Reviewerly/exHarmony huggingface dataset:
Bewertung und Nutzungsbereitschaft des interaktiven Küchenroboters AuRorA bei älteren Erwachsenen

Abstract Background The number of older adults in need of care and living at home is increasing in Europe. At the same time, the number of professional caregivers is decreasing. This development reinforces the need for assistive technology to support care recipients in their own homes and promote their independence. One of the main challenges of independent living is the preparation of food. Interactive robots could assist older adults with difficulties performing physically demanding tasks. Within the project AuRorA (full German project title: Wiederverwendbare, interaktive Verhalten für proaktive Roboter im Smart Home), an interactive voice-controlled robot arm was developed as an assistance system in the kitchen. Objective The aim of the study was to assess how older adults evaluate the AuRorA system and to collect data on actual willingness to use the technology. Older adults were asked to evaluate the system in terms of usefulness, usability, accessibility and intention to use. Material and methods Due to the ongoing coronavirus disease 2019 (COVID-19) pandemic, the older adults evaluated the system via an online survey. The validated questionnaire Technology Usage Inventory (short: TUI) was used in conjunction with self-developed questionnaires to collect data on study population characteristics. Results A total of 106 participants were included in the analysis. The acceptance, usability and usefulness of the system were rated as medium, while the intention to use was rated as low. A significant strong correlation was found between the TUI subscales intention to use and usefulness. Conclusion It can be assumed that the actual need of the individual participant for such a robotic assistive system had an influence on the evaluation of the system. The perceived usefulness may have been a crucial influence on the intention to use and the overall assessment of the system.
Forecasting Electric Power Generation in a Photovoltaic Power Systems for Smart Energy Management

Solar electricity is generated using photovoltaic (PV) systems all over the world. Solar power sources are irregular in nature since PV system output power is intermittent and highly dependent on environmental conditions. Irradiance, humidity, PV surface temperature, and wind speed are only a few of these variables. The uncertainty in photovoltaic generating, it's crucial to plan ahead for solar power generation. Solar power forecasting is required for electric grid supply and demand planning. Because solar power generation is weather-dependent and unregulated, this forecast is complicated and difficult. Selective developed to this goal. Traditional approaches such as statistics, autoregressive moving average, regression, and others were used to forecast PV power before the widespread usage variables are assessed for prediction models based on Artificial Neural Networks (ANN) and regression models. Several PV forecasting algorithms have been of machine learning technologies. Artificial Neural Networks, Support Vector Machines, and hybrid techniques have grown popular as a result of recent advances in machine learning methodologies and access to huge data. This study examines the impacts of numerous environmental conditions on PV system output, as well as the working principle and application of various PV forecasting approaches, in order to better comprehend the insights of PV prediction. Furthermore, the important parameters influencing PV generation are calculated using real-time data.
Solar Irradiance Forecasting Using an Artificial Intelligence Model

The paper presents the solar energy prediction using ANN in order to effectively predict solar irradiance. With increasing interest in the scope for renewable energy, many countries are adopting new technologies of solar photovoltaic which has higher solar resource potential. If in advance of 24 hours, solar irradiance can be predicted, then it would help us immensely to optimize the energy production efficiency. Traditional methods which were used involved empirical, analytical, and physics-based models, statistical forecasting of solar data, and numerical methods to effectively predict the amount of solar irradiation. With the increasing use of Machine Learning better predictive models are being developed which help us to forecast better thereby reducing the error and increasing the efficiency.
Self-Supervised Representation Learning for Video Quality Assessment

No-reference (NR) video quality assessment (VQA) is a challenging problem due to the difficulty in model training caused by insufficient annotation samples. Previous work commonly utilizes transfer learning to directly migrate pre-trained models on the image database, which suffers from domain inadaptation. Recently, self-supervised representation learning has become a hot spot for the independence of large-scale labeled data. However, existing self-supervised representation learning method only considers the distortion types and contents of the video, there needs to investigate the intrinsic properties of videos for the VQA task. To amend this, here we propose a novel multi-task self-supervised representation learning framework to pre-train a video quality assessment model. Specifically, we consider the effects of distortion degrees, distortion types, and frame rates on the perceived quality of videos, and utilize them as guidance to generate self-supervised samples and labels. Then, we optimize the ability of the VQA model in capturing spatio-temporal differences between the original video and the distorted version using three pretext tasks. The resulting framework not only eases the requirements for the quality of the original video but also benefits from the self-supervised labels as well as the Siamese network. In addition, we propose a Transformer-based VQA model, where short-term spatio-temporal dependencies of videos are modeled by 3D-CNN and 2D-CNN, and then the long-term spatio-temporal dependencies are modeled by Transformer because of its excellent long-term modeling capability. We evaluated the proposed method on four public video quality assessment databases and found that it is competitive with all compared VQA algorithms.
Achieving Efficient and Secure Query in Blockchain-based Traceability Systems

With the rapid development of blockchain technology, it provides a new technical solution for secure storage of data and trusted computing. However, in the actual application of data traceability, blockchain technology has an obvious disadvantage: the large amount of data stored in the blockchain system will lead to a long response time for users to query data. Higher query delay severely restricts the development of block chain technology in the traceability system. In order to solve this problem, we propose an efficient, secure and low storage overhead blockchain query scheme. Specifically, we design an index structure independent of Merkle tree to support efficient intra-block query, and create new fields in the block header to optimize inter-block query. Compared with several existing schemes, our scheme ensures the security of data. Finally, we simulate and evaluate our proposed scheme. The results show that the proposed scheme has better execution efficiency while reducing additional overhead.
A comparison between PMBM Bayesian track initiation and labelled RFS adaptive birth

This paper provides a comparative analysis between the adaptive birth model used in the labelled random finite set literature and the track initiation in the Poisson multi-Bernoulli mixture (PMBM) filter, with point-target models. The PMBM track initiation is obtained via Bayes' rule applied on the pre-dicted PMBM density, and creates one Bernoulli component for each received measurement, representing that this measurement may be clutter or a detection from a new target. Adaptive birth mimics this procedure by creating a Bernoulli component for each measurement using a different rule to determine the probability of existence and a user-defined single-target density. This paper first provides an analysis of the differences that arise in track initiation based on isolated measurements. Then, it shows that adaptive birth underestimates the number of objects present in the surveillance area under common modelling assumptions. Finally, we provide numerical simulations to further illustrate the differences.
Target threat estimation based on discrete dynamic Bayesian networks with small samples

The accuracy of target threat estimation has a great impact on command decision-making.The Bayesian network, as an effective way to deal with the problem of uncertainty, can be used to track the change of the target threat level.Unfortunately, the traditional discrete dynamic Bayesian network (DDBN) has the problems of poor parameter learning and poor reasoning accuracy in a small sample environment with partial prior information missing.Considering the finiteness and discreteness of DDBN parameters, a fuzzy k-nearest neighbor (KNN) algorithm based on correlation of feature quantities (CF-FKNN) is proposed for DDBN parameter learning.Firstly, the correlation between feature quantities is calculated, and then the KNN algorithm with fuzzy weight is introduced to fill the missing data.On this basis, a reasonable DDBN structure is constructed by using expert experience to complete DDBN parameter learning and reasoning.Simulation results show that the CF-FKNN algorithm can accurately fill in the data when the samples are seriously missing, and improve the effect of DDBN parameter learning in the case of serious sample missing.With the proposed method, the final target threat assessment results are reasonable, which meets the needs of engineering applications.
Digital Twin Enabled Multi-task Federated Learning in Heterogeneous Vehicular Networks

In the heterogeneous vehicular networks (HetVNets), the base stations (BSs) can exploit the massive amounts of valuable data collected by vehicles to complete federated learning tasks. However, most of the existing studies consider the scenario of one task requester (TR) and ignore the fact that multiple TRs may concurrently generate their model training requests in the HetVNets. In this paper, we consider the scenario of multi-TR and multi-BS and propose a digital twin enabled scheme for multitask federated learning to address the two-way selection problem between the TRs and the BSs. We first analyze the diversified requirements of the TRs in the HetVNets. Then, we develop a novel model that jointly considers the available training data, the declared price, and the training experience to evaluate the differentiated training capabilities of the BSs. After that, based on the requirements of the TRs and the training capabilities of the BSs, the two-way selection problem between the TRs and the BSs is formulated as a matching game in the digital twin networks, where a matching algorithm is designed to obtain their optimal strategies. The simulation results demonstrate that the proposed scheme can obtain the highest model accuracy and bring the highest utility to the TRs compared with the conventional schemes.
Study on Optimization of Data-Driven Anomaly Detection

In the paper, according to the original data and the value of the sensor at different moments, the box diagram method is used to process the data, and divides the normal value and outliers. The two types of outliers were distinguished based on the persistence of the outliers in the longitudinal time of the data and the linkage of the lateral sensors, and the clustering algorithm was used to reclassify the data. Then, persistence and linkage were calculated within each class, dividing the sum of persistence and linkage by the result of the maximum number of possible anomalies as the risk coefficient, and then defining a threshold to distinguish between risk-specific and non-risk anomalies. Later, a comprehensive evaluation model of anomaly degree was established through quantitative score, principal component analysis and 0,1 planning. Finally, this quantitative evaluation method is evaluated objectively.
Sentiment Analysis of Jakarta Bus Rapid Transportation Services using Support Vector Machine

Jakarta Bus Rapid Transportation is state-own company which have services in public transportation. On October 2021, Jakarta Bus Rapid Transportation was recently trending on Twitter. Twitter public views might be utilized for the company as a decision support system for enhance and evaluate the services of the company. A sentiment analysis method may be used to examine public opinion especialy users of Jakarta Bus Rapid Transportation on Twitter. The goal of this research is to better understand Jakarta's public opinion trends about services. The researchers manually classified tweets from the Tweepy collection as Informasi, Apresiasi, Saran, or Komplain. Professionals will classify the sentiment as favorable, negative, or neutral. The data was then pre-processed to eliminate duplicates and extraneous information. The sentiment of fresh data will then be predicted using machine learning. The machine learning algorithms were then examined using a number of tests to discover which kernels and features provided the best accuracy. The result of this method shows of 92.00 percent of accuracy, 91.00 percent of precision, 92.00 percent of recall, and 2123 of support. The majority of Jakartans, according to the data, have an unfavorable impression of bus rapid transit. The majority of customers were disappointed with the services.
An adaptive approach-based ensemble for 1 day-ahead production prediction of solar PV systems

The world is becoming more reliant on renewable energy sources to satisfy its growing energy demand. The primary disadvantage of such sources is their significant uncertainty in power production. As appropriate energy production planning and scheduling necessitate a solid and confident assessment of renewable power production, the necessity for developing reliable prediction models grows by the day. This paper proposes an adaptive approach-based ensemble for 1-day ahead production prediction of solar Photovoltaic (PV) systems. Different ensembles of Artificial Neural Networks (ANNs) prediction models are established, whose architectures (number of the ANNs that comprise the ensembles) and configurations (number of hidden nodes required by the ANNs models of the ensembles) change adaptively at each hour h, h∈ [1, 24] of a day, for accommodating the hour seasonality in the solar PV data and, thus, enhancing the 1 day-ahead predictions accuracy. The suggested approach is tested on a 264 kW solar PV system installed at Applied Science Private University, Jordan. Its prediction performance is evaluated, particularly for different weather conditions (seasons) experienced by the concerned PV system, using standard performance metrics. Results show the effectiveness of the suggested approach in predicting solar PV power production and its superiority compared to another prediction approach of the literature that uses single ANNs at each hour h of the day. Specifically, for 1-day ahead prediction, the obtained enhanced accuracy, on average, was around 8%–10% on the test “unseen” datasets.
CC2020 Visualization Tool

A significant part of the recently published ACM and IEEE-CS report "Computing Curricula 2020: Paradigms for Global Computing Education" [1] was the development of visualizations of any computing degree and the comparison with the ACM and IEEE-CS approved curricula. This visualization covers discipline areas that have approved curricula: Computer Engineering, Computer Science, Software Engineering, Information Systems, Information Technology and Cyber Security. The CC2020 report developed a "Landscape of Computing Knowledge" table (see table 1) aggregating all the topic areas of all six approved curricula into 34 topic areas and then assigned a minimum and maximum value for each of topic areas. As part of the visualizations project an online application has been developed (see Figure 1) where stakeholders can assign a minimum and maximum value to each of the topic areas that are required in their degree program and then that program can be matched against the current ACM/IEEE-CS approved curricula and other degree programs globally. This will have significant importance for educators and other stakeholders of computing degree programs.
Randomized approximate class-specific kernel spectral regression analysis for large-scale face verification

Kernel methods are known to be effective to analyse complex objects by implicitly embedding them into some feature space. The approximate class-specific kernel spectral regression (ACS-KSR) method is a powerful tool for face verification. This method consists of two steps: an eigenanalysis step and a kernel regression step, however, it may suffer from heavily computational overhead in practice, especially for large-sample data sets. In this paper, we propose two randomized algorithms based on the ACS-KSR method. The main contribution of our work is four-fold. First, we point out that the formula utilized in the eigenanalysis step of the ACS-KSR method is mathematically incomplete, and we give a correction to it. Moreover, we consider how to efficiently solve the ratio-trace problem and the trace-ratio problem involved in this method. Second, it is well known that kernel matrix is approximately low-rank, however, to the best of our knowledge, there are few theoretical results that can provide simple and feasible strategies to determine the numerical rank of a kernel matrix without forming it explicitly. To fill-in this gap, we focus on the commonly used Gaussian kernel and provide a practical strategy for determining numerical rank of the kernel matrix. Third, based on numerically low-rank property of the kernel matrix, we propose a modified Nyström method with fixed-rank for the kernel regression step, and establish a probabilistic error bound on the approximation. Fourth, although the proposed Nyström method can reduce the computational cost of the original method, it is required to form and store the reduced kernel matrix explicitly. This is unfavorable to extremely large-sample data sets. To settle this problem, we propose a randomized block Kaczmarz method for kernel regression problem with multiple right-hand sides, in which there is no need to compute and store the reduced kernel matrix explicitly. The convergence of this method is established. Comprehensive numerical experiments on real-world data sets are performed to show the effectiveness of our theoretical results and the efficiency of the proposed methods.
Prediction of response to neoadjuvant chemotherapy in breast cancer with recurrent neural networks and raw ultrasound signals

Objective. Prediction of the response to neoadjuvant chemotherapy (NAC) in breast cancer is important for patient outcomes. In this work, we propose a deep learning based approach to NAC response prediction in ultrasound (US) imaging.Approach.We develop recurrent neural networks that can process serial US imaging data to predict chemotherapy outcomes. We present models that can process either raw radio-frequency (RF) US data or regular US images. The proposed approach is evaluated based on 204 sequences of US data from 51 breast cancers. Each sequence included US data collected before the chemotherapy and after each subsequent dose, up to the 4th course. We investigate three pre-trained convolutional neural networks (CNNs) as back-bone feature extractors for the recurrent network. The CNNs were pre-trained using raw US RF data, US b-mode images and RGB images from the ImageNet dataset. The first two networks were developed using US data collected from malignant and benign breast masses.Main results. For the pre-treatment data, the better performing network, with back-bone CNN pre-trained on US images, achieved area under the receiver operating curve (AUC) of 0.81 (±0.04). Performance of the recurrent networks improved with each course of the chemotherapy. For the 4th course, the better performing model, based on the CNN pre-trained with RGB images, achieved AUC value of 0.93 (±0.03). Statistical analysis based on the DeLong test presented that there were no significant differences in AUC values between the pre-trained networks at each stage of the chemotherapy (p-values > 0.05).Significance. Our study demonstrates the feasibility of using recurrent neural networks for the NAC response prediction in breast cancer US.
Analysis of interoperability, security and usability of digital repositories in Kenyan Institutions of Higher Learning

Kenya has experienced a significant growth in the number of institutional repositories in the recent past. The number grew from a paltry two (2) in 2009 to 42 in August 2020. The growth is a positive indicator as repositories play a crucial role in solving some of the problems experienced in the broader area of scholarly communication. This study sought to establish the current extent to which institutions of higher learning in Kenya have established and implemented digital repositories, from a technical perspective. To achieve this goal, the study undertook a technical analysis of institutional repositories implemented by accredited universities in Kenya by the Commission for University Education as at June 2020. The analysis focused on numerous metrics on interoperability, security and usability of the analyzed institutional repositories. The study employed an exploratory approach to collecting the data. The data collected was stored on a MySQL database using the PhpMyAdmin tool. Data analysis was done by SQL querying and the result set copied to MS Excel for generation of graphical visualizations. From a total of 49 institutions examined, 34 (69%) had institutional repositories while 15 (39%) did not have institutional repositories. All the 34 institutions with repositories were using Dspace software. Of all the metrics analyzed, the study established that most of the institutional repositories did not implement essential features that improve interoperability, security and usability of their repository platforms. The study recommends either further training for repository managers or outsourcing of the technical process of establishing and maintaining functional institutional repositories. We further recommend more comprehensive studies to cover all the aspects of the FAIR principles of data management in Kenya.
Object detection of inland waterway ships based on improved SSD model

To realise the rational utilisation of inland waterway resources, the intelligent identification method based on Convolutional Neural Network (CNN) is used to track and monitor the ships. Introducing the Repulsion Loss function and Soft-NMS algorithm to improve model, improve the detection precision of the partially occluded ships. The Feature Pyramid Networks (FPN) is used to realise the fusion of semantic information and spatial information of feature map to solve the problem of difficult detection of small object ships. Three up-sampling methods are used to extend and smooth the feature map. Through the above multiple algorithm improvements, the partial occlusion ships and small object ships in inland waterways are effectively detected.