Reviewerly/exHarmony huggingface dataset:
Regularization for Hybrid N-Bit Weight Quantization of Neural Networks on Ultra-Low Power Microcontrollers

We propose a novel regularization method for hybrid quantization of neural networks, enabling efficient deployment on ultra-low power microcontrollers in embedded systems. Our approach introduces alternative regularization functions and a uniform hybrid quantization scheme targeting {2, 4, 8}-bits. The method offers flexibility to the weight matrix level, negligible costs, and seamless integration into existing 8-bit post-training quantization pipelines. Additionally, we propose novel schedule functions for regularization, addressing the critical yet often overlooked timing aspect and providing new insights into pacing quantization. Our method achieves a substantial reduction in model byte size, nearly halving it with less than 1% accuracy loss, effectively minimizing power and memory footprints on microcontrollers. Our contributions advance resource-efficient models in resource-constrained devices and the emerging field of tinyML, overcoming limitations of existing approaches and providing new perspectives on the quantization process. The practical implications of our work span diverse real-world applications, including IoT, wearables, and autonomous systems.
Population Coding Can Greatly Improve Performance of Neural Networks: A Comparison

Artificial neural networks oftentimes operate on continuous inputs. While biological neural networks usually represent information through the activity of a population of neurons, the inputs of an artificial neural network are typically provided as a list of scalars. As the information content of each of the input scalars depends heavily on the problem domain, representing them as individual scalar inputs, irrespective of the amount of information they contain, may prove to be suboptimal for the network. We therefore compare and examine four different Population Coding schemes and demonstrate on two toy datasets and one real world benchmark that applying Population Coding to information rich, low dimensional inputs can vastly improve a network’s performance.
Guiding the Comparison of Neural Network Local Robustness: An Empirical Study

Local robustness refers to whether a deep neural network (DNN) can correctly classify an image under certain perturbations (e.g. Gaussian noise and the $$L_{\infty }$$ perturbation). Since the discovery of adversarial examples, the local robustness of DNNs has received much attention, and researchers have proposed many formal verification techniques to measure it. One important application of these verification techniques is to compare the local robustness of different DNNs. However, these techniques contain some parameters that need to be manually set, and it is unclear whether the selection of parameters will affect the comparison results. In this paper, we conduct an empirical study to explore DNNs’ local robustness towards perturbations. We find that two widely-used assumptions in existing papers are not always true in practice. Based on our experimental results, we discuss defects of existing local robustness comparison methods and provide some possible solutions.
Analysis of Various Blockchain-Based Solutions for Electronic Health Record System

Digitization helps most of the countries in the world to adopt electronic health record (EHR) system to store and access sensitive patient medical record easily. Developing secure IT environment for EHR is a challenging task due to repeatedly cyber attack on healthcare system. Recently, use of blockchain in healthcare applications increases due to its inherent features. Blockchain addresses various security issues faced by healthcare system. The main objective of the paper is to review and analyze the use of blockchain-based solutions in healthcare domain for EHR. Comparative analysis is carried out based on type of blockchain used, their pros and cons along with various security parameters and performance parameters. Based on the analysis, the future research direction have been suggested.
TransNoise: Transferable Universal Adversarial Noise for Adversarial Attack

Deep neural networks have been proven to be vulnerable to adversarial attacks. The early attacks mostly involved image-specific approaches that generated specific adversarial noises for each individual image. More recent studies have further demonstrated that neural networks can also be fooled by image-agnostic noises, called “universal adversarial perturbation”. However, the current universal adversarial attacks mainly focus on untargeted attacks and exhibit poor transferability. In this paper, we propose TransNoise, a new approach for implementing a transferable universal adversarial attack that involves modifying only a few pixels of the image. Our approach achieves state-of-art success rates in the universal adversarial attack domain for both targeted and nontarget settings. The experimental results demonstrate that our method outperforms the current methods from three aspects of universality: 1) by adding our universal adversarial noises to different images, the fooling rates of our method on the target model are almost all above 95%; 2) when no training data are available for the targeted model, our method is still able to implement targeted attacks; 3) the method transfers well across different models in the untargeted setting.
Generating Distinctive Facial Images from Natural Language Descriptions via Spatial Map Fusion

Due to the abstract nature of language, creating accurate visual representations of faces using textual descriptions is a complex task. To overcome this challenge, we propose a novel approach called the Spatial-Text Semantic Fusion GAN (STSF-GAN) network that leverages multiple descriptions to generate distinct facial features. Our proposed method includes a new module called the Spatial Map Merge module, which predicts masks as the spatial condition to refine image feature maps based on textual semantics. Additionally, we introduce an attention mechanism called the Local Semantic Attention module that utilizes the potential distribution of each word in the description to compute local attention. Our experiments on Multi-Modal CelebA-HQ and CelebAText-HQ dataset demonstrate the effectiveness of our proposed approach.
Multi-convolution and Adaptive-Stride Based Transferable Adversarial Attacks

Adversarial attacks pose a significant threat to deep neural networks, as they can be exploited to manipulate the models and cause incorrect predictions. One type of adversarial attack, known as black-box attacks based on transferability, seeks to generate adversarial examples that can be effective against multiple models. However, existing transferable attacks have a low success rate against deeply trained models, which limits their effectiveness. To address this issue, a novel method called Multi-Convolution Adaptive-stride Nesterov Fast Gradient Method (MCAN-FGM) has been proposed. This method incorporates Multi-Convolution and Adaptive-stride modules, which leverage Gaussian convolution and optimization algorithms to generate more transferable adversarial examples. The Multi-Convolution module designs multi-scale gradients to improve the forward-looking update direction, while the Adaptive-stride module adjusts the stride adaptively to control the change range of the stride. Experimental results have shown that MCAN-FGM has a higher attack success rate than state-of-the-art gradient-based attack methods.
Universal Object Detection Under Unconstrained Environments

This paper presents a universal object detection framework for unconstrained environment settings where machines can only learn from massive unlabeled multimodal data and a few labeled data. This research aims to tackle key challenges in computer vision and expects to produce next-generation object detection techniques that can effectively detect objects of diversified categories in complex application settings. The proposed universal object detection framework is based on a novel formulation to solve anomaly detection problem leveraging multimodal heterogeneous data sources and denoising diffusion models and application to a wide set of complex application settings.
Reconstructing Masked Face Using GAN Technique

The COVID-19 pandemic has made face recognition and identification a complex task, as people often cover a significant portion of their face with masks as a precautionary measure. This creates difficulties for biometric devices and secure authentication systems, as masks obstruct facial key points that are necessary for face detection. The presence of masks also presents challenges for face identification. There is a shortage of paired and aligned face images that show faces both with and without masks. This study proposes a framework for reconstructing the occluded part of the face that is covered by a mask. The GAN-based unpaired image translation method is used to translate masked face images into unmasked face images as the reconstructed faces. A synthetic paired face dataset is created to evaluate the performance of the model in reconstructing the unmasked face from a masked face and is used to train the proposed GAN-based face reconstruction model. The model is based on transfer learning and the pix2pix cGAN architecture and the results of the comparison analysis show that our model outperforms other state-of-the-art face reconstruction models in both quality and quantity.
Towards Background and Foreground Color Robustness with Adversarial Right for the Right Reasons

Deep learning models are robust to classify complex inputs with high accuracy. However, as these models automatically select the important input features based on the training data, there is no assurance that the right input information drives the model inference process. Different techniques guide the training model process to focus on the right features for the problem. These methods usually minimize the input gradients of the non-important features dimension, forcing the model to use signal features and be right for the right reasons. However, some tasks have a bias in their signal features, so if the model learns to focus on it, the model will be biased by the signal bias. In addition, these strategies expose the important features to attacks because the input gradients of the important features have a high norm. In this work, we propose a new loss function that jointly teaches the model to be right for the right reasons and be adversarial robust. We evaluate the proposed approach with two categories of problems: texture-based and structure-based. The proposed method presented SOTA results in the structure-based problems and competitive results in the texture-based ones.
A Unique Method of Detection of Edges and Circles of Multiple Objects in Imaging Scenarios Using Line Descriptor Concepts

This paper proposes a novel method for detecting the edges and circles of multiple objects in imaging scenarios using line descriptor concepts. The method involves analyzing the intensity gradient and the orientation of the pixels in the image, and using this information to identify lines and circles that are likely to correspond to object boundaries. The proposed approach is compared with existing methods and is shown to provide superior performance in terms of accuracy and computational efficiency. The method is particularly useful for applications such as object recognition and tracking, where accurate detection of object boundaries is essential. The experimental results demonstrate the effectiveness of the proposed method in detecting edges and circles of multiple objects in different imaging scenarios.
Low-Frequency Features Optimization for Transferability Enhancement in Radar Target Adversarial Attack

Ensuring the transferability of adversarial examples between different recognition models is one of the keys to radar target adversarial attack technology. Analyzing the common transfer principles of different perturbations in various radar target recognition models is an important method to improve the transferability of adversarial examples. The features of radar targets can be divided in frequency domain. The high-frequency features are affected by the flicker phenomenon and have low stability, while the low-frequency features reflect the main structure of the target and have strong generalizations. Given the characteristic, this paper proposes a semantic-inspired synthetic aperture radar target adversarial attack algorithm based on low-frequency optimization and feature bias. By introducing low-frequency optimization in the process of perturbation generation, the generated adversarial examples focus on the low-frequency features of attacked targets, which are more generalized. The adversarial examples are guided to attack the high-level semantic features of the target, and the transferability of adversarial examples is improved. Experimental results on moving and stationary target acquisition and recognition (MSTAR) dataset show the effectiveness of the proposed method.
The Six Sigma Methodology Implementation in Agile Domain

Six Sigma is a method for updating processes to reduce variability and defects. Although many world-class manufacturing businesses have adopted Six Sigma, it is still relatively new in the software sector. This article discusses how the Six Sigma technique may be used to reduce faults in software maintenance projects. To solve the fundamental problem of minimizing customer reported defects during the maintenance phase of the software, the Define–Measure–Analyze–Improve–Control (DMAIC) technique was used. The purpose of this study was to show how a software process may adopt a systematic approach for achieving world-class quality while also achieving customer satisfaction as well as the overall profit of a company. The project discuss is the live maintenance project done by the software QA team which was responsible for ensuring that the software meets quality standards and customer satisfaction. The work done in this project shows that the implementation of Six Sigma during the maintenance or QA process has improved the total profit of the Software Service company and thus saved the ruining of brand of the company.
Connectionist Temporal Sequence Decoding: M-ary Hopfield Neural-Network with Multi-limit Cycle Formulation

Recently, we have proposed a novel M-ary (multi-level) Hopfield neural-network (MHNN) based auto-associative memory formulation for storage and retrieval of variable length sequences. This formulation modeled variable length sequences in a M-ary Hopfield network in the form of multiple variable length limit cycles. In the current paper, we extend this limit cycle formulation and propose a novel ‘connectionist temporal sequence decoding’ algorithm to solve the generic ‘temporal sequence decoding’ problem (which is the central decoding process in sequence-to-sequence learning tasks such as automatic speech recognition (ASR), movie scene recognition (MSR) etc.). Here, we model multiple prototype movie sub-sequences (scenes) of variable lengths from a given vocabulary as multiple limit cycles of a single MHNN of size 10000 neurons (and 100 million interconnection weights) and apply the proposed decoding algorithm. The proposed algorithm, uses the trained MHNN model along with Hopfield energy based End-of-Sequence markers to decode a very long noisy movie scene (comprising sub-sequence scenes with non-linear temporal and statistical variability). The proposed method solves the movie scene segmentation and labelling task with zero error. We note that this is a first of its kind solution in associative-memory based formulation using multi-level multi-limit cycle Hopfield networks for realization of such a ‘connectionist temporal sequence decoding’, representing a pleasing solution with biological-realism for such sequence decoding problems, which are conventionally solved by dynamic programming frameworks such as Viterbi decoding and finite-state networks in ASR kind of problems.
A Comparison of the Characteristics of Teaching Methods of Spoken language and Written Language

This study explored the need for different methods of teaching written and spoken language thotough the methods of literature research. The conclusion is as follows.&#x0D; First, in spoken language education, subsidiary point of teaching is effective. Amigdala in the brain is responsible for emotions, and it is effective to educate indirectly to allow Amigdala to control itself.&#x0D; Second, focal point of teaching is effective in written language education. The Thalamus and visual cortex of the brain are primarily responsible for reason. However, abstract written language is objectified as information is transmitted from Thalamus to visual cortex, and in this process, the objectified information is transmitted to Amigdala. Forcal point of teaching is effective because objectified information, such sa written language, has strong abstract.&#x0D; Third, debate education combines spoken and written language. When using spoken language, subsidiary point od teaching os effective to control Amigdala, and when using written langauge, focal point of teaching is effective because of the function of Thalamus and visual cortex.
Cyberbullying Severe Classification Using Deep Learning Approach

Given the accessibility of the Internet, social media has become a popular means of communication. Social media facilitates communication, but it has also created several issues. People may be bullied despite the advantages of using social media. Due to the extensive variety of language used, identifying cyberbullying in posts is complex. The detrimental effects of bullying on social media are getting worse every day, which is frightening. In this study, we proposed cyberbullying detection and its severe classification model using roBERTa, convolutional neural network (CNN), and long short-term memory (LSTM). We used an annotated dataset with a total of 7547 negative tweets derived from the Nigerian top influential Twitter. The proposed model demonstrates nearly 97% accuracy in detecting and classifying posts into four severe classes: Very severe, severe, moderate, and mild.
Comparative Study of Metaheuristic Algorithms for Scheduling in Cloud Computing Based on QoS Parameters

Cloud computing (CC) has gained huge superiority in recent era by providing the feature of sharing a pool of computing resources on demand among various cloud users over the internet. It provides benefits of scalability, flexibility, and pay-per-use facility using virtualization technology to its clients which attract large enterprises that work on distributed computing. One important considered research issue in cloud computing is task scheduling which means that the cloud tasks need to be appropriately mapped to the existing cloud resources to optimize single or multiple objectives. The complexity and large search space of task scheduling classify it as a NP-hard problem. A brief analysis of existing heuristic and metaheuristic strategies and their application in scheduling cloud environments has been presented in this paper followed by the comparative study of few metaheuristic algorithms. The heuristic algorithms cannot produce an exact optimal solution in an acceptable time. To solve this problem, metaheuristic algorithms based on swarm intelligence and bio-inspired techniques like Particle Swarm Optimization (PSO), Genetic algorithm (GA), and Ant Colony Optimization (ACO) algorithm are a good choice for finding the near-optimal solution. These have been implemented to run in cloud scenarios and their performance has been compared to optimize the parameters makespan, average resource usage, and average response time. PSO algorithm is found to be outperformed ACO and GA in these optimization metrics in various test conditions in the cloud environment.
GC-GAN: Photo Cartoonization Using Guided Cartoon Generative Adversarial Network

Image cartoonization, a subfield of image translation, has gained increasing attention in recent years. However, the overall performance of existing methods is limited by shortcomings such as poorly fitting regions. To address these issues, our paper proposes the Guided Cartoon Generative Adversarial Network (GC-GAN). Our approach introduces a segmentation step before the training process, which splits and guides mixed training images into a human face set and a scenery set. This enables our model to extract features specifically from cartoon faces and generate more realistic results. Furthermore, we include a loss function called triplet loss in our framework, which drives the network to bring output closer to a referenced image and focus more on the detailed parts of the training images. This improves the overall quality of the generated images and addresses the issue of poorly fitting regions. Compared to the state-of-the-art White-box CartoonGAN, our work improves Fréchet Inception Distance by 18.7% and Kernel Inception Distance by 17.6%, respectively. Additionally, our work surpasses AnimeGAN in terms of Fréchet Inception Distance by 40.7% and Kernel Inception Distance by 47.4%.
Impact of Spatial Distribution of Repeated Samples on the Geometry of Hyperplanes

Support vector machines (SVMs) and their uses in various scientific domains have been the subject of extensive research in recent years. SVMs are among the most potent and reliable classification and regression algorithms in various application areas. In the proposed work, the impact of location and multiple occurrences of support vectors on SVM has been studied by noticing the geometrical differences. Multiple occurrences or repetitions of data points are generally done; in case of imbalance classes to balance the data otherwise, results will be biased toward the majority class. Multiple occurrences of the same data points will result in a change of behavior and orientation of the hyperplane. The hyperplane will change if the support vectors are deleted or added.
Generative Event Extraction via Internal Knowledge-Enhanced Prompt Learning

Event extraction is a crucial research task in information extraction. In order to maximize the performances of the pre-trained language model (PLM), some works formulating event extraction as a conditional generation problem. However, most existing generative methods ignore the prior information between event entities, and are usually over-dependent on hand-crafted designed templates, which causing subjective intervention. In this paper, we propose a generative event extraction model named KEPGEE based on internal knowledge-enhanced prompt learning. We firstly use relational graph neural networks (RGCN) to encode the event triples entities and fuse them with the word embeddings to obtain the knowledge representation. Then the knowledge representation is concatenated with task-specific virtual tokens to compose knowledge-enhanced soft prompts, which can provide additional event information to adapt the sequence-to-sequence PLM for the generative event extraction task. Besides, in template design, we add the related topic words into the prompt templates to enhance the implicit event information. We evaluate our model on ACE2005 and ERE datasets, and the results show that our model achieves matched or better performances with several classification-based or generation-based event extraction models (including the state-of-the-art models).