Reviewerly/exHarmony huggingface dataset:
One-shot multi-object tracking using CNN-based networks with spatial-channel attention mechanism

Deep learning algorithms for multi-object tracking have made great progress and have powered the emergence of state-of-the-art models to address multi-object tracking problems. Though a lot of efforts have been made, false detections (named "FP") and missed detections (named "FN") caused by inaccurate tracking still cannot be well addressed especially in extremely crowded driving situations. To address these problems, we develop a new online one-shot multi-object tracking system based on convolutional neural networks with spatial-channel attention mechanism. Firstly, we propose a feature combination module (FCM) that uses dilated convolution to obtain different receptive fields to adapt to the deformation of the targets, instead of introducing a large number of parameters to deal with the problem of target scale transformation like the recent feature pyramid network. Then, an attention mechanism network (AM-Net) is designed to allow the model to dynamically focus on certain parts of the input that help perform the task and ignore irrelevant information. Finally, we introduce a combination of triple loss and online instance matching loss (TOIM Loss) to distinguish similar instances within a class. Our proposed method is evaluated in three commonly used multi-object tracking datasets including 2DMOT15, MOT16 and MOT20. The results show that our proposed method is generally superior to the compared models.
Workload Time Series Cumulative Prediction Mechanism for Cloud Resources Using Neural Machine Translation Technique

Abstract Dynamic resource allocation and auto-scaling represent effective solutions for many cloud challenges, such as over-provisioning (i.e., energy-wasting, and Service level Agreement “SLA” violation) and under-provisioning (i.e., Quality of Service “QoS” dropping) of resources. Early workload prediction techniques play an important role in the success of these solutions. Unfortunately, no prediction technique is perfect and suitable enough for most workloads, particularly in cloud environments. Statistical and machine learning techniques may not be appropriate for predicting workloads, due to instability and dependency of cloud resources’ workloads. Although Recurrent Neural Network (RNN) deep learning technique considers these shortcomings, it provides poor results for long-term prediction. On the other hand, Sequence-to-Sequence neural machine translation technique (Seq2Seq) is effectively used for translating long texts. In this paper, workload sequence prediction is treated as a translation problem. Therefore, an Attention Seq2Seq-based technique is proposed for predicting cloud resources’ workloads. To validate the proposed technique, real-world dataset collected from a Google cluster of 11 k machines is used. For improving the performance of the proposed technique, a novel procedure called cumulative-validation is proposed as an alternative procedure to cross-validation. Results show the effectiveness of the proposed technique for predicting workloads of cloud resources in terms of accuracy by 98.1% compared to 91% and 85% for other sequence-based techniques, i.e. Continuous Time Markov Chain based models and Long short-term memory based models, respectively. Also, the proposed cumulative-validation procedure achieves a computational time superiority of 57% less compared to the cross-validation with a slight variation of 0.006 in prediction accuracy.
Circuit Routing Using Monte Carlo Tree Search and Deep Reinforcement Learning

We propose a new approach to circuit routing by modeling it as a sequential decision problem and solving it in MCTS with DRL-guided rollout. Compared with conventional routing algorithms that are either manually designed with domain knowledge or tailored to specific design rules, our approach can be reconfigured for nearly any routing constraints and goals without changing the algorithm itself because the AI agent explores solutions in a general search strategy. Experimental results on both randomly generated circuits and popular open-source hardware projects show that our method achieves 33.3% higher success rate than traditional A *-based approach.
DRL-Based Improvement for Autonomous UAV Motion Path Planning in Unknown Environments

In this paper, we proposed an empirical playback-based deep reinforcement learning (DRL) approach to solve the problems related to autonomous path planning for unmanned aerial vehicles (UAVs), which is different from the traditional DRL-Deep Q Network (DQN). To improve the learning efficiency, we also proposed a step reward scheme to replace the traditional simple reward function and introduce it into the experience replay-based DRL-DQN approach in this paper to construct a new experience replay-based DRL-DQN. The results show that our scheme has a significant improvement in performance compared with the traditional scheme, and also makes a significant improvement in the adaptation of the UAV to the dynamic environment.
Cloud Computing Security and Challenges: Issues, Threats, and Solutions

Cloud Computing has become a point of interest to institutions, companies, and individuals because of the many advantages it provides, and its low-cost infrastructure, in addition to the ease of managing it and accessing it remotely anywhere there is an Internet connection that it has become a great innovation in the information technology. With the rapid evolution of cloud computing, information security concerns have emerged that hinder the evolution of cloud computing and need a solution, as security has become the main challenge of cloud computing. This paper will focus on cloud computing security, challenges, issues, threats, and solutions.
Barriers for Prosumers’ Open Business Models: A Resource-Based View on Assets and Data-Sharing in Electricity Markets

This article explores the barriers for open business models in support of sustainability in electricity markets. It puts forward privacy and data protection concerns about sharing prosumers’ physical assets as well as data due to to their critical role in decentralized modes of electricity/flexibility trading. In particular, it uses a multiple case study approach to identify actors’ resources, examine other interested actors in each resource, define their objectives, and consider privacy and data protection concerns of sharing prosumers’ physical assets and data. The findings yield new insights into sharing opportunities beyond electricity/flexibility trading. In doing so, our study contributes to theories of the firm by applying the resource-based view in a new context and to the business model literature by shedding light on barriers in applying open business models.
Recommendations for machine learning benchmarks in neuroimaging

The field of neuroimaging has embraced methods from machine learning in a variety of ways. Although an increasing number of initiatives have published open-access neuroimaging datasets, specifically designed benchmarks are rare in the field. In this article, we first describe how benchmarks in computer science and biomedical imaging have fostered methodological progress in machine learning. Second, we identify the special characteristics of neuroimaging data and outline what researchers have to ensure when establishing a neuroimaging benchmark, how datasets should be composed and how adequate evaluation criteria can be chosen. Based on lessons learned from machine learning benchmarks, we argue for an extended evaluation procedure that, next to applying suitable performance metrics, focuses on scientifically relevant aspects such as explainability, robustness, uncertainty, computational efficiency and code quality. Lastly, we envision a collaborative neuroimaging benchmarking platform that combines the discussed aspects in a collaborative and agile framework, allowing researchers across disciplines to work together on the key predictive problems of the field of neuroimaging and psychiatry.
Self-weighted learning framework for adaptive locality discriminant analysis

Linear discriminant analysis (LDA) is one of the most important dimensionality reduction techniques and applied in many areas. However, traditional LDA algorithms aim to capture the global structure from data and ignore the local information. That may lead to the failure of LDA in some real-world datasets which have a complex geometry distribution. Although there are many previous works that focus on preserving the local information, they are all stuck in the same problem that the neighbor relationships of pairwise data points obtained from the original space may not be reliable, especially in the case of heavy noise. Therefore, we proposed a novel self-weighted learning framework, named Self-Weighted Adaptive Locality Discriminant Analysis (SALDA), for locality-aware based dimensionality reduction. The proposed framework can adaptively learn an intrinsic low-dimensional subspace, so that we can explore the better neighbor relationships for samples under the ideal subspace. In addition, our model can automatically learn to assign the weights to data pairwise points within the same class and takes no extra parameters compared to other classical locality-aware methods. At last, the experimental results on both synthetic and real-world benchmark datasets demonstrate the effectiveness and superiority of the proposed algorithm.
Federated Learning: A Survey of a New Approach to Machine Learning

Massive clients can use large-scale machine learning using federated learning without revealing their raw data to the outside world. It's capable of preserving client personal information while also achieving great learning performance for the client's benefit. Existing research on federated learning is primarily concerned with increasing learning efficiency and model accuracy. But in reality, customers are unwilling to take part in the learning process unless they are compensated for their time and effort consequently, it is critical to figure out how to get customers involved in federated learning by motivating them successfully. Other areas like crowdsourcing, cloud computing, smart grid, etc. are simpler than designing an incentive structure for federated learning. To begin, it's impossible to determine the exact worth of the training data collected from each individual client. Second, different federated learning algorithms' learning performance is challenging to model. This work examines the design of a federated learning incentive system. Before we evaluate and contrast different strategies, we provide taxonomy of existing federated learning incentive mechanisms. There have also been some innovative ideas for enticing customers to take part in federated learning.
Sentiment Analysis of User-Generated Reviews of Women Safety Mobile Applications

Google play store is an application store from where people get various kinds of applications for android certified devices which makes life a lot easier and faster through the diverse functionalities the apps contain. Numerous users are using applications as per their needs and putting their experience, thoughts of using that application via reviews in form of ratings and texts. As the safety of women is threatened, whether applications like women's safety apps are appreciated, can be detected through text reviews and ratings by the users. This paper analyzes the positive, negative, neutral polarity of the sentences or text reviews that are given by the users of the women's safety app through the google play store. To detect the emotions of the users through the given text reviews and star ratings, the machine learning (ML) algorithms using natural language processing (NLP) are conducted to analyze the sentiments of the review given by the users. For this study, the data was collected from the app reviews and star ratings provided by the users of the women's safety related applications whose main purpose is to provide necessary functionality that can keep women safe in any dangerous and unwanted situation. The purpose of this paper is to mine the opinion of the users and get their viewpoint about those apps of specific polarity levels. As the current user's ratings, reviews, or their viewpoint helps the new user understand the performance of the applications and insights in advance, so the mining of their opinion is helpful for both parties - developers and general users. To detect the level of the sentiment, several machine learning algorithms were applied, namely Multinomial Naive Bayes (MNB), Logistic Regression (LR), Support Vector Machine (SVM), and k-nearest neighbor (k-NN). Among these algorithms, the MNB has outperformed all other algorithms in terms of accuracy (85.42%).
Application of an Improved TF-IDF Method in Literary Text Classification

Literature is extremely important in the advancement of human civilization. Every day, many literary texts of various genres are produced, dating back to ancient times. An urgent concern for managers in the current literary activity is how to classify and save the expanding mass of literary text data for easy access by readers. In the realm of text classification, the TF-IDF algorithm is a widely used classification algorithm. However, there are significant issues with utilizing this approach, including a lack of distribution information inside categories, a lack of distribution information between categories, and an inability to adjust to skewed datasets. It is possible to improve classification accuracy by using the TF-IDF algorithm in this paper’s application situation by exploiting the association between feature words and the quantity of texts in which they appear, while ignoring the variation in feature word distribution across categories. With the purpose of classifying the literary texts in this study, this work proposes an improved IDF method for the problem of feature words appearing several times and having diverse meanings in different fields. The meanings of feature words in distinct domains are separated to increase the trust in the TF-IDF algorithm’s output. Using the improved TF-IDF method suggested in this research with the random forest (RF) classifier, the experimental results show that the classifier has a good classification impact, which can meet the actual work needs, based on comparative experiments on feature dimension selection, feature selection algorithm, feature weight algorithm, and classifier. It has a fair amount of historical significance.
A Novel Patient Similarity Network (PSN) Framework Based on Multi-Model Deep Learning for Precision Medicine

Precision medicine can be defined as the comparison of a new patient with existing patients that have similar characteristics and can be referred to as patient similarity. Several deep learning models have been used to build and apply patient similarity networks (PSNs). However, the challenges related to data heterogeneity and dimensionality make it difficult to use a single model to reduce data dimensionality and capture the features of diverse data types. In this paper, we propose a multi-model PSN that considers heterogeneous static and dynamic data. The combination of deep learning models and PSN allows ample clinical evidence and information extraction against which similar patients can be compared. We use the bidirectional encoder representations from transformers (BERT) to analyze the contextual data and generate word embedding, where semantic features are captured using a convolutional neural network (CNN). Dynamic data are analyzed using a long-short-term-memory (LSTM)-based autoencoder, which reduces data dimensionality and preserves the temporal features of the data. We propose a data fusion approach combining temporal and clinical narrative data to estimate patient similarity. The experiments we conducted proved that our model provides a higher classification accuracy in determining various patient health outcomes when compared with other traditional classification algorithms.
A note on the Assmus–Mattson theorem for some binary codes

We previously proposed the first nontrivial examples of a code having support t-designs for all weights obtained from the Assmus–Mattson theorem and having support $$t'$$ -designs for some weights with some $$t'>t$$ . This suggests the possibility of generalizing the Assmus–Mattson theorem, which is very important in design and coding theory. In the present paper, we generalize this example as a strengthening of the Assmus–Mattson theorem along this direction. As a corollary, we provide a new characterization of the extended Golay code $${\mathcal {G}}_{24}$$ .
Fully extracting feature correlation between and within stages for semantic segmentation

Feature correlation plays a key role in improving accuracy of semantic segmentation. To fully extract correlation information, we design several special structures to propose a Multi-scale Feature Attention Network (MFANet). For the sake of capturing internal correlation within the encoding stage, we design a Channel Attention Module with two inputs (CAM2). The two inputs usually have different resolutions, and they can be from any two layers of the encoding backbone network. To obtain attention features, we first design a Mixed Pooling module (MP), in which a global pooling path generates attention coefficients to weight the output of an adaptive pooling path. Then we connect several MPs with different scales in parallel to propose a Multi-scale Mixed Pooling module (MMP) for efficiently aggregating multi-scale feature maps. To extract external correlation between encoding and decoding stages, we propose a Feature Attention Fusion (FAF) module in a manner of attention mechanism for fusing features from different stages. In FAF, the feature map from the encoding stage is squeezed and excited to generate attention coefficients for weighting all channels of the feature map from the decoding stage. All the proposed modules use a way of attention mechanism to extract feature correlation between different stages and within the same stage. Extensive experiments on several public datasets show that our method achieves excellent results and significantly outperforms state-of-the-art methods.
A survey of structural representation learning for social networks

Social networks have a plethora of applications, and analysis of these applications has been gaining much interest from the research community. The high dimensionality of social network data poses a significant obstacle in its analysis, leading to the curse of dimensionality. The mushrooming of representation learning in various research fields facilitates network representation learning (also called network embedding), which will help us address the above-mentioned issue. Structural Representation Learning aims to learn low-dimensional vector representations of high-dimensional network data, allowing maximal preservation of network structural information. This representation can then serve as a backbone for various network-based applications. First, we investigate the techniques used in network representation learning and similarity indices. We then categorize the representative algorithms into three types based on the network structural level used in their learning process. We also introduce algorithms for representation learning of edges, subgraphs, and the whole network. Finally, we introduce the evaluation metrics and the applications of network representation learning and promising future research directions.
Deep Convolutional Neural Networks for Multiclass Cervical Cell Classification

Cervical intraepithelial neoplasia (CIN) is a major problem women face worldwide. The classic Pap smear analysis (Papanicolaou) is a suitable method for assessing cell images to diagnose cervical disorders. Many computer vision algorithms may be utilized to identify the cancerous and non-cancerous pap smear cell images. The majority of existing research focuses on binary classification techniques that use different methods. However, they have intrinsic difficulties with the excision of minor features and exact categorization. We propose a novel approach for performing multiclass classification of cervical cells with optimal feature extraction, minimal parameters, and less computing power than competing models. The implementation of ConvNet with the Transfer Learning approach validates significant cancer cell diagnosis. The suggested binary and multiclass classification techniques obtained 99.3&#x0025; and 97.3&#x0025; accuracy results, respectively, on the dataset.
Interpretable Visual Reasoning via Induced Symbolic Space

We study the problem of concept induction in visual reasoning, i.e., identifying concepts and their hierarchical relationships from question-answer pairs associated with images; and achieve an interpretable model via working on the induced symbolic concept space. To this end, we first design a new framework named object-centric compositional attention model (OCCAM) to perform the visual reasoning task with object-level visual features. Then, we come up with a method to induce concepts of objects and relations using clues from the attention patterns between objects’ visual features and question words. Finally, we achieve a higher level of interpretability by imposing OCCAM on the objects represented in the induced symbolic concept space. Experiments on the CLEVR and GQA datasets demonstrate: 1) our OCCAM achieves a new state of the art without human-annotated functional programs; 2) our induced concepts are both accurate and sufficient as OCCAM achieves an on-par performance on objects represented either in visual features or in the induced symbolic concept space.
Firefly Algorithms

The firefly algorithm (FA) is a swarm intelligence based algorithm for optimization. FA is a nonlinear system that uses attraction to evolve its multi-agent system. This chapter introduces the basic ideas of FA and its variants. A demo implementation is also provided.
Weakly Supervised Representation Learning with Coarse Labels

With the development of computational power and techniques for data collection, deep learning demonstrates a superior performance over most existing algorithms on visual benchmark data sets. Many efforts have been devoted to studying the mechanism of deep learning. One important observation is that deep learning can learn the discriminative patterns from raw materials directly in a task-dependent manner. Therefore, the representations obtained by deep learning outperform hand-crafted features significantly. However, for some real-world applications, it is too expensive to collect the task-specific labels, such as visual search in online shopping. Compared to the limited availability of these task-specific labels, their coarse-class labels are much more affordable, but representations learned from them can be suboptimal for the target task. To mitigate this challenge, we propose an algorithm to learn the fine-grained patterns for the target task, when only its coarse-class labels are available. More importantly, we provide a theoretical guarantee for this. Extensive experiments on real-world data sets demonstrate that the proposed method can significantly improve the performance of learned representations on the target task, when only coarse-class information is available for training.