Reviewerly/exHarmony huggingface dataset:
The Using of Gaussian Pyramid Decomposition, Compact Watershed Segmentation Masking and DBSCAN in Copy-Move Forgery Detection with SIFT

The case of image manipulation is rising, along with the growth of digital imaging development. One of the many dangerous image manipulation types is a copy-move forgery. In this manipulation, the user can cover some parts in images with another piece in the same picture. SIFT keypoint extraction is one of many methods used by many researchers to detect whether a digital photo is copy-move manipulation result or not. Usually, this keypoint extraction method had combined with other methods for improving its accuracy. This paper explained our experiment with Gaussian Pyramid Decomposition, Compact Watershed segmentation mask, and DBSCAN clustering in copy-move forgery with SIFT. This combination shows the average precisions at more than 78% and can detect in shorter average times.
Experiments with IBM Quantum Devices for Random Number Generation and String Matching

Quantum physics is old and mature. Quantum computing and algorithms arose 3 decades ago. Quantum programming on real Quantum Computational Devices (QCD) is something new. Less than the last five years, researchers and scientists could apply quantum programming to QCD since several vendors like IBM, Rigetti, Microsoft or Google could provide access to real machines. In this paper, we experiment and investigate the accuracy of a 5-qubits QCD applying a string search algorithm. Simulating environment used also in order to evaluate the technique and the results obtained were very promising. Even if the accuracy of a real device was around 48% since the possibilities of the other outcomes were extremely low proved the reliability of the real machine. The number of existing qubits was the main obstacle that limits their processing power. In addition, a random number generator was implemented which overcomes all the disadvantages of pseudo-random generators.
Bagged random causal networks for interventional queries on observational biomedical datasets

Learning causal effects from observational data, e.g. estimating the effect of a treatment on survival by data-mining electronic health records (EHRs), can be biased due to unmeasured confounders, mediators, and colliders. When the causal dependencies among features/covariates are expressed in the form of a directed acyclic graph, using do-calculus it is possible to identify one or more adjustment sets for eliminating the bias on a given causal query under certain assumptions. However, prior knowledge of the causal structure might be only partial; algorithms for causal structure discovery often provide ambiguous solutions, and their computational complexity becomes practically intractable when the feature sets grow large. We hypothesize that the estimation of the true causal effect of a causal query on to an outcome can be approximated as an ensemble of lower complexity estimators, namely bagged random causal networks. A bagged random causal network is an ensemble of subnetworks constructed by sampling the feature subspaces (with the query, the outcome, and a random number of other features), drawing conditional dependencies among the features, and inferring the corresponding adjustment sets. The causal effect can be then estimated by any regression function of the outcome by the query paired with the adjustment sets. Through simulations and a real-world clinical dataset (class III malocclusion data), we show that the bagged estimator is -in most cases- consistent with the true causal effect if the structure is known, has a good variance/bias trade-off when the structure is unknown (estimated using heuristics), has lower computational complexity than learning a full network, and outperforms boosted regression. In conclusion, the bagged random causal network is well-suited to estimate query-target causal effects from observational studies on EHR and other high-dimensional biomedical databases.
Hardware Implementation and optimization Design of Lightweight RECTANGLE Algorithm

With the development of information security, RFID and other fields, lightweight algorithms have been widely used. In order to make the lightweight algorithm applied to more situations, this design optimizes the hardware implementation of the lightweight RECTANGLE algorithm, which mainly includes: solving the Boolean expression of S-box, deriving the inverse key schedule in decryption, designing the cyclic iterative structure. Finally, under UMC55 process, the areas of REC-80 and REC-128 encryption modules are 2152. 8GE and 2916GE, which are 13.16% and 8.38% less than before optimization. And the throughput can reach 980Mbps under 400MHz. The areas of REC-80 and REC-128 encryption/decryption modules are 3336. 8GE and 4866. 8GE under the inverse key schedule mode. And the throughput can reach 820Mbps under 333MHz. Compared with before, these modules have obvious optimization effect, which can achieve higher throughput area ratio. It can be applied to hardware implementation situations with smaller area and higher throughput.
Digital media assignments in undergraduate science education: an evidence-based approach

Digital media assignments empower students to become co-creators of knowledge rather than passive consumers of content. The Internet explosion and the affordability of digital technologies and devices such as smartphones, tablets and action cameras have created opportunities to use digital media in the classroom. This article aims to present an evidence-based approach to help educators to design, implement and evaluate digital media assignments in the classroom. For this purpose, four theoretical models were tested to inform the design of digital media assignments in undergraduate science education. These models helped to identify the student training in digital media needed, develop effective marking rubrics, and inform the design, implementation and evaluation of digital media assessment tasks. Trials were conducted in Spring 2016 (n&nbsp;= 458) and Autumn 2017 (n&nbsp;= 1329), respectively. Data collection used a mixed-methods approach, including a qualitative survey, open-ended questions, group contribution data and marks attained. Data analysis showed positive outcomes of the systematic implementation of digital media assignments. In conclusion, students enjoyed the support they received, being creative, working in groups and learning with digital media. To date, this intervention is one of the most comprehensive and practical approaches to digital media assignments in the classroom, which has been undertaken.
Comparison of Neural Networks for Foreign Exchange Rate Prediction

The paper incorporates a comparison between long short term memory (LSTM), simple recurrent neural networks (SRNN), and gated recurrent unit (GRU) to predict currency exchange rate for 22 countries-currencies against United States Dollar(USD) simultaneously. The models predict foreign currency exchange rates for 30 consecutive days by taking the last 365 days of data as input. The models work with the same number of neural network layers, input, targeted output, optimizer, and learning rate.
Blockchain Based Approach for Managing Medical Practitioner Record: A Secured Design

Proper management of medical practitioner record (MPR) and its accuracy is essential for protecting public health. Medication errors can cause several physical injury and possible death to patients. Most of the medical institutes and organizations maintain such records locally in a centralised manner which can be steal by hackers for creating fake identities. Blockchain ledger has immutability, integrity and security benefits that allows patients to access accurate medical practitioner’s record and to take treatment from genuine practitioner only. Purpose of this study is to design a blockchain based immutable and secured model for MPR. This can be a very useful mechanism to guarantee right record availability at the time of registering medical practitioner for practice in any health organization in order to avoid fake medical Practitioner from playing with the public health. Proposed model enables medical institutions to transfer MPR content on the blockchain network and empowers demanding entities (hospitals, patients, medical colleges) to retrieve it to validate medical practitioner’s record. Unique IDs are given to each MPR by universally unique identifier (UUID) and the hash computation of blocks containing MPR data is done using secure hash algorithm (SHA)-256 algorithm. The developed prototype comprises four modules for different purposes such as MPR transferring, MPR block mining, MPR block verification and addition in blockchain and MPR retrieval. All modules of the created model are tested effectively and few test outcomes are presented. The test results shows that created model is suitable for MPR management.
Reduced-Reference Stereoscopic Image Quality Assessment Based on Entropy of Gradient Primitives

Stereoscopic image quality assessment (SIQA) has always been challenging due to the remarkable distinction between human monocular and binocular vision. This paper proposes a novel gradient-based dictionary learning method for SIQA, which effectively integrates the gradients are sparser than the image itself. Specifically, we first compute the gradient maps of each view image of stereopair by applying contrast sensitivity of human visual system (HVS) and neighborhood gradient information to weight the gradient magnitudes in a locally adaptive manner. Afterwards, the binocular perceptual information of gradient (GBPI) is represented by the distribution statistics of visual primitives in gradient maps of left and right views' images, which are extracted by sparse representation. Furthermore, the entropy of gradient maps of each views' images are utilized to represent monocular cue. Their mutual information is used to represent binocular cue. The difference of the reference and distorted images' GBPIs is taken as quality-ware features. Finally, the kernel ridge regressing (KRR) is utilized to simulate a nonlinear relationship between the quality-ware features and human opinions. The performance of the proposed metric is evaluated over the LIVE 3D phase II asymmetric datasets, and shown to be competitive with the state-of-the-art SIQA algorithms.
On the fly classification of traffic in Anonymous Communication Networks using a Machine Learning approach

Anonymous Communication Networks (ACNs) provide privacy and anonymity to the users of the Internet. Traffic classification in ACNs is an emerging area of research due to its benefits in network management tasks like network security, Quality of Service provisioning, and in Research and Development of ACNs. Out of the well-known traffic classification approaches available, Machine Learning (ML) based approach has proven to be advantageous over the port-based and payload based approach. Using a publicly released Anon17 dataset, this work presents an ML-based traffic classification technique in ACNs. The proposed technique performs on the fly classification, which involves the classification of traffic as early as possible using the first few packets of traffic flow. The proposed on the fly classification technique outperforms the state of the art technique in ACNs with increased classification accuracy, F measure and requires less number of packets in traffic flow to achieve highest possible performance metrics.
Digital engineering school on the way to digital production

Abstract Identifies approaches for improving and enhancing the quality of pre, basic, and additional school’ engineering education according to key paradigm "Industry 4.0" based on the development and wide application of information and communication technology with opportunities in research areas of Industrial Informatics. As an example of implementation, innovative research and educational project of the Digital engineering school is considered, including designed for the practical implementation of continuous engineering education "School-University-Enterprise". The main objectives of the project "Digital engineering school" are formulated and disclosed, the approach to teaching programming in high-level languages using microcontrollers and microprocessor technology is highlighted, practical recommendations are given for its implementation in the structure of school education for both students and teachers.
A novel sensitive chaotic image encryption algorithm based on SHA-3 and steganography

Chaotic systems are used in many image encryptions. This paper based on SHA-3 and steganography proposes a chaotic image encryption algorithm, which is extremely sensitive to plaintext content. The process of the algorithm is related to the hash value of plain image, makes the process extremely sensitive to the pixels of plain image. Different from other algorithms, this algorithm uses steganography to embed the hash value into the ciphertext image. The scheme does not require an extra channel to transmit the hash value, it has no hash transmission cost and transmission leakage risk. Our Experimental and security analysis show that the proposed algorithm has good security performance and can resist various attacks.
Hand Stability Based Features for Touch Behavior Smartphone Authentication

Recently, smartphones evolve, not only used for communication, but also used for more substantial necessity in daily life. Smartphone store more sensitive information such as banking account, or private information about its owner. Due to the needs for safer smartphone security systems, continuous touch behavior authentication is gain significant attention among researchers. However, recent development shown that touch behavior can be compromised by robotic attack. In this paper, we proposed new set of features based on hand stability to authenticate smartphone users. Hand stability are difficult for robotic system to impersonate due to its micro-movement. Our method using hand stability as features for behavioral authentication achieved EER 9.64 % which is potential for future development.
Raspberry PI Based Smart Projector

The growth of the I Projection plays important activities in various sectors like education sectors, conferences, and meetings. Anything that is seen visually makes more impact on the human brain and gets registered easily when compared to something said orally. Hence, the concept of projector can be recycled in our all aspects of needs. In the proposed work, implementation of the idea and show the PDF, Word in addition as Demonstration documents on the projector, and also to share the screen of the laptop computer and projector and also to view on mobile. Continuous monitoring has been possible by the implementation of remote manage control over the documents This is the main usage of the proposed system by the usage of any electronic communication devices like mobile or from a laptop. The paper explains the method of creation of the system and also the working of the scheme.
Computational Efficiency of a Modular Reservoir Network for Image Recognition

Liquid state machine (LSM) is a type of recurrent spiking network with a strong relationship to neurophysiology and has achieved great success in time series processing. However, the computational cost of simulations and complex dynamics with time dependency limit the size and functionality of LSMs. This paper presents a large-scale bioinspired LSM with modular topology. We integrate the findings on the visual cortex that specifically designed input synapses can fit the activation of the real cortex and perform the Hough transform, a feature extraction algorithm used in digital image processing, without additional cost. We experimentally verify that such a combination can significantly improve the network functionality. The network performance is evaluated using the MNIST dataset where the image data are encoded into spiking series by Poisson coding. We show that the proposed structure can not only significantly reduce the computational complexity but also achieve higher performance compared to the structure of previous reported networks of a similar size. We also show that the proposed structure has better robustness against system damage than the small-world and random structures. We believe that the proposed computationally efficient method can greatly contribute to future applications of reservoir computing.
Concept Drift Detection in Phishing Using Autoencoders

When machine learning models are built with non-stationary data their performance will naturally decrease over time due to concept drift, shifts in the underlying distribution of the data. A common solution is to retrain the machine learning model which can be expensive, both in obtaining new labeled data and in compute time. Traditionally many approaches to concept drift detection operate upon streaming data. However drift is also prevalent in semi-stationary data such as web data, social media, and any data set which is generated from human behaviors. Changing web technology causes concept drift in the website data that is used by phishing detection models. In this work, we create “Autoencoder Drift Detection” (ADD) an unsupervised approach for a drift detection mechanism that is suitable for semi-stationary data. We use the reconstruction error of the autoencoder as a proxy to detect concept drift. We use ADD to detect drift in a phishing detection data set which contains drift as it was collected over one year. We also show that ADD is competitive within ±24% with popular streaming drift detection algorithms on benchmark drift datasets. The average accuracy on the phishing data set is .473 without drift detection and using ADD is increased to .648.
Adaptive just-noticeable difference profile for image hashing

A novel algorithm to extract image features related to human perception of colors is presented in this paper. The proposed profile is based on the generation of a lookup table with thresholds for just-noticeable color difference against all possible colors from RGB color gamut. The profile is further used to reduce the computational cost of image hashing. The host color image is scanned to generate the maximum magnitude difference profile. The lookup table is then used for thresholding of magnitude differences to generate the binary adaptive just-noticeable difference profile. The generated profile can be used for image watermarking, hashing and similar image processing applications. Performance of the proposed profile has been tested for image hashing. Average similarity of 0.97 for 66 tampered versions of 42 host images and discrimination of 0.138 among 79,800 different image hash pairs have been obtained while area under receiver operating characteristic curve is 0.9980.
Domain-Slot Relationship Modeling Using a Pre-Trained Language Encoder for Multi-Domain Dialogue State Tracking

Dialogue state tracking for multi-domain dialogues is challenging because the model should be able to track dialogue states across multiple domains and slots. As using pre-trained language models is the de facto standard for natural language processing tasks, many recent studies use them to encode the dialogue context for predicting the dialogue states. Model architectures that have certain inductive biases for modeling the relationship among different domain-slot pairs are also emerging. Our work is based on these research approaches on multi-domain dialogue state tracking. We propose a model architecture that effectively models the relationship among domain-slot pairs using a pre-trained language encoder. Inspired by the way the special [CLS] token in BERT is used to aggregate the information of the whole sequence, we use multiple special tokens for each domain-slot pair that encodes information corresponding to its domain and slot. The special tokens are run together with the dialogue context through the pre-trained language encoder, which effectively models the relationship among different domain-slot pairs. Our experimental results on the datasets MultiWOZ-2.0 and MultiWOZ-2.1 show that our model outperforms other models with the same setting. Our ablation studies incorporate three main parts. The first component shows the effectiveness of our approach exploiting the relationship modeling. The second component compares the effect of using different pre-trained language encoders. The final component involves comparing different initialization methods that could be used for the special tokens. Qualitative analysis of the attention map of the pre-trained language encoder shows that our special tokens encode relevant information through the encoding process by attending to each other.
Assessing the Difficulty of Labelling an Instance in Crowdworking

In supervised machine learning solutions, obtaining labels for data is either expensive or labels are very difficult to come by. This has resulted in reliance on crowdworking for label acquisition. However, these labels come with a penalty of unreliability, which gives rise to the need for assessing the reliability of labels. One such assessment can be performed by determining the difficulty of the labeling task performed by the crowdworker. Assessing annotator stress levels while performing a task can be indicative of its difficulty. We propose a time series classification approach that learns on stress signals of crowdworkers to distinguish between easy and difficult exemplary instance-labeling tasks. To transfer this classifier for a labeling task of different nature, we propose two types of time series classification models: a global model trained on the data of all label annotators, and individual-centric models trained on the time series of each annotator. We incorporate this approach into an instance-labeling framework that encompasses one phase for learning on exemplary tasks and one phase for the characterization of unknown tasks. In other words, the model is trained on a data distribution and is then used for classifying data from another distribution. We show that the individual-centric models achieve better performance than their global counterparts on many occasions, and we report notable performance by the classification models overall.
Decision Support System for Selection of Staples Food and Food Commodity Price Prediction Post-COVID-19 Using Simple Additive Weighting and Multiple Linear Regression Methods

In the pandemic after the occurrence of COVID-19, there are significant changes in economic statistics, this is influenced by economic activity that is not stable compared to before.The price of food staples was also affected by the pandemic, meetings between buyers and traders, usually held in traditional and modern markets, were hampered due to government restrictions on the territory.This causes a decrease in existing transactions in the market, therefore foodstuffs have the possibility of price volatility.Multiple Linear Regression (MLR) algorithm is a method that can overcome predictions with the type of seasonal dataset prediction, therefore the MLR algorithm is implemented to predict food prices, especially in the modern market, based on the predicted prices, then a decision support system is made to make an alternative ranking of food selection accumulation.Based on the available food ingredients there are nutrients contained in these foods, therefore experts are needed to determine the weighting of nutrition in each food ingredient.Simple Additive Weighting (SAW) method is a method that can do weighting and ranking of alternatives.Therefore the SAW method is applied to rank alternative food staples that have nutritional weight and price.Based on the application of MLR, the error level testing concluded that the prediction of the price of food "Rice" has the least error results compared to other foodstuffs with the value of MSE 21261.04,MAE 145.79,RMSE 145.812,MAPE 0.81 while for the best R2 values found at food ingredients "Garlic" with a value of 0.576.Based on testing of the application of SAW, the same results are obtained between manual calculations and calculations provided by the system, so that the accuracy of the system can be ascertained.