Reviewerly/exHarmony huggingface dataset:
EMM-LC Fusion: Enhanced Multimodal Fusion for Lung Cancer Classification

Lung cancer (LC) is the most common cause of cancer-related deaths in the UK due to delayed diagnosis. The existing literature establishes a variety of factors which contribute to this, including the misjudgement of anatomical structure by doctors and radiologists. This study set out to develop a solution which utilises multiple modalities in order to detect the presence of LC. A review of the existing literature established failings within methods to exploit rich intermediate feature representations, such that it can capture complex multimodal associations between heterogenous data sources. The methodological approach involved the development of a novel machine learning (ML) model to facilitate quantitative analysis. The proposed solution, named EMM-LC Fusion, extracts intermediate features from a pre-trained modified AlignedXception model and concatenates these with linearly inflated features of Clinical Data Elements (CDE). The implementation was evaluated and compared against existing literature using F1 score, average precision (AP), and area under curve (AUC) as metrics. The findings presented in this study show a statistically significant improvement (p &lt; 0.05) upon the previous fusion method, with an increase in F-Score from 0.402 to 0.508. The significance of this establishes that the extraction of intermediate features produces a fertile environment for the detection of intermodal relationships for the task of LC classification. This research also provides an architecture to facilitate the future implementation of alternative biomarkers for lung cancer, one of the acknowledged limitations of this study.
Entanglement-based Satellite FSO/QKD System using Dual-Threshold/Direct Detection

This paper proposes a new implementation of the BBM92 protocol in satellite continuous-variable quantum key distribution (CV-QKD) systems using dual-threshold/direct detection (DT/DD). The proposed method is less complex and thus possibly cheaper than current discrete-variable (DV) and CV-QKD systems using coherent detection. We model and analyze the performance of the proposed system in the context that a satellite distributes secret keys to two legitimate users. The analytical results are derived by considering the channel loss, atmospheric turbulence-induced fading, and receiver noises. The Gaussian beam model is considered to evaluate the impact of geometrical spreading on the signal received by legitimate users and the possibility of being eavesdropped on. Based on the design criteria for the system and analytical results, we find suitable parameters for the transmitter and the receivers to properly achieve the QKD function for distributing secret keys between two legitimate parties.
Enhancement or Super-Resolution: Learning-based Adaptive Video Streaming with Client-Side Video Processing

The rapid development of multimedia and communication technology has resulted in an urgent need for high-quality video streaming. However, robust video streaming under fluctuating network conditions and heterogeneous client computing capabilities remains a challenge. In this paper, we consider an enhancement-enabled video streaming network under a time-varying wireless network and limited computation capacity. "Enhancement" means that the client can improve the quality of the downloaded video segments via image processing modules. We aim to design a joint bitrate adaptation and client-side enhancement algorithm toward maximizing the quality of experience (QoE). We formulate the problem as a Markov decision process (MDP) and propose a deep reinforcement learning (DRL)-based framework, named ENAVS. As video streaming quality is mainly affected by video compression, we demonstrate that the video enhancement algorithm outperforms the super-resolution algorithm in terms of signal-to-noise ratio and frames per second, suggesting a better solution for client processing in video streaming. Ultimately, we implement ENAVS and demonstrate extensive testbed results under real-world bandwidth traces and videos. The simulation shows that ENAVS is capable of delivering 5%−14% more QoE under the same bandwidth and computing power conditions as conventional ABR streaming.
Scalability of Distributed Intelligence Architecture for 6G Network Automation

Distributed automation is expected to play a significant role in the management of 6G networks, as it avoids the drawbacks of a single point of failure and signaling overhead inherent in a centralized paradigm. However, the issue of conflicts is intrinsic to a distributed architecture and when left unaddressed, may severely impair system KPIs. Considering the conflict problem, it is unclear if distributed automation would be scalable to realize the potential of 6G networks. In this paper, we validate the scalability of distributed intelligence, specifically based on Q-Learning, Q-Learning for Cooperation (QLC), consisting of intelligent agents that learn to cooperate on a discrete state space. Results show that the performance of QLC is scalable when compared to the optimal, computed by a centralized solution. Scalability may be limited by the convergence time that increases with the number of agents and the size of the discrete state space. The cooperation overhead is also not critical. These findings indicate that QLC is promising and may be applied to other use cases if the speed of convergence is not a significant detriment in distributing intelligence in 6G.
MAMMO-Net: An Approach for Classification of Breast Cancer using CNN with Gabor Filter in Mammographic Images

In today’s times, one of the most prevalent form of cancer in women is breast. Early detection of breast cancer improves patients’ chances of survival by allowing them to get the best treatment available. Convolutional Neural Networks has been very popular to extract the relevant features and classification. To develop a real time breast cancer model, we have developed a light weighted convolutional Neural Network (MAAMMO-Net). In this work we have proposed a convolutional Neural Network (MAMMO-Net) for automated diagnosis of breast cancer. We have also processed the mammographic images before giving input to the MAMMO-Net by applying Gabor filter. We experimented with the publicly accessible mammographic dataset CBISDDSM to verify the performance of the suggested model and obtained accuracy of 98%.
Blockchain-Oriented Privacy Protection with Online and Offline Verification in Cross-Chain System

User privacy is an attractive and valuable task to the success of blockchain systems. However, user privacy protection's performance and data capacity have not been well studied in existing access control models of blockchain systems because of traceability and openness of the P2P network. This paper focuses on investigating performance and data capacity from a blockchain infrastructure perspective, which adds secondary encryption to shield confidential information in a non-invasive way. First, we propose an efficient asymmetric encryption scheme by combining homomorphic encryption and state-of-the-art multi-signature key aggregation to preserve privacy. Second, we use smart contracts and CA infrastructure to achieve attribute-based access control. Then, we use the non-interactive zero-knowledge proof scheme to achieve secondary confidentiality explicitly. Finally, experiments show our scheme succeeds better performance in data capacity and system than other schemes. This scheme improves availability and robust scalability, solves the problem of multi-signature key distribution and the unlinkability of transactions. Our scheme has established a sound security cross-chain system and privacy confidentiality mechanism and that has more excellent performance and higher system computing ability than other schemes.
Botnet Detection Based on Machine Learning

A botnet is a new type of attack method developed and integrated on the basis of traditional malicious code such as network worms and backdoor tools, and it is extremely threatening. This course combines deep learning and neural network methods in machine learning methods to detect and classify the existence of botnets. This sample does not rely on any prior features, the final multi-class classification accuracy rate is higher than 98.7%, the effect is significant.
Service Discovery Method Based on Knowledge Graph and Word2vec

Mashup is a new type of application that integrates multiple Web APIs. For mashup application development, the quality of the selected APIs is particularly important. However, with the rapid development of Internet technology, the number of Web APIs is increasing rapidly. It is unrealistic for mashup developers to manually select appropriate APIs from a large number of services. For existing methods, there is a problem of data sparsity, because one mashup is related to a few APIs, and another problem of over-reliance on semantic information. To solve these problems in current service discovery approaches, we propose a service discovery approach based on a knowledge map (SDKG). We embed service-related information into the knowledge graph, alleviating the impact of data sparsity and mining deep relationships between services, which improves the accuracy of service discovery. Experimental results show that our approach has obvious advantages in accuracy compared with the existing mainstream service discovery approaches.
Pixel type classification based reversible data hiding for hyperspectral images

The digital files are becoming larger and larger with the development of computer hardware and computing power, and the fast processing for large files, e.g., hyperspectral images, is becoming feasible for not only companies but also individuals. However, the acquirement of hyperspectral images still costs a lot. Therefore, security issues like copyright ownership of hyperspectral images need to be taken seriously. Reversible data hiding (RDH) is a technology that can embed watermarking information into multimedia cover to protect copyright. However, the natural-images-based RDH methods cannot exploit the large amount of inter-band redundancy contained by hyperspectral images, which leads to a low efficiency for copyright protection. In this paper, a novel RDH method specially designed for hyperspectral images is proposed. We use the value information from the pixel of an adjacent band to classify each pixel into one of the five types when predicting it, and an adaptive predictor is matched for the pixels of each type to achieve a high prediction accuracy. Finally, a complexity calculation method containing three components is put forward to further improve the embedding performance. Experiments show that the proposed method outperforms the existing RDH method for hyperspectral images and other state-of-the-art RDH methods for natural images.
FacialSCDnet: A deep learning approach for the estimation of subject-to-camera distance in facial photographs

Facial biometrics play an essential role in the fields of law enforcement and forensic sciences. When comparing facial traits for human identification in photographs or videos, the analysis must account for several factors that impair the application of common identification techniques, such as illumination, pose, or expression. In particular, facial attributes can drastically change depending on the distance between the subject and the camera at the time of the picture. This effect is known as perspective distortion, which can severely affect the outcome of the comparative analysis. Hence, knowing the subject-to-camera distance of the original scene where the photograph was taken can help determine the degree of distortion, improve the accuracy of computer-aided recognition tools, and increase the reliability of human identification and further analyses. In this paper, we propose a deep learning approach to estimate the subject-to-camera distance of facial photographs: FacialSCDnet. Furthermore, we introduce a novel evaluation metric designed to guide the learning process, based on changes in facial distortion at different distances. To validate our proposal, we collected a novel dataset of facial photographs taken at several distances using both synthetic and real data. Our approach is fully automatic and can provide a numerical distance estimation for up to six meters, beyond which changes in facial distortion are not significant. The proposed method achieves an accurate estimation, with an average error below 6 cm of subject-to-camera distance for facial photographs in any frontal or lateral head pose, robust to facial hair, glasses, and partial occlusion.
Extractive summarization using concept‐space and keyword phrase

Abstract PROBLEM Text summarization is widely used to extract relevant information from documents. The extensive unstructured data are generated in various domains, developing a concise and coherent form of information has become an inevitable and assertive problem among the research community. Automatic text summarization plays a crucial role in acquiring potent knowledge effectively and efficiently for each domain of information. Medical data contains crucial information regarding human diseases and their symptoms. The proposed method will benefit the research community and health experts as it saves a lot of time and resources to compute the patients' summary during diagnosis. APPROACH In this research, an unsupervised approach is proposed for extractive summarization, based on semantic similarity and keyword‐phrase extraction. Merging Concept Map and the RAKE method, a generic summary is computed based on threshold values. Both single‐document and multi‐document summarization can be accomplished with the approach. RESULTS To evaluate the proposed unsupervised approach, various biomedical transcripts of neuro‐science, general medicine, gastroenterology, orthopaedic and radiology domain are used. MT Sample Dataset is used to collect 1040 different transcripts. Using the proposed approach, an average ROUGE score of 0.77 for single‐document summarization; however, for generic summary, an average ROUGE is 0.72. The proposed technique is validated for the previous corpus of BioMed articles, and results are better with state‐of‐the‐art techniques.
Semantic textual similarity for modern standard and dialectal Arabic using transfer learning

Semantic Textual Similarity (STS) is the task of identifying the semantic correlation between two sentences of the same or different languages. STS is an important task in natural language processing because it has many applications in different domains such as information retrieval, machine translation, plagiarism detection, document categorization, semantic search, and conversational systems. The availability of STS training and evaluation data resources for some languages such as English has led to good performance systems that achieve above 80% correlation with human judgment. Unfortunately, such required STS data resources are not available for many languages like Arabic. To overcome this challenge, this paper proposes three different approaches to generate effective STS Arabic models. The first one is based on evaluating the use of automatic machine translation for English STS data to Arabic to be used in fine-tuning. The second approach is based on the interleaving of Arabic models with English data resources. The third approach is based on fine-tuning the knowledge distillation-based models to boost their performance in Arabic using a proposed translated dataset. With very limited resources consisting of just a few hundred Arabic STS sentence pairs, we managed to achieve a score of 81% correlation, evaluated using the standard STS 2017 Arabic evaluation set. Also, we managed to extend the Arabic models to process two local dialects, Egyptian (EG) and Saudi Arabian (SA), with a correlation score of 77.5% for EG dialect and 76% for the SA dialect evaluated using dialectal conversion from the same standard STS 2017 Arabic set.
Simultaneous label inference and discriminant projection estimation through adaptive self-taught graphs

Processing structured data has become an interesting topic in recent years. The development of graph-based semi-supervised learning models has attracted much attention from machine learning researchers. In this paper, we present a novel approach for graph-based semi-supervised learning. We provide an effective method for simultaneous label recovery and linear transformation estimation. The targeted linear transformation is to obtain a discriminant subspace. The most important factor in this work to improve the semi-supervised learning is to exploit the data structure and soft labels of the available unlabeled samples. In the iterative optimization scheme used, the prior estimation of the labels increases the monitoring information in an indirect way through an introduced label-graph, avoiding the use of confidence-based hard decisions as used in self-supervised methods. It also enforces label smoothing and projected data smoothing through the use of hybrid graphs. For each smoothing type, the hybrid graph is an adaptive fusion of the two graphs encoding the similarity of the data information and the similarity of the label information. The proposed method leads to an improved discriminant linear transformation. Several experimental results on real image datasets confirm the effectiveness of the proposed method. This work also shows superior performance compared to semi-supervised methods that use simultaneous embedding and inference of labels.
Biogeochemical prospecting for gold at the Yellowknife City Gold Project, Northwest Territories, Canada: Part 1 - Species optimization

The Yellowknife City Gold Project (YCGP), located close to the city of Yellowknife, Northwest Territories, Canada, is exploring and drilling the extension of the Au-bearing shear zones that host the Giant and Con Mines. A biogeochemical survey was conducted over the Crestaurum and Barney deposits, which are hosted within two well-known high Au grade shear zones at the YCGP. A total of 625 samples were collected from the alder (Alnus incana) (n = 76), Labrador tea (Ledum groenlandicum) (n = 40), black spruce (Picea mariana) (n = 189), and juniper (Juniperus) (n = 320), and analyzed using ICP-MS. Robust statistical analyses were applied to biogeochemical data to (1) identify the biogeochemical response in plant samples with respect to the underlying mineralogy, (2) detect the most preferred plant species in the study area, and (3) identify Au pathfinder elements and subsequently zones of Au enrichment. Principal component analysis (PCA) and K-means clustering analysis were employed to achieve these objectives. The preliminary statistical analysis showed that plant samples can successfully accumulate anomalous Au values up to 147 ppb. Also, it showed that black spruce and Labrador tea can accumulate higher concentrations of Au than alder and juniper. According to the PCA, mineralization/geological (PC1) and physiological (PC2) factors control the distribution of elements in plant samples. According to K-means clustering, plant samples were classified into three clusters based on the vegetation type and their affinity to accumulate suites of elements. Black spruce samples were identified as the optimum sample media for biogeochemical exploration at the YCGP as they were clustered in proximity to the mineralization/geological factor (PC1) responsible for loading vectors of Au, Ag, As, Se, Bi, Pb, Cu, Co, Fe, Zn, Cd, Tl, and Cr. Au is strongly correlated with its pathfinder elements (Ag, As, Se, Bi, Tl and Sb), specifically in the south of the Crestaurum Shear. The presented results in this study demonstrate that zones of Au enrichment can be detected using biogeochemical and robust statistical methods.
Thangka Yidam classification based on DenseNet and SENet

Thangka Yidam classification is of great importance for understanding the image content and experiencing the Tibetan living culture. However, few samples, complex composition, and diverse Thangka images give the present methods a challenge for Thangka Yidam classification. To this end, we propose an improved method for Thangka Yidam classification based on DenseNet and SENet, which improves the transmission and reuse of feature information. First, a weighted pooling function is designed in the squeeze operation of SENet to alleviate the loss of feature information. Second, we further embed the improved SENet between dense blocks and transition layers of DenseNet to learn the weight correlation between feature channels. Finally, we adopt the Leaky ReLU function to optimize the network in DenseNet. The experimental results demonstrate that the accuracy of our method on the constructed Thangka dataset is increased by 5.54% to 95.89% compared with DenseNet model, and the accuracy on the Cifar-10 dataset is increased by 2.23% to 92.46%. Moreover, the experiments on Thangka dataset also show the significant improvements of our approach over the state-of-the-art methods, which proves the generalization ability of our method.
A Fuzzy MLP Approach for Nonlinear System Identification

In case of decision making problems, identification of nonlinear systems is an important issue. Identification of nonlinear systems using a multilayer perceptron (MLP) trained with back propagation becomes much complex with an increase in number of input data, number of layers, number of nodes, and number of iterations in computation. In this paper, an attempt has been made to use fuzzy MLP and its learning algorithm for identification of nonlinear system. The fuzzy MLP and its training algorithm which allows to accelerate a process of training, which exceeds in comparison with classical MLP is proposed. Results show a sharp reduction in search for optimal parameters of a neuro fuzzy model as compared to the classical MLP. A training performance comparison has been carried out between MLP and the proposed fuzzy-MLP model. The time and space complexities of the algorithms have been analyzed. It is observed that the number of epochs has sharply reduced and performance increased compared with classical MLP.
Personal or General? A Hybrid Strategy with Multi-factors for News Recommendation

News recommender systems have become an effective manner to help users make decisions by suggesting the potential news that users may click and read, which has shown the proliferation nowadays. Many representative algorithms made great efforts to discover users’ preferences from the histories for triggering news recommendations. However, there exist some limitations due to the following two main issues. First, they mainly rely on the sufficient user data, which cannot well capture users’ temporal interests with very limited records. Second, always perceiving users’ histories for recommendation may ignore some important news (e.g., breaking news). In this article, we propose a novel Multi-factors Fusion model for news recommendation by integrating both user-dependent preference effect and user-independent timeliness effect together. First, to track the preference of a certain user, we decompose her reading history into two user-related factors, including the long-term habit and the short-term interest. Specifically, we extract her persistent habit by exploring the category effect of news that she focuses on from her whole records. Then, we characterize her temporary interests by proposing a recurrent neural network of analyzing the homogeneous relations between her latest clicked news and the candidate ones. Second, to describe the user-independent news timeliness effect, we propose a novel survival analysis model to estimate the instantaneous click probability of a certain news as the occurring probability of an event, where much sensational news tends to be picked out. Last, we fuse all effects to determine the probability of a user clicking on a certain news under the independent event assumption. We conduct extensive experiments on two real-world datasets. Experimental results demonstrate that our model can generate better news recommendations on both general scenario and cold-start scenario.