Reviewerly/exHarmony huggingface dataset:
Sentiment analysis and emotion detection of post-COVID educational Tweets: Jordan case

Education evolved dramatically under Covid-19, and owing to the conditions, distant learning became mandatory. However, this has opened new realities to the educational business under the label of "Hybrid-Learning," where educational institutions are still using online learning in addition to face-to-face learning, which has changed people's lives and split their opinions and emotions. As a result, this study investigated the Jordanian community's perspectives and feelings on the transition from pure face-to-face education to blended education by examining related tweets in the post-COVID era. Specifically, using NLP Emotion detection and Sentiment Analysis approaches, as well as deep learning models. As a result of analyzing the collected tweets, 18.75% of studied Jordanian's community sample are dissatisfied (Anger and Hate), 21.25% are negative (Sad), 13% are Happy, and 24.50 percent are Neutral about it.
IPC prediction of patent documents using neural network with attention for hierarchical structure

International patent classifications (IPCs) are assigned to patent documents; however, since the procedure for assigning classifications is manually done by the patent examiner, it takes a lot of time and effort to select some IPCs from about 70,000 IPCs. Hence, some research has been conducted on patent classification with machine learning. However, patent documents are very voluminous, and learning with all the claims (the part describing the content of the patent) as input would run out of the necessary memory, even if the batch size is set to a very small size. Therefore, most of the existing methods learn by excluding some information, such as using only the first claim as input. In this study, we propose a model that considers the contents of all claims by extracting important information for input. In addition, we focus on the hierarchical structure of the IPC, and propose a new decoder architecture to consider it. Finally, we conducted an experiment using actual patent data to verify the accuracy of the prediction. The results showed a significant improvement in accuracy compared to existing methods, and the actual applicability of the method was also discussed.
Large Language Models Are State-of-the-Art Evaluators of Translation Quality

We describe GEMBA, a GPT-based metric for assessment of translation quality, which works both with a reference translation and without. In our evaluation, we focus on zero-shot prompting, comparing four prompt variants in two modes, based on the availability of the reference. We investigate nine versions of GPT models, including ChatGPT and GPT-4. We show that our method for translation quality assessment only works with GPT~3.5 and larger models. Comparing to results from WMT22's Metrics shared task, our method achieves state-of-the-art accuracy in both modes when compared to MQM-based human labels. Our results are valid on the system level for all three WMT22 Metrics shared task language pairs, namely English into German, English into Russian, and Chinese into English. This provides a first glimpse into the usefulness of pre-trained, generative large language models for quality assessment of translations. We publicly release all our code and prompt templates used for the experiments described in this work, as well as all corresponding scoring results, to allow for external validation and reproducibility.
A Mixed Clustering Approach for Real Time Anomaly Detection

Anomaly Detection in real time data is accepted as a vital research area. Clustering has effectively been tried for this purpose. As the datasets are real time, the time of generating of the data is also important. In this article, we introduce a mixture of partitioning and agglomerative hierarchical approach to detect anomalies from such datasets. It is a two-phase method which follows partitioning approach first and then agglomerative hierarchical approach. The dataset can have mixed attributes. In phase-1, a unified metric defined on mixed attributes is used. The same is also used for merging of similar clusters in phase-2. Also, we have kept the track of time attribute of each data instance which produces the clusters with their lifetimes in phase-1. Then in phase-2, we merge the similar clusters. While merging, the similar clusters, the lifetimes of the corresponding clusters with overlapping cores are to be superimposed producing fuzzy time intervals. This way, each cluster will have an associated fuzzy lifetime. The data instances either belonging sparse clusters or not belonging to any of the clusters can be treated as anomalies. The efficacy of the algorithms can be established using both complexity analysis as well as experimental studies.
Identity-Based Proxy Re-Encryption Scheme Using Fog Computing and Anonymous Key Generation

In the fog computing architecture, a fog is a node closer to clients and responsible for responding to users’ requests as well as forwarding messages to clouds. In some medical applications such as the remote healthcare, a sensor of patients will first send encrypted data of sensed information to a nearby fog such that the fog acting as a re-encryption proxy could generate a re-encrypted ciphertext designated for requested data users in the cloud. Specifically, a data user can request access to cloud ciphertexts by sending a query to the fog node that will forward this query to the corresponding data owner who preserves the right to grant or deny the permission to access his/her data. When the access request is granted, the fog node will obtain a unique re-encryption key for carrying out the re-encryption process. Although some previous concepts have been proposed to fulfill these application requirements, they either have known security flaws or incur higher computational complexity. In this work, we present an identity-based proxy re-encryption scheme on the basis of the fog computing architecture. Our identity-based mechanism uses public channels for key distribution and avoids the troublesome problem of key escrow. We also formally prove that the proposed protocol is secure in the IND-PrID-CPA notion. Furthermore, we show that our work exhibits better performance in terms of computational complexity.
Digital Forensic Tools: A Literature Review

Digital forensics is a process of collection, identification, extraction, and documentation of electronic evidence which is used in a court of law.There are a large number of tools that help us to make this process easy and simple.In this paper, four tools have been chosen to explore and study.The best digital forensic tools have been chosen according to different parameters in each type of digital forensics.The (Stellar) basis tool and (Forensic Tool Kit) have been explored for computer forensic tools while (Network Map) has been chosen for network forensic tools and (OSFmount) has been studied as a live forensic tool.This paper also covers other types of forensic tools like Database forensic tools, O.S. forensic tools, and Mail forensic tools.The role of Artificial intelligence in Digital Forensic tools has been discussed in this paper by using both Decision Stump and Bayes net machine learning techniques.After making an investigation of the IoT device traffic dataset using these two techniques, Decision Stump gives us less accurate results compared with Bayes net.
The Hitchhiker’s Guide to Facebook Web Tracking with Invisible Pixels and Click IDs

Over the past years, advertisement companies have used various tracking methods to persistently track users across the web. Such tracking methods usually include first and third-party cookies, cookie synchronization, as well as a variety of fingerprinting mechanisms. Facebook (FB) (now Meta) recently introduced a new tagging mechanism that attaches a one-time tag as a URL parameter (namely FBCLID) on outgoing links to other websites. Although such a tag does not seem to have enough information to persistently track users, we demonstrate that despite its ephemeral nature, when combined with FB Pixel, it can aid in persistently monitoring user browsing behavior across i) different websites, ii) different actions on each website, iii) time, i.e., both in the past as well as in the future. We refer to this online monitoring of users as FB web tracking.
Eastwood-Tidy: C Linting for Automated Code Style Assessment in Programming Courses

Computer Science students receive significant instruction towards writing functioning code that correctly satisfies requirements. Auto-graders have been shown effective at scalably running student code and determining whether the code correctly implements a given assignment or project. However, code functionality is only one component of "good" code, and there are few studies on the correlation between code style and code quality. There are even fewer studies contributing a tool equivalent to auto-graders for code style checking and grading. We put forth two contributions. First, a style guide for the C programming language focused on readability for student programs. Second, an automated linting tool Eastwood-Tidy providing on-demand style violation and fix feedback for students and automated style grading for course staff. Finally, we survey students and find a positive response to both a code standard and an automated tool to support the standard and make recommendations for the inclusion of both in programming focused courses based on these results.
Wisdom of Crowds in Quantum Machine Learning

A collective outcome or decision from a crowd often prevails over that of a single expert. Here we study this phenomenon through the lens of quantum machine learning. We compare the performance of an expert, a highly trained quantum network, to a crowd, several poorly trained ones, for quantum information processing tasks such as quantum tomography and entanglement recognition. We also show quantum-enhanced performance from a temporal crowd by using time multiplexing on a single poorly trained quantum network. Given the same resources for training, we show that the crowd outperforms the expert by a definitive margin.
Implementation and goals of quantum optics experiments in undergraduate instructional labs

As quantum information science and technology (QIST) is becoming more prevalent and occurring not only in research labs but also in industry, many educators are considering how best to incorporate learning about quantum mechanics into various levels of education. Although much of the focus has been on quantum concepts in nonlab courses, current work in QIST has a substantial experimental component. Many instructors of undergraduate lab courses want to provide their students the opportunity to work with quantum experiments. One common way this is done is through a sequence of quantum optics experiments often referred to as the “single-photon experiments.” These experiments demonstrate fundamental quantum phenomena with equipment common to research labs; however, they are resource intensive and cannot be afforded by all institutions. It is therefore imperative to know what unique affordances these experiments provide to students. As a starting point, we surveyed and interviewed instructors who use the single-photon experiments in undergraduate courses, asking how and why they use the experiments. We describe the most commonly used experiments in both quantum and beyond-first-year lab courses, the prevalence of actions the students perform, and the learning goals, ranging from conceptual knowledge to lab skills to student affect. Finally, we present some strategies from these data demonstrating how instructors have addressed the common challenges of preparing students to work with conceptually and technically complex experiments and balancing the practice of technical skills with the completion of the experiments.Received 15 November 2022Accepted 9 February 2023DOI:https://doi.org/10.1103/PhysRevPhysEducRes.19.010117Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article’s title, journal citation, and DOI.Published by the American Physical SocietyPhysics Subject Headings (PhySH)Professional TopicsUpper undergraduate studentsPhysics Education Research
Modulation Signal Recognition of Underwater Acoustic Communication Based on Archimedes Optimization Algorithm and Random Forest

This paper researches the recognition of modulation signals in underwater acoustic communication, which is the fundamental prerequisite for achieving noncooperative underwater communication. In order to improve the accuracy of signal modulation mode recognition and the recognition effects of traditional signal classifiers, the article proposes a classifier based on the Archimedes Optimization Algorithm (AOA) and Random Forest (RF). Seven different types of signals are selected as recognition targets, and 11 feature parameters are extracted from them. The decision tree and depth obtained by the AOA algorithm are calculated, and the optimized random forest after the AOA algorithm is used as the classifier to achieve the recognition of underwater acoustic communication signal modulation mode. Simulation experiments show that when the signal-to-noise ratio (SNR) is higher than -5dB, the recognition accuracy of the algorithm can reach 95%. The proposed method is compared with other classification and recognition methods, and the results show that the proposed method can ensure high recognition accuracy and stability.
Deep learning for face mask detection: a survey

The Coronavirus Disease (Covid-19) was declared as a pandemic by WHO (World Health Organization) on 11 March 2020, and it is still currently going on, thereby impacting tremendously the whole world. As of September 2021, more than 220 million cases and 4.56 million deaths have been confirmed, which is a vast number and a significant threat to humanity. Although, As of 6 September 2021, a total of 5,352,927,296 vaccine doses have been administered, still many people worldwide are not fully vaccinated yet. As stated by WHO, "Masks" should be used as one of the measures to restrain the transmission of this virus. So, to reduce the infection, one has to cover their face, and to detect whether a person's face is covered with a mask or not, a "Face mask detection system" is needed. Face Mask Detection falls under the category of "Object Detection," which is one of the sub-domains of Computer Vision and Image Processing. Object Detection consists of both "Image Classification" and "Image Localization". Deep learning is a subset of Machine learning which, in turn, is a subset of Artificial intelligence that is widely being used to detect face masks; even some people are using hybrid approaches to make the most use of it and to build an efficient "Face mask detection system". In this paper, the main aim is to review all the research that has been done till now on this topic, various datasets and Techniques used, and their performances followed by limitations and improvements. As a result, the purpose of this study is to give a broader perspective to a researcher to identify patterns and trends in Face mask detection (Object Detection) within the framework of covid-19.
A Secure Medical Record Sharing Scheme Based on Blockchain and Two-fold Encryption

Usually, a medical record (MR) contains the patients’ disease-oriented sensitive information. In addition, the MR needs to be shared among different bodies, e.g., diagnostic centres, hospitals, physicians, etc. Hence, retaining the privacy and integrity of MR is crucial. A blockchain based secure MR sharing system can manage these aspects properly. This paper proposes a blockchain based electronic (e-) MR sharing scheme that (i) considers the medical image and the text as the input, (ii) enriches the data privacy through a two-fold encryption mechanism consisting of an asymmetric cryptosystem and the dynamic DNA encoding, (iii) assures data integrity by storing the encrypted e-MR in the distinct block designated for each user in the blockchain, and (iv) eventually, enables authorized entities to regain the e-MR through decryption. Preliminary evaluations, analyses, comparisons with state-of-the-art works, etc., imply the efficacy of the proposed scheme.
Qualitative Choice Logic For Modeling Experts Recommendations of Antibiotics

Qualitative Choice Logic (QCL) is a logic-based formalism for preference handling. The logic adds to classical propositional logic a new connective called ordered disjunction (~).x~y intuitively means: if possible x, but if x is not possible then at least y. The aim of this paper is to explore QCL in healthcare domain, particularly for modeling experts reasoning for providing recommendations of antibiotics. We show that QCL is fully adapted for modeling this problem.
Rethinking the Backbone Architecture for Tiny Object Detection

Tiny object detection has become an active area of research because images with tiny targets are common in several important real-world scenarios. However, existing tiny object detection methods use standard deep neural networks as their backbone architecture. We argue that such backbones are inappropriate for detecting tiny objects as they are designed for the classification of larger objects, and do not have the spatial resolution to identify small targets. Specifically, such backbones use max-pooling or a large stride at early stages in the architecture. This produces lower resolution feature-maps that can be efficiently processed by subsequent layers. However, such low-resolution feature-maps do not contain information that can reliably discriminate tiny objects. To solve this problem we design 'bottom-heavy' versions of backbones that allocate more resources to processing higher-resolution features without introducing any additional computational burden overall. We also investigate if pre-training these backbones on images of appropriate size, using CIFAR100 and ImageNet32, can further improve performance on tiny object detection. Results on TinyPerson and WiderFace show that detectors with our proposed backbones achieve better results than the current state-of-the-art methods.
Efficient Use of Quantum Linear System Algorithms in Interior Point Methods for Linear Optimization

Quantum computing has attracted significant interest in the optimization community because it potentially can solve classes of optimization problems faster than conventional supercomputers. Several researchers proposed quantum computing methods, especially Quantum Interior Point Methods (QIPMs), to solve convex optimization problems, such as Linear Optimization, Semidefinite Optimization, and Second-order Cone Optimization problems. Most of them have applied a Quantum Linear System Algorithm at each iteration to compute a Newton step. However, using quantum linear solvers in QIPMs comes with many challenges, such as having ill-conditioned systems and the considerable error of quantum solvers. This paper investigates how one can efficiently use quantum linear solvers in QIPMs. Accordingly, an Inexact Infeasible Quantum Interior Point Method is developed to solve linear optimization problems. We also discuss how we can get an exact solution by Iterative Refinement without excessive time of quantum solvers. Finally, computational results with a QISKIT implementation of our QIPM using quantum simulators are analyzed.
Kidney Segmentations Using CNN models

For medical diagnostic tests, kidney segmentation from high-volume imagery is an important major. Since 3D medical images need a lot of GPU memory, slices and patches are used for training and inference in traditional neural network variant architectures, which necessarily slows down contextual learning. In this research, Mobile Net and Efficient Net CNN models were trained for segmenting human kidney images generated from The Human Biomolecular Atlas Program (HuBMAP). The purpose of this work is to evaluate the effectiveness of different strategies for Glomeruli identification in order to solve the issue. The high size images were decoded to be fitted and trained in the models first, then the CNN models were trained. The CNN models result show that the Efficient Net has the highest accuracy rate with 99.49 %, and Mobile Net with 99.33 %.
Prediction of Power Generation of a Photovoltaic Power Plant Based on Neural Networks

Photovoltaic energy production is an important factor for increasing the electricity supply. The ability to predict the electric power production (EPP) of a photovoltaic (PV) farm supports from the management process of the power grid to the trade in the energy market and much more. Also, by predicting the production of PV power (PVP), it is possible to monitor the lifetime of the solar cells that form the backbone of any solar PV system. As a critical result, sudden failures of the PV plant can be avoided. Using a long short-term memory recurrent neural network (LSTM-RNN) model, this work evaluates the prediction accuracy of two forecasting strategies: the recursive strategy and the non-recursive Multiple-Input and Multiple-Output, respectively. The dataset consists of 5-years in-filed production data measurements collected from the CETATEA photovoltaic power plant, a research site facility for renewable energies located in Cluj-Napoca, Romania. The high granularity of the electric power production dataset values recorded each 1 hour guarantees the overall prediction accuracy of the system. The impact of the dataset size, the number of previous observations, and the forecast horizon on the neural network prediction accuracy is evaluated for each strategy. The performance metrics used to evaluate the prediction accuracy are the root mean square error, the mean bias error, and the mean average error. The results analysis demonstrates the ability of the implemented machine learning models to predict electric power production, as well as their importance in the energy loss management process.
Quantum asymmetric key crypto scheme using Grover iteration

Abstract Here, we propose a quantum asymmetric key cryptography scheme using Grover’s quantum search algorithm. In the proposed scheme, Alice generates a pair of public and private keys, keeps the private keys safe, and only discloses public keys to the outside. Bob uses Alice's public key to send a secret message to Alice and Alice uses her private key to decrypt the secret message. Furthermore, we discuss the safety of quantum asymmetric key encryption techniques based on quantum mechanical properties.
Multi-camera BEV video-surveillance system for efficient monitoring of social distancing

The current sanitary emergency situation caused by COVID-19 has increased the interest in controlling the flow of people in indoor infrastructures, to ensure compliance with the established security measures. Top view camera-based solutions have proven to be an effective and non-invasive approach to accomplish this task. Nevertheless, current solutions suffer from scalability problems: they cover limited range areas to avoid dealing with occlusions and only work with single camera scenarios. To overcome these problems, we present an efficient and scalable people flow monitoring system that relies on three main pillars: an optimized top view human detection neural network based on YOLO-V4, capable of working with data from cameras at different heights; a multi-camera 3D detection projection and fusion procedure, which uses the camera calibration parameters for an accurate real-world positioning; and a tracking algorithm which jointly processes the 3D detections coming from all the cameras, allowing the traceability of individuals across the entire infrastructure. The conducted experiments show that the proposed system generates robust performance indicators and that it is suitable for real-time applications to control sanitary measures in large infrastructures. Furthermore, the proposed projection approach achieves an average positioning error below 0.2 meters, with an improvement of more than 4 times compared to other methods.