Reviewerly/exHarmony huggingface dataset:
Classification with Quantum Measurements

This paper reports a novel method for supervised machine learning based on the mathematical formalism that supports quantum mechanics. The method uses projective quantum measurement as a way of building a prediction function. Specifically, the relationship between input and output variables is represented as the state of a bipartite quantum system. The state is estimated from training samples through an averaging process that produces a density matrix. Prediction of the label for a new sample is made by performing a projective measurement on the bipartite system with an operator, prepared from the new input sample, and applying a partial trace to obtain the state of the subsystem representing the output. The method can be seen as a generalization of Bayesian inference classification and as a type of kernel-based learning method. One remarkable characteristic of the method is that it does not require learning any parameters through optimization. We illustrate the method with different 2-D classification benchmark problems and different quantum information encodings.
Quantum Algorithm for the Direct Calculations of Vertical Ionization Energies

Recently, a quantum algorithm that is capable of directly calculating the energy gap between two electronic states having different spin quantum numbers without inspecting the total energy of the individual electronic states was proposed. This quantum algorithm guarantees an exponential speedup, like quantum phase estimation (QPE)-based full-CI, with much lower costs. In this work, we propose a modified quantum circuit for the direct calculations of spin state energy gaps to reduce the number of qubits and quantum gates, extending the quantum algorithm to the direct calculation of vertical ionization energies. Numerical quantum circuit simulations for the ionization of light atoms (He, Li, Be, B, C, and N) and small molecules (HF, BF, CF, CO, O2, NO, CN, F2, H2O, and NH3) revealed that the proposed quantum algorithm affords the vertical ionization energies within 0.1 eV of precision.
Automatic Modulation Classification Based on Deep Feature Fusion for High Noise Level and Large Dynamic Input

Automatic modulation classification (AMC) is playing an increasingly important role in spectrum monitoring and cognitive radio. As communication and electronic technologies develop, the electromagnetic environment becomes increasingly complex. The high background noise level and large dynamic input have become the key problems for AMC. This paper proposes a feature fusion scheme based on deep learning, which attempts to fuse features from different domains of the input signal to obtain a more stable and efficient representation of the signal modulation types. We consider the complementarity among features that can be used to suppress the influence of the background noise interference and large dynamic range of the received (intercepted) signals. Specifically, the time-series signals are transformed into the frequency domain by Fast Fourier transform (FFT) and Welch power spectrum analysis, followed by the convolutional neural network (CNN) and stacked auto-encoder (SAE), respectively, for detailed and stable frequency-domain feature representations. Considering the complementary information in the time domain, the instantaneous amplitude (phase) statistics and higher-order cumulants (HOC) are extracted as the statistical features for fusion. Based on the fused features, a probabilistic neural network (PNN) is designed for automatic modulation classification. The simulation results demonstrate the superior performance of the proposed method. It is worth noting that the classification accuracy can reach 99.8% in the case when signal-to-noise ratio (SNR) is 0 dB.
Robonomics in the 6G Era: Playing the Trust Game With On-Chaining Oracles and Persuasive Robots

6G is anticipated to become more human-centered than 5G and should not only explore more spectrum at high-frequency bands but, more importantly, converge driving technological trends such as blockchain technologies and connected robotics. This paper focuses on the emerging field of robonomics, which studies the sociotechnical impact of blockchain technologies on social human-robot interaction and behavioral economics for the social integration of robots into human society. Advanced blockchain technologies such as oracles enable the on-chaining of blockchain-external off-chain information stemming from human users. In doing so, they leverage on human intelligence rather than machine learning only. In this paper, we investigate the widely studied trust game of behavioral economics in a blockchain context, paying close attention to the importance of developing efficient cooperation and coordination technologies. After identifying open research challenges of blockchain-enabled implementations of the trust game, we first develop a smart contract that replaces the experimenter in the middle between trustor and trustee and demonstrate experimentally that a social efficiency of up to 100% can be achieved by using deposits to enhance both trust and trustworthiness. We then present an on-chaining oracle architecture for a networked N-player trust game that involves a third type of human agents called observers, who track the players' investment and reciprocity. The presence of third-party reward and penalty decisions helps raise the average normalized reciprocity above 80%, even without requiring any deposit. Finally, we experimentally demonstrate that mixed logical-affective persuasive strategies for social robots improve the trustees' trustworthiness and reciprocity significantly.
Preventing Counterfeit Products using Cryptography, QR Code and Webservice

Counterfeit production is a threat for every genuine business causing damage to their brand image and stealing their revenues. The aim of this paper is topresenta novel method to prevent counterfeit products using cryptography, QR code and webservice. The method requires that every original product manufacturer obtain a cryptographic key pair, securely store their private key and publish their public key on their website as a QR code. The product manufacturer needs to print a unique item code on their product packs and provide inside the pack a QR code encoding the ciphertext generated by encrypting the item code with their private key. For product verification by buyers, the manufacture is required to provide a QR code scanning app for download on their website, Google Play Store or iPhone App Store. The scanning app should have additional cryptographic functionality to decrypt ciphertext of the item code encoded in the QR code. The manufacturer also needs to launch a simple webservice on his hosting server to accept requests from the mobile app and verify the item code and buyer’s name in its database. Technicalimplementation and the verification process are described in detail through figures and flowchart. The method can be implemented even by small manufacturers with nominal cost by obtaining a key pair and creating a scanning app and webservices. We have also tested the method with an actual software code written for cryptographic operations using the Java cryptography Extension and QR code operations using Google Zxing libraries.
The Effectiveness of Augmented Reality in Learning Vector to Improve Students’ Spatial and Problem-Solving Skills

&lt;p class="0abstract"&gt;The researchers implemented the quasi-experimental method of research. This research was conducted at SMAN 1 Ngemplak, Indonesia. The research was conducted from March to April 2020. In this study, the samples consisted of 70 students divided into two classes, namely Class A and Class B. The students in Class A received treatment by using the Augmented Reality Module (ARM). Meanwhile, Class B students were taught conventionally using a conventional module from the government and GeoGebra application. The instruments used in this study were written tests, the first test was in the form of multiple choices and consisted of 25 questions to measure students’ spatial skills, and the second test was in the form of essays, consisted of five questions in order to measure students’ problem-solving skills. The reliability of the tests had been tested before collecting the data. The data were analyzed using descriptive statistics and inferential statistics (normality test, homogeneity test, paired t-test, and independent t-test). Based on the analyses' results, it can be concluded that Augment-ed Reality Module has a significant effect on students' problem-solving and spatial skills since there is an improvement in students' mean scores in the pre-test and post-test results. Besides, it can be concluded that Augmented Reality learning is more effective than conventional learning since the students are usually taught using conventional modules from the government and GeoGebra application&lt;span&gt;.&lt;/span&gt;&lt;/p&gt;
Sentiment Analysis Method based on Kmeans and Online Transfer Learning

Sentiment analysis is a research hot spot in the field of natural language processing and content security. Traditional methods are often difficult to handle the problems of large difference in sample distribution and the data in the target domain is transmitted in a streaming fashion. This paper proposes a sentiment analysis method based on Kmeans and online transfer learning in the view of fact that most existing sentiment analysis methods are based on transfer learning and offline transfer learning. We first use the Kmeans clustering algorithm to process data from one or multiple source domains and select the data similar to target domain data to establish the classifier, so that the processed data does not negatively transfer the data in the target domain. And then create a new classifier based on the new target domain. The source domain classifier and target domain classifier are combined with certain weights by using the homogeneous online transfer learning method to achieve sentiment analysis. The experimental results show that this method has achieved better performance in terms of error rate and classification accuracy.
Data Validation Infrastructure for <i>R</i>

Checking data quality against domain knowledge is a common activity that pervades statistical analysis from raw data to output.The R package validate facilitates this task by capturing and applying expert knowledge in the form of validation rules: logical restrictions on variables, records, or data sets that should be satisfied before they are considered valid input for further analysis.In the validate package, validation rules are objects of computation that can be manipulated, investigated, and confronted with data or versions of a data set.The results of a confrontation are then available for further investigation, summarization or visualization.Validation rules can also be endowed with metadata and documentation and they may be stored or retrieved from external sources such as text files or tabular formats.This data validation infrastructure thus allows for systematic, user-defined definition of data quality requirements that can be reused for various versions of a data set or by data correction algorithms that are parameterized by validation rules.
Application of the Zoom Meeting Application in Online Learning During the Pandemic

In the midst of the Covid-19 pandemic outbreak, almost all Indonesians, including Bengkulu province, expect to experience the spike in positive cases of Covid-19, particularly in the Rejang Lebong Regency, resulting in many very significant changes in almost all fields, especially in the field of education. The learning process, consisting of synchronous learning, is carried out internet (online), namely face-to-face by video calls/ zoom meetings and asynchronous, namely by assignments. Using observational data methods, interviews, and notes, the research approach used is a qualitative research method. The aim of this study is to provide some explanations of the use of the zoom meeting application during a pandemic in online learning, to evaluate the constraints of using the zoom meeting application and the benefits of the application for zoom meeting from several features during a pandemic in online learning. The findings of this study show that it can be easier for lecturers and students to interact synchronously in the learning process by applying the zoom meeting application to learning during this pandemic.
Fast semi-supervised evidential clustering

Semi-supervised clustering is a constrained clustering technique that organizes a collection of unlabeled data into homogeneous subgroups with the help of domain knowledge expressed as constraints. These methods are, most of the time, variants of the popular k-means clustering algorithm. As such, they are based on a criterion to minimize. Amongst existing semi-supervised clusterings, Semi-supervised Evidential Clustering (SECM) deals with the problem of uncertain/imprecise labels and creates a credal partition. In this work, a new heuristic algorithm, called SECM-h, is presented. The proposed algorithm relaxes the constraints of SECM in such a way that the optimization problem is solved using the Lagrangian method. Experimental results show that the proposed algorithm largely improves execution time while accuracy is maintained.
A Safe and Secured Medical Textual Information Using an Improved LSB Image Steganography

Safe conveyance of medical data across unsecured networks nowadays is an essential issue in telemedicine. With the exponential growth of multimedia technologies and connected networks, modern healthcare is a huge step ahead. Authentication of a diagnostic image obtained from a specialist at a remote location which is from the sender is one of the most challenging tasks in an automated healthcare setup. Intruders were found to be able to efficiently exploit securely transmitted messages from previous literature since the algorithms were not efficient enough leading to distortion of information. Therefore, this study proposed a modified least significant bit (LSB) technique capable of protecting and hiding medical data to solve the crucial authentication issue. The application was executed and established by utilizing MATLAB 2018a, and it used a logical bit shift operation for execution. The investigational outcomes established that the proposed technique can entrench medical information without leaving a perceptible falsification in the stego image. The result of this implementation shows that the modified LSB image steganography outperformed the standard LSB technique with a higher PSNR value and lower MSE value when compared with previous research works. The number of shifts was added as a new performance metric for the proposed system. The study concluded that the proposed secured medical information system was evidenced to be proficient in secreting medical information and creating undetectable stego images with slight entrenching falsifications when likened to other prevailing approaches.
Age and Gender as Cyber Attribution Features in Keystroke Dynamic-Based User Classification Processes

Keystroke dynamics are used to authenticate users, to reveal some of their inherent or acquired characteristics and to assess their mental and physical states. The most common features utilized are the time intervals that the keys remain pressed and the time intervals that are required to use two consecutive keys. This paper examines which of these features are the most important and how utilization of these features can lead to better classification results. To achieve this, an existing dataset consisting of 387 logfiles is used, five classifiers are exploited and users are classified by gender and age. The results, while demonstrating the application of these two characteristics jointly on classifiers with high accuracy, answer the question of which keystroke dynamics features are more appropriate for classification with common classifiers.
Business Organization Security Strategies to Cyber Security Threats

It is argued that the advancement of Information, Communication and Technology went hand in hand with the emergence of certain threats and vulnerabilities to cybersecurity. In several cases, cyber attacks have targeted the information, communication and infrastructure networks of numerous organizations. Today, hackers and intruders have advanced technology within their scope that lets them access the organizational information system. The present study highlights numerous internet security related problems, it offers a broad-based overview of internet threats from the perspective of business enterprises, along with prevention measures and enhanced safety strategies. A systematic analysis of secondary literature was introduced by researchers, the study found that it is critical for organizations to choose an IT security management tool that can be categorized as best practices and standards. The Security Incident Event Management (SIEM) framework is one key instrument proposed here. SIEM instruments help security analysts gain insight into the security threats targeting the IT structures of a given organization.
Prediction of Lung Cancer using Meta-Heuristic based Optimization Technique: Crow Search Technique

In this paper, a meta-heuristic-based optimization technique is used for predicting cancer in the lungs. Lung cancer is one of the dreadful diseases that occurred in human beings and cause a lot of damage. Even though, lots of techniques have been proposed to predict lung cancer. But, still, the prediction of cancer has become a challenging task, due to the multifaceted structures in the CT scan. The automated meta-heuristic-based optimization technique namely crow search algorithm is used to find the feasible position and then searching the similar pixels for the clustering process. The exact position of the tumor is finding out using the crow search algorithm and can be better visualized. This process helps the radiologist to find the tumor in the beginning stage and acts as an efficient method for the prediction process. The sensitivity values (99.12 %) produced by the crow search technique is promising than the conventional techniques.
FastPathology: An Open-Source Platform for Deep Learning-Based Research and Decision Support in Digital Pathology

Deep convolutional neural networks (CNNs) are the current state-of-the-art for digital analysis of histopathological images. The large size of whole-slide microscopy images (WSIs) requires advanced memory handling to read, display and process these images. There are several open-source platforms for working with WSIs, but few support deployment of CNN models. These applications use third-party solutions for inference, making them less user-friendly and unsuitable for high-performance image analysis. To make deployment of CNNs user-friendly and feasible on low-end machines, we have developed a new platform, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">FastPathology</i> , using the FAST framework and C++. It minimizes memory usage for reading and processing WSIs, deployment of CNN models, and real-time interactive visualization of results. Runtime experiments were conducted on four different use cases, using different architectures, inference engines, hardware configurations and operating systems. Memory usage for reading, visualizing, zooming and panning a WSI were measured, using FastPathology and three existing platforms. FastPathology performed similarly in terms of memory to the other C++-based application, while using considerably less than the two Java-based platforms. The choice of neural network model, inference engine, hardware and processors influenced runtime considerably. Thus, FastPathology includes all steps needed for efficient visualization and processing of WSIs in a single application, including inference of CNNs with real-time display of the results. Source code, binary releases, video demonstrations and test data can be found online on GitHub at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/SINTEFMedtek/FAST-Pathology/</uri> .
Attention-based Relational Graph Convolutional Network for Target-Oriented Opinion Words Extraction

Target-oriented opinion words extraction (TOWE) is a subtask of aspect-based sentiment analysis (ABSA).It aims to extract the corresponding opinion words for a given opinion target in a review sentence.Intuitively, the relation between an opinion target and an opinion word mostly relies on syntactics.In this study, we design a directed syntactic dependency graph based on a dependency tree to establish a path from the target to candidate opinions.Subsequently, we propose a novel attention-based relational graph convolutional neural network (ARGCN) to exploit syntactic information over dependency graphs.Moreover, to explicitly extract the corresponding opinion words toward the given opinion target, we effectively encode target information in our model with the target-aware representation.Empirical results demonstrate that our model significantly outperforms all of the existing models on four benchmark datasets.Extensive analysis also demonstrates the effectiveness of each component of our models.Our code is available at https: //github.com/wcwowwwww/towe-eacl.
Recognizing Chinese judicial named entity using BiLSTM-CRF

Named entity recognition (NER) plays an essential role in natural language processing systems. Judicial NER is a fundamental component of judicial information retrieval, entity relation extraction, and knowledge map building. However, Chinese judicial NER remains to be more challenging due to the characteristics of Chinese and high accuracy requirements in the judicial filed. Thus, in this paper, we propose a deep learning-based method named BiLSTM-CRF which consists of bi-directional long short-term memory (BiLSTM) and conditional random fields (CRF). For further accuracy promotion, we propose to use Adaptive moment estimation (Adam) for optimization of the model. To validate our method, we perform experiments on judgment documents including commutation, parole and temporary service outside prison, which is acquired from China Judgments Online. Experimental results achieve the accuracy of 0.876, recall of 0.856 and F1 score of 0.855, which suggests the superiority of the proposed BiLSTM-CRF with Adam optimizer.
SICK-NL: A Dataset for Dutch Natural Language Inference

We present SICK-NL (read: signal), a dataset targeting Natural Language Inference in Dutch.SICK-NL is obtained by translating the SICK dataset of Marelli et al. (2014) from English into Dutch.Having a parallel inference dataset allows us to compare both monolingual and multilingual NLP models for English and Dutch on the two tasks.In the paper, we motivate and detail the translation process, perform a baseline evaluation on both the original SICK dataset and its Dutch incarnation SICK-NL, taking inspiration from Dutch skipgram embeddings and contextualised embedding models.In addition, we encapsulate two phenomena encountered in the translation to formulate stress tests and verify how well the Dutch models capture syntactic restructurings that do not affect semantics.Our main finding is all models perform worse on SICK-NL than on SICK, indicating that the Dutch dataset is more challenging than the English original.Results on the stress tests show that models don't fully capture word order freedom in Dutch, warranting future systematic studies.