Reviewerly/exHarmony huggingface dataset:
Why Does Surprisal From Larger Transformer-Based Language Models Provide a Poorer Fit to Human Reading Times?

Abstract This work presents a linguistic analysis into why larger Transformer-based pre-trained language models with more parameters and lower perplexity nonetheless yield surprisal estimates that are less predictive of human reading times. First, regression analyses show a strictly monotonic, positive log-linear relationship between perplexity and fit to reading times for the more recently released five GPT-Neo variants and eight OPT variants on two separate datasets, replicating earlier results limited to just GPT-2 (Oh et al., 2022). Subsequently, analysis of residual errors reveals a systematic deviation of the larger variants, such as underpredicting reading times of named entities and making compensatory overpredictions for reading times of function words such as modals and conjunctions. These results suggest that the propensity of larger Transformer-based models to ‘memorize’ sequences during training makes their surprisal estimates diverge from humanlike expectations, which warrants caution in using pre-trained language models to study human language processing.
Enhancing Effective Learning Capability of SOAR Agent based Episodic Memory

In order to increase the agent's intelligence, the fundamental goal of the research paper is to learn from its experiences. In order to solve a problem, learning is accomplished with the aid of episodic memory, which stores previous experiences as episodes. This study illustrates the improvement in intelligence with the use of the application of an example in soar. Working memory elements (WMEs), which are episodes (past experiences) in the form, are discussed in this paper as a means of enhancing intelligence. The purpose of this work is to demonstrate that knowledge always leads to performance improvement, meaning that as an agent studies more, performance will extend.
Framework for Deployment of Smart Motor Starter using Android Automation Tool

In today's era of technology a control system without an automation is worthless and not that much efficient. A control system must be integrated with an automation to conduct the day-to-day challenging task in an easy way and can save a time and cost of manual operation. In India some of the farmers are using their own techniques of smart farming. But in some areas smart farming is not yet developed due to less knowledge about the technology. Some farmers are using the technologies that can increase the crop yield directly but in some scenarios crop yield can be increased by using some indirect ways. Some of them are borewell charging, poly house water management automation, submersible motor automation, etc. in our survey we found that if farmers are using a new technology there are some misleading concepts, they have regarding it so to nullify the problem of farmers we are introducing a smart device that can be used to automate the motor starter remotely.
TransCNN: Hybrid CNN and transformer mechanism for surveillance anomaly detection

Surveillance video anomaly detection (SVAD) is a challenging task due to the variations in object scale, discrimination and unexpected events, the impact of the background, and the wide range of definitions of anomalous events in different surveillance contexts. In this work, we introduce an end-to-end hybrid convolution neural network (CNN) and vision transformer-based framework for anomaly detection. The proposed framework uses spatial and temporal information from a surveillance video to detect anomalous events and operates in two steps: in the first step, an efficient backbone CNN model is used for spatial feature extraction, while in the second step, these features are passed from the transformer-based model to learn the long-term temporal relationships between various complex surveillance events. The features from the backbone model are fed to a sequential learning model in which temporal self-attention is utilised to generate an attention map; this allows the proposed framework to learn the spatiotemporal features effectively and to detect anomalous events. Our experimental results on various benchmark VAD datasets prove the validity of the proposed framework, which outperforms other state-of-the-art approaches by achieving high AUC values of 94.6%, 98.4%, and 89.6% on the ShanghaiTech, UCSD Ped2 and CUHK avenue datasets, respectively.
Toward an integration of blockchain technology in the food supply chain

The traceability of what we eat is a lingering issue. Blockchain enables transparency across the value chain as it tracks a product's origin, location, and history. In this work we adopt a mix-method approach - experiment plus qualitative evidence - to understand why consumers consider the traceability offered by blockchain important and what are the barriers that suppliers face when considering blockchain adoption. Our findings show that, when the food is local, blockchain increases consumers' trust and, in turn, attitudes and behaviors toward the sharing experience. On the supply side, we find that, while operators see key benefits in blockchain adoption – including enhanced trust, they are still hesitant in using the technology because of a greater need for intra-organizational support and a concern in data sharing. By looking at both consumers and suppliers, we offer a complete picture on the integration of blockchain technology in the food supply chain.
Multi-Modal 3D Object Detection in Autonomous Driving: A Survey and Taxonomy

Autonomous vehicles require constant environmental perception to obtain the distribution of obstacles to achieve safe driving. Specifically, 3D object detection is a vital functional module as it can simultaneously predict surrounding objects' categories, locations, and sizes. Generally, autonomous vehicles are equipped with multiple sensors, including cameras and LiDARs. The fact that single-modal methods suffer from unsatisfactory detection performance motivates utilizing multiple modalities as inputs to compensate for single sensor faults. Although many multi-modal fusion detection algorithms exist, there is still a lack of comprehensive and in-depth analysis of these methods to clarify how to fuse multi-modal data effectively. Therefore, this paper surveys recent advancements in fusion detection methods. First, we present the broad background of multi-modal 3D object detection and identify the characteristics of widely used datasets along with their evaluation metrics. Second, instead of the traditional classification method of early, middle, and late fusion, we categorize and analyze all fusion methods from three aspects: feature representation, alignment, and fusion, which reveals how these fusion methods are implemented in an essential way. Third, we provide an in-depth comparison of their pros and cons and compare their performance in mainstream datasets. Finally, we further summarize current challenges and research trends for realizing the full potential of multi-modal 3D object detection.
Genetic algorithms as classical optimizer for the Quantum Approximate Optimization Algorithm

Optimization is one of the research areas where quantum computing could bring significant benefits. In this scenario, a hybrid quantum–classical variational algorithm, the Quantum Approximate Optimization Algorithm (QAOA), is receiving much attention for its potential to efficiently solve combinatorial optimization problems. This approach works by using a classical optimizer to identify appropriate parameters of a problem-dependent quantum circuit, which ultimately performs the optimization process. Unfortunately, learning the most appropriate QAOA circuit parameters is a complex task that is affected by several issues, such as search landscapes characterized by many local optima. Moreover, gradient-based optimizers, which have been pioneered in this context, tend to waste quantum computing resources. Therefore, gradient-free approaches are emerging as promising methods to address this parameter-setting task. Following this trend, this paper proposes, for the first time, the use of genetic algorithms as gradient-free methods for optimizing the QAOA circuit. The proposed evolutionary approach has been evaluated in solving the MaxCut problem for graphs with 5 to 9 nodes on a noisy quantum device. As the results show, the proposed genetic algorithm statistically outperforms the state-of-the-art gradient-free optimizers by achieving solutions with a better approximation ratio.
3D Object Detection for Autonomous Driving: A Comprehensive Survey

Autonomous driving, in recent years, has been receiving increasing attention for its potential to relieve drivers' burdens and improve the safety of driving. In modern autonomous driving pipelines, the perception system is an indispensable component, aiming to accurately estimate the status of surrounding environments and provide reliable observations for prediction and planning. 3D object detection, which aims to predict the locations, sizes, and categories of the 3D objects near an autonomous vehicle, is an important part of a perception system. This paper reviews the advances in 3D object detection for autonomous driving. First, we introduce the background of 3D object detection and discuss the challenges in this task. Second, we conduct a comprehensive survey of the progress in 3D object detection from the aspects of models and sensory inputs, including LiDAR-based, camera-based, and multi-modal detection approaches. We also provide an in-depth analysis of the potentials and challenges in each category of methods. Additionally, we systematically investigate the applications of 3D object detection in driving systems. Finally, we conduct a performance analysis of the 3D object detection approaches, and we further summarize the research trends over the years and prospect the future directions of this area.
Self-attention enhanced deep residual network for spatial image steganalysis

As a specially designed tool and technique for the detection of image steganography, image steganalysis conceals information under the carriers for covert communications. Being developed on the BOSSbase dataset and released a decade ago, most of the Convolutional Neural Network (CNN) architectures for spatial image steganalysis fail to achieve satisfactory performance on new challenging datasets, i.e. ALASKA#2, which was released recently and is more complex yet consistent with the real scenarios. In this paper, we propose an enhanced residual network (ERANet) with self-attention ability, which utilizes a more complex residual method and a global self-attention technique, to alleviate the problem. Compared to the residual network that was widely used in the state-of-the-art, the enhanced residual network mathematically employed a more sophisticated way to extract more effective features in the images and hence it is suitable for more complex situations in the new dataset. Our proposed Enhanced Low-Level Feature Representation Module can be easily mounted on other CNNs in selecting the most representative features. Although it comes with a slightly extra computational cost, comprehensive experiments on the BOSSbase and ALASKA#2 datasets at various sizes have demonstrated the effectiveness of the proposed methodology. In short, ERANet provides an improvement of about 3.77% on average, compared to a few state-of-the-art CNNs.
Short-term solar radiation forecasting using hybrid deep residual learning and gated LSTM recurrent network with differential covariance matrix adaptation evolution strategy

Developing an accurate and robust prediction of long-term average global solar irradiation plays a crucial role in industries such as renewable energy, agribusiness, and hydrology. However, forecasting solar radiation with a high level of precision is historically challenging due to the nature of this source of energy. Challenges may be due to the location constraints, stochastic atmospheric parameters, and discrete sequential data. This paper reports on a new hybrid deep residual learning and gated long short-term memory recurrent network boosted by a differential covariance matrix adaptation evolution strategy (ADCMA) to forecast solar radiation one hour-ahead. The efficiency of the proposed hybrid model was enriched using an adaptive multivariate empirical mode decomposition (MEMD) algorithm and 1+1EA-Nelder–Mead simplex search algorithm. To compare the performance of the hybrid model to previous models, a comprehensive comparative deep learning framework was developed consisting of five modern machine learning algorithms, three stacked recurrent neural networks, 13 hybrid convolutional (CNN) recurrent deep learning models, and five evolutionary CNN recurrent models. The developed forecasting model was trained and validated using real meteorological and Shortwave Radiation (SRAD1) data from an installed offshore buoy station located in Lake Michigan, Chicago, United States, supported by the National Data Buoy Centre (NDBC). As a part of pre-processing, we applied an autoencoder to detect the outliers in improving the accuracy of solar radiation prediction. The experimental results demonstrate that, firstly, the hybrid deep residual learning model performed best compared with other machine learning and hybrid deep learning methods. Secondly, a cooperative architecture of gated recurrent units (GRU) and long short-term memory (LSTM) recurrent models can enhance the performance of Xception and ResNet. Finally, using an effective evolutionary hyper-parameters tuner (ADCMA) reinforces the prediction accuracy of solar radiation.
Person re-identification: A retrospective on domain specific open challenges and future trends

Person Re-Identification (Re-ID) is a critical aspect of visual surveillance systems, which aims to automatically recognize and locate individuals across a multi-camera network with non-overlapping fields-of-view. Despite significant progress in recent years through the use of deep learning-based approaches, there remain many vision-related challenges, such as occlusion, pose, background clutter, misalignment, scale, viewpoint, low resolution & illumination, and cross-domain generalization across camera modalities, that hinder the accurate identification of individuals. The majority of the proposed approaches directly or indirectly aim to solve one or multiple of these existing challenges. To further advance the development of Re-ID solutions, a comprehensive review of the current approaches is necessary. However, no focused review currently exists that analyses and highlights specific aspects for further development. To fill this gap, we present a systematic challenge-specific literature survey of about 300 papers published between 2015 and 2022, which reviews Re-ID approaches from a solution-oriented perspective. This survey is the first of its kind to provide an in-depth analysis of the different approaches used to address the various challenges in Re-ID. Furthermore, our review highlights several prominent and diverse research trends in the Re-ID domain. These trends offer a visionary perspective regarding ongoing person Re-ID research, and they may eventually lead to the development of practical real-world solutions. We highlighted the AI ethics that must be followed while developing a Re-ID solution, and recently being practiced as well. Another exciting future dimension of person Re-ID research is the long-term Re-ID, which is still under evolution. Overall, our survey aims to serve as a valuable resource for researchers and practitioners working in the field of Re-ID and to inspire the development of innovative and effective Re-ID solutions.
An Efficient Hybrid Deep Learning Model for Denial of Service Detection in Cyber Physical Systems

Security is critical in the Cyber-Physical Systems (CPS) model for smart healthcare networks, and it will likely have a significant impact on the industry, medical and healthcare; and farming-related substructures shortly. Due to an increase in the frequency of security and privacy attacks in present times in healthcare networks, this article addressed a fundamental component of intrusion detection systems (IDS) based on the important parameter security. The limitations of IDS in reacting to cyberattacks as well as in establishing private controls in the field of smart healthcare have motivated this research. An efficient and lightweight deep learning-based CNN-Bidirectional LSTM is proposed for the DDoS detection that uses the features of Convolutional Neural Networks (CNNs) to classify traffic flows as benign and malicious in this study. The results are achieved using Python where four convolutional layers, Maximum Pooling, that ends with the Dense Layer. The hyperparameters used are batch size of 500, epochs 20, number of classes 25, and Relu and softmax pooling activation function along with the softmax.
Biases in Large Language Models: Origins, Inventory, and Discussion

In this article, we introduce and discuss the pervasive issue of bias in the large language models that are currently at the core of mainstream approaches to Natural Language Processing (NLP). We first introduce data selection bias, that is, the bias caused by the choice of texts that make up a training corpus. Then, we survey the different types of social bias evidenced in the text generated by language models trained on such corpora, ranging from gender to age, from sexual orientation to ethnicity, and from religion to culture. We conclude with directions focused on measuring, reducing, and tackling the aforementioned types of bias.
Social companionship with artificial intelligence: Recent trends and future avenues

The social companionship (SC) feature in conversational agents (CAs) enables the emotional bond and consumer relationships. The heightened interest in SC with CAs led to exponential growth in publications scattered across disciplines with fragmented findings, thus limiting holistic understanding of the domain and warrants a macroscopic view of the domain to guide future research directions. The present study fills the research void by offering a comprehensive literature review entailing science performance and intellectual structure mapping. The comprehensive review revealed the research domain's major theories, constructs, and thematic structure. Thematic and content analysis of intellectual structure resulted in a conceptual framework encompassing antecedents, mediators, moderators, and consequences of SC with CAs. The study discusses future research directions guiding practitioners and academicians in designing efficient and ethical AI companions.
Reinforcement learning algorithms: A brief survey

Reinforcement Learning (RL) is a machine learning (ML) technique to learn sequential decision-making in complex problems. RL is inspired by trial-and-error based human/animal learning. It can learn an optimal policy autonomously with knowledge obtained by continuous interaction with a stochastic dynamical environment. Problems considered virtually impossible to solve, such as learning to play video games just from pixel information, are now successfully solved using deep reinforcement learning. Without human intervention, RL agents can surpass human performance in challenging tasks. This review gives a broad overview of RL, covering its fundamental principles, essential methods, and illustrative applications. The authors aim to develop an initial reference point for researchers commencing their research work in RL. In this review, the authors cover some fundamental model-free RL algorithms and pathbreaking function approximation-based deep RL (DRL) algorithms for complex uncertain tasks with continuous action and state spaces, making RL useful in various interdisciplinary fields. This article also provides a brief review of model-based and multi-agent RL approaches. Finally, some promising research directions for RL are briefly presented.
Artificial intelligence for health message generation: an empirical study using a large language model (LLM) and prompt engineering

Introduction This study introduces and examines the potential of an AI system to generate health awareness messages. The topic of folic acid, a vitamin that is critical during pregnancy, served as a test case. Method We used prompt engineering to generate awareness messages about folic acid and compared them to the most retweeted human-generated messages via human evaluation with an university sample and another sample comprising of young adult women. We also conducted computational text analysis to examine the similarities between the AI-generated messages and human generated tweets in terms of content and semantic structure. Results The results showed that AI-generated messages ranked higher in message quality and clarity across both samples. The computational analyses revealed that the AI generated messages were on par with human-generated ones in terms of sentiment, reading ease, and semantic content. Discussion Overall, these results demonstrate the potential of large language models for message generation. Theoretical, practical, and ethical implications are discussed.
Dynamic Cryptography Integrated Secured Decentralized Applications with Blockchain Programming

Blocks and chains are the building blocks of the blockchain, which is a community network. Blocks and chains are two terms used to describe collections of data information. The most fundamental need for a blockchain is that these postings be connected by cryptography, which is the case here. Cryptography. the entries in each block are added to as the list grows. Although the concept of blockchain cryptography is difficult, we have made it easier for you to understand. Asymmetric-key cryptography and hash functions are used in blockchains. Hash functions provide participants with a complete image of the internet. The SHA-256 hashing algorithm is often used in blockchains. In Bitcoin, where addresses are tracked by public-private key pairs, blockchains are often used. The public key in blockchain cryptography is a person's address. All participants have access to the participant's public key. The private key is used to get access to the address database and to authorise activities using the address. To ensure the integrity of the blockchain ledger, encryption plays a key role. Each event on the blockchain is recorded using encrypted data. As long as each user has access to their cryptographic keys, they may buy or trade cryptocurrencies. The root hashes of all transactions are stored in blockchains via cryptographic hashing. If somebody attempts to tamper with any data upon that blockchain, the main hash will have a completely new hash. Root hash comparisons may be performed on any other system to check whether the data is safe.
Co-benefits of carbon neutrality in enhancing and stabilizing solar and wind energy

Solar photovoltaic (PV) and wind energy provide carbon-free renewable energy to reach ambitious global carbon-neutrality goals, but their yields are in turn influenced by future climate change. Here, using a bias-corrected large ensemble of multi-model simulations under an envisioned post-pandemic green recovery, we find a general enhancement in solar PV over global land regions, especially in Asia, relative to the well-studied baseline scenario with modest climate change mitigation. Our results also show a notable west-to-east interhemispheric shift of wind energy by the mid-twenty-first century, under the two global carbon-neutral scenarios. Both solar PV and wind energy are projected to have a greater temporal stability in most land regions due to deep decarbonization. The co-benefits in enhancing and stabilizing renewable energy sources demonstrate a beneficial feedback in achieving global carbon neutrality and highlight Asian regions as a likely hotspot for renewable resources in future decades. Achieving global carbon neutrality will lead to a low-warming future and impact renewable energy production. This study demonstrates that deep mitigation pathways can bring positive feedback that enhances and stabilizes solar and wind energy production.