Reviewerly/exHarmony huggingface dataset:
SIA: Smartwatch-Enabled Inference Attacks on Physical Keyboards Using Acoustic Signals

The convergence of various technologies, such as smartwatches, smartphones, etc. has proven to be beneficial, although poses various security and privacy risks. In this paper, we explore one such risk where a smartwatch can be exploited to infer what a user is typing on a physical keyboard while wearing the smartwatch. We exploited the acoustic emanations of the keyboard as recorded by the smartwatch to perform the proposed attack-SIA. To address various environment-related challenges, SIA employs four stages: Noise Cancelling, Keystroke Detection, Key Identification, and Word Correction, where several digital signal processing, machine learning, and natural language processing techniques are utilized to produce the final inference. Our results show that an acoustic emanation of a physical keyboard captured by a smartwatch recovers up to 98% of the typed text. We also showed that utilizing the noise cancellation, SIA is robust to the changes in the attack environment, which further boosts the practicality of the attack. The findings are alarming and call for further investigation on methods to cope with inference attacks due to the convergence of those technologies.
TDCMR: Triplet-Based Deep Cross-Modal Retrieval for Geo-Multimedia Data

Mass multimedia data with geographical information (geo-multimedia) are collected and stored on the Internet due to the wide application of location-based services (LBS). How to find the high-level semantic relationship between geo-multimedia data and construct efficient index is crucial for large-scale geo-multimedia retrieval. To combat this challenge, the paper proposes a deep cross-modal hashing framework for geo-multimedia retrieval, termed as Triplet-based Deep Cross-Modal Retrieval (TDCMR), which utilizes deep neural network and an enhanced triplet constraint to capture high-level semantics. Besides, a novel hybrid index, called TH-Quadtree, is developed by combining cross-modal binary hash codes and quadtree to support high-performance search. Extensive experiments are conducted on three common used benchmarks, and the results show the superior performance of the proposed method.
Positive Computing in Virtual Reality Industrial Training

This research investigates the application of positive computing principles to Virtual Reality (VR) training scenarios where the Virtual Environment (VE) has not a direct influence on operator learning. We propose to place the 3D models of the only objects needed for the task in a VE consisting of 360° panoramas of natural environments. We made a preliminary evaluation of the user experience which showed that the hedonic quality is significantly higher with this VE than a 3D modeled empty room. However, we also observed a reduction of the pragmatic quality, due to potential distractions. Thus, further research is needed to demonstrate the efficacy of our positive computing approach in training against a traditional one based on the faithful 3D reproduction of the real environment.
Research on MySQL Database Recovery and Forensics Based on Binlog

MySQL database is more and more widely used, and the problems of data recovery and forensics brought about by it have become more and more people's attention. The current common method of MySQL data recovery is to analyze the redo log. However, the redo log is based on the InnodDB storage engine. The storage engine in MySQL is not only InnoDB, so this method is not universal. In MySQL, there is also a very important log, which is a binary log with high efficiency and flexibility. This article analyzes the structure of MySQL binary log and uses binlog to restore the database by studying the principle of MySQL binary log function. This article analyzes and compares the mysqlbinlog tool and its defects, and uses Python to write a better binlog analysis tool to better adapt and generate standard SQL, and has a rollback function to recover data better. Use new tools to prove its practical significance and conduct post-research on forensics.
Face Mask Recognition Using MobileNetV2

The pandemic of Corona Virus Disease is generating a public health emergency. Wearing a mask is one of the most efficient ways to combat the infection. This paper presents the detection of face masks, through mitigating, evaluating, preventing, and preparing actions regarding COVID-19. In this work, face mask identification is achieved using Machine Learning technique and the Image Classification algorithms are MobileNetV2 with major changes which includes Label Binarizer, ImageNet, and Binary Cross-Entropy. The methods involved in building the model are collecting the data, pre-processing, image generation, model construction, compilation, and finally testing. The proposed method can recognize people with and without masks. The training accuracy of the proposed method is 98.5% and the testing accuracy is 99%. This model is implemented in an image or video stream to detect faces with mask.
Edge Enhancement for Image Super-Resolution using Deep Learning Approach

In recent times the use of digital images has increased the demand for high-resolution images. The images captured are sometimes affected by noise, making visualization of the objects difficult, so the image super-resolution method is used to solve this problem. This research is based on a predefined Edge Informed Single Image Super-Resolution(EISR). The model is based on a deep learning approach that uses a convolutional neural network(CNN) and works on single image super-resolution(SISR). The first stage of the proposed model is the bi-cubic interpolation stage, followed by the Edge enhancement and Image completion stage. A qualitative comparison between the existing and proposed models on the x2 scaling factor is made.
Safety Helmet Wearing Detection Based on Super-resolution Reconstruction

As the most commonly used labor protection products, safety helmets can effectively reduce accident damage, and it is particularly necessary to supervise the wearing of workers' helmets. This paper designs and implements a safety helmet wearing detection system optimized based on super-resolution reconstruction, obtains images through the RTSP video stream, detects and marks whether the characters in the video image wearing a helmet, and outputs the detection results in the form of a real-time video stream. The detection system applies the ESPCN super-resolution reconstruction neural network algorithm to preprocess the image data, enhances the subtle characteristics of the distant characters in the original image, and improves the detection accuracy of whether the distant target characters wear a helmet. The test results show that the method can achieve the image and video detection of hard hat wearing well, and the detection rate and detection accuracy of small targets in the distance are significantly better than the results of single YOLOv3 neural network detection.
Heterogeneous Graph Attention Network-Enhanced Web Service Classification

Service classification helps to improve the efficiency of service discovery. Previous methods mainly focus on homogeneous graph-based service classification. However, due to the heterogeneity of service data in the real world, these methods cannot deal with many types of nodes and edges in service relationship network well, and lack the usage of rich semantic information. The emergence of heterogeneous graph attention network can effectively solve the problems, because it can more completely and naturally extracts the relationships and nodes from the service relationship network, and well distinguishes the importance of neighbor nodes and meta paths. Therefore, this paper proposes a heterogeneous graph attention network-enhanced Web service classification method. In this method, firstly, a heterogeneous information service network is constructed by using composite service information, atomic service information and their attribute information. Then, the meta path is defined according to different semantic information, and the similarity matrix of service is constructed by using the commuting matrix and the similarity measurement technology based on meta path. Finally, a two-layer attention model is designed to calculate the node-level attention and meta path-level attention of the service, so as to obtain the node-level representations and meta path-level representations of the services, and generate more representative embedding features of services for achieving more accurate service classification. Finally, the experimental results on real datasets of ProgrammableWeb show that our method is better than GAT, GCN, Metapath2Vec, Node2Vec, BiLSTM and LDA in terms of precision, recall and macro F1, and improves the accuracy of Web service classification.
ACO Inspired GA Mutation Applied to the TSP

Meta-heuristics Genetic Algorithms (GA) and Ant Colony Optimisation (ACO) have both shown success when applied to permutation problems such as the Traveling Salesman Problem (TSP). Therefore, combining the approaches could prove beneficial. This paper presents an ACO inspired mutation operator for use in a GA based on Partial-ACO. Applied to a range of TSP instances the ACO mutation operator demonstrates considerable improvements over standard GA mutation operators finding solutions within a few percent of optimal for TSP instances of nearly 4000 cities. In fact, experiments demonstrate performance close to that of local search with standard mutation operators. Moreover, when integrating local search the ACO inspired mutation operator continues to provide improved results over standard mutation and local search.
Real-Time Mask Recognition

While a variety of image processing studies have been performed to quantify the potential performance of neural network-based models using high-quality still images, relatively few studies seek to apply those models to a real-time operational context. This paper seeks to extend prior work in neural-network-based mask detection algorithms to a real-time, low-power deployable context that is conducive to immediate installation and use. Particularly relevant in the COVID-19 era with varying rules on mask mandates, this work applies two neural network models to inference of mask detection in both live (mobile) and recorded scenarios. Furthermore, an experimental dataset was collected where individuals were encouraged to use presentation attacks against the algorithm to quantify how perturbations negatively impact model performance. The results from evaluation on the experimental dataset are further investigated to identify the degradation caused by poor lighting and image quality, as well as to test for biases within certain demographics such as gender and ethnicity. In aggregate, this work validates the immediate feasibility of a low-power and low-cost real-time mask recognition system.
Deep Learning for Recommender Systems: Literature Review and Perspectives

During the last few years, deep learning revolutionized several fields including: image analysis, speech recognition and language processing. Deep learning has also become pervasive and demonstrated effectiveness in the field of recommender systems and information retrieval. Unlike the conventional recommendation systems, deep learning have the unique ability to successfully capture non-trivial and non-linear interactions between user and item, allowing for the codification of more complicated abstractions. We begin by providing a brief overview of recommender systems and deep learning. Second, we present a complete overview of the current state of the art in deep learning-based RS. Then, we describe a possible future research direction of the field. Finally, we conclude the review.
Hadoop Data Reduction Framework: Applying Data Reduction at the DFS Layer

Big-data processing systems such as Hadoop, which usually utilize distributed file systems (DFSs), require data reduction schemes to maximize storage space efficiency. These schemes have different tradeoffs, and there are no all-purpose schemes applicable to all data. Users must select a suitable scheme in accordance with their data. To accommodate this requirement, application software or file system (FS) have a fixed selection of these schemes. However, these provided schemes are insufficient for all data types, and when novel schemes emerge, extending the selection can be problematic. If the source code of the application or FS is available, the source code could potentially be extended with extensive labor, but could be virtually impossible without the code maintainers’ assistance. If the source code is unavailable, there is no way to tackle the problem. This paper proposes an unexplored solution through a modular DFS design that eases data reduction scheme usage through existing programming techniques. The advantages of this presented approach are threefold. First, adding new schemes is easy and they are transparent to the application code requiring no extensions to it. Second, the modular structure requires minimal modification to the existing DFSs and performance overhead. Third, users can compile schemes separately from the DFS without the FS or DFS source code. To demonstrate the design’s effectiveness, we implemented it by minimally extending the Hadoop DFS (HDFS) and named it the Hadoop Data Reduction Framework (HDRF). We designed HDRF to work with minimal overhead and tested it extensively. Experimental results indicate that it has negligible overhead over existing approaches. In a number of cases, it can offer up to 48.96% higher throughput while achieving the best result in storage reduction within our tested setups because of the incorporated data reduction schemes.
A comparative analysis of consensus protocols for dealing power theft issue in Pakistan

Power is considered as a boon for life, lifeline of a country's economy; a key instrument for the socio-economic evolution of any country. Pakistan faces the issue of power theft that worsens the economy of utilities. The purpose of research is to modernize Pakistan's power sector with the usage of Blockchain. Research is based on a comparative analysis of proof of stake (POS) and proof of authority (POA) consensus protocols for the power theft issue for small, medium, or large scales of consumers; results showing automated and pre-selected processing time of transactions running on different nodes of POS and POA along. By comparative analysis of POS and POA; it is shown that the performance of Blockchain remains unaffected by the consensus protocol. To further strengthen the findings the statistical analysis data is carried out to establish the uniqueness of sampled data. The result establishes that Blockchain performs equally well for different architectures of the proposed architecture. The consensus protocol helps in achieving a suitable governing style with no effect on Blockchain performance.
Evaluation of Morphological Operators in Gaussian Mixture Model for Moving Object Detection

Moving object detection is fundamental technology for intelligent video surveillance. The Gaussian Mixture Model (GMM) is one of the most commonly used algorithms for moving object detection, but it does not cope with complex scenes efficiently. Thus, morphological operators are always in consideration after applying GMM. This study is to evaluate the influence of morphological operators on the moving object detection result based on the GMM. In our experiment, the open operation is used to remove the small noises outside the silhouette, and then the close operation is used to fill the small holes inside the object region. We evaluated the detection results of different complex scenes with respect to precision and speed. The experimental results show that the performance of the Gaussian mixture model algorithm with morphological operators is inconsistent based on different complex scenes, it is more useful in some specific scenes than some others.
Quantum Safe Cloud Computing Using Hash-based Digital Signatures

Cloud computing provides its clients with on-demand resources, such as storage and performance. The idea behind cloud computing is sharing of resources. With growth of cloud computing, the risks of security and privacy are arising. Also, the cloud resources are not secure against quantum computers, as quantum computing threatens the best security controls implemented today, that include ECC (Elliptic Curve Cryptography), RSA, DSA (Digital Signature Algorithm) and ECDSA (Elliptic Curve Digital Signature Algorithm). Fortunately, there are quantum-safe digital signatures called Hash-based Signatures, which can also protect integrity, authentication and privacy on the cloud. In this work, a hash-based digital signature scheme called WOTS is implemented to avoid security risks imposed by quantum computing on cloud computing. The implementation is carried out in CloudSim simulator. Apart from achieving quantum safety, the results show that our proposed solution is computationally cost effective.
PointerNet: Spatiotemporal Modeling for Crowd Counting in Videos

The existing video crowd counting methods via deep learning technique are mainly involved in how to leverage the temporal correlation to improve the model. Studies have shown that convolutional neural networks with spatiotemporal three-dimensional kernels (3D CNNs) are promising architectures on video crowd counting. However, the existing methods based on 3D CNNs are insufficient for very deep neural networks in 2D-based CNNs owing to their considerable number of parameters and lack of labeled data, which gives rise to overfitting of 3D CNNs and results in an unsatisfying video crowd counting performance. To address this issue, a novel end-to-end video crowd counting framework, named PointerNet (PseudO-3D (P3D) CNNs INtegrated with Temporal channEl-awaRe (TCA) block) is proposed. The use of P3D kernels causes our framework to possess greater structural diversity and go deep, while having a limited computational cost and memory demand. In addition, the temporal context-aware block was proposed and integrated into our architecture, which assists in exploiting the temporal interdependencies among video sequences. Experiments on three benchmark datasets indicates that the proposed method delivers a state-of-the-art performance.
Investigation on Impression of Streetscape toward Traveling Route Recommendation Considering User Experience

This paper investigates the impression of streetscapes users receive from street view movies. In travel recommendation, travelers do not only want to reach the destination, but also want to enjoy streetscapes throughout the journey. While traditional route recommendation systems tend to focus on how to get users to the destination faster, we think routes to the destination should be determined considering user experience on the route. To consider such a user experience, the lack of actual route conditions due to 2D maps is also a problem of the traditional route recommendation systems. Therefore, we are developing a recommendation system that can provide street view movies of the recommended routes. As the preliminary investigation for realizing the system, this paper compares the impression users receive from street view movies and that from 2D map. The results show that even while presenting information of the same route with 2D Map and Street view, users still receive the different impressions.
A replication study on implicit feedback recommender systems with application to the data visualization recommendation

Abstract In this study, we compare the Bayesian personalized ranking (BPR) algorithms with two recent state‐of‐the‐art algorithms, namely, noisy‐label robust Bayesian point‐wise optimization (NBPO) and Light Graph Convolution Network (LightGCN) algorithms, to validate and generalize their performance by using six publicly available datasets and one proprietary dataset containing web‐based data visualization usage records. We follow the guidelines explained in the original studies to pre‐process the input data and evaluate these algorithms using various evaluation metrics. We also perform hyperparameter tuning for the recommendation algorithms to determine the optimal configuration resulting in the best recommendation quality. We observe that the best hyperparameter configuration varies based on the algorithms and the datasets. The results of our analysis show some similarities with the results of the original studies while differing in certain respects. We observe that adaptive oversampling BPR (AOBPR) and LightGCN algorithms generate higher quality recommendations than the other algorithms. However, algorithm convergence rates vary significantly for each dataset. We note that the AOBPR approach is particularly useful for data visualization recommendation task, and can contribute to the improved recommendations in practice.
Research on Solar Radiation Estimation Based on Singular Spectrum Analysis-Deep Belief Network

Because of its clean, safe, convenient and efficient characteristics, the photovoltaic power generation act as an important part to effectively improving the climate change, energy security as well as environmental pollution. To effectively use solar energy resources and increase the photovoltaic power generation, accurate solar radiation estimation is an indispensable basis and premise. A new estimation algorithm based on singular spectral analysis-deep belief network (SSA-DBN) is proposed in this paper. Firstly, considering meteorological factors, the air pollution data is mainly studied. A deep belief network model is established based on the input quantity selected by the maximum correlation minimum redundancy algorithm (mRMR). Afterwards, the singular spectrum analysis of historical data sequences, including singular value decomposition and reconstruction, is applied to the radiation estimation model improvement, and a daily solar radiation estimation model based on singular spectrum analysis-deep belief network (SSA-DBN) is established. It could improve the accuracy of radiation estimation and provide more effective information for photovoltaic grid-connection and power system operation and scheduling.
Fuzzy Emulated Symbolic Regression for Modelling and Control of Markov Jump Systems With Unknown Transition Rates

In this brief, a novel fuzzy emulated symbolic regression (FESR) model is proposed for modelling and control of Markov jump systems with unknown transition rates. Conventional symbolic regression model is improved in multi-layered form where internal functions, operations and sparse connections are determined via random-learning strategy. A clustering based fuzzy system is designed as a preprocessing-layer that brings an additional power to the model capability. Proposed model includes small number of the output parameters so it is parametrically parsimonious, implementable and easy designed model for future embedded-design applications. In numerical applications first, open-loop black-box modelling results of nonlinear Markov jump systems are shown to discuss the model accuracies where the systems are excited by multi-frequency sine inputs. Modelling results are compared in terms of mean squared-error (MSE) and minimum-descriptive length (MDL) criteria. Second, generalized predictive controller is designed to control the Markov jump systems with proposed model and unknown transition rate where stabilization results are discussed for future applications.