Reviewerly/exHarmony huggingface dataset:
Toward Student Classification in Educational Video Courses Using Knowledge Tracing

Using videos as a learning resource has received a lot of attention as an effective learning tool. Knowledge Tracing is intended to track students' knowledge acquisition when they answer a serie of problems. In this paper, we describe an experiment to model students' knowledge acquisition in educational video courses. For this purpose, Deep Knowledge tracing is used to classify and predict learners' performance as they interact with an educational video course in the subject of "C programming language". Learners' responses in previous quizzes were analyzed in order to forecast their next responses. The implementation of DKT in our dataset, led to an AUC (Area Under the Receiver Operating Characteristic Curve) of 0.73 which is a notable performance.
Video Steganography Using Edge Detection Techniques

For transmitting secret information, security is very important because hackers may utilize weak link over communique network to take desired information. Video Steganography is the process of hiding some secret information inside a video. The addition of this information to the video is not perceptible by the human eye as the change of a pixel color is minor. In this paper, I have designed new algorithm to hide the data into video. Hidden data has been taken as text and hide it into frames of video.
A Smart Campus System based on Intention Recognition and Internet of Things

Improving the efficiency and experience of information query and teaching management through Natural Language Processing (NLP) is a rising trend of the next-generation Smart Campus. To support campus information interaction and classroom management, the author developed The Campus Information Interaction and Classroom Teaching Support System based on NLP, Face Recognition, and Internet of Things (IoT). The author also innovatively developed the "Intention Recognition Algorithm Based on Sentence Structure and Text Similarity", which realized accurate identification of user's intention in campus scenarios. In this system, NLP is combined with the campus-specific application situations. Only one voice or text command can make the required information automatically and quickly pushed to the user's eyes. Moreover, only one voice or text command is needed to enable the system to quickly and automatically complete the equipment management task and realize "what you say is what you get". By improving the efficiency and experience of information query and teaching management in the campus scenarios, this system is a beneficial exploration on the development direction of the next generation Smart Campus.
Detection of local structures in images using local entropy information

Recently one deep learning technique, Convolutional Neural Networks (CNN), has gained immense popularity. Their success is particularly noticeable on image data, but falls short on non-image data. New methods have been developed to transform non-image data to exhibit image like local structures. That would enable the transformed data to take advantage of CNN architectures. Question then arises, how to measure the presence of local structures, the quality of those local structures, and how to know if there is any optimal shape of the local structures that might result in superior performance for CNN. In this paper, we answer these three questions. We present three methods to identify presence of local structures by measuring entropy. We show experimental results that provide intuitions about the quality of the local structures. Finally, we provide results showing that the performances of CNN models corresponding to the lowest entropy producing datasets were superior.
Rethinking ReID: Multi-Feature Fusion Person Re-identification Based on Orientation Constraints

Person re-identification (ReID) aims to identify the specific pedestrian in a series of images or videos. Recently, ReID is receiving more and more attention in the fields of computer vision research and application like intelligent security. One major issue downgrading the ReID model performance lies in that various subjects in the same body orientations look too similar to distinguish by the model, while the same subject viewed in different orientations looks rather different. However, most of the current studies do not particularly differentiate pedestrians in orientation when designing the network, so we rethink this problem particularly from the perspective of person orientation and propose a new network structure by including two branches: one handling samples with the same body orientations and the other handling samples with different body orientations. Correspondingly, we also propose an orientation classifier that can accurately distinguish the orientation of each person. At the same time, the three-part loss functions are introduced for orientation constraint and combined to optimize the network simultaneously. Also, we use global and local features int the training stage in order to make use of multi-level information. Therefore, our network can derive its efficacy from orientation constraints and multiple features. Experiments show that our method not only has competitive performance on multiple datasets, but also can let retrieval results aligned with the orientation of the query sample rank higher, which may have great potential in the practical applications.
The Value of Communication in Agile Project Management

The purpose of this research is to understand the importance and impact of communication in an Agile environment so as to suggest ways to overcome communication challenges in Agile projects. In this research, the latest use of communication methods, approaches, and challenges faced by Agile teams are thoroughly evaluated to know the best solutions to communication problems. A literature review in this study proposes that a lack of proper communication is one of the main challenges faced by Agile projects. The authors used a systematic literature review to examine pre-existing literature and to select the most pertinent literature to assess these variables in depth and to create an assessment model. The ultimate outcome of this study was the development of the best practices that improve communication in Agile project environments.
An Improved SMOTE Algorithm for Processing Unbalanced Electric Charge Data Sets

research-article An Improved SMOTE Algorithm for Processing Unbalanced Electric Charge Data Sets Share on Authors: Li Wu State Grid Electronic Commerce Co Ltd State Grid Electronic Commerce Co LtdView Profile , Bei Yu State Grid Electronic Commerce Co Ltd State Grid Electronic Commerce Co LtdView Profile , Xinrui Ju State Grid Electronic Commerce Co Ltd State Grid Electronic Commerce Co LtdView Profile , Lihua Jiang State Grid Information & Communication Co Ltd State Grid Information & Communication Co LtdView Profile Authors Info & Claims CONF-CDS 2021: The 2nd International Conference on Computing and Data ScienceJanuary 2021 Article No.: 95Pages 1–5https://doi.org/10.1145/3448734.3450825Published:28 January 2021 0citation3DownloadsMetricsTotal Citations0Total Downloads3Last 12 Months3Last 6 weeks1 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Online Digital Cheque Clearance and Verification System using Block Chain

Cheque Truncation System (CTS) is an image-based cheque clearing framework used in Sri Lanka. This semi manual process has certain limitations and takes up to 3 working days to clear an inter-bank national cheque in Sri Lanka. Faced with the limitations of this system, cheque users and commercial banks must need an efficient and a secured system which can clear a cheque within less than 24 hours along with providing integrity and confidentiality to the system. This research portrays an automated solution, which is feasible for any commercial bank in Sri Lanka, to address above-mentioned issues. The proposed system is based on the blockchain where all banks willing to take an interest in this framework must connect the proposed blockchain based system to supply the quicker cheque clearance to its clients. Answers were proposed with a complete framework consisting of four main phases: (i) paper cheque clearing process, (ii) digital cheque issuing and clearing process, (iii) cheque fraud detection process and (iv) cheque transaction securing process. Python along with Flutter framework and Ethereum were the major technologies used for implementing the system. The proposed system is highly scalable as Ethereum provides added integrity to the system. The approach advocates the customer as well as the bank with much simpler and speedier cheque clearing process with increased security. It also contributes with a paper cheque fraud detection system with faster and reliable results. The proposed system provides benefits to the user as well as the bank by addressing the requirement of producing a secure, effective and environment friendly system. Finally, CheckMate permits a consistent stream of cheque clearance operation for the payer and the payee without any mediators.
Teaching-Learning Based Optimization of Dual Watermarking of Color Images

Digital watermarking has proven to be a promising solution for copyright protection. However, several issues relating to robustness and tampered area detection remain unresolved. In this article, two watermarks are embedded in a color image. Using the Stationary Wavelet Transform (SWT) and Singular Value Decomposition (SVD), a gray-scale image is embedded as one of the watermarks for ownership identification. The least significant bit approach is used to embed a binary watermark to locate the tampers. Teaching Learning Based Optimization (TLBO) is employed to boost the watermark's robustness. A new fitness function is proposed for color image watermarking. This technique is tested on both standard and medical images. The performance of this method is evaluated in terms of imperceptibility, robustness, and embedding capacity.
A technique for developping mobile applications

Software engineering principles are very required for the development of mobile applications, which are necessary for many life applications. In this paper, we present a development process based on transformation of UML models, which describe mobile applications based on migrant objects to Mobile Maude language that extends the Maude language and implements the Rewriting Logic (RL). The generated rewriting theories will be executed for simulation using located configurations, which are produced from UML object diagrams.
A Solution to UAV Route Planning Problem Based on Chaotic Genetic Algorithm

The paper addresses un-crewed aerial vehicles (UAV) route planning with its tracking trajectory of preventing obstacles by implementing a chaotic genetic algorithm (CGA). In complex environments, the dynamic 2D-grid model of low-altitude aircraft is discretized according to the constraints of 3D-space, divided into several 2D spaces. The improved CGA code is used to optimize the UAV's route planning phase for obstacles in space. The experimental results depict that the proposed scheme can effectively design an estimated optimal path and overcome route planning in the 3D space by converting engineering practicability spaces.
Maintaining Consistency with Constraints: A Constrained Deep Clustering Method

Constrained clustering has been intensively explored in the data mining. Popular clustering algorithms such as k-means and spectral clustering are combined with prior knowledge to guide the clustering process. Recently, constrained clustering with deep neural network gains superior performance by jointly learning cluster-oriented feature representations and cluster assignments simultaneously. However, these methods face a common issue that they have poor performance when only minimal constraints are available because of their single way to mine constraint information. In this paper, we propose an end-to-end clustering method that learns unsupervised information and constraint information in two consecutive modules: an unsupervised clustering module to obtain feature representations and cluster assignments followed by a constrained clustering module to tune them. The constrained clustering module is composed of a Siamese or triplet network to maintain consistency with constraints. To capture more information from minimal constraints, the consistency is maintained from two perspective simultaneously: embedding space distance and cluster assignments. Extensive experiments on both pairwise and triplet constrained clustering validate the effectiveness of the proposed algorithm.
On Random Editing in LZ-End

LZ-End is a variant of the LZ77 compression algorithm which allows random access to the compressed data. In this paper, we use the random-access capability of LZ-End to perform random edits on the compressed data.
중국 대학 한국어학과 초·중급 듣기 교육과정의 개발과 개선 방향 -북경대학교 조선(한국)언어문화학부 학습자-교사 집단의 인식 및 요구 조사를 중심으로-

Students studying Korean at Chinese universities have extremely limited opportunities and time to learn Korean outside of their classes. In particular, considering learners' Korean language levels, teachers often help learners understand in elementary courses by using the Chinese language. To provide learners with more listening input and to improve their listening skills in their classes, it is necessary to ensure that an effective listening curriculum is available. As the listening curriculum is a part of the overall curriculum of a Korean language department, improving the listening curriculum can bring about positive changes in related curricula and will also help to form a systematic Korean language curriculum. Accordingly, this study aims to investigate the current status of elementary and intermediate listening curricula and to uncover the perceptions and needs of learners and teachers in listening courses, thereby facilitating the design and improvement of their listening curriculum they use. For this purpose, this article initially defines the characteristics of the listening curricula used in Korean language departments and introduces the research method. Next, the results of a seven-part survey containing questions about subject organization, teaching and learning goals, teaching and learning contents, textbooks, teachers, evaluations, and difficulties in teaching and learning are analyzed. Based on this, several suggestions are presented on how to design and improve the elementary and intermediate listening curricula used by Korean language departments at Chinese universities. (Seoul National University)
Learning to Fly with Deep Reinforcement Learning

In this work, we applied deep reinforcement learning for a simulated quadrotor to fly autonomously from a fixed start point to an arbitrary set of goals. The agent was trained using only two random goal positions and the generalization was tested using more than two goal points. A new OpenAI-gym environment was created which was based on Microsoft Airsim and Unity game engine, this environment will serve as a benchmark for this task. The results of this procedure showed that the agent could perform even in more than-two-random goals tasks but at a lower success rate, while it performed poorly on goals that were far away from targets in the training data.
The Role of the Family in Assistance of Learning Process Reading 'Children's Poetry' Mi Ihsaniyah Tegal Students in the Pandemic COVID-19

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Real-World Presence in the Era of Ubiquitous Computing

In the era of everyday ubiquitous computing, all of us have at least once lost track of time, space or our social environment when using our smartphone. In such situations, we might not feel present in the here and now in the physical real world. Presence has been a long term topic of interest in virtual reality research as the feeling of being there, in a technology-mediated virtual world. However, we have yet to understand our feeling of being here and now in the physical real world under the wide availability and easy accessibility of smartphones. In my thesis, I want to examine the concept of real-world presence from a theoretical, users’ and technology development perspective. I aim at understanding current and developing new mobile solutions, with the goal of supporting our presence in the physical real world.
Meta-Subject Training As a Condition for Forming the Readiness of Future Professionals for Professional Activities

In the article as a research task was defined an attempt to evaluate meta-subject training as a condition for the formation of the readiness of the future specialist for professional activities. There is a stage in the study of the meta-subject approach to learning. Much attention has been paid to establishing the conceptual certainty of meta-subjectivity and meta-subject approach. The main content of the work is devoted to the disclosure of the meaningful part of meta-subject training, its organizational foundations. A series of meta-effects as an integrated form of knowledge and action with them is substantiated. The article provides meta-subject competencies and features of the organization of meta-subject activities.
A Method and System for Program Management of Security Chip Production

This paper analyzes the current situation and shortcomings of traditional security chip production program management, then proposes a management approach of a chip issue program management method and develope a management system based on Webservice technology. The program management method and system of chip production proposed in this paper simplifies the program management process of chip production and improves the working efficiency of chip production management.
Quality-based Representation for Unconstrained Face Recognition

Significant advances have been achieved in face recognition in the last decade thanks to the development of deep learning methods. However, recognizing faces captured in uncontrolled environments is still a challenging problem for the scientific community. In these scenarios, the performance of most of existing deep learning based methods abruptly falls, due to the bad quality of the face images. In this work, we propose to use an activation map to represent the quality information in a face image. Different face regions are analyzed to determine their quality and then only those regions with good quality are used to perform the recognition using a given deep face model. For experimental evaluation, in order to simulate unconstrained environments, three challenging databases, with different variations in appearance, were selected: the Labeled Faces in the Wild Database, the Celebrities in Frontal-Profile in the Wild Database, and the AR Database. Three deep face models were used to evaluate the proposal on these databases and in all cases, the use of the proposed activation map allows the improvement of the recognition rates obtained by the original models in a range from 0.3 up to 31%. The obtained results experimentally demonstrated that the proposal is able to select those face areas with higher discriminative power and enough identifying information, while ignores the ones with spurious information.
The Method of Forming a Panorama with Increased Resolution in the Spatial Direction

The creation of one panorama from several images is today a popular user application in modern multimedia devices. Now, existing stitching methods strive to ensure maximum visual quality, while the algorithms allow the appearance of unreliable objects, such as additional lines and distorted proportions, which entails the loss of information. The task of the proposed method is to maximize the number of pixels in the original images. This gives advantages in the subsequent analysis of the merged images in areas such as security, pattern recognition, etc. In this paper, we present a method for forming a panorama with an increased resolution in the spatial direction with the possibility of a wide basis for the location of electrooptical converters for video analysis from moving platforms. Our method connects the pixel coordinates of the two images using graphs to optimally find the seam boundaries. An algorithm has also been developed to calculate a consistent alignment of overlapping frames.
Role of polycultural space in formation of ethnopedagogical competence of future teachers of primary education

This article is devoted to the actual problem of multicultural education, reveals its essence and justifies its importance in the development of ethno-pedagogical competencies of future primary school teachers. The ethnopedagogical competence developed in the multicultural educational space will allow primary school teachers to prepare the younger generation for intercultural interaction in the conditions of complex social relations of the modern world in their further pedagogical practice.
Security Analysis of Information Technology

As we know security is always important challenges in technology. In cyberworld is full of lack of security. We need to protect our cyber worlds in real time. This article has described basics of security, security impact and provide and an overview on security areas and their vulnerable places in our cyber world such as Computer security, Network Security, and Cyber security. These security features must not be limited to protecting confidential information, but also needs to address safety critical systems such as brake, accelerator or steering etc.
An Image Retrieval System Based on Automatic Image Annotation to Facilitate Digital Humanities Research

In addition to digital text, the digital image has become an important data type used to support digital humanities research. An image retrieval system based on automatic image annotation (IRS-AIA) is therefore developed based on Mask R-CNN to enhance image retrieval performance through enriching the metadata of an image by the recognized object labels for digital humanities research. In contrast, the metadata of an image manually determined by a human is called subjective labels. To verify whether IRS-AIA can assist digital humanists in the effectiveness of image interpretation, counterbalanced design in quasi-experimental research was applied. A total of 14 users were divided into two groups to complete the designed image interpreting tasks by operating IRS-AIA and general image retrieval tool (GIRT) through crossing the use of two systems. Analytical results show that IRS-AIA could effectively assist users in interpreting image situations. The use of IRS-AIA could acquire better image retrieval precision and recall rates than GIRT. However, the image interpretation effectiveness between the use of GIRT and IRS-AIA does not achieve a statistically significant difference. Subject label and object label present distinct purposes in image retrieval and both labels could satisfy users' needs for different information retrieval ways.