Reviewerly/exHarmony huggingface dataset:
Interactive spatio-temporal feature learning network for video foreground detection

Abstract Video foreground detection (VFD), as one of the basic pre-processing tasks, is very essential for subsequent target tracking and recognition. However, due to the interference of shadow, dynamic background, and camera jitter, constructing a suitable detection network is still challenging. Recently, convolution neural networks have proved its reliability in many fields with their powerful feature extraction ability. Therefore, an interactive spatio-temporal feature learning network (ISFLN) for VFD is proposed in this paper. First, we obtain the deep and shallow spatio-temporal information of two paths with multi-level and multi-scale. The deep feature is conducive to enhancing feature identification capabilities, while the shallow feature is dedicated to fine boundary segmentation. Specifically, an interactive multi-scale feature extraction module (IMFEM) is designed to facilitate the information transmission between different types of features. Then, a multi-level feature enhancement module (MFEM), which provides precise object knowledge for decoder, is proposed to guide the coding information of each layer by the fusion spatio-temporal difference characteristic. Experimental results on LASIESTA, CDnet2014, INO, and AICD datasets demonstrate that the proposed ISFLN is more effective than the existing advanced methods.
MOJ-DB: A new database of Arabic historical handwriting and a novel approach for subwords extraction

The digitalization of historical documents is vital to preserving their content and the historical memory of nations. Although, the results of historical Arabic handwritten text recognition and word spotting are still unsatisfactory. The increasing research efforts during the last few years are still not sufficient since handwriting recognition systems rely heavily on robust databases. In this paper, we present a new contour-based method of subword extraction from Arabic historical documents and a novel database of Arabic historical subwords MOJ-DB. The proposed method of subword extraction includes a process of touching components resolving. It proved high performance and consistency while tested on different databases and compared with other methods from the literature. The proposed database contains 560000 subwords distributed on 5600 different classes. It was built using 64 pages extracted from 10 books written in the 17th and 16th centuries. MOJ-DB database is divided into three sets; 70%,20%, and 10% for training, testing, and validation, respectively. Ground truth is established iteratively to guarantee minimal error. It includes information about the subword as of the sourcebook and page. We conducted several experiments to verify the robustness of the proposed database as well as the validity of the segmentation process. The database is freely available for the public research community. It can be used for word and subword recognition, word spotting, subword extraction, and database construction.
Attention-Based Bi-DLSTM for Sentiment Analysis of Beijing Opera Lyrics

We propose a sentiment analysis model based on Bi-DLSTM to solve the problem of sentiment analysis of Beijing Opera lyrics. A Bi-LSTM network with dilated recurrent skip connections (Bi-DLSTM) is introduced in this model, which can improve the ability to exact long-sequence information. The proposed model can learn the dependence of long sequences in different time dimensions and effectively improve the semantic extraction performance of lyrics. The attention mechanism is introduced to ensure the recognition of the more important words in the text sequence, which further improves the performance of the network. In order to solve the problem of lack of data on lyric sentiment analysis on the Internet, we build a dataset that can be used for lyric sentiment analysis. This paper completes multiple experiments on four datasets and verifies the effectiveness of the proposed model.
Color Image Contrast Enhancement Using Modified Firefly Algorithm

The image enhancement process is used for improving the standard of the image, it's inspired by the development of human perception pictorial information. Increasing the contrast of the image, removing the unwanted noise from the images is the picture enhancement process. A histogram of the low contrast images and depth image is employed to enhance image contrast. In the proposed work, a color image as input and extract out the red, green, and blue pixel matrixes from it, then obtain the optimized histogram using the modified firefly algorithm and compare the performance matrices like PSNR and Entropy, etc. with other optimization techniques.
Bridging unlinkability and data utility: Privacy preserving data publication schemes for healthcare informatics

Publishing patient data without revealing their sensitive information is one of the challenging research issues in the healthcare sector. Patient records contain useful information that is often released to healthcare industries and government institutions to support medical and census research. There are several existing privacy models in protecting healthcare data privacy, which are mainly built upon the anonymity of patients. In this paper, we incorporate unlinkability in the context of healthcare data publication, where two new privacy notions namely identity unlinkability and attribute unlinkability are introduced. We design two schemes using the proposed models to address identity disclosure and attribute disclosure problems in publishing healthcare data. Experimental results on real and synthetic datasets show that our schemes efficiently achieve data utility preservation and privacy protection simultaneously.
Interest-Based Content Clustering for Enhancing Searching and Recommendations on Smart TV

Smart TV has become a pervasive device due to its support for numerous entertainment options. These capabilities of smart TV make it attractive for viewers and researcher. Besides, a plethora of multimedia content continues to grow, which makes searching and browsing the desired content a difficult, time-consuming, and contributes to cognitive overload problem. In the case of smart TV, making clusters of the related content based on user’s interest is among the best solutions. In this connection, this study proposed a dynamic approach for clustering the TV-related online multimedia content and presenting them in a manageable format on smart TV to mitigate the issue of searching and relevant recommendations. We collected and clustered the content from diverse data sources based on the viewer’s interest. This further recommends novel content to the viewers without social metadata, such as rates, tags, which is normally insignificant in for smart TV viewership due to its shared nature. We used bisecting <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mi>K</mi> </math> -means, Lingo, and Suffix Tree Clustering (STC) algorithms. A comparative analysis of these algorithms and suitability in the context of smart TV is also presented. Results show that the proposed approach enhances search results and recommends relevant content based on user’s interests.
User-centred multimodal authentication: securing handheld mobile devices using gaze and touch input

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Requirements engineering for autonomous vehicles

Context: Autonomous Vehicles (AVs) will transform the way we live and work. Several benefits are envisaged, including: reduction in traffic deaths, drop in harmful emissions, improvement in fuel economy, reduction in travel time, and consumer savings. Indeed, the trend in the automobile industry is the development of AVs in preparation for the introduction and mass implementation of driverless vehicles. However, given the complexity and increasing connectivity of the AV, the challenges for effective and efficient development are immense. Many problems are related to misconceptions in the requirements engineering phase for AVs. Hence, a Requirements Engineering (RE) process is crucial in the development of AVs. Objective: The purpose of this work is to identify and analyse the current RE approaches used for AVs development. The analysis is based on answers related to the type of RE problems addressed by the study, the RE phases covered by the approach, requirements modelling styles used, the type of requirements described in the study, the specific AVs considered in the study, and the open problems reported. Method: We conducted a Systematic Literature Review (SLR) as the basis for our work. Results: Our SLR draws on 31 studies in which we identified the RE problems addressed by the studies and the phases of the RE processes considered. We also uncovered the languages and description styles used to describe the requirementes of the AVs. Special attention was paid to the non-functional requirements of interest. Different types of AVs were identified. Last but not least, several challenges were revealed. Conclusions: This paper reports the current state of the art of RE for AVs and identifies some open issues that deserve further investigation.
SFGN: Representing the sequence with one super frame for video person re-identification

Video-based person re-identification (V-Re-ID) is more robust than image-based person re-identification (I-Re-ID) because of the additional temporal information. However, the high storage overhead of video sequences largely stems the applications of V-Re-ID. To reduce the storage overhead, we propose to represent each video sequence with only one frame. However, directly picking one frame from each sequence will reduce the performance dramatically. Thus, we propose a brand-new framework called super frame generation network (SFGN), which can encode the spatial–temporal information of a video sequence into a generated frame, which is called “super frame” to distinguish from the directly picked “key frame”. To achieve super frames of high visual quality and representation ability, we carefully design the specific-frame-feature fused skip-connection generator (SFSG). SFSG takes the role of a feature encoder and the co-trained image model can be seen as the corresponding feature decoder. To reduce the information loss in the encoding–decoding process, we further propose the feature recovery loss (FRL). To the best of our knowledge, we are the first to propose and relieve this issue. Extensive experiments on Mars, iLIDS-VID, and PRID2011 show that the proposed SFGN can generate super frames of high visual quality and representation ability. For the code, please visit the project website: .
CandyCodes: simple universally unique edible identifiers for confirming the authenticity of pharmaceuticals

Abstract Counterfeit or substandard medicines adversely affect the health of millions of people and cost an estimated $200 billion USD annually. Their burden is greatest in developing countries, where the World Health Organization estimates that one in ten medical products are fake. In this work, I describe a simple addition to the existing drug manufacturing process that imparts an edible universally unique physical identifier to each pill, tablet, capsule, caplet, etc. This technique uses nonpareils (also called sprinkles and “hundreds and thousands”), tiny inexpensive multicolor candy spheres that are normally added to other candies or desserts as decorations. If nonpareils are applied at random to a pill immediately after manufacture, the specific pattern they form is unlikely to ever be repeated by random chance; this means that the pattern (or “CandyCode”) can be used to uniquely identify the pill and distinguish it from all other pills. By taking a photograph of each CandyCoded pill after manufacture and recording the location and color of each nonpareil, a manufacturer can construct a database containing the CandyCodes of all known-authentic pills they produce. A consumer can then simply use a cellphone to photograph a pill and transfer its image to the manufacturer’s server, which determines whether the pill’s CandyCode matches a known-good CandyCode in their database (meaning that the pill is authentic) or does not have a match in the database (in which case the consumer is warned that the pill may be counterfeit and should not be consumed). To demonstrate the feasibility of using random particles as universal identifiers, I performed a series of experiments using both real CandyCodes (on commercially produced chocolate candies) and simulated CandyCodes (generated by software). I also developed a simple method for converting a CandyCode photo to a set of strings for convenient storage and retrieval in a database. Even after subjecting CandyCodes to rough handling to simulate shipping conditions, the CandyCodes were still easily verifiable using a cellphone camera. A manufacturer could produce at least $$10^{17}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msup> <mml:mn>10</mml:mn> <mml:mn>17</mml:mn> </mml:msup> </mml:math> CandyCoded pills—41 million for each person on Earth—and still be able to uniquely identify each CandyCode. By providing universally-unique IDs that are easy to manufacture but hard to counterfeit, require no alteration of the existing drug formulation and minimal alteration of the manufacturing process, and need only a cameraphone for verification, CandyCodes could play an important role in the fight against fraud in pharmaceuticals and many other products.
Multi-Source Selection Transfer Learning with Privacy-Preserving

Transfer learning has ability to create learning task of weakly labeled or unlabeled target domain by using knowledge of source domain to help, which can effectively improve the performance of target learning task. At present, the increased awareness of privacy protection restricts access to data sources and poses new challenges to the development of transfer learning. However, the research on privacy protection in transfer learning is very rare. The existing work mainly uses differential privacy technology and does not consider the distribution difference between data sources, or does not consider the conditional probability distribution of data, which causes negative transfer to harm the effect of algorithm. Therefore, this paper proposes multi-source selection transfer learning algorithm with privacy-preserving MultiSTLP, which is used in scenarios where target domain contains unlabeled data sets with only a small amount of group probability information and multiple source domains with a large number of labeled data sets. Group probability means that the class label of each sample in target data set is unknown, but the probability of each class in a given data group is available, and multiple source domains indicate that there are more than two source domains. The number of data set contains more than two data sets of source domain and one data set of target domain. The algorithm adapts to the marginal probability distribution and conditional probability distribution differences between domains, and can protect the privacy of target data and improve classification accuracy by fusing the idea of multi-source transfer learning and group probability into support vector machine. At the same time, it can select the representative dataset in source domains to improve efficiency relied on speeding up the training process of algorithm. Experimental results on several real datasets show the effectiveness of MultiSTLP, and it also has some advantages compared with the state-of-the-art transfer learning algorithm.
Matching Knowledge Graphs with Compact Niching Evolutionary Algorithm

To address the Knowledge Graph (KG) heterogeneity issue, we need to determine a set of entity correspondences, which requires aggregating several complementary similarity measures to improve the confidence of the results. How to determine the suitable aggregating weights for the similarity measures to improve the KG alignment's quality is called the KG meta-matching problem, whose challenge of scalability remains significant in the Semantic Web (SW) domain. To face this challenge, this work proposes a Compact Niching Evolutionary Algorithm (CNEA) based matching technique. We first propose an approximate evaluation metric on the alignment's quality, and on this basis, a multi-modal optimization model is constructed to formally define the KG meta-matching problem. Then, a niching strategy is combined with EA's evolutionary paradigm to address it, which is able to effectively locate and maintain multiple global optimal solutions. Moreover, a serial matching framework and the compact evolutionary mechanism are presented to improve CNEA's efficiency. In particular, the former reduces the algorithm's search space of the instance matching phase with schema-level alignments, and the latter uses the probability representation on the population to reduce the algorithm's memory consumption and run time. The experiment utilizes Ontology Alignment Evaluation Initiative (OAEI)'s KG track to test our proposal's performance, and experimental results show that CNEA-based KG matching technique is both effective and efficient.
Polarization-based smoke removal method for surgical images

Smoke generated during surgery affects tissue visibility and degrades image quality, affecting surgical decisions and limiting further image processing and analysis. Polarization is a fundamental property of light and polarization-resolved imaging has been studied and applied to general visibility restoration scenarios such as for smog or mist removal or in underwater environments. However, there is no related research or application for surgical smoke removal. Due to differences between surgical smoke and general haze scenarios, we propose an alternative imaging degradation model by redefining the form of the transmission parameters. The analysis of the propagation of polarized light interacting with the mixed medium of smoke and tissue is proposed to realize polarization-based smoke removal (visibility restoration). Theoretical analysis and observation of experimental data shows that the cross-polarized channel data generated by multiple scattering is less affected by smoke compared to the co-polarized channel. The polarization difference calculation for different color channels can estimate the model transmission parameters and reconstruct the image with restored visibility. Qualitative and quantitative comparison with alternative methods show that the polarization-based image smoke-removal method can effectively reduce the degradation of biomedical images caused by surgical smoke and partially restore the original degree of polarization of the samples.
Blockchain Technology in the Fashion Industry

The concept of fashion has been coupled with technology, where technology has become the protagonist. The transparency between an organization and a customer works as a catalyst, and the customer has taken a more mainstream role. With blockchain technology, companies can reconnect with customers and customers can track the journey of a product from its raw materials to the finished goods. The primary focus of the study is on services and data collected from the following sectors, namely fashion, apparel, and online platforms. The author's main goals are (1) to illustrate an overview of how big data is transforming the service industry, especially the fashion and design sector, and (2) to present various mechanisms adopted in the service industry. The study aims to investigate a model that fits through EXT-TAM and uses additional attributes of blockchain technology with a special reference to fashion apparel. The findings of this study depict a model, where PEOU, PU, and attitude are the major constructs and present a win-win scenario for both the customer and the organization.
Structure Diversity-Induced Anchor Graph Fusion for Multi-View Clustering

The anchor graph structure has been widely used to speed up large-scale multi-view clustering and exhibited promising performance. How to effectively integrate the anchor graphs on multiple views to achieve enhanced clustering performance still remains a challenging task. Existing fusing strategies ignore the structure diversity among anchor graphs and restrict the anchor generation to be same on different views, which degenerates the representation ability of corresponding fused consensus graph. To overcome these drawbacks, we propose a novel structural fusion framework to integrate the multi-view anchor graphs for clustering. Different from traditional integration strategies, we merge the anchors and edges of all the view-specific anchor graphs into a single graph for the structural optimal graph learning. Benefiting from the structural fusion strategy, the anchor generation of each view is not forced to be same, which greatly improves the representation capability of the target structural optimal graph, since the anchors of each view capture the diverse structure of different views. By leveraging the potential structural consistency among each anchor graph, a connectivity constraint is imposed on the target graph to indicate clusters directly without any post-processing such as k -means in classical spectral clustering. Substantial experiments on real-world datasets are conducted to verify the superiority of the proposed method, as compared with the state-of-the-arts over the clustering performance and time expenditure.
Reinforcement Learning Applicability for Resource-Based Auto-scaling in Serverless Edge Applications

Serverless computing is an alternative deployment paradigm for cloud computing platforms, aimed to provide scalability and cost reduction without requiring any additional deployment overhead from developers. Generally, open-source serverless computing platforms rely on two auto-scaling approaches: workload-based and resource-based. In the former, a designated algorithm scales instances according to the number of incoming requests. In the latter, instances are scaled when a certain resource usage limit, such as maximum Central Processing Unit (CPU) utilization, is reached. Resource-based auto-scaling is usually implemented leveraging Kubernetes Horizontal Pod Autoscaler (HPA). In this work, we investigate the applicability of a reinforcement-based approach to resource-based auto-scaling in OpenFaaS, the most widely used open-source serverless platform. Serverless technologies are particularly convenient when dealing with edge computing on constrained devices or resource-limited machines. Our experimental analysis has been conducted on constrained Kubernetes-based nodes, to simulate such an edge application scenario. Its preliminary results show that our proposed model learns an effective scaling policy, based on CPU utilization, to provide minimal service latency within a limited number of iterations.