Reviewerly/exHarmony huggingface dataset:
Electronic document authenticity verification of diploma and transcript using smart contract on Ethereum blockchain

Ethereum is one of the oldest examples of blockchain technology provides a system that converts centralized storage to distributed and records transactions by way of decentralized and not by a centralized system and can be verified by each node, therefore it is suitable for storing fingerprints from official diploma documents and transcripts that are published. Smart contract is needed for making contract transactions to Ethereum with programming code, so contracts such as diplomas and transcripts uploaded on the Ethereum blockchain can distribute and produce diploma validation and the authenticity of transcripts with transaction hash, consensus, and comply with ERC-721 token standardization. The results showed that a sample of 5 electronic documents in pdf format with a transaction speed of 1 second on each file that were published and secured with Ethereum blockchain technology can be easily verified for authenticity, the system proposed and developed by us takes in consideration invalid and failure cases by giving the necessary feedback to the user.
SIMULATION OF MOBILE ROBOT CLAMPING MAGNETS BY CIRCLE-FIELD METHOD

Abstract There are a list of complicated tasks need to be solved to increase the working productivity and decrease working cost in modern shipbuilding and ship repair. Good results in solving those problems are shown whether automation with varied robots implementation. The mobile robots able to move and perform given technological operations on different-spaced ferromagnetic surfaces are equipped with own control systems, movers and clamping devices. Usually, reliability and safety of such robots are in direct dependence on designers’ adequate representation of their behavior that is described by mathematical description of separate parts or the robot in the whole to correct control problem solving. The article amply considers the process of the climbing mobile robot clamping electromagnet simulation model building using the improved circle-field method on the example of BR-65/30 clamping electromagnet. The model is built on the basis of interpolated dependences of flux coupling and electromagnetic force on the magnetomotive force and the value of the air gap obtained by numerical calculations of the magnetic field. The dynamic properties of the electromagnet are investigated and a family of its traction characteristics is obtained by the developed model, which can be used for automatic control of the robot clamping device. References 25, figures 5, tables 3.
Experimental analysis of the electromagnetic instruction skip fault model and consequences for software countermeasures

Microcontrollers storing valuable data or using security functions are vulnerable to fault injection attacks. Among the various types of faults, instruction skips induced at runtime proved to be effective against identification routines or encryption algorithms. Until recently, most research works assessed a fault model that consists in a single instruction skip, i.e. the ability to prevent one chosen instruction in a program from being executed. We question this fault model for EM fault injection on experimental basis and report the possibility to induce several consecutive instructions skips. Such an extended fault model proved to be effective against a duplication-based software countermeasure as our experiments revealed.
ECLB: Edge-Computing-Based Lightweight Blockchain Framework for Mobile Systems

The blockchain technology achieves security by sacrificing prohibitive storage and computation resources. However, in mobile systems, the mobile devices usually offer weak computation and storage resources. It prohibits the wide application of the blockchain technology. Edge computing appears with strong resources and inherent decentralization, which can provide a natural solution to overcoming the resource-insufficiency problem. However, applying edge computing directly can only relieve some storage and computation pressure. There are some other open problems, such as improving confirmation latency, throughput, and regulation. To this end, we propose an edge-computing-based lightweight blockchain framework (ECLB) for mobile systems. This paper introduces a novel set of ledger structures and designs a transaction consensus protocol to achieve superior performance. Moreover, considering the permissioned blockchain setting, we specifically utilize some cryptographic methods to design a pluggable transaction regulation module. Finally, our security analysis and performance evaluation show that ECLB can retain the security of Bitcoin-like blockchain and better performance of ledger storage cost in mobile devices, block mining computation cost, throughput, transaction confirmation latency, and transaction regulation cost.
Robust and Secure Watermarking for Propagation of Digital Multimedia by Paillier Homomorphic Cryptosystem With Arnold Transformation

Sharing of digital media over internet is becoming easier due to content authentication and security provided by digital watermarking. It also locates application in other fields like copyright protection, tele-medicine, military, tamper detection, and many more. This paper represents the robust watermarking approach using Paillier homomorphic cryptosystem with Arnold transformation. In this, the watermarking system is primarily prepared at an encrypted DWT-DCT domain. Cryptosystem is exploited at this time to encrypt the original media. For more security of multimedia content, the watermark image is scrambled through Arnold scrambling technique. The embedding process is done here to produce the encrypted watermark image followed by the encryption process. At recovery phase, decryption of encrypted watermark image to get the original and watermark image is done. The experimental result has shown that watermarking method is more robust and offers the security of digital media.
HSB-SPAM: An Efficient Image Filtering Detection Technique

Median filtering is being used extensively for image enhancement and anti-forensics. It is also being used to disguise the traces of image processing operations such as JPEG compression and image resampling when utilized in image de-noising and smoothing tool. In this paper, a robust image forensic technique namely HSB-SPAM is proposed to assist in median filtering detection. The proposed technique considers the higher significant bit-plane (HSB) of the image to highlight the statistical changes efficiently. Further, multiple difference arrays along with the first order pixel difference is used to separate the pixel difference, and Laplacian pixel difference is applied to extract a robust feature set. To compact the size of feature vectors, the operation of thresholding on the difference arrays is also utilized. As a result, the proposed detector is able to detect median, mean and Gaussian filtering operations with higher accuracy than the existing detectors. In the experimental results, the performance of the proposed detector is validated on the small size and post JPEG compressed images, where it is shown that the proposed method outperforms the state of art detectors in the most of the cases.
Online recognition of unsegmented actions with hierarchical SOM architecture

Abstract Automatic recognition of an online series of unsegmented actions requires a method for segmentation that determines when an action starts and when it ends. In this paper, a novel approach for recognizing unsegmented actions in online test experiments is proposed. The method uses self-organizing neural networks to build a three-layer cognitive architecture. The unique features of an action sequence are represented as a series of elicited key activations by the first-layer self-organizing map. An average length of a key activation vector is calculated for all action sequences in a training set and adjusted in learning trials to generate input patterns to the second-layer self-organizing map. The pattern vectors are clustered in the second layer, and the clusters are then labeled by an action identity in the third layer neural network. The experiment results show that although the performance drops slightly in online experiments compared to the offline tests, the ability of the proposed architecture to deal with the unsegmented action sequences as well as the online performance makes the system more plausible and practical in real-case scenarios.
Impact of Deep Learning on Localizing and Recognizing Handwritten Text in Lecture Videos

Now-a-days, the video recording technologies have turned out to be more and more forceful and easier to utilize.Therefore, numerous universities are recording and publishing their lectures online in order to make them reachable for learners or students.These lecture videos encapsulate the handwritten text written either on a paper or blackboard or on a tablet using a stylus.On the other hand, this mechanism of recording the lecture videos consumes huge quantity of multimedia data in a faster manner.Thus, handwritten text recognition on the lecture video portals has turned out to be an incredibly significant and demanding task.Thus, this paper intends to develop a novel handwritten text detection and recognition approach on the video lecture dataset by following four major phases, viz.(a) Text Localization, (b) Segmentation (c) Pre-processing and (d) Recognition.The text localization in the lecture video frames is the initial phase and here the arbitrarily oriented text on video frames is localized using the Modified Region Growing (MRG) algorithm.Then, the localized words are subjected to segmentation via the K-means clustering, in which the words from the detected text regions are segmented out.Subsequently, the segmented words are pre-processed to avoid the blurriness artifacts as well.Finally, the pre-processed words are recognized using the Deep Convolutional Neural Network (DCNN).The performance of the proposed model is analyzed in terms of the performance measures like accuracy, precision, sensitivity and specificity to exhibit the supremacy of the text detection and recognition in lecture video.Experimental results reveal that at Learning Percentage of 70, the presented work has the highest accuracy of 89.3% for 500 count of frames.
"I’m Controlling and Composing": The Role of Metacognition in The Incredible Machine

The Role of Metacognition in The Incredible Machine 2 Up, Up, and Away: IntroductionThe mouse sets the bowling ball in motion, which falls and squeezes the bellows, which sends out a puff of air, which sends the balloon into the gears that are connect by a belt to another mouse's exercise wheel. 1 The balloon pops.Having learned how this routine functions, I then move my mouse to connect the rest of the on-screen mice so that the pulleys of all of the caged mice spin with their wheels to finish the puzzle in time allowing me to move to the next level.Eventually, I will be able to make my own versions of Rube Goldberg machines turned into puzzles based on what I have seen and learned in playing through the eighty challenges provided for Mort the mouse, Bob the fish, and me.Although it is more than twenty-five years old, by teaching about games, learning through games, and learning itself, The Incredible Machine (Dynamix, 1992) continues to defy several key deterministic viewpoints about video games.Said another way, The Incredible Machine (TIM) anticipates Douglas Hacker's defining study of metacognition-i.e., "knowledge of one's own knowledge processes"in games and simulations. 2 As a game about learning, TIM resists the prevailing scholarly notion that pleasurable games must follow the cultural imperative for
Utilization of <i>K</i>-nearest neighbor algorithm for classification of white blood cells in AML M4, M5, and M7

Abstract Acute myeloid leukemia (AML) M4, M5, and M7 are subtypes of leukemia derived from myeloid cell derivatives that influences the results of the identification of AMLs, which includes myeloblast, monoblast, and megakaryoblast. Furthermore, they are divided into more specific types, including myeloblasts, promyelocytes, monoblasts, promonocytes, monocytes, and megakaryoblasts, which must be clearly identified in order to further calculate the ratio value in the blood. Therefore, this research aims to classify these cell types using the K -nearest neighbor (KNN) algorithm. Three distance metrics are tested, namely, Euclidean, Chebychev, and Minkowski, and both the weighted and unweighted were tested. The features used as parameters are area, nucleus ratio, circularity, perimeter, mean, and standard deviation, and about 1,450 objects are used as training and testing data. In addition, to ensure that the classification is not overfitting, K- fold cross validation was conducted. The results show that the unweighted Minkowski distance acquired about 240 of 290 objects at K = 19, which is the best. Therefore, the unweighted Minkowski distance is selected for further analysis. The accuracy, recall, and precision values of KNN with unweighted Minkowski distance obtained from fivefold cross validation are 80.552, 44.145, and 42.592%, respectively.
Joint image encryption and compression schemes based on hexa-coding

This research proposes a new image compression and encryption method depend on a modified JPEG technique combined with the Hexa-Coding algorithm.The compression algorithm starts by dividing an image into 8x8 blocks, then DCT (Discrete Cosine Transform) is applied to all blocks independently followed by uniform quantization.Additionally, the size of blocks is reduced by eliminating insignificant coefficients, and then Arithmetic coding is applied to compress residual coefficients.Finally, Hexa-encoding is applied to the compressed data to further reduce compression size as well as provide encryption.The encryption is accomplished based on five different random keys.The decompression uses a searching method called FMSA (Fast Matching Search Algorithm) which is used for decoding the previously compressed data, followed by Arithmetic decoding) to retrieve residual coefficients.These residuals are padded with zeros to rebuild the original 8x8 blocks.Finally, inverse DCT is applied to reconstruct approximately the original image.The experimental results showed that our proposed image compression and decompression has achieved up to 99% compression ratio while maintaining high visual image quality compared with the JPEG technique.
Improving Explainable Recommendations by Deep Review-Based Explanations

Many e-commerce sites encourage their users to write product reviews, in the knowledge that they exert a considerable influence on users' decision-making processes.These snippets of real-world experience provide an essential source of data for interpretable recommendations.However, current methods relying on user-generated content to make recommendations can run into problems because of well-known issues with reviews, such as noise, sparsity and irrelevant content.On the other hand, recent advances in text generation methods demonstrate significant text quality improvements and show promise in their ability to address these problems.In this paper, we develop two character-level deep neural network-based personalised review generation models, and improve recommendation accuracy by generating high-quality text which meets the input criteria of text-aware recommender systems.To make fair comparisons, we train review-aware recommender systems by human written reviews and attain advanced recommendations by feeding generated reviews at the inference step.Our experiments are conducted on four large review datasets from multiple domains.We leverage our methods' performance by comparing with non-review based recommender systems and advanced review-aware recommender systems.The results demonstrate that we beat baselines on a range of metrics and obtain state-of-the-art performance on both rating prediction and top-N ranking.Our sparsity experiments validate that our generation models can produce high-quality text to tackle the sparsity problem.We also demonstrate the generation of useful reviews so that we can achieve up to 13.53% RMSE improvements.For explanation evaluation, quantitative analyses reveal good understandable scores for our generated review-based explanations, and qualitative case studies substantiate we can capture critical aspects in generating explanations.
Bibliometric Analysis of the Research on Seamless Learning

Seamless learning has a significance that has been increasing in recent years, and an increasing number of studies on the subject in the literature draws attention. This study aimed to examine the research on seamless learning between 1996 and 2020 with the bibliometric analysis method. The Scopus database was used in the collection of the data. After various screening processes, a total of 389 publications were included in the analysis. Descriptive analysis and bibliometric analysis were used in the analysis of the data. The distribution of publications by years, types of publications, sources, and languages were analyzed in the research. Additionally, visual maps were created with analyses of co-author, co-citation, and co-word. At the end of the study, it was seen that there has been an increase in the number of publications from the past to the present, articles and papers were predominant, and that most of the studies were carried out in English. As a result of bibliometric analysis, it was concluded that the most efficient countries in seamless learning were the United Kingdom, the United States, and Singapore. Also, it has been determined that the National Institute of Education, Center for International Education and Exchange, and Kyushu University institutions are dominant. The most frequently mentioned authors cited in studies in many different fields are M. Sharples, L.-H. Wong, and H. Ogata. According to the co-word analysis, the keywords seamless learning, mobile learning, ubiquitous learning, and mobile-assisted language learning stand out in the field of seamless learning.
SIMRES-TV: NOISE AND RESIDUAL SIMILARITY FOR PARAMETER ESTIMATION IN TOTAL VARIATION

Abstract. Image restoration with regularization models is very popular in the image processing literature. Total variation (TV) is one of the important edge preserving regularization models used, however, to obtain optimal restoration results the regularization parameter needs to be set appropriately. We propose here a new parameter estimation approach for total variation based image restoration. By utilizing known noise levels we compute the regularization parameter by reducing the similarity between residual and noise variances. We use the split Bregman algorithm for the total variation along with this automatic parameter estimation step to obtain a very fast restoration scheme. Experimental results indicate the proposed parameter estimation obtained better denoised images and videos in terms of PSNR and SSIM measures and the computational overload is less compared with other approaches.
Foveated light culling

In this paper, we propose a novel Foveated Light Culling (FLC) method to efficiently approximate global illumination for foveated rendering in virtual reality applications. The key idea is to cull the virtual point lights (VPLs) in accordance with their contribution to the visual perception, which makes the loss of details caused is imperceptible to the human eyes. Based on this idea and the features of human visual system (HVS), we calculate the contribution of each VPL according to samples generated from a perceptual importance map. By estimating the local visibility between VPLs and samples, our method can greatly improve the performance during the process of evaluating contributions. Our method is able to produce high-quality single-bounce diffuse-to-diffuse indirect illumination in the foveal region at real-time frame rate while supporting dynamic scenes and light sources. Furthermore, the experiments show that our approach makes a good balance between foveal quality and render efficiency.
Combination of Natural Language Understanding and Reinforcement Learning for Booking Bot

At present, some popular messaging applications have evolved specifically with bots starting to emerge into development. One of the developments of chatbots is to help humans booking flight with Named Entity Recognition in the text, trace sentences to detect user intentions, and respond even though the context of the conversation domain is limited. This study proposes to conduct analysis and design chatbot interactions using NLU (Natural Language Understanding) with the aim that the bot understands what is meant by the user and provides the best and right response. Classification using Support Vector Machine (SVM) method with (erm Frequency-Inverse Document Frequency (TF-IDF) feature extraction is suitable combination methods that produce the highest accuracy value up to 97.5%. Conversation dialogue on chatbots developed using NLU which consists of NER and intent classification then dialog manager using Reinforcement Learning could make a low cost for computing in chatbots.
Feature-Enhanced RefineDet: Fast Detection of Small Objects

Object detection has been studied for many years. The convolutional neural network has made great progress in the accuracy and speed of object detection. However, due to the low resolution of small objects and the representation of fuzzy features, one of the challenges now is how to effectively detect small objects in images. Existing target detectors for small objects: one is to use high-resolution images as input, the other is to increase the depth of the CNN network, but these two methods will undoubtedly increase the cost of calculation and time-consuming. In this paper, based on the RefineDet network framework, we propose our network structure RF2Det by introducing Receptive Field Block to solve the problem of small object detection, so as to achieve the balance of speed and accuracy. At the same time, we propose a Medium-level Feature Pyramid Networks, which combines appropriate high-level context features with low-level features, so that the network can use the features of both the low-level and the high-level for multi-scale target detection, and the accuracy of the small target detection task based on the low-level features is improved. Extensive experiments on the MS COCO dataset demonstrate that compared to other most advanced methods, our proposed method shows significant performance improvement in the detection of small objects.
Exploring the Role of Artifacts in Collective Dance Re-staging

Preparing a new dance performance involves more than learning individual steps. We are interested in understanding how dancers collaborate as they rehearse a new dance piece, with a particular emphasis on how they use physical and digital artifacts to support this process. We conducted a 12-month longitudinal observational study with a dance company that re-staged a dance piece, taken from the contemporary repertoire and unknown to the dancers. The study focused on the role that artifacts play in shaping the learning of a dance piece. We showed how dancers produced an ecology of artifacts with the aim of analyzing the choreographic ideas behind the dance and sharing them with other learners. We showed that sharing these artifacts was challenging because they are idiosyncratic and embody their creator perspective and vocabulary. We then illustrated how dancers overcome this challenge by compiling artifacts and distributing the learning task among the group in order to create a common knowledge of the piece which improves the learning process. We conclude with design opportunities for technologies supporting long-term dance learning processes.