Reviewerly/exHarmony huggingface dataset:
BERT-based Automatic Text Scoring for Collaborative Learning

A method called Collaboration Scenario-based Scale for Emotion Regulation (CSSER) was proposed for evaluating coordination ability in collaborative learning. The method presents cartoons of several scenarios that occur in collaborative learning to the learners, and then they are asked to fill in blank balloons with their thoughts and statements about some of the scenarios; their responses are evaluated manually based on a rubric. In this paper, we propose a BERT-based classification model for automatic text scoring in CSSER, which enables us to group learners based on their abilities measured by CSSER. Further, we demonstrate the effectiveness of data-augmentation techniques for the proposed method using lexical substitution and back-translation.
Design and Realization of Gaze Gesture Control System for Flight Simulation

Abstract In airplane cockpit, flight crew, display interface, control device and onboard control device constitute a complex human computer interaction system. As a natural interaction way, eye movement can simplify the interaction process of pilots, avoiding mistakes caused by fatigue or distraction. What’s more, since this interactive modality only requires eye movement as input, it can be adapted to some special situations and free the user’s hands. In this paper, we design a gaze gesture interaction system for flight simulation and compare it with the existing prominent interaction methods.
GridSet: Visualizing Individual Elements and Attributes for Analysis of Set-Typed Data

We present GridSet, a novel set visualization for exploring elements, their attributes, intersections, as well as entire sets. In this set visualization, each set representation is composed of glyphs, which represent individual elements and their attributes utilizing different visual encodings. In each set, elements are organized within a grid treemap layout that can provide space-efficient overviews of the elements structured by set intersections across multiple sets. These intersecting elements can be connected among sets through visual links. These visual representations for the individual set, elements, and intersection in GridSet facilitate novel interaction approaches for undertaking analysis tasks by utilizing both macroscopic views of sets, as well as microscopic views of elements and attribute details. In order to perform multiple set operations, GridSet supports a simple and straightforward process for set operations through dragging and dropping set objects. Our use cases involving two large set-typed datasets demonstrate that GridSet facilitates the exploration and identification of meaningful patterns and distributions of elements with respect to attributes and set intersections for solving complex analysis problems in set-typed data.
A Bayesian Probabilistic Argumentation Framework for Learning from Online Reviews

In the real world it is common for agents to posit arguments concerning an issue but not directly specify the attack relations between them. Nonetheless the agent may have these attacks in mind and instead they may provide a proxy indicator through which one can infer the agent's intended argument graph (arguments and attacks). Consider online reviews, where reviews are collections of arguments for and against the product (positive and negative) under review and the rating indicates whether the positive or negative arguments succeed ultimately. In previous work [1] we have proposed a method that formalises this intuition and uses the constellations approach to probabilistic argumentation to construct a probability distribution over the set of arguments graphs the agent may have had in mind. In this paper we extend this proposal and provide a method, that uses Bayesian inference, to update the initial probability distribution using real data. We evaluate our proposal by conducting a number of simulations using synthetic data.
Archival resonances: embodied libraries and the corporeal lives of sonic effects

This article examines the relationship between the postproduction sound practitioner and their library of sound files. The paucity of phenomenological accounts of digital library use means there is opportunity to use theories of embodiment as well as existing understandings of archives in order to reframe understandings of the digital library, particularly in a film sound production context. To abstract the sound library from embodiment is to miss the opportunity to further deconstruct the notion of technicity and data as divorced from the bodies of users. Film sound postproduction provides a rich canvas with which to pursue a discussion of embodied libraries. This article first examines the recording and building of a library as a creative resource; it then examines how sound files as units of a larger library archive, live multiple sonic lives across many projects. Further, the role of location, practices of library engagement, including auditioning and selection of sound, as well as metadata management are examined. In this way, the sound library becomes reconfigured as a sensory archive, and the sound file becomes the sensory kindling rich with personal meanings. These meanings are lived and re-lived, exchanged and reinvented with each new incarnation of a sound work.
Server Load Balancing for Small to Medium-Sized Enterprises

Nowadays, SMBs (Small to Medium-Sized Business) are booming and competing with, SMEs (Small to Medium-Sized Enterprises) and large enterprises. The evolution of IT and it's need are common to all sectors. SMBs IT infrastructure needs a proper website management, scalability, very high reliability, high performance on par with their counterparts SMEs and large enterprises. So, it's time to provide an imperative service to achieve all these above mentioned requirements in SMBs. Some vendors offer application delivery controllers and server load balancers to achieve these requirements in SMBs. This survey article gives an overview of how application delivery controllers and load balancers helps to achieve the needs of SMBs.
Low-cost Solar Tracking and Irradiance Measurement using Miniature 5-DoF Robotic Arm and Raspberry Pi

The solar tracking system is gaining popularity owing to its capability to increase energy production by adjusting the solar panel's angle of incidence throughout the day. This paper aims to develop a low-cost solar tracking and irradiance measurement system. A miniaturized 5-DoF robotic arm is deployed as the mechanical interface for the sun's movement. A mini-solar panel and camera are attached to the end effector of the robotic arm. The sky images are captured for aligning the mini-solar panel to the sun by adjusting the joint angles of the robotic arm. The power output of the mini-solar panel from the solar irradiance is estimated in real-time. The embedded Raspberry Pi zero processor controls this system. The system is capable of following the sun in real-time, and its low cost contributes to a low-precision alternative for sophisticated and expensive sensors.
Filler Prediction Based on Bidirectional LSTM for Generation of Natural Response of Spoken Dialog

Most of the conventional response generation models do not generate speech disfluencies including fillers, because they are trained from a written language corpus. It is necessary to insert fillers to written sentences for training a response generation model for the spoken language. In this paper, we proposed the filler prediction model based on bidirectional LSTM (BLSTM). This approach can consider a whole utterance and model both positions and kinds of fillers simultaneously. The experiments showed that the proposed method surpasses the conventional approach in terms of the prediction accuracy.
DynamicHS: Streamlining Reiter’s Hitting-Set Tree for Sequential Diagnosis

Given a system that does not work as expected, sequential diagnosis aims at suggesting a series of system measurements to isolate the true explanation for the system’s misbehavior from a potentially large set of possible explanations. To reason about the best next measurement, sequential diagnosis methods usually require a sample of possible fault explanations at each step of the iterative diagnostic process. The computation of this sample can be accomplished by various diagnostic search algorithms. Among those, Reiter’s HS-Tree is one of the most popular due to its desirable properties and general applicability. Usually, HS-Tree is used in a stateless fashion throughout the diagnosis process to (re)compute a sample of possible fault explanations per iteration, each time given the latest (updated) system knowledge including all so-far collected measurements. At this, the built search tree is discarded between two iterations, albeit often large parts of the tree have to be rebuilt in the next iteration, involving redundant operations and calls to costly reasoning services. As a remedy to this, we propose DynamicHS, a variant of HS-Tree that maintains state throughout the diagnostic session and embraces special strategies to minimize the number of expensive reasoner invocations. DynamicHS provides an answer to a longstanding question posed by Raymond Reiter in his seminal paper from 1987, where he wondered if there is a reasonable strategy to reuse an existing search tree to compute fault explanations after new system information is obtained. We conducted extensive evaluations on real-world diagnosis problems from the domain of knowledge-based systems—a field where the usage of HS-Tree is state-of-the-art—under various diagnosis scenarios in terms of the number of fault explanations computed and the heuristic for measurement selection used. The results prove the reasonability of the novel approach and testify its clear superiority to HS-Tree wrt. computation time. More specifically: (1) DynamicHS required less time than HS-Tree in 96 % of the executed sequential diagnosis sessions. (2) DynamicHS exhibited substantial and statistically significant time savings over HS-Tree in most scenarios, with median and maximal savings of 52 % and 75 %, respectively. (3) The relative amount of saved time appears to neither depend on the number of computed fault explanations nor on the used measurement selection heuristic. (4) In the hardest (most time-intensive) cases per diagnosis scenario, DynamicHS achieved even higher savings than on average, and could avoid median and maximal time overheads of over 175 % and 800 %, respectively, as opposed to a usage of HS-Tree. Remarkably, DynamicHS achieves these performance improvements while preserving all desirable properties as well as the general applicability of HS-Tree.
Quick Response Code Attendance System with SMS Location Tracker

Over the years, the manual checking of attendance has been carried across most of the educational institutions. Manual attendance monitoring results in a lot of time consumed. To overcome the problem for manual attendance, the researchers proposed a Quick Response (QR) Code Attendance System with SMS Location Tracker that can provide information about the student's arrival and departure time in school. The main purpose of the study is to design a QR Code Attendance System to improve the manual/traditional attendance and to provide a Global Positioning System (GPS) that can track the location of the students. The researchers used an Incremental Methodology to approach the study. It is a method in which the product is incrementally designed, implemented, and evaluated until the product is complete. The design project was tested and evaluated by 50 users and 10 experts. Based on the series of testing the system can provide an easier and more convenient recording and checking of attendance using the QR Code Scanner, it is also capable of providing the information about attendance by sending a text message and can provide location by requesting on the Android Application.
A Feature Tree and Dynamic QoS based Service Integration and Customization Model for Multi-tenant SaaS Application

Typically, a service platform of SaaS provides a specific domain of application services for tenants having the same or similar business needs, such as enterprise resource planning, customer relationship management or warehouse management, etc. Tenants customize and use application services in the SaaS platform according to their own business requirements and quality of service (QoS), and to achieve the targets of low-cost, on-demand, and rapid deployment. To meet the requirements of multi-tenant SaaS application customization, we analyze the related concepts and issues involved in tenants' application customization from service integration perspective and QoS perspective. Then we proposed a feature tree and dynamic QoS based model for multi-tenant SaaS application. The feature tree is used to decompose the fuzzy tenants' requirements and construction integrate rules to gather scattered services on SaaS platform. Dynamic QoS supports tenants to customize their applications' functions and service quality. Finally, to demonstrate the effectiveness of the model, this paper run a customization process of SaaS application on the case of warehouse management system in the field of logistics distribution.
Detection of Phishing URL using Bayesian Optimized SVM Classifier

This paper aims to collect, map and model elements that will lead to the finding of phishing UR automatically, for this purpose data mining is used as a basic tool and in this sense, it is considered that the existing patterns in a URL will make it possible to distinguish the legitimate link for pages. Whereas, the identification of these patterns will serve to model a successful classification method. For this purpose, the attributes found in the database “phishing web” that correspond to patterns of phishing pages will be validated, at the same time it will be evaluated algorithms extracted from the literature that allow a better classification of records, finally, a model with the highest precision results is delivered and it consists of Bayesian optimized support vector machine classifier.
Potter-Cramer Mine, Maricopa County, Arizona

The Potter-Cramer mine is an inactive lead, silver, and gold mine, originally claimed in the 1920s. It is the type locality for the rare fluorescent mineral wickenburgite. Also found at the mine ar...
The Present and Future of Continual Learning

This paper addresses a continual lifelong learning problem that learns incremental multiple tasks in real-world environments. We overview and summarize representative approaches and categorization of the state-of-the-art in continual learning. Comparable scenarios, benchmark datasets, and baseline approaches for different continual scenarios introduced in this paper. We suggested a comparison of the differences and similarities with other machine learning methods. We also report real-world applications, especially robots and healthcare fields. We summarize current states and suggest future direction of continual learning problems.
Introducing Latent Space Correlation to Conditional Autoencoders for Intra Prediction

Intra prediction has been an integral part of image and video coders for a long time. A predominant method is angular prediction that extends the reference area in a certain angle into the block. Recently many deep-learning-based methods have been proposed. Since intra prediction uses multiple modes this usually requires training a large number of networks. With a conditional autoencoder we are able to generate an arbitrary number of modes with only one network. In this paper we introduce a novel loss function enforcing a spatially correlated latent space and extend the network structure to the same end. Thereby we are able to propose a simple spatial mode prediction scheme using most-probable-mode lists. By replacing matrix-based intra prediction in VVC with our method, we obtain average rate savings of 0.84% with peak gains of 2.37%.
Pseudo Ground Truth Segmentation Mask to Improve Video Prediction Quality

Video prediction to foresee future events is an extremely difficult job since it involves spatial feature extraction and temporal sequential analysis. We identified that the semantic information is actually crucial to prediction, and proposed using “pseudo ground truth” segmentation masks which are generated automatically in real time and add them to the input layers as extra information to predict future frames. Experiments conducted on our self-defined network demonstrated drastically higher quality predictions are achieved when compared with other state-of-the-art direct video prediction models.
Mobile learning in non-English Speaking Countries: Designing a Smartphone Application of English Mathematical Terminology for Students of Mathematics Teacher Education

With the expeditious development of the 4.0 technology era, learning English in non-English speaking countries is more crucial and meaningful than ever. Thus the application of information technology for teaching has been increasingly focused and interested, particularly the application of m-learning to support teaching and learning is also progressively popular. Following this trend, this paper conducts to build the application of learning Mathematics by using the English language for the primary object of Students of Mathematics Teacher Education through three steps: First of all, survey the need for studying Mathematics by using English language and analyze the factors of the affecting to students' English proficiency. After that, plan and design an application for teaching Mathematics by using the English language. Finally, experiment and get opinions. The results obtained in the study show that learners who receive this application are quite positive.
RETRACTED: Real-time monitoring of the athlete's musculoskeletal health based on an embedded processor

In this paper, we use wireless sensor network in motion analysis and computer vision processing. We mainly monitor and analyze various kinds of motion through wireless sensor network. When dealing with computer vision, we use wireless sensor networks to capture moving objects and analyze their detailed motion parameters. We mainly use two types of sensors to receive information, and then fuse the data they receive. These data can be applied in various fields, such as robot field. In this study, we also proposed the predictive data analysis technology, which can be used to determine the health status of the body by monitoring the robot&apos;s movement under certain conditions. At the same time, this technology can also be applied to the rehabilitation training of the injured patients. This technique can not only be used in exercise analysis, but also provide some useful suggestions and optimal exercise time for patients&apos; rehabilitation training, which is very helpful for patients&apos; rehabilitation and strength training. In this paper, the pilot&apos;s work is simulated by different actions, and a large number of visual motion data are generated in the experiment. In addition, we also explored some elderly patients with dyskinesia, and carried out disease monitoring and exercise monitoring on them.
A Trust-based Recommender System by Integration of Graph Clustering and Ant Colony Optimization

Recommender systems (RSs) are intelligent systems to help e-commerce users to find their preferred items among millions of available items by considering the profiles of both users and items. These systems need to predict the unknown ratings and then recommend a set of high rated items. Among the others, Collaborative Filtering (CF) is a successful recommendation approach and has been utilized in many real-world systems. CF methods seek to predict missing ratings by considering the preferences of those users who are similar to the target user. A major task in Collaborative Filtering is to identify an accurate set of users and employing them in the rating prediction process. Most of the CF-based methods suffer from the cold-start issue which arising from an insufficient number of ratings in the prediction process. This is due to the fact that users only comment on a few items and thus CF methods faced with a sparse user-item matrix. To tackle this issue, a new collaborative filtering method is proposed that has a trust-aware strategy. The proposed method employs the trust relationships of users as additional information to help the CF tackle the cold-start issue. To this end, the proposed integrated trust relationships in the prediction process by using the Ant Colony Optimization (ACO). The proposed method has four main steps. The aim of the first step is ranking users based on their similarities to the target user. This step uses trust relationships and the available rating values in its process. Then in the second step, graph clustering methods are used to cluster the trust graph to group similar users. In the third step, the users are weighted based on their similarities to the target users. To this end, an ACO process is employed on the users' graph. Finally, those of top users with high similarity to the target user are used in the rating prediction process. The superiority of our method has been shown in the experimental results in comparison with well-known and state-of-the-art methods.
Armor Within: Defending Against Vulnerabilities in Third-Party Libraries

Vulnerabilities in third-party software modules have resulted in severe security flaws, including remote code execution and denial of service.However, current approaches to securing such libraries suffer from one of two problems.First, they do not perform sufficiently well to be applicable in practice and incur high CPU and memory overheads.Second, they are also harder to apply to legacy and proprietary systems when the source code of the application is not available.There is, therefore, a dire need to secure the internal boundaries within an application to ensure vulnerable software modules are not exploitable via crafted input attacks.We present a novel approach to secure third-party software modules without requiring access to the source code of the program.First, using the foundations of language-theoretic security, we build a validation filter for the vulnerable module.Using the foundations of linking and loading, we present two different ways to insert that filter between the main code and the vulnerable module.Finally, using the foundations of ELFbased access control, we ensure any entry into the vulnerable module must first go through the filter.We evaluate our approaches using three known real-world exploits in two popular libraries-libpng and libxml.We were able to successfully prevent all three exploits from executing.
Migrating Complex Applications From On-Premises to Cloud Interface

Migrating applications on the cloud storage from the systems physically available on the premises is a difficult task. There are a lot of research articles providing solutions for the current problem of migration of applications by software industry. The chapter is shedding light on how to migrate the application efficiently using mathematical approach. The dependency of migration is directly proportional to the size of the data and the speed of the network. There are a number of storage options available on cloud for easy accessibility, cache-ability, and consistency. This chapter focuses on difficult migration of an application.
A Discrete Cosine Model of Light Field Sampling for Improving Rendering Quality of Views

A number of theories have been proposed for reducing sampling rate of light field. But these theories still need a great many of samples (images) to obtain sufficient geometric information. In this paper, we utilize the sparse representation of light field in Discrete Cosine Transform domain to present a Discrete Cosine Sparse Basis (DCSB). Thus, we can find out the zeros of DCSB to reduce sampling requirement of light field for alias-free rendering. Finally, experimental results demonstrate the effectiveness of our approach without lose information.