Reviewerly/exHarmony huggingface dataset:
Effectiveness of Kumospace Apps on the Academic Performance of Grade 9 Students in Online Physics Class

Kumospace is platform that provides an immersive and fun experience online, designed to recreate physical experience and atmosphere of being in a room, you can move around and connect with others virtually with the simplicity of a video chat and the creativity of video games. This study aimed to determine the effectiveness of Kumospace on the academic performance of Grade 9 students in online Physics class. This research is limited only to two sections in Grade 9, Academic Year 2021-2022. It used the quantitative research design with 30 participants for the control group and 30 participants for experimental group. Pretest and posttest were taken thru google form attached to the learning management system, which were prepared with a TOS ahead and validated by the experts before it was conducted to test. Results showed a high mean score of 3.333, 3.442, and 3.383 respectively, in terms of content quality, learners need and website quality which means that the students strongly agree and are satisfied with the features of Kumospace, while mean scores of the posttest for both sections gave an excellent result. The finding of the study showed that t-test analysis on the effectiveness of Kumospace on the academic performance of Grade 9 online Physics class resulted a p-value of 0.401 which means that they are not significant. Therefore, with consideration of the results, disregarding the H1, it can be concluded that Kumospace is an effective intervention to improve the academic performance of learners in the conduct of online physics classes but cannot be significantly compared to other online applications, specifically Zoom.
Video Summarization Overview

With the broad growth of video capturing devices and applications on the web, it is more demanding to provide desired video content for users efficiently. Video summarization facilitates quickly grasping video content by creating a compact summary of videos. Much effort has been devoted to automatic video summarization, and various problem settings and approaches have been proposed. Our goal is to provide an overview of this field. This survey covers early studies as well as recent approaches which take advantage of deep learning techniques. We describe video summarization approaches and their underlying concepts. We also discuss benchmarks and evaluations. We overview how prior work addressed evaluation and detail the pros and cons of the evaluation protocols. Last but not least, we discuss open challenges in this field.
Intrusion Detection Quantum Sensor Networks

This paper proposes a perimeter detection scheme based on the quantum physical properties of photons. Existing perimeter intrusion detection schemes, if using light, rely on the classical properties of light only. Our quantum sensor network uses the quantum property of spatial superposition of photons, meaning that a photon can simultaneously follow two different paths after going through a beam splitter. Using multiple Mach–Zehnder interferometers, an entire web of paths can be generated, such that one single photon occupies them all. If an intruder violates this web in some arbitrary point, the entire photon superposition is destroyed, the photon does not self-interfere any more and this event is detected by measurements. For one single photon, the intruder detection probability is limited theoretically but can be increased arbitrarily with the usage of a sequence of photons. We show both theoretical bounds as well as practical results of the proposed schemes. The practical results are obtained by simulation experiments on IBM Quantum platforms. The benefits of our quantum approach are: low power, invisibility to potential intruders, scalability and easy practical implementation.
A Derivative Fidelity-Based Total Generalized Variation Method for Image Restoration

Image edge is the most indicative feature that forms a significant role in image analysis and image understanding, but edge-detail preservation is a difficult task in image restoration due to noise and blur during imaging. The balance between edge preservation and noise removal has always been a difficult problem in image restoration. This paper proposes a derivative fidelity-based total generalized variation method (D-TGV) to improve this balance. First, an objective function model that highlights the ability to maintain details is proposed for the image restoration problem, which is combined with a fidelity term in derivative space and a total generalized variation regularization term. This is designed to achieve the advantage of preserving details in derivative space and eliminate the staircase effect caused by traditional total variation. Second, the alternating direction method of the multipliers (ADMM) is used to solve the model equations by decomposing the original, highly complex model into several simple sub-problems to attain rapid convergence. Finally, a series of experiments conducted on standard grayscale images showed that the proposed method exhibited a good balance between detail preservation and denoising but also reached completion with the fewest iterations compared with the currently established methods.
Distributing Polarization-Entangled Photon Pairs with High Rate Over Long Distances through Standard Telecommunication Fiber

Entanglement distribution over long distances is essential for many quantum communication schemes like quantum teleportation, some variants of quantum key distribution, or implementations of a quantum internet. Distributing entanglement through standard telecommunication fiber is particularly important for quantum key distribution protocols with low vulnerability over metropolitan distances. However, entanglement distribution over long distance through optical fiber so far could only be accomplished with moderate photon pair rates. In this work, we present entanglement distribution over 50km of standard telecommunication fiber with pair rate more than 10,000 s$^{-1}$ using a bright non-degenerate photon pair source. Signal and idler wavelengths of this source are optimized for low dispersion in optical fiber and high efficiency for single-photon avalanche diode detectors, respectively. The resulting modest hardware requirement and high rate of detected entangled photon pairs could significantly enhance practical entanglement-based quantum key distribution in existing metropolitan fiber networks.
Feature generation and contribution comparison for electronic fraud detection

Abstract Modern money transfer services are convenient, attracting fraudulent actors to run scams in which victims are deceived into transferring funds to fraudulent accounts. Machine learning models are broadly applied due to the poor fraud detection performance of traditional rule-based approaches. Learning directly from raw transaction data is impractical due to its high-dimensional nature; most studies construct features instead by extracting patterns from raw transaction data. Past literature categorizes these features into recency, frequency, monetary, and anomaly detection features. We use various machine learning algorithms to examine the performance of features in these four categories with real transaction data; we compare them with the performance of our feature generation guideline based on the statistical perspectives and characteristics of (non)-fraudulent accounts. The results show that except for the monetary category, other feature categories used in the literature perform poorly regardless of which machine learning algorithm is used; anomaly detection features perform the worst. We find that even statistical features generated based on financial knowledge yield limited performance on a real transaction dataset. Our atypical detection characteristic of normal accounts improves the ability to distinguish them from fraudulent accounts and hence improves the overall detection results, outperforming other existent methods.
Valid Blockchain-Based E-Voting Using Elliptic Curve and Homomorphic Encryption

Improving the voting system has become a widely discussed issue. Paper-based elections are not safe because of the possibility of changing and adding ballots. Consequently, many countries use e-voting systems to ensure security, authenticity and time efficiency. Blockchain e-voting systems can be adopted to reduce fraud and increase voting access from home, especially in pandemics. This paper suggests a blockchain e-voting system that tackles two security and authentication issues. The security has been ensured using hybrid public-key cryptography; the voter information is encrypted using the regional election office elliptic public key, while the homomorphic public supreme election authority encrypts the vote. Using homomorphic encryption for voice enables the calculations of results as the authority encrypts it without revealing the vote itself. Authentication has been improved for home voting by a robust login system. This login system consists of two steps. In the first step, the voter enters the site using his unique QR code number scanned by webcam; in the second step, the system checks the voter's face using a face recognition system by web camera to be routed to the voting page. Voting public keys are also authenticated using a digital certificate schema. The system has been tested to show its efficiency and suitability in block establishment time and the encryption and key generator randomness using NIST tests.
Avoiding Plagiarism of Students' Scientific Writing by Using the QuillBot Paraphraser

This research reviews the use of QuillBot to paraphrase the students' scientific writing in avoiding plagiarism. This research is descriptive qualitative. Observation is used as the main instrument to collect data. The data uses the app of QuillBot as an object of research. The analysis data use three ways of qualitative analysis, namely reducing data, displaying data, and conclusions. The analysis shows that several ways QuillBot in paraphrasing includes changing the word/phrase/ clause order, using synonyms, changing the sentence structure from active to passive voice, and vice versa, also separating the information into separate sentences. QuillBot Premium contains different modes of paraphrasing depending on writing needs, such as Standard, Fluency, Formal, Simple, Creative, Expand, and Shorten. Standard mode rewrites text reliably to maintain meaning, Fluency mode sure text is readable and free of errors; Formal mode presents the text in a more sophisticated and professional way; Simple mode makes writing more readable and accessible; Creative mode presents the text in a way most people can understand; Expand mode adds more detail and depth to increase sentence length; and Shorten mode strips away extra words to provide a clear message. QuillBot can be used by students to rewrite any writings or as an alternative tool when they cannot paraphrase manually. However, the site is a product of technology. Of course, human intelligence remains the main. Students need to understand in-depth and re-check their writing to get better or higher-quality paraphrasing.
Comparison of Mortality Predictive Models of Sepsis Patients Based on Machine Learning

Objective To compare the performance of five machine learning models and SAPS II score in predicting the 30-day mortality amongst patients with sepsis. Methods The sepsis patient-related data were extracted from the MIMIC-IV database. Clinical features were generated and selected by mutual information and grid search. Logistic regression, Random forest, LightGBM, XGBoost, and other machine learning models were constructed to predict the mortality probability. Five measurements including accuracy, precision, recall, F1 score, and area under curve (AUC) were acquired for model evaluation. An external validation was implemented to avoid conclusion bias. Results LightGBM outperformed other methods, achieving the highest AUC (0.900), accuracy (0.808), and precision (0.559). All machine learning models performed better than SAPS II score (AUC=0.748). LightGBM achieved 0.883 in AUC in the external data validation. Conclusions The machine learning models are more effective in predicting the 30-day mortality of patients with sepsis than the traditional SAPS II score.
Development of a PSO Modification with Varying Cognitive Term

A new modification of particle swarm optimization (PSO) was developed in the work. It involves changing the cognitive term of a particle movement. The frequency of such changing through iterations execution is considered as an additional parameter to tune. A reasonable range of the parameter was stated in the work. A comparative study supported the superiority of the developed PSO modification. In addition, its mature performance showed the efficiency of the algorithm iterations number increasing.
Distributed Hardware Accelerated Secure Joint Computation on the COPA Framework

Performance of distributed data center applications can be improved through use of FPGA-based SmartNICs, which provide additional functionality and enable higher bandwidth communication and lower latency. Until lately, however, the lack of a simple approach for customizing SmartNICs to application requirements has limited the potential benefits. Intel's Configurable Network Protocol Accelerator (COPA) provides a customizable FPGA framework that integrates both hardware and software development to improve computation and commu-nication performance. In this first case study, we demonstrate the capabilities of the COPA framework with an application from cryptography - secure Multi-Party Computation (MPC) - that utilizes hardware accelerators connected directly to host memory and the COPA network. We find that using the COPA framework gives significant improvements to both computation and communication as compared to traditional implementations of MPC that use CPUs and NICs. A single MPC accelerator running on COPA enables more than 17Gb/s of communication bandwidth while using only 3% of Stratix 10 resources. We show that utilizing the COPA framework enables multiple MPC accelerators running in parallel to fully saturate a 100Gbps link enabling higher performance compared to traditional NICs.
IDALib: a Python library for efficient image data augmentation

The accuracy obtained with deep learning-based systems usually depends on the availability of large image datasets, which is not always possible. Consequently, it is necessary to apply techniques to increase the size of these datasets and their variability in a reliable and efficient way. In this respect, a novel tool for image augmentation and the associated data, IDALib, is presented. It provides an automatic method to perform transformation operations jointly on the images and related data, such as landmarks or masks, with the main aim of minimising the computational overhead. Thus, it applies automatically a set of optimisations, such as the vectorisation and composition of operations. Furthermore, the transformations are performed in GPU, which leads to a notable speedup, specially in dual GPU setups. IDALib is publicly available in the PyPI repository, https://pypi.org/project/ida-lib/
Suspicious Activity Detection

Abstract: In today's insecure world, video surveillance systems play a significant role in keeping both indoors and outdoors secure. Real-time applications can utilize video surveillance components, such as behavior recognition, understanding and classifying activities as normal or suspicious. People are at risk from suspicious activities when it comes to the potential danger they pose. Detecting criminal activities in urban and suburban areas is necessary to minimize such incidents as criminal activity increases. The early days of surveillance were carried out manually by humans and involved a lot of fatigue, since suspicious activities were rare compared to everyday activities. Various surveillance approaches were introduced with the advent of intelligent surveillance systems. This paper analyzes two cases that could pose a threat to human lives if ignored, namely the detection of gun-related crimes, the detection of abandoned luggage, the detection of human violence, the detection of lock hammering, the theft of wallets, and the tempering of ATMs on surveillance video frames. In these papers they have used a neural network model that is Faster R-CNN and YOLOv3 technique to detect these activities.
Putting IMT to the Test: Revisiting and Expanding Interval Matching Techniques and their Calibration for SCA

Side-Channel Analysis (SCA) requires the detection of the specific time frame Cryptographic Operations (COs) take place in the side-channel signal. Under laboratory conditions with full control over the Device under Test (DuT), dedicated trigger signals can be implemented to indicate the start and end of COs. For real-world scenarios, waveform-matching techniques have been established which compare the side-channel signal with a template of the CO's pattern in real time to detect the CO in the side channel. State-of-the-Art approaches describe implementations based on Field-Programmable Gate Arrays (FPGAs). However, the maximal length of the template is restricted by the resources available on an FPGAs. Particularly, for high sampling rates the recording of an entire CO may need more samples than the maximum template length supported by a waveform-matching system. Consequently, the template has to be reduced such that it fits the resources while still containing all features relevant for detecting the COs via waveform matching.
Investigating Normalization Methods for CNN-Based Image Quality Assessment

Prior to training convolutional neural networks (CNNs) for image quality assessment (IQA), input normalization is sometimes recommended and sometimes not, according to the literature. Although input normalization is known to improve model training and helps in learning important features, it may result in the loss of information such as contrast, color, and luminance. To better explore this issue, we conduct an empirical study to first investigate the effect of normalization on model performance and then which normalization method best fits IQA among existing methods. The performances of the selected methods are statistically compared with three basic scaling methods. The application of normalization is found to be statistically significant on three IQA databases. The performance improvement on the overall databases, as well as per-individual degradation, is demonstrated in the experimental results.
A Self-Contrastive Learning Framework for Skin Cancer Detection Using Histological Images

Cutaneous spindle cell (CSC) neoplasms are a group of tumors that represent a formidable diagnostic challenge for dermatopathologists. Digital pathology has enabled the application of new methods based on artificial intelligence to reduce the workload of pathologists’ daily practice. In this work, we propose a self-learning framework to detect tumor regions in histological images. The use of a teacher-model paradigm increases the annotated database while avoiding manual annotation. The pre-trained latent space of this model is then used in a second stage by another model to differentiate between leiomyomas (benign cases) and leiomyosarcomas (malignant cases). A contrastive learning approach allows separating the latent space of samples from different classes. This framework was tested on an independent database. This novel approach supposes a step forward in the CSC detection as the obtained results suggest (Acc = 0.90 and 0.8451, respectively).
Transfer Learning from Vision Transformers or ConvNets for 360-Degree Images Quality Assessmentƒ

Currently, there are debates on the accuracy of vision transformers (ViTs) compared to ConvNets for image processing tasks. Image quality assessment (IQA) and particularly 360-IQA is lacking insights regarding their performances and robustness compared to the widely used ConvNets. This paper aims to investigate transfer learning from two pre-trained versions of ViTs and two ConveNets (ResNet-50 and EfficientNet-B3) for 360-degree image quality assessment with a focus on (i) the prediction accuracy and generalization ability and (ii) their adaptation to the specific characteristics of 360-degree images. Furthermore, the influence of adaptive patches sampling compared to simply using equirectangular content is analyzed with each architecture. Experimental findings on publicly available datasets (OIQA, CVIQ and MVAQD) show the superiority of ResNet-50 over ViTs and EfficientNet-B3 while requiring less computational time. Also, the base version of ViTs outperforms the larger one. Finally, except for CVIQ, both ViTs and ConveNets benefit from the adaptive sampling strategy, depicting the interest of taking 360-degree characteristics into account.
Evaluation of Lontar Prasi Bali Application based on Augmented Reality Using User Experience Questionnaire

The digitalization era supports the use of technology to preserve and strengthen local culture, such as using Augmented Reality (AR) to visualize Lontar Prasi figures and stories. To design and operate the Lontar Prasi Bali AR application optimally based on user demands, User Experience (UE) testing was conducted on 56 respondents with 8 questions on the application using User Experience Questionnaire (UEQ) test shows that the average pragmatic and hedonic quality is 1.94, meaning the application is good at providing user pleasure and comfort. The Lontar Prasi Bali AR app provides above-average results (Excellent) and matches user expectations.
Revisiting Language Support for Generic Programming: When Genericity Is a Core Design Goal

Context: Generic programming, as defined by Stepanov, is a methodology for writing efficient and reusable algorithms by considering only the required properties of their underlying data types and operations. Generic programming has proven to be an effective means of constructing libraries of reusable software components in languages that support it. Generics-related language design choices play a major role in how conducive generic programming is in practice. Inquiry: Several mainstream programming languages (e.g. Java and C++) were first created without generics; features to support generic programming were added later, gradually. Much of the existing literature on supporting generic programming focuses thus on retrofitting generic programming into existing languages and identifying related implementation challenges. Is the programming experience significantly better, or different when programming with a language designed for generic programming without limitations from prior language design choices? Approach: We examine Magnolia, a language designed to embody generic programming. Magnolia is representative of an approach to language design rooted in algebraic specifications. We repeat a well-known experiment, where we put Magnolia's generic programming facilities under scrutiny by implementing a subset of the Boost Graph Library, and reflect on our development experience. Knowledge: We discover that the idioms identified as key features for supporting Stepanov-style generic programming in the previous studies and work on the topic do not tell a full story. We clarify which of them are more of a means to an end, rather than fundamental features for supporting generic programming. Based on the development experience with Magnolia, we identify variadics as an additional key feature for generic programming and point out limitations and challenges of genericity by property. Grounding: Our work uses a well-known framework for evaluating the generic programming facilities of a language from the literature to evaluate the algebraic approach through Magnolia, and we draw comparisons with well-known programming languages. Importance: This work gives a fresh perspective on generic programming, and clarifies what are fundamental language properties and their trade-offs when considering supporting Stepanov-style generic programming. The understanding of how to set the ground for generic programming will inform future language design.