Reviewerly/exHarmony huggingface dataset:
LCCspm: l-Length Closed Contiguous Sequential Patterns Mining Algorithm to Find Frequent Athlete Movement Patterns from GPS

The analysis of athletes’ spatiotemporal data provides actionable insights for strength and conditioning and customized training designs. The identification of unique movements and adjacent match events of team-sport athletes is important. It helps to understand the demands of a match and to advance training programs by improving training specificity. In this paper we present a novel l-length Closed Contiguous sequential pattern mining (LCCspm) algorithm. To validate LCCspm, England Rugby Football League (RFL) Super League players’ movements and Fédération Internationale de Football Association (FIFA) 2018 football world cup events datasets were used. The algorithm was compared with the other existing algorithm (i.e., CCspan). Empirically, the most frequently discovered closed contiguous patterns from RFL were 1-7 length movement patterns while 10-40 length patterns were those discovered in men’s FIFA 2018 world cup. This reflects the duration at which RFL and FIFA football match events usually occur and how data granularity influence results. LCCspm greatly outperforms the CCSpan in terms of scalability, runtime and memory usage. The use of LCCspm instead of CCspan for mining closed contiguous sequences regardless of the length of patterns and size of the database is recommended as it offers timely retrieval of patterns with lesser compute.
A Don’t-Care-Based Approach to Reducing the Multiplicative Complexity in Logic Networks

Reducing the number of AND gates in logic networks benefits the applications in cryptography, security, and quantum computing. This work proposes a don’t-care-based (DC-based) approach to reduce the number of AND gates further in the well-optimized network. Furthermore, this work also proposes an enhanced synthesis flow by integrating our approach with the state-of-the-art. The experimental results show that our approach can further reduce up to 25% of the number of AND gates in the network. For the experiments about the enhanced synthesis flow, we achieve a speedup of almost <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$10\times $ </tex-math></inline-formula> on average for the cryptography benchmarks while having competitive results as compared to the flow in the state-of-the-art.
Overview of UAV Target Detection Algorithms Based on Deep Learning

With the widespread use of UAVs in military struggles, how to take effective measures to achieve the detection of UAV targets has become a focus of attention in various countries. Obtaining target images based on photoelectric detection can overcome the shortcomings of traditional observation methods and effectively achieve detection and tracking for “low, slow, small” flying targets. In response to this problem, this article combines the development of target detection algorithms, discusses traditional target detection algorithms, two-stage and one-stage target detection algorithms based on deep learning, and summarize the advantages and disadvantages of different models based on the test results on the public data set.
Cybertwin Assisted Wireless Asynchronous Federated Learning Mechanism for Edge Computing

The significant advances in wireless communication together with edge intelligent (EI) technology have facilitated the decentralized edge computing paradigm for data-intensive and delay-sensitive solution on massive Internet of Things (IoT) devices. In this paper, a Cybertwin assisted asynchronous federated learning (AFL) mechanism is proposed for realizing efficient edge computing by taking full advantage of local computation capability under heterogeneous wireless environment. First, Cybertwin is introduced as intermediary communication assistant to coordinate individual model aggregation between the users and the cloud server under AFL training process. Second, for the sake of flexible and effective utilization of communication-computation resources for edge computing, Cybertwin plays the role of intelligent agent to jointly take the local computing and up-link transmission into consideration. A resource optimization problem considering the diversified computing power, varied data size, and available communication bandwidth is formulated and we leverage the block coordinate descent (BCD) method to obtain optimal resource management solution. Extensive simulations are conducted to demonstrate the effectiveness of our proposed Cybertwin assisted AFL mechanism, which can shed further light on the application of data-intensive edge computing paradigm over wireless communication network.
Estimation on the Importance of Semantic Web Integration for Art and Culture Related Online Media Outlets

Since the dawn of the new millennium and even earlier, a coordinated effort has been underway to expand the World Wide Web into a machine-readable web of data known as the Semantic Web. The field of art and culture has been one of the most eager to integrate with the Semantic Web, since metadata, data structures, linked-data, e.g., the Getty vocabularies project and the Europeana LOD initiative—and other building blocks of this web of data are considered essential in cataloging and disseminating art and culture-related content. However, art is a constantly evolving entity and as such it is the subject of a vast number of online media outlets and journalist blogs and websites. During the course of the present study the researchers collected information about how integrated the media outlets that diffuse art and culture-related content and news are to the Semantic Web. The study uses quantitative metrics to evaluate a website’s adherence to Semantic Web standards and it proceeds to draw conclusions regarding how that integration affects their popularity in the modern competitive landscape of the Web.
StyleDNA: A High-Fidelity Age and Gender Aware Kinship Face Synthesizer

High-fidelity kinship face synthesis receives increasing interest in this technology for visual kinship applications, including law enforcement, social media analysis, finding lost children, etc. However, it is a challenging task because of the unresolved ambiguities by the limited amount of available kinship data and severe data noise. To address these issues, we leverage the pretrained state-of-the-art face synthesis model, StyleGAN2, to assist the synthesis. With StyleGAN2, we develop three different kinship face synthesis strategies: (1) synthesis based on the kinship statistics, (2) synthesis using the latent code interpolation of the parents, and (3) synthesis based on the latent code interpolation from the disentangled age and gender independent latent space. The first two methods synthesize kinship faces through the direct manipulation of the original StyleGAN2 latent codes. The third one, on the other hand, is a two-stage synthesis method which first learns an age and gender invariant latent representation upon the one of StyleGAN2 to represent the genes. Combining with the maximal selection process to fuse the corresponding representation of parents, we form the genes of the child followed by them feeding back to StyleGAN2 for the final synthesis. With extensive ablation studies and experiments, we observe that all three methods can generate more photo-realistic and clearer faces than the previous state-of-the-art method. In addition, the third method achieves the best kinship verification results on the FIW dataset. Surprisingly, the subjective evaluation results of the three proposed methods are very close because typical humans are not good at recognizing unfamiliar kinship faces.
Proliferation of Cyber Situational Awareness: Today’s Truly Pervasive Drive of Cybersecurity

Situation awareness (SA) issues necessitate a comprehension of present activities, the ability to forecast, what will happen next, and strategies to assess the threat or impact of current internet activities and projections. These SA procedures are universal, domain-independent and can be used to detect cyber intrusions. This study introduces cyber situation awareness (CSA), its origin, conception, aim, and characteristics based on an analysis of function shortages and development requirements. Furthermore, we discussed the CSA research framework and examined the research history, which is the essential aspect, and assessed the present issues of the research as well. The assessment approaches were divided into three methods: mathematics model, knowledge reasoning, and pattern recognition. The study then goes into detail regarding the core idea, assessment procedure, strengths, and weaknesses of novel approaches, and then, it addresses CSA from three perspectives: model, knowledge representation, and assessment methods. Many common approaches are contrasted, and current CSA application research in the realms of security, transmission, survivability, and system evaluation is discussed. Finally, this study summarized the findings of the present from technical and application systems, outlined CSA’s future development directions, and provided adversary activities and information that can be used to improve an organization’s SA operations.
Neural speech-rate conversion with multispeaker WaveNet vocoder

Speech-rate conversion technology, which can expand or compress speech waveforms while preserving the pitch of the sound, is traditionally realized by signal-processing-based approaches. To improve the synthesis quality, this paper proposes a machine-learning-based approach using neural vocoders, to perform neural speech-rate conversion. The proposed approach introduces a multispeaker WaveNet vocoder trained with a multispeaker corpus. Speech-rate conversion for many and unspecified speakers, not included in the training data, is realized by resampling acoustic features or hidden features along the time direction in inference. In experiments, the multispeaker WaveNet vocoder was trained using the JVS corpus and two types of resampling methods were compared. Conventional WSOLA and STRAIGHT were also compared as signal-processing-based baselines. The test sets included Japanese speaker corpora for the monolingual condition, and an English multispeaker corpus (CMU ARCTIC) for the cross-lingual condition. The results of the experiments demonstrate that the proposed approach with resampling of hidden features can achieve higher quality speech-rate conversion than the conventional methods, in both monolingual and cross-lingual conditions, except for speakers with low fundamental frequency in conversion of fast speech.
Face Aging Using Generative Adversarial Networks

When computationally aging a face, it is desirable that the age output is close to the expected age and the individual’s characteristics are maintained. Traditionally, there have been two kinds of modeling techniques used in this task: prototype-based and model-based methods. The first calculates the mean difference between age groups, and the latter uses parametric models to simulate change over time. Both approaches fail to keep individual characteristics when transforming a face from a younger domain to an aged one. With advances in computer vision, generative models have been applied to perform this task, especially generative adversarial networks (GANs). With them, it becomes possible to generate realistically aged faces of specific individuals. These networks can encode latent information, by enabling a generator model to create new images conditional on an input image. This could lead to improvements in biometric systems, assist in the search for missing people and in the identification of criminals in an automated way. In addition, generative models are currently used in multiple applications in entertainment. In the last decade, an increasing number of publications focused on applying state-of-the-art generative models on facial aging. Most of these works were done by using a general architecture and customizing it to fit the aging problem’s needs. This work enumerates the most frequent architectures, some of the challenges involved in the task, the available benchmark databases, and their applications. Additionally, it compares different face aging architectures, by using the three most frequent databases: FG-NET, UTKFaces, and CACD. Finally, age estimation and face verification techniques were used to measure the effectiveness of the resulting faces.
Adaptive and Personalized Visualization

There is ample evidence in the visualization community that individual differences matter. These prior works highlight various personality traits and cognitive abilities that can modulate the use of t
Insults, Criminalisation, and Calls for Violence: Forms of Hate Speech and Offensive Language in German User Comments on Immigration

This chapter studies target groups, forms, and intensities of hate speech in German user comments on refugees and immigration, and considers offensive comments that are below the line of hate speech but can potentially escalate online discussions and provoke hate speech. The analysis uses a modularised framework that goes beyond the common “hate/no-hate” dichotomy in the field. Results show that offensive language is used significantly more often than hate speech in user comments. While statements with offensive language are predominantly directed against politicians, hate speech statements are mostly targeting refugees and immigrants. Comments that stereotype refugees and Muslims as criminals portraying them as a danger are noteworthy, while extreme forms of hate speech, such as calls for violence and killing, are rare in the sample.
A Reversible Database Watermark Scheme for Textual and Numerical Datasets

In this paper, we present our design of a new, blind, reversible, robust, watermarking scheme for relational databases storing textual and numerical data, which is not only guaranteed to meet the above seven requirements and to provide proof of ownership for the legal owner of a relational database but also does not require the existence of the primary key attribute in the relational database. Extensive analysis and our experimental results showed that the proposed scheme achieves strong robustness under a variety of possible attacks, e.g., deletion attacks, modification attacks, addition attacks, and sorting attacks. In addition, in comparison with six existing schemes, the proposed scheme was more secure and robust, as evidenced by our experimental results and robustness analysis.
GCN2defect : Graph Convolutional Networks for SMOTETomek-based Software Defect Prediction

With the introduction of network metrics into the field of software defect prediction, the dependency network of software modules is widely used. The network embedding models aim to represent nodes as low-dimensional vectors, thereby preserving the topological structure of the network. However, in software engineering, traditional network embedding models do not concern deep learning strategies, while recently, graph neural networks (GNNs) have been proved to be an effective deep learning framework for learning graph data. As a variant of GNN, graph convolution neural network (GCN) has achieved appealing results in node classification and link prediction. Inspired by the performance of GCN, we propose GCN2defect, which extends GCN to automatically learn to encode the software dependency network and ultimately improve software defect prediction. Specifically, we firstly construct a program's Class Dependency Network, and then use node2vec for embedded learning to obtain the structural features of the network automatically. After that, we combine the learned structural features with traditional software code features to initialize the attributes of nodes in the Class Dependency Network. Next, we feed the dependency network to GCN to get much deeper representation of the class. Meanwhile, to enhance the accuracy of prediction, we also employ the SMOTETomek sampling to solve the problem of data imbalance. Finally, we evaluate the proposed method on eight open-source programs and demonstrate that, on average, GCN2defect improves the state-of-the-art approach by 6.84% ~ 23.85% in terms of the F-measure.
An Ensemble Model for Combating Label Noise

The labels crawled from web services (e.g. querying images from search engines and collecting tags from social media images) are often prone to noise, and the presence of such label noise degrades the classification performance of the resulting deep neural network (DNN) models. In this paper, we propose an ensemble model consisting of two networks to prevent the model from memorizing noisy labels. Within our model, we have one network generate an anchoring label from its prediction on a weakly-augmented image. Meanwhile, we force its peer network, taking the strongly-augmented version of the same image as input, to generate prediction close to the anchoring label for knowledge distillation. By observing the loss distribution, we use a mixture model to dynamically estimate the clean probability of each training sample and generate a confidence clean set. Then we train both networks simultaneously by the clean set to minimize our loss function which contains unsupervised matching loss (i.e., measure the consistency of the two networks) and supervised classification loss (i.e. measure the classification performance). We theoretically analyze the gradient of our loss function to show that it implicitly prevents memorization of the wrong labels. Experiments on two simulated benchmarks and one real-world dataset demonstrate that our approach achieves substantial improvements over the state-of-the-art methods.
Co-clustering for Fair Recommendation

Collaborative filtering relies on a sparse rating matrix, where each user rates a few products, to propose recommendations. The approach consists of approximating the sparse rating matrix with a simple model whose regularities allow to fill in the missing entries. The latent block model is a generative co-clustering model that can provide such an approximation. In this paper, we show that exogenous sensitive attributes can be incorporated in this model to make fair recommendations. Since users are only characterized by their ratings and their sensitive attribute, fairness is measured here by a parity criterion. We propose a definition of fairness specific to recommender systems, requiring item rankings to be independent of the users' sensitive attribute. We show that our model ensures approximately fair recommendations provided that the classification of users approximately respects statistical parity.
Blockchain-Based Decentralized Authorization Technique for Data Sharing in the Internet of Things

The idea behind the emergence of the Internet of Things (IoT) is to offer smart services and applications to make our daily life more convenient through the connectivity of smart devices. Smart devices are meant to observe the environment and accumulate data that are distributed to authorized users. IoT data collection, administration, and access indulge several stakeholders that comprise resource, resource owner, resource consumer, admin, data repository hosts, and data providers. In this paper, we presented a blockchain-based decentralized authorization model for the IoT environment. The proposed blockchain-enabled model is based on the concepts of the smart contract which is free from a single point of failure and offers a trusted, and decentralized data sharing scheme. Additionally, in the proposed work, IoT devices are not part of the blockchain network which results in making the system more scalable. The implementation validates the feasibility and usefulness of the proposal from the consumers as well as the owner's perspectives.
Sustainable Hate: How Gab Built a Durable “Platform for the People”

Background: Gab is a social network and a haven for racism, sexism, and extremism. With accessible design and tech-forward products, this “platform for the people” has surged in popularity. Analysis: This article examines Gab News, analyzing its topics, sources, and platform integration. Its mix of topics creates an expansive world. Its mix of neutral and hyperpartisan sources creates a believable (yet still conservative) world. And its mix of moderate and explicit content establishes a sustainable (yet still hateful) world. Conclusion: Gab’s strategic communication offers a sustainable foundation for the radical right; understanding this is key for counteracting hate.
High-Capacity Reversible Data Hiding Using Lossless LZW Compression

Reversible data hiding is a form of steganography where the cover data is recovered completely after extracting the secret message. This paper presents a novel reversible data hiding scheme for compressed images. For the cover image to be recovered completely, the compression algorithm employed must be lossless. The proposed method processes the image to achieve higher compression efficiency using LZW lossless compression. LZW compression exploits the redundancy in the form of repetition present in the input data. Less redundancy increases the size of the coded output. The proposed pre-processing method increases the input data’s redundancy, thereby compressing the input image with a very high compression ratio. The redundant bits present in the LZW output are used to store the secret information. After retrieving the hidden bits at the receiver after transmission, the cover image is recovered completely after decompression. The proposed method is compared with existing techniques in terms of compression ratio and data hiding capacity. The proposed data compression model increased the compression ratio to a great extent. The experimental results show that the proposed method showcased a very high compression ratio and good data hiding capacity.
Searching for Controllable Image Restoration Networks

We present a novel framework for controllable image restoration that can effectively restore multiple types and levels of degradation of a corrupted image. The proposed model, named TASNet, is automatically determined by our neural architecture search algorithm, which optimizes the efficiency-accuracy trade-off of the candidate model architectures. Specifically, we allow TASNet to share the early layers across different restoration tasks and adaptively adjust the remaining layers with respect to each task. The shared task-agnostic layers greatly improve the efficiency while the task-specific layers are optimized for restoration quality, and our search algorithm seeks for the best balance between the two. We also propose a new data sampling strategy to further improve the overall restoration performance. As a result, TASNet achieves significantly faster GPU latency and lower FLOPs compared to the existing state-of-the-art models, while also showing visually more pleasing outputs. The source code and pre-trained models are available at https://github.com/ghimhw/TASNet.
Zero-watermarking Algorithm for Audio and Video Matching Verification

&lt;abstract&gt; &lt;p&gt;For the needs of tamper-proof detection and copyright identification of audio and video matching, this paper proposes a zero-watermark algorithm that can be used for audio and video matching verification. The algorithm segments audio and video in smaller time units, generates a video frame feature matrix based on NSCT, DCT, and SVD, and generates a sound watermark based on methods such as DWT and K-means. The zero watermark combines video, audio and copyright information. The experimental results show that the zero watermark generated by this algorithm can not only realize highly accurate matching detection and positioning of audio and video, but also well resist common single attack and combination attacks such as noise, scaling, rotation, frame attack and format conversion, which has good robustness.&lt;/p&gt; &lt;/abstract&gt;