Reviewerly/exHarmony huggingface dataset:
LFIENet: Light Field Image Enhancement Network by Fusing Exposures of LF-DSLR Image Pairs

Plenoptic cameras can record both spatial and angular information of incident rays as 4D light field (LF) images, which have unique advantages in a wide range of computer vision and graphics applications. However, plenoptic cameras usually suffer from image quality degradation due to limited resolution, very small sub-apertures for sub-views, improper exposure and color quantization of image sensors. Raw macro-pixel LF images captured by plenoptic cameras are usually decomposed into an array of sub-views, during which decomposition and improper correction would further damage the quality of LF images. Therefore, the sub-views of an LF image always have tricky problems of low dynamic range, brightness reduction, color deviation and missing textural details in improper exposure areas due to the small sub-aperture for each sub-view. We observed that it is hard to tell that the brightness (tone) ranges of DSLR (Digital Single Lens Reflex) Camera images are always better than that of LF images even captured from the same real-world scenes. Thus, instead of directly taking the accompanying DSLR images as ground truths for enhancing LF images, we propose an unsupervised neural network, called LFIENet, for properly fusing the exposures of LF-DSLR image pairs. With the help of corresponding DSLR images, the enhanced LF images would contain much abundant textural details and have extended dynamic ranges and better contrast. Since histogram equalization enhancement for brightness range is able to extend dynamic range and improve image contrast, we propose a Histogram Equalization Attention Module (HEAM) to discover the over/under-exposed areas for properly fusing LF-DSLR image pairs. In addition, for learning the proposed neural network, we propose a real-world LF-DSLR image pair dataset. Extensive experiments on various challenging real-world LF images demonstrate the effectiveness of our network.
A Classification Method for English Texts Based on Hybrid Recurrent Neural Network and Graph Construction in Social Recommendation Systems

Text classification (TC) methods are widely used in the field of recommendation systems. Text data has the characteristics of short length, large capacity, and variable data distribution, resulting in sparsity and concept drift issues, which pose challenges to text classification methods. In view of this, a recursive neural network and graph construction method has been proposed for classifying English texts in social recommendation systems. First, the English text document serves as the original dataset. Then, the data is preprocessed through tokenization, removal of stop words, stemming, and min-max normalization. Meanwhile, the principal component analysis is used to extract text features from English texts. Finally, feature selection is performed using information gain technology to improve text classification performance. The simulation results show that compared with the comparison method, the proposed algorithm has ideal results in accuracy, precision, recall, F1-score, true positive rate, and false positive rate. Meanwhile, based on experimental data, it was found that the proposed method outperforms state-of-the-art methods in text analysis of English text works.
Sentiment Analysis of Twitter Data on ‘The Agnipath Yojana’

On June 14, 2022, India’s government introduced “The Agnipath Recruitment Scheme” to allow Indian youth, called Agniveers, to serve in the armed forces for a four-year tenure. This scheme has been criticized for the lack of consultation and public debate. Democratic institutions must know the opinion of the public as it signals the public’s preferences. This paper proposes a framework for determining the sentiment of the comments posted by the public. The comments related to our scheme are extracted from the Twitter platform with related tags. The sentiment identification for the cleaned comments is performed with the help of the Valence Aware Dictionary for Sentiment Reasoning technique. A base-line model is created by training Multinomial Naïve Bayes and RoBERTa models on the data. RoBERTa model has achieved a precision of 84% on the data. This work also performs topic modeling using the LDA algorithm to determine the most important vocabulary used in comments.
Performance Analysis of Blockchain Networks through Smart Contracts

Blockchain systems became popular, not just because they provide the underlying mechanism of the most prominent cryptocurrencies. They provide temper resistant features with decentralised architecture that are useful for many Internet of Things applications. However, one of the most limiting factors of its usability, are the slow throughput and latency. Even in Bit-coin, the most popular blockchain-based cryptocurrency system, speed causes a serious problem. Performance measurements and analysis are crucial to reveal possible bottlenecks and trigger improvements. They can also be used to compare different services with similar purposes. The goal of this paper is to find a way to differentiate blockchain services with a novel, lightweight approach. We developed a wallet application to be able to carry out experimental measurements by executing parameterizable computational tasks with smart contracts. We performed an evaluation of four different blockchain implementations with our approach, and investigated the correlation between the computational need of the smart contract and the elapsed time or used gas, and compared the overall latency.
Keyphrase-based Refinement Functions for Efficient Improvement on Document-Topic Association in Human-in-the-Loop Topic Models

Human-in-the-loop topic models allow users to encode feedback to modify topic models without changing the core machinery of the topic models. Basic refinement functions have been proposed in prior works in which the main focus was to modify the top word lists of topics, e.g., add a single word in a topic having distribution over a large vocabulary set. In this work, we point out that such refinements have very little to no effect on document-topic associations, which are rather important in practical applications, and propose keyphrase-based refinement functions that are designed to improve document-topic associations efficiently. In the proposed method, these keyphrases are extracted by using a neural keyphrase generation model that summarizes a document in a few keyphrases which are human-interpretable representations of each given document. The proposed refinement functions are as simple as word-based refinements but directly modify the document-topic association in functionality by referring to the keyphrase representations of documents. To examine the capability of the refinement functions for revising topic models, we conducted experiments based on a simulated user that has a fixed preference for the document-topic association using 20Newsgroups dataset. Our results showed that the proposed keyphrase-based refinements outperform the basic word-based refinements in terms of the F1 score computed on the fixed preference.
From Diffusion to Confusion of RGB Pixels Using a New Chaotic System for Color Image Encryption

Over the past decade, the transmission of images over digital and social networks, as well as their archiving on clouds, has increased dramatically. It has become necessary and urgent to secure them during their transmission or when archiving. In this paper, we propose a new color image encryption (CIE) scheme based on a single scroll chaotic system, using neurons with two dendrites. The originality of the proposed CIE scheme is that the main encryption step is based only on color pixel scrambling, which is traditionally used for diffusion. Based on the proposed CIE scheme we have developed three different approaches. From color pixel scrambling (CPS), to full color pixel component scrambling (FCPCS), passing through intra-color pixel component scrambling (ICPCS), we show that color pixel component scrambling can act not only as a permutation, but also as a substitution, ensuring both confusion and diffusion properties. The experimental results show that using the proposed scheme, regardless of the approach used, we can efficiently obtain encrypted color images. In particular, the results obtained with the FCPCS approach show that statistical and differential attack analyses are comparable to those obtained with current state-of-the-art methods that combine permutation and substitution steps.
Hyperbolic Tangent Like Relied Banach Space Valued Neural Network Multivariate Approximations

Abstract Here we examine the multivariate quantitative approximations of Banach space valued continuous multivariate functions on a box or ℝ N , N ∈ ℕ, by the multivariate normalized, quasi-interpolation, Kantorovich type and quadrature type neural network operators. We treat also the case of approximation by iterated operators of the last four types. These approximations are derived by establishing multidimensional Jackson type inequalities involving the multivariate modulus of continuity of the engaged function or its high order Fréchet derivatives. Our multivariate operators are defined by using a multidimensional density function induced by a hyperbolic tangent like sigmoid function. The approximations are pointwise and uniform. The related feed-forward neural network is with one hidden layer.
Assessment of the Distributed Ledger Technology for Energy Sector Industrial and Operational Applications Using the MITRE ATT&amp;CK® ICS Matrix

In recent times, Distributed Ledger Technology (DLT) has gained significant attention for its potential application in the energy sector. Utilizing blockchain and DLT has demonstrated the ability to enhance the resilience of the electric infrastructure, which will support a more flexible infrastructure and advance grid modernization. However, the deployment of these technologies increases the overall attack surface. The MITRE ATT&CK® matrices have been developed to document an adversary’s tactics and techniques based on real-world observations. The MITRE ATT&CK® matrices provide a common taxonomy for offense and defense and have become a valuable conceptual tool across multiple cybersecurity disciplines for conveying threat intelligence, performing testing through red teaming or adversary emulation, and enhancing network and system defenses against intrusions. The MITRE ATT&CK® for Industrial Control Systems (ICS) matrix was created to provide knowledge about adversary behavior in the ICS technology domain. This study analyzes the relevance of various tactics and techniques across a seven-layer DLT engineering and cybersecurity stack, known as the DLT stack, designed by the Cybersecurity Taskforce under IEEE P2418.5 - Standard for Blockchain in Energy working group sponsored by Power and Energy Systems - Smart Buildings, Loads and Customer Systems (PES/SBLC) Technical Committee. Additionally, this paper identifies specific mitigation strategies tailored to the energy ICS environment.
Offset-Guided Attention Network for Room-Level Aware Floor Plan Segmentation

Recognition of floor plans has been a challenging and popular task. However, existing approaches are struggling to make accurate room-level unified predictions, seriously limiting their visual quality and applicability. In this paper, we propose a novel approach to recognize the floor plan layouts with a newly proposed Offset-Guided Attention mechanism to improve the semantic consistency within a room. In addition, we present a Feature Fusion Attention module that leverages the channel-wise attention to encourage the consistency of the room, wall, and door predictions, further enhancing the room-level semantic consistency. Experimental results manifest our approach is able to improve room-level semantic consistency and outperforms the existing works both qualitatively and quantitatively. To summarize, this paper tackles inconsistent semantic prediction issues in floor plan segmentation and enhances visual plausibility, quantitative performance, and practical applicability.
A Standardized and Cost-Effective VR Approach for Powered Wheelchair Training

Mastering wheelchair driving skills is essential for the safety of wheelchair users (WUs), yet the acquisition of these skills can be challenging, and training resources can be costly or not available. Technologies such as virtual reality (VR) have grown in popularity as they can provide a motivating training environment without the risks found in real-life training. However, these approaches often deploy navigation controllers which are different from the ones WUs utilise, and do not use a standardised approach in assessing the acquisition of skills. We propose a VR training system based on the wheelchair skills training program (WSTP) and utilizing a sensor device that can be retrofitted to any joystick and communicates wirelessly with a Head-Mounted Display. In this paper, we present a first-validation study with fourteen able-bodied participants, split between a VR test group and a non-VR control group. To determine the acquisition of skills, participants complete tasks in real-life before and after the VR training, where completion time and length of joystick movements are measured. We also assess our system using heart rate measurements, the WSTP questionnaire, the simulator sickness questionnaire and the igroup presence questionnaire. We found that the VR training facilitates the acquisition of skills for more challenging tasks; thus, our system has the potential of being used for training skills of powered wheelchair users, with the benefit of conducting the training in safely and in a low-cost setup.
Nature-Based Prediction Model of Bug Reports Based on Ensemble Machine Learning Model

In software development systems, the maintenance process of software systems attracted the attention of researchers due to its importance in fixing the defects discovered in the software testing by using bug reports (BRs) which include detailed information like descriptions, status, reporter, assignee, priority, and severity of the bug and other information. The main problem in this process is how to analyze these BRs to discover all defects in the system, which is a tedious and time-consuming task if done manually because the number of BRs increases dramatically. Thus, the automated solution is the best. Most of the current research focuses on automating this process from different aspects, such as detecting the severity or priority of the bug. However, they did not consider the nature of the bug, which is a multi-class classification problem. This paper solves this problem by proposing a new prediction model to analyze BRs and predict the nature of the bug. The proposed model constructs an ensemble machine learning algorithm using natural language processing (NLP) and machine learning techniques. We simulate the proposed model by using a publicly available dataset for two online software bug repositories (Mozilla and Eclipse), which includes six classes: Program Anomaly, GUI, Network or Security, Configuration, Performance, and Test-Code. The simulation results show that the proposed model can achieve better accuracy than most existing models, namely, 90.42% without text augmentation and 96.72% with text augmentation.
Visual Spatial Reasoning

Abstract Spatial relations are a basic part of human cognition. However, they are expressed in natural language in a variety of ways, and previous work has suggested that current vision-and-language models (VLMs) struggle to capture relational information. In this paper, we present Visual Spatial Reasoning (VSR), a dataset containing more than 10k natural text-image pairs with 66 types of spatial relations in English (e.g., under, in front of, facing). While using a seemingly simple annotation format, we show how the dataset includes challenging linguistic phenomena, such as varying reference frames. We demonstrate a large gap between human and model performance: The human ceiling is above 95%, while state-of-the-art models only achieve around 70%. We observe that VLMs’ by-relation performances have little correlation with the number of training examples and the tested models are in general incapable of recognising relations concerning the orientations of objects.1
Augmented reality learning media based on tetrahedral chemical representation: How effective in learning process?

The implementation of technology in the era of Society 5.0 runs massively in the world of education. One of them is in the form of augmented reality (AR) learning media. AR technology that can visualize abstract chemical topics in line with the concept of tetrahedral chemical representation. Therefore, this study aims to design and test the effectiveness of AR learning media based on tetrahedral chemical representation. This study used research and development methods with ADDIE (analysis, design, development, implementation, and evaluation) model. The topic of chemical equilibrium chemistry was chosen in this study to develop AR media. This research was conducted in three representative schools in Surakarta, Central Java, Indonesia. A total of 168 students from three representative schools (66 male and 102 female) participated as subjects in the Implementation stage. In addition, a multiple-choice instrument with 24 parallel questions on the pre- and post-test was used to determine the effect of the developed media on the experimental and control classes. The results showed that the design of AR learning media based on tetrahedral chemical representations was successfully developed and proved effective in improving learning outcomes. Student response sheets are given after using the media to find user experience regarding the strength and weaknesses of AR media.
Klima DAO: a crypto answer to carbon markets

Abstract Tackling major societal challenges often requires a significant level of organizational novelty. One such recent example is Klima DAO, a decentralized autonomous organization (DAO) that seeks to transform voluntary markets through the adoption of carbon credits. DAOs rely on self-executing rules and have a high level of decentralization. By leveraging blockchain and related technologies, Klima DAO offers innovative solutions to universal problems of organizing in both its governance and product delivery. Blockchain tools enable Klima DAO to operate uniquely, allowing for rapid scaling while maintaining decentralized governance and promoting transparent carbon credit trading with low transaction fees. As such, Klima DAO may serve as a model for future organizations in search of greater transparency and flatter governance structures.
Semantic Communication With Memory

While semantic communication succeeds in efficiently transmitting due to the strong capability to extract the essential semantic information, it is still far from the intelligent or human-like communications. In this paper, we introduce an essential component, memory, into semantic communications to mimic human communications. Particularly, we investigate a deep learning (DL) based semantic communication system with memory, named Mem-DeepSC, by considering the scenario question answer task. We exploit the universal Transformer based transceiver to extract the semantic information and introduce the memory module to process the context information. Moreover, we derive the relationship between the length of semantic signal and the channel noise to validate the possibility of dynamic transmission. Specially, we propose two dynamic transmission methods to enhance the transmission reliability as well as to reduce the communication overheads by masking some unessential elements, which are recognized through training the model with mutual information. Numerical results show that the proposed Mem-DeepSC is superior to benchmarks in terms of answer accuracy and transmission efficiency, i.e., number of transmitted symbols.
Suspicious Behavior Detection with Temporal Feature Extraction and Time-Series Classification for Shoplifting Crime Prevention

The rise in crime rates in many parts of the world, coupled with advancements in computer vision, has increased the need for automated crime detection services. To address this issue, we propose a new approach for detecting suspicious behavior as a means of preventing shoplifting. Existing methods are based on the use of convolutional neural networks that rely on extracting spatial features from pixel values. In contrast, our proposed method employs object detection based on YOLOv5 with Deep Sort to track people through a video, using the resulting bounding box coordinates as temporal features. The extracted temporal features are then modeled as a time-series classification problem. The proposed method was tested on the popular UCF Crime dataset, and benchmarked against the current state-of-the-art robust temporal feature magnitude (RTFM) method, which relies on the Inflated 3D ConvNet (I3D) preprocessing method. Our results demonstrate an impressive 8.45-fold increase in detection inference speed compared to the state-of-the-art RTFM, along with an F1 score of 92%,outperforming RTFM by 3%. Furthermore, our method achieved these results without requiring expensive data augmentation or image feature extraction.
Safety-critical computer vision: an empirical survey of adversarial evasion attacks and defenses on computer vision systems

Abstract Considering the growing prominence of production-level AI and the threat of adversarial attacks that can poison a machine learning model against a certain label, evade classification, or reveal sensitive data about the model and training data to an attacker, adversaries pose fundamental problems to machine learning systems. Furthermore, much research has focused on the inverse relationship between robustness and accuracy, raising problems for real-time and safety-critical systems particularly since they are governed by legal constraints in which software changes must be explainable and every change must be thoroughly tested. While many defenses have been proposed, they are often computationally expensive and tend to reduce model accuracy. We have therefore conducted a large survey of attacks and defenses and present a simple and practical framework for analyzing any machine-learning system from a safety-critical perspective using adversarial noise to find the upper bound of the failure rate. Using this method, we conclude that all tested configurations of the ResNet architecture fail to meet any reasonable definition of ‘safety-critical’ when tested on even small-scale benchmark data. We examine state of the art defenses and attacks against computer vision systems with a focus on safety-critical applications in autonomous driving, industrial control, and healthcare. By testing a combination of attacks and defenses, their efficacy, and their run-time requirements, we provide substantial empirical evidence that modern neural networks consistently fail to meet established safety-critical standards by a wide margin.
Dynamic Twitter Topic Summarization Using Speech Acts

The enormous growth of social media platforms such as Facebook and Twitter caused content and comment explosion. Summarization of such comments help in value derivation and become useful in policy making. The tweets are different in nature like noisy, short, and dissimilar. Twitter’s capacity to summarize a message or issue concisely and effectively is what gives it this power. To improve their tweets and reach a larger audience, users frequently use strategies like threading, hashtags, and multimedia attachments. There are several methods applied by researchers, but these are error prone. To summarize the tweets, we have taken the help of speech acts. The speech acts are helpful to guide the summarization based on behavior of the tweet with an organized view of the tweets. The approach is collecting the tweets, preprocessing, classification, and summarization. After the summarization evaluate the tweets based on different categories and nature of the tweet.