Reviewerly/exHarmony huggingface dataset:
Pruning Deep Feature Networks Using Channel Importance Propagation

Deep convolutional neural networks use their powerful feature representation capability to extract deep information of the targets, which is conducive to the improvement of model accuracy. However, its model is more complex, with a heavier computational burden and greater demand on computational and memory resources, which affects the real-time performance and lightness of the model. To address the above limitations of deep convolutional neural networks, we define a new metric for measuring the importance of convolutional kernels in conjunction with feature maps, introduce a non-linear mapping function that maps feature maps to important convolutional kernels, propose a continuous and smooth pruning strategy for deep convolutional neural networks, and obtain the Pruning deep feature networks using channel importance propagation model to reduce the complexity of the network and reduce the computational burden, and improve the accuracy and training efficiency of the model, while ensuring the feature network representation capability and the system performance loss is small. Our proposed model was tested on three datasets, CIFAR-10, CIFAR-100 and SVHN, and the test results demonstrated the validity of the model.
Fireworks Image Classification with Deep Learning

Before the advent of deep learning, traditional image recognition used algorithms to find features and then classify images using classical machine learning algorithms. But it is difficult to define features for variable types of images. Instead, Representation learning is a way to allow a system to discover the representations of feature for image processing. Deep-learning algorithms attempt to learn multiple levels of representation and play the key role of modern Representation learning. When deep learning neural networks recognize images, the shallow layers usually extract lower-level features, then start with intermediate-level features, and finally the full images. This study uses CNN (Convolutional Neural Network) to classify fireworks images, and the trained modules are used to evaluate the accuracy and suitability.
A Machine-Learning-based Approach for Parameter Control in Bee Colony Optimization for Traveling Salesman Problem

Metaheuristics are a set of algorithms which is capable of solving Combinatorial Optimization Problems (COPs). When a metaheuristic is used to solve a COP, one of the major aspects is to determine an appropriate parameter setting. Poor practice of determining parameter values may lead to inability to find optimal solutions or getting invalid conclusions from the experimental results. This research proposes a machine-learning-based parameter control mechanism for a metaheuristic, i.e. the Bee Colony Optimization (BCO) algorithm. The proposed mechanism consists of three main phases: Data Collection, Model Training, and Deployment. In order to examine the performance of the BCO algorithm with the parameter control mechanism, a set of 16 TSP instances is used as test bed. The experimental results show that it is significantly better than the BCO implementation using the parameter values that are determined via a manual tuning process. The proposed parameter control mechanism overcomes the shortcomings of manual parameter tuning and dynamically adjust the parameter values throughout the BCO optimization process.
Deep learning real-time detection and correction system for stroke rehabilitation posture

Cerebrovascular diseases are becoming younger, and rehabilitation treatment becomes the importance of future recovery. Some rural areas have limited medical re-sources or people living in more remote mountainous areas have certain difficulties to receive treatment in hospitals. To solve this problem to build a set of OpenPose based human posture recognition algorithms to apply to stroke rehabilitation, to process the action of the characters in the image. Use the frame extraction algorithm of motion capture data to obtain the key frames of the character's movement, so as to analyze the details of the human movement .Then identify the body movements, carry out the stroke rehabilitation OpenPose human body model training, and use the Law of Cosines to calculate the angles of 3 points in BODY25, so as to analyze the details of the body movements to identify the body movements and provide the correct posture to guide the rehabilitation patients to the posture angle Rehabilitation.The experimental results show that the test subjects have a correct rate of posture and movement of 53.3% before using the system and 97% after using the system, and an improvement rate of 43.7%.
Person Detection in Collaborative Group Learning Environments Using Multiple Representations

We introduce the problem of detecting a group of students from classroom videos. The problem requires the detection of students from different angles and the separation of the group from other groups in long videos (one to one and a half hours).We use multiple image representations to solve the problem. We use FM components to separate each group from background groups, AM-FM components for detecting the back-of-the-head, and YOLO for face detection. We use classroom videos from four different groups to validate our approach. Our use of multiple representations is shown to be significantly more accurate than the use of YOLO alone.
An Interactive Approach to Bias Mitigation in Machine Learning

Underrepresentation and misrepresentation of protected groups in the training data is a significant source of bias for Machine Learning (ML) algorithms, resulting in decreased confidence and trustworthiness of the generated ML models. Such bias can be mitigated by incorporating both objective as well as subjective (through human users) measures of bias, and compensating for them by means of a suitable selection algorithm over subgroups of training data. In this paper, we propose a methodology of integrating bias detection and mitigation strategies through interactive visualization of machine learning models in selected protected spaces. In this approach, a (partially generated) ML model performance is visualized and evaluated by a human user or a community of human users in terms of potential presence of bias using both objective and subjective criteria. Guided by such human feedback, the ML algorithm can implement a variety of remedial sampling strategies to mitigate the bias using an iterative human-in-the-loop approach. We also provide experimental results with a benchmark ML dataset to demonstrate that such an interactive ML approach holds considerable promise in detecting and mitigating bias in ML models.
Privacy of Study Participants in Open-access Health and Demographic Surveillance System Data: Requirements Analysis for Data Anonymization

Data anonymization and sharing have become popular topics for individuals, organizations, and countries worldwide. Open-access sharing of anonymized data containing sensitive information about individuals makes the most sense whenever the utility of the data can be preserved and the risk of disclosure can be kept below acceptable levels. In this case, researchers can use the data without access restrictions and limitations.This study aimed to highlight the requirements and possible solutions for sharing health surveillance event history data. The challenges lie in the anonymization of multiple event dates and time-varying variables.A sequential approach that adds noise to event dates is proposed. This approach maintains the event order and preserves the average time between events. In addition, a nosy neighbor distance-based matching approach to estimate the risk is proposed. Regarding the key variables that change over time, such as educational level or occupation, we make 2 proposals: one based on limiting the intermediate statuses of the individual and the other to achieve k-anonymity in subsets of the data. The proposed approaches were applied to the Karonga health and demographic surveillance system (HDSS) core residency data set, which contains longitudinal data from 1995 to the end of 2016 and includes 280,381 events with time-varying socioeconomic variables and demographic information.An anonymized version of the event history data, including longitudinal information on individuals over time, with high data utility, was created.The proposed anonymization of event history data comprising static and time-varying variables applied to HDSS data led to acceptable disclosure risk, preserved utility, and being sharable as public use data. It was found that high utility was achieved, even with the highest level of noise added to the core event dates. The details are important to ensure consistency or credibility. Importantly, the sequential noise addition approach presented in this study does not only maintain the event order recorded in the original data but also maintains the time between events. We proposed an approach that preserves the data utility well but limits the number of response categories for the time-varying variables. Furthermore, using distance-based neighborhood matching, we simulated an attack under a nosy neighbor situation and by using a worst-case scenario where attackers have full information on the original data. We showed that the disclosure risk is very low, even when assuming that the attacker's database and information are optimal. The HDSS and medical science research communities in low- and middle-income country settings will be the primary beneficiaries of the results and methods presented in this paper; however, the results will be useful for anyone working on anonymizing longitudinal event history data with time-varying variables for the purposes of sharing.
A Brief Analysis of Soft Computing Techniques in Software Fault Prediction

Software testing and fault prediction is major part of any software development. It not only reduces the overall cost and efforts but also increase the robustness of the system. Tester has to find or predicts the fault in advance to design premium fault free software. Soft-computing is basically is used for identifying defect in advance through techniques like Fuzzy logic, Neural Network and other metaheuristic techniques. In this paper we are doing a brief analysis of different soft computing techniques used in software fault prediction. Different datasets and parameter selection is used in the calculation of faulty module, model's efficiency and accuracy. Recent studies with their benefit and loss are being compared to find out best possibilities and combination of model for better stabilized results
Identifing the Future Security Issues Methods for Secure Data in the Cloud Computing

In the fast-growing world, Cloud Computing is an important tool to store sensitive data in the cloud database in a secure manner. Cloud Computing provides the largest resource allocation to cloud clients like a computer, memories, operating system and application through the internet that passes across the world. The major problem in every year IT sectors facing security issues and challenges for securing the sensitive data in the cloud database. Though there are handling many techniques on developing security in the cloud and this has made a very confusing state for the cloud users. To overcome this problem, thispaper to identify the issues and challenges in the cloud using the new method EIR. This paper also provides suggestions for future cloud challenges and cloud users use these suggestions to aware of Cloud computing in the future also.
Using EM technique for Juvenile crime zoning

The primary elements for child criminal behavior are education levels and family background of children and others like friends, money problems, internet, drugs etc. In this research paper, we use expectation-maximization cluster method of data mining to segment juvenile crimes in India with a major risk element of education levels of children. This research will help to visualize the major zones of children involving in crimes and helps to take early preventive steps to mitigate juvenile delinquency
Cause and Effect: Concept-based Explanation of Neural Networks

In many scenarios, human decisions are explained based on some high-level concepts. In this work, we take a step in the interpretability of neural networks by examining their internal representation or neuron’s activations against concepts. A concept is characterized by a set of samples that have specific features in common. We propose a framework to check the existence of a causal relationship between a concept (or its negation) and task classes. While the previous methods focus on the importance of a concept to a task class, we go further and introduce four measures to quantitatively determine the order of causality. Through experiments, we demonstrate the effectiveness of the proposed method in explaining the relationship between a concept and the predictive behaviour of a neural network.
Factors Influencing CNN Performance

The current drive toward incorporating machine learning (ML) models within automated closed-loop workflows renews existing issues for image quality prediction; specifically, maintaining the operator-in-the-loop's trust in system operation. This paper reviews methods to assess information content in the reconstruction process to maintain trust in closed-loop workflows. We build ML models from commonly used architectures. For previous manual exploitation, the interpretability of an image indicates the potential intelligence value of the data. Historically, the National Imagery Interpretability Rating Scale (NIIRS) has been the standard for quantifying the intelligence potential based on image analysis by human observers. Empirical studies have demonstrated that spatial resolution is the dominant predictor of the NIIRS level of an image and that compression to 1 bit per pixel can maintain that NIIRS level. However, with modern ML that digests images rather than extracted features, what is the corresponding size of the latent space required to maintain the NIIRS levels? To those ends, we operate on moderate size images, 480×480 pixels, to provide realistic generalizable estimates over those experiments against similar sprite size (<100×100 pixels) images.
Physical Adversarial Attacks in Simulated Environments

Adversarial attacks against machine learning algorithms are increasingly a threat as object detection, tracking, and identification systems are more frequently and widely deployed in the real world. Current research on the evaluation of adversarial physical attacks and defenses utilize open and static datasets, such as APRICOT, to provide a benchmark for comparison. Evaluating defenses in the real world can be time consuming as the collection of data in the environment of concern with enough variation such as distance, lighting, and viewing angle is quite tedious. Even so, the digital insertion of attacks (such as adver-sarial patches) has been shown to overestimate effectiveness when compared to physical insertion in the real world, often because environmental variations are not taken into consideration. This work focuses on creating a pipeline in a simulated environment to evaluate the effectiveness of adversarial patches and correct for these real world variations. In this paper, we leverage Car Learning to Act (CARLA), a hyper-realistic autonomous driving simulator, and DAPRICOT (a method to digitally insert realistic adversarial attacks into real-world scenes) to simulate and correct for realistic operational conditions.
A Dynamic Thermal IR Display for Physical Adversarial Attacks

Machine learning systems, and specifically deep neural networks, are increasingly being utilized in domains beyond the standard visible spectrum in imagery applications. Work researching adversarial vulnerabilities in these domains, meanwhile, is still catching up. While some work has been done to extend digital attacks to various domains, most research largely focuses on virtual attacks, or visible spectrum imaging, where a color printer is all that is typically needed to deploy such attacks in the physical world. In this work, we demonstrate adversarial attacks on thermal imaging in long-wave infrared (IR). After demonstrating these attacks digitally, we introduce a programmable prototype tool developed for displaying these adversary thermal patches in the physical domain. This tool allows us to rapidly test different attacks and evaluate their efficacy as a virtual versus physical attack, which is work that has previously only been demonstrated with imagery in the human-visible spectrum, and opens the door to future adversarial attacks against multi-modal ML systems.
Improving Stance-based Fake News Detection using BERT Model with Synonym Replacement and Random Swap Data Augmentation Technique

The amount of fake news on the internet remains to grow due to its low time and cost of publishing information. A fake news detection system can be implemented to combat its spread. In this research, a stance-based fake news detection model is built with a pretrained Bidirectional Encoder Representations of Transformers (BERT) model fine-tuned for stance detection between headline and body text with data augmentation. The data augmentation utilized in this research includes synonym replacement which replaces chosen words with their synonym, and random swap, which randomly replaces position between two words. The experiment is done by using the two data augmentation techniques separately, combining the two techniques where half of each augmentation is done by one technique, and mixing the two techniques. The evaluation on the test set by cross-validation shows that random swap augmentation provides the best result overall with 42.63&#x0025; sensitivity, 82.14&#x0025; specificity, 32.44 &#x0025; Fl-score, with the least cost on accuracy with 71.52 &#x0025; accuracy.
Research on Progress of Blockchain Access Control

With the continuous development of blockchain as a data management technology, while improving data management efficiency, reducing data maintenance costs, and improving data security, it also faces serious privacy leakage problems. Access control is one of the important means of privacy protection, the traditional access control model is mostly built based on central trusted entities, which has problems such as high cost of trust construction or single point of failure. Combined with the features of blockchain such as decentralization and transparency, some blockchain-based access control schemes are proposed, but they also bring problems such as access policy leakage and user privacy exposure. This paper systematically summarizes the existing achievements in the field of blockchain access control: firstly, it analyzes the demand for blockchain-based access control and the technical difficulties faced; secondly, it summarizes the developing access control methods and introduces 16 access technology principles and implementation ideas; then it comprehensively analyzes the 4 security risks and main problems of blockchain access control technology; finally, it summarizes and discusses the future development trend of blockchain. The future development trend of access control technology is summarized and discussed.
Relation Extraction based on Data Partition and Representation Integration

Relation extraction (RE) is the cornerstone of many natural language processing applications. The success of machine learning algorithms generally depends on the embedding representation of data, since it involves different explanatory factors of variation behind the data. This paper explores integrating multiple representations to improve relation extraction. Note that the shortest dependency path (SDP) can retain relevant information and eliminate irrelevant words in the sentence, we partition the input dataset into multiple subsets based on the deformed SDP. For the input dataset and each subset, we train different encoders respectively. The input dataset encoder pays more attention to words in SDP, while the subset encoders pay more attention to words beyond SDP. In this way, we can get multiple embedding representations of diversity for the same sentence and improve the performance of RE by integrating them with predefined strategies. Experimental results on a widely used dataset demonstrate the effectiveness of our approach.
A Survey of DeepFakes Generation and Detection

The abuse of DeepFakes poses a potential threat to individuals and society. In order to eliminate the negative effects of DeepFakes techniques, researchers have conducted in-depth research on the detection of DeepFakes. Despite the remarkable progress, there is still a lack of systematic summarization of DeepFakes generation and detection techniques due to the different focus of researchers. In this paper, the development of DeepFakes generation and detection technology is reviewed, and the existing approaches and datasets are systematically summarized and scientifically classified. In doing so, we hope to provide a reference for further research in the field of DeepFakes detection.
User Experience based Perspective Guidance Method for Immersive Interactive Video

With the development of science and technology, immersive interactive video, as a new form of artistic expression in recent years. It has been widely concerned by artists, scientists and the whole society. People try to use new media to create new art. Because users have more independent perspective selection ability when experiencing immersive interactive videos, how to more intelligently guide users&#x0027; perspective to focus on the highlights of videos, and how to view such videos more personalized, has become a problem that people pay attention to and expect to solve. This paper proposes an intelligent perspective guidance solution based on user experience. It integrates the needs of users and creators, and combines the psychological and social factors of users&#x0027; actual experience. Through the test, this solution can meet the personalized viewing needs of users to a certain extent.
Within-Project Software Aging Defect Prediction Based on Active Learning

Long-running software systems tend to exhibit performance degradation and increase failure rate, and the phenomenon is known as software aging. The bugs that cause the aging phenomenon are called Aging-Related Bugs (ARBs), and may bring serious economic loss or even endanger human security. To discover and remove ARBs, ARBs prediction is presented. But ARBs prediction model often needs a large number of training data in order to train a high performance classification model. In practice, the labeled data are rare in many cases. In addition, it is difficult to label all samples manually. Furthermore, there is a serious class imbalance problem in ARBs datasets. In order to address the two problems, we propose a framework named QUIRE-HUE. On the one hand, we use a approach named Active Learning by Querying Informative and Representative Examples (QUIRE) to select a few informative and representative samples to label for training set, which can reduce the cost of labeling and get a high performance classification model. On the other hand, we apply a Hashing-Based Undersampling Ensemble (HUE) by constructing diversified training subspaces for undersampling to alleviate class imbalance problem. A set of experiments are performed on two large open-source projects (MySQL, Linux) with six different machine learning classifiers. We use Balance and AUC as the evaluation metrics. Experimental results indicate that QUIRE-HUE achieves encouraging results. Average AUC and Balance are 0.769 and 0.812 respectively on MySQL dataset, 0.772 and 0.828 respectively on Linux dataset, which significantly outperforms all baseline methods.