Reviewerly/exHarmony huggingface dataset:
High Throughput JPEG 2000 for Broadcast and IP-based Applications

High Throughput JPEG 2000 (HTJ2K) is a royalty-free image compression standard published in 2019 that enhances JPEG 2000 by replacing its slow block coder with a fast block coder. The resulting speedup (e.g. > 30x for lossless coding) accelerates the encoding and decoding of images which can have a great impact for users. A less obvious benefit of the speedup is the enablement of low latency applications that were previously not possible with the original JPEG 2000 standard. Examples of these new potential applications are live, high-quality, low-latency broadcast video contribution, remote production, and IP-based production on-premise and on public cloud. — This paper examines various HTJ2K encoding parameters and their impact on quality, bitrate, latency, and multi-generation encode/decode cycle performance in the context of requirements of broadcast and IP-based applications. Configurations include a number of different wavelet filters, code-block sizes and wavelet decomposition structures that are available with HTJ2K. The performance of low-latency HTJ2K is also compared to other low-latency wavelet codecs like VC-2, JPEG XS and JPEG 2000 Part-1. The JPEG 2000 Part-1 comparisons use the full frame Broadcast Profile as well as the Ultra-Low Latency (ULL) configuration as per VSF TR-01:2018. A trade-off between latency and compression quality is shown.
Experimental model of professional training of pedagogical college students

The article substantiates the necessity to create an educational space for student development to solve problem nodes. The aim is to substantiate an experimental training model of students in the Pedagogical College. The organization of the educational process partly transfers to school base. This model of student training includes four components: the educational program, the results of student training, resources, and the college school interaction. The emphasis is placed on preparing students for professional activities through the development of professional competencies and solving professional problems. The article reveals three forms of organizing classes: immersion, workshop, and club. The student's training in this model is checked by a demo exam, in the playback of the model situation of the project task. Analysis of the results of the pilot study demonstrated that the implementation of the experimental model of student training provides productive interaction with employers. Introduction of experimental models of training contributed to enrich the psycho-pedagogical knowledge, improve professional skills, acquire terminology of project activities and methodology to students of pedagogical college. However, the model also helped to identify issues in need of improvement: the adjustment of the content of work programs in order to strengthen the practical training of students, the formation of a unified system of control assessment tools for assessing students' professional competencies for school teachers and college teachers, and also emphasized the need to organize and conduct reflexive analytical seminars.
Classification of Student Complaints with the Naive Bayes and Literature Methods

E-Complaint Data is a collection of data which contains comments or complaints from students towards the University. Lots of comments - comments that occur in the university environment about the performance facilities of lecturers etc ... Text mining is also known as text data mining or knowledge search in a textual database is a semi-automatic process of extracting data patterns. The purpose of text mining is to get useful information from a collection of documents. In this study using the naïve Bayes method with TFIDF weighting features. The stages will be taken to determine its classification. First is taking data from the E-Complaint System then the data will go through the preprocessing stage using literary libraries, after going through the preprocessing stage the data will be divided into 2 namely training data and testing data. Then the training data will be carried out the TF-IDF weighting process up to the probability, if it has, the next is to process the testing data by determining priors. Next is the data testing stage between the testing data and the training data, the results of the testing data will come in the form of a predetermined category. The trial results show that the classification of complaints with the naïve Bayes algorithm and with the TF-IDF feature and literary libraries in the preprocessing process has an average accuracy that is quite high at 82%.
POITrack

Maps enable complex decision making, such as planning a day trip in a foreign city This kind of task often requires combining information from different parts of the map leading to a sequence of visual searches and map extent changes. Hereby, the user can easily get lost, not being able to find back to relevant points of interest (POI). In this paper, we present POITrack, a novel gaze-adaptive map which supports a user in finding previously inspected POIs faster by providing highlights. Our approach allows filtering inspected POIs based on their category and automatically adapting the current map extent. Not only could participants find visited locations faster with our system, but they also rated the interaction as more pleasing. Our findings can contribute to improving the interaction with high-density visual information, which requires revisiting of previously seen objects whose relevance for the task may not have been clear initially.
Disruption and Deception in Crowdsourcing

While crowdsourcing has become increasingly popular among organizations, it also has become increasingly susceptible to unethical and malicious activities. This article discusses recent examples of disruptive and deceptive efforts on crowdsourcing sites, which impacted the confidentiality, integrity, and availability of the crowdsourcing efforts' service, stakeholders, and data. From these examples, the authors derive an organizing framework of risk types associated with disruption and deception in crowdsourcing based on commonalities among incidents. The framework includes prank activities, the intentional placement of false information, hacking attempts, DDoS attacks, botnet attacks, privacy violation attempts, and data breaches. Finally, the authors discuss example controls that can assist in identifying and mitigating disruption and deception risks in crowdsourcing.
Deep Learning

Deep learning is a major concept in machine learning. Deep learning follows the nested hierarchy mechanism for data representation, and it simulates the human brain. Deep learning can take the advantage of processing huge data input and achieve results with accuracy rates higher than traditional classification techniques. Many domains, such as computer vision, speech recognition, natural language processing, and pattern detection are using deep learning algorithms to solve real-world problems. Many notable companies have been developing projects related to deep learning, with various applications developed in the big data age. Nevertheless, these accomplishments come with several challenges. To address the existing challenges, the computer scientists are working closely with researchers in other domains to enhance deep learning algorithms, which indicates a promising future of deep learning.
Summarized Logical Forms for Controlled Question Answering

Providing a high-end question answering (Q/A) requires a special knowledge representation technique, which is based on phrasing-independent partial formalization of the essential information contained in domain answers. We introduce Summarized Logical forms (SLFs), the formalized expressions which contain the most important knowledge extracted from answers to be queried. Instead of indexing the whole answer, we index only its SLFs as a set of expressions for most important topics in this answer. By doing that, we achieve substantially higher Q/A precision since foreign answers will not be triggered. We also introduce Linked SLFs connected by entries of a domain-specific ontology to increase the coverage of a knowledge domain. A methodology of constructing SLFs and Linked SLFs is outlined and the achieved Q/A accuracy is evaluated. Meta-programming issues of matching query representations (QRs) against SLFs and domain taxonomy are addressed as well.
Cloud Security

Cloud solutions are third-party solutions available to any enterprise/user. Cloud users avail themselves of a number of different services deployed in the cloud provider’s infrastructure. The initial reaction to this is one of security, which is of course a major concern. The objective of this chapter is to present security-related concepts in the cloud. The reader should get a thorough understanding of how security is provided in different service models such as IaaS, PaaS, and SaaS. By the end of this chapter the reader should also understand that putting security in place is a shared responsibility and that the cloud user is responsible for data security, compliance, and regulatory and privacy requirements. Security is a topic that attracts a lot of attention and users tend to ask the following motivational questions.
A Survey to Nature Inspired Soft Computing

This article describes how swarm intelligence (SI) and bio-inspired techniques shape in-vogue topics in the advancements of the latest algorithms. These algorithms can work on the basis of SI, using physical, chemical and biological frameworks. The authors can name these algorithms as SI-based, inspired by biology, physics and chemistry as per the basic concept behind the particular algorithm. A couple of calculations have ended up being exceptionally effective and consequently have turned out to be the mainstream devices for taking care of real-world issues. In this article, the reason for this survey is to show a moderately complete list of the considerable number of algorithms in order to boost research in these algorithms. This article discusses Ant Colony Optimization (ACO), the Cuckoo Search, the Firefly Algorithm, Particle Swarm Optimization and Genetic Algorithms in detail. For ACO a real-time problem, known as Travelling Salesman Problem, is considered while for other algorithms a min-sphere problem is considered, which is well known for comparison of swarm techniques.
Structure Construction and Performance Analysis of RNN Aiming for Reduction of Calculation Costs

This paper presents novel RNN structures. In order to reduce the calculation as well as to overcome the analysis difficulty of gate structure and the problem of data dependence related normalization in the conventional method, 4 types of new RNNs inspired by shortcut connection were constructed while taking the advantages of gated RNNs and a sech function. Experiments and analyses are carried out to evaluate the performance of the RNNs. In detail, the experiments are performed using two corpus of WikiText-2 and IMDB, which have different properties. As a result, in the binary classification task using IMDB, the accuracy was about the same as LSTM and GRU with the parameter of 1/6 or less. However, in the language modeling task using WikiText-2, even if multilayer and the parameters of the intermediate layer were increased, the results were worse than that of conventional RNNs. This should be clarified in our future research.
A Blended Learning Approach to American Literature in the Era of “New Liberal Arts” Based on Modern Information Technologies

Modern information technologies, which are highly valued in the era of the new liberal arts in China and the practice of blended learning, can bring fascinating effects to China’s higher education of foreign languages. This paper sets out to discuss a pedagogical reform of American Literature in application-oriented universities. With an analysis of current situations in this course, it points out that we should answer the call of new liberal arts construction and implement blended learning based on the school running characteristics and objectives of local application- oriented universities. During the process of teaching practice, various modern information technologies, such as web-based Learning Management Systems and data association mining are employed to update teaching contents and resources, innovate teaching modes, perfect teaching evaluation, improve teaching efficiency and eventually help to cultivate compound talents to satisfy the needs of local economic development.
Foreign Language Teaching Practices in 3D Multi-User Virtual Immersive Learning Environments

3D multi-user virtual environments (MUVE) bring new opportunities to foreign language teaching and learning as a platform for teaching practices. Due to immersive and interactive nature of 3D MUVE, these tools have gained popularity in the field of foreign language learning and teaching. The power of virtual learning environments lies in creating immersive 3-D spaces that give users a sense of learning by doing. Moreover, the environment is interactive and is much the same as real world interaction. MUVE provides strong support for synchronous collaborations, interactions, and immersive environments for experiential and constructivist learning settings. Some key features of the virtual worlds comprise the ability to construct 3D environments that simulate real world situations, as well as the ability to have a virtual identity to create a cross-cultural self and the ability to have participants from around the world acting virtually in designated roles that convey new types of learning within this environment.
Emergent Pathways for the Future of Instructional Design

This chapter aims at providing an overview of the entire field of instructional design, starting with a brief historical account of the field but focusing on current and promising trends for the future of instructional design based on advances in instructional technology and human-computer interaction capabilities. The discussion encompasses several parallel trending areas, such as adaptive learning, digital storytelling, gamification, simulation technologies, augmented reality, cybernetics, the xAPI standard, mobile and ubiquitous learning, as well as implications of these trends for the field from both the theoretical and practical point of view. Even though this discussion is by no means an exhaustive account of these trends, it is the aim of this monogram to provide a centralized literature review of multiple paths currently being carved in the field and a glimpse to a multiplicity of potential futures for all those involved in designing and delivering learning or effecting human behavior and performance change.
Research on Intelligent Operation Support Technology for 5G Nuclear-Powered Ships

In order to improve the informatization level of the operation support of the nuclear-powered ship platform, an open system of digital capabilities based on 5G is constructed to realize intelligent decision-making and agile empowerment of the power system. Based on the technical characteristics of 5G network resources virtualization and capability service, a new 5G digital intelligence enabling architecture is proposed by breaking through the cloud edge collaboration and multi-source heterogeneous data fusion technology, to realize the opening of micro-service capability of the operation system, and flexible arrangement, give birth to new intelligent application styles such as intelligent operation and maintenance management, unmanned intelligent patrol inspection, virtual maintenance of equipment, etc. for nuclear power ships. The deployment of applications will multiply the operation information capability of nuclear-powered ships.
Artificial Intelligence

Machine learning and deep learningDeep learning as artificial intelligenceArtificial intelligence (AI) methods use algorithms to learn from data to make predictions, not from programming. Big dataBig Data are the enablers and the backbone for any artificial intelligence application. The data quality with outlier and noise detection becomes of significant importance. Machine learning and deep learning have become distinguishable and separable methods with own tools and algorithms. Basic algorithms for regression and classification in machine learningMachine learning, as well as single and multilayer neural networks for deep learning are developed as basis for the machine learning game in Sect. 9.11 and the online manufactureManufacture course as explained in Sect. 8.3 .
Study on the Application of Artificial Intelligence Technology in Empowering Education : Taking "Intelligent Learning Partner" as an Example

With the rapid development of artificial intelligence technology, society has entered an era where artificial intelligence is integrated with all aspects of life, and the impact of artificial intelligence on education has attracted more attention. How does artificial intelligence technology development and what are key technologies integrated with education? And its current development status in the field of education. This article takes the "Smart Learning Partner", an educational product that integrates several number of smart technologies from the Beijing Normal University Future Education Advanced Innovation Center, as an example, and its three application cases in teaching practice: 1. Hybrid based on "Smart Learning Partner" The exploration and analysis of the model teaching model, 2. "Smart Study Partner " to promote the exploration of the precision teaching of junior high school history, 3. The construction and application model research of the discipline ability development evaluation learning and teaching system.
Research on Fast Detection Algorithm of Intelligent Transportation Target Based on Mobilenet_SSD

With the rapid development of 5G and artificial intelligence, autonomous driving and smart transportation have attracted more and more attention. Traditional target detection algorithms have deep network layers and a large number of parameters, and a lot of time is required for model detection, which seriously affects the real-time performance of the detection system. The Mobilenet_SSD network uses deep separable convolution to reduce model parameters, thereby shortening computing time. In this paper, we use the Mobilenet_SSD model and optimize the frozen network structure using the openvino tool and combine it with asynchronous operations for computation. Experimental results show that the use of Mobilenet_SSD in combination with the openvino tool and asynchronous operation techniques further improves the detection performance of the system.
Fusion of Fuzzy Multi-Criteria Decision Making Approaches for Discriminating Risk with Relate to Software Project Performance

The prediction and estimation software risks ahead have been key predictor for evaluating project performance. Discriminating risk is vital in software project management phase, where risk and performance has been closely inter-related to each other. This chapter aims at hybridization of fuzzy multi-criteria decision making approaches for building an assessment framework that can be used to evaluate risk in the context of software project performance in following dimensions: 1) user, 2) requirements, 3) project complexity, 4) planning and control, 5) team, and 6) organizational environment. For measuring the risk for effectiveness of project performance, we have integrated Fuzzy Multi-Criteria Decision Making (FMCDM) and Fuzzy Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) approaches. Moreover the fusion of FMCDM and TOPSIS has not been adequately investigated in the exiting studies.
Extracting Rules for Decreasing Body Fat Mass Using Various Classifiers from Daily Lifestyle Habits Data

This study was performed to extract rules and factors for reducing body fat mass and to compare Inductive Logic Programming (ILP) and common classifiers to provide necessary steps for the healthcare system. Many researchers have focused on lifestyle-related diseases; however, few have attempted to extract rules and factors for decreasing body fat mass. The authors obtained lifestyle habits data. This data includes a variety of features (e.g., sleep, exercise, and nutrient intake). These features are easier for patients to understand. ILP and common classifiers are applied to this data. In terms of accuracy, random forest outperformed all other methods, and random forest is suitable for extracting factors among common classifiers. However, in terms of rules, ILP is more suitable than others, because ILP can extract rules covering many positive and negative examples, and it is easy to apply to the healthcare system because these rules cover a range of features.
A Framework for Vietnamese Email Phishing Detection

Currently, the attacks on network information systems are increasing rapidly in number and level of danger. Phishing email distribution method is widely used by hackers today. This is an attacking technique that exploits human behaviors in the system. This type of attack, though it is not too complicated, becomes very effective for attackers if users are unaware of information security and unable to identify phishing emails. This attack is particularly more commonly effective in developing countries where the information security is still overlooked. As a result, email phishing detection problem has become a hot topic for information security researchers. There have been some published methods to detect phishing emails on given email attacking datasets. However, one of the important issues in email phishing detection relates to the language used in emails. Each particular language used in different emails may lead to a different phising detection approach. In this article, a Vietnamese email phishing detection system is investigated. The research includes a feature selection method and a combination of machine learning algorithms to improve the performance of phishing email detection in Vietnamese language. The proposed method is evaluated using two datasets. The first dataset includes phishing emails from Vietnamese collected from Vietnamese volunteers. The second dataset is the widely used English emails as introduced in [16,17]. The experimental results show that our method is applicable for real Vietnamese email phishing detection systems.
Depth Maps and Deep Learning for Facial Analysis

Gathering and examining progressively multi-modular sensor information of human faces is a critical issue in PC vision, with applications in examinations, entertainment, and security. However, due to the exigent nature of the problem, there is a lack of affordable and easy-to-use systems, with real-time, annotations capability, 3D analysis, replay capability and with a frame speed capable of detecting facial patterns in working behavior environments. In the context of an ongoing effort to develop tools to support the monitoring and evaluation of the human affective state in working environments, the authors investigate the applicability of a facial analysis approach to map and evaluate human facial patterns. The challenge is to interpret this multi-modal sensor data to classify it with deep learning algorithms and fulfill the following requirements: annotations capability, 3D analysis, and replay capability. In addition, the authors want to be able to continuously enhance the output result of the system with a training process in order to improve and evaluate different patterns of the human face.