Reviewerly/exHarmony huggingface dataset:
Modeling of Artificial Intelligence Enabled Crowd Density Classification for Smart Communities

Smart cities are a contemporary phenomenon to involve information and communication technologies (ICTs) in the advancement of large urban cities. It will be helpful in determining the movement of a city through observing general flow of visitors and traffic jams. Crowd management can be one key aspect of smart cities, assisting in enjoyable and safety experiences for visitors and residents. As crowd density (CD) classification methods encounter difficulties such as inter-scene and intra-scene deviations, non-uniform density, occlusion and convolutional neural network (CNN) methods were valuable. This manuscript designs a wolf pack algorithm with deep learning enabled crowd density classification (WPADL-CDC) model for smart communities. The presented WPADL-CDC technique assists in improving the quality of life in smart community environment. In addition, the presented WPADL-CDC model employs deep convolutional neural network (DCNN) based densely connected network (DenseNet) model for feature extraction purposes. Moreover, the WPA is exploited for the optimal hyper parameter tuning of the DenseNet201 method. Furthermore, fuzzy radial basis neural network (FRBNN) model can be utilized for the identification and classification of CDs in the video surveillance system. For examining the enhanced CD classification outcomes of the presented WPADL-CDC method, a detailed experimental analysis is performed. The experimental values demonstrate the promising performance of the WPADL-CDC model.
Cybersecurity Risk with Wearable Technology in Sports: Why Should We Care?

With technology becoming ever more prevalent in the world of sports, with collecting of data also becoming essential for analysis, the use of wearable medical technology in sports has increased drastically. Take a professional athlete as an example, their use is to track their biodata and optimize their performance. Compared to people who participate in recreational activities, they may simply want to view their BMI, calories burned, and heart rate to help guide them towards a healthier lifestyle. The purpose of this study is to explore the possibilities and limitations due to cybersecurity of wearable technology in sports, and in what, way can it influence athletes, trainers, and an organization. This technology is not limited to performance and health, as it can also help predict sudden cardiac arrests which could ultimately even prevent deaths. Despite all the benefits, like any industry with a large market, there is always the issue of conflicting motives and conflict of interests. This could result in unethical behavior and hinder the research and development of wearable technology for all users including one in the sports, with cyber-risks. Implementing a data governance council who has the best interest for a sports individual's security and privacy is key to formulating a successful risk management framework in sports. To protect wearable technologies in sports, this research took the novel initiative.
Robotic System Control using Embedded Machine Learning and Speech Recognition

The focus of this work is to control a computing-resource-constrained robotics system using embedded machine learning (also known as tiny machine learning). The objective of this effort was to develop a potential lab exercise for students in a robotics class, with the goal of giving them a better understanding of how artificial intelligence applications can be used to control a robotics system. This paper discusses the fundamentals of embedded machine learning and the convolution neural network training process used for this project. The dataset used for this project and its preparation for use with the machine learning model is also discussed. An overview of the technology and data used for this project is provided describing hardware features used to control two motors on a robotic system using speech recognition by keyword spotting. Data validation and future improvements of the project related to the speech-controlled system are also discussed.
The Human Continuity Activity Semisupervised Recognizing Model for Multiview IoT Network

With advances in spatial–temporal Internet of Things (IoT) technologies, human activity recognition (HAR) has played a major role in human safety and medical health. Recently, most works focus on activity deep feature extraction, offering promising alternatives to manually engineered features. However, how to extract the effective and distinguishable continuity activity features and meanwhile reduce the heavy dependence on labels still remains the key challenge for HAR. This article proposes the human continuity activity semisupervised recognizing method in multiview IoT network scenarios. Our innovation combines supervised activity feature extraction with unsupervised encoder–decoder modules, which can capture continuity activity features from sensor data streams. To be more specific, our work applies a convolutional neural network (CNN) to capture the local dependence of sensor data and designs an encoder–decoder architecture to extract temporal features in an unsupervised manner. Then, we fuse these two features to recognize activities and train them with manual labels, thereby refining the temporal feature extraction and training CNN module. Experiments on five public data sets demonstrate the exceptional performance of our proposed method, which can achieve a higher recognition accuracy on almost all the data sets and is more robust and adaptive among different data sets.
Accurate and Robust Eye Center Localization by Deep Voting

Eye Center Localization (ECL) is one of the most crucial technologies for various computer vision applications, such as eye gazing estimation and eye-tracking. Current conventional implementations consist of two phases, including locating the approximate eye regions and finding the eye center position by extracting the semantic features around the corresponding eye region. However, the combination pipeline results in the ECL accuracy being influenced by not only the environmental factors, such as the variability of photographing angles, illuminations, and the probable occlusions by eyelids or glasses, but also the quality of preceding procedures. Inspired by the ensemble mechanism in machine learning, we formulate ECL problem as a process of end-to-end voting, and the core is to select a set of local descriptors which can capture efficient independent information to vote for eye centers. With the help of deep convolutional neural networks, we are able to determine semantic descriptors around the eye regions. Each descriptor proposes a vote pointing to the corresponding eye center, and all the votes indicate the eye centers finally. The experimental results on the public databases, BioID and GI4E, show that our method achieves 80.3% and 95.2% accuracy, respectively, which outperforms the existing state-of-the-art methods, and the results based on our customized challenging database verify the robustness of our method.
Anomaly Detection Using Multiscale C-LSTM for Univariate Time-Series

Computers generate network traffic data when people go online, and devices generate sensor data when they communicate with each other. When events such as network intrusion or equipment failure occur, the corresponding time-series will show abnormal trends. By detecting these time-series, anomalous events can be detected instantly, ensuring the security of network communication. However, existing time-series anomaly detection methods are difficult to deal with sequences with different degrees of correlation in complex scenes. In this paper, we propose three multiscale C-LSTM deep learning models to efficiently detect abnormal time-series: independent multiscale C-LSTM (IMC-LSTM), where each LSTM has an independent scale CNN; combined multiscale C-LSTM (CMC-LSTM), that is, the output of multiple scales of CNN is combined as an LSTM input; and shared multiscale C-LSTM (SMC-LSTM), that is, the output of multiple scales of CNN shares an LSTM model. Comparative experiments on multiple data sets show that the proposed three models have achieved excellent performance on the famous Yahoo Webscope S5 dataset and Numenta Anomaly Benchmark dataset, even better than the existing C-LSTM based latest model.
Knowledge-aware Global Reasoning for Situation Recognition

The task of situation recognition aims to solve the visual reasoning problem with the ability to predict the activity happening (salient action) in an image and the nouns of all associated semantic roles playing in the activity. This poses severe challenges due to long-tailed data distributions and local class ambiguities. Prior works only propagate the local noun-level features on one single image without utilizing global information. We propose a Knowledge-aware Global Reasoning (KGR) framework to endow neural networks with the capability of adaptive global reasoning over nouns by exploiting diverse statistical knowledge. Our KGR is a local-global architecture, which consists of a local encoder to generate noun features using local relations and a global encoder to enhance the noun features via global reasoning supervised by an external global knowledge pool. The global knowledge pool is created by counting the pairwise relationships of nouns in the dataset. In this paper, we design an action-guided pairwise knowledge as the global knowledge pool based on the characteristic of the situation recognition task. Extensive experiments have shown that our KGR not only achieves state-of-the-art results on a large-scale situation recognition benchmark, but also effectively solves the long-tailed problem of noun classification by our global knowledge.
Task-Adaptive Feature Disentanglement and Hallucination for Few-Shot Classification

Few-shot classification is a challenging task of computer vision and is critical to the data-sparse scenario like rare disease diagnosis. Feature augmentation is a straightforward way to alleviate the data-sparse issue in few-shot classification. However, mimicking the original feature distribution from a small amount of data is challenging. Existing augmentation-based methods are task-agnostic: the augmented feature is not with optimal intra-class diversity and inter-class discriminability concerning a certain task. To address this drawback, we propose a novel Task-adaptive Feature Disentanglement and Hallucination framework, dubbed TaFDH. Concretely, we first perceive the task information to disentangle the original feature into two components: class-irrelevant and class-specific features. Then more class-irrelevant features are decoded from a learned variational distribution, fused with the class-specific feature to get the augmented features. Finally, a generalized prior distribution over a quadratic classifier is meta-learned, which can be fast adapted to the class-specific posterior, thus further alleviating the inadequacy and uncertainty of feature hallucination via the nature of Bayesian inference. In this way, we construct a more discriminable embedding space with reasonable intra-class diversity instead of simply restoring the original embedding space, which can lead to a more precise decision boundary. We obtain the augmented features equipped with enhanced inter-class discriminability by highlighting the most discriminable part while boosting the intra-class diversity by fusing with the diverse generated class-irrelevant parts. Experiments on five multi-grained few-shot classification datasets demonstrate the superiority of our method.
Assessing TD Macro-Management: A Nested Modeling Statistical Approach

Quality improvement can be performed at the: (a) micro-management level: interventions applied at a fine-grained level (e.g., at a class or method level, by applying a refactoring); or (b) macro-management level: interventions applied at a large-scale (e.g., at project level, by using a new framework or imposing a quality gate). By considering that the outcome of any activity can be characterized as the product of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">impact</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">scale</i> , in this paper we aim at exploring the impact of Technical Debt (TD) Macro-Management, whose scale is by definition larger than TD Micro-Management. By considering that TD artifacts reside at the micro-level, the problem calls for a nested model solution; i.e., modeling the structure of the problem: artifacts have some inherent characteristics (e.g., size and complexity), but obey the same project management rules (e.g., quality gates, CI/CD features, etc.). In this paper, we use the Under-Bagging based Generalized Linear Mixed Models approach, to unveil project management activities that are associated with the existence of HIGH_TD artifacts, through an empirical study on 100 open-source projects. The results of the study confirm that micro-management parameters are associated with the probability of a class to be classified as HIGH_TD, but the results can be further improved by controlling some project-level parameters. Based on the findings of our nested analysis, we can advise practitioners on macro-technical debt management approaches (such as “ <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">control the number of commits per day</i> ”, “ <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">adopt quality control practices</i> ”, and “ <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">separate testing and development teams</i> ”) that can significantly reduce the probability of all software artifacts to concentrate HIGH_TD. Although some of these findings are intuitive, this is the first work that delivers empirical quantitative evidence on the relation between TD values and project- or process-level metrics.
Preventing a Cryptoapocalypse: From mathematics to circuits for postquantum cryptography

Communication over the Internet is an integral part of the connected world. As the Internet is a public network, the data packets exchanged by the communicating parties pass through various insecure channels and untrusted servers before they reach their destinations. Still, we feel it is safe to send e-mails, visit social media websites, watch movies of our own preferences online, and shop online using credit cards. Cryptography or encryption makes it possible to protect our private information in the presence of third parties. When we browse a secured website, we see a lock symbol on the browser; it means our communication with the website is encrypted, and hence, no third party will be able to read the data packets exchanged between the browser and the website. Hypertext Transfer Protocol Secure (HTTPS) is used to establish an encrypted, hence secured communication channel between a web browser and a website. The browser website is just one example; cryptography is happening in your mobile phones, smart cards, IoT devices, and in almost all connected devices.
TCNet: A Novel Triple-Cooperative Network for Video Object Detection

Video object detection aims at accurately localizing the objects in videos and correctly recognizing their categories. Off-the-shelf video object detection methods have made some progress in recent years but they still suffer from the problems of inaccurate object localization, incorrect object recognition or insufficient relation learning, resulting in limited detection performance. In this paper, we propose a novel triple-cooperative network (TCNet) for high-performance video object detection, with three substantial improvements to ameliorate the problems of existing methods. First, we develop a context-aware proposal refinement module to generate high-quality proposals, enabling our TCNet to achieve more accurate object localization. Second, we present a similarity-aware semantic distillation module that innovatively leverages the semantic knowledge of class labels as additional supervisory signals to enhance the object recognition ability of our TCNet. Third, we design a structure-aware relation learning module to effectively model the structural relations between features with an adaptive-pruning residual graph convolutional network, making our TCNet perform more effective feature aggregation. We conduct extensive experiments on the challenging ImageNet VID dataset and the experimental results demonstrate that our TCNet outperforms current state-of-the-art methods. More remarkably, our TCNet achieves 85.2% mAP and 86.3% mAP with ResNet-101 and ResNeXt-101, respectively.
A Universal Low Complexity Compression Algorithm for Sparse Marked Graphs

Many modern applications involve accessing and processing graphical data, i.e. data that is naturally indexed by graphs.Examples come from internet graphs, social networks, genomics and proteomics, and other sources.The typically large size of such data motivates seeking efficient ways for its compression and decompression.The current compression methods are usually tailored to specific models, or do not provide theoretical guarantees.In this paper, we introduce a low-complexity lossless compression algorithm for sparse marked graphs, i.e. graphical data indexed by sparse graphs, which is capable of universally achieving the optimal compression rate defined on a per-node basis.The time and memory complexities of our compression and decompression algorithms are optimal within logarithmic factors.In order to define universality we employ the framework of local weak convergence, which allows one to make sense of a notion of stochastic processes for sparse graphs.Moreover, we investigate the performance of our algorithm through some experimental results on both synthetic and real-world data.
High Performance Time Series Anomaly Detection Using Brain Inspired Cortical Coding Method

Accurate and automated anomaly detection in time series data sets has an increasingly important role in a wide range of applications. Inspired by coding in the cortical networks of the brain, here we introduce a novel approach for high performance real-time anomaly detection. Cortical coding method is adaptive and dynamic, consisting of self-organized networks. In the cortical coding network introduced herein, the morphological structuring is driven by a brain inspired feature extraction strategy that aims the minimization of the signal energy dissipation while increasing the information entropy of the system. We combine the cortical coding network with transform coding and multi resolution analysis for anomaly detection. As we demonstrate here, the new coding methodology provides high computational efficiency in addition to scalability with respect to target accuracy compared to the traditional clustering algorithms. A wide variety of data sets are used to demonstrate time series anomaly detection performance. In a preliminary work presented here, we detected 77.6% of the present anomalies correctly, using the same hyperparameters for every stage of the method. The results are compared with several clustering algorithms such as K-means and its variants mini-batch K-means, sequential K-means and finally with hierarchical agglomerative clustering. Additionally, the performance of all the clustering methods are compared by memorizing all input data set without performing any clustering. The cortical coding method has shown the best performance compared to the other methods. From the results achieved so far, it appears that there is still a significant room for improvement of the success rate by, specifically, performing hyperparameter and filter optimization according to the characteristics of data sets and using a more advanced fusion model at the output layer. Low time and space complexity, high generalization performance, suitability to real-time anomaly detection, and in-memory processing compatibility are distinct advantages of the cortical coding method in a variety of anomaly detection problems, such as predictive maintenance, cybersecurity, telemedicine, risk management, and transportation safety.
Magma: Robust and Flexible Multi-Party Payment Channel

The lack of scalability is a leading issue of blockchain. By transferring transactions to off-chain, 2-party payment channels achieve instant transaction confirmation between channel users and enhance the blockchain throughput, thereby becoming a promising solution. By extending the channel from 2-party to multi-party, richer application scenarios could be supported. Meanwhile, new and exclusive requirements emerge in the multi-party off-chain payments, including robustness and flexibility. The robustness requires that the channel operation would not be impeded by any uncooperative channel member, and the flexibility guarantees that parties could join or exit the channel dynamically. However, all the current attempts either fail to achieve the new emerging properties or sacrifice some merits of 2-party channels. In this paper, we propose a new multi-party channel construction, Magma, which has good scalability. Magma outperforms the previous solutions to the multi-party payment channel for the following reasons. By canceling the heavy reliance on the cooperation of all channel members when implementing the channel operation, Magma achieves robustness. Magma also allows parties to join or exit one channel flexibly, without violating the balance security. Meanwhile, Magma's whole transaction process is performed off-chain, thereby inheriting the instant confirmation and low-cost features of 2-party channels. To guarantee the security of Magma, we formalize the multi-party channel's functionality and prove that Magma is secure in the UC framework. Moreover, our implementation and comparison show that Magma is practical and performs better than existing solutions in providing off-chain payment services.
Binarized Neural Network With Parameterized Weight Clipping and Quantization Gap Minimization for Online Knowledge Distillation

As the applications for artificial intelligence are growing rapidly, numerous network compression algorithms have been developed to restrict computing resources such as smartphones, edge, and IoT devices. Knowledge distillation (KD) leverages soft labels derived from a teacher model to a less parameterized model achieving high accuracy with reduced computational burden. Moreover, online KD provides parallel computing through collaborative learning between teacher and student networks, thus enhancing the training speed. A binarized neural network (BNN) offers an intriguing opportunity to facilitate aggressive compression at the expense of drastically degraded accuracy. In this study, two performance improvements are proposed for online KD when a BNN is applied as a student network: 1) parameterized weight clipping (PWC) to reduce dead weights in the student network and 2) quantization gap-aware adaptive temperature scheduling between the teacher and student networks. In contrast to constant weight clipping (CWC), PWC demonstrates a 3.78% top-1 test accuracy enhancement with trainable weight clipping by decreasing the gradient mismatch with CIFAR-10 dataset. Furthermore, the quantization gap-aware temperature scheduling increases the top-1 test accuracy by 0.08% over online KD at a constant temperature. By aggregating both methodologies, the top-1 test accuracy for CIFAR-10 dataset was 94.60%, and that for Tiny-ImageNet dataset was comparable to that of the 32-bit full-precision neural network.