Reviewerly/exHarmony huggingface dataset:
Meta-learning on Heterogeneous Information Networks for Cold-start Recommendation

Cold-start recommendation has been a challenging problem due to sparse user-item interactions for new users or items. Existing efforts have alleviated the cold-start issue to some extent, most of which approach the problem at the data level. Earlier methods often incorporate auxiliary data as user or item features, while more recent methods leverage heterogeneous information networks (HIN) to capture richer semantics via higher-order graph structures. On the other hand, recent meta-learning paradigm sheds light on addressing cold-start recommendation at the model level, given its ability to rapidly adapt to new tasks with scarce labeled data, or in the context of cold-start recommendation, new users and items with very few interactions. Thus, we are inspired to develop a novel meta-learning approach named MetaHIN to address cold-start recommendation on HINs, to exploit the power of meta-learning at the model level and HINs at the data level simultaneously. The solution is non-trivial, for how to capture HIN-based semantics in the meta-learning setting, and how to learn the general knowledge that can be easily adapted to multifaceted semantics, remain open questions. In MetaHIN, we propose a novel semantic-enhanced tasks constructor and a co-adaptation meta-learner to address the two questions. Extensive experiments demonstrate that MetaHIN significantly outperforms the state of the arts in various cold-start scenarios. (Code and dataset are available at https://github.com/rootlu/MetaHIN.)
Using VADER sentiment and SVM for predicting customer response sentiment

Customer support is important to corporate operations, which involves dealing with disgruntled customer and content customers that can have different requirements. As such, it is important to quickly extract the sentiment of support errands. In this study we investigate sentiment analysis in customer support for a large Swedish Telecom corporation. The data set consists of 168,010 e-mails divided into 69,900 conversation threads without any sentiment information available. Therefore, VADER sentiment is used together with a Swedish sentiment lexicon in order to provide initial labeling of the e-mails. The e-mail content and sentiment labels are then used to train two Support Vector Machine models in extracting/classifying the sentiment of e-mails. Further, the ability to predict sentiment of not-yet-seen e-mail responses is investigated. Experimental results show that the LinearSVM model was able to extract sentiment with a mean F1-score of 0.834 and mean AUC of 0.896. Moreover, the LinearSVM algorithm was also able to predict the sentiment of an e-mail one step ahead in the thread (based on the text in the an already sent e-mail) with a mean F1-score of 0.688 and the mean AUC of 0.805. The results indicate a predictable pattern in e-mail conversation that enables predicting the sentiment of a not-yet-seen e-mail. This can be used e.g. to prepare particular actions for customers that are likely to have a negative response. It can also provide feedback on possible sentiment reactions to customer support e-mails.
Benchmark Analysis of Jetson TX2, Jetson Nano and Raspberry PI using Deep-CNN

Hardware, low power consumption, high accuracy and performance are crucial factors for deep learning applications. High level graphics processing units (GPU) are commonly used in high performance deep learning applications. However, it is a lot in terms of cost and power consumption to build a high-performance platform. In this study, performances of single-board computers in NVIDIA Jetson Nano, NVIDIA Jetson TX2 and Raspberry PI4 through CNN algorithm created by using fashion product images dataset are compared. 2D CNN model has been developed so as to classify 13 different fashion products in tests. Data set is comprised of 45K pictures. Parameters for performance analysis has been defined as consumption (GPU, CPU, RAM, Power), accuracy and cost. Data set is divided into parts of 5K, 10K, 20K, 30K and 45K in training and test of the model in order to expand on the differences of single-board computers. Eventually, performance of the embedded system boards in different data set in CNN algorithm is analyzed. It is, thus, aimed to attain high accuracy preference by minimum hardware requirements in deep learning applications.
A Deep Learning-based Cryptocurrency Price Prediction Scheme for Financial Institutions

A cryptocurrency is a network-based digital exchange medium, where the records are secured using strong cryptographic algorithms such as Secure Hash Algorithm 2 (SHA-2) and Message Digest 5 (MD5). It uses blockchain technology to make the transactions secure, transparent, traceable, and immutable. Due to these properties, the cryptocurrencies have gained popularity in almost all the sectors especially in financial sectors. Though, cryptocurrencies are getting recognition form the approval bodies, but still, the uncertainty and dynamism in their prices risk the investments substantially. Cryptocurrency price prediction has become a trending research topic globally. Many machine learning and deep learning algorithms such as Gated Recurrent Unit (GRU), Neural Networks (NN), and Long short-term memory (LSTM) have been used by the researchers to predict and analyze the factors affecting the cryptocurrency prices. In this paper, a LSTM and GRU-based hybrid cryptocurrency prediction scheme is proposed, which focuses on only two cryptocurrencies, namely Litecoin and Monero. The results depict that the proposed scheme accurately predicts the prices with high accuracy, revealing that the scheme can be applicable in various cryptocurrencies price predictions.
DenseNet-201-Based Deep Neural Network with Composite Learning Factor and Precomputation for Multiple Sclerosis Classification

( Aim ) Multiple sclerosis is a neurological condition that may cause neurologic disability. Convolutional neural network can achieve good results, but tuning hyperparameters of CNN needs expert knowledge and are difficult and time-consuming. To identify multiple sclerosis more accurately, this article proposed a new transfer-learning-based approach. ( Method ) DenseNet-121, DenseNet-169, and DenseNet-201 neural networks were compared. In addition, we proposed the use of a composite learning factor (CLF) that assigns different learning factor to three types of layers: early frozen layers, middle layers, and late replaced layers. How to allocate layers into those three layers remains a problem. Hence, four transfer learning settings (viz., Settings A, B, C, and D) were tested and compared. A precomputation method was utilized to reduce the storage burden and accelerate the program. ( Results ) We observed that DenseNet-201-D (the layers from CP to T3 are frozen, the layers of D4 are updated with learning factor of 1, and the final new layers of FCL are randomly initialized with learning factor of 10) can achieve the best performance. The sensitivity, specificity, and accuracy of DenseNet-201-D was 98.27± 0.58, 98.35± 0.69, and 98.31± 0.53, respectively. ( Conclusion ) Our method gives better performances than state-of-the-art approaches. Furthermore, this composite learning rate gives superior results to traditional simple learning factor (SLF) strategy.
SpAGNN: Spatially-Aware Graph Neural Networks for Relational Behavior Forecasting from Sensor Data

In this paper, we tackle the problem of relational behavior forecasting from sensor data. Towards this goal, we propose a novel spatially-aware graph neural network (SpAGNN) that models the interactions between agents in the scene. Specifically, we exploit a convolutional neural network to detect the actors and compute their initial states. A graph neural network then iteratively updates the actor states via a message passing process. Inspired by Gaussian belief propagation, we design the messages to be spatially-transformed parameters of the output distributions from neighboring agents. Our model is fully differentiable, thus enabling end-to-end training. Importantly, our probabilistic predictions can model uncertainty at the trajectory level. We demonstrate the effectiveness of our approach by achieving significant improvements over the state-of-the-art on two real-world self-driving datasets: ATG4D and nuScenes.
Byzantine-Resilient Secure Federated Learning

Secure federated learning is a privacy-preserving framework to improve machine learning models by training over large volumes of data collected by mobile users. This is achieved through an iterative process where, at each iteration, users update a global model using their local datasets. Each user then masks its local update via random keys, and the masked models are aggregated at a central server to compute the global model for the next iteration. As the local updates are protected by random masks, the server cannot observe their true values. This presents a major challenge for the resilience of the model against adversarial (Byzantine) users, who can manipulate the global model by modifying their local updates or datasets. Towards addressing this challenge, this paper presents the first single-server Byzantine-resilient secure aggregation framework (BREA) for secure federated learning. BREA is based on an integrated stochastic quantization, verifiable outlier detection, and secure model aggregation approach to guarantee Byzantine-resilience, privacy, and convergence simultaneously. We provide theoretical convergence and privacy guarantees and characterize the fundamental trade-offs in terms of the network size, user dropouts, and privacy protection. Our experiments demonstrate convergence in the presence of Byzantine users, and comparable accuracy to conventional federated learning benchmarks.
Terahertz pulse shaping using diffractive surfaces

Recent advances in deep learning have been providing non-intuitive solutions to various inverse problems in optics. At the intersection of machine learning and optics, diffractive networks merge wave-optics with deep learning to design task-specific elements to all-optically perform various tasks such as object classification and machine vision. Here, we present a diffractive network, which is used to shape an arbitrary broadband pulse into a desired optical waveform, forming a compact pulse engineering system. We experimentally demonstrate the synthesis of square pulses with different temporal-widths by manufacturing passive diffractive layers that collectively control both the spectral amplitude and the phase of an input terahertz pulse. Our results constitute the first demonstration of direct pulse shaping in terahertz spectrum, where a complex-valued spectral modulation function directly acts on terahertz frequencies. Furthermore, a Lego-like physical transfer learning approach is presented to illustrate pulse-width tunability by replacing part of an existing network with newly trained diffractive layers, demonstrating its modularity. This learning-based diffractive pulse engineering framework can find broad applications in e.g., communications, ultra-fast imaging and spectroscopy.
A survey on data‐efficient algorithms in big data era

Abstract The leading approaches in Machine Learning are notoriously data-hungry. Unfortunately, many application domains do not have access to big data because acquiring data involves a process that is expensive or time-consuming. This has triggered a serious debate in both the industrial and academic communities calling for more data-efficient models that harness the power of artificial learners while achieving good results with less training data and in particular less human supervision. In light of this debate, this work investigates the issue of algorithms’ data hungriness. First, it surveys the issue from different perspectives. Then, it presents a comprehensive review of existing data-efficient methods and systematizes them into four categories. Specifically, the survey covers solution strategies that handle data-efficiency by (i) using non-supervised algorithms that are, by nature, more data-efficient, by (ii) creating artificially more data, by (iii) transferring knowledge from rich-data domains into poor-data domains, or by (iv) altering data-hungry algorithms to reduce their dependency upon the amount of samples, in a way they can perform well in small samples regime. Each strategy is extensively reviewed and discussed. In addition, the emphasis is put on how the four strategies interplay with each other in order to motivate exploration of more robust and data-efficient algorithms. Finally, the survey delineates the limitations, discusses research challenges, and suggests future opportunities to advance the research on data-efficiency in machine learning.
Resampling imbalanced data for network intrusion detection datasets

Abstract Machine learning plays an increasingly significant role in the building of Network Intrusion Detection Systems. However, machine learning models trained with imbalanced cybersecurity data cannot recognize minority data, hence attacks, effectively. One way to address this issue is to use resampling, which adjusts the ratio between the different classes, making the data more balanced. This research looks at resampling’s influence on the performance of Artificial Neural Network multi-class classifiers. The resampling methods, random undersampling, random oversampling, random undersampling and random oversampling, random undersampling with Synthetic Minority Oversampling Technique, and random undersampling with Adaptive Synthetic Sampling Method were used on benchmark Cybersecurity datasets, KDD99, UNSW-NB15, UNSW-NB17 and UNSW-NB18. Macro precision, macro recall, macro F1-score were used to evaluate the results. The patterns found were: First, oversampling increases the training time and undersampling decreases the training time; second, if the data is extremely imbalanced, both oversampling and undersampling increase recall significantly; third, if the data is not extremely imbalanced, resampling will not have much of an impact; fourth, with resampling, mostly oversampling, more of the minority data (attacks) were detected.
DeepCervix: A deep learning-based framework for the classification of cervical cells using hybrid deep feature fusion techniques

Cervical cancer, one of the most common fatal cancers among women, can be prevented by regular screening to detect any precancerous lesions at early stages and treat them. Pap smear test is a widely performed screening technique for early detection of cervical cancer, whereas this manual screening method suffers from high false-positive results because of human errors. To improve the manual screening practice, machine learning (ML) and deep learning (DL) based computer-aided diagnostic (CAD) systems have been investigated widely to classify cervical Pap cells. Most of the existing studies require pre-segmented images to obtain good classification results. In contrast, accurate cervical cell segmentation is challenging because of cell clustering. Some studies rely on handcrafted features, which cannot guarantee the classification stage's optimality. Moreover, DL provides poor performance for a multiclass classification task when there is an uneven distribution of data, which is prevalent in the cervical cell dataset. This investigation has addressed those limitations by proposing DeepCervix, a hybrid deep feature fusion (HDFF) technique based on DL, to classify the cervical cells accurately. Our proposed method uses various DL models to capture more potential information to enhance classification performance. Our proposed HDFF method is tested on the publicly available SIPaKMeD dataset and compared the performance with base DL models and the late fusion (LF) method. For the SIPaKMeD dataset, we have obtained the state-of-the-art classification accuracy of 99.85%, 99.38%, and 99.14% for 2-class, 3-class, and 5-class classification. This method is also tested on the Herlev dataset and achieves an accuracy of 98.32% for 2-class and 90.32% for 7-class classification. The source code of the DeepCervix model is available at: https://github.com/Mamunur-20/DeepCervix.
Deep learning based forecasting of photovoltaic power generation by incorporating domain knowledge

Solar energy constitutes an effective supplement to traditional energy sources. However, photovoltaic power generation (PVPG) is strongly weather-dependent, and thus highly intermittent. High-precision forecasting of PVPG forms the basis of the production, transmission, and distribution of electricity, ensuring the stability and reliability of power systems. In this work, we propose a deep learning based framework for accurate PVPG forecasting. In particular, taking advantage of the long short-term memory (LSTM) network in solving sequential-data based regression problems, this paper considers the specific domain knowledge of PV and proposes a physics-constrained LSTM (PC-LSTM) to forecast the hourly day-ahead PVPG. It aims to overcome the shortcoming of recent machine learning algorithms that are applied based only on massive data, and thus easily producing unreasonable forecasts. Real-life PV datasets are adopted to evaluate the feasibility and effectiveness of the models. Sensitivity analysis is conducted for the selection of input feature variables based on a two-stage hybrid method. The results indicate that the proposed PC-LSTM model possesses stronger forecasting capability than the standard LSTM model. It is more robust against PVPG forecasting, and more suitable for PVPG forecasting with sparse data in practice. The PC-LSTM model also demonstrates superior performance with higher accuracy of PVPG forecasting compared to conventional machine learning and statistical methods.
Beyond Triplet Loss: Person Re-Identification With Fine-Grained Difference-Aware Pairwise Loss

Person Re-IDentification (ReID) aims at re-identifying persons from different viewpoints across multiple cameras. Capturing the fine-grained appearance differences is often the key to accurate person ReID, because many identities can be differentiated only when looking into these fine-grained differences. However, most state-of-the-art person ReID approaches, typically driven by a triplet loss, fail to effectively learn the fine-grained features as they are focused more on differentiating large appearance differences. To address this issue, we introduce a novel pairwise loss function that enables ReID models to learn the fine-grained features by adaptively enforcing an exponential penalization on the images of small differences and a bounded penalization on the images of large differences. The proposed loss is generic and can be used as a plugin to replace the triplet loss to significantly enhance different types of state-of-the-art approaches. Experimental results on four benchmark datasets show that the proposed loss substantially outperforms a number of popular loss functions by large margins; and it also enables significantly improved data efficiency.
The Performance Evaluation of Blockchain-Based Security and Privacy Systems for the Internet of Things: A Tutorial

This article presents research challenges and a tutorial on performance evaluation of blockchain-based security and privacy systems for the Internet of Things (IoT). We start by summarizing the existing surveys that deal with blockchain security for IoT networks. Then, we review the blockchain-based security and privacy systems for seventeen types of IoT applications, e.g., Industry 4.0, software-defined networking, edge computing, Internet of Drones, Internet of Cloud, Internet of Energy, Internet of Vehicles, etc. We also review various consensus algorithms and provide a comparison with respect to the nine properties, such as latency, throughput, computation, storage, and communication costs, scalability, attack model, advantage, disadvantage, etc. Moreover, we present the security analysis techniques and provide a classification into four categories, including Burrows, Abadi, and Needham (BAN) logic, game theory, theory analysis, and AVISPA tool. In addition, we analyze the performance metrics, blockchain testbeds, and cryptography libraries used in the performance evaluation of blockchain-based security and privacy systems for the IoT networks. Based on the current survey, we discuss the major steps to follow for building and evaluating blockchain-based security and privacy systems. Finally, we discuss and highlight open challenges and future research opportunities.
Distribution Alignment: A Unified Framework for Long-tail Visual Recognition

Despite the recent success of deep neural networks, it remains challenging to effectively model the long-tail class distribution in visual recognition tasks. To address this problem, we first investigate the performance bottleneck of the two-stage learning framework via ablative study. Motivated by our discovery, we propose a unified distribution alignment strategy for long-tail visual recognition. Specifically, we develop an adaptive calibration function that enables us to adjust the classification scores for each data point. We then introduce a generalized re-weight method in the two-stage learning to balance the class prior, which provides a flexible and unified solution to diverse scenarios in visual recognition tasks. We validate our method by extensive experiments on four tasks, including image classification, semantic segmentation, object detection, and instance segmentation. Our approach achieves the state-of-the-art results across all four recognition tasks with a simple and unified framework.
Implementation of solar energy in smart cities using an integration of artificial neural network, photovoltaic system and classical Delphi methods

Energy supply of megacities is considered as an active research topic in the new aspects of urban management, especially in developing countries like Iran. With an introduction to the sustainable development goals, the smart city concept presents a novel idea for providing energy in a city with the use of Artificial Intelligence (AI), renewable energy, such as Photovoltaic (PV) technologies, and Transformational Participation (TP) based on motivational programs for citizens. This study aims to evaluate the electrical energy consumption in Mashhad, Iran, based on machine learning tools and present the dynamic strategies for promoting citizens’ willingness for renewable energy generation based on the experts’ knowledge. The main novelty of this research is simultaneous application of Artificial Neural Network (ANN) and statistical analysis for creating a Decision Support System (DSS). Then, the solar energy potential is appraised by the PV system simulation tool during one year in our case study in Mashhad, Iran. Furthermore, a Classical Delphi (CD) method is applied for motivational strategies and further TP implementation. In particular, the motivational strategies are suggested by 45 experts and then are prioritized in sequential expert meetings. The outcomes of this research indicate that the ANN model can successfully forecast the electrical energy consumption in summer and winter periods with a 99% accuracy. Then, based on the solar energy computations in the PV system, the peak of electrical energy consumption can be controlled in the hottest and coldest months. Last but not least, the superposition of experts’ and citizens’ opinions reveal A4 (sharing benefits of optimized costs with the citizens by solar energy generation), B2 (reducing the electrical energy cost for solar energy generation, especially in peak times) and C1 (creating the energy coin in the city with credits instead of spending money in urban activities fits to solar energy generation) as the main motivational strategies for solar energy generation in short, middle and long-term planning horizons.