Reviewerly/exHarmony huggingface dataset:
Shaping the Future of Teaching Profession

In the context of education debates, systemic and program solutions, the teacher undoubtedly plays a crucial role.It is the teacher who is assigned a strategic role in the functioning and effectiveness of the education system.This is confirmed by various global and our domestic educational reports in Poland, where it is emphasized that the quality of the school and the pupil's educational results largely depend on the quality of the teacher's work, while the quality of the teacher's work depends on the quality of their education (see Madalińska-Michalak, 2017).Without a doubt, the strength of current research is that it notices something that has long been known in pedagogical discourse and has often been ignored in practice: teachers play a vital role in the educational process to which pupils are subject (Darling-Hammond, 2017).This role is particularly vital when we perceive it through the lens of learning and development at school.In this paper I put forward the thesis that reinforcing the socio-professional position of a teacher -a teacher perceived as an actor on the educational stage
Lossless Text Image Compression using Two Dimensional Run Length Encoding

Text images are used in many types of conventional data communication where texts are not directly represented by digital character such as ASCII but represented by an image, for instance facsimile file or scanned documents. We propose a combination of Run Length Encoding (RLE) and Huffman coding for two dimensional binary image compression namely 2DRLE. Firstly, each row in an image is read sequentially. Each consecutive recurring row is kept once and the number of occurrences is stored. Secondly, the same procedure is performed column-wise to the image produced by the first stage to obtain an image without consecutive recurring row and column. The image from the last stage is then compressed using Huffman coding. The experiment shows that the 2DRLE achieves a higher compression ratio than conventional Huffman coding for image by achieving more than 8:1 of compression ratio without any distortion.
Electric Wheelchair Control Mechanism Using Eye-mark Key Point Detection

People with deficiency hands and feet, especially people with multiple disabilities has difficulty for navigation.Therefore, this research proposed an approach to help them using an electric wheelchair and control it using their eyeball movements.However, eyeball movement detection is still a problem due to the lack of standardized methods such as low accuracy for several gazes especially for downward and forward movements that caused by failure tracking mechanisms.Failure tracking mechanism mostly caused by failure pupil detection and tracking processes.According to the conducted research, there is a relation between eyeball movement and eyelid movement.Due to the relation, it can be concluded that the movement of the eyeball can be detected by utilize the eyelid movement without using the tracking mechanism.Hence, in this research, we propose an approach to detect the five movements of the eyeball using Eye-mark based on contour and edge detection.Our proposed method produces a brisk result with 91,2% of accuracy and better computational time compared with other marking methods.Accordingly, it is interesting on how to relate between eyeball movements and electric wheelchair navigation mechanism.
Center Manifold Analysis of Plateau Phenomena Caused by Degeneration of Three-Layer Perceptron

A hierarchical neural network usually has many singular regions in the parameter space due to the degeneration of hidden units. Here, we focus on a three-layer perceptron, which has one-dimensional singular regions comprising both attractive and repulsive parts. Such a singular region is often called a Milnor-like attractor. It is empirically known that in the vicinity of a Milnor-like attractor, several parameters converge much faster than the rest and that the dynamics can be reduced to smaller-dimensional ones. Here we give a rigorous proof for this phenomenon based on a center manifold theory. As an application, we analyze the reduced dynamics near the Milnor-like attractor and study the stochastic effects of the online learning.
Affective State Based Anomaly Detection in Crowd

Abstract To distinguish individuals with dangerous abnormal behaviours from the crowd, human characteristics (e.g., speed and direction of motion, interaction with other people), crowd characteristics (such as flow and density), space available to individuals, etc. must be considered. The paper proposes an approach that considers individual and crowd metrics to determine anomaly. An individual’s abnormal behaviour alone cannot indicate behaviour, which can be threatening toward other individuals, as this behaviour can also be triggered by positive emotions or events. To avoid individuals whose abnormal behaviour is potentially unrelated to aggression and is not environmentally dangerous, it is suggested to use emotional state of individuals. The aim of the proposed approach is to automate video surveillance systems by enabling them to automatically detect potentially dangerous situations.
Facial Image Completion Using Bi-Directional Pixel LSTM

Structural features of facial images directly affect the performance of the image completion model. However, most existing work does not make full use of spatial dependence to extract features, and cause the semantics and structure of completion being inconsistent with the context. This paper addresses this issue using a bi-directional pixel long-short time memory (LSTM) network. Specifically, it consists of two LSTM subnetworks and can simultaneously scan the input image row by row or column by column, thereby the extracted features contain the dependence information among rows or among columns. Through a fusion operation of these features, the complete spatial dependence information is included. In addition, the parameters of the decoder and discriminator are automatically adjusted to accommodate the proposed bi-directional pixel LSTM. Finally, compared with existing state-of-the-art methods, experimental results show our superior performance.
Into the Deep Web: Understanding E-commerce Fraud from Autonomous Chat with Cybercriminals

E-commerce miscreants heavily rely on instant messaging (IM) to promote their illicit businesses and coordinate their operations.The threat intelligence provided by IM communication, therefore, becomes invaluable for understanding and mitigating the threats of e-commerce frauds.However, such information is hard to obtain since it is usually shared only through one-on-one conversations with the criminals.In this paper, we present the first chatbot, called Aubrey, to actively collect such intelligence through autonomous chats with real-world e-commerce miscreants.Our approach leverages the questiondriven conversation pattern of small-time workers, who seek jobs and/or attack resources from e-commerce fraudsters, to model the interaction process as a finite state machine, thereby enabling an autonomous conversation.Aubrey successfully chatted with 470 real-world e-commerce miscreants and gathered a large amount of fraud-related artifacts, including previously-unknown SIM gateways, account trading websites, and attack toolkits, etc. Further, the conversations revealed the supply chain of ecommerce fraudulent activities on the deep web and the complicated relations (e.g., complicity and reselling) among miscreants.
Editable video creation based on embedded simulation engine and GAN

With the progress of artificial intelligence, the embedded generation of images and videos by AI has become a hot topic. This technology is tried to be applied in real-time processing of monitors, cameras and smart phones. Using GAN embedder networks, some research attempts to transfer the style, action and content of one video to another target video. Unfortunately, this generation is often difficult to control. We have created an Engine-GAN (E-GAN) model process, which effectively combines engine method with embedder GAN content to create "real" videos that can be edited in real time. This makes the image and content generated by AI directly controlled. We have made progress in E-GAN architecture, E-GAN workflow, tag generation and entity stylization. We use cuckoo algorithm to optimize the migration target and improve the migration efficiency.
An Image Watermarking Method Using Discrete Tchebichef Transform and Singular Value Decomposition Based on Chaos Embedding

A good watermarking method should be robust against attacks but have good visual quality.This research proposes a combination of two transformations i.e.Tchebichef and singular value decomposition (SVD).Arnold's algorithm is also integrated to improve the security of the watermark and visual quality of the watermarked image.Arnold's transformation was carried out on the cover image with the aim to spread watermark.Furthermore, the cover image is divided into small blocks and transformed using Tchebichef.The lowest coefficient of each Tchebichef block is collected in a matrix for decomposition with SVD, then the watermark is embedded in the singular matrix.Based on the results of testing the proposed method can have excellent visual quality with PSNR values ranging from 45dB and SSIM averaging 0.998.The proposed method is also robust to various attacks such as filtering, noise addition, geometry, and signal processing.
Quantum Dual Signature with Coherent States Based on Chained Phase-Controlled Operations

A novel encryption algorithm called the chained phase-controlled operation (CPCO) is presented in this paper, inspired by CNOT operation, which indicates a stronger correlation among message states and each message state depending on not only its corresponding key but also other message states and their associated keys. Thus, it can prevent forgery effectively. According to the encryption algorithm CPCO and the classical dual signature protocols, a quantum dual signature scheme based on coherent states is proposed in this paper. It involves three participants, the customer Alice, the merchant Bob and the bank Trent. Alice expects to send her order message and payment message to Bob and Trent, respectively. It is required that the two messages must be linked to guarantee the payment is paid for the corresponding order. Thus, Alice can generate a quantum dual signature to achieve the goal. In detail, Alice firstly signs her two messages with the shared secret key. Then She connects the two signatures into a quantum dual signature. Finally, Bob and Trent severally verify the signatures of the order message and the payment message. Security analysis shows that our scheme can ensure its security against forgery, repudiation and denial. In addition, simulation experiments based on the Strawberry Fields platform are performed to valid the feasibility of CPCO. Experimental results demonstrate that CPCO is viable and the expected coherent states can be acquired with high fidelity, which indicates that the encryption algorithm of the scheme can be implemented on quantum devices effectively.
Delay-Aware Adaptive Wireless Video Streaming in Edge Computing Assisted Ultra-Dense Networks

Server and Network Assisted Dynamic adaptive streaming over HTTP (SAND) is a promising technology to cope with the dramatic increase in video streaming traffic. The new emerging Mobile Edge Computing (MEC) paradigm may further facilitate bitrate adaptation and video transcoding in a SAND system with the help of local edge servers. A critical issue in MEC-SAND framework is to guarantee Quality of Experience (QoE) for clients while achieving efficient utilization of edging network resources. In this paper, we aim to develop an adaptive video delivery scheme to minimize the delay in MEC assisted ultra-dense networks. In our scheme, each client is mapped to a server that better fits its requirements and transmission condition, and a bitrate selection mechanism is exploited to decide the best video version for the client. Besides, time-consuming transcoding tasks are carefully scheduled considering edge computing capacity. We formulate a Mix-Integer Non-Linear Programming (MINLP) problem to jointly determine cell association, bitrate adaption, and computing resource allocation. We exploit the generalized Benders decomposition method to reduce the solving complexity of the formulated problem. Numerical results validate the effectiveness and efficiency of the proposed scheme.
Author Correction: Chip-to-chip quantum teleportation and multi-photon entanglement in silicon

An amendment to this paper has been published and can be accessed via a link at the top of the paper.
Syllable-Dependent Discriminative Learning for Small Footprint Text-Dependent Speaker Verification

This study proposes a novel scheme of syllable-dependent discriminative speaker embedding learning for small footprint text-dependent speaker verification systems. To suppress undesired syllable variation and enhance the power of discrimination inherited in the frame-level features, we design a novel syllable-dependent clustering loss to optimize the network. Specifically, this loss function utilizes syllable labels as auxiliary supervision information to explicitly maximize inter-syllable divisibility and intra-syllable compactness between the learned frame-level features. Successively, we propose two syllable-dependent pooling mechanisms to aggregate the frame-level features to several syllable-level features by averaging those features corresponding to each syllable. The utterance-level speaker embeddings with powerful discrimination are then obtained by concatenating the syllable-level features. Experimental results on Tencent voice wake-up dataset show that our proposed scheme can accelerate the network convergence and achieve significant performance improvement against the state-of-the-art methods.
An Accurate Fourier-Based Method for Three-Dimensional Reconstruction of Transparent Surfaces in the Shape-From-Polarization Method

The Fourier-based gradient field integration method can efficiently reconstruct transparent surfaces from the measured gradient data in the Shape-from-polarization method. However, for the differentiation operator having large truncation errors and lacking constraints on adjacent heights, it will increase the reconstructing error of the Fourier-based integration method. This paper presents an accurate Fourier-based integration approach to improve the reconstruction accuracy, in which a new differentiation operator is derived by limiting the truncation error and increasing heights and slopes in the operator. In order to modify the data obtained by the new operator to meet the requirements of both periodicity and size for the use of the discrete Fourier transform, we propose a method to extend the raw gradient data by first performing antisymmetric extension and then performing periodic extension. A series of simulations and experiments have been developed to verify the performance of the proposed method. By comparing the approach of this paper with other Fourier-based approaches, including Frankot-Chellappa, Southwell-FT and Simpson-FT, both of the simulation and experiment results show that the proposed Fourier-based integration method performs a higher accuracy than other approaches. In the reconstruction experiment, the reconstruction error can be reduced from 0.065 ~ 0.081 mm to 0.020 mm for the spherical surface, and from 0.060 ~ 0.12 mm to 0.016 mm for the free-form surface.
The Use of Augmented Reality in Education: Development and Use of Application for the Course "Physics - Explore and Discover" in the 5th Class of the Primary School

The Use of Augmented Reality in Education: Development and Use of Application for the Course "Physics - Explore and Discover" in the 5th Class of the Primary School Panagiotis Tsiavos, Alivisos Sofos Abstract This research paper initially attempts to give a brief bibliographic overview of the concept of augmented reality, its applications and corresponding outcomes in the field of education, since enhancing the real-world environment with digital information is considered to offer additional opportunities for teaching and learning. Subsequently, it is designed, developed and implemented in the 5th class of Primary School, an original Augmented Reality (AR) educational textbook application in the "Physics" section of the "Physics - Explore and discover" course to explore the effect of Physics AR textbooks on learning outcomes and on students' interest and attitudes about the subject. The results of this application show a positive impact on learning outcomes and reinforce students' interest and positive attitude towards the Physics lesson, a desire derived towards the extension this type of application to other knowledge objects. Full Text: PDF DOI: 10.15640/jehd.v8n4a17
Alpha-N: Shortest Path Finder Automated Delivery Robot with Obstacle Detection and Avoiding System

Alpha-N - A self-powered, wheel-driven Automated Delivery Robot (ADR) is presented in this paper. The ADR is capable of navigating autonomously by detecting and avoiding objects or obstacles in its path. It uses a vector map of the path and calculates the shortest path by Grid Count Method (GCM) of Dijkstra’s Algorithm. Landmark determination with Radio Frequency Identification (RFID) tags are placed in the path for identification and verification of source and destination, and also for the re-calibration of the current position. On the other hand, an Object Detection Module (ODM) is built by Faster R-CNN with VGGNet-16 architecture for supporting path planning by detecting and recognizing obstacles. The Path Planning System (PPS) is combined with the output of the GCM, the RFID Reading System (RRS) and also by the binary results of ODM. This PPS requires a minimum speed of 200 RPM and 75 s duration for the robot to successfully relocate its position by reading an RFID tag. In the result analysis phase, the ODM exhibits an accuracy of 83.75%, RRS shows 92.3% accuracy and the PPS maintains an accuracy of 85.3%. Stacking all these 3 modules, the ADR is built, tested and validated which shows significant improvement in terms of performance and usability comparing with other service robots.
An Energy-Efficient Task Scheduling Mechanism with Switching On/Sleep Mode of Servers in Virtualized Cloud Data Centers

In recent years, the energy consumption of cloud data centers has continued to increase. A large number of servers run at a low utilization rate, which results in a great waste of power. To save more energy in a cloud data center, we propose an energy-efficient task-scheduling mechanism with switching on/sleep mode of servers in the virtualized cloud data center. The key idea is that when the number of idle VMs reaches a specified threshold, the server with the most idle VMs will be switched to sleep mode after migrating all the running tasks to other servers. From the perspective of the total number of tasks and the number of servers in sleep mode in the system, we establish a two-dimensional Markov chain to analyse the proposed energy-efficient mechanism. By using the method of the matrix-geometric solution, we mathematically estimate the energy consumption and the response performance. Both numerical and simulated experiments show that our proposed energy-efficient mechanism can effectively reduce the energy consumption and guarantee the response performance. Finally, by constructing a cost function, the number of VMs hosted on each server is optimized.
Determining the Probability of Cyberattacks

The use of information is inextricably linked with its security. The presence of vulnerabilities enables a third party to breach the security of information. Threat modeling helps to identify those infrastructures, which would be most likely exposed to cyberattacks. In some cases, however, threat modeling can not be classified as sufficient method of protection. This paper entitled “Determining the probability of cyberattacks” presents an analysis of different techniques with an attempt to identify the most informative parameters and cyberattack prediction markers, which would lay the foundation for the development of cyberattack probability functions. Next, it would be relevant to design such cyberattack probability functions, which would be used upon the initial identification of a cyberattack. The findings of this research could be applied during the future assessment of risk levels of information systems to ensure more effective information security management.
Social Tags of Select Books Written by Mahatma Gandhi a Comparative Study of Library Thing Tags and OCLC Fast Subject Headings

&#x0D; &#x0D; &#x0D; Retrieval to specific information is critical to user satisfaction in today’s semantic web environment. Users use various terms, nomenclatures and words for aiding mnemonic value to identify concepts for future access and sharing with community members. Social tagging has emerged as a popular option for the millennials to address this issue Tagging is a convenient way used by today’s users for naming their web resources in their own terms. In this study a comparative study was carried out between the standardised Online Computer Library Centre (OCLC) Faceted Application of Subject Terminology (FAST) Subject headings and the popular user-generated tags observed on the LibraryThing website for select books written by Mahatma Gandhi, the iconic Father of Modern India, who was also a prolific writer. M K Gandhi wrote many books, letters, newsletters, essays and edited journals on various subjects like health, vegetarianism, diet, religion and social reforms. A close examination of the subject headings and tags given by users to works authored by M K Gandhi reveal that there is a vast amount of difference between the standard terms assigned using OCLC FAST Subject Headings and tags assigned by the users. The study reveals that neither subject headings nor tags are perfect systems by themselves, but they may complement each other in library catalogues. The inclusion of user-generated keywords into catalogues will greatly enhance representation, organisation and retrieval of resources in a library environment.&#x0D; &#x0D; &#x0D;
Pengembangan Media Booklet Berbasis Keanekaragaman Jenis Jamur Makroskopis

This study aims to develop a booklet media based on the diversity of macroscopic mushroom species in Lubuklinggau City. The method used is the Research and Development (R&amp;D) method, with two stages of research namely making media booklets and validating media booklets. Data collection using the JAS method. The validity of the booklet with questionnaires for media experts and material experts. Response readability and practicality using a questionnaire questionnaire lecturers and students, knowing the response to the effectiveness of the booklet by looking at learning outcomes with tests. Data analysis techniques using content analysis Validity Ratio (CVR). The results of stage 1 research found that there were 32 types of macroscopic fungi, from 4 classes, 9 orders, and 16 families. Fungi that can be consumed include Auricularia-auricula-juda, A. cornea, Schizophyllum commune, Lentinus strigosus, L. triginus, Favolus brasiliensis, F. tenuiculus, Pleuretus ostreatus, Agaricus silvaticus, Marasmius oreades, Tremella mesentere, Cook and Cavina tricholoma While the potential as a drug are Cordyceps militaris and Coprinellus disseminatus, and the type of poisonous mushroom is Chlorophyllum molybdites. The results of the analysis and validation of the media booklet of each criterion received a CVR value of 1.00 with valid or proper information. The lecturer and student questionnaire data for the average attractiveness of 3.3 were categorized as attractive, for readability 3.1 was categorized as good, for efficiency 3.5 was categorized very efficient, and for practicality an average of 3.7 was categorized very practical. In conclusion, the booklet that was developed was declared suitable for use, the booklet was categorized as attractive, good legibility, efficient and practical to use.&#x0D; Keywords: booklet, macroscopic mushroom, lubuklinggau, development