Reviewerly/exHarmony huggingface dataset:
Binarized network embedding with community structural information

Network embedding aims to learn low-dimensional vector representations for network nodes by preserving the network structure. The vast majority of existing network embedding methods are typically represented in continuous vectors, which impose formidable challenges in storage and computation costs, especially in large-scale applications. To this end, we propose a novel Binarized Network Embedding (BINE) method to learn binary node representations while retaining the community structural information in the network. Specifically, to preserve the community structure among node representations, we absorb the modularity constraint into the matrix factorization-based framework that can approximate the first-order and second-order proximity. Meanwhile, the cyclic coordinate descent method is adopted to implement the binary node representations for improving storage and computing efficiency. Finally, the conjugate gradient algorithm is employed to jointly optimize the objective function. Extensive experimental results on a variety of real-world network datasets demonstrate that BINE exhibits lower storage and computational complexity than state-of-the-art network embedding methods while achieving competitive experimental performance.
VIBE: Looking for Variability In amBiguous rEquirements

Variability is a characteristic of a software project and describes the fact that a system can be configured in different ways, obtaining different products (variants) from a common code base, accordingly to the software product line paradigm. This paradigm can be conveniently applied in all phases of the software process, starting from the definition and analysis of the requirements. We observe that often requirements contain ambiguities which can reveal an unintentional and implicit source of variability, that has to be detected. To this end we define VIBE, a tool supported process to identify variability aspects in requirements documents. VIBE is defined on the basis of a study of the different sources of ambiguity in natural language requirements documents that are useful to recognize potential variability, and is characterized by the use of a NLP tool customized to detect variability indicators. The tool to be used in VIBE is selected from a number of ambiguity detection tools, after a comparison of their customization features. The validation of VIBE is conducted using real-world requirements documents.
Script Tainting Was Doomed From The Start (By Type Conversion): Converting Script Engines into Dynamic Taint Analysis Frameworks

Data flow analysis is an essential technique for understanding the complicated behavior of malicious scripts. For tracking the data flow in scripts, dynamic taint analysis has been widely adopted by existing studies. However, the existing taint analysis techniques have a problem that each script engine needs to be separately designed and implemented. Given the diversity of script languages that attackers can choose for their malicious scripts, it is unrealistic to prepare taint analysis tools for the various script languages and engines.
Context-Auditor: Context-sensitive Content Injection Mitigation

Cross-site scripting (XSS) is the most common vulnerability class in web applications over the last decade. Much research attention has focused on building exploit mitigation defenses for this problem, but no technique provides adequate protection in the face of advanced attacks. One technique that bypasses XSS mitigations is the scriptless attack: a content injection technique that uses (among other options) CSS and HTML injection to infiltrate data. In studying this technique and others, we realized that the common property among the exploitation of all content injection vulnerabilities, including not just XSS and scriptless attacks, but also command injections and several others, is an unintended context switch in the victim program’s parsing engine that is caused by untrusted user input.
An improved image encryption algorithm based on Bézier coefficients matrix

In recent years, numerous images are engaged over various unprotected broad networks, causing safety and privacy threats to users. This is where image encryption plays a role in ensuring the secrecy of multimedia data. This article suggests a cryptosystem that employs Elliptic Curve Cryptosystem (ECC) on Hill Cipher matrix known as ECCHC and cubic Bezier coefficient matrix. The cubic Bezier coefficient matrix is utilized as an extra layer of security over ECCHC for image encryption and decryption. The proposed technique is designed to produce an efficient cryptosystem algorithm that provides a high level of security with no discernible difference in the decrypted image. The suggested approach will be subjected to a series of security tests to ensure its performance and efficiency. According to simulation outcomes, the suggested technique has a decent encryption and decryption impact. The proposed scheme includes more than 2100 key search, making it extremely difficult for unauthorized third parties to predict the keys in order to breach the proposed system. In statistical analysis, the image histograms are regularly distributed, and entropy value of the ciphered images are nearing 8, indicating that the ciphered images are extremely unpredictable. Finally, the PSNR values of the decrypted images of grayscale and RGB with additional noise intensities up to 0.1 are 10.6264 and 10.3707, respectively, demonstrating that the approach is capable of dealing with a certain degree of noise assault.
Deep Neural Classification of Darknet Traffic

Darknet is an encrypted portion of the internet for users who intend to hide their identity. Darknet’s anonymous nature makes it an effective tool for illegal online activities such as drug trafficking, terrorist activities, and dark marketplaces. Darknet traffic recognition is essential in monitoring and detection of malicious online activities. However, due to the anonymizing strategies used for the darknet to conceal users’ identity, traffic recognition is practically challenging. The state-of-the-art recognition systems are empowered by artificial intelligence techniques to segregate the Darknet traffic data. Since they rely on processed features and balancing techniques, these systems suffer from low performance, inability to discover hidden relations in data, and high computational complexity. In this paper, we propose a novel decision support system named Tor-VPN detector to classify raw darknet traffic into four classes of Tor, non-Tor, VPN, and non-VPN. The detector discovers complex non-linear relations from raw darknet traffic by our deep neural network architecture with 79 input artificial neurons and 6 hidden layers. To evaluate the performance of the proposed method, analyses are conducted on a benchmark dataset of DIDarknet. Our model outperforms the state-of-the-art neural network for darknet traffic classification with an accuracy of 96%. These results demonstrate the power of our model in handling darknet traffic without using any preprocessing techniques, like feature extraction or balancing techniques.
P‐2.3: The Development of Virtual Reality Technology and the Origin of the Metaverse

VR virtual reality technology is an emerging human‐computer interaction technology, which brings a variety of near‐real experiences to users, and has extremely broad application prospects in many fields such as education, medical care, retail and games. The explosion of the metaverse concept in 2021 will push VR technology to a new stage of development. Virtual reality technology is the technical cornerstone of the Metaverse. This paper sorts out the generation and development of virtual reality technology, expounds the origin of the Metaverse, and provides a certain reference value for the development of virtual reality and the Metaverse.
P‐2.6: Based on the status quo of virtual reality and prospects for future development

In the context of the explosive growth of virtual reality technology, the author analyzes the degree of attention, development trend and hotspot distribution of VR technology in recent years through research on VR‐related market research and large‐scale project applications. Specifically, the author investigates and summarizes the history, development and latest progress of VR technology, and lists the latest achievements of VR technology in the field of industrial applications. The development prospects in the fields of patriotic education, smart medical care, and smart social networking are prospected and discussed.
13.2: Review of Subjective evaluation method of high‐quality TV images

Vision is the most direct and effective way for humans to obtain information from the outside world. More than 80% of the information is transmitted to the brain through eyes. With the development of display technology, the picture received by the eyes is brighter, more vivid and clear. For such high‐quality display images, the objective evaluation system is slightly insufficient. Merely describing the image quality with parameters such as brightness, color gamut, and contrast is not completely consistent with consumers' real feelings. Therefore, subjective evaluation based on human vision is necessary to add to the image quality evaluation system. This article focuses on the domestic and international standards of subjective assessment methods for image quality, and reviews it from the perspectives of viewing conditions, observers, test preparation, test methods, and result analysis. Based on the above research, a subjective evaluation method suitable for high‐quality display devices is summarized.
HARDeNN: Hardware-assisted attack-resilient deep neural network architectures

We propose HARDeNN, a low-overhead end-to-end inference accelerator methodology to armor the underlying pre-trained neural network architecture against black-box non-input adversarial attacks. In order to find the most vulnerable neural network architectures parameters, a hardware-assisted fault injection tool and a statistical stress model have been proposed to synergy uniform fault assessment across layers and targeted in-layer fault assessment to realize a holistic, rigorous fault evaluation in NN topologies susceptible to non-input adversarial black-box attacks. The key observation from the assessment shows that the weights and activation functions are the most vulnerable neural network parameters that are susceptible to both single-bit and multiple-bit flip attacks. Concerning the aforementioned parameters, a multi-objective design space exploration is conducted to find a superior design under different resource constraints. The error-resiliency magnitude offered by HARDeNN can be adjusted based on the given boundaries. The experimental results show that HARDeNN methodology enhances the error-resiliency magnitude of cnvW1A1 by 17.19% and 96.15% for 100 multi-bit upsets that target weight and activation layers, respectively, during CIFAR-10 classification .© 2017 Elsevier Inc. All rights reserved.
Application of Sports Clustering Deconstruction Based on Neural Network

Sports cluster analysis can mainly provide more teaching ideas for physical education. Teachers can make scientific and reasonable arrangements for the teaching plan according to the analyzed data results, so as to achieve better teaching purposes. However, due to various factors such as exercise time and course time conflict, this method cannot be widely used. The neural network had a good memory function, and it can be used to integrate physical education resources in sports. Then, a knowledge framework was formed by simulating a large number of human brain neuron structures, which can solve the problems existing in the motion cluster analysis to a certain extent. In this paper, the sports based on neural network is used to improve the problems existing in the practical teaching application of sports clustering analysis. A teaching system model based on motion recognition was established to improve motion cluster analysis and promote the implementation of data-based education. According to the experimental data obtained from the experimental test, people can know that the detection rate of the sports cluster analysis model is about 89.5%, and the average missed detection probability is about 5.5%.
Lifelong Online Learning from Accumulated Knowledge

In this article, we formulate lifelong learning as an online transfer learning procedure over consecutive tasks, where learning a given task depends on the accumulated knowledge. We propose a novel theoretical principled framework, lifelong online learning, where the learning process for each task is in an incremental manner. Specifically, our framework is composed of two-level predictions: the prediction information that is solely from the current task; and the prediction from the knowledge base by previous tasks. Moreover, this article tackled several fundamental challenges: arbitrary or even non-stationary task generation process, an unknown number of instances in each task, and constructing an efficient accumulated knowledge base. Notably, we provide a provable bound of the proposed algorithm, which offers insights on the how the accumulated knowledge improves the predictions. Finally, empirical evaluations on both synthetic and real datasets validate the effectiveness of the proposed algorithm.
A Optimized BERT for Multimodal Sentiment Analysis

Sentiment analysis of one modality (e.g., text or image) has been broadly studied. However, not much attention has been paid to the sentiment analysis of multi-modal data. As the research on and applications of multi-modal data analysis are becoming more and more broad, it is necessary to optimize BERT internal structure. This article proposes a hierarchical multi-head self-attention and gate channel BERT, which is an optimized BERT model. The model is composed of three modules: the hierarchical multi-head self-attention module realizes the hierarchical extraction process of features; the gate channel module replaces BERT’s original Feed Forward layer to realize information filtering; and the tensor fusion model based on a self-attention mechanism is utilized to implement the fusion process of different modal features. Experiments show that our method achieves promising results and improves accuracy by 5–6% when compared with traditional models on the CMU-MOSI dataset.
Temporal Blockchains for Intelligent Transportation Management and Autonomous Vehicle Support in the Internet of Vehicles

In the internet of vehicles (IoV) field, blockchain technology has been proposed for durable and trustworthy bookkeeping of the exchanged data. However, block timestamps assigned by miners are usually delayed with respect to events that generate the stored data, making them unusable for applications dealing with exact timing, like traffic law enforcement and insurance accident investigation. To overcome this shortcoming, the authors propose to add new timestamps to the blockchain, which are assigned by data originators to represent the valid time of data recorded within a transaction. The resulting enhanced blockchain data model, named BiTchain, can be considered from a temporal database perspective as a bitemporal data model. In order to let users and applications enjoy the potential of BiTchain, they also introduce an expressive temporal query language, named BiTEQL, defined as a TSQL2-like temporal extension of the EQL blockchain query language.
SIMCard: Toward better connected electronic health record visualization

Summary Recently, several healthcare organizations use decision‐making systems based on electronic health record (EHR) data in order to guarantee patient's safety and improve the quality of healthcare. In essence, the evolutions of Internet of Things (IoT) technologies have been of great help for implementing an integrated and interoperable decision‐making system based on EHR and medical devices (MDs). Those IoT‐based systems allow Clinicians collecting real‐time health data and provide accurate patient's monitoring. Nevertheless, several studies have shown that it is hard to improve the quality of healthcare using the current EHR IoT‐based systems since they do not allow to easily express clinician needs. Interactive visualization tools have been proposed to improve the efficacy and utility of these EHR based systems. However, there is no framework that provides a visual summary of patient data to clinician for planning specific clinical tasks, subsequently evaluating clinician responses, visually exploring EHR data and MDs data, gaining insights, supporting dynamic coordination processes care, and forming and validating hypotheses and risks. This article addresses this problem and introduces SIMCard, an aggregation‐based connected EHR visualization framework for patient monitoring, interpreting and predicting with MDs. The proposed framework aims to synthesize patient's clinical data into a single aggregating model for both EHR and MD conforming to health standard and terminologies. It also allows to link the aggregating model to the relevant medical knowledge in order to provide a connected and dynamic care and preventive plan. Last but not least, it provides an aggregated visualization model capable of displaying graphically a patient's personal data from databases, healthcare devices and sensors to reduce cognitive barriers related to the complexity of medical information and interpretation of health data. To demonstrate the refinement and design of our system and to observe user's actual practice of visualizing and analyzing real‐world dataset, we evaluated our system and compare to existing ones.
Efficient quantum state tomography with mode-assisted training

Neural networks (NNs) representing quantum states are typically trained using Markov chain Monte Carlo based methods. However, unless specifically designed, such samplers only consist of local moves, making the slow-mixing problem prominent even for extremely simple quantum states. Here, we propose to use mode-assisted training that provides global information via the modes of the NN distribution. Applied to quantum state tomography using restricted Boltzmann machines, this method improves the quality of reconstructed quantum states by orders of magnitude. The method is applicable to other types of NNs and may efficiently tackle problems previously unmanageable.
A Knowledge Graph Perspective on Knowledge Engineering

Abstract For over 50 years researchers and practitioners have searched for ways to elicit and formalize expert knowledge to support AI applications. Expert systems and knowledge bases were all results of these efforts. The initial efforts on knowledge bases were focused on defining a domain and task intensionally with rather complex ontologies. The increasing complexity of knowledge and knowledge-based systems eventually led to the development of knowledge engineering methodologies. Knowledge graphs, in contrast to the traditional knowledge bases, represent knowledge more extensionally with a very large set of explicit statements and rather simpler and smaller ontologies. This paradigm change calls for a new take on knowledge engineering that focuses on the curation of ABox statements. In this paper, we introduce various aspects of the knowledge graphs lifecycle namely creation, hosting, curation and deployment. We define each task, give example approaches from the literature and explain our approach with a running example. Additionally, we present the German Tourism Knowledge Graph that is being implemented with our methodology.
A scene flow estimation method based on fusion segmentation and redistribution for autonomous driving

Abstract A novel approach is presented here to solve the problem of motion occlusion and motion edge blurring in the existing scene flow estimation. First instance segmentation and superpixels are combined to segment the target and other regions in fusion segmentation. The pixels in each block are then redistributed by the optical flow to ensure the motion of pixels in the subblocks is consistent. Moreover, the 3D motion of subblocks with sufficient pixels is estimated by the energy function, and the others are considered outliers. Finally, the Driving and the KITTI benchmarks are used to evaluate the proposed method. The results demonstrated that the fusion of segmentation and redistribution is positive for the estimation, and this method outperforms the other state‐of‐the‐art methods both qualitatively and quantitatively.
Open data becoming more acceptable

Researchers worldwide are becoming more open to sharing their data, a survey of 6,000 researchers shows . The State of Open Data , an annual survey in its seventh year, is conducted by the software company Figshare, the technology firm Digital Science, and the publisher Springer Nature. Most responses came from people in Asia (38%) and Europe (33%); 13% were from North America. Among the findings, around 80% of researchers said that data should be openly available, and 74% said that they have shared their data during publishing. Most respondents (70%) said they were required to follow a data-sharing policy for their research. Researchers said they are motivated to share their data primarily because it increases citations to their research papers (67%) and enhances the impact and visibility of those papers (61%). Journal or publisher mandates for scientists to share data freely and efforts to benefit the public also provided
Personalized Dynamic Attention Multi-task Learning model for document retrieval and query generation

In the actual retrieval scenario, queries from different users tend to have different retrieval intentions. Accurately understanding users’ retrieval intentions is a fundamental challenge for search engines. In this paper, we propose a Personalized Dynamic Attention Multi-task Learning model (PDAML) to solve this problem, which can clarify the user’s retrieval intention and generate a personalized document list. Specifically, we design a personalized retrieval model that can learn user retrieval preferences based on the user’s historical behavior. In addition, we propose an ad-hoc model based on multi-task learning to train query generation tasks and document retrieval tasks to enhance query representation and integrate document-aware interactive information into query representation through a dynamic attention mechanism. We can generate query representations with intent information and rank candidate documents through the interaction of the two sub-models. Experimental results on the publicly available AOL and SogouQ datasets demonstrate the effectiveness of PDAML, which improves the retrieval accuracy and personalized experience for users.
Application of Image Super-Resolution Reconstruction in Gymnastics Training by Using Internet of Things Technology

Image super-resolution reconstruction, in short, is to restore some not too clear images, so that the image is more convenient to identify. It is generally used in intelligent surveillance systems and medical imaging systems in hospitals. This kind of technology is applied in many traditional algorithms. Today, the ultra-high-resolution algorithm has been revised. This paper analyzes in detail the convergence characteristics of two widely used pixel-by-pixel loss functions from theoretical and experimental perspectives, fully considers each convergence characteristic, and studies the combination of MAE and network. The MSE advantage learning method uses a single loss function to train the network. This training method can improve network performance. The level of Chinese gymnasts is relatively high, and it can even be said that they represent the most advanced level in the world. For many gymnasts, training for sharp turns is the focus of training, as well as training. Chinese gymnasts face similar problems. How to help Chinese gymnasts to improve their training quality has become a major issue in gymnastics training at this stage.