Reviewerly/exHarmony huggingface dataset:
Empirical Investigation of role of Meta-learning approaches for the Improvement of Software Development Process via Software Fault Prediction

Context: Software Engineering (SE) community has empirically investigated software defect prediction as a proxy to benchmark it as a process improvement activity to assure software quality. In the domain of software fault prediction, the performance of classification algorithms is highly provoked with the residual effects attributed to feature irrelevance and data redundancy issues. Problem: The meta-learning-based ensemble methods are usually carried out to mitigate these noise effects and boost the software fault prediction performance. However, there is a need to benchmark the performance of meta-learning ensemble methods (as fault predictor) to assure software quality control and aid developers in their decision making. Method: We conduct an empirical and comparative study to evaluate and benchmark the improvement in the fault prediction performance via meta-learning ensemble methods as compared to their component base-level fault predictors. In this study, we perform a series of experiments with four well-known meta-level ensemble methods Vote, StackingC (i.e., Stacking), MultiScheme, and Grading. We also use five high-performance fault predictors Logistic (i.e., Logistic Regression), J48 (i.e., Decision Tree), IBK (i.e. k-nearest neighbor), NaiveBayes, and Decision Table (DT). Subsequently, we performed these experiments on public defect datasets with k-fold (k=10) cross-validation. We used F-measure and ROC-AUC (Receiver Operating Characteristic-Area Under Curve) performance measures and applied the four non-parametric tests to benchmark the fault prediction performance results of meta-learning ensemble methods. Results and Conclusion: we conclude that meta-learning ensemble methods, especially Vote could outperform the base-level fault predictors to tackle the feature irrelevance and redundancy issues in the domain of software fault prediction. Having said that, their performance is highly related to the number of base-level classifiers and the set of software fault prediction metrics.
What Is Meaningful Human-Computer Interaction? Understanding Freedom, Responsibility, and Noos in HCI Based on Viktor Frankl’s Existential Philosophy

Meaningfulness is a profound aspect of our lives. So far, a concrete reflection of what meaning means in HCI is still rare. To understand users as humans, and thus, the humanity in being a user, we adopt Viktor Frankl’s understanding of users as meaning-seeking subjects. To make the concept of meaningfulness more graspable, we refer to what Frankl calls the three existentials of life: freedom, responsibility, and noos. We elaborate four dimensions, namely the existentials as an outcome, as their embeddedness in technology, their role in interaction, and their (non-)usage. This is followed by a discussion on the interwovenness of the three existentials as well as their potential impact on HCI research. By that, we aim to contribute to a profound understanding of meaning for HCI, from the epistemological to the methodological perspective, to enable meaning-centered design.
Forcasting Stock Price PT. Indonesian Telecomunication with ARCH-GARCH Model

This research discusses the modeling of time series using R software, focusing on forecasting the stock price of PT. Indonesian telecommunications with ARCH-GARCH model. The data used daily closing data on stock prices from January 6, 2020, to January 6, 2021 was obtained from the website www.finance.yahoo.com. The goal is to find out the best model arch-garch on PT. Indonesian telecommunications to find out the results of stock price forecasting the next day using the ARCH-GARCH model. The best model was ARIMA (2,1,3). The results of the ARCH-LM test showed the data contained heteroskedasticity effects or ARCH elements. The research models proposed in this study are ARCH (1) and ARCH-GARCH (1,1). The smallest AIC and BIC values of these two models are ARCH-GARCH (1,1) which is the best model for forecasting the stock price of PT. Indonesian telecommunications for the next 10 days. The study attempts to conduct stock price forecasting with the ARCH-GARCH model. The result of the forecasting of the share price of PT. Indonesian telecommunications from January 07, 2021 to January 20, 2021 respectively except for holidays is IDR 3374.884, IDR 3379.617,IDR 3378.305, IDR 3376.610, IDR 3380.050, IDR 3376.372, IDR 3379.071, IDR 3377.964, IDR 3377.515, IDR 3379.002. Forecasting results are close to factual data for forecasting the next 10 days so that they can be taken into consideration in investing by investors.
Scoping Analysis of Leveraging IoT with Blockchain for Monitoring and Ensuring Efficacy of Vaccine Cold Chains

The recent pandemic has brought an unprecedented tangible effect on the adversaries of understanding the importance of Vaccines. Prior to this situation there were scenarios where the drip in Vaccine efficacy has affected the public heal th sector and even the Government's. Vaccination is the only assertive way of procuring immunization against such deadly pandemics. This study discusses such events which motivated the need of managing and monitoring the vaccine supply chain. The applications of IoT and Blockchain have been depicted and converged in this regard. Blockchain is the apparent solution to manage and monitor the vaccines effectiveness by delivering the same to the public health centers timely. Moreover, the counterfeit vaccines can also be identified. Internet of Things (IoT) has given a significant contribution in terms of fetching the real time readings in many applications. In our study temperature and moistu re parameters are collected using Sensors in IoT and then the complete voyage of vaccine cold units will be monitored using Blockchain Transactions till it has been delivered. A framework is proposed on a Ethereum platform and con sensu s mechanism which will eventually avoid any possible alterations and counterfeiting of the deliverable VaccineUnits ensuring a safe transit which will be accessible and transparent even to the receiving patients, healthcare members along with the Governments body.
A New Age in Health: Metaverse

With the ever-growing technology, people's needs and expectations in both the real and virtual worlds are changing rapidly over time. Metaverse, which has recently entered our lives, is predicted to be a central concept in the health sector, as it is in many other sectors. Many tools, such as virtual reality, artificial intelligence, and digital applications, are already being used in the health sector. By ensuring synchronization with Metaverse in the coming years, it will be possible to create an adaptable platform in all health services using this framework. The aim of this study is to share information in the form of a compilation about the benefits of Metaverse applications in the field of health by referring to Metaverse’ conceptual information.
Object Perceptibility Prediction for Transmission Load Reduction in Vehicle-Infrastructure Cooperative Perception

Vehicle-infrastructure cooperative perception is an ingenious way to eliminate environmental perception blind areas of connected and autonomous vehicles (CAVs). However, if the infrastructure transmits all environmental information to the nearby CAVs, the transmission load is so heavy that it causes a waste of network resources, such as time and bandwidth, because parts of the information are redundant for the CAVs. It is an efficient manner for the infrastructure to merely transmit the information about objects which cannot be perceived by the CAVs. Therefore, the infrastructure needs to predict whether an object is perceptible for a CAV. In this paper, a machine-leaning-based model is established to settle this problem, and a data filter is also designed to enhance the prediction accuracy in various scenarios. Based on the proposed model, the infrastructure transmits the environmental information selectively, which significantly reduces the transmission load. The experiments prove that the prediction accuracy of the model achieves up to 95%, and the transmission load is reduced by 55%.
Non-bijectivity-based image obfuscation method for deep learning based medical applications

As more and more deep learning (DL) solutions are employed in the healthcare domain using the Machine Learning as a Service (MLaaS) paradigm, concerns regarding personal data privacy have been raised. In this context, especially in medical imaging, the demand for privacy-preserving techniques, that allow for DL model development, has recently increased significantly. Herein, we propose a medical image obfuscation algorithm based on pixel intensity shuffling and non-bijective functions. The proposed algorithm is evaluated on a medical use case based on coronary angiographic images. Multiple convolutional neural networks are trained to measure the utility of the obfuscated images. An attack configuration based on artificial intelligence (AI) is evaluated to validate the level of privacy. The classification performance on the obfuscated images is satisfactory, while the computational time is not affected significantly. Visual and metrics-based analyses show that the data is protected from human perception and from AI-based image reconstruction approaches.
Channel-wise quantization without accuracy degradation using Δloss analysis

Recent studies have pointed out that the effect of quantization of convolutional neural networks on accuracy varies from layer to layer. For this reason, partial quantization or mixed-precision quantization on a layer basis have been considered for quantization. However, the layer quantization has a large impact on accuracy because its granularity is large; so, it generally requires retraining of the network, which incurs high computational cost. In this study, we proposed a new search algorithm for partial quantization, which aims to derive practical combinations of quantized channels without retraining. The proposed method successfully quantizes 83.3% of the parameters without degrading the accuracy in ResNet18 4-bit quantization. In addition, the proposed method succeeded in compressing 80.8% of the parameters in ResNet34 without degrading the accuracy.
Invertible Sharpening Network for MRI Reconstruction Enhancement

High-quality MRI reconstruction plays a critical role in clinical applications. Deep learning-based methods have achieved promising results on MRI reconstruction. However, most state-of-the-art methods were designed to optimize the evaluation metrics commonly used for natural images, such as PSNR and SSIM, whereas the visual quality is not primarily pursued. Compared to the fully-sampled images, the reconstructed images are often blurry, where high-frequency features might not be sharp enough for confident clinical diagnosis. To this end, we propose an invertible sharpening network (InvSharpNet) to improve the visual quality of MRI reconstructions. During training, unlike the traditional methods that learn to map the input data to the ground truth, InvSharpNet adapts a backward training strategy that learns a blurring transform from the ground truth (fully-sampled image) to the input data (blurry reconstruction). During inference, the learned blurring transform can be inverted to a sharpening transform leveraging the network's invertibility. The experiments on various MRI datasets demonstrate that InvSharpNet can improve reconstruction sharpness with few artifacts. The results were also evaluated by radiologists, indicating better visual quality and diagnostic confidence of our proposed method.
AFedAvg: communication-efficient federated learning aggregation with adaptive communication frequency and gradient sparse

Federated learning enables a large number of clients (such as edge computing devices) to learn a model jointly without data sharing. However, the high amount of communication of the federated learning aggregation algorithm hinders the realisation of artificial intelligence in the last mile. Although FederatedAveraging (FedAvg) is the leading algorithm, its communication cost is still high. The method of communication delay and gradient sparse can reduce the communication cost, but there is no previous work to analyse the relationship and common effects of these two dimensions. Aiming at the problems that federated learning communication is expensive and it has become a training bottleneck, we improve the FedAvg algorithm and propose an adaptive communication frequency FederatedAveraging algorithm (AFedAvg). The gradient sparse operation in the algorithm reduces the quantity of parameters for a single communication, while the communication delay operation allows training to converge faster and obtain smaller losses. The number of sparse parameters is used to select the communication frequency of next round dynamically. Experimental results prove that, the AFedAvg algorithm is superior to the FedAvg and its variants in terms of communication cost. It achieves 2.4X–23.1X communication compression in different data distributions with minimal communication rounds required by the algorithm to converge.
A pointing-based method for spacecraft attitude maneuver path planning under time-varying pointing constraints

The requirement to observe moving targets raises the time-varying pointing constraint for spacecraft attitude maneuver, which is seldom considered in existing attitude maneuver path planning methods. Therefore, a pointing-based method for attitude maneuver path planning under time-varying pointing constraints is proposed. Considering that pointing constraints are exerted on the perceptional sensor of spacecraft, an improved rapidly-exploration random tree method is designed to obtain the rotational pointing nodes while satisfying the pointing constraints. To this end, the key point is to define the time property of sampled nodes to determine their relations with time-varying constraints. Subsequently, the attitude quaternion nodes are generated according to pointing nodes. Finally, to find a continuous quaternion curve passing through quaternion nodes, component-wise and piecewise quaternion interpolation functions are adopted to calculate the angular velocity and control torque by the inverse dynamics method. The attitude motion spherical shell is designed by using the radial component to represent the timeline to visually display the satisfaction of time-varying constraints. Simulation results are used to validate the feasibility of the proposed method.
Interactive clustering and high-recall information retrieval using language models

Clustering is a crucial text mining technique for organizing digital document sets, enabling users to understand their data better. It has been demonstrated that involving users can often significantly improve clustering quality. We propose a novel system that combines deep language models (SBERT, Infer-Sent, and Universal Sentence Encoder) with interactive clustering enabling users to steer the clustering algorithm towards results meaningful to them through interactive document and cluster visualizations. Our system is comprised of several visual components, each of which allows the user to apply their domain knowledge to the clustering process. The use of deep language models for representing sentences addresses the vocabulary mismatch problem that affects bag-of-words representations of documents. We employ sentence embeddings to obtain document embeddings as an input to the clustering algorithm, a modified version of K-means. We conduct a two-stage evaluation of our system. First, we evaluate the proposed clustering models in automatic clustering of various publicly available data sets, and we confirm that they are competitive with state-of-the-art. Second, we conduct a formal expert study of a specific data set consisting of our research group’s readings (research papers in machine learning, text mining, and natural language processing) over several years. The domain expert is a graduate student whose thesis is in the above field. The expert study concludes that our system is significantly better at producing meaningful clusters than the baseline system (Vis-Kt).
Semi-Supervised Medical Insurance Fraud Detection by Predicting Indirect Reductions Rate using Machine Learning Generalization Capability

There is 10% fraud in medical insurance based on published statistics in Insurance Research Institute of Islamic Republic of Iran in 1399 –solar system eq. 2020 in the Gregorian calendar-which cost about 28 thousand billion RIALs –the official currency of Iran eq. to about 320 million dollars-. This study proposes a machine learning-based technique to predict the claim cost based on other patients’ history and predict fraud or abnormal costs in claims that significantly differ from other claims. Besides, a new data sampling approach is proposed to lead the machine learning algorithms that focus on exceptional cases. A real-world private dataset is used to evaluate 700,000 claims of the RASA web portal, used for supplementary insurance by famous companies like Day. The proposed data sampling approach reduced absolute error in exceptional cases from 35 to 23 errors for deduction rate. The evaluation results show about 0.5% of abnormal cases in the dataset with a higher than 20% absolute error. The abnormal rates can be adjusted to a lower or higher range.
Guaranteed bounds for posterior inference in universal probabilistic programming

We propose a new method to approximate the posterior distribution of probabilistic programs by means of computing guaranteed bounds. The starting point of our work is an interval-based trace semantics for a recursive, higher-order probabilistic programming language with continuous distributions. Taking the form of (super-/subadditive) measures, these lower/upper bounds are non-stochastic and provably correct: using the semantics, we prove that the actual posterior of a given program is sandwiched between the lower and upper bounds (soundness); moreover, the bounds converge to the posterior (completeness). As a practical and sound approximation, we introduce a weight-aware interval type system, which automatically infers interval bounds on not just the return value but also the weight of program executions, simultaneously. We have built a tool implementation, called GuBPI, which automatically computes these posterior lower/upper bounds. Our evaluation on examples from the literature shows that the bounds are useful, and can even be used to recognise wrong outputs from stochastic posterior inference procedures.
Experimental analysis of crack initiation and propagation in dynamically shear-loaded aluminium specimens using the digital image correlation method

The present paper investigates crack growth in specially designed specimens of 6061 aluminium alloy sheets subjected to dynamic shear loads. The specimens were tested with a low-frequency alternating shear load until complete fracture. The main objective of this work was to observe the crack initiation and growth under alternating shear load using the Digital Image Correlation (DIC) method. Each time the peak amplitude was reached, a photograph of the shear area on the specimen surface was taken to investigate crack grow. The length of the crack in every load cycle was then estimated by an algorithm using the DIC strain fields as input data. The DIC results were compared to FE. The observation of the shear strain component is presented as the most appropriate method to predict the location of the crack even before its initiation.
Integrity verification in cloud key-value stores

Database-as-a-Service (DBaaS) is a common approach for storing data in the cloud. However, this approach introduces several concerns regarding data integrity. As a client does not maintain the data, its completeness and correctness might be compromised by the cloud provider or a malicious entity that penetrated the cloud. We are introducing a novel method for verifying data integrity in cloud databases with a focus on key-value stores.
Detection algorithm of aerial vehicle target based on improved YOLOv3

Abstract In order to solve the problems of large number, small size and low detection accuracy of vehicle targets in aerial photography, a target detection algorithm based on improved YOLOv3 is proposed in this paper. Firstly, aiming at the problem of small target information loss of road vehicles, a new detection size is added.Secondly, in order to better detect small targets, a 104x104 scale detection layer is added on the basis of the three detection layers of the traditional yolov3 network structure.The k-means + + algorithm is used to cluster the data set, and a new ground target detection candidate frame is obtained. The loss function is improved by using Focal loss function in the classification loss function and using DIoUloss function based on IoUloss improvement in the regression loss function.The improved YOLOv3 algorithm can identify the road vehicle target more accurately without the decline of speed, and reduce the miss rate. The improved algorithm is tested on Visdrone dataset, and the experimental data show that the average accuracy of the improved algorithm is 94.04%, and the average detection accuracy (mAP) is improved by 2.94%.The detection accuracy of the proposed improved YOLOv3 algorithm is better than that of YOLOv3.
Scratch‐RL: A preference‐driven adversarial reinforcement reasoning framework over knowledge graphs for explainable recommendation of Scratch

Nowadays, Scratch, as a widely-used educational programming platform, has gathered a huge number of programming users all over the world. Facing massive programming resources, how to make satisfactory programming recommendations has attracted increasing attention, especially on explainable recommendations. Existing Scratch recommendation systems overlook to provide why a project is recommended, which prevents users from making better decisions and trusting in the system. To resolve this problem, we design the Scratch-RL, an explainable reinforcement learning framework over knowledge graphs for Scratch recommendation. First, we devise a preference-driven Actor-Critic network to simulate users' local preferences and explore the potential interested projects along the reasoning paths. In the Actor-Critic network, we elaborate a preference state function, a preference-based reward function, and a preference-conditional action pruning strategy for the agent. Then, we leverage a directive discriminator network to help evaluate the correctness of recommendations from the agent and return an extra guidance reward accordingly. A high guidance reward is given when the agent generates correct recommendations, which guarantees that the agent quickly and accurately comprehends the preferences of users. Finally, we jointly train the Actor-Critic network and the discriminator, when the whole training is done, the reasoning paths are taken as the interpretability of the recommendations. Extensive experiments on both the Scratch data set and public data set show that, Scratch-RL obtains favorable recommendation results compared with the state-of-the-art models.
Preparing Teachers for the 21st Century

This chapter provides an overview of (1) the current situation concerning teacher professional development (TPD) programs through studies referring to existing challenges; (2) the TPD programs under discussion that have been implemented during the last three years (2018-2021) in the context of European projects, including their structure and descriptions of the educational content; (3) teachers' views and feedback concerning the TPD program they attended, based on a specific evaluation framework, with focus on issues relevant to emerging technologies. The researchers provide directions towards an effective framework for horizontal TPD programs targeting large numbers of teachers, aiming to allow them to gain the appropriate knowledge and skills in order to integrate emerging technologies as concepts in interdisciplinary STE(A)M-based instructional scenarios, especially in the levels of Secondary general (Gymnasium and Lyceum in Greece) and (post)secondary vocational education (EPAL and IEK in Greece).