Reviewerly/exHarmony huggingface dataset:
A Clearer Image: Improving Object Detection in Real Rainy Conditions with Two-Stage Processing

Object detection is one of the most important and challenging branches of computer vision, which has been widely applied in social life, such as intelligent security, autonomous driving, and so on. However, the performance of object detection could degrade rapidly under challenging weather scenarios including rainy conditions. Despite existing technologies having made impressive advancements, they are almost under synthetic rainy-clean pairs, which have a huge domain gap between synthetic and real rain. In this work, we propose an unsupervised deraining method under real rain and utilize a non-local contrastive learning for better decoupling the rain layer from the clean image. Furthermore, we introduce a semi-supervised detector during detection module to reduce the inconsistency of pseudo labels for better facilitating contrastive learning. Extensive experiments with different deraining and object detection methods demonstrate that our two-stage method obtains competitive performance in complex weather scenarios.
DRSDetector: Detecting Gambling Websites by Multi-level Feature Fusion

With the development of the Internet, online gambling has gradually replaced the traditional way of gambling and became a popular way of making money for illegal organizations. In many countries, online gambling is prohibited by law. But in some countries, these online gambling activities can still attract a variety of victims through their secret promotion channels. In this paper, we propose a gambling website detection method called DRSDetector, which combines domain features, resource features, and semantic features. And this method uses the idea of ensemble learning to fuse different modules. Specifically, we learn the character features of domains based on two stacked Transformer Encoder structures, use the LightGBM to learn resource feature of websites, and learn the semantic feature of websites based on the HAN model. The experimental results show that the performance of DRSDetector is better than the traditional website detection methods. In addition, we also investigated the promotion channels of gambling websites and took China as an example to reveal the 10 major entertainment companies behind these websites. These will help the government to combat online gambling activities more accurately and effectively.
Mobile Phone Decoder of Two-Cell Contracted Braille for Filipino Words Using Convolutional Neural Networks

This paper presents the results of using MobileNetV2, EfficientNetV1, and EfficientNetV2 for decoding 25 classes of two-cell contracted Filipino braille words. Test accuracies of 91.20% and 89.20%, and F1-scores of 0.91 and 0.89 for the top two models, the EfficientNetV1B0 and EfficientNetV2B0, respectively, were acquired. Transfer learning was used from these CNN models using the weights from ImageNet for pre-training. 1250 images were used for this research, with 50 images per class. For training the models, 70% of the images were allocated, 10% for validation, and 20% for testing. An equal number of samples were allocated for each class in this arrangement of the datasets. The model was implemented in an Android phone application through TensorFlow lite to allow mobile decoding of the braille codes. This output was aimed at creating a reliable and portable platform that will aid Special Education (SPED) teachers in the Philippines in studying and teaching two-cell contracted Filipino braille words.
$\text{VVC} +\mathrm{M}$: Plug and Play Scalable Image Coding for Humans and Machines

Compression for machines is an emerging field, where inputs are encoded while optimizing the performance of down-stream automated analysis. In scalable coding for humans and machines, the compressed representation used for machines is further utilized to enable input reconstruction. Often performed by jointly optimizing the compression scheme for both machine task and human perception, this results in sub-optimal rate-distortion (RD) performance for the machine side. We focus on the case of images, proposing to utilize the pre-existing residual coding capabilities of video codecs such as VVC to create a scalable codec from any image compression for machines (ICM) scheme. Using our approach we improve an existing scalable codec to achieve superior RD performance on the machine task, while remaining competitive for human perception. Moreover, our approach can be trained post-hoc for any given ICM scheme, and without creating a coupling between the quality of the machine analysis and human vision.
Information Flow in Graph Neural Networks: A Clinical Triage Use Case

Graph Neural Networks (GNNs) have gained popularity in healthcare and other domains due to their ability to process multi-modal and multi-relational graphs. However, efficient training of GNNs remains challenging, with several open research questions. In this paper, we investigate how the flow of embedding information within GNNs affects the prediction of links in Knowledge Graphs (KGs). Specifically, we propose a mathematical model that decouples the GNN connectivity from the connectivity of the graph data and evaluate the performance of GNNs in a clinical triage use case. Our results demonstrate that incorporating domain knowledge into the GNN connectivity leads to better performance than using the same connectivity as the KG or allowing unconstrained embedding propagation. Moreover, we show that negative edges play a crucial role in achieving good predictions, and that using too many GNN layers can degrade performance.
Overview of Methods for Collision Avoidance for Unmanned Aerial Vehicles

The paper describes an overview of approaches for anti-collision systems of unmanned aerial vehicles. These systems can be used both for individual drones and for drones forming a swarm.
Innovative Colormap for Emphatic Imaging of Human Voice for UAV-Based Disaster Victim Search

Unmanned aerial vehicles (UAVs) are being utilized for damage assessment in natural disasters and for search and rescue operations. Currently, the search for victims primarily relies on analyzing images captured by cameras mounted on UAVs. However, this approach has limitations when it comes to locating victims who are not within the camera's field of view. As a result, sound-based search methods are being considered. In this method, a voice message is transmitted to the disaster area through a speaker mounted on a UAV, and the presence of victims is confirmed by detecting their response using the onboard microphone of the UAV. Nevertheless, the UAV's microphone captures both the sound of the victim and the propeller rotation, posing a significant challenge in extracting the victim's voice from this combined audio. To address this issue, we propose a solution that involves generating spectrogram images of the sound mixture and the propeller sound, and extracting the human sound by subtracting them. We found that the conventional colormap was ineffective in emphasizing the human sound in the spectrogram images. To overcome this limitation, this paper introduces a new colormap based on the normal distribution. This colormap enhances human voices while attenuating propeller sounds by adjusting the mean and variance. Through the results of our experiments, we confirm that the proposed colormap effectively reduces propeller sound interference in the sound mixing and simultaneously emphasizes the voice of a disaster victim. By utilizing the proposed colormap, it becomes possible to visualize the victim's voice from the audio mixture acquired by the UAV's onboard microphone, enabling the identification of the victim.
How Does Oversampling Affect the Performance of Classification Algorithms?

To address the issue of imbalanced datasets classification, this study explores how different oversampling algorithms and imbalance ratios affect the performance of classification algorithms. Two oversampling algorithms, including random oversampling and Synthetic Minority Oversampling Technique (SMOTE), are used to adjust the imbalance ratio of the training dataset to 999:1, 99:1, 9:1, 3:1, and 1:1. Four classification methods, including the Convolutional Neural Network, Vision Transformer, XGBoost and CatBoost, are evaluated using performance metrics such as precision, recall, AUC, and F2-Score. We conduct more than 240 experiments and observe that oversampling ratio has a significant positive impact on AUC and recall rate, but a negative impact on precision. The study also identifies the best oversampling algorithm and imbalance ratio for each classification algorithm. It is noteworthy that the Vision Transformer algorithm used in this study has not been employed in previous research on imbalanced data classification.
Named Entity Recognition in Electronic Medical Records Based on RoBERTa Embedding and BiLSTM-CRF

With the advent of the information age, electronic medical record resources are growing exponentially and have diverse data forms. In this paper, we propose SoftLexicon-RoBERTa-BiLSTM-CRFS (SLRBC) to implement electronic medical records named entity recognition. It can embed known lexical information into a character-level-based model to obtain lexical information, and yet avoid the impact of superposition errors caused by word-level-based models due to word separation. The pre-trained model RoBERTa enables the model to obtain a better embedding representation, which can effectively improve the accuracy of the model. We conduct experiments on the public datasets CCKS2018 and CCKS2019, and achieve better results to verify the effectiveness of the model.
Differentially Private Functional Mechanism for Broad Learning System

To avoid the complex structure of deep learning models and significant training costs associated with them, the Broad Learning System (BLS) based on Random Vector Functional Link Neural Network was developed. BLS only has one input layer and one output layer and uses ridge regression theory to approximate the pseudoinverse of input, greatly simplifying the network and reducing training expenses. Despite its benefits, an attacker may use membership inference attacks to determine if some sample belongs to the model's training data when model parameters are revealed, leading to information leakage. Till now there is no related work on protecting BLS against membership inference attacks. To overcome this privacy issue we present a Privacy-Preserving Broad Learning System (PPBLS), by perturbing the objective function based on Functional Mechanism (FM) in differential privacy. We theoretically prove that PPBLS can satisfy E-differential privacy, and also demonstrate its effectiveness on both regression and classification tasks.
Performance Analysis of a Handwritten Digit Recognition Using CNN-WOA

Handwritten Digit Recognition (HDR) techniques are acquiring favor in academia and business. Because of their complex and irregular patterns, handwritten texts are difficult to recognize. Word retrieval in limited-resource scripts is complex and time-consuming. Randomizing samples and increasing the size of training datasets enhance deep learning and neural network, models. HDR techniques heavily rely on manually-programmed key point information. The writing styles and dimensions of the input sample make numerical identification and categorization difficult. This work circumvents these limitations by employing a CNN model and an effective HDR scheme. HDR enhances the classification accuracy of the MNIST dataset. The Gaussian filter and the Convolution algorithm polish the numbers. CNN-WOA is indicated as the model because the Algorithm for Whale Optimization Algorithm (WOA) fine-tunes CNN parameters. Using accuracy, precision, recall, specificity, AUC, F1-score, and false positive rate, simulation outcomes of the CNN-WOA model are compared to state-of-the-art techniques.
Extractive Question Answering for Kazakh Language

This article provides research and development of an extractive question answering system based on the BERT-like model for the Kazakh language. Developing an extractive question answering system requires large training datasets - tens of thousands of annotated question-answer pairs. Such datasets are not available in the majority of languages, including Kazakh. To address this issue, the Kazakh Question Answering Dataset (KazQA) is introduced, which is based on the Stanford Question Answering Dataset (SQuAD) and generated through machine translation using the Google Cloud Translation API. Different large pretrained contextual language models are used as the baseline models - ALBERT and multilingual BERT and are compared with the newly trained monolingual Kazakh model KazBERT. The results demonstrate that the proposed approach can effectively generate question answering systems in low-resourced Kazakh language.
A Powerful Load Balancing Algorithm for Cloud Performance Evaluation

The energy consumption of data centers plays a significant role in the cost estimation of maintenance, performance evaluation, and environmental pollution in cloud computing. Carbon emissions are one of the major drawback of data centers in this scenario. To manage the data center temperature as well as the necessity to reduce power consumption while satisfying customer demands. This is extremely beneficial to both the data center and the environment. The data center is a collection of servers from which consumers can benefit by using their services. In this context, cloud service providers are working hard to reduce energy usage and avoid environmental contamination. This research work describes the novel algorithm for scheduling virtual machines in servers in order to consume a minimal amount of energy. The weight approximation algorithm arranges servers and virtual machines in decreasing order depending on their configuration to allocate VMs with jobs to optimize power usage. The power consumption of the server is calculated before and after VM allocation in this algorithm. To avoid CRAC modifications, the critical threshold of each server is maintained in this instance. If it exceeds the server's critical threshold, the VM is moved to another ideal server to avoid migration. In this method of VM allocation, the ordered tasks are allocated to the server using round-robin fashion. The model is simulated using cloudsim tools, and the server's performance and energy consumption are better than the random selection algorithm.
Speaker Identification, Differentiation and Verification Using Deep Learning for Human Machine Interface

Speaker distinction is critical in criminology and security organizations. Speaker differentiation is a form of voice recognition. The problem of precise differentiation is challenging. Researchers used various models to compare and classify voice comparison problems. But when it comes to accuracy deep learning is gaining traction once trained with the data in bulk for orator recognition and differentiation. This article presents a model that is efficient and accurate enough to segregate two or many voices. Also, the model finds the level of similarity of different voice samples. Even if the same speaker speaks twice after an age gap or with different emotions, the model can detect it as a different utterance of the same speaker. This paper exploits Deep learning- based methods for identification, fake speech detection, and orator diarization. The paper is giving insight into the past practices for speaker differentiation using deep learning. This short study will help novices and researchers comprehend the field of speech recognition and comparison.
Preservation of Confidentiality Based on Homomorphic Encryption Library for IoT

The Internet of Things (IoT) is the term used to describe the connectivity of different physical objects, including sensors, appliances, vehicles, and other devices, which are equipped with electronics, software, and sensors to gather and share data. These devices produce a significant volume of confidential information that requires safeguarding against unauthorized access or disclosure. Homomorphic encryption can play a significant role in preserving confidentiality in IoT environments. It enables data to be processed without revealing the underlying data or the results of the computation. This means that sensitive data generated by IoT devices can be encrypted using homomorphic encryption, and computations can be performed on the encrypted data, while maintaining confidentiality. A homomorphic encryption library designed for IoT can allow IoT devices to securely and efficiently perform computations on encrypted data, without exposing the data to any third party or intermediary. This can help prevent data breaches and unauthorized access to sensitive IoT data, which is crucial in many industries, such as healthcare, finance, and transportation. The number of smart devices is growing at an incredible rate, as is the interest of people in these devices. Devices send data wirelessly and are good targets for hackers. To prevent such situations, you must first protect the microcontroller built into the devices. For this purpose, the fully homomorphic encryption library «HomomorphicControllerVersion_01» was developed. The article proposes the architecture of the library of fully homomorphic operations on integers. The library supports basic homomorphic operations (addition, subtraction, multiplication, division) on integers. On the basis of the proposed method of homomorphic division and the architecture of the library, the library of homomorphic operations on integers was implemented. The article also provides measurements of the time required to perform certain operations on encrypted data and analyzes the efficiency of the developed implementation of the library.
"Would life be more interesting if I were in AI?" Answering Counterfactuals based on Probabilistic Inductive Logic Programming

Probabilistic logic programs are logic programs where some facts hold with a specified probability. Here, we investigate these programs with a causal framework that allows counterfactual queries. Learning the program structure from observational data is usually done through heuristic search relying on statistical tests. However, these statistical tests lack information about the causal mechanism generating the data, which makes it unfeasible to use the resulting programs for counterfactual reasoning. To address this, we propose a language fragment that allows reconstructing a program from its induced distribution. This further enables us to learn programs supporting counterfactual queries.
On Feasibility of Declarative Diagnosis

The programming language Prolog makes declarative programming possible, at least to a substantial extent. Programs may be written and reasoned about in terms of their declarative semantics. All the advantages of declarative programming are however lost when it comes to program debugging. This is because the Prolog debugger is based solely on the operational semantics. Declarative methods of diagnosis (i.e. locating errors in programs) exist, but are neglected. This paper discusses their possibly main weaknesses and shows how to overcome them. We argue that useful ways of declarative diagnosis of logic programs exist, and should be usable in actual programming.
Quantification and Aggregation over Concepts of the Ontology

We argue that in some KR applications, we want to quantify over sets of concepts formally represented by symbols in the vocabulary.We show that this quantification should be distinguished from second-order quantification and meta-programming quantification.We also investigate the relationship with concepts in intensional logic.We present an extension of first-order logic to support such abstractions, and show that it allows writing expressions of knowledge that are elaboration tolerant.To avoid nonsensical sentences in this formalism, we refine the concept of well-formed sentences, and propose a method to verify well-formedness with a complexity that is linear with the number of tokens in the formula.We have extended FO(•), a Knowledge Representation language, and IDP-Z3, a reasoning engine for FO(•), accordingly.We show that this extension was essential in accurately modelling various problem domains in an elaboration-tolerant way, i.e., without reification.
A quantitative analysis of training effects for the touch screen-based flashcard game attention training

Attention problems are common problems in adolescents and even adults. In this study, a training protocol based on flashcard game paradigm was designed, and reaction time and eye movement data were recorded during the training. The subjects received attention training twice a week for one month. Subjects took the IVA-CPT attention test before and after training. The study compared and analysed parameters such as reaction time, fixation distribution, fixation time, scanning length, etc. The experimental results showed that the task reaction time of the subjects was significantly shortened after attention training, and various parameters of eye movements were improved and statistically different. After training the attention quotients of IVA-CPT test were greatly improved and statistically different from those before training. This study provides a theoretical basis and technical support for the study of attention training and training effects.
Natlog: Embedding Logic Programming into the Python Deep-Learning Ecosystem

Driven by expressiveness commonalities of Python and our Python-based embedded logic-based language Natlog, we design high-level interaction patterns between equivalent language constructs and data types on the two sides. By directly connecting generators and backtracking, nested tuples and terms, coroutines and first-class logic engines, reflection and meta-interpretation, we enable logic-based language constructs to access the full power of the Python ecosystem. We show the effectiveness of our design via Natlog apps working as orchestrators for JAX and Pytorch pipelines and as DCG-driven GPT3 and DALL.E prompt generators. Keyphrases: embedding of logic programming in the Python ecosystem, high-level inter-paradigm data exchanges, coroutining with logic engines, logic-based neuro-symbolic computing, logic grammars as prompt-generators for Large Language Models, logic-based neural network configuration and training.
Explainable and Trustworthy Traffic Sign Detection for Safe Autonomous Driving: An Inductive Logic Programming Approach

Traffic sign detection is a critical task in the operation of Autonomous Vehicles (AV), as it ensures the safety of all road users. Current DNN-based sign classification systems rely on pixel-level features to detect traffic signs and can be susceptible to adversarial attacks. These attacks involve small, imperceptible changes to a sign that can cause traditional classifiers to misidentify the sign. We propose an Inductive Logic Programming (ILP) based approach for stop sign detection in AVs to address this issue. This method utilises high-level features of a sign, such as its shape, colour, and text, to detect categories of traffic signs. This approach is more robust against adversarial attacks, as it mimics human-like perception and is less susceptible to the limitations of current DNN classifiers. We consider two adversarial attacking methods to evaluate our approach: Robust Physical Perturbation (PR2) and Adversarial Camouflage (AdvCam). These attacks are able to deceive DNN classifiers, causing them to misidentify stop signs as other signs with high confidence. The results show that the proposed ILP-based technique is able to correctly identify all targeted stop signs, even in the presence of PR2 and ADvCam attacks. The proposed learning method is also efficient as it requires minimal training data. Moreover, it is fully explainable, making it possible to debug AVs.