Reviewerly/exHarmony huggingface dataset:
DHAN: Encrypted JPEG image retrieval via DCT histograms-based attention networks

In image retrieval, the images to be retrieved are stored on remote servers. Since the images contain amounts of privacy information and the server cannot be fully trusted, people usually encrypt their images before uploading them to the server, which raises the demand for encrypted image retrieval (EIR). Current EIR techniques extract ruled hand-craft features from cipher images first and then build retrieval models (e.g., support vector machine, SVM) by these features, or use deep learning models (e.g., Convolutional Neural Network, CNN) to learn cipher-image representations in an end-to-end manner. However, SVM is not skilled at learning non-linear embedding in complex image database, and end-to-end EIR leads to low image security or retrieval performance because CNN is sensitive to extreme chaotic cipher images. Not-end-to-end EIR offers excellent encryption performance, and deep learning can further mine non-linear embedding from ruled hand-craft features. To this end, we propose a novel EIR scheme, named discrete cosine transform (DCT) Histograms-based Attention Networks (DHAN), which is based on deep learning to enhance expression ability of cipher-image in a not-end-to-end manner. Specifically, the DCT coefficients of images are encrypted by value replacement and block permutation encryption, and then the effective histogram features of DCT coefficients are extracted from the cipher images since the sets of DCT frequency in encrypted images are similar to that of plain images. After that, to dynamically learn the salient features of cipher images, we propose a new module named ResAttention and design deep attention networks to provide retrieval. Extensive experiments on two datasets demonstrate that DHAN not only provides high image security but also greatly improves retrieval performance than that of existing schemes.
How to realize the full potentials of artificial intelligence (AI) in digital economy? A literature review

Artificial intelligence (hereafter AI) is widely considered as a driving force in the current digital economy, with many firms having already invested in AI. Since AI is unconstrainted by humans' cognitive limitations and inflexibility, and thus a key assumption in popular press is that AI is crucial for firms' success in digital economy. However, surprisingly, many managers indicate they are yet to benefit from their AI investments. To address this issue, the main purpose of this paper is to summarize the extant literature on AI in business and management fields to identify how AI can create competitive advantages and underpin the key barriers that prevent AI from realizing its full potentials. Our results suggest AI can increase revenue by improving employee productivity, increasing consumer evaluation, setting competitive price and creating unique resources. AI can also reduce cost by improving efficiency and reducing risks. However, our results also indicate that AI adoption, task nature and AI management are the key barriers preventing AI from realizing its full potentials. This is because AI lacks interpersonal skills. Thus, we encourage future research to focus on improving AI's interpersonal skills.
Multi-Scale and spatial position-based channel attention network for crowd counting

Crowd counting algorithms have recently incorporated attention mechanisms into convolutional neural networks (CNNs) to achieve significant progress. The channel attention model (CAM), as a popular attention mechanism, calculates a set of probability weights to select important channel-wise feature responses. However, most CAMs roughly assign a weight to the entire channel-wise map, which makes useful and useless information being treat indiscriminately, thereby limiting the representational capacity of networks. In this paper, we propose a multi-scale and spatial position-based channel attention network (MS-SPCANet), which integrates spatial position-based channel attention models (SPCAMs) with multiple scales into a CNN. SPCAM assigns different channel attention weights to different positions of channel-wise maps to capture more informative features. Furthermore, an adaptive loss, which uses adaptive coefficients to combine density map loss and headcount loss, is constructed to improve network performance in sparse crowd scenes. Experimental results on four public datasets verify the superiority of the scheme.
Quantitative characterization of out-of-plane fiber wrinkling in thick CFRP with Double-side inverse-variance weight-synthetic ultrasonic imaging

Fiber wrinkling-induced beam deviation and wave attenuation in thick carbon fiber reinforced polymer (CFRP) pose a great challenge to accurate and robust ultrasonic imaging of fiber-wrinkling. Addressing this challenge, based on finite element and experimental studies, a double-side inverse-variance weight-synthetic ultrasonic imaging method is developed featuring i) circular variance-based weight estimation for synthetic imaging and ii) circular-circular correlation-based evaluation of imaging quality. The phase-based B-scan images with upper and lower side scanning are segmented into multiples patches, whose circular variances of ply angles extracted with the structure tensor are reciprocal of the weights to synthesize the phases. The imaging quality is quantitatively characterized with the circular-circular correlation of ply angles between the imaging and the baseline for CFRP up to 10 mm thick and ply angles around 15°. Results prove the developed approach can achieve a circular-circular correlation coefficient ≥ 0.7631, compared to the single-side imaging only ≥ 0.3783.
Transformer-based multilevel region and edge aggregation network for magnetic resonance image segmentation

To improve the quality of magnetic resonance (MR) image edge segmentation, some researchers applied additional edge labels to train the network to extract edge information and aggregate it with region information. They have made significant progress. However, due to the intrinsic locality of convolution operations, the convolution neural network-based region and edge aggregation has limitations in modeling long-range information. To solve this problem, we proposed a novel transformer-based multilevel region and edge aggregation network for MR image segmentation. To the best of our knowledge, this is the first literature on transformer-based region and edge aggregation. We first extract multilevel region and edge features using a dual-branch module. Then, the region and edge features at different levels are inferred and aggregated through multiple transformer-based inference modules to form multilevel complementary features. Finally, the attention feature selection module aggregates these complementary features with the corresponding level region and edge features to decode the region and edge features. We evaluated our method on a public MR dataset: Medical image computation and computer-assisted intervention atrial segmentation challenge (ASC). Meanwhile, the private MR dataset considered infrapatellar fat pad (IPFP). Our method achieved a dice score of 93.2% for ASC and 91.9% for IPFP. Compared with other 2D segmentation methods, our method improved a dice score by 0.6% for ASC and 3.0% for IPFP.
Survey of Credit Card Anomaly and Fraud Detection Using Sampling Techniques

The rapid growth in e-commerce has resulted in an increasing number of people shopping online. These shoppers depend on credit cards as a payment method or use mobile wallets to pay for their purchases. Thus, credit cards have become the main payment method in the e-world. Given the billions of transactions that occur daily, criminals see tremendous opportunities to be gained from finding different ways of attacking and stealing credit card information. Fraudulent credit card transactions are a serious business issue, and such ‘scams’ can result in significant financial and personal losses. As a result, businesses are increasingly investing in the development of new ideas and methods for detecting and preventing fraud to secure their customers’ trust to protect their privacy. In recent years, learning algorithms have emerged as important in research areas aimed at developing optimal solutions to this issue. The core challenge currently facing researchers is that of the imbalanced credit card dataset, in which the data are highly skewed and the number of normal transactions is much higher than fraudulent transactions, which thus negatively affects the performance of credit card fraud detection. This paper reviews the sampling techniques and their importance in solving the imbalanced data problem. Past research is found to show that hybrid sampling techniques will produce excellent results that can improve the fraud detection system.
Enhanced Regional Earthquake Catalog with Alaska Amphibious Community Seismic Experiment Data

Abstract The Alaska Amphibious Community Seismic Experiment (AACSE) comprised 75 ocean-bottom seismometers and 30 land stations and covered about 650 km along the segment of the subduction zone that includes Kodiak Island, the Alaska Peninsula and the Shumagin Islands between May 2018 and September 2019. This unprecedented onshore-offshore dataset provided an opportunity to compile a greatly enhanced earthquake catalog for the region by both increasing the number of detected earthquakes and improving the accuracy of their source parameters. We use all available regional and AACSE campaign seismic data to compile an earthquake catalog for the region between Kodiak and the Shumagin Islands including the Alaska Peninsula (51° N–59° N, 148° W–163° W). We apply the same processing and reporting standards to additional picks and events as the Alaska Earthquake Center currently uses for compilation of the authoritative regional earthquake catalog. Over 7200 events (both newly detected and previously reported) have been processed with AACSE data. We added about 30% more events, 60% more phase picks, lowered the magnitude of completeness by about 0.2 on average across the region, and improved location errors. All data have been published in public data archives. In addition, we test the machine-learning earthquake detection and picking algorithm EarthquakeTransformer (EQT) on the AACSE seismic dataset, comparing EQT-determined P and S picks with the new catalog. EQT is entirely trained on land data, whereas AACSE is amphibious. Overall, EQT finds 59% of P and 63% of S arrivals in the catalog within 300 km epicentral distance. The percent of catalog picks detected by EQT varies inversely with earthquake epicentral distance, and EQT performs particularly poorly on data from earthquakes recorded by instruments in the outer rise.
Enhancing recommendations with contrastive learning from collaborative knowledge graph

There have been excellent results using knowledge graphs in recommender systems. Knowledge graphs can be used as auxiliary information to alleviate data sparsity and strengthen the modeling of item sets and the representation of user preferences. However, users as the Core subject in the recommendation process, should be taken seriously. We believe that the user's choice of items will be affected by internal and external factors. Internal factors refer to the users' fuzzy interest sets, which initially affect the users' choices. External factors refer to the influence of similar users and similar items in the users' selection of items. Inspired by the success of contrastive learning in graph collaborative filtering, we propose the Knowledge Augmented User Representation (KAUR) model to explore contrastive learning in collaborative knowledge graphs, learning semantic neighbors (external factors) and extract fuzzy interest sets (internal factors) from collaborative knowledge graphs. Specifically, we use the graph neural network to learn the representation of each node in the collaborative knowledge graph and regard the information of nodes and their propagated neighbors' information as positive contrastive pairs, and then use contrastive learning to enhance the node representations. To further explore the potential interests of users, we regard users (or items) with other similar users (or items) as semantic neighbors and incorporate them into contrastive learning as positive pairings as well. Then the extracted fuzzy interest sets are merged into the user representations to get better interpretability. We conduct extensive experiments on three standard datasets and the results show that our KAUR model outperforms current state-of-the-art baselines.
Temporal convolutional networks and data rebalancing for clinical length of stay and mortality prediction

It is critical for hospitals to accurately predict patient length of stay (LOS) and mortality in real-time. We evaluate temporal convolutional networks (TCNs) and data rebalancing methods to predict LOS and mortality. This is a retrospective cohort study utilizing the MIMIC-III database. The MIMIC-Extract pipeline processes 24 hour time-series clinical objective data for 23,944 unique patient records. TCN performance is compared to both baseline and state-of-the-art machine learning models including logistic regression, random forest, gated recurrent unit with decay (GRU-D). Models are evaluated for binary classification tasks (LOS > 3 days, LOS > 7 days, mortality in-hospital, and mortality in-ICU) with and without data rebalancing and analyzed for clinical runtime feasibility. Data is split temporally, and evaluations utilize tenfold cross-validation (stratified splits) followed by simulated prospective hold-out validation. In mortality tasks, TCN outperforms baselines in 6 of 8 metrics (area under receiver operating characteristic, area under precision-recall curve (AUPRC), and F-1 measure for in-hospital mortality; AUPRC, accuracy, and F-1 for in-ICU mortality). In LOS tasks, TCN performs competitively to the GRU-D (best in 6 of 8) and the random forest model (best in 2 of 8). Rebalancing improves predictive power across multiple methods and outcome ratios. The TCN offers strong performance in mortality classification and offers improved computational efficiency on GPU-enabled systems over popular RNN architectures. Dataset rebalancing can improve model predictive power in imbalanced learning. We conclude that temporal convolutional networks should be included in model searches for critical care outcome prediction systems.
Blockchain-enabled cooperative computing strategy for resource sharing in fog networks

Several heterogeneous network designs have been developed to fulfill user experience needs, driven by widely growing applications needing large data processing, such as Industry 4.0. Fog computing is one of them since it allows for the efficient integration and exploitation of ubiquitous computing resources. Willingness and service billing concerns become important in fog computing situations for computing resource sharing. The present fog systems, on the other hand, are vulnerable to malicious attacks. The integration of blockchain technology into the fog computing environment is characterized by successfully allowing consensus in an untrustworthy environment. However, creating public blockchains requires a lot of processing power, which can quickly deplete the computational resources of IoT-enabled Smart Machines (SM) in Industry 4.0. This study suggests partitioning the fog system into fog clusters (FC), with fog nodes (FN) in each cluster sharing the same access control list (ACL) that is protected by a public blockchain. Therefore, Blockchain-enabled Resource Sharing inside SM in Fog networks (B-RSSF), a novel architecture for computing resource sharing in fog networks is proposed. B-RSSF has specified physical architecture, design principles, and inner workings. To make full and effective use of ubiquitous computing resources, fog computing’s wireless features and blockchain technology are tightly integrated. In addition, an enhanced PoW consensus mechanism for reaching consensus in an untrustworthy fog environment has been discussed. Finally, the experimental results have been conducted to evaluate the performance of the proposed scheme over other existing schemes in a fog-based environment.
Perceptual Hashing of Deep Convolutional Neural Networks for Model Copy Detection

In recent years, many model intellectual property (IP) proof methods for IP protection have been proposed, such as model watermarking and model fingerprinting. However, with the increasing number of models transmitted and deployed on the Internet, quickly finding the suspect model among thousands of models on model-sharing platforms such as GitHub is in great demand, which concurrently triggers the new security problem of model copy detection for IP protection. As an important part of the model IP protection system, the model copy detection task has not received enough attention. Due to the high computational complexity, both model watermarking and model fingerprinting lack the capability to efficiently find suspected infringing models among tens of millions of models. In this article, inspired by the hash-based image retrieval methods, we introduce a novel model copy detection mechanism: perceptual hashing for convolutional neural networks (CNNs). The proposed perceptual hashing algorithm can convert the weights of CNNs to fixed-length binary hash codes so that the lightly modified version has the similar hash code as the original model. By comparing the similarity of a pair of hash codes between a query model and a test model in the model library, similar versions of a query model can be retrieved efficiently. To the best of our knowledge, this is the first perceptual hashing algorithm for deep neural network models. Specifically, we first select the important model weights based on the model compression theory, then calculate the normal test statistics (NTS) on the segments of important weights, and finally encode the NTS features into hash codes. The experiment performed on a model library containing 3,565 models indicates that our perceptual hashing scheme has a superior copy detection performance.
Robust Financial Fraud Alerting System Based in the Cloud Environment

The digitalisation of finance influenced the emergence of new technological concepts for existing user needs. Financial technology, or fintech, provides improved services for customers and new economic value for businesses. As such, fintech services require on-demand availability on a 24/7 basis. For this reason, they are often deployed in cloud environments that allow connectivity with ubiquitous devices. This allows customers to perform online transactions, which are overseen by the respective financial institutions. However, such cloud-based systems introduce new challenges for information security. On one hand, they represent attractive targets for cyberattacks. On the other, financial frauds can still go unnoticed by the financial institutions in charge. This paper contributes to both challenges by introducing the concept for a cloud-based system architecture for fraud detection and client profiling in the banking domain. Therefore, a systematic risk assessment was conducted in this context, and exploitation probabilities were inferred for multiple attack scenarios. In addition, formal verification was accomplished in order to determine the effects of successful vulnerability exploits. The consequences of such security violations are discussed, and considerations are given for improving the resilience of fintech systems.
Ensemble of RNN Classifiers for Activity Detection Using a Smartphone and Supporting Nodes

Nowadays, sensor-equipped mobile devices allow us to detect basic daily activities accurately. However, the accuracy of the existing activity recognition methods decreases rapidly if the set of activities is extended and includes training routines, such as squats, jumps, or arm swings. Thus, this paper proposes a model of a personal area network with a smartphone (as a main node) and supporting sensor nodes that deliver additional data to increase activity-recognition accuracy. The introduced personal area sensor network takes advantage of the information from multiple sensor nodes attached to different parts of the human body. In this scheme, nodes process their sensor readings locally with the use of recurrent neural networks (RNNs) to categorize the activities. Then, the main node collects results from supporting sensor nodes and performs a final activity recognition run based on a weighted voting procedure. In order to save energy and extend the network's lifetime, sensor nodes report their local results only for specific types of recognized activity. The presented method was evaluated during experiments with sensor nodes attached to the waist, chest, leg, and arm. The results obtained for a set of eight activities show that the proposed approach achieves higher recognition accuracy when compared with the existing methods. Based on the experimental results, the optimal configuration of the sensor nodes was determined to maximize the activity-recognition accuracy and reduce the number of transmissions from supporting sensor nodes.
Applications of Virtual Machine Using Multi-Objective Optimization Scheduling Algorithm for Improving CPU Utilization and Energy Efficiency in Cloud Computing

Financial costs and energy savings are considered to be more critical on average for computationally intensive workflows, as such workflows which generally require extended execution times, and thus, require efficient energy consumption and entail a high financial cost. Through the effective utilization of scheduled gaps, the total execution time in a workflow can be decreased by placing uncompleted tasks in the gaps through approximate computations. In the current research, a novel approach based on multi-objective optimization is utilized with CloudSim as the underlying simulator in order to evaluate the VM (virtual machine) allocation performance. In this study, we determine the energy consumption, CPU utilization, and number of executed instructions in each scheduling interval for complex VM scheduling solutions to improve the energy efficiency and reduce the execution time. Finally, based on the simulation results and analyses, all of the tested parameters are simulated and evaluated with a proper validation in CloudSim. Based on the results, multi-objective PSO (particle swarm optimization) optimization can achieve better and more efficient effects for different parameters than multi-objective GA (genetic algorithm) optimization can.