Reviewerly/exHarmony huggingface dataset:
<i>In situ</i> detection of RF breakdown on microfabricated surface ion traps

Microfabricated surface ion traps are a principle component of many ion-based quantum information science platforms. The operational parameters of these devices are pushed to the edge of their physical capabilities as the experiments strive for increasing performance. When the applied radio-frequency (RF) voltage is increased too much, the devices can experience damaging electric discharge events known as RF breakdown. We introduce two novel techniques for in situ detection of RF breakdown, which we implemented while characterizing the breakdown threshold of surface ion traps produced at Sandia National Laboratories. In these traps, breakdown did not always occur immediately after increasing the RF voltage, but often minutes or even hours later. This result is surprising in the context of the suggested mechanisms for RF breakdown in vacuum. Additionally, the extent of visible damage caused by breakdown events increased with applied voltage. To minimize the probability for damage when RF power is first applied to a device, our results strongly suggest that the voltage should be ramped up over the course of several hours and monitored forbreakdown.
Anomaly Detection of Photovoltaic Systems Installed in Renewable Energy Housing Support Project Sites by Analyzing Power Generation Data

In this study, we proposed a new method of detecting abnormalities by analyzing power generation data of photovoltaic (PV) systems installed in renewable energy housing support project sites. The study site is north of Gakbuk-myeon, Cheongdo-gun, Gyeongsangbuk-do, Korea, where 63 PV systems have been installed and operated. Based on the system design and surrounding environment, the 63 PV systems were clustered into 6 groups using the K-means clustering method, which is an unsupervised machine learning algorithm. The power production data from the PV systems in each group were analyzed and set as abnormal values if they deviated from the range of ±2.58 times the standard deviation from the mean (assuming a normal distribution and 99% confidence interval). As a result, several abnormalities were detected in the PV systems in November 2020. The cause of the abnormalities was confirmed through site investigation. The proposed method is expected to accelerate the diagnosis of PV systems in renewable energy housing support project sites.
Making Few-Shot Object Detection Simpler and Less Frustrating

Few-shot object detection is useful in order to extend object detection capabilities in media production and archiving applications with specific object classes of interest for a particular organization or production context. While recent approaches for few-shot object detection have advanced the state of the art, they still do not fully meet the requirements of practical workflows, e.g., in media production and archiving. In these applications, annotated samples for novel classes are drawn from different data sources, they differ in numbers and it may be necessary to add a new class quickly to cover the requirements of a specific production. In contrast, current frameworks for few-shot object detection typically assume a static dataset, which is split into the base and novel classes. We propose a toolchain to facilitate training for few-shot object detection, which takes care of data preparation when using heterogeneous training data and setup of training steps. The toolchain also creates annotation files to use combined data sets as new base models, which facilitates class-incremental training. We also integrated the toolchain with an annotation UI.
Toward Robust Response Selection Model for Cross Negative Sampling Condition

Open-domain dialogues can be formulated as a dialogue response selection task that selects the most appropriate response in a given dialogue context. In such a task, neural dialogue models typically predict responses based on context-response content similarity, but models with over-reliance on the similarity are not robust in real-world scenarios. Although various methods have been proposed to utilize adversarial negative responses enabling selection models to learn features beyond content similarity, selection models that rely on adversarial negative responses rather do not distinguish random negative responses well. We need robust selection models independent of the different types of negative responses. To mitigate this problem, this paper suggests the need for a novel training method or model architecture by identifying the performance of a selection model dependent on the type of negative response. Using both random and adversarial negative responses for training may be a basic solution, but we can observe that this solution still shows performance degradation in the random test dataset. Such an observation supports the need for robust selection models for the distribution shift of negative responses.
Research on deep correlation filter tracking based on channel importance

Abstract Correlation filter tracking requires little prior knowledge of the tracking target (e.g., the shape, and the posture) but has a fast-tracking speed. The deep features extracted by the deep convolutional neural network have strong representation ability, so the tracking method based on the combination of correlation filter and deep convolutional neural network, named as deep correlation filter tracking, is a hot issue in the field of target tracking at present. However, the deep convolutional neural network largely restricts the real-time performance of the deep correlation filter tracking because of its complex network structure and heavy computation burden. To balance the contradiction between tracking speed and tracking accuracy, a new channel importance is defined and the channel importance based method of how to select the important channels is given in this paper. And then, a deep correlation filter tracking method based on channel importance is proposed to lighten the feature network, reduce the computation load and improve the tracking speed under the premise of ensuring the tracking accuracy. In the process of tracking, the structural similarity index measurement (SSIM) of the predicted tracking target in two consecutive frames is calculated in real-time. Based on the SSIM, determine whether the feature network needs to be updated, and decide whether the tracking fails. If the feature network needs to be updated, the feature network will be updated online while the tracking is on. If the tracking fails, the target will be searched again, and the tracking is recovered from the failure. The tracking algorithm proposed in this paper is tested on the OTB2013 data set, and the experiment shows that the tracking algorithm designed in this paper can improve the real-time performance while meeting the requirement of tracking accuracy. The online update of the feature network can make the network adapt to the complex background and target changes to improve tracking accuracy; In the case of tracking failure, the re-tracking module can search for the target again and resume tracking given that the target is always present.
Development of Visually Induced Motion Sickness Evaluation Index Using Gaze Data of Elderly People

The benefit of using a noncontact eye-tracking system is its low a burden on individuals for measuring biological signals. The goal of this study was to develop a visually induced motion sickness (VIMS) evaluation index using data collected with a noncontact eye-tracking system for driving simulator (DS) experiments.The participants included nine elderly people with visual and balance functions that did not interfere with their daily life. The gaze data of the participants were measured at rest-both before and after DS trials. The participants answered followed up the simulator sickness questionnaire (SSQ) before and after each trial. The participants were divided into two groups on the basis of their SSQ results. One group experienced VIMS during the DS trial (four people; average age, 79.0 years), whereas the other group did not experience it (five people; average age, 71.2 years).The results of VIMS symptoms were confirmed: data concerning the locus of eye-tracking were lengthened, the eye-tracking data were diffused. This experiment demonstrated the usefulness of sparse density as a quantification index based on eye-tracking data in the evaluation of VIMS.Regarding the application of the findings of this study, it is believed that it will be easier to detect VIMS symptoms induced by DS operations if the index can be used for an eye-tracking data-based evaluation of VIMS.
INFORMATION AND COMMUNICATION TECHNOLOGIES AT THE 1&lt;sup&gt;ST &lt;/sup&gt;STAGE OF THE PRIMARY SCHOOL DURING PANDEMIOE COVID-19

The aim of this paper was to find out how primary school teachers perceive modern technology as a tool for distance learning in the wave of the Covid-19 pandemic.What was their experience in this area, time and what equipment (software and hardware) they had to fulfill the educational essence of their profession.As a method for the research survey, we used a questionnaire, where we evaluated the answers from a total of 149 respondents -teachers working at the first stage of primary schools.The questionnaire consisted of 17 questions, partly socio-demographic and, in connection with them, questions with regard to the essence of the research survey.From the overall output of the research survey, it is clear that primary school teachers, with regard to the demographic distribution, have a positive attitude towards modern technologies.This relationship / attitude was evaluated with a final grade of 1.74 (grade taken as part of the school environment).The material provision of distance learning or technology in ordinary frontal teaching is not at all surprising.Educators make extensive use of a technique that is generally known and used in other professions or in the daily activities of the ordinary person.It is mainly a laptop and a printer.The issue of modern technologies and online tools is very complex, because it is a completely new era that the current population is going through.Technology development is advancing very intensively.It is now the educators who guide current pupils through basic education and give them a foundation in digital and information technologies who need to deepen their knowledge and skills.Given the pandemic situation within Covid-19, it is clear that there has been a radical reversal in the ability of teachers and the schools themselves to access distance learning with the help of modern technology.
What is “fake news” and “hate speech” and how do they work in practice?

Bader, Lea, and Jochen Bender. 2022. “What Is “fake news” and ‘hate speech’ and How Do They Work in Practice?”. Central and Eastern European EDem and EGov Days 342 (March):17-36. https://doi.org/10.24989/ocg.v.342.1.
Artificial Intelligence and Online Hate Speech Moderation: A Risky Match?

Artificial Intelligence is increasingly being used by social media platforms to tackle online hate speech. The sheer quantity of content, the speed at which is it developed and the enhanced pressure companies are facing by States to remove hate speech quickly from their platforms have led to a tricky situation. This commentary argues that automated mechanisms, which may have biased datasets and be unable to pick up on the nuances of language, should not be left unattended with hate speech as this can lead to issues of violating freedom of expression and the right to non-discrimination.
Advancing Human Understanding with Deep Learning Go AI Engines

Humans mainly learned from other human beings for thousands of years. Recent advancements in artificial intelligence (AI) seem to have changed this setup. Due to deep learning, we now have access to automatically generated high-quality statistical knowledge beyond human expert intuition in many fields. However, the representation is not human-friendly: an opaque mass of pure associations instead of narrative, causal explanations. Here, we investigate the epistemological problem of using AI data for human understanding and suggest an active approach based on the scientific method. Following tradition in AI, we focus on a game. Go is a well-defined problem domain that is complex enough that our approach may provide solutions in other fields of knowledge, too, where AI technology outperforms humans.
Visualizing the customization endeavor in product-based-evolving software product lines: a case of action design research

Abstract Software Product Lines (SPLs) aim at systematically reusing software assets, and deriving products (a.k.a., variants) out of those assets. However, it is not always possible to handle SPL evolution directly through these reusable assets. Time-to-market pressure, expedited bug fixes, or product specifics lead to the evolution to first happen at the product level, and to be later merged back into the SPL platform where the core assets reside. This is referred to as product-based evolution. In this scenario, deciding when and what should go into the next SPL release is far from trivial. Distinct questions arise. How much effort are developers spending on product customization? Which are the most customized core assets? To which extent is the core asset code being reused for a given product? We refer to this endeavor as Customization Analysis , i.e., understanding the functional increments in adjusting products from the last SPL platform release. The scale of the SPLs’ code-base calls for customization analysis to be conducted through Visual Analytics tools. This work addresses the design principles for such tools through a joint effort between academia and industry, specifically, Danfoss Drives, a company division in charge of the P400 SPL. Accordingly, we adopt an Action Design Research approach where answers are sought by interacting with the practitioners in the studied situations. We contribute by providing informed goals for customization analysis as well as an intervention in terms of a visual analytics tool. We conclude by discussing to what extent this experience can be generalized to product-based evolving SPL organizations other than Danfoss Drives.
Automatic Demand Response Method for the Energy Storage Resource System Based on the Blockchain Technology Combined with Sensors

In order to improve the efficiency of the automatic demand response of the energy storage resource system, a user authentication and key agreement scheme for wireless sensor networks based on blockchain is proposed. The user does not need to register with the gateway node. As the light node of the blockchain network, the two can directly authenticate each other. The user obtains the public information of the sensor node from the blockchain through the client and verifies its legitimacy. It solves the problem that the traditional wireless sensor network user authentication scheme cannot be applied to the distributed Internet of things environment and the authentication process is low in security and efficiency. Mode 1 is the disordered charging mode. The charging facility provides continuous constant power charging service for the connected response subject until the user leaves. If the response subject is full before this, the charging will stop. Mode 2 is the BADR operation mode under charging optimization only, that is, the V2G function of PEV is not considered. In this case, <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mn>0</mn> <mo>≤</mo> <msub> <mrow> <mi>P</mi> </mrow> <mrow> <mi>l</mi> <mo>,</mo> <mi>k</mi> </mrow> </msub> <mo>≤</mo> <msubsup> <mrow> <mi>P</mi> </mrow> <mrow> <mi>l</mi> </mrow> <mrow> <mi mathvariant="normal">max</mi> <mi>c</mi> </mrow> </msubsup> </math> . Mode 3 is the BADR operating mode with V2G functionality in mind. The ELN load characteristics under the above three operating modes are discussed, respectively. Calculate and compare with economy on both sides of supply and demand. The experimental results show that the discharge compensation coefficient will not affect mode 2. In mode 3, as the compensation coefficient increases (from 0.4 to 1.2 at intervals of 0.2), the average total cost of the PEV cluster continues to decrease and the average total cost of the energy local network (ELN) system increases monotonically. Comparing the curves of the economic impact of the compensation coefficient on both sides of mode 2 and mode 3, it can be seen that the reasonable setting of the compensation coefficient is one of the key parameters for controlling the distribution of benefits. It proves that the proposed blockchain-based automated demand response (BADR) method can not only meet the satisfaction of energy demand but also coordinate the available energy storage resources in the system, so that the load level curve can track the output of new energy to the greatest extent and improve the balance between supply and demand of the system, which is suitable for a large-scale decentralized energy storage system.
Academic performance in virtual learning environments post COVID-19 pandemic in higher education

The aim of this study is to analyze academic performance in virtual learning environments during the Covid 19 pandemic for Institutions Incorporated into the University located in the State of Mexico. For this, a review of scientific literature was carried out, which is based on current references in specialized databases. A quantitative design of this study based on an experimental study with a sample of 15 institutions, 1200 professors who taught at distance or virtually in the academic period, also 700 students were applied instruments during scholar period of year 2020- 2021, 2022. Data collection used different surveys. The information analysis is described narrative and presented by graphic, tables, and diagrams. The results of these surveys shown that, 80% learnt at distance, nevertheless 20% decided to quit the classes. The most used platform was Google classroom, the main communication plataforms used were Facebook, Zoom and WhatsApp. The 70% of the contents of the programs were completed. The 90% consider that academic performance is subjective. The 90% of theachers require developing digital competences 95% consider that access to technology is an oportunity to achieve learning.
A hybrid approach towards movie recommendation system with collaborative filtering and association rule mining

There is a huge information stockpile available on the internet. But the available information still throws a stiff challenge to users while selecting the needed information. Such an issue can be solved by applying information filtering for locating the required information through a Recommender System. While using a RS, the users find it easy to curate and collect relevant information out of massive databanks. Though various types of RS are currently available, yet the RS developed by Collaborative Filtering techniques has proven to be the most suitable for many problems. Among the various Recommended Systems available, movie recommendation system is the most widely used one. In this system, the recommendations will be made based on the similarities in the characteristics as exhibited by users / items. The movie recommendation system contains a huge list of user objects and item objects. This paper combines Collaborative Filtering Technique with association rules mining for better compatibility and assurance while delivering better recommendations. Hence, in the process, the produced recommendations can be considered as strong recommendations. The hybridization involving both collaborative filtering and association rules mining can provide strong, high-quality recommendations, even when enough data is unavailable. This article combines various recommendations for creating a movie recommendation system by using common filtering techniques and data mining techniques
Research on super-resolution reconstruction of single-frame image of infrared focal plane polarization imaging

Since the low resolution of infrared focal plane arrays may degrade the performance of polarization imaging significantly, it is necessary to study the super-resolution reconstruction method for superior image resolution and contrast. Four typical single-frame image reconstruction methods are studied in this paper, and the comparison of reconstruction result of these methods is conducted by using subjective and objective evaluation. The experiments show that the reconstruction method based on generative adversarial network performs poorly in the evaluation indexes such as peak signal-to-noise ratio and structural similarity, but its reconstructed image has good visual effects, rich texture and details, and has a strong ability to suppress background noise, and using the reconstructed images for polarization information parsing can significantly improve the accuracy of polarization information parsing and the effect of polarization image fusion.
PopNet: Real-Time Population-Level Disease Prediction with Data Latency

Population-level disease prediction estimates the number of potential patients of particular diseases in some location at a future time based on (frequently updated) historical disease statistics. Existing approaches often assume the existing disease statistics are reliable and will not change. However, in practice, data collection is often time-consuming and has time delays, with both historical and current disease statistics being updated continuously. In this work, we propose a real-time population-level disease prediction model which captures data latency (PopNet) and incorporates the updated data for improved predictions. To achieve this goal, PopNet models real-time data and updated data using two separate systems, each capturing spatial and temporal effects using hybrid graph attention networks and recurrent neural networks. PopNet then fuses the two systems using both spatial and temporal latency-aware attentions in an end-to-end manner. We evaluate PopNet on real-world disease datasets and show that PopNet consistently outperforms all baseline disease prediction and general spatial-temporal prediction models, achieving up to 47% lower root mean squared error and 24% lower mean absolute error compared with the best baselines.
Integrating Mobile Computing in University Information Management Systems to Improve Access and User Experience

Objectives: To develop a mobile application and integrate it with an existing University Management System for improving access and user experience for services to be accessed anytime, anywhere through connected smartphones.Methods: An online survey, interviews, and observations to collect the system requirements were gathered and analyzed essential requirements for the design and development of the mobile application.Agile software methodology was used because of its flexibility to accommodate changes.The integration architecture was based on RESTful API and SOAP web services.The System Usability Scale (SUS) methodology was applied to assess the user's perception and usefulness of the mobile application.Findings: The developed Pwani University (PU) mobile application was able to successfully connect with the Students Information Management System (SIMS) to access services.Unit, integration, and system testing were performed to ensure the intended specifications were met.The key findings from the survey show that smartphones, the majority of which run on the Android platform, are the most commonly owned and used mobile devices among students.The majority of students 97% agreed that the justifications for developing the app were valid.The study indicated that 68% of students want access to course information, hostel information, and exam results via their mobile gadgets.The usability and acceptance testing were very successful with more than 90% of users liking the app and more than 80% did not need any assistance to use the app and did not find it cumbersome.Novelty : The developed mobile application provides seamless integration with the PU SIMS, increasing access to services, portability, and convenience to staff and students.