Reviewerly/exHarmony huggingface dataset:
Automated Summarization of Stack Overflow Posts

Software developers often resort to Stack Overflow (SO) to fill their programming needs. Given the abundance of relevant posts, navigating them and comparing different solutions is tedious and time-consuming. Recent work has proposed to automatically summarize SO posts to concise text to facilitate the navigation of SO posts. However, these techniques rely only on information retrieval methods or heuristics for text summarization, which is insufficient to handle the ambiguity and sophistication of natural language. This paper presents a deep learning based framework called Assortfor SO post summarization. Assortincludes two complementary learning methods, <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{Assort}_{S}$</tex> and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{Assort}_{IS}$</tex> , to address the lack of labeled training data for SO post summarization. <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{Assort}_{S}$</tex> is designed to directly train a novel ensemble learning model with BERT embeddings and domain-specific features to account for the unique characteristics of SO posts. By contrast, <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{Assort}_{IS}$</tex> is designed to reuse pre-trained models while addressing the domain shift challenge when no training data is present (i.e., zero-shot learning). Both <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{Assort}_{S}$</tex> and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{Assort}_{IS}$</tex> outperform six existing techniques by at least 13% and 7% respectively in terms of the F1 score. Furthermore, a human study shows that participants significantly preferred summaries generated by <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{Assort}_{S}$</tex> and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{Assort}_{IS}$</tex> over the best baseline, while the preference difference between <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{Assort}_{S}$</tex> and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{Assort}_{IS}$</tex> was small.
Snake-Stega: A snake game-based steganography scheme

When two people want to communicate secretly, they can use steganography, which entails hiding the message within a seemingly innocuous medium. This research proposes a novel approach to encrypt hidden data in the famous Snake game, which is available online. The method builds a stego-snake game resembling online snake games with several information-concealing features. This paper primarily focuses on hiding messages in the snakes' food grid, followed by a simulation of a stego-snake game with an embedded message.
DCT: A lightweight approach for the real-time video anomaly detection in multi camera

CNN and transformer are the popular techniques that have been used in the past to design video anomaly detection approaches. But due to the spatial inductive bias nature of convolution, the CNN-based approaches completely rely upon the local features. Whereas transformer-based approaches are extremely heavy in terms of weight as they required deeper and wider layers to learn the visual representation. In addition, most of the approaches proposed in the past are mainly designed for the single camera-based scenario. In real world scenario, there can be N number of distributed cameras monitoring the different locations of an area. Considering these issues in this paper we design a novel lightweight approach to detect the video anomaly in the multi-camera scenario. We fuse the CNN and transformer in our approach and provide the CT module which has the property like convolution permitting it for global processing. We extracted the local features using CNN and global features using the transformer. We identify the video anomalies using the PSNR and anomaly score. We detect and track an abnormal object using the YOLO and DeepSORT algorithms. To verify the two people in different cameras are the same we use our modified architecture DCT. We evaluated our approach using our custom multicamera dataset GEU, For the comparison with state-of-the-art approaches we use standard datasets Ped1, Ped2, Avenue, and ShanghaiTech in which we achieve AUC of 98.21%, 99.32%, 98.46%, and 98.45% and EER of 2.36%, 1.55%, 1.34%, and 2.15% respectively. The average running time of our approach was 0.0092 (108 FPS).
Actualities: Seamless Live Performance with the Physical and Virtual Audiences in Multiverse

During the pandemic, the popularity of VR concerts increased, providing the audience with a novel experience of watching performances where performers and online attendees typically exist in different spaces and times. However, as the pandemic recovered gradually, people returned to live performances physically due to the diminished sense of co-presence experienced in VR concerts. We present Actualities, a seamless live performance consisting of onsite and online form with a multiverse interaction to create an engaging experience for all participants. By utilizing various sensors, we capture signals from the performance venue and digitalize all onsite elements into virtual scenes. The visual content is projected onto screens for the onsite audience, while being simultaneously broadcast via live-streaming to the online audience.
Calligraphy Experience System That Conveys the Relationship Between Kanji and Nature

To date, systems dealing with Kanji characters have been produced mainly for the purpose of learning, but it is important not only to learn the meaning and writing order of Kanji characters, but also to understand the cultural aspects of them. In regions where Kanji characters are used, various cultures, including Kanji characters, are influenced by nature, so in this study, we propose a calligraphy experience system that conveys the relationship between nature and Kanji characters. When a user looks at the writing order projected on the tabletop and writes Kanji characters created from natural shapes with a brush, the written characters change into the original natural shapes. The user's handwriting could be transformed into a natural form by using projected images.
Reflections on Socio-political, Cultural and Environmental Challenges through Computational Media Arts and Design

The field of emerging computational media arts and design is rapidly evolving as an interdisciplinary and high-impact domain. Beyond its technological affordances, it could also serve as a lens for addressing the pressing environmental, social, cultural, and political challenges. Within this context, this panel highlights the contributions of four women that shed light on the nuanced topics of inclusivity, transparency, accessibility, awareness, and action. These experts bring diverse perspectives and engage with technical advancements critically through their practice and research.
Exploring Multiple-Display Interaction for Live XR Performance

Although VR concerts offer a unique experience for audience to watch a performance, people still tend to participate in a live performance physically for the co-presence and shared experience are difficult to perceive in VR. To address this issue, we propose Actualities, a live XR performance system that integrates onsite and online concerts to create a seamless experience across multiple displays. Our system utilizes various sensors to detect signals from musical instruments and onsite audiences, digitalizing onsite performance elements into a virtual world. We project the visuals onto screens and live-stream the content for audiences to watch through various devices, and we also designed several interactive elements for the audience to interact with the public display. To evaluate our system, we conducted exploratory research to help us refine our system and improve the cross-reality experience.
SinkInSync: A Brainwave Synchronization Experience in VR Towards Enhancing Social Connectedness

SinkInSync presents the design and prototype of a VR-based cross-person EEG neurofeedback platform. This generative VR platform uses one user's brainwave data to procedurally render 3D scenes and passively displays visual cues that are synchronized with the real-time brainwave frequency to another user. With the platform, we aim to explore the potential of VR as an avenue for augmenting cognitive and emotional social connectedness in remote interactions via externally-induced brainwave synchronization between pairs of individuals.
Deformable Neural Radiance Fields for Object Motion Blur Removal

In this paper, we present a novel approach to remove object motion blur in 3D scene renderings using deformable neural radiance fields. Our technique adapts the hyperspace representation to accommodate shape changes induced by object motion blur. Experiments on Blender-generated datasets demonstrate the effectiveness of our method in producing higher-quality images with reduced object motion blur artifacts.
DLInfer: Deep Learning with Static Slicing for Python Type Inference

Python programming language has gained enor-mous popularity in the past decades. While its flexibility signifi-cantly improves software development productivity, the dynamic typing feature challenges software maintenance and quality assurance. To facilitate programming and type error checking, the Python programming language has provided a type hint mechanism enabling developers to annotate type information for variables. However, this manual annotation process often requires plenty of resources and may introduce errors. In this paper, we propose a deep learning type inference technique, namely DLInfer, to automatically infer the type infor-mation for Python programs. DLInfer collects slice statements for variables through static analysis and then vectorizes them with the Unigram Language Model algorithm. Based on the vectorized slicing features, we designed a bi-directional gated recurrent unit model to learn the type propagation information for inference. To validate the effectiveness of DLInfer, we conduct an extensive empirical study on 700 open-source projects. We evaluate its accuracy in inferring three kinds of fundamental types, including built-in, library, and user-defined types. By training with a large-scale dataset, DLInfer achieves an average of 98.79% Top-1 accuracy for the variables that can get type information through static analysis and manual annotation. Further, DLInfer achieves 83.03% type inference accuracy on average for the variables that can only obtain the type information through dynamic analysis. The results indicate DLInfer is highly effective in inferring types. It is promising to apply it to assist in various software engineering tasks for Python programs.
A Metalanguage for Cost-Aware Denotational Semantics

We present metalanguages for developing synthetic cost-aware denotational semantics of programming languages. Extending recent advances by Niu et al. in cost and behavioral verification in dependent type theory, we define two successively more expressive metalanguages for studying cost-aware metatheory. We construct synthetic denotational models of the simply-typed lambda calculus and Modernized Algol, a language with first-order store and while loops, and show that they satisfy a cost-aware generalization of the classic Plotkin-type computational adequacy theorem. Moreover, by developing our proofs in a synthetic language of phase-separated constructions of intension and extension, our results easily restrict to the corresponding extensional theorems. Consequently, our work provides a positive answer to the conjecture raised in op. cit. and contributes a framework for cost-aware programming, verification, and metatheory.
Badminton Court Management System

A badminton court management project can be designed to help manage the scheduling and booking of badminton courts, as well as the payment and maintenance of the courts. The project can be developed as a web-based application or a mobile application, or both, depending on the needs of the users. They can help automate and streamline many of the processes involved in managing badminton courts. The system can provide an online platform for users to view court availability, make bookings, and make payments. It can also keep track of maintenance schedules and send reminders to staff when maintenance is due. This system can solve some problems such as it is time consuming for customer to find a badminton hall to play badminton and badminton hall’s owner need a system to easily manage user’s booking of their badminton court. The objective of this system is to design a system that can generate a list of badminton halls or badminton courts based on user's preferences. By using this system, customer can book a badminton court via online and badminton hall’s owner can use this system to manage user’s booking of their badminton court.
Secure integration of extremely resource-constrained nodes on distributed ROS2 applications

<ns3:p><ns3:bold>Background</ns3:bold>: modern robots employ artificial intelligence algorithms in a broad ange of applications. These robots acquire information about their surroundings and use these highly-specialized algorithms to reason about their next actions. Despite their effectiveness, artificial intelligence algorithms are highly susceptible to adversarial attacks. This work focuses on mitigating attacks aimed at tampering with the communication channel between nodes running micro-ROS, which is an adaptation of the Robot Operating System (ROS) for extremely resource-constrained devices (usually assigned to collect information), and more robust nodes running ROS2, typically in charge of executing computationally costly tasks, like processing artificial intelligence algorithms.</ns3:p><ns3:p> <ns3:bold>Methods</ns3:bold>: we followed the instructions described in the Data Distribution Service for Extremely Resource Constrained Environments (DDS-XRCE) specification on how to secure the communication between micro-ROS and ROS2 nodes and developed a custom communication transport that combines the application programming interface (API) provided by eProsima and the implementation of the Transport Security Layer version 1.3 (TLS 1.3) protocol developed by wolfSSL.</ns3:p><ns3:p> <ns3:bold>Results</ns3:bold>: first, we present the first open-source transport layer based on TLS 1.3 to secure the communication between micro-ROS and ROS2 nodes, providing initial benchmarks that measure its temporal overhead. Second, we demystify how the DDS-XRCE and DDS Security specifications interact from a cybersecurity point of view.</ns3:p><ns3:p> <ns3:bold>Conclusions</ns3:bold>: by providing a custom encrypted transport for micro-ROS and ROS2 applications to communicate, extremely resource-constrained devices can now participate in DDS environments without compromising the security, privacy, and authenticity of their message exchanges with ROS2 nodes. Initial benchmarks show that encrypted single-value messages present around 20% time overhead compared to the default non-encrypted micro-ROS transport. Finally, we presented an analysis of how the DDS-XRCE and DDS Security specifications relate to each other, providing insights not present in the literature that are crucial for further investigating the security characteristics of combining these specifications.</ns3:p>
Fuzzy Model Parameter and Structure Optimization Using Analytic, Numerical and Heuristic Approaches

Fuzzy systems are widely used in most fields of science and engineering, mainly because the models they produce are robust, accurate, easy to evaluate and capture real-world uncertainty better than do the classical alternatives. We propose a new methodology for structure and parameter tuning of Takagi–Sugeno–Kang fuzzy models using several optimization techniques. The output parameters are determined analytically, by finding the minimum of the root-mean-square error (RMSE) for a properly defined error function. The membership functions are simplified by considering symmetry and equispacing, to reduce the optimization problem of finding their parameters, and allow it to be carried out using the numerical method of gradient descent. Both algorithms are fast enough to finally implement a strategy based on the hill climbing approach to finding the optimal structure (number and type of membership functions) of the fuzzy system. The effectiveness of the proposed strategy is shown by comparing its performance, using four case studies found in current relevant works, to the popular adaptive network-based fuzzy inference system (ANFIS), and to other recently published strategies based on evolutionary fuzzy models. In terms of the RMSE, performance was at least 28% better in all case studies.
Effective Information Retrieval Framework for Twitter Data Analytics

The widespread adoption of opinion mining and sentiment analysis in higher cognitive processes encourages the need for real time processing of social media data to capture the insights about user's sentiment polarity, user's opinions, and current trends. In recent years, lots of studies were conducted around the processing of data to achieve higher accuracy. But reducing the time of processing still remained challenging. Later, big data technologies came into existence to solve these challenges but those have its own set of complexities along with having hardware deadweight on the system. The contribution of this article is to touch upon mentioned challenges by presenting a climbable, quick and fault tolerant framework to process real-time data to extract hidden insights. This framework is versatile enough to support batch processing along with real time data streams in parallel and distributed environment. Experimental analysis of proposed framework on twitter posts concludes it as quicker, robust, fault tolerant, and comparatively more accurate with traditional approaches.
Modelling a stacked dense network model for outlier prediction over medical-based heart prediction data

Recently, deep learning has been used in enormous successful applications, specifically considering medical applications. Especially, a huge number of data is captured through the Internet of Things (IoT) based devices related to healthcare systems. Moreover, the given captured data are real-time and unstructured. However, the existing approaches failed to reach a better accuracy rate, and the processing time needed to be lower. This work considers the medical database for accessing the patient’s record to determine the outliers over the dataset. Based on this successful analysis, a novel approach is proposed where some feasible and robust features are extracted to acquire the emotional variations for various ways of expression. Here, a novel dense-Convolutional Neural Network (CNN) with ResNet (CNN-RN) extracts features from patients’, while for establishing visual modality, deep residual network layers are used. The significance of feature extraction is less sensitive during outlier prediction while modeling the context. To handle these issues, this dense network model is used for training the network in an end-to-end manner by correlating the significance of CNN and RN of every stream and outperforming the overall approach. Here, MATLAB 2020b is used for simulation purposes, and the model outperforms various prevailing methods for consistent prediction. Some performance metrics include detection accuracy, F1-score, recall, MCC, p-value, etc. Based on this evaluation, the experimental results attained are superior to other approaches.
Vehicle leasing credit risk assessment modeling by applying extended logistic regression

In vehicle leasing industry which presents a great business opportunity, information completed by applicants was assessed and judged by leasing associates manually in most cases; therefore, assessment results would be affected by their personal experience of leasing associates and decisions would be further affected accordingly. There are few researches on applicant credit risk assessment due to not easy to obtain of vehicle leasing data. Further, the difficulty in vehicle leasing risk assessment is increased due to class imbalance problems in vehicle leasing data. In order to address such issue, a research on credit risk assessment in vehicle leasing industry was conducted in this study. The great disparity in the ratio of high risk and low risk data was addressed by applying synthetic minority over-sampling technique (SMOTE). Then, classification effect of risk assessment model was improved by applying logistic regression in a two-phase manner. In the section of empirical analysis, the feasibility and effectiveness of the approach proposed in this study was validated by using data of actual vehicle leasing application cases provided by a financial institution in Taiwan. It is found that the proposed approach provided a simple yet effective way to build a credit risk assessment model for companies that provide vehicle leasing.
Reviewing the Usability of Web Authentication Procedures: Comparing the Current Procedures of 20 Websites

A sustainable online environment is essential to protecting businesses from abuse and data breaches. To protect sustainability, websites’ authentication procedures should continuously keep up with new technologies and the ways in which these technologies are used. Thus, a continuous assessment of these authentication procedures is required to ensure their usability. This research aimed to compare the status of the sign-up, sign-in, and password recovery processes on 20 websites. The researcher checked every website in a separate session and used the “think-aloud” technique while recording the screen to ensure accurate data analysis. Specific items were checked during every session to detect the similarities and differences between the tested websites in their authentication processes. The results led to valuable discussions and recommendations for improving authentication procedures. Some of these recommendations include best practices for better design of password rules, determining when two-factor authentication should be compulsory, and understanding how to improve password reset processes and keep accounts secure.
融合B2C和G2C的Metaverse： 旅游业的新商业模式

The Metaverse, a buzzword after last pandemic, is accepted as a functional tool enable for multi sided platforms in a single phygital world. The key contributions of this article include an organized evaluation of metaverse concepts, components, main facilitator technologies and implementation in tourism and a critical analysis of how metaverse can be facilitated in tourism including designing a new metaverse ecosystem by means of Metaverse as a service (MaaS) platform. In designed metaverse, it is aimed to merge two business models B2C &amp;amp;G2C to construct a new hybrid business model B2CG2Ccamping, glamping activities. In order to concrete the designed metaverse, two scenarios were generated by means of scenario-based approach. The metaverse designed by means of Spatial.io on a desktop computer with a 512 GB capacity with processor 11th Gen Intel(R) Core (TM) i9-11950H @ 2.60GHz 2.61 GHz, 16.0 GB RAM, 64-bit -Windows 10 Enterprises. Results show that designed metaverse is applicable for tourism activities such as glamping as a case on this study. Besides, the designed ecosystem approved the two business models can be merged in a one platform: Metaverse.