Reviewerly/exHarmony huggingface dataset:
Dependency-aware Form Understanding

Form understanding is an important task in many fields such as software testing, AI assistants, and improving accessibility. One key goal of understanding a complex set of forms is to identify the dependencies between form elements. However, it remains a challenge to capture the dependencies accurately due to the diversity of UI design patterns and the variety in development experiences. In this paper, we propose a deep-learning-based approach called DependEX, which integrates convolutional neural networks (CNNs) and transformers to help understand dependencies within forms. DependEX extracts semantic features from UI images using CNN-based models, captures contextual patterns using a multilayer transformer encoder module, and models dependencies between form elements using two embedding layers. We evaluate DependEX with a large-scale dataset from mobile Web applications. Experimental results show that our proposed model achieves over 92% accuracy in identifying dependencies between UI elements, which significantly outperforms other competitive methods, especially for heuristic-based methods. We also conduct case studies on automatic form filling and test case generation from natural language (NL) instructions, which demonstrates the applicability of our approach.
Violinists Employ More Expressive Gesture and Timing Around Global Musical Resolutions

Performers express musical structure using variations in dynamics, timbre, timing, and physical gesture. Previous research on instrumental performance of Western classical music has identified increased nontechnical motion (movement considered supplementary to producing sound) and ritardando at cadences. Cadences typically provide resolution to built-up tension at differing levels of importance according to the hierarchical structure of music. Thus, we hypothesized that performers would embody these differences by employing nontechnical motion and rubato, even when not explicitly asked to express them. Expert violinists performed the Allemande from Bach’s Flute Partita for motion capture and audio recordings in a standing position, then we examined nontechnical motion and rubato in four cadential excerpts (two locally important, two globally important) and four noncadential excerpts. Each excerpt was segmented into the buildup to and departure from the dominant-tonic progression. Increased ritardando as well as nontechnical motion such as side-to-side whole-body swaying and torso rotation in cadential excerpts were found compared to noncadential excerpts. Moreover, violinists used more nontechnical motion and ritardando in the departure segments of the global cadences, while the buildups also showed the global-local contrast. Our results extend previous findings on the expression of cadences by highlighting the hierarchical nature of embodied musical resolution.
DC-BiGRU-CNN Algorithm for Irony Recognition in Chinese Social Comments

Irony recognition is an important research direction in text sentiment analysis, which contributes to discover ironic tone to judge text emotion correctly. The object of this paper is the application of deep learning to irony recognition, and the research aims to solve the problem that existing machine learning algorithms have difficulty in discriminating ironic tones. Aiming at the irony recognition of Chinese social comments, DC-BiGRU-CNN is proposed from the perspective of structural optimization rather than a text vectorization mechanism point of view, which is a dual-channel CNN combined with BiGRU, and incorporates attention mechanism and multigranularity convolutional neural network as the main framework. This paper first briefly discusses the difficulties of irony recognition and provides an overview of existing text sentiment analysis algorithms. This is followed by a detailed discussion of DC-BiGRU-CNN. Furtherly, it is compared with the main irony recognition methods on a social comment dataset containing Chinese ironic comments, and the experimental results show that DC-BiGRU-CNN can improve the accuracy of irony recognition.
Multi Modal Analysis of memes for Sentiment extraction

Memes are one of the most ubiquitous forms of social media communication. The study and processing of memes, which are intrinsically multimedia, is a popular topic right now. The study presented in this research is based on the Memotion dataset, which involves categorising memes based on irony, comedy, motivation, and overall-sentiment. Three separate innovative transformer-based techniques have been developed, and their outcomes have been thoroughly reviewed.The best algorithm achieved a macro F1 score of 0.633 for humour classification, 0.55 for motivation classification, 0.61 for sarcasm classification, and 0.575 for overall sentiment of the meme out of all our techniques.
Analysis of Color and Texture Features for Samarinda Sarong Classification

Samarinda sarong or Tajong Samarinda is a traditional woven fabric originating from Samarinda, East Borneo, Indonesia. It is made through a weaving process using a loom called a Gedokan (a traditional machine). Unfortunately, many Samarinda people still lack knowledge regarding the type of Samarinda sarong; hence they cannot recognize it. Therefore, an automatic method of image processing-based needed to recognizing and classifying the motif of Samarinda sarong. This method requires appropriate and discriminatory features to obtain the optimal classification results. This work aims to analyze color and texture features to produce discriminative features. The color features used are color moments applied on RGB and HSV color spaces, while texture features were extracted using Gray Level Co-occurrence Matrix (GLCM). Subsequently, those features were reduced using correlation-based feature selection (CFS) followed by applying the Support Vector Machine (SVM) classifier. The dataset used consists of 150 sarong images (50 Belang Hata, 50 Belang Negara, and 50 Kuningsau). The method performance successfully achieved the accuracy of 100% using only 10 color features from a total of 34 features.
AccGuard: Secure and Trusted Computation on Remote FPGA Accelerators

Application-specific acceleration has prevailed in cloud computing and data centers. But the current infrastructure design provides little or no support for security in external accelerators. Existing trusted computing solutions such as Intel SGX or ARM TrustZone only target CPU-only environments, leaving external accelerators and peripheral devices unprotected. This work proposes AccGuard, a new scheme to extend trust computation for remote FPGA accelerators. AccGuard consists of a security manager (SM) with hardware root of trust and remote attestation through standard cryptographic primitives to form an enclave framework for FPGA accelerators. It minimizes the performance overhead (due to the security features) compared to a state-of-the-art CPU-based enclave framework, Intel SGX, while enjoying the benefit of improved performance through hardware acceleration.
A Coarse-to-Fine Framework for Automatic Video Unscreen

Video unscreen, a technique to extract foreground from given videos, has been playing an important role in today's video production pipeline. Existing systems developed for this purpose which mainly rely on video segmentation or video matting, either suffer from quality deficiencies or requiring tedious manual annotations. In this work, we aim to develop a fully automatic video unscreen framework that is able to obtain high-quality foreground extraction without the need of human intervention in a controlled environment. Inspired by the alpha composition equation, our frame adopts a coarse-to-fine strategy, where the obtained background estimate given an initial mask prediction in turn helps the refinement of the mask. We conducted experiments on two datasets, 1) the Adobe's Synthetic-Composite dataset, and 2) DramaStudio, our newly collected large-scale green screen video matting dataset, exhibiting the controlled environments. The results show that the proposed framework outperforms existing algorithms and commercial software, both quantitatively and qualitatively. We also demonstrate its utility in person replacement in videos, which can further support a variety of video editing applications.
Cryptocurrency Price Prediction using Time Series Forecasting (ARIMA)

Cryptocurrency is a tangible or digital currency protected with the help of Cryptography, making it almost impossible to counterfeit or double. Many cryptocurrency networks are categorized primarily based on blockchain technology. The present socio-economic situation also creates an environment for people to hold less cash and remain marginalized by the market trends. The objective of the project is to build a profitable Machine Learning prediction model. We begin by collecting the data from Yahoo Finance website using inbuilt python libraries. Our objective was to perform price prediction of various Cryptocurrencies using Machine Learning, and we have implemented the Autoregressive Integrated Moving Average (ARIMA) Model. We have performed feature engineering on various set of lagged values, on previous day, one for 7 days and another looking back for 30 days. We have forecasted the outcome of the model and plotted the outcome in a responsive chart using Plot graph.
LearnPhi: a Real-Time Learning Model for Early Prediction of Phishing Attacks in IoV

The Internet of Vehicles (IoV) can deliver services for intelligent transportation systems. However, it typically relies on the exchange of sensitive information, including passwords and personal data, which makes it vulnerable to many security risks, such as phishing attacks, which have largely increased in the last decade. Cryptography-based security measures look as a form of protection to preserve sensitive information, but they can be bypassed by inside attackers and, in addition, they increase the burden on the network. To tackle the aforementioned issues, in this paper, we present an approach to control phishing attacks in the IoV environment, based on Machine Learning (ML) and Deep Learning (DL) techniques.
Image splicing detection using discriminative robust local binary pattern and support vector machine

Purpose This paper aims to propose a novel splicing detection method using a discriminative robust local binary pattern (DRLBP) with a support vector machine (SVM). Reliable detection of image splicing is of growing interest due to the extensive utilization of digital images as a communication medium and the availability of powerful image processing tools. Image splicing is a commonly used forgery technique in which a region of an image is copied and pasted to a different image to hide the original contents of the image. Design/methodology/approach The structural changes caused due to splicing are robustly described by DRLBP. The changes caused by image forgery are localized, so as a first step, localized description is divided into overlapping blocks by providing an image as input. DRLBP descriptor is calculated for each block, and the feature vector is created by concatenation. Finally, features are passed to the SVM classifier to predict whether the image is genuine or forged. Findings The performance and robustness of the method are evaluated on public domain benchmark data sets and achieved 98.95% prediction accuracy. The results are compared with state-of-the-art image splicing finding approaches, and it shows that the performance of the proposed method is improved using the given technique. Originality/value The proposed method is using DRLBP, an efficient texture descriptor, which combines both corner and inside design detail in a single representation. It produces discriminative and compact features in such a way that there is no need for the feature selection process to drop the redundant and insignificant features.
MLogNet: A Logarithmic Quantization-Based Accelerator for Depthwise Separable Convolution

In this article, we propose a novel logarithmic quantization-based deep neural network (DNN) architecture for depthwise separable convolution (DSC) networks. Our architecture is based on selective two-word logarithmic quantization (STLQ), which improves accuracy greatly over logarithmic-scale quantization while retaining the speed and area advantage of logarithmic quantization. On the other hand, it also comes with the synchronization problem due to variable-latency processing elements (PEs), which we address through a novel architecture and a compile-time optimization technique. Our architecture is dynamically reconfigurable to support various combinations of depthwise versus pointwise convolution layers efficiently. Our experimental results using layers from MobileNetV2 and ShuffleNetV2 demonstrate that our architecture is significantly faster and more area efficient than previous DSC accelerator architectures as well as previous accelerators utilizing logarithmic quantization.
Object Estimation Method for Edge Devices Inspired by Multimodal Information Processing in the Brain

To realize real-time mobile augmented reality applications, various objects in the real world need to be instantly identified, located, and represented as a digital twin through sensor devices and edge IoT systems. However, it is challenging to make a fast and accurate decision on what the object is from real-time noisy streaming information. Multimodal decision making has been expected to mitigate such incomplete information and improve the accuracy of simplified recognition algorithms tuned for edge devices. In this paper, we propose an object estimation method inspired from the multimodal information processing mechanism of the brain, which makes decisions based on multiple types of uncertain observed information. Through computer simulations, we show that our proposed method identifies an object accurately and quickly from uncertain observed information.
Blind Recognition of Channel Coding Based on CNN-BLSTM

In cognitive radio or military communication systems, the channel coding type recognition of the primary user signal is an important task to realize full awareness of the wireless communication environment. Previous methods to solve this problem usually have high computational complexity, which are not suitable for real-time applications and require rich experience and professional knowledge in manual feature extraction. In this paper, a blind channel coding recognition algorithm based on CNN-BLSTM is proposed. Firstly, this method uses convolutional neural network to extract the data features of coding sequence and also avoids the problem of low recognition accuracy caused by inputting the original codeword data with inconspicuous features directly into neural network. Then, the context dependence of features is obtained through bidirectional long short-term memory network. Finally, the classification task is accomplished by softmax function. The experiments use spatially coupled LDPC codes and 5G NR LDPC codes as candidate codes. The experimental results show that the algorithm achieves quite high recognition accuracy under good channel conditions.
Ubiquitous computing

Ubiquitous computing promises new paradigms for serving users’ everyday experiences. Such a technique is energized by the copious sensor information, computational capabilities, and access to the remote data management systems. For the recent year, the application-driven and user-centered studies on ubiquitous computing have pushed wearable computing away from the traditional computer-centered system and thus changed the relationship between the users and machines. In this chapter, we would focus on how the Ubiquitous computing enhances the functions of wearable medicine with common or specific computational devices. With existing healthcare studies, the sensor fusion processes, data management and communication methods, system operability and user-system interaction are introduced in detail. The advanced developments of several real-life applications were also described with particular system designs. Finally, we highlight the possible challenges and issues of Ubiquitous computing technique.
A Comparative Analysis for Single Person and Multi Person Pose Estimation Using Deep Learning Algorithms

Due to numerous application of Human posture application in the real world, it has recently gained a lot of attention. This paper gives a complete assessment of deep learning-based human pose estimation methods and analyses the methodology used, because deep learning can improve the performance of state-of-the-art human pose estimation methods. Pipelines for single people and multi-person are examined individually first. The Alpha pose and Open pose deep learning frameworks used in these pipelines and, are then compared and analyzed.
UIT-Anomaly: A Modern Vietnamese Video Dataset for Anomaly Detection

Anomaly detection in videos is of utmost importance for numerous tasks in the field of computer vision. We introduce the UIT-Anomaly dataset captured in Vietnam with a total duration of 200 minutes. It contains 224 videos with six different types of anomalies. Moreover, we apply a method for weakly supervised video anomaly detection, called Robust Temporal Feature Magnitude learning (RTFM) based on feature magnitude learning to detect abnormal snippets. The approached method yields competitive results, compared to other state-of-the-art algorithms using publicly available datasets such as ShanghaiTech and UCF–Crime.
Intelligent Approaches for Natural Language Processing for Indic Languages

Natural Language Processing (NLP) is a subfield of semantics, software engineering, and artificial intelligence dealing with the coordination between computers and human language, specifically how to program computers to process and investigate a lot of natural language information. The objective is to program a computer to understand the written texts, including the context of the language inside them. India is a diverse nation and communicates in various dialects. India faces incredible difficulties for research in the field of NLP. In this work we present the various works done in the field of NLP for Indian Languages and also present the various challenges that are faced by the research community working in this field in India. We also discuss our proposed methodology for machine translation from Sanskrit to Hindi.
Addressing the Influence of End User Human Aspects on Software Engineering

We outline some of the key challenges in supporting diverse human aspects of end users in software engineering. This includes examples of age, gender, physical and mental challenges, human values, personality, emotions, language and culture. We review key related work from a range of disciplines, and propose an initial taxonomy of end user human aspects that need careful consideration throughout the software engineering life cycle. Finally we outline a research road map of key areas requiring further investigation and work.
A File Encryption System Based on Attribute Based Encryption

Attribute Based Encryption (ABE) is a kind of public-key cryptosystem that is commonly used to protect file data security and achieve the fine-grained sharing of files. However, the existing attribute-based encryption algorithms are very complex and involve a large amount of additional cryptographic data, which is not convenient to implement. To solve this problem, an attribute-based data encryption scheme is proposed in this paper to achieve secure storage and access control of files. This system is convenient to implement and reliable in operation. In this file encryption system, the attribute private key is divided into a server-side secret share and a user-side secret share. When a user is authorized to access a file, the client and the server decrypt the encrypted file by collaborative computation using the user-side secret shares and server-side secret shares of the user's attribute private keys.
Mixed Dimensions: Exploring Novel Material and Extrusion Tool for 2.5D Shape Painting

This paper explores a novel tool and materials for brush-like expression in 3D forms. We envision a 2.5D painting device that aims at closing the gap between painterly expression, digital fabrication, and the incorporation of expressive elements present in traditional painting with structural capability. The paper describes the development of the new pigment material, a custom handheld extrusion device, and discusses a user study investigating emerging relationships with the developed medium. We find the reactivity and variability of the medium enabling strong expressive agency and new interaction opportunities. With these, we discuss what are aspects in making that could be embraced to enhance users' creative agency and ownership in making, postulating a space between artistic expression and utilitarian making–e.g. digital fabrication.
Phonological variation on Twitter: Evidence from letter repetition in three French dialects

Abstract Writing on social media often departs from prescriptive norms through the use of non-standard words, spellings and punctuation. Amongst these traits is the repetition of letters (e.g. &lt;ouiiiii&gt; for oui ‘yes’). In this study, we draw upon a corpus of over 65 million tweets from three dialects of French (Laurentian, Metropolitan and Midi) to test phonological motivations for the choice of repeated letter in a word with repetition. Using mixed-effects multinomial regression, we compare dialectal differences in whether repetition targets final consonants (silent or pronounced), word-final orthographic &lt;e&gt; corresponding to phonological schwa, and prosodically accented penults. We demonstrate that repetition covertly signals phonological properties. We conclude that prosody mediates morphological and phonological effects and that grapheme-to-phoneme correspondences vary between regions, thereby producing phonological patterns that writers likely did not intend to convey at the time of writing. We also propose that orthographic repetition on Twitter has two prosodic sources: the default pitch accent in French (shifted or not) and focus.
ReFixar: Multi-version Reasoning for Automated Repair of Regression Errors

Software programs evolve naturally as part of the ever-changing customer needs and fast-paced market. Software evolution, however, often introduces regression bugs, which un-duly break previously working functionalities of the software. To repair regression bugs, one needs to know when and where a bug emerged from, e.g., the bug-inducing code changes, to narrow down the search space. Unfortunately, existing state-of-the-art automated program repair (APR) techniques have not yet fully exploited this information, rendering them less efficient and effective to navigate through a potentially large search space containing many plausible but incorrect solutions. In this work, we revisit APR on repairing regression errors in Java programs. We empirically show that existing state-of-the-art APR techniques do not perform well on regression bugs due to their algorithm design and lack of knowledge on bug inducing changes. We subsequently present ReFixar, a novel repair technique that leverages software evolution history to generate high quality patches for Java regression bugs. The key novelty that empowers ReFixar to more efficiently and effectively traverse the search space is two-fold: (1) A systematic way for multi-version reasoning to capture how a software evolves through its history, and (2) A novel search algorithm over a set of generic repair templates, derived from the principle of incorrectness logic and informed by both past bug fixes and their bug-inducing code changes; this enables ReFixar to achieve a balance of both genericity and specificity, i.e., generic common fix patterns of bugs and their specific contexts. We compare ReFixar against the state-of-the-art APR techniques on a data set of 51 real regression bugs from 28 large real-world programs. Experiments show that ReFixar significantly outperforms the best baseline by a large margin, i.e., ReFixar can fix correctly 24 bugs while the best baseline can only correctly fix 9 bugs.