Reviewerly/exHarmony huggingface dataset:
Automated Essay Scoring (AES) using English Essay Question

Automated Essay Scoring (AES) is a one of the important research areas in educational technology. Research about AES is started in the early 1960s and keep growing in development along with the advances of the computing technology. AES is more widely use by the educational system for assessment and classroom activities, it being use in all layers of education. The potential for Automated Essay Scoring is being used widely becomes a reality as more and more classroom activities, assessment, and test preparation materials are supplied online. These essay questions might include everything from arithmetic, where students are asked to explain how they arrived at their answers, to science, where they might be asked to define words or explain experiments, or history, where they must ask to explain or discuss an event. In this study we try to design the AES using Natural Language Processing (NLP) and machine learning techniques to evaluate the student's answer by comparing it with the answer scheme. It is not simply a string-matching program and need a proper system to process the students answer. We propose the 4 main process to the system that is Spelling Checking, Pre-Processing, Latent Semantic Analysis and Thesaurus Checking. All this process is combined and analyze to become a good AES system. Each of this process mean give somethings important to the system flow on how the essay going to be evaluate. The system has been done some experiment and the system get the average correlation between system scores and human appraiser scores is only 70%. However, there needs to be more research on the future use of this Automatic Essay Scoring system.
Phase Modulation of the Input Signal Improves Performance of Reservoir Computing

In reservoir computing, semiconductor lasers with delayed feedback can efficiently solve difficult and time-consuming problems. The input data is optically injected into the laser. We show numerically that far better performance is observed using phase modulation of the injected field rather than intensity modulation.
AnalogVNN: A Fully Modular Framework for Photonic Analog Neural Networks

Optimal hyperparameters and inference accuracy for analog-based deep learning hardware is highly dependent on system architecture and component noise. We present AnalogVNN as a fully modular framework to easily model and train arbitrary analog photonic neural networks using the simple modular layer structures of PyTorch.
Entanglement Assisted Multistatic Radars

An entanglement assisted (EA) multistatic radar is proposed that significantly outperforms corresponding EA bistatic, coherent state-based quantum, and classical radars. Proposed EA multistatic radar employs multiple entangled transmitters performing transmit side optical phase conjugation, multiple coherent detection-based receivers serving as EA detectors, and a joint detector.
Migrating Regional Development Plan Forum System to Service Platform Based on Microservices

Regional Development Plan Forum (Musrenbang) is an activity to support the development planning and budgeting process of local government in Indonesia. Medan City has developed an information system to manage that activity and manage the data of development planning. The information system is referred to as DevPlan and is built in monolithic approach. In the utilization of DevPlan problems arises. Complicated when it is repaired an error occurs or when it is updated due to modifications in Regional Development Planning Body (Bappeda) business process or regulations. Also, the repetitive activities in the same data management on the development planning system built by the Ministry of Home Affairs. These problems could be solved by migrating the system into service platform based on microservice to increase the efficiency and effectiveness in the Musrenbang process, with the service platform the two systems or others can be integrated and use the resources together. Service computing system engineering (SCSE) combined with migration strategy is used as a methodology to help the migration and produce the design and prototype of IT Services.
Digital Forensic Readiness Information System For EJBCA Digital Signature Web Server

As the nature of the website, the EJBCA digital signatures may have vulnerabilities. The list of web-based vulnerabilities can be found in OWASP's Top 10 2021. Anticipating the attack with an effective and efficient forensics application is necessary. The concept of digital forensic readiness can be applied as a pre-incident plan with a digital forensic lifecycle pipeline to establish an efficient forensic process. Managing digital evidence in the pre-incident plan includes data collection, examination, analysis, and findings report. Based on this concept, we implemented it in designing an information system that carries out the entire flow, provides attack evidence collection, visualization of attack statistics in executive summary, mitigation recommendation, and forensic report generation in a physical form when needed. This research offers an information system that can help the digital forensic process and maintain the integrity of the EJBCA digital signature server web.
Data Transmission And Off-board Object Detection Of A Visual Inspection Drone In The Factory Shop Floor Area

Drone can be used to perform a visual inspection of the machine's condition in the shop floor area of the factory. In this work, a drone has been created to reduce the duration of visual inspections and to do object detection on milling and grinding machines. To carry out the functionality, it is necessary to have system parts that are useful for providing visual data for inspectors and the drone pilot. This article discusses the design, implementation, and testing of product parts related to image acquisition from a visual sensor, image data transmission to an off-board computer, and machine object detection. There are four product specifications related to these parts, which are video frame rate, video delay, bounding box accuracy, and detection functionality. The results obtained from the tests are in accordance with what has been specified.
SupTech Governance in Regulatory/Supervisory Government Agencies: A Systematic Literature Review

Supervision is the process of ensuring that organizational and management objectives are achieved. Supervision as a control tool in monitoring and reporting activities. Supervision starts from planning, implementing, monitoring, and evaluating to produce the right policies or decisions. Supervision can not be separated from the rules that have been set. The supervision aspect is also assessed by how obedient it is in implementing the regulations that have been set, including the aspect of accelerating regulatory changes in the event of regulatory changes that must be adjusted. The limitations of human resources in analyzing several existing regulations and conducting supervision from existing data sources directly result in the quality of monitoring results being not optimal. The limited time for supervision is considered less than optimal for supervising more complex problems. SupTech is here as an innovation to change the monitoring system to be more accurate, effective, and efficient. This paper describes the systematic literature on governance, technology oversight, and sub-regulators in government agencies. The research process by searching for articles through the database of article sources published from 2017 to 2022 by carrying out the stages of the literature study process based on the research objectives. Several stages were carried out to obtain the final study article. There is a research article as a final study related to research questions that can answer SupTech that can be applied to the field of supervision. Researchers feel the need to research more deeply about the standardization of IT governance guidelines for SupTech.
$\ell\rho$-suppression: A Privacy Preserving Data Anonymization Method for Customer Transaction Data Publishing

Publishing a transaction database to other parties is becoming a common way to harvest useful information from the database. Though, the database usually contains privacy concerns such as personal private itemset. Therefore, it is crucial to protect sensitive information prior to sharing the database to other parties. A solution to preserve the privacy of data subject in the transaction database is by modifying the transaction records using specific data anonymization method. Data anonymization method such as local generalization is widely adopted to solve the issue. However, the method induces excessive item loss and reduce data utility significantly. In this paper, a privacy preserving method to protect private itemsets of individual data subjects namely <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\ell\rho$</tex> -suppression is proposed. The method adopts cell suppression technique where personal sensitive items in transaction records are omitted. The method is distinct from an existing method, where it uses maximum term frequency normalization of the items in the database and it only suppresses items from transaction records having the privacy ration higher than that of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\ell\rho$</tex> value. Experimental results indicate the proposed methods can successfully enhance the privacy protection level and has the ability to minimize the number of item loss as well as maintaining data utility level in an anonymized database.
Development of Distributed Multimedia System for Ensuring Quality of Experience (QoE) on Video-on-Demand (VOD) Streaming Service

The rise of technological adoption of video-based information distribution accelerated by the COVID-19 pandemic leads to a substantial increase in video streaming on-demand service users. Video streaming services such as Twitch, and YouTube have enormous amount of active users so the system developers need to develop a system capable of handling numerous requests. The increase of video streaming on-demand service users poses a challenge for system developers to guarantee the Quality of Experience (QoE) for all system users. Said surge of users also resulted in high resource usage to serve each user while securing QoE. In order to ensure the QoE on video streaming on-demand service, this work develops the system using the waterfall model that implements a distributed approach for the video streaming service system. The system has reverse proxy and load balancing capabilities to forward and equally distribute HTTP requests and uses DASH to segment the video file to stream the video without sending the whole file. The developed system for this work subjectively ensures the QoE with a mean opinion score (MOS) of 4.434 from 53 respondents.
GHz-Rate Neuromorphic Photonic Spiking Neural Network with a Vertical-Cavity Surface-Emitting Laser

We report a high-speed (GHz rate) and hardware-friendly Neuromorphic Photonic Spiking Neural Network (pSNN) architecture formed by up to >1000 spiking nodes and built with a single off-the-shelf telecom Vertical-Cavity Surface Emitting Laser (VCSEL). We demonstrate the successful application of the proposed pSNN architecture to a complex nonlinear classification task, running at sub-nanosecond speeds per spike directly in the optical domain.
Using an integrative meta-analyzis to examine the effects of smart classroom-based instruction on academic achievement: a technological change perspective

With the development of education technology, Smart classroom has evolved to version 2.0. Currently, the meta-analysis literature on the effects of smart classroom-based instruction on academic achievement ignores the impact of technological changes and time on the effect sizes. This study incorporated the impact of technological changes and time, and proposed an integrative algorithm suitable for meta-analysis of students' academic achievement in applying education technology. The research results indicate that the overall average weighted effect size is 0.8, revealing that smart classroom-based instruction has a larger positive effect on students' academic achievement than non-smart classroom-based instruction. In addition, the average effect size is affected by moderator variables such as smart classroom type and year, learning management system and year, teaching strategy, sample size (experimental group), and school location, but is not affected by educational stage, learning domain, and experiment duration. The advantages of the proposed algorithm include: (1) using this algorithm, we can understand the changes in the effect size with the different types of education technology and the passage of time; and (2) this algorithm provides a quantitative analysis process, which can be used as a reference for the related research.
Submodular maximization over data streams with differential privacy noise

In the big data era, data often comes in the form of streams and fast data stream analysis has recently attracted intensive research interest. Submodular optimization naturally appears in many streaming data applications such as social network influence maximization with the property of diminishing returns. However, in a practical setting, streaming data frequently comes with noises that are small but significant enough to impact the optimality of submodular optimization solutions. Following the framework of differential privacy (DP), this paper considers a streaming model with DP noise that is small by construction. Within this noisy streaming model, the paper strives to address the general problem of submodular maximization with a cardinality constraint. The main theoretical result we obtained is a streaming algorithm that is one-pass and has an approximation guarantee of 1(2+(1+1k)2)(1+1k)−δ for any δ>0. Finally, we implement the algorithm and evaluate it against several baseline methods. Numerical results support the practical performance of our algorithm across several real datasets.
A UAV to UAV tracking benchmark

Visual object tracking has achieved remarkable progress in recent years. However, current research in the vision community mainly focuses on tracking of generic objects, while less attention is paid to tracking unmanned aerial vehicle (UAV) especially from a moving platform. In this work, we explore this problem by constructing a UAV to UAV (UAV2UAV) tracking dataset. Specifically, this dataset consists of 44 videos (23k frames). Each video is manually annotated with bounding boxes. To the best of our knowledge, this dataset is the first work dedicated to UAV2UAV tracking. We extensively evaluate 19 state-of-the-art trackers using both hand-crafted feature based and deep-learning based approaches to understand how existing tracking methods perform and to provide a baseline for future works. The evaluation results show that more research works are needed to improve UAV2UAV tracking. In addition, we introduce a novel tracker, which leverages data augmentation technique for UAV tracking to encourage future research. By releasing this dataset, we expect to facilitate future studies and applications of UAV2UAV tracking in both academia and industry communities. Our dataset is available at: https://github.com/hapless19/UAV2UAV-dataset.
The applications of hyperspectral remote sensing techniques in the identification of subsurface faults. An experimental study

The study shows that geological faults can be identified by hyperspectral remote sensing techniques. The study was carried out at the Balapur fault, the longest evident fault in the Kashmir valley, NW Himalaya. The results indicate that the faults constitute a good oxidation and reduction environment, which causes some mineral elements in the soil to undergo oxidation and reduction reactions. The absorption features at the strike of the fault are typical of Fe3+ and Fe2+ and the soil spectrum across the fault is different and can be separated by clustering and discrete wavelet transform.
Attention-guided complex denoising network for automatic modulation recognition

With the rapid progress in artificial intelligence technology in recent years, deep learning has gradually become the main method in the field of radar signal automatic modulation recognition (AMR). Under harsh condition of lower signal–noise ratio (SNR), extracting the useful features of the noisy radar signal in the time or the time–frequency domain is extremely difficult because of serious noise corruption of the clean radar signals. Considering the complex-valued characteristic of radar signal, we propose an attention-guided complex denoising network (ACDNet) that consists of denoising and recognition modules. By utilizing the denoising module, we can estimate denoised signal in the noisy background, and thus the more distinctive features of the clean signal are extracted. The recognition module uses the extracted information to complete the modulation recognition task. Furthermore, squeeze-and-excitation blocks (channel attention mechanism) and the network are merged to guide the network to obtain more efficient performance. The experimental results demonstrate that ACDNet has obvious advantages over comparison deep learning algorithms at lower SNRs.
Birefringence learning: A new global optimization technology model based on birefringence principle in application on artificial bee colony

Optimization technologies play a key role in addressing sophisticated optimization problems. However, with the increasing scale and dimension of problems, the performance of optimization technologies has been greatly challenged. To better address them, a new global optimization model is proposed inspired by the birefringence phenomenon, namely a birefringence learning (BRL) model which simulates the birefringence phenomenon of two refracted beams formed by the incident beam in the nature when the natural light enters some certain mediums. To substantiate the optimization performance of the model, it is applied to the artificial bee colony algorithm (ABC) to enhance its global optimization performance further and make its local optimization (exploitation) and global optimization (exploration) balance to some extent. In ABC, the BRL model as a mutation operator is employed in the phase of the scout bee to decrease the probability of ABC trapping into the local extreme region and then a novel ABC algorithm using the birefringence learning (NABC-BRL) is proposed. Via conducting abundant numerical experiments on some universally known benchmark functions, the experimental results and analysis indicate that it can obtain higher accuracy of solutions and faster convergence on majority of benchmark functions compared with some well-acknowledged algorithms, which also proves the effectiveness of global optimization of the BRL model.
Effective algorithms for mining frequent-utility itemsets

The current pattern mining algorithms focus on discovering either frequent itemsets or high-utility itemsets. The goal of this research is to study the problem of mining frequent-utility itemsets. To solve this problem, two novel algorithms named FUIMTWU-Tree (Frequent-utility Itemset Mining based on TWU-Tree) and FUIMTF-Tree (Frequent-utility Itemset Mining based on TF-Tree) are presented based on the integration of IHUP and HUI-Miner. The TWU-tree and TF-Tree structures are utilised to avoid the unnecessary utility-list construction of itemsets that do not appear in a transaction dataset. The performance of the proposed algorithms is evaluated on various datasets. The results of the experiments demonstrate that FUIMTWU-Tree and FUIMTF-Tree perform efficiently in terms of speed, pruning performance and scalability.
Confidently Comparing Estimates with the c-value

Modern statistics provides an ever-expanding toolkit for estimating unknown parameters. Consequently, applied statisticians frequently face a difficult decision: retain a parameter estimate from a familiar method or replace it with an estimate from a newer or more complex one. While it is traditional to compare estimates using risk, such comparisons are rarely conclusive in realistic settings. In response, we propose the “c-value” as a measure of confidence that a new estimate achieves smaller loss than an old estimate on a given dataset. We show that it is unlikely that a large c-value coincides with a larger loss for the new estimate. Therefore, just as a small p-value supports rejecting a null hypothesis, a large c-value supports using a new estimate in place of the old. For a wide class of problems and estimates, we show how to compute a c-value by first constructing a data-dependent high-probability lower bound on the difference in loss. The c-value is frequentist in nature, but we show that it can provide validation of shrinkage estimates derived from Bayesian models in real data applications involving hierarchical models and Gaussian processes. Supplementary materials for this article are available online.
An induction principle for the Bombieri-Vinogradov theorem over <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si1.svg"><mml:msub><mml:mrow><mml:mi mathvariant="double-struck">F</mml:mi></mml:mrow><mml:mrow><mml:mi>q</mml:mi></mml:mrow></mml:msub><mml:mo stretchy="false">[</mml:mo><mml:mi>t</mml:mi><mml:mo stretchy="false">]</mml:mo></mml:math> and a variant of the Titchmarsh divisor problem

Let Fq[t] be the polynomial ring over the finite field Fq. For arithmetic functions ψ1,ψ2:Fq[t]→C, we establish that if a Bombieri-Vinogradov type equidistribution result holds for ψ1 and ψ2, then it also holds for their Dirichlet convolution ψ1⁎ψ2. As an application of this, we resolve a version of the Titchmarsh divisor problem in Fq[t]. More precisely, we obtain an asymptotic for the average behaviour of the divisor function over shifted products of two primes in Fq[t].
New social contracts and digital intimacy in mammalian diving reflex’s the lockdown resolution

In July 2021, Canadian performance company Mammalian Diving Reflex premiered The Lockdown Resolution at Arnolfini, a contemporary art centre in Bristol, UK. Combining virtual reality (VR) hardware, 360° film and Zoom teleconferencing, the performance connected ten ‘in real life’ (IRL) audience members to ten remote audience members around the world. Through conversations and immersive, tech-mediated interaction, audiences found out about each other and the lives of three young Bristolians during lockdown. Drawing on research in VR and social dynamics, this paper examines human-tech interfacing in The Lockdown Resolution, exploring synchrony, social presence and intimacy to social cohesion. Examining social engagement through multiple-reality performance, producer Nadia Abdelaziz and Mammalian Founder Darren O’Donnell investigate digital intimacy in performance and its role in creating an opportunity for civic development. At the centre of this activity, Mammalian create space for young people to lead these interactions. Bristol-based young performers Chris Lewis (18), Alke Schwarz (19) and Germain Loud (21) co-devised the show working with local creatives over a series of workshops, meetings and rehearsals. Offering insight into Mammalian Diving Reflex’s working methods, we imagine and discuss the civic potential of new social contracts that might be created through intimate and immersive digital performance.