Miracle-dz/UniFAR-FTUs huggingface dataset:
Target-X: An Efficient Algorithm for Generating Targeted Adversarial Images to Fool Neural Networks

Deep neural networks (DNNs) have achieved a series of significant successes in a wide spectrum of critical domains. For instance, in the field of computer vision, DNNs become the first choice in developing image recognition and classification solutions. However, DNNs have been recently found vulnerable to manipulations of input samples, called adversarial images. The adversarial images can be classified into two categories: untargeted adversarial images which aim to manipulate the output of the DNNs to any incorrect label and targeted adversarial images which force the prediction of the DNNs to a specified target label predefined by the adversary. That being said, the construction of targeted adversarial images requires careful crafting of the targeted perturbations. Different research works have been done to generate targeted adversarial images. However, the majority of them have two limitations: (1) adding large size of perturbations to generate successfully targeted images, and (2) they require extensive computational resources to be utilized in large-scale datasets. This paper introduces Target-X, a novel and fast method for the construction of adversarial targeted images on large-scale datasets that can fool the state-of-the-art image classification neural networks. We evaluate the performance of Target-X using the well-trained image classification neural networks of different architectures and compare it with the well-known T-FGSM and T-UAP targeted attacks. The reported results demonstrate that Target-X can generate targeted adversarial images with the least perturbations on large-scale datasets that can fool the image classification neural networks and significantly outperform the T-FGSM and T-UAP attacks.
Investigating Code Generation Performance of ChatGPT with Crowdsourcing Social Data

The recent advancements in Artificial Intelligence, particularly in large language models and generative models, are reshaping the field of software engineering by enabling innovative ways of performing various tasks, such as programming, debugging, and testing. However, few existing works have thoroughly explored the potential of AI in code generation and users’ attitudes toward AI-assisted coding tools. This knowledge gap leaves it unclear how AI is transforming software engineering and programming education. This paper presents a scalable crowdsourcing data-driven framework to investigate the code generation performance of generative large language models from diverse perspectives across multiple social media platforms. Specifically, we utilize ChatGPT, a popular generative large language model, as a representative example to reveal its insights and patterns in code generation. First, we propose a hybrid keyword word expansion method that integrates words suggested by topic modeling and expert knowledge to filter relevant social posts of interest on Twitter and Reddit. Then we collect 316K tweets and 3.2K Reddit posts about ChatGPT’s code generation, spanning from Dec. 1, 2022 to January 31, 2023. Our data analytics show that ChatGPT has been used in more than 10 programming languages, with Python and JavaScript being the two most popular, for a diverse range of tasks such as code debugging, interview preparation, and academic assignment solving. Surprisingly, our analysis shows that fear is the dominant emotion associated with ChatGPT’s code generation, overshadowing emotions of happiness, anger, surprise, and sadness. Furthermore, we construct a ChatGPT prompt and corresponding code dataset by analyzing the screen-shots of ChatGPT code generation shared on social media. This dataset enables us to evaluate the quality of the generated code, and we have released this dataset to the public. We believe the insights gained from our work will provide valuable guidance for future research on AI-powered code generation.
Fixed-time stabilization for impulsive neural networks

This paper presents finite-time and fixed-time stabilization for impulsive neural networks, for which a kind of optimal control problem was investigated. A new method based on dual dynamic programming was applied. A verification theorem for finite-time and fixed-time stability was proved. Additionally, numerical algorithms were presented, which demonstrate that impulses are necessary for achieving finite-time stability in neural networks.
Quantifying Domain Knowledge in Large Language Models

Transformer based Large language models such as BERT, have demonstrated the ability to derive contextual information from the words surrounding it. However, when these models are applied in specific domains such as medicine, insurance, or scientific disciplines, publicly available models trained on general knowledge sources such as Wikipedia, it may not be as effective in inferring the appropriate context compared to domain-specific models trained on specialized corpora. Given the limited availability of training data for specific domains, pre-trained models can be fine-tuned via transfer learning using relatively small domain-specific corpora. However, there is currently no standardized method for quantifying the effectiveness of these domain-specific models in acquiring the necessary domain knowledge. To address this issue, we explore hidden layer embeddings and introduce domain_gain, a measure to quantify the ability of a model to infer the correct context. In this paper, we show how our measure could be utilized to determine whether words with multiple meanings are more likely to be associated with domain-related meanings rather than their colloquial meanings.
Comparative Study of Random Forest Algorithm and Logistic Regression in the Analysis of Fake News

The onset of the World Wide Web and the dramatic rise of social networking sites, including Facebook and Twitter, have aided an unparalleled delivery of content throughout humanity's history. Consumers today generate and share more information than ever before. However, the present use of social media platforms, which is often misleading and has no relevance to reality, undermines the reliability of the information available. Automated categorization of a content editorial as false info or psyops presents a complex contest. Even an expert in a particular field must consider multiple aspects before passing judgement on an article's veracity. This study presents by using a machine learning forms a partnership to classify media articles auto. This study explores numerous different texts assets which can discern between good and what is bad product. Practicing a combination of different machine learning techniques employing numerous different classification techniques and try to assess their success on four real-world datastores feature. The explored different of our envisioned as a technical beginner attitude proves its hegemony over leaners. The rapid increase of knowledge on social media and its free entry and exit have made it challenging to tell apart between correct and incorrect knowledge. The ease for sharing information has contributed to the proliferation of falsified information, jeopardizing the credibility of social media networks. As a result, a research challenge has emerged to automatically verify information's authenticity based on its source, content, and publisher using a variety of text classifiers. While machine learning has been useful in information classification, it has some limitations. This study examines it many computational methods, such like Logistic Regression and Random Forest, for characterizing falsify and faked information. The limitations of these methods and approaches are discussed, along with potential ways to improve them.
4D UAV Waypoint Arrival Guidance Based on Baseline Tau Vector Field

This paper introduces a novel baseline tau vector field (BτVF) method to provide four-dimensional (4D) guidance in time-constrained waypoint arrival tasks of Unmanned Aerial Vehicle (UAV). To guide a UAV to reach the desired position with expected speed at the certain arrival time, the bio-inspired tau guidance laws are adopted to design the vector guidance field. The UAV guided by $\mathrm{B}\tau\text{VF}$ can approach the baseline between two waypoints, and more importantly, the vehicle can arrive at the destination with strict time and velocity constraints of 4D flight tasks. A challenging 4D waypoint arrival dynamic simulation is designed for rotor UAV and solved by the proposed $\mathrm{B}\tau\text{VF}$ method. The simulation result demonstrates that the proposed method can provide 4D guidance between waypoints efficiently and effectively.
A Wearable Brain-Computer Interface System based on Flexible and Air-Permeable Electronic Skin

In this paper, a wearable brain-computer interface system based on flexible and air-permeable electronic skin is implemented. Wearable brain-computer interfaces need to be expanded into more aspects of daily life. Research in these areas requires devices that are closer to life. The flexible electronic skin has the advantages of high permeability (1.904 kg $\cdot \mathrm{m}^{-2} \cdot$ day $^{-1}$ at body temperature) and low skin contact impedance ($4.683 \mathrm{k} \Omega$ at 1 kHz), and can be used to expand the research of single lead brain computer interface in the field of appetite. Because the orbitofrontal cortex plays an important role in food decisions, the device placed electrodes on the forehead to collect electrical signals from people when they were presented with images of different delicacies. Watching their brain activity as they made judgments about their food choices. Found that it was possible to distinguish between delicious pictures and boring ones. To look at the brain’ s attraction and rejection decisions when faced with food cues.
Exploiting Deep Learning for Sentence-Level Lipreading

Lipreading, also called visual speech recognition, is an excellent technique to understand what a speaker says without audios. Based on deep learning, many studies have gained outstanding achievements in English lipreading. English is dominated by polysyllables, with the proportion of homonyms as low as 1%. Compared to English, Mandarin (the official language of Chinese) is dominated by monosyllables, with the ratio of homonyms as high as 72%. The high ratio of homonyms makes the lipreading in Mandarin much more challenging. However, little attention has been paid to Mandarin lipreading within the deep learning framework, especially at the sentence level. In this paper, we first introduce a dataset, named Mandarin-Lipreading, which is recorded in the controlled lab environment and is the largest dataset so far for sentence-level lipreading in Mandarin. We further investigate the modeling units and propose an end-to-end Mandarin lipreading system. The experimental results show that the proposed system achieves 14.77% CER on Mandarin-Lipreading and 8.7% CER for unseen speakers evaluation on the GRID corpus.
Detection of Asperger Syndrome at Early Stages in Children using Deep Neural Networks

ASD (Autism Spectrum Disorder) is behavioral and considered to be a developmental disorder found in human beings. It includes disorders in brain development, eventually affecting functional brain capabilities. It also affects how a person converses with others and causes problems in interaction and communication. The vast range of symptoms, abilities, and functioning levels in ASD patients is known as the "spectrum. " It is essential to diagnose ASD at early stages so that autistic child can reach their full potential.In some cases, parents cannot accept that their child's mental growth is not developing along with physical development. With early diagnosis, children with autism can lead fulfilling and joyful lives through the provision of appropriate medication. Screening tests are used to diagnose this type of disorder but are time-consuming and costly. This research study has employed a convolutional neural network to detect the likelihood of ASD in young children at an early stage. Deep Neural Networks function well in many applications. This study has used Xception and Resnet50 model to predict ASD based on static facial images, especially in children. According to the results obtained, ResNet50's performance is better than Xception in Autism detection.
Convolutional Neural Networks Do Work with Pre-Defined Filters

We present a novel class of Convolutional Neural Networks called Pre-defined Filter Convolutional Neural Networks (PFCNNs), where all $n\times n$ convolution kernels with $n > 1$ are pre-defined and constant during training. It involves a special form of depthwise convolution operation called a Pre-defined Filter Module (PFM). In the channel-wise convolution part, the $1\times n\times n$ kernels are drawn from a fixed pool of only a few (16) different pre-defined kernels. In the $1\times 1$ convolution part linear combinations of the pre-defined filter outputs are learned. Despite this harsh restriction, complex and discriminative features are learned. These findings provide a novel perspective on the way how information is processed within deep CNNs. We discuss various properties of PFCNNs and prove their effectiveness using the popular datasets Caltech101, CIFAR10, CUB-200-2011, FGVC-Aircraft, Flowers102, and Stanford Cars. Our implementation of PFCNNs is provided on Github https://github.com/Criscraft/PredefinedFilterNetworks.
Anonymous Spreaders Detection of False Information using Incremental Learning Approach

Considering the amount of false information spread rapidly, identifying the spreaders of false information is significant and has become a need of time. Although some models detect the spreaders of incorrect information, authors found that identifying the anonymous spreaders of false information needs to be paid more attention to and addressed. Therefore, the authors proposed the anonymous spreader detection model that extracts relevant implicit features from the text and user-based features using incremental learning techniques. Authors have developed a novel healthcare dataset with an anonymity parameter and a machine learning-based model to detect and classify the anonymous spreaders of false information. With an incremental learning approach, the Logistic Regression and Content Similarity Measure outperformed other classifiers, and also it was observed that their accuracy increased by 5% and 2%.
Anomaly-based Filtering of Application-Layer DDoS Against DNS Authoritatives

Authoritative DNS infrastructures are at the core of the Internet ecosystem. But how resilient are typical authoritative DNS name servers against application-layer Denial-of-Service attacks? In this paper, with the help of a large country-code TLD operator, we assess the expected attack load and DoS countermeasures. We find that standard botnets or even single-homed attackers can overload the computational resources of authoritative name servers—even if redundancy such as anycast is in place. To prevent the resulting devastating DNS outages, we assess how effective upstream filters can be as a last resort. We propose an anomaly detection defense that allows both, well-behaving high-volume DNS resolvers as well as low-volume clients to continue name lookups—while blocking most of the attack traffic. Upstream ISPs or IXPs can deploy our scheme and drop attack traffic to reasonable query loads at or below 100k queries per second at a false positive rate of 1.2% to 5.7% (median 2.4%).
Design of a Novel Priority Mux Based Indoor Autonomous Surveillance Robot

Bank robberies have exacerbated with time. It is a daunting task to monitor the banks for human-based security, considering long hours. Autonomous surveillance robots, in such situations, are beneficial to patrol a particular environment by collecting data with an electronic payload. DRISHTI, the proposed robot model designed using Robot Operating System (ROS), includes facial recognition to raise an alert if any suspect is detected, and human tracking is implemented during night patrolling. An Intrusion Alarm Panel (IAP) is employed using external sensors to detect any unethical breaking of the vault in addition to the anti-theft feature. The robot surveillance is aided by autonomous patrolling using Cartographer-based Simultaneous Localization and Mapping (SLAM). The accuracy of the generated map of the prototype is found to be 95.31% with respect to the corresponding ground truth. The design of a novel priority mux is proposed to integrate the surveillance functionalities with the navigation node. The alert velocity command node is made to override the navigation velocity commands with a 10 times priority ratio using the novel priority mux with a latency of less than one millisecond.
Multi-task Scheduling with Dependencies in Heterogeneous Edge Arithmetic Networks

The current development of edge computing technology provides a possibility for real-time processing of network stream data, but in edge computing scenarios, the heterogeneity of computing resources and the personalized demand of tasks for computing resources pose a great challenge for task scheduling. This challenge is even greater when there are dependencies between the multiple tasks being scheduled, and there is a lack of relevant research. In this paper, we propose a multi-task scheduling method for heterogeneous edge computing networks with dependency relationships. The dependency relationships between tasks to be scheduled are represented as directed acyclic graphs, and by constructing a comprehensive matching degree model, we consider the matching situation between tasks and computing devices in terms of computing power resources and storage resources, and prioritize tasks to computing devices with high matching degree, so as to improve the utilization of computing power resources. By optimizing the bandwidth allocation to reduce the response time of task data transmission and scheduling multiple tasks based on the idea of minimum cost flow, the response time of task computation is effectively reduced and the utilization of computing and storage resources of computing devices is improved. The experimental validation of the multi-task scheduling method with dependencies proposed in the thesis is carried out based on a simulation environment. The experimental results show that the multi-task scheduling method proposed has obvious advantages in terms of task processing response time, computing power resource utilization and storage resource utilization compared with the random scheduling method.
5G mmWave Dualstrate Broad Bandwidth and Wide-beamwidth Conformal Antenna for On-Body Wearable Applications

A new radio (NR) fifth generation (5G) millimeter (mm) wave dualstrate textile-based antenna operating at (36.91 - 48.92 GHz) is designed and investigated for on-body wearable applications. The antenna is engraved on a 1mm denim substrate and occupies a volume of $10\times 10\times 5\text{mm}3$. The main radiator designed on the lower dualstrate is resonant at 40GHz, whereas the gamma-shaped radiator printed on the upper dualstrate along with four vias broadens the 3 dB beamwidth from 49.5° to 149.8°, as well as increases the -10 dB impedance bandwidth by 11.75%. The realization of the dualstrate antenna is investigated in both free space as well as on a flat equivalent human phantom. Finally, a bending analysis confirms the flexibility of the antenna, which retains its performance along the curvature of the human body.
E-STDP: A Spatio-Temporally Local Unsupervised Learning Rule for Sparse Coded Spiking Convolutional Autoencoders

Sparse coding algorithms can be used to train a network to perform compression and image reconstruction similar to autoencoders. These algorithms have allowed unsupervised learning of biologically plausible image filters in Spiking Neural Networks (SNNs) that can be used for image classification tasks. These methods have the advantage of being more power efficient due to their sparsity and spiking nature. However, in the absence of a temporally local learning rule, the training step has to be done offline in a separate conventional computing framework. We propose a spike-based temporally local update rule that implements sparse coding in SNNs. We trained a Convolutional Neural Network (CNN) which achieves a test accuracy of 98.35% on the MNIST dataset. Furthermore, it improves the power efficiency by representing an MNIST digit by around 10,000 spikes which is 4x more efficient representation, in terms of bits required, compared to a standard non-spiking CNN. Since the updates are both temporally and spatially local, it also performs the required learning within a small multiple of 10,000 operations per sample compared to around 1,870,000 operations per sample required to train a typical non-spiking CNN having a similar structure. Such a spatio-temporally local learning rule opens the way for implementation on neuromorphic hardware without the systemic and operational inefficiencies of global information broadcast.
Importance of Land Use Maps in Statistical Studies of Urban Mobility Based on Signaling Information Provided by Mobile Devices

The study emphasizes the importance of land use maps in the calculation of statistical indicators from urban mobility studies, based on the signaling of mobile equipment in a mobile phone network. In the restrictive context of the GDPR, the introduction of land use maps in the process of statistical analysis of urban mobility becomes important and helps to distribute mobile network users who have signaled in an administrative territorial unit as close as possible to reality. The study found that, in some cases, it is necessary to use different types of land use maps for an area that is part of an administrative territorial unit, depending on the time of day and the social events that take place in the observation interval in which the statistical study of urban mobility is carried out. The study shows the difference between the methods of distributing mobile network users present in an administrative area. From the tests carried out in the study, a significant difference was found between the uniform distribution of mobile network users detected in a territorial administrative unit and the distribution based on the weight of population density given by land use in each area of the territorial administrative unit. Such weighted distributions, used in urban mobility statistical studies, can bring an advantage in business decision-making by large retailers, marketing agencies, as well as local authorities in planning public transport networks.
Hypercube-based caching: a multidimensional semantic architecture for efficient information mediation

Using semantic caching to meet user requests has been of great interest. Many information systems, including distributed databases, data warehouses and mediation systems encounter challenges related to network unavailability and congestion. In such cases, semantic caches can be extremely useful. In a semantic cache, queries that have already been answered are stored with their respective formulas to be used in future demands. In this paper, we propose a new semantic query caching for mediation systems. In our caching model, cached regions are semantically represented on multiple semantic dimensions in the form of hyper-cubes. We provide an algorithm to project queries into their own hyper-cubes and store them in regions. We also examine when it is advisable to use a given hypercube in the cache to partially answer a user query and when regions should be skipped to reduce response times and obtain relevant answers.