Reviewerly/exHarmony huggingface dataset:
User Experience Evaluation in Immersive Virtual Reality Serious Game: A Research Design

The design of research projects is crucial in science, social science, and many other areas. The most crucial step for the researcher is to choose the proper study design after choosing the research subject and creating questions. However, many studies have no research design, lack rigor and have a confusing structure toward user experience evaluation in virtual reality serious games. Therefore, this paper proposed a research design to construct the VirtualSG Model to evaluate user experience (UX) in an Immersive Virtual Reality-Serious Game (IVRSG). This paper offers suggestions for a research design involving a Problem Investigation Phase, Model Development Phase, Model Evaluation and Documentation Phase. Moreover, this paper reviews elements of UX evaluation on virtual reality and serious games. We hope this concept paper will inspire more interesting and impactful research to accelerate contribution to theory and practice in the UX field.
Lean Software Startup Practices and Software Engineering Education

In the modern economy, software drives innovation and economic growth. Studies show how software increasingly influences all industry sectors. Over the past 5 decades, software engineering has also changed significantly to advance the development of various types and scales of software products. Software engineering education plays an essential role in apprising students of software technologies, processes, and practices popular in industries. Furthermore, approaches to teaching software engineering are becoming more interdisciplinary and team-centered, comparable to startup contexts. In this PhD work, I want to answer the following research questions: (1) To what extent are software engineering trends present in software engineering education research? (2) What set of common software engineering practices employed in lean software startups is transferable to the software engineering education context? (3) What is the impact of lean startup practices on software engineering students and curricula? I utilize (1) a literature review, (2) mixed-methods approaches in gathering empirical evidence, and (3) design-based research. In the first phase of the research, I pinpoint the relevance of the lean startup in software engineering education through an extensive literature review. I gather empirical evidence on lean startup practices and assess their potential transferability to software engineering education during the second research phase. I demonstrate that the lean startup is an emerging trend in software engineering education research. I demonstrate that students can acquire soft, hard, and project management skills in a more realistic context in the introduction of the growth phase of lean startup practices throughout external course activities. I expect software engineering curricula to benefit from the model and framework that I propose and validate, thus facilitating lean startup practice transfer to software engineering curricula.
Automatic Door Un-locking and Security System Using Raspberry Pi

As a critical constituent of many institutions domestic safety and protection precedence, surveillance has mounted its significance and advantages several instances with the aid of using offering instant supervising of possessions, humans having access to your private home and property. This project deals with the layout method of an Embedded Real-Time automated door lock safety device the use of pi digital camera primarily based totally on Raspberry Pi 3 for intruder remark that enhances surveillance generation to offer critical safety to homes and related control. An embedded internet server creates a clean manner for tracking & routinely locking the door any device. The Raspberry Pi primarily based totally Automatic Door Un-locking system the use of a pi Camera affords the concept of tracking a selected area in a far flung vicinity and stumble on the unknown face if they are attempting to enter. The door gets unlock only when it detects the known face and switches on the home lights as home application. When unknown or unauthorized person's face is detected, this device will ship SMS and the picture of the unauthorized person is sent to the owner’s mail. The diligent of contemporary COVID-19 situation. As temperature test performs crucial position in controlling unfold of covid, the proposed project has an extra characteristic of temperature test of the individual that attempts to go into the house. So, it has even temperature sensor and LCD which will ask the person to measure his temperature and display the temperature, the measured temperature is also send to cloud so that owner can view the person temperature.
An Efficient and Economical Combination of Exploration Methods for Pb-Zn Polymetallic Skarn Deposits: A Case Study of the Periphery of Hetaoping Deposit, Yunnan Province, China

The Hetaoping ore district in Baoshan City, Yunnan Province, is one of the major localities of Pb-Zn polymetallic skarn deposits in China, where geophysical and geochemical surveys play an important role in exploring Pb-Zn polymetallic mineral resources. Based on the exploration and prospecting carried out at the periphery of the Hetaoping Pb-Zn polymetallic deposit, this study proposed an aero-ground joint exploration method to determine the metallogenic model of distal skarns in the Hetaoping ore district, achieving ideal prospecting results. The steps of this method are as follows. First, the locations of ore-induced anomalies were determined using high-amplitude aeromagnetic anomalies. Then, the ore-induced anomalies were determined to be anomalies of Pb-Zn polymetallic deposits through geochemical surveys of soil samples and ground geophysical surveys. Based on these data, a quantitative analysis and metallogenic potential assessment of ore bodies and their surrounding rocks were conducted using the interactive 2.5D magnetic inversion. In addition, the 3D inversion of regional gravity data was also performed in order to determine the spatial location of the deep magma chamber. Accordingly, the metallogenic geological process in this area was analyzed by determining the spatial morphology of the deep magma chamber, and a prospecting model of the Pb-Zn polymetallic deposits was finally built. The results show that the aero-ground joint exploration method, which first conducts a rapid scanning survey using the aeromagnetic method and then locates, distinguishes, and assesses significant aeromagnetic anomalies by combining comprehensive verification means such as ground geophysical, geochemical, and geological surveys, is efficient and economical. This study will guide regional metallogenic research and the exploration and prospecting of Pb-Zn polymetallic deposits.
Evaluating Commit Message Generation: To BLEU Or Not To BLEU?

Commit messages play an important role in several software engineering tasks such as program comprehension and understanding program evolution. However, programmers neglect to write good commit messages. Hence, several Commit Message Generation (CMG) tools have been proposed. We observe that the recent state of the art CMG tools use simple and easy to compute automated evaluation metrics such as BLEU4 or its variants. The advances in the field of Machine Translation (MT) indicate several weaknesses of BLEU4 and its variants. They also propose several other metrics for evaluating Natural Language Generation (NLG) tools. In this work, we discuss the suitability of various MT metrics for the CMG task. Based on the insights from our experiments, we propose a new variant specifically for evaluating the CMG task. We re-evaluate the state of the art CMG tools on our new metric. We believe that our work fixes an important gap that exists in the understanding of evaluation metrics for CMG research. CCS CONCEPTS• Software and its engineering $\rightarrow$Software verification and validation.
Evolution of One-Dimensional and Two-Dimensional Discrete Fourier Transform

The rapid development of information technology has significantly expanded the scope of application of digital Fourier processing of finite signals. We note tomography, active and passive sonar, radar, seismology, technical diagnostics, medicine, forensic cybernetics, and artificial intelligence among these applications. The complication of the tasks solved by information technologies in these subject areas stimulated, firstly, the transition from one-dimensional to two-dimensional digital Fourier processing, and secondly, it posed an urgent theoretical and applied problem of finding new basic systems, both in one-dimensional and two-dimensional case. Systems analysis has shown that the transition from the one-dimensional to the two-dimensional case is far from trivial and is primarily of a qualitative rather than quantitative nature. At the same time, the generalization of the results of the two-dimensional case to the multidimensional one, as a rule, does not cause difficulties, since it is mainly quantitative, and not qualitative. A systematic analysis of the actual theoretical and applied problem of searching for new basic systems has shown that the most important requirements for basic systems are: orthogonality, symmetry and multiplicativity. The article provides the detailed analysis of the analytical properties of new two discrete Fourier transforms developed by the authors. These are Parametric Discrete Fourier Transform (DFT-P) for Fourier processing of scalar functions of scalar arguments and 2D Discrete Fourier Transform with variable parameters (2D DFT-VP) for Fourier processing of scalar functions of vector arguments. DFT-P and 2D DFT-VP transforms are based on a generalization of exponential basis systems of DFT and 2D DFT transforms.
VoiceTalk: Multimedia-IoT Applications for Mixing Mandarin, Taiwanese, and English

The voice-based Internet of Multimedia Things (IoMT) is the combination of IoT interfaces and protocols with associated voice-related information, which enables advanced applications based on human-to-device interactions. An example is Automatic Speech Recognition (ASR) for live captioning and voice translation. Three major issues of ASR for IoMT are IoT development cost, speech recognition accuracy, and execution time complexity. For the first issue, most non-voice IoT applications are upgraded with the ASR feature through hard coding, which are error prone. For the second issue, recognition accuracy must be improved for ASR. For the third issue, many multimedia IoT services are real-time applications and, therefore, the ASR delay must be short. This article elaborates on the above issues based on an IoT platform called VoiceTalk. We built the largest Taiwanese spoken corpus to train VoiceTalk ASR (VT-ASR) and show how the VT-ASR mechanism can be transparently integrated with existing IoT applications. We consider two performance measures for VoiceTalk: speech recognition accuracy and VT-ASR delay. For the acoustic tests of PAL-Labs, VT-ASR's accuracy is 96.47%, while Google's accuracy is 94.28%. We are the first to develop an analytic model to investigate the probability that the VT-ASR delay for the first speaker is complete before the second speaker starts talking. From the measurements and analytic modeling, we show that the VT-ASR delay is short enough to result in a very good user experience. Our solution has won several important government and commercial TV contracts in Taiwan. VT-ASR has demonstrated better Taiwanese Mandarin speech recognition accuracy than famous commercial products (including Google and Iflytek) in Formosa Speech Recognition Challenge 2018 (FSR-2018) and was the best among all participating ASR systems for Taiwanese recognition accuracy in FSR-2020.
IDE Augmented with Human-Learning Inspired Natural Language Programming

Natural Language (NL) programming, the concept of synthesizing code from natural language inputs, has garnered growing interest among the software community in recent years. Unfortunately, current solutions in the space all suffer from the same problem, they require many labeled training examples due to their data-driven nature. To address this issue, this paper proposes an NLU-driven approach that forgoes the need for large numbers of labeled training examples. Inspired by how humans learn programming, this solution centers around Natural Language Understanding and draws on a novel graph-based mapping algorithm. The resulting NL programming framework, HISyn, uses no training examples, but gives synthesis accuracies comparable to data-driven methods trained on hundreds of samples. HISyn meanwhile demonstrates advantages in terms of interpretability, error diagnosis support, and cross-domain extensibility. To encourage adoption of HISyn among developers, the tool is made available as an extension for the Visual Studio Code IDE, thereby allowing users to easily submit inputs to HISyn and insert the generated code expressions into their active programs. A demo of the HISyn Extension can be found at https://youtu.be/KKOqJS24FNo.
Crafting universal adversarial perturbations with output vectors

Recently, researches on universal adversarial perturbations have deepened the public’s attention to the security of deep neural networks (DNNs). Most researchers use iterative methods or generative adversarial networks (GANs) to find universal adversarial perturbations (UAPs). However, just focusing on outputs still cannot separate adversarial examples in feature space and there is almost no correlation between the perturbations generated by random noise and the target network. To cope with these problems, we propose a novel end-to-end UAPs generation framework that uses constant activation information as the input of the generator and adopts a feature adaptive loss function. Experiments show that the attack success rate of our UAPs has reached a competitive level against different classification networks on CIFAR-10 and ImageNet where the average fooling rate can reach 86% and 96%, leading to 19% and 16% improvements over UAP, respectively. Furthermore, we demonstrate the great generalization ability and strong transferability of proposed attacker. Finally, the proposed attacker can achieve lower mAP and mean IoU than several classical white box attackers in object detection task and image segmentation task.
TauPad: Test Data Augmentation of Point Clouds by Adversarial Mutation

Point clouds have been widely used in a large number of application scenarios to handle with various deep learning (DL) tasks. Testing is an essential means to guarantee the robustness of DL models, which places high demands on test data. Therefore, it is crucial to design a reliable and effective test data augmentation tool of point clouds to generate high-quality data to test the robustness of the target model. However, although common mutation methods can increase the amount of point clouds, the quality of the augmented data still needs to be improved based on the specify of the spatial structure of the point clouds. In this paper, we develop a point clouds augmentation tool, namely TauPad, of which the specific mutation direction is guided by adversarial attacks. Based on the point clouds pre-processing, point clouds adversarial mutation, and spatial distribution restoration, TauPad can generate augmented test data that are significantly deceptive to the target model. Preliminary experiments show that TauPad can reliably and effectively augment point clouds for testing. Its video is at https://youtu.be/Y9nDIEW13_g/ and TauPad can be used at http://1.13.193.98:2600/.
An Empirical Study on the Current Adoption of Quantum Programming

Quantum computing is no longer just a scientific curiosity; it is rapidly evolving into a commercially viable technology that has the potential to surpass the limitations of classical computation. As a result of this transition, a new discipline known as quantum software engineering has emerged, which is needed to describe unique methodologies for developing large-scale quantum applications. In the pursue of building this new body of knowledge, we undertake a mining study to elicit the purposes quantum programming is being used for, and steer further research.
Global Higher Education in 2050

Abstract This article outlines an onto-epistemic perspective for the transformation of the university, an institution currently at the service of an unsustainable and defuturing mode of social and ecological existence, into a pluriversity attuned to the imperatives of being, life, and the Earth. It argues that the key to constructing livable worlds lies in the cultivation of ways of knowing, being, and acting based on a profound awareness of the fundamental interdependence of everything that exists. This shift in vision is seen as necessary for healing our bodies, ecosystems, cities, and the planet at large—in short, for much-needed civilizational transitions, in which a redesigned academy could be a fundamental factor.
De-raining using Dual Graph Convolutional Network

Drastic climatic conditions, such as rain, snow, and other extreme weather, have a negative impact on the visual quality of photographs taken, rendering them unusable for use and sharing. As a result, it’s critical to develop a solution to this type of single- image de-raining problem. So, we try to apply deep CNN-based de-raining algorithms, which are built by stacking vanilla convolutional layers that can only simulate local relations. As a result, for this specific activity, long-range contextual information is rarely considered. To overcome these issues when training the system, we’d want to use a neural network to retrieve rain stripes from a single picture. called "Dual Graph Convolutional Network. "We incorporate these two graphs, as well as multi-scale dilated convolution, into symmetrically skip- connected network architecture to perform image rain removal. As a result, by exploring numerous representations, such as multi- scale local feature, global spatial coherence, and cross-channel correlation, this dual graph convolutional network can handle complicated and geographically extended rain streaks. Meanwhile, this model is simple to implement, trainable from beginning to end, and computationally efficient. Extensive tests on simulated and actual data show that our method outperforms current state-of-the- art methods by a wide margin.
Network Audio Data and Music Composition Teaching Based on Heterogeneous Cellular Network

With the rapid development of services such as Industry 4.0 and Internet of Vehicles, it is difficult for traditional cellular networks to meet the needs of network users for quantification, diversification, and greenness in the future. Various cellular networks expand multiple micro-cell nodes and relay nodes under macro-cells to form a multilayer network architecture. Based on this, in the process of data transmission, the links have been repeatedly reduced, and at the same time, the terminal power consumption has been reduced and the running system has been improved. This article will use the ratio of the capacity, energy consumption, and resource allocation of different cellular networks as the main means to optimize the cost. Using graph theory, auction theory, and multipurpose optimization algorithms, we have conducted in-depth research topics on upstream and downstream wireless resource allocation, network relay deployment and transmission scheduling, MMW large-scale multi-antenna transmission technology, and base station energy management. A series of optimization schemes and algorithms are proposed. This dissertation is based on the research of educational system design theory in the field of educational technology so as to carry out the research of music education system design theory suitable for the nature of music subjects and learning and education characteristics. Based on the necessity and importance of music education system design theory, the research framework of music education system design theory is constructed in advance. The voice data acquisition system collects voice data through a network grabber and real-time recording and uses signal processing and pattern recognition technology to automatically classify the collected voice data into three categories: voice, environmental sound, and music. After establishing the audio data deployment strategy, simulation method, and architecture design based on heterogeneous cellular network, this paper designs the corresponding music composition teaching system, mainly including score editing, viewing, and content display of the composition teaching system, and the final test shows that the system designed in this paper can be effectively used in various music school teaching combined with heterogeneous cellular networks.
Topology of the Documentation Landscape

Every software system (ideally) comes with one or more forms of documentation. Beside source code comments, other structured and unstructured sources (e.g., design documents, API references, wikis, usage examples, tutorials) constitute critical assets. Cloud-based repositories for collaborative development (e.g., GitHub, Bitbucket, GitLab) provide many functionalities to create, persist, and version documentation artifacts. On the other hand, the last decade has seen the rise of rich instant messaging clients used as global software community platforms (e.g., Slack, Discord). Although completely detached from a specific versioning system or development workflow, they allow developers to discuss implementation issues, report bugs, and, in general, interact with one another.We refer to this evolving heterogeneous collection of information sources and documentation artifacts as the documentation landscape. It is important to have tools to extract information from these sources and integrate them in a topological visualization, to ease comprehension of a software system. How can we automatically generate this topology? How can we link elements in the topology back to the source code they refer to?The goal of this PhD research is to automatically mine the documentation landscape of a system by disclosing pieces of information to aid, for example, in program maintenance tasks. We present our classification of possible documentation sources. The long term vision is to provide a domain model of the documentation landscape to build, visualize, and explore its instances for real software systems and evaluate the usefulness of the metaphor we propose.
Are average Faces Master Faces?

So-called master faces are faces for which the probability of achieving a false match when being compared against an unknown set of face images is significantly higher than that of a random impostor. In this work, we investigate whether average faces exhibit said property of master faces. In order to generate average faces for different demographic groups, face morphing is applied to images of public face databases. Subsequently, average faces are compared against different databases in a biometric identification scenario considering various demographic groups. Obtained results indicate that for some demographic groups, e.g. Asian females, average faces tend to yield moderately higher chances for a false match, while generally this is not the case.
Design of High Speed 8-Bit Magnitude Comparator for Security Application

A comparator is a digital or electronic circuit which is used to compare the given two inputs to give the result (greater than or lesser than or equal). This is widely used to compare the voltages and current in electrical as well as electronic circuits. This project proposed a design of 8-bit comparator which compares 8 bits of two binary data. This 8bit comparator is slightly different from conventional comparators, which can be built with two 4 bit comparators using XNOR gates or by neglecting the logic for greater or lower output; that result in reduction of gates and area. The simulations of the comparator can be done using Verilog code in Xilinx tool and analyzing its performance for various inputs in the same way a security password circuit and the cascading circuit are design as a application using proposed 8-Bit comparator.