Reviewerly/exHarmony huggingface dataset:
An Empirical Study of IR-based Bug Localization for Deep Learning-based Software

As the impact of deep-learning-based software (DLSW) increases, automatic debugging techniques for guaranteeing DLSW quality are becoming increasingly important. Information-retrieval-based bug localization (IRBL) techniques can aid in debugging by automatically localizing buggy entities (files and functions). The low-cost advantage of IRBL can alleviate the difficulty of identifying bug locations due to the complexity of DLSW. However, there are significant differences between DLSW and traditional software, and these differences lead to differences in search space and query quality for IRBL. That is, IRBL performance must be validated in DLSW. We empirically validated IRBL performance for DLSW from the following four perspectives: 1) similarity model, 2) query generation, 3) ranking model for buggy file localization, and 4) ranking model for buggy function localization. Based on four research questions and a large-scale experiment using 2,365 bug reports from 136 DLSW projects, we confirmed the salient char-acteristics of DLSW from the perspective of IRBL and derived four recommendations for practical IRBL usage in DLSW from the empirical results. Regarding IRBL performance, we validated that IRBL performance with the combination of bug-related features outperformed that of using only file similarity by 15 % and IRBL ranked buggy files and functions on average of 1.6th and 2.9th, respectively. Our study is valuable as a baseline for IRBL researchers and as a guideline for DLSW developers who wish to apply IRBL to ensure DLSW quality.
Using Eye Tracking Data for Enhancing Adaptive Learning Systems

Adaptive learning systems analyse a learner's input and respond on the basis of it, for example by providing individual feedback or selecting appropriate follow-up tasks. To provide good feedback, such a system must have a high diagnostic capability. The collection of gaze data alongside the traditional data obtained through mouse and keyboard input seems to be a promising approach for this. We use the example of graphical differentiation to investigate whether and how the integration of eye tracking data into such a system can succeed. For this purpose, we analyse students' eye tracking data and gather empirical understanding about which measures are suitable as decision support for adaptation
Estimating Virtual Machine Approach for Energy Consideration in Cloud Computing

The cloud environment is a heterogeneous one where more users are deployed over it. The cloud providers are continuously addressing the challenges of increased data centers and maintenance. Moreover, the cloud data centers are being incorporated into a more number of computational resources which in turn lead to the huge power consumption in the data centers. Virtualization is one of the techniques which serves the application and server consolidation also the placement of VM's on the Physical machine. The technique we implemented is the machine learning classification in determining the proficiency of VM and datacenter resource management, energy consumption, energy saving, load balance and robustness in the network.
Real-Time Correction Based on wheel Odometry to Improve Pedestrian Tracking Performance in Small Mobile Robot

The process of learning good features for machine learning applications can be very computationally expensive and may prove difficult in cases where little data is available.A prototypical example of this is the one-shot learning setting, in which we must correctly make predictions given only a single example of each new class.In this paper, we explore a method for learning siamese neural networks which employ a unique structure to naturally rank similarity between inputs.Once a network has been tuned, we can then capitalize on powerful discriminative features to generalize the predictive power of the network not just to new data, but to entirely new classes from unknown distributions.Using a convolutional architecture, we are able to achieve strong results which exceed those of other deep learning models with near stateof-the-art performance on one-shot classification tasks.
Detection of Cloud Cover Average Percentage based on Adaptive Threshold Method for Energy Forecasting in Photovoltaic System

Over the past decade, photovoltaic (PV) technology has become one of the fastest-growing renewable energy technologies. However, due to the different energy outputs according to the weather and solar occlusion by the clouds, this will be a challenge for utility companies to use solar energy effectively. Solar Power Producers (SPP) are also asked to submit solar PV generation forecasts. In this context, our study aims to develop a method of analyzing the percentage of cloud cover based on satellite images of peninsular Malaysia. The Adaptive Threshold method is chosen to be used in this study to classify satellite images. The Adaptive Threshold algorithm is coded in Python to detect clouds and analyze the average percentage of cloud cover in Peninsular Malaysia from 8 am (0800) to 7 pm (1900). The analysis of the average percentage of cloud cover in Peninsular Malaysia is done based on the Frequency Cloud Index which has been identified by the maximum average percentage of cloud cover in Peninsular Malaysia which is from 0% to 60%. The average percentage of cloud cover data in Peninsular Malaysia was used to analyze two (2) case studies. The first case study is an analysis for each month to see the trends, maximum and minimum cloud cover in Peninsular Malaysia while the second analysis is to analyze the trend, maximum and minimum cloud cover in Peninsular Malaysia throughout the year from 5 April 2018 to 16 December 2018. Through analysis made, such an approach is seen can be used as an initial process in making predictions about solar irradiance towards solar energy efficiency.
Enhanced Graph Learning for Recommendation via Causal Inference

The goal of the recommender system is to learn the user’s preferences from the entity (user–item) historical interaction data, so as to predict the user’s ratings on new items or recommend new item sequences to users. There are two major challenges: (1) Datasets are usually sparse. The item side is often accompanied by some auxiliary information, such as attributes or context; it can help to slightly improve its representation. However, the user side is usually presented in the form of ID due to personal privacy. (2) Due to the influences of confounding factors, such as the popularity of items, users’ ratings on items often have bias that cannot be recognized by the traditional recommendation methods. In order to solve these two problems, in this paper, (1) we explore the use of a graph model to fuse the interactions between users and common rating items, that is, incorporating the “neighbor” information into the target user to enrich user representations; (2) the do() operator is used to deduce the causality after removing the influences of confounding factors, rather than the correlation of the data surface fitted by traditional machine learning. We propose the EGCI model, i.e., enhanced graph learning for recommendation via causal inference. The model embeds user relationships and item attributes into the latent semantic space to obtain high-quality user and item representations. In addition, the mixed bias implied in the rating process is calibrated by considering the popularity of items. Experimental results on three real-world datasets show that EGCI is significantly better than the baselines.
Database Modeling Technology Research Based on Requirement Ontology

Abstract Aiming at the complexity and uncertainty of large-scale system requirements, this paper introduces the concept of ontology, through the requirement ontology model, the system requirements are formed into a unified modular unit, and each modular unit has a structured logical relationship. Finally, the structured and modularized requirement model will form a complete requirement management platform in the DOORS tool, which will realize the basis of civil aircraft requirement management and improve the capability and efficiency of civil aircraft product design.
Cyber threat response using reinforcement learning in graph-based attack simulations

In this ongoing project we employ reinforcement learning in a simulation environment to learn policies for cyber defense. The environment is based on attack graphs produced using the Meta Attack Language, a modeling language used to assess the security of systems.Two RL algorithms are utilized to prevent a simulated attacker agent to reach a series of targets within attack graphs. The defensive agent has to make decisions based on the value of keeping assets enabled, or suffering the consequence of the attacker reaching its goal.The initial results are promising, and show that both algorithms are able to find distinct strategies for defense. However, further analysis is needed to evaluate policy quality, including the implementation of sensible baseline policies for comparison.
TACC

We present a Secure Accelerator Enclave design, which includes heterogeneous accelerator running AI workloads into the protection scope of Trusted Execution Environment, called TACC (Trusted Accelerator). TACC supports dynamic user switching and context clearing of accelerator enclave from the microarchitecture level; The physical isolation of in-package memory (3D chip package) and off-package memory is used to realize the full stack (from hardware to software) isolation of enclave internal running memory and external ciphertext memory; It is also equipped with independent hardware AES-GCM module (including DMA engine) to be responsible for the interaction between internal and external memory. On a FPGA development board containing Xilinx xc7z100-ffg900-2 chip, we implemented two versions of TACC prototypes: FAT (144 multipliers and 48 blockRAMs) and SLIM (36 multipliers and 12 blockRAMs). We deployed and ran the RepVGG inference neural networks on them respectively under different batch sizes. The average overhead of our security mechanism is no more than 1.76%.
UAV Aerial Photography Target Detection Algorithm Based On Improved YOLOv5

Abstract Aiming at the characteristics of complex background and less target pixels in UAV aerial images, a UAV aerial target detection algorithm based on improved yolov5 is proposed in this paper. First, the low-level feature map with richer small target information is introduced into the feature fusion network, so a new prediction head is added. Then, the structure of the feature fusion network is improved to realize cross layer connection and assign different weights to each feature map. Finally, the loss function is optimized to further improve the detection accuracy of the model. The model is trained with VisDrone data set. The results show that the average accuracy of the improved algorithm is improved by 4.6 percentage points compared with yolov5x, and it has better detection performance for scenes with complex background and dense small targets.
Principal Components Analysis

Today, cryptocurrencies are rapidly gaining popularity and sweeping all the economies of the world, but the bulk of the literature is devoted to a few cryptocurrencies only. The purpose of this chapter is to analyze of the cryptocurrency market. More than 2000 cryptocurrencies are examined, and a set of 70 cryptocurrencies were recovered for a sample spanning 2015-2018. The degree of relationship between the variables was then investigated. The PCA was performed. This analysis allows the initial variables to be replaced by five factors that retain almost all of the information (91.028% of the total information) and have the advantage of being uncorrelated. Therefore, the authors have concluded that the first factor corresponds to the cci30 index used by the crypto funds while the rest of the factors can be distinguished according to some variants.
Cloud Workload Clustering

With countless businesses and millions of customers dependent upon cloud infrastructure, cloud resource manage-ment is more critical now than ever. Workloads like VMs, Databases and micro services use cloud resources and their usage data is monitored by the cloud service providers. With the cloud workload data, workloads can be categorized based on their usage of CPU, Memory and Disk I/O Operations. Clustering the workload data based on these categories will infer an understanding of the characteristics of workloads, usage of resources and efficient allocation of resources to the cloud service providers. Multiple clustering methods are compared and analysed thereby helping in smooth scaling without impacting the QoS (Quality of Service) of existing users. In past, Disk IO operations weren&#x0027;t CPU bottlenecked due to their low Disk IOPS, but with rising IOPS in storage devices, it can be seen if this still holds true.
A Few Ripe Red-Blue Cherries

SummaryWe construct games of “Red-Blue Cherries” with values n, n+12, 12n, and 1−12n for all integers n. We also show games with values that are not numbers, specifically games with a value called star, an example of a switch game, and a game with a value called uparrow. We prove that games on trees always have integer values, and we present a previously discovered but unpublished algorithm to compute values of games on paths efficiently. These results correct a claim and answer an open question posed about this game.
A Digital Watermarking Algorithm Based on Digital Wavelet and Singular Value Division

Discrete wavelet has excellent characteristics such as multi-scale analysis, fast operation speed, noise elimination, and singular value decomposition has stable characteristics, so the blind watermarking algorithm combined with the two has high practical value. At the same time, the watermark is scrambled by Arnold transform to improve its secrecy. After using JPEG compression and shearing to attack the image after embedding the watermark, the clearer watermark can still be extracted from it. The obtained PSNR value is greater than 30. At the same time, the NC value is very close to 1. Therefore, the algorithm has good robustness and can provide effective protection for the copyright of various digital works.
Healthcare Billing Fraud Detection Through Machine Learning And Using Homographic Encryption Technique For Prevention

Fraud on health care costs has become dangerous. The healthcare industry has focused on detecting and preventing fraud and increasing its focus on safety and security. Although much research has been done to resolve this issue, no standard solution has been adopted. Now the day app used to detect fraud is riddled with misconceptions and very false ideas. Machine learning allows the app to view locations based on data. The basis of previous self-awareness algorithms can be designed that allow the model to apply what it has learned from previous experiences. Machine learning tools are used to analyze fraudulent situations. Another important area we face in our project is the detection of fraud in real-time. We purpose to design a fraudulent detection model in this paper that provides us a large amount of efficiency, working with the kaggle fraud database. This novel idea is useful for predicting fraud less than 300 times, the current method will be phased with an accuracy of 99.62%, which can prevent many health billing security problems thus making the model more secure.
Lightweight Review: Challenges and Benefits of Adopting DevOps

This review gives a clear picture of DevOps and its benefits when it is adopted by an organization. This study is based on the formal guidelines for the systematic literature review process. We used the standard methodology for the preparation of literature review, we employed a manual search with the use of search strings that have a DevOps as a center topic and this manual search has been done on four databases. The objective of this study is to review the current status of DevOps from 2010 to 2020 using primary and secondary studies to review the articles related to DevOps.
Time‐Varying Channel Estimation Based on Air‐Ground Channel Modelling and Modulated Learning Networks

To improve the time-varying channel estimation accuracy of orthogonal frequency division multiplexing air-ground datalink in complex environment, this paper proposes a time-varying air-ground channel estimation algorithm based on the modulated learning networks, termed as MB-ChanEst-TV. The algorithm integrates the modulated convolutional neural networks (MCNN) with the bidirectional long short term memory (Bi-LSTM), where the MCNN subnetworks accomplish channel interpolation in frequency domain and compress the network model while the Bi-LSTM subnetworks achieve channel prediction in time domain. Considering the unique characteristics of airframe shadowing for unmanned aircraft systems, we propose to combine the classical 2-ray channel model with the tapped delay line model and present a more realistic channel impulse response samples generation approach, whose code and dataset have been made publicly available. We demonstrate the effectiveness of our proposed approach on the generated dataset, where experimental results indicate that the MB-ChanEst-TV model outperforms existing state-of-the-art methods with a lower estimation error and better bit error ratio performance under different signal to noise ratio conditions. We also analyze the effect of roll angle of the aircraft and the duration percentage of the airframe shadow on the channel estimation.
Can Explainable AI Foster Trust in a Customer Dialogue System?

In this poster paper we present a web user study about a customer dialogue system, in which participants assigned tickets based on an automatic classification to different departments and answered questions about the perceived classification performance. Completion times were significantly shorter when offering explanations on the classification process, while task success and trust in the interface did not depend on showing explanations or not. Based on the results, future studies should be confined to smaller scopes and investigate more techniques for explainable AI.
Use of Games Forms of Learning in Distance Education on the Example of Physics Experiments

The article discusses the use of game forms of learning in the process of distance education for schoolchildren. Using the example of the demonstrating of Coulomb's law, the application of the game approach in the development of teaching and knowledge-testing material in physics is shown. An application has been developed in the Unity environment that simulates the physical interaction of electric charges and visualizes the physics experiment. Application placement on the Internet portal for distance education. An example of an application to demonstrate the law of conservation of energy is shown too.
Visualizing Instructor’s Gaze Information for Online Video-based Learning: Preliminary Study

Video-based online educational content has been more popular nowadays. However, due to the limited communication and interaction between the learners and instructors, various problems regarding learning performance have occurred. Gaze sharing techniques received much attention as a means to address this problem, however, there still exists a lot of room for improvement. In this work-in-progress paper, we introduce some possible improvement points regarding gaze visualization strategies and report the preliminary results of our first step towards our final goal. Through a user study with 30 university students, we found the feasibility of the prototype system and the future directions of our research.
Investigating the effects of a programming course using flipped learning

In recent years, flipping classrooms has become a popular topic of discussion. However, few previous studies have focused on the effect of the flow experience on programming self-efficacy. To address this gap, the present researchers developed a model that included six research hypotheses. The study applied a flipped classroom model to a programming course in order to examine the relationship between self-efficacy, flow experience, and learning performance in a flipped programming course. The research model was empirically examined using a survey to collect data from 46 college students in Taiwan. Programming courses were conducted for 180 minutes once a week, with six weeks of total teaching time. The findings of this study indicate that there is a positive relationship between programming self-efficacy and enjoyment, engagement, and flow control. The study also revealed that certain important implications and factors to improve the design and delivery of such courses.
Impact of Pooling Methods on Image Quality Metrics

Image quality assessment using objective metrics is becoming more widespread, and an impressive number of image quality metrics have been proposed in the literature. An aspect that has received little attention compared to the design of these metrics is pooling. In pooling the quality values, usually from every pixel is reduced to fewer numbers, usually a single value, that represents overall quality. In this paper we investigate the impact of different pooling techniques on the performance of image quality metrics. We have tested different pooling methods with the SSIM and S-CIELAB image quality metrics in the CID:IQ database, and found that the pooling technique has a significant impact on their performance.
Prototyping InContext: Exploring New Paradigms in User Experience Tools

The technologies we use in everyday contexts are designed and tested, using existing standards of usability. As technology advances standards are still based on planar displays and simple screen-based interactions. End-user digital devices need to consider context and physicality as additional influences on design. Additionally, accessibility and multi-modal interaction must be considered as we build technologies with interactions such as soundscapes to support user experience. When considering the tools we use to design existing interactions, we can evaluate new ways of working with software to support the development of the changing face of interactive devices. This paper presents two prototypes which explore the space of user experience design tools, first in the space of contextual cues when looking at multi device interaction, and second, in the space of physical prototyping. These prototypes are starting points for a wider discussion around the changing face of usability. We also discuss extending the scope of existing user experience design tools and rethinking what ”user experience” means when the devices we own are becoming ’aware’ of their surroundings, context, and have increasing agency.