Reviewerly/exHarmony huggingface dataset:
A Point-of-Interest Recommendation Method Exploiting Sequential, Category and Geographical Influence

Point of interest (POI) recommendation as an important service in location-based social networks has developed rapidly, which can help users find more interesting unknown locations and facilitate service providers to provide users with more accurate notifications or advertisements. Some existing work has addressed the data sparsity problem of collaborative filtering by incorporating contextual information into the model. However, they ignore the sequence relationship contained in the user’s historical check-in records, which makes it difficult to accurately model the user’s preference and affects the final recommendation results. To acquire users’ preference for a location more accurately, this paper proposes a new POI recommendation framework exploiting sequential, category, and geographical influence. Firstly, we obtain the latent vector of POI and the latent vector of the user’s preference for POI from the user’s check-in sequence based on the word embedding model. Next, a virtual common access sequence for users is constructed according to the user’s check-ins, a new similarity computation method is present combining category differentiation and POI latent vector. Then, we apply it to the collaborative filtering framework to get the user’s behavioral preference probability of POI. In addition, the kernel density estimation method is employed to get the user’s geographical preference probability of POI by considering the geographical influence. Finally, the POI recommendation list is obtained by the weighted fusion of the two users’ preference probability to improve the performance of the POI recommendation. Experimental results on two datasets indicate that the proposed method has better performance in terms of three evaluation metrics than the other five POI recommendation methods.
Human Performance Capture from Monocular Video in the Wild

Capturing the dynamically deforming 3D shape of clothed human is essential for numerous applications, including VR/AR, autonomous driving, and human-computer interaction. Existing methods either require a highly specialized capturing setup, such as expensive multi-view imaging systems, or they lack robustness to challenging body poses. In this work, we propose a method capable of capturing the dynamic 3D human shape from a monocular video featuring challenging body poses, without any additional input. We first build a 3D template human model of the subject based on a learned regression model. We then track this template model’s deformation under challenging body articulations based on 2D image observations. Our method outperforms state-of-the-art methods on an in-the-wild human video dataset 3DPW. Moreover, we demonstrate its efficacy in robustness and generalizability on videos from iPER datasets.
A dynamic hand gesture recognition dataset for human-computer interfaces

Computer vision systems are commonly used to design touch-less human-computer interfaces (HCI) based on dynamic hand gesture recognition (HGR) systems, which have a wide range of applications in several domains, such as, gaming, multimedia, automotive, home automation. However, automatic HGR is still a challenging task, mostly because of the diversity in how people perform the gestures. In addition, the number of publicly available hand gesture datasets is scarce, often the gestures are not acquired with sufficient image quality, and the gestures are not correctly performed. In this data article, we propose a dataset of 27 dynamic hand gesture types acquired at full HD resolution from 21 different subjects, which were carefully instructed before performing the gestures and monitored when performing the gesture; the subjects had to repeat the movement in case the performed hand gesture was not correct, i.e., the authors of this paper that were observing the gesture found that it did not correspond to the exact expected movement and/or the camera recorded a viewpoint did not allow for a plain visualizing of the gesture. Each subject performed 3 times the 27 hand gestures for a total of 1701 videos collected and corresponding 204,120 video frames.
CroLSSim: Cross‐language software similarity detector using hybrid approach of LSA‐based AST‐MDrep features and CNN‐LSTM model

Software similarity in different programming codes is a rapidly evolving field because of its numerous applications in software development, software cloning, software plagiarism, and software forensics. Currently, software researchers and developers search cross-language open-source repositories for similar applications for a variety of reasons, such as reusing programming code, analyzing different implementations, and looking for a better application. However, it is a challenging task because each programming language has a unique syntax and semantic structure. In this paper, a novel tool called Cross-Language Software Similarity (CroLSSim) is designed to detect similar software applications written in different programming codes. First, the Abstract Syntax Tree (AST) features are collected from different programming codes. These are high-quality features that can show the abstract view of each program. Then, Methods Description (MDrep) in combination with AST is used to examine the relationship among different method calls. Second, the Term Frequency Inverse Document Frequency approach is used to retrieve the local and global weights from AST-MDrep features. Third, the Latent Semantic Analysis-based features extraction and selection method is proposed to extract the semantic anchors in reduced dimensional space. Fourth, the Convolution Neural Network (CNN)-based features extraction method is proposed to mine the deep features. Finally, a hybrid deep learning model of CNN-Long-Short-Term Memory is designed to detect semantically similar software applications from these latent variables. The data set contains approximately 9.5K Java, 8.8K C#, and 7.4K C++ software applications obtained from GitHub. The proposed approach outperforms as compared with the state-of-the-art methods.
A self-embedding secure fragile watermarking scheme with high quality recovery

In recent years, with the development of cloud storage, more and more people upload images to the cloud for storage. However, confidentiality and integrity issues may arise during transmission and storage to the cloud. Aiming at these security problems, a fragile watermarking scheme based on the encrypted domain is proposed. A watermark is divided into two types, one is for detection, the other is for recovery. After embedding the two types of watermarks into the host image, the watermarked image will be transferred to the cloud for storage. A three-level tamper detection mechanism is used in the detection process, and the first-level tamper detection can be processed in the cloud. While in recovery process, a mechanism of "block-level detection, pixel-level recovery" is proposed to recover the tampered area. The experimental results show that the watermarked image has greatly changed the original image and guarantees the confidentiality. The three-level tamper detection mechanism can accurately detect the tampered area, the image can be effectively restored in different situations, when the tampering rate is as high as 80%, the average PSNR reaches 34.62 dB, and the average SSIM is higher than 0.93.
Adaptive-order proximity learning for graph-based clustering

Recently, structured proximity matrix learning, which aims to learn a structured proximity matrix with explicit clustering structures from the first-order proximity matrix, has become the mainstream of graph-based clustering. However, the first-order proximity matrix always lacks several must-links compared to the groundtruth in real-world data, which results in a mismatched problem and affects the clustering performance. To alleviate this problem, this work introduces the high-order proximity to structured proximity matrix learning, and explores a novel framework named Adaptive-Order Proximity Learning (AOPL) to learn a consensus structured proximity matrix from the proximities of multiple orders. To be specific, AOPL selects the appropriate orders first, then assigns weights to these selected orders adaptively. In this way, a consensus structured proximity matrix is learned from the proximity matrices of appropriate orders. Based on AOPL framework, two practical models with different properties are derived, namely AOPL-Root and AOPL-Log. Besides, AOPL and the derived models are regarded as the same optimization problem subjected to some slightly different constraints. An efficient algorithm is proposed to solve them and the corresponding theoretical analyses are provided. Extensive experiments on several real-world datasets demonstrate superb performance of our model.
Hypergraph regularized semi-supervised support vector machine

At present, graph regularized semi-supervised methods achieve excellent performance in various fields. However, the manifold regularization term of most methods only considers the pairwise relationship between data, thus it cannot accurately represent the multivariate and complex structure of data. In this paper, we exploit the multivariate manifold structure by hypergraph, and propose a hypergraph regularized semi-supervised support vector machine (HGSVM) algorithm. To accelerate the training process of HGSVM, we further develop a fast algorithm based boundary sample selection algorithm, termed fast-HGSVM. Moreover, two SMOTE-variant techniques and the one-vs-rest strategy are introduced in fast-HGSVM, and two multi-category semi-supervised algorithms called fast-ASHGSVM and fast-KSHGSVM are proposed. Experiments on two moons and UCI datasets validate the effectiveness of the proposed algorithms.
Using an ontology for systematic practice adoption in agile methods: Expert system and practitioners-based validation

As many software development teams have started to adopt agile methods, a vast amount of valuable experiences have been reported on in both academic and industrial knowledge bases. This information has been used through various approaches to guide and help practitioners finding suitable practices for their software development projects. Nevertheless, not many of these approaches could gather the available experiences to make them systematically reusable and help practitioners understanding agile practices in depth. To the best of our knowledge, only one ontology has been created to solve this problem; some limitations related to its quality and usability make it nevertheless unqualified to serve the intended purpose. The aim of this paper is to build an expert system (i.e. an evidence-based tool) to ease agile practices adoption by efficiently and effectively providing information on them. Firstly, we improve the concepts and relationships in the aforementioned ontology and theoretically validate it using a large data-set of agile practices adoption experiences collected through a Systematic Literature Review (SLR). Secondly, we develop a supporting tool having a friendly Graphical User Interface (GUI) allowing to use the ontology as a concrete agile practice knowledge provider. Finally, we empirically validate the enhanced ontology and evaluate the supporting tool using a survey with agile experts. Our supporting tool can help practitioners to decide what practice to adopt, how to adopt it, how to solve practical issues, etc. The ontology and the tool materialize our contribution to the field of systematic agile practices adoption.
Uniform and scalable sampling of highly configurable systems

Abstract Many analyses on configurable software systems are intractable when confronted with colossal and highly-constrained configuration spaces. These analyses could instead use statistical inference, where a tractable sample accurately predicts results for the entire space. To do so, the laws of statistical inference requires each member of the population to be equally likely to be included in the sample, i.e., the sampling process needs to be “uniform”. SAT-samplers have been developed to generate uniform random samples at a reasonable computational cost. However, there is a lack of experimental validation over colossal spaces to show whether the samplers indeed produce uniform samples or not. This paper (i) proposes a new sampler named , (ii) presents a new statistical test to verify sampler uniformity, and (iii) reports the evaluation of and five other state-of-the-art samplers: , , , , and . Our experimental results show only satisfies both scalability and uniformity.
A Manifesto for Posthuman Creativity Studies

This article advances a manifesto for a posthuman creativity studies that highlights the emergent, collective, and ecological aspects of creativity, offering propositions that problematize any individualist or human-exceptionalist approach to the field. We attend to a range of extra/ordinary affects, encounters, and modalities for expanding creative possibilities in the 21st century. Beginning with a recognition that creativity must be rewilded from its current capture in economic and educational discourses, we argue for more sustainable re-engagements. In the specificity of these encounters, we manifest 10 commitments to posthuman creativity as the foundations for a more dynamic and more-than-human creative agency.
Maximum Entropy Markov Model for Human Activity Recognition Using Depth Camera

Activity recognition is an essential factor in the determination of daily routine of a human being. There exist numerous Human Activity Recognition (HAR) systems; however, an HAR with a practical accuracy is still in search and a challenge at large. The classifiers utilized in existing systems offer degrading recognition rates in various key environments such as depth camera environment among others. To address the limitation of degrading recognition rates, in this paper, we propose Maximum Entropy Markov Model (MEMM) that solves the degrading recognition rate problem. In MEMM, we model the states of activity recognition as the states of the model itself i.e. as the states of MEMM, and hence consider the observations of video-sensor as the observations of MEMM. Further, we use a modified version of Viterbi, a machine learning algorithm, to generate the most likely probable state sequence based on these observations. Then, from such a state sequence, we use MEMM to predict the activity state. We evaluate the performance MEMM against depth dataset having eleven different types of activities in a large-scale experimentation process. The results show that MEMM outperforms existing well-known methods by achieving a weighted average recognition rate 96.3% across the naturalistic dataset collected using depth camera.
Attitudes Toward Mobile Apps for Pandemic Research Among Smartphone Users in Germany: National Survey

During the COVID-19 pandemic, but also in the context of previous epidemic diseases, mobile apps for smartphones were developed with different goals and functions, such as digital contact tracing, test management, symptom monitoring, quarantine compliance, and epidemiological and public health research.The aim of this study was to explore the potential for the acceptance of research-orientated apps (ROAs) in the German population. To this end, we identified distinctive attitudes toward pandemic apps and data sharing for research purposes among smartphone users in general and with a focus on differences in attitudes between app users and nonusers in particular.We conducted a cross-sectional, national, telephone-based survey of 1003 adults in Germany, of which 924 were useable for statistical analysis. The 17-item survey assessed current usage of pandemic apps, motivations for using or not using pandemic apps, trust in app distributors and attitudes toward data handling (data storage and transmission), willingness to share coded data with researchers using a pandemic app, social attitudes toward app use, and demographic and personal characteristics.A vast majority stated that they used a smartphone (778/924, 84.2%), but less than half of the smartphone users stated that they used a pandemic app (326/778, 41.9%). The study focused on the subsample of smartphone users. Interestingly, when asked about preferred organizations for data storage and app distribution, trust in governmental (federal or state government, regional health office), public-appointed (statutory health insurance), or government-funded organizations (research institutes) was much higher than in private organizations (private research institutions, clinics, health insurances, information technology [IT] companies). Having a university degree significantly (P<.001) increased the likelihood of using a pandemic app, while having a migration background significantly (P<.001) decreased it. The overwhelming majority (653/778, 83.9%) of smartphone users were willing to provide their app data for state-funded research. Regarding attitudes toward app usage, striking differences between users and nonusers were found. Almost all app users (317/327, 96.9%) stated they would be willing to share data, whereas only 74.3% (336/452) of nonusers supported data sharing via an app. Two-thirds (216/326, 66.3%) of app users fully or rather agreed with the statement that using a pandemic app is a social duty, whereas almost the same proportion of nonusers entirely or rather disagreed with that statement (273/451, 60.5%).These findings indicate a high potential for the adoption of ROAs among smartphone users in Germany as long as organizational providers engaged in development, operation, and distribution are state-funded or governmental institutions and transparency about data-using research institutions is provided.
Ensuring the Security and Performance of IoT Communication by Improving Encryption and Decryption With the Lightweight Cipher uBlock

Internet of Things (IoT) devices expect a secure communication method or protocol with communication performance that matches the network bandwidth in a public network. With the advancement of network bandwidths, such as through wireless technology 5G, encryption and decryption throughput can be the performance bottleneck of IoT communication security, as IoT devices have limited computing power. As a result, it is a challenge for IoT devices to obtain the required level of security and communication performance. In this article, we improve the encryption and decryption performance with a lightweight cipher algorithm called uBlock for IoT devices. By using sequential circuits, the encryption and decryption throughput can reach the 1 Gb/s level with 90 nm technology in our simulation environment. We can expect a more than two fold improvement in the performance with better nanotechnology from hardware vendors. Our proposed solution can achieve the required level of security and communication performance with a little power consumption.
TGAVC: Improving Autoencoder Voice Conversion with Text-Guided and Adversarial Training

Non-parallel many-to-many voice conversion remains an interesting but challenging speech processing task. Recently, AutoVC, a conditional autoencoder based method, achieved excellent conversion results by disentangling the speaker identity and the speech content using information-constraining bottlenecks. However, due to the pure autoencoder training method, it is difficult to evaluate the separation effect of content and speaker identity. In this paper, a novel voice conversion framework, named $\boldsymbol T$ext $\boldsymbol G$uided $\boldsymbol A$utoVC(TGAVC), is proposed to more effectively separate content and timbre from speech, where an expected content embedding produced based on the text transcriptions is designed to guide the extraction of voice content. In addition, the adversarial training is applied to eliminate the speaker identity information in the estimated content embedding extracted from speech. Under the guidance of the expected content embedding and the adversarial training, the content encoder is trained to extract speaker-independent content embedding from speech. Experiments on AIShell-3 dataset show that the proposed model outperforms AutoVC in terms of naturalness and similarity of converted speech.
Enhancing the security of E-Health services in Bangladesh using blockchain technology

The telemedicine service concept was mainly established to benefit the underprivileged people from rural areas of a country. However, due to the low literacy and awareness rates among rural population of Bangladesh, the service is not much effective. This paper represents a study on the awareness of the rural population of telemedicine service in Bangladesh and few key findings indicate how the awareness could be increased. The research also suggests that utilizing blockchain technology can enhance the data security and privacy. The research reveals some of the findings which can raise the awareness and popularity of telemedicine service among rural population. We have proposed implementation of blockchain technology which can vastly improve the security issue.
Financial fraud detection using the related-party transaction knowledge graph

Financial fraud detection has gained constant attention from researchers, practitioners, and regulators. Because of its concealment and ease of manipulation, related-party transactions (RPTs) among firms have become a usual way to implement financial fraud. However, the traditional quantitative analysis methods regard each firm as an independent individual, failing to mine the intricate relation and transactions among related parties. A knowledge graph can mine valuable hidden knowledge from large-scale associated data as a new form of knowledge representation. Therefore, in this paper, the RPT knowledge graph is utilized to detect financial fraud, where the feature of the transaction's scale and category is obtained. The experiment on the Chinese listed companies from 2000 to 2019 shows that these features enhance financial fraud detection performance, suggesting that type, size, and frequency of RPTs may imply fraud. More details, the feature importance indicates that regulators should pay more attention to the loan-based RPTs and the total number of RPTs.
Gradually Enhanced Adversarial Perturbations on Color Pixel Vectors for Image Steganography

Compared to element-wise embedding, vector-wise embedding based on CPV (color pixel vector) shows its superiority in color image steganography. However, when working with an adversarial embedding scheme for introducing adversarial perturbations, its success rate of deceiving a target CNN (convolutional neural network) steganalyzer dramatically drops. In this paper, inspired by the I-FGSM (iterative fast gradient sign method), we present an effective steganography for color images. Specifically, after decomposing an image into several non-overlapped sub-images, we iteratively and gradually increase the possibilities of generating adversarial perturbations for the CPVs in each sub-image by changing their adversarial costs. The costs are incrementally adjusted with a small step so that their maximum relative variation is minimized. Leveraging a new designed cost adjustment criterion, more modification patterns of CPV can participate in producing effective adversarial perturbations. Extensive experiments demonstrate that the proposed method achieves a high success rate in deceiving the target CNN steganalyzer and stably defending against the detection of other non-target steganalytic schemes for color images.