Reviewerly/exHarmony huggingface dataset:
Improved DeepSORT-Based Object Tracking in Foggy Weather for AVs Using Sematic Labels and Fused Appearance Feature Network

The presence of fog in the background can prevent small and distant objects from being detected, let alone tracked. Under safety-critical conditions, multi-object tracking models require faster-tracking speed while maintaining high object-tracking accuracy. The original DeepSORT algorithm used YOLOv4 for the detection phase, and a simple neural network for deep appearance descriptor. Consequently, the feature map generated loses relevant details about the track being matched with a given detection in fog. Targets with a high degree of appearance similarity on the detection frame are more likely to be mismatched, resulting in identity switches or track failures in heavy fog. We propose an improved multi-object tracking model based on the DeepSORT algorithm to im-prove tracking accuracy and speed under foggy weather conditions. First, we employed our camera-radar fusion network (CR-YOLOnet) in the detection phase for faster and more accurate object detection. We proposed an appearance feature network to replace the basic convolutional neural network. We incorporated GhostNet to take the place of the traditional convolutional layers to generate more features and reduce computational complexities and cost. We adopted a segmentation module and fed the semantic labels of the corresponding input frame to add rich semantic information to the low-level appearance feature maps. Our proposed method outperformed YOLOv5 + DeepSORT with a 35.15% increase in multi-object tracking accuracy, a 32.65% increase in multi-object tracking precision, the speed increased by 37.56%, and identity switches decreased by 46.81%.
Introduction: Artificial intelligence should meet natural stupidity. But it cannot!

The simulation of human mind might be possible with the advancement of cognitive science research on memory, thinking, reasoning, emotion, decision making and embodied cognition. But the attempt should avoid the Rationality Bias of “Human Intelligence as Optimal Computation Device”, that is thinking that the mind is mainly the ability to process optimally data and to conform to the principle of logic, probability and utility. On the contrary AI should consider the imperfect formal features of human intelligence and how many times it is adaptively better to be biased than to be formally perfect. The chapter presents some theoretical and empirical reasons in support of this argument. The two main theses that will be developed are 1) The current programs of emulative AI, based on the processing of Big Data through machine and deep learning programs, are flawed in many contexts and in particular they manage to find patterns of past data, but they are not able to forecast future unstable events, such as financial ones, characterized by complexity and uncertainty; 2) The Simulative AI, also called Psychology AI (Gigerenzer, 2022), despite being more suited to grasping more realistic aspects of human intelligence, in fact has not been able to achieve this goal due to the lack of the body dimension of the simulated cognitive experience . In any case, according to some philosophers, the construction of machines that have the same intelligence as man is an a priori impossible undertaking.
Post Hoc Explanations of Language Models Can Improve Language Models

Abstract Large Language Models (LLMs) have demonstrated remarkable capabilities in performing complex tasks. Moreover, recent research has shown that incorporating human-annotated rationales (e.g., Chain-of-Thought prompting) during in-context learning can significantly enhance the performance of these models, particularly on tasks that require reasoning capabilities. However, incorporating such rationales poses challenges in terms of scalability as this requires a high degree of human involvement. In this work, we present a novel framework, Amplifying Model Performance by Leveraging In-Context Learning with Post Hoc Explanations (AMPLIFY), which addresses the aforementioned challenges by automating the process of rationale generation. To this end, we leverage post hoc explanation methods which output attribution scores (explanations) capturing the influence of each of the input features on model predictions. More specifically, we construct automated natural language rationales that embed insights from post hoc explanations to provide corrective signals to LLMs. Extensive experimentation with real-world datasets demonstrates that our framework, AMPLIFY, leads to prediction accuracy improvements of about 10-25% over a wide range of tasks, including those where prior approaches which rely on human-annotated rationales such as Chain-of-Thought prompting fall short. Our work makes one of the first attempts at highlighting the potential of post hoc explanations as valuable tools for enhancing the effectiveness of LLMs. Furthermore, we conduct additional empirical analyses and ablation studies to demonstrate the impact of each of the components of AMPLIFY, which, in turn, lead to critical insights for refining in-context learning.
FANet: Flexibility and Adaptivity Net for 3D Point Cloud Detection

3D object detection is essential for an accurate and reliable autonomous driving system. Currently, the methods used by the state-of-the-art two-stage detectors are not flexible enough and their fea-ture extraction capabilities are very limited to cope effectively with the disorder and irregularity of point clouds. In this paper, we combine the advantages of both PV-RCNN and PAConv (Position Adaptive Convolution) to create a completely new network, FANet, in order to overcome the ir-regularity and disorder of point clouds. The convolution in our network builds convolutional ker-nels from a basic weight matrix, whose combined coefficients are learned adaptively by LearnNet from relative points. This network allows for flexible modeling of complex spatial variations and geometric structures in the 3D point cloud, enabling better extraction of point cloud features and producing high-quality 3D proposal boxes. Compared to other methods, FANet is superior in terms of 3D object detection accuracy. Extensive experiments on the KITTI dataset have shown a signif-icant improvement in our approach.
Adaptive search query generation and refinement in systematic literature review

Systematic literature reviews (SLRs) are a central part of evidence-based research, which involves collecting and integrating empirical evidence on specific research questions. A key step in this process is building Boolean search queries, which are at the core of information retrieval systems that support literature search. This involves turning general research aims into specific search terms that can be combined into complex Boolean expressions. Researchers must build and refine search queries to ensure they have sufficient coverage and properly represent the literature. In this paper, we propose an adaptive query generation and refinement pipeline for SLR search that uses reinforcement learning to learn the optimal modifications to a query based on feedback from researchers about its performance. Empirical evaluations with 10 SLR datasets showed our approach achieves comparable performance to queries manually composed by SLR authors. We also investigate the impact of design decisions on the performance of the query generation and refinement pipeline. Specifically, we study the effects of the type of input seed, the use of general versus domain-specific word embedding models, the sampling strategy for relevance feedback, and number of iterations in the refinement process. Our results provide insights into the effects of these choices on the pipeline’s performance.
Decentralised Access Control Framework using Blockchain: Smart Farming Case

The convergence of farming with cutting-edge technologies, like the Internet of Things (IoT), has led to the emergence of a smart farming revolution.IoT facilitates the interconnection of numerous devices across different agricultural ecosystems, enabling automation and ultimately enhancing the efficiency and quality of production.However, the implementation of IoT entails an array of potential risks.The accelerated adoption of IoT in the domain of smart farming has amplified the existing cybersecurity concerns, specifically those pertaining to access control.In extensive IoT environments that require scalability, the conventional centralized access control system is insufficient.Therefore, to address these gaps, we propose a novel decentralized access control framework.The framework applies blockchain technology as the decentralization approach with smart contract application focuses on the application scenario in smart farming to protect and secure IoT devices from unauthorised access by anomalous entities.The proposed framework adopted attribute-based access control (ABAC) and role-based access control (RBAC) to establish access rules and access permissions for IoT.The framework is validated via simulation to determine the price of gas consumption when executing smart contracts to retrieve attributes, roles and access rules between three smart contracts and provide the baseline value for future research references.Thus, this paper offers valuable insight into ongoing research on decentralized access control for IoT security to protect and secure IoT resources in the smart farming environment.
An Enhanced SVM Model for Implicit Aspect Identification in Sentiment Analysis

Opinion Mining or Sentiment Analysis (SA) is a key component of E-commerce applications where a vast number of reviews are generated by customers.SA operates on aspect level where the views are expressed on a specific aspect of a product and have a big influence on the customers' choices and businesses' reputation.Aspect Based Sentiment Analysis (ABSA) is the task of categorizing text by aspect and identifying the sentiment attributed to it.Implicit Aspect Identification (IAI) is a subtask of ABSA.This paper aims to empirically investigate how external knowledge (e.g.WordNet) is integrated into SVM model to address some of its intrinsic issues when dealing with classification.To achieve this research goal, we propose an approach to improve Support Vector Machines (SVM) model to deal with IAI.Using WordNet (WN) semantic relations, we suggest an enhancement for the SVM kernel computation.Experiments are conducted on three benchmark datasets of products, laptops, and restaurant reviews.The effects of our approach are examined and analyzed according to three criteria: (i) kernel function used, (ii) different experimental settings, and (iii) SVM behavior towards Overfitting and Underfitting.The research finding of our work is that the integration of external knowledge (e.g.WordNet) is experimentally proved to be significantly helpful to SVM classification for IAI and especially for addressing Overfitting and Underfitting that are considered as two of the main structural SVM issues.The empirical results demonstrate that our approach helps SVM (i) improve its performance for the three considered kernels and under different experimental settings, and (ii) deal better with Overfitting and Underfitting.
The Tokenomics Audit Checklist: Presentation and Examples from the Audit of a DeFi project, Terra/Luna and Ethereum 2.0

With the increasing popularity of Web 3.0, tokenomics is becoming even more important as a scientific discipline.Tokenomics auditing is a new area of interest in the context of Web 3.0 whose aim is to stress test the design and structure of token economies, to ensure they are robust and safe.The importance of this area has increased considering multiple project failures and fraud that were witnessed in the recent history of the space.In this paper we present a framework to audit the tokenomics of blockchain-based projects.The auditing framework can be used by stakeholders to audit their tokens and by investors to assess the risks associated with investing in a given token.We demonstrate how this framework can be applied to assess risks and improve the structure of a token economy.We give three examples: First, an example is based on a real case study of a newly launched DeFi protocol called Algem which was the original inspiration for this protocol.Then, we perform retrospective study of Terra/Luna.Finally, we show how that framework could be used for the analysis of Ethereum 2.0.While the auditing framework is not without its shortcomings, it represents the first attempt at standardising tokenomics auditing.Therefore, this provides a foundation upon which the community will build and improve, as tokenomics auditing matures as a framework.
Cascaded Degradation-Aware Blind Super-Resolution

Image super-resolution (SR) usually synthesizes degraded low-resolution images with a predefined degradation model for training. Existing SR methods inevitably perform poorly when the true degradation does not follow the predefined degradation, especially in the case of the real world. To tackle this robustness issue, we propose a cascaded degradation-aware blind super-resolution network (CDASRN), which not only eliminates the influence of noise on blur kernel estimation but also can estimate the spatially varying blur kernel. With the addition of contrastive learning, our CDASRN can further distinguish the differences between local blur kernels, greatly improving its practicality. Experiments in various settings show that CDASRN outperforms state-of-the-art methods on both heavily degraded synthetic datasets and real-world datasets.
Interrogating the text: questioning to mediate junior secondary school students’ barriers to reading comprehension

Reading comprehension is the capacity to analyze and comprehend the text’s meaning. Reading through questioning techniques can improve the skill of thinking, but it can also be a problem for some individuals. This study aims to unveil EFL students’ barriers in reading comprehension through questioning technique. Eight students and one English teacher at a private junior secondary school in Medan, South Sumatera, Indonesia participated in this study. Nested in qualitative descriptive method, classroom observation and semi-structured interview were used to gather data. Study findings expose that the researchers found the students’ problems in reading comprehension through questioning techniques, such as problems in concentration, problems in understanding vocabulary, comprehending long sentences, arranging words in English, applying grammatical rules, and understanding how to pronounce the words (error pronunciation). In response to the students’ barriers, this study recommends to solve these problems through organizing an ice-breaking, giving some questions to students, and providing opportunities for students to ask questions.
Intelligent Video Surveillance System with Abnormal Behavior Recognition and Metadata Retrieval

Huge-scale video surveillance systems have become essential in crime prevention and situation recording. Traditional surveillance systems relied on human monitoring of video streams, which often led to errors and difficulties in understanding events. Furthermore, locating specific scenes within recorded videos required extensive human investigation. To overcome these challenges of inefficiency, inconvenience, and potential risks, we propose an intelligent analysis scheme that utilizes abnormal behavior recognition and metadata retrieval algorithms to replace human monitoring. The proposed method consists of three stages: i) metadata generation through object detection and tracking, ii) abnormal behavior recognition, and iii) SQL-based metadata retrieval. By incorporating specific information such as object color and aspect ratio, our technique enhances the usability of retrieval. Moreover, our abnormal behavior recognition module demonstrates robust classification capabilities for activities such as pushing, violence, falling, and crossing barriers. The proposed method can be seamlessly deployed on both edge cameras and analysis servers, making it adaptable to various surveillance setups. This approach revolutionizes the traditional surveillance paradigm, enabling more efficient, reliable, and secure video monitoring and analysis.
Enhancing Voice Cloning Quality through Data Selection and Alignment-based Metrics

Voice cloning, an emerging field in the speech processing area, aims to generate synthetic utterances that closely resemble the voices of specific individuals. In this study, we investigate the impact of various techniques on improving the quality of voice cloning, specifically focusing on a low-quality dataset. To contrast our findings, we also use two high-quality corpora for comparative analysis. We conduct exhaustive evaluations of the quality of the gathered corpora in order to select the most suitable audios for the training of a Voice Cloning system. Following these measurements, we conduct a series of ablations by removing audios with lower SNR and higher variability in utterance speed from the corpora in order to decrease their heterogeneity. Furthermore, we introduce a novel algorithm that calculates the fraction of aligned input characters by exploiting the attention matrix of the Tacotron 2 Text-to-Speech (TTS) system. This algorithm provides a valuable metric for evaluating the alignment quality during the voice cloning process. We present the results of our experiments, demonstrating that the performed ablations significantly increase the quality of synthesised audios for the challenging low-quality corpus. Notably, our findings indicate that models trained on a 3-hour corpus from a pre-trained model exhibit comparable audio quality to models trained from scratch using significantly larger amounts of data.
Recurrent general type-2 fuzzy neural networks for nonlinear dynamic systems identification

This paper introduces a recurrent general type-2 Takagi-Sugeno-Kang fuzzy neural network (RGT2-TSKFNN) for the identification of nonlinear systems. In the proposed structure, the general type-2 fuzzy set (GT2FS) and a recurrent fuzzy neural network (RFNN) are combined to obviate the data uncertainties. The fuzzy firing strengths in the developed structure are returned to the network input as internal variables. In the proposed structure, GT2FS is utilized to characterize the antecedent parts while the consequent parts are performed using TSK type. The issues of constructing a RGT2-TSKFNN involve type reduction, structure learning as well as parameter learning. An efficient strategy is developed by utilizing alpha-cuts to decompose a GT2FS into several interval type-2 fuzzy sets (IT2FSs). In order to solve the computation time of the type-reduction issue, a direct defuzzification method is used instead of iterative nature of Karnik-Mendel​ (KM) algorithm. Type-2 fuzzy clustering and Lyapunov criteria are utilized for online structure learning as well as the antecedent and consequent parameters, respectively for reducing the number of rules and guaranteeing the stability of the proposed RGT2-TSKFNN. The reported comparative analysis of the simulation results is utilized to estimate the performance of the proposed RGT2-TSKFNN with respect to other popular type-2 FNNs (T2FNNs) methodologies.
GRADES-NDA'23: 6th Joint Workshop on Graph Data Management Experiences &amp; Systems (GRADES) and Network Data Analytics (NDA)

GRADES-NDA is the premier workshop series on graph data management and analytics that aims to bring together researchers from academia, industry, and government. GRADES-NDA'23 is a forum for discussing recent advances in (large-scale) graph data management and analytics systems, as well as proposing and discussing novel methods and techniques for addressing domain-specific challenges or handling noise in real-world graphs. In 2023, GRADES-NDA is in its sixth edition.
Multimodal Transformer for Risk Classification: Analyzing the Impact of Different Data Modalities

Risk classification plays a critical role in domains such as finance, insurance, and healthcare. However, identifying risks can be a challenging task when dealing with different types of data. In this paper, we present a novel approach using the Multimodal Transformer for risk classification, and we investigate the use of data augmentation for risk data through automated retrieval of news articles. We achieved this through keyword extraction based on the title and descriptions of risks and using various selection metrics. We evaluate our approach using a real-world dataset containing numerical, categorical, and textual data. Our results demonstrate that the use of the Multimodal Transformer for risk classification outperforms other models that only utilize textual data. We show that the inclusion of numerical and categorical data improves the performance of the model, particularly for risks that are difficult to classify based on textual data alone. Additionally, our research indicates that the utilization of data augmentation techniques yields enhanced performance outcomes in models. This methodology presents a promising avenue for enterprises to effectively mitigate risks and make well-informed decisions.
Challenges &amp; Opportunities – The Road Ahead for Management of Internet Waste &amp; Carbon Emissions for a Safe &amp; Green Digital World

Internet, the most powerful tool responsible for the growth and development of any facet of the economy at large is growing exponentially. It has become an indispensable part of our lives accelerating trade, easing faster communication, aiding research and providing recreation. The growing complexity of its services and our dependence have further led to new inventions namely as Internet of things (IOT), Artificial Intelligence (AI), Augmented Reality (AR), Metaverse, Block Chain Technology, Virtual Currency and Over the Top (OTT) Platforms. These new inventions coupled with regular innovations in form of updates and versions have surged the demand for uninterrupted supply of fast speed internet services across the globe. The telecom service providers are constantly on their toes for providing fast pace internet services with minimum glitches at affordable prices in order to win over its customers. Nothing in this world is free, is rightly applicable in our current case. Everything comes at cost of the another. It entirely depends on the fact that Is the cost feasible? If yes them how much &amp; its impact on other variables. The same internet which is like a magic wand in the hands of its users, is also responsible for creation of wastes in the form of energy, land, material resources &amp; time contributing to un amounted tons of carbon emissions affecting the Green House Gases and ecological system. It is further giving birth to a new form of pollution under the name Internet Pollution. Diagnostics of the issue paves the way for its treatment that needs to be done in the most effective &amp; efficient manner. The journey to solve any problem isn’t always easy due to presence of limiting factors. In the process of management of internet waste, numerous numbers of challenges have to be taken care of, coupled with the new horizons in name of opportunities which are yet to be explored. The present article is a step ahead to bring forth the challenges that come across in the course of minimising the CO2 emissions resulting in use of Internet services and also suggests the possible ways to reduce its impact on the ecological system to continue our journey in creation of Green Planet with the help of Green Technology. Key Words Internet Waste. Carbon Emissions. Internet Pollution. Sustainable Environment