hadoop Distributed File System

Information security issues in public clouds are amplified by Big Data, s unique security challenges originating from its volumetric data storage from a wide variety of sources and structures. Apache Hadoop (AH) framework is driving the Big Data paradigm is for its effectiveness in processing large datasets. AH is a typical Platform-as-a-Service cloud computing model. It is centered on the underlying Hadoop Distributed File System (HDFS). AH was originally designed to run in a well controlled private computing environment. However, when AH operates in a public cloud in large clusters, its built-in security mechanisms are subject to different types of threats. Motivated by such fundamental design concept and deployment computing environment, and for HDFS being a core component of AH, the contribution of this paper is to identify, expose, and discuss security threats and vulnerabilities in public cloud-based HDFS.