Reviewerly/exHarmony huggingface dataset:
Efficient methods for mining weighted clickstream patterns

Pattern mining has been an attractive topic for many researchers since its first introduction. Clickstream mining, a specific version of sequential pattern mining, has been shown to be important in the age of the Internet. However, most previous works have simply exploited and applied existing sequential pattern algorithms to the mining of clickstream patterns, and few have studied clickstreams with weights, which also have a wide range of application. In this paper, we address this problem by proposing an approach based on the average weight measure for clickstream pattern mining and adapting a previous state-of-the-art algorithm to deal with the problem of weighted clickstream pattern mining. Following this, we propose an improved method named Compact-SPADE to enhance both the efficiency and memory consumption. Through various tests on both real-life and synthetic databases, we show that our proposed algorithms outperform state-of-the-art alternatives in terms of efficiency, memory requirements and scalability.
A lightweight and scalable attribute-based encryption system for smart cities

In the near future, a technological revolution will involve our cities, where a variety of smart services based on the Internet of Things will be developed to facilitate the needs of the citizens. Sensing devices are already being deployed in urban environments, and they will generate huge amounts of data. Such data is typically outsourced to some cloud service in order to lower capital and operating expenses and guarantee high availability. However, cloud services may suffer from data breaches due to software and hardware vulnerabilities, or they may have incentives to release stored data to unauthorized entities. In this work we present ABE-Cities, an encryption system for urban sensing which solves the above problems while ensuring fine-grained access control on data by means of Attribute-Based Encryption (ABE). ABE-Cities senses data from the city and stores it on the cloud in an encrypted form. Then, it provides users with keys able to decrypt only data sensed from authorized paths or zones of the city. In ABE-Cities, sensors perform only lightweight symmetric-key encryption, thus we can employ constrained sensor devices such as battery powered motes. ABE-Cities allows us to plan an expiration date for each key, as well as to revoke a given key in an unplanned fashion. We prove that ABE-Cities scales well with the number of users and the number of streets by simulating it with 30 000 users on the Beijing street network, which consists of more than 30 000 streets. In addition to the "vanilla" ABE-Cities scheme, we propose an "advanced" one that leverages the presence of IoT gateways to reduce the computational load otherwise weighing on a single Trusted Third Party. We validate this by testing the advanced scheme on the simulated Houston and Beijing street networks.
Convolutional Neural Network for Copy-Move Forgery Detection

Digital image forgery is a growing problem due to the increase in readily-available technology that makes the process relatively easy. In response, several approaches have been developed for detecting digital forgeries. This paper proposes a novel scheme based on neural networks and deep learning, focusing on the convolutional neural network (CNN) architecture approach to enhance a copy-move forgery detection. The proposed approach employs a CNN architecture that incorporates pre-processing layers to give satisfactory results. In addition, the possibility of using this model for various copy-move forgery techniques is explained. The experiments show that the overall validation accuracy is 90%, with a set iteration limit.
Recommending New Features from Mobile App Descriptions

The rapidly evolving mobile applications (apps) have brought great demand for developers to identify new features by inspecting the descriptions of similar apps and acquire missing features for their apps. Unfortunately, due to the huge number of apps, this manual process is time-consuming and unscalable. To help developers identify new features, we propose a new approach named SAFER. In this study, we first develop a tool to automatically extract features from app descriptions. Then, given an app, we leverage the topic model to identify its similar apps based on the extracted features and API names of apps. Finally, we design a feature recommendation algorithm to aggregate and recommend the features of identified similar apps to the specified app. Evaluated over a collection of 533 annotated features from 100 apps, SAFER achieves a Hit@15 score of up to 78.68% and outperforms the baseline approach KNN+ by 17.23% on average. In addition, we also compare SAFER against a typical technique of recommending features from user reviews, i.e., CLAP. Experimental results reveal that SAFER is superior to CLAP by 23.54% in terms of Hit@15.
Improving Night-Time Pedestrian Retrieval With Distribution Alignment and Contextual Distance

Night-time pedestrian retrieval is a cross-modality retrieval task of retrieving person images between day-time visible images and night-time thermal images. It is a very challenging problem due to modality difference, camera variations, and person variations, but it plays an important role in night-time video surveillance. The existing cross-modality retrieval usually focuses on learning modality sharable feature representations to bridge the modality gap. In this article, we propose to utilize auxiliary information to improve the retrieval performance, which consistently improves the performance with different baseline loss functions. Our auxiliary information contains two major parts: cross-modality feature distribution and contextual information. The former aligns the cross-modality feature distributions between two modalities to improve the performance, and the latter optimizes the cross-modality distance measurement with the contextual information. We also demonstrate that abundant annotated visible pedestrian images, which are easily accessible, help to improve the cross-modality pedestrian retrieval as well. The proposed method is featured in two aspects: the auxiliary information does not need additional human intervention or annotation; it learns discriminative feature representations in an end-to-end deep learning manner. Extensive experiments on two cross-modality pedestrian retrieval datasets demonstrate the superiority of the proposed method, achieving much better performance than the state-of-the-arts.
Blockchain-based Land Registration: Possibilities and Challenges

In recent decades land registration systems operating in Europe and worldwide have been subject to modernisation processes consisting in implementation of information and communication technologies. Such reforms have gradually led to facilitating access to land information, improving effectiveness of land registration proceedings and even introducing possibilities to dispose of the ownership of land electronically by developing electronic conveyancing mechanisms. Another innovative concept much discussed nowadays is the application of blockchain technology in the land registration sector. This solution is currently being tested in a number of countries.Distributed ledger technology underlying blockchain is expected to revolutionise land registration by offering a secure architecture to store land transactions with the use of cryptographic protocol. This shall bring advantages of increased trust and processing efficiency as well as reduction of costs. However, the above idea raises concerns given that, under the assumptions of the “original” blockchain model, transactions are irreversible and are carried out without intermediaries, which means the lack of any external control and independent verification of the transactions to be recorded.The article examines potential benefits and risks of automatisation of land transactions as well as practical experiences of selected countries in implementing blockchain in the area of land registration. On this basis, an assessment will be made as to whether blockchain-based registration could indeed replace the existing methodology of registering rights to land.
Generalizable Feature Learning in the Presence of Data Bias and Domain Class Imbalance with Application to Skin Lesion Classification

Training generalizable data-driven models for medical imaging applications is especially challenging as acquiring and accessing sufficiently large medical datasets is often unfeasible. When trained on limited datasets, a high capacity model, as most leading neural network architectures are, is likely to overfit and thus generalize poorly to unseen data. Further aggravating the problem, data used to train models in medicine are typically collected in silos and from narrow data distributions that are determined by specific acquisition hardware, imaging protocols, and patient demographics. In addition, class imbalance within and across datasets is a common complication as disease conditions or sub-types have varying degrees of prevalence. In this paper, we motivate the need for generalizable training in the context of skin lesion classification by evaluating the performance of ResNet across 7 public datasets with dataset bias and class imbalance. To mitigate dataset bias, we extend the classification and contrastive semantic alignment (CCSA) loss that aims to learn domain-invariant features. As the CCSA loss requires labelled data from two domains, we propose a strategy to dynamically sample paired data in a setting where the set of available classes varies across domains. To encourage learning from underrepresented classes, the sampled class probabilities are used to weight the classification and alignment losses. Experimental results demonstrate improved generalizability as measured by the mean macro-average recall across the 7 datasets when training using the weighted CCSA loss and dynamic sampler.
Touch Well Before Use

Internet of Things (IoT) are densely deployed in smart environments, such as homes, factories and laboratories, where many people have physical access to IoT devices. How to authenticate users operating on these devices is thus an important problem. IoT devices usually lack conventional user interfaces, such as keyboards and mice, which makes traditional authentication methods inapplicable. We present a virtual sensing technique that allows IoT devices to virtually sense user 'petting' (in the form of some very simple touches for about 2 seconds) on the devices. Based on this technique, we build a secure and intuitive authentication method that authenticates device users by comparing the petting operations sensed by devices and those captured by the user wristband. The authentication method is highly secure as physical operations are required, rather than based on proximity. It is also intuitive, adopting very simple authentication operations, e.g., clicking buttons, twisting rotary knobs, and swiping touchscreens. Unlike the state-of-the-art methods, our method does not require any hardware modifications of devices, and thus can be applied to commercial off-the-shelf (COTS) devices. We build prototypes and evaluate them comprehensively, demonstrating their high effectiveness, security, usability, and efficiency.
Health Record Management through Blockchain Technology

The world is moving towards progress, to achieve the desired progress, the world should have a healthy population and health records are the projections of an individual's health over time. The centralised approach of maintaining the health records lead to data breaches. According to 2017 Ponemon Cost of Data Breach Study, the cost of the data breach for healthcare organizations estimated to be $380 per record. According to 2016 Breach Barometer Report, 27,314,647 patient records were affected. So we moved towards institution-driven approach of record maintenance, which didn't make much difference with the previously existing one. Since the patient have no control over the data, the chances of data being misused is high. So we need a patient-centered approach which is completely decentralised, which can identify data thefts, prevent data manipulation, and patient has the right in access control. Blockchain Technology serves as a best solution to address all the problems and fulfill the needs. Blockchain being a decentralised and distributed ledger it can also impact on billing, record sharing, medical research, identify thefts and financial data crimes in days to come. Implementation of smart contracts in health care can simplify things even better. Where invoking, record creation and validation will be done on Blockchain. This paper highlights on the patient-driven model of record maintenance using Blockchain technology where smart contracts can be incorporated in future days making it more potential in data exchange. Finding its huge scope, hoping that more researches will be carried out and practically implemented.
Synthetic dataset generation for object-to-model deep learning in industrial applications

The availability of large image data sets has been a crucial factor in the success of deep learning-based classification and detection methods. Yet, while data sets for everyday objects are widely available, data for specific industrial use-cases (e.g., identifying packaged products in a warehouse) remains scarce. In such cases, the data sets have to be created from scratch, placing a crucial bottleneck on the deployment of deep learning techniques in industrial applications. We present work carried out in collaboration with a leading UK online supermarket, with the aim of creating a computer vision system capable of detecting and identifying unique supermarket products in a warehouse setting. To this end, we demonstrate a framework for using data synthesis to create an end-to-end deep learning pipeline, beginning with real-world objects and culminating in a trained model. Our method is based on the generation of a synthetic dataset from 3D models obtained by applying photogrammetry techniques to real-world objects. Using 100K synthetic images for 10 classes, an InceptionV3 convolutional neural network was trained, which achieved accuracy of 96% on a separately acquired test set of real supermarket product images. The image generation process supports automatic pixel annotation. This eliminates the prohibitively expensive manual annotation typically required for detection tasks. Based on this readily available data, a one-stage RetinaNet detector was trained on the synthetic, annotated images to produce a detector that can accurately localize and classify the specimen products in real-time.
Robiot: A Design Tool for Actuating Everyday Objects with Automatically Generated 3D Printable Mechanisms

Users can now easily communicate digital information with an Internet of Things; in contrast, there remains a lack of support to automate physical tasks that involve legacy static objects, e.g. adjusting a desk lamp's angle for optimal brightness, turning on/off a manual faucet when washing dishes, sliding a window to maintain a preferred indoor temperature. Automating these simple physical tasks has the potential to improve people's quality of life, which is particularly important for people with a disability or in situational impairment.
CNAVER: A Content and Network-based Academic VEnue Recommender system

The phenomenon of rapidly developing academic venues poses a significant challenge for researchers: how to recognize the ones that are not only in accordance with one’s scholarly interests but also of high significance? Often, even a high-quality paper is rejected because of a mismatch between the research area of the paper and the scope of the journal. Recommending appropriate scholarly venues to researchers empowers them to recognize and partake in important academic conferences and assists them in getting published in impactful journals. A venue recommendation system becomes helpful in this scenario, particularly when exploring a new field or when further choices are required. We propose CNAVER: A Content and Network-based Academic VEnue Recommender system. It provides an integrated framework employing a rank-based fusion of paper-paper peer network (PPPN) model and venue-venue peer network (VVPN) model. It only requires the title and abstract of a paper to provide venue recommendations, thus assisting researchers even at the earliest stage of paper writing. It also addresses cold start issues such as the involvement of an inexperienced researcher and a novel venue along with the problems of data sparsity, diversity, and stability. Experiments on the DBLP dataset exhibit that our proposed approach outperforms several state-of-the-art methods in terms of precision, nDCG, MRR, accuracy, F−measuremacro, average venue quality, diversity, and stability.
Sentiment classification within online social media using whale optimization algorithm and social impact theory based optimization

In recent years, social networks have become an important part of daily life and are becoming increasingly important. In this study, sentiment analysis or opinion mining, which is one of the most well-known social network analysis problems, is considered as an optimization problem for the first time. In the same way, for the first time, sentiment analysis is considered as a multi-objective problem. Whale Optimization Algorithm and Social Impact Theory based Optimization Algorithm, which are the current intelligent optimization algorithms, are adapted for the sentiment analysis problem. Furthermore, memory feature is integrated into Social Impact Theory based Optimization Algorithm in order to obtain effective results in this study. The obtained results from the proposed algorithms are also compared with thirty-three supervised learning algorithms within real IMDB, Polarity, and Amazon data sets. In order to evaluate the performance of the results; accuracy percentage, precision, recall, F-Measure, and MCC that are the five most commonly used measure in the literature are used. When the results are examined, it is observed that adapted metaheuristic optimization algorithms give more successful results in sentiment analysis problem.
Quantum Supremacy Circuit Simulation on Sunway TaihuLight

With the rapid progress made by industry and academia, quantum computers with dozens of qubits or even larger size are being realized.However, the fidelity of existing quantum computers often sharply decreases as the circuit depth increases.Thus, an ideal quantum circuit simulator on classical computers, especially on highperformance computers, is needed for benchmarking and validation.We design a large-scale simulator of universal random quantum circuits, often called "quantum supremacy circuits", and implement it on Sunway TaihuLight.The simulator can be used to accomplish the following two tasks: 1) Computing a complete output state-vector; 2) Calculating one or a few amplitudes.We target the simulation of 49-qubit circuits.For task 1), we successfully simulate such a circuit of depth 39, and for task 2) we reach the 55-depth level.To the best of our knowledge, both of the simulation results reach the largest depth for 49-qubit quantum supremacy circuits.
Learning for Detection: MIMO-OFDM Symbol Detection Through Downlink Pilots

In this paper, we introduce a reservoir computing (RC) structure, namely, windowed echo state network (WESN), for multiple-input-multiple-output orthogonal frequency-division multiplexing (MIMO-OFDM) symbol detection. We show that adding buffers in input layers is able to bring an enhanced short-term memory (STM) to the standard echo state network. A unified training framework is developed for the introduced WESN MIMO-OFDM symbol detector using both comb and scattered patterns, where the training set size is compatible with those adopted in 3GPP LTE/LTE-Advanced standards. Complexity analysis demonstrates the advantages of WESN based symbol detector over state-of-the-art symbol detectors when the number of OFDM sub-carriers is large, where the benchmark methods are chosen as linear minimum mean square error (LMMSE) detection and sphere decoder. Numerical evaluations suggest that WESN can significantly improve the symbol detection performance as well as effectively mitigate model mismatch effects using very limited training symbols.
A meta-learning approach for selecting image segmentation algorithm

Image segmentation is a key issue in image processing. New image segmentation algorithms have been proposed in the last years. However, there is no optimal algorithm for every image processing task. The selection of the most suitable algorithm usually occurs by testing every possible algorithm or using knowledge from previous problems. These processes can have a high computational cost. Meta-learning has been successfully used in the machine learning research community for the recommendation of the most suitable machine learning algorithm for a new dataset. We believe that meta-learning can also be useful to select the most suitable image segmentation algorithm. This hypothesis is investigated in this paper. For such, we perform experiments with eight segmentation algorithms from two approaches using a segmentation benchmark of 300 images and 2100 augmented images. The experimental results showed that meta-learning can recommend the most suitable segmentation algorithm with more than 80% of accuracy for one group of algorithms and with 69% for the other group, overcoming the baselines used regarding recommendation and segmentation performance.
An encryption scheme for securing multiple medical images

With the rise in medical imaging tools and telemedicine technology, the distance barriers in providing health care have been reduced by communicating patients data over the internet. To provide patients with privacy, the data are encrypted while transmitting over the insecure network. The paper proposes an encryption scheme for multiple medical images using a new finding in elliptic curve analogue ElGamal cryptosystem and Mersenne Twister pseudo-random number generator. The new finding quickens the encryption time as well as solve the problem of data expansion associated with ElGamal cryptosystem. Results from simulation, security and statistical analyses show that the proposed encryption algorithm can be used for multiple medical images encryption.