Reviewerly/exHarmony huggingface dataset:
Parallelizing Automatic Temporal Cognitive Tool for Large-Scale Online Learning Analytics

With the advent of Massive Online Open Courses (MOOCs), the data scale of student learning behavior has significantly increased. In order to analyze these datasets efficiently and present on-the-fly intelligent tutoring to online learners, it is necessary to improve existing learning analytics tools in a parallel and automatic way. We introduce Automatic Temporal Cognitive (ATC) model to describe temporal progress of online learners and evaluate their mastery of course knowledge. As a complex dynamic Bayesian network model, it often causes high computational overhead of training the ATC model via Probabilistic Programming tools. The time-consuming Monte Carlo sampling adopted by the mainstream implementations renders parameter fitting for the model a slow execution process. To address the issue, this paper proposes to transform the ATC model into the form of nonlinear Kalman filter and presents a new parallel ATC tool based on the Spark framework with the method of Unscented Kalman Filter (UKF). This tool improves the ATC model by using a parallel UKF method with the capability of automatically estimating the parameters in the whole sequential process. Experimental results demonstrate that this tool can achieve the fast execution speed and greatly improve the robustness of training parameters on different sizes of real educational data sets.
Enhance Your Student Membership With IEEE Educational Opportunities

"As the existing body of technical knowledge continues to grow exponentially, engineers and technology professionals are working hard to stay current, competent, and informed," stated IEEE President Toshio Fukuda in his article "IEEE President: Let's Focus on Continuing Education" in The Institute (Fukuda, 2020). "Today, technologists are seeking trusted sources to support their long-term and just-in-time learning needs, and this demand will continue to grow in the future. IEEE has a vital role to play in delivering 'educational value' to its members," he continued.
The Model of Influence in Cybersecurity with Frames

The Model of Influence in Cybersecurity with Frames unifies the current literature around influence and media effects in cybersecurity messaging. Building on the Process Model of Framing Research by Scheufele, this new model applies directly to the cybersecurity area and provides a macro-level view to further researcher understand of cybersecurity influence and provide options for intervention by organizational security professionals. This analysis included 42 documents concerning the work of influencing users to engage in secure behavior covering topics in persuasion, user interface design, equivalency framing, managing, and understanding user perceptions, and exploring user mental models regarding cybersecurity. This review also investigates the use of framing in cybersecurity and the definitions needed to contextualize and understand research in cybersecurity that uses framing. This model is intended as a starting point with which to build a larger understanding of cybersecurity communication to address human factors in cybersecurity.
Research on Personnel File Management Based on Computer Information Technology

Abstract Informatization has become an inevitable trend in office, and it is also the main way for modern business society to improve work efficiency. From the observation of the reform of file management in practice, its main direction is based on computer technology. This research selects three aspects based on computer technology, including information resource management, technology management and service management. It mainly discusses the integration and utilization of information resources, technology management promotion measures and innovative service management system paths.
Usage of Decision Support Systems for Modelling of Conflicts During Recognition of Information Operations

This chapter describes an application of decision support systems to conflict modelling in information operations recognition. An information operation is considered as a complex weakly structured system. We suggest a model of conflict between two subjects, based on the second-order reflexive model. We describe a method for construction of the design pattern for knowledge bases of decision support systems. We also suggest a methodology allowing to use decision support systems for modelling of conflicts during information operations recognition. The methodology is based on the use of expert assessments and content monitoring data.
A Characterization of Data Exchange between Visual Analytics Tools

Over the past years, the visualization of large and complex data sets brought up various Visual Analytics (VA) tools in order to solve domain-specific tasks. These VA tools are typically implemented as individual software components in data-flow-oriented models, meaning that data is transferred from one component to the next. While most VA frameworks rely on a monolithic architecture with features for the integration of specialized analysis methods, we consider a loose coupling of independent applications, where autonomous VA tools are used in predefined analysis sequences. To this end, we provide a characterization of the data exchange process among individual VA tools in the form of a taxonomy. This taxonomy can be used as a checklist to identify characteristics and improve the data flow of one's own multi-tool VA setup. For this purpose, we conducted a systematic investigation of the individual aspects of data exchange that are commonly found across different usage scenarios. We apply our taxonomy to three existing multi-tool frameworks, the open-source library ReVize, the toolchain editor AnyProc, and the visualization and monitoring framework Plant@Hand3D.
Human eye-fixation prediction based on Convolutional Neural Network in RGB images

Abstract Using neural networks to simulate and predict human visual attention mechanism is a hot topic in the field of computer vision. In this paper, we propose an end-to-end encoder-decoder network architecture to predict the fixation mechanism of human eyes, which consists of modules composed of multiple convolutional layers with different expansion rates to capture multi-scale features in parallel. In addition, the attention module is added on the basis of the encoder network structure, and a self-attention mechanism is introduced to capture the visual feature dependency in the channel size, and the semantic interdependence in the channel dimension is modeled to predict the visual salience more accurately. In this paper, five data sets and selected examples are used to demonstrate the effectiveness of the proposed method. Our method achieves competitive and consistent results on multiple evaluation indicators on MIT1003 and CAT2000 datasets.
BCGAN: Facial Expression Synthesis by Bottleneck-Layered Conditional Generative Adversarial Networks

Facial expression synthesis is widely applied to emotion prediction and face recognition for human-computer interaction. This task is challenging because it is difficult to reconstruct realistic and accurate facial expressions. Early deep learning methods focus only on pixel-level manipulation and are not suitable for generating realistic facial expressions. In this paper, we propose a bottleneck-layered conditional generative adversarial networks (BCGAN) for more realistic and accurate facial expression synthesis. BCGAN adopts a bottleneck layer that uses channel-wise concatenation in the generator to train with meaningful features only. In addition, a dense connection that links all bottleneck layers is added to generate an image which preserves the facial details of the original image. Both quantitative and qualitative evaluations were performed using the Radboud Faces Database (RaFD). Experimental results showed that BCGAN had 2% higher classification accuracy (98.7%) on the generated images as well as faster training speed compared to state-of-the-art approach.
Semiotic Recognition System

The Web is one of the most powerful platforms in recent years, a large number of applications are built over the platform using API's provided in HTML5 and various other web technologies. WebRTC (Real-Time Communication) standard allows supporting browsers to stream video and audio content directly from native devices such as web cam, enabling gesture recognition within the browser window. The project aims to develop a system that would allow users to interact with web applications using simple hand gestures. This system would eliminate the use of more complex input devices and easy to use. It is cost-efficient, as no additional hardware is required. It requires less processing power. It has a reduced latency as there is no 3D image manipulation required. One practical use is within a mass casualty management framework to help the operator briefly check the traffic cameras to suggest the routes for first responders.
Fake Review Detection System Through Analytics of Sales Data

A standout among the most vital types of expression reviews. Most of the destinations have made it necessary for their clients to post reviews. It is discovered that 97 individuals, who made a buy-in view of online audits, observed the survey to be accurate. What if these surveys are incorrect? There are numerous cases, in which individuals either independently or in gathering, post off base audits about some products. Sometimes these people groups are paid extremely well. Such kinds of spam surveys can be extensively characterized as constructive and antagonistic reviews. Such sorts of surveys ought to be recognized and wiped out to furnish clients with certified involvement of the business, so our application will give you promising surveys by separating them from other sites. And then you can choose what you need to purchase or not.
Acceleration of Large Deep Learning Training with Hybrid GPU Memory Management of Swapping and Re-computing

Deep learning has achieved overwhelmingly better accuracy than existing methods in various fields. To further improve deep learning, a deeper and larger neural network (NN) model is indispensable, but graphics processing unit (GPU) memory is not large enough to train such NN models. One promising method to reduce GPU memory consumption is data swapping, which eases the burden on GPU memory by swapping out intermediate data from GPU memory to central processing unit (CPU) memory while the data are not necessary. However, this method introduces communication overhead in transferring data between GPU and CPU memory. Another method is recomputation, which discards intermediate data once and then computes them again when necessary. Unlike the data-swapping method, the re-computation method introduces additional computation but does not require CPU-GPU communication. Therefore, it may reduce CPU-GPU communication by introducing it effectively in the data-swapping method. In this paper, we developed a faster training method for large NN models by combining the re-computation and the data-swapping methods. This method edits the graph of TensorFlow automatically. We developed heuristics about which part of the graph should be recomputed. The heuristics divides the graph into sub-graphs and applies the re-computation in the decreasing order of the amount of swapping data in the sub-graphs. This heuristics is intended to reduce maximal communications by re-computing minimal sub-graphs. Our hybrid method improves performance by up to 15.5% in image size 8000×8000 of ResNet50, 15.3% in image size 7500×7500 of ResNet152, and 12.4% in image size 1000×1000 of DeepLabV3+ compared with the existing data-swapping method.
Capsule Network Improved Multi-Head Attention for Word Sense Disambiguation

Word sense disambiguation (WSD) is one of the core problems in natural language processing (NLP), which is to map an ambiguous word to its correct meaning in a specific context. There has been a lively interest in incorporating sense definition (gloss) into neural networks in recent studies, which makes great contribution to improving the performance of WSD. However, disambiguating polysemes of rare senses is still hard. In this paper, while taking gloss into consideration, we further improve the performance of the WSD system from the perspective of semantic representation. We encode the context and sense glosses of the target polysemy independently using encoders with the same structure. To obtain a better presentation in each encoder, we leverage the capsule network to capture different important information contained in multi-head attention. We finally choose the gloss representation closest to the context representation of the target word as its correct sense. We do experiments on English all-words WSD task. Experimental results show that our method achieves good performance, especially having an inspiring effect on disambiguating words of rare senses.
Consumers' Brand Awareness Structure for Sour Citrus Fruits

Although citrus consumption has been declining, producing districts are currently attempting to increase their market appeal by branding their citrus fruit products. To determine basic knowledge regarding effective branding, this report seeks to clarify consumers’ brand awareness structure by conducting a study on regional citrus brands. For this purpose, we used the technique of network analysis. In doing so, it was possible to illustrate a cognitive structure connected to the word kabosu. This analysis demonstrates that the word kabosu is closely associated with Oita. However, consumers don’t make a detailed differentiation between sour citrus fruits. Therefore, the brand parity level of Oita kabosu is quite high.
Current innovative trends in the CIS national libraries

The findings of the study of the development strategies of the CIS na-tional libraries with the reference to the national specifics of the innovative ac-tivities are presented. The goal of the study is to facilitate the cooperation of the libraries within the Commonwealth of Independent States. The analyzed vast em-pirical data is based on expert assessments, consultation papers, publications in periodicals, reports and the libraries’ websites. Sociological and statistical me-thods and comparative analysis were applied. The major problem the national libraries are facing is due to their different degree of digitalization. Based on the empirical data analysis, the major trends were revealed: the key projects are aimed at providing online access to the possibly most complete collections of the CIS national libraries. The current data provided in the paper are predominantly about technological innovations. The CIS national libraries are focused on the programs of e-libraries including national electronic libraries (NELs); projects of global academic Open Access, and structural transformations (designing virtual reading rooms, digital resources sections, book museums, etc.), and educational projects. Innovations have been the strategic priority of the CIS national libraries. Implementing modern information communication technologies facilitates buil-ding common library and information space of the CIS states, while innovative activities enables sharing plans and projects for the CIS national libraries.
Particle Swarm Optimization-Based Resource Allocation in Service Composition

Service-oriented architecture (SOA) tremendously increases attention due to use in the industry to build complex web service applications. The web service plays a very important role in Service Oriented Computing (SOC). The service composition is the activity by which concatenation of the services to the already existing service with functional attributes is done rapidly. The resource allocation in the service composition is one of the steps for the allocation of web service resources to every candidate service from composite service. In a web service, service holds computational resources on the web. The work in the paper solves the problem of resource allocation using the dynamic programming approach and particle swarm optimization algorithm. The computational simulations were carried out, and a comparison between these methods was performed. It has been observed in experimental comparison that the PSO-based algorithm is best suitable for allocation problem.
What am I looking at: Contextualizing subject headings through linked open data

Many libraries have deployed discovery layers to bring various library resources under a single search interface. These tools, which mostly rely on keyword indexing, do not easily bring together items on similar topics described under different thesauri. Michigan State University Libraries (MSUL) undertook an experimental project to enhance subject discovery through subject knowledge cards that utilize subject authority data and linked open data (LOD). Subject knowledge cards are currently deployed in the MSUL digital repository to improve the subject discovery experience by displaying contextual information on the subject, links to repository items on broader, narrower and related subjects, and links to external library resources on the same subject. This approach demonstrates a new way to enhance the subject discovery experience through LOD.
2020 Sequestered Data Evaluation for Known Activities in Extended Video: Summary and Results

This paper presents a summary and results for the ActEV'20 SDL (Activities in Extended Video Sequestered Data Leaderboard) challenge that was held under the CVPR'20 ActivityNet workshop [38]. The primary goal of the challenge was to provide an impetus for advancing research and capabilities in the field of human activity detection in untrimmed multi-camera videos. Advancements in activity detection will help with a wide range of public safety applications. The challenge was administered by the National Institute of Standards and Technology (NIST), where anyone could submit their system which run on sequestered data with the resulting score posted to a public leaderboard. Ten teams submitted their systems for the ActEV'20 SDL competition on the Multiview Extended Video with Activities (MEVA) test set with 37 target activities. The performance metric for the leaderboard ranking is the partial, normalized Area Under the Detection Error Tradeoff (DET) curve (nAUDC). The top rank on activity detection was by UCF at 37%, followed by CMU at 39% and OPPO at 41%.
qMDP: DASH Adaptation using Queueing Theory within a Markov Decision Process

Adaptive bitrate (ABR) streaming algorithms play an important role in ensuring a high Quality of Experience (QoE) for the consumer. However, a lot of ABR algorithms tend to be too ad hoc. In response, methods based on a Markov Decision Process (MDP) offer more intelligent models. In particular, Reinforcement Learning (RL) methods typically do so via QoE metrics. However, RL methods are plagued by high complexity and long convergence times due to their model-free nature. This paper proposes qMDP, which is an RL method with an MDP partially modeled by an M/D/1/K queue. Our study shows that qMDP results in higher QoE and faster convergence compared to a QoE-only model-free version.
Mathematical and Algorithmic Aspects of Scalable Machine Learning

Although a number of machine learning models have been proposed and successfully deployed in organizations, there is a new emerging challenge that the organizations are going to face in the upcoming years. As various organizations are relying on data for decision-making and optimization of processes, the volume of data is an important factor for developing precise models. The ever-increasing volume of data and its storage is contributed by the advancement of communication technology and storage services like cloud computing. The large volume of data collected is usually stored in a distributed storage and computing environment to ensure fault tolerance and scalability. The development of machine learning models is quite inefficient in a distributed environment using traditional machine learning algorithms. The inefficiency is attributed to the distributed nature of the dataset and computing. The development of the models needs to be carried out in a distributed manner. Thus, additional challenges related to distributed computing need to be addressed by the machine learning algorithms. Scalable machine learning is an updation of traditional machine learning in a distributed environment. As the nature of computing changes, the mathematical formulas and equations need to be revisited along with the algorithms to make it suitable for a distributed environment. This chapter discusses the challenges faced by the traditional machine learning algorithms in distributed environments, the various mathematical backgrounds of scalable machine learning models, and the state-of-the-art distributed algorithms for scalable machine learning models.
Instructional Design for virtual courses for university teachers affected by COVID-19

Due to the 2020 scenario, the pandemic caused by the COVID-19 virus prompted a substantial change in the methods used by teachers when teaching and implementing their classes.As a support mechanism for teachers in the face of the radical change from physical presence to virtual presence, two courses were designed by the School of Pedagogy of the Universidad Panamericana: the first on Instructional Design and the second on didactic tools that enhance interaction and revitalization of classes.The summer of 2020 was the ideal time to train and prepare teachers in the instructional design of online classes because, although the educational model of the university is face-to-face, the circumstances and health limitations forced a strategic change to carry on with the quality that characterizes the institution.The preliminary results that we analyze with the SPSS software indicate that for 89.4% of the teachers the content of the course contributed to their professional development and that 87.9% have sufficient elements to redesign their subject for the following semester, which is executed in virtual mode.
Environmental legislation and variable communicative and pedagogical competence formation model for employees of penal system

The article describes the issue of formation of communicative and pedagogical competence among employees of penal system. The proposed method of using the means of professional mentoring is stipulated as a social order for training of specialists of penal system in the process of carrying out practical activities in an institution executing criminal sentences or in a pretrial detention center.
Parsing via Regular Expressions

Since programming languages exist, parsing the code - primarily for compilation - is an ever-present necessity. As of today, there is a deep theory of this topic, describing approaches and useful constructs, categories and capabilities of parsers, which this paper not intends to dive into deeply, but to describe and use the official technical terms where possible. The presented approach of parsing utilizes regular expressions and forms a PEG (Parsing Expression Grammar), which is more expressive than simply regular expressions [1]. This paper aims to present an approach specially for parsing complex input recursively using PEG approach. An easy to configure and understand interpreter based on regular expressions over characters, tokens and schemas is outlined.