Reviewerly/exHarmony huggingface dataset:
Keystroke Dynamics Based User Authentication and its Application in Online Examination

Currently user authentication and identity monitoring are required in various collaborative computer supported systems, such as online assessments and examinations. However, existing authentication methods such as passwords checking are less reliable. In addition, identity monitoring is hard to be realized effectively and efficiently for applications based on collaborative architecture. In this paper, we leverage keystroke dynamics to explore biometrics security and propose a user authentication framework based on edge computing architecture to address these issues. To support both static and continuous authentications with high accuracy and efficiency, dynamically improved keystroke profiles, Gaussian model based anomaly detector and keystroke stream processing are designed in the framework. The feasibility and effectiveness of the framework are verified by three representative public data sets and a real-world case study. The results show that the authentication can proceeds efficiently to enable an undisturbed and secure environment for online examinations.
Compact high-aperture interferometer with a diffractive reference wave for high-precision referenceless aberration measurements of optical elements and systems

We present the optical layout and actual design of a compact high-aperture referenceless point diffraction interferometer with a diffraction reference wave. The basic concept of this interferometer is based on a single-mode optical fibre with a subwave output aperture, which is used as a source of spherical reference waves. The interferometer is designed for high-precision measurements of the surface shape and aberrations of optical elements. It makes use of a diffraction reference wave and is developed for mass industrial applications. The proposed design does not require the involvement of special external conditions, and reduces the need for calibrated reference surfaces. We evaluate the performance of our interferometer for highprecision measurements and demonstrate its use by presenting results obtained from measuring the shapes of spherical references from various manufacturers and the aberration of a five lens objective.
I/O Associations in Scientific Software: A Study of SWMM

Understanding which input and output variables are related to each other is important for metamorphic testing, a simple and effective approach for testing scientific software. We report in this paper a quantitative analysis of input/output (I/O) associations based on co-occurrence statistics of the user manual, as well as association rule mining of a user forum, of the Storm Water Management Model (SWMM). The results show a positive correlation of the identified I/O pairs, and further reveal the complementary aspects of the user manual and user forum in supporting scientific software engineering tasks.
Toward Effective Automated Content Analysis via Crowdsourcing

Many computer scientists use the aggregated answers of online workers to represent ground truth. Prior work has shown that aggregation methods such as majority voting are effective for measuring relatively objective features. For subjective features such as semantic connotation, online workers, known for optimizing their hourly earnings, tend to deteriorate in the quality of their responses as they work longer. In this paper, we aim to address this issue by proposing a quality-aware semantic data annotation system. We observe that with timely feedback on workers’ performance quantified by quality scores, better informed online workers can maintain the quality of their labeling throughout an extended period of time. We validate the effectiveness of the proposed annotation system through i) evaluating performance based on an expert-labeled dataset, and ii) demonstrating machine learning tasks that can lead to consistent learning behavior with 70%–80% accuracy. Our results suggest that with our system, researchers can collect high-quality answers of subjective semantic features at a large scale.
Construction of an Aggregated Fall Detection Model Utilizing a Microwave Doppler Sensor

Fall accidents are serious problems, especially for elderly people living alone. This is why many fall event detection methods have been proposed in the past. When constructing multiple classification models, a method is expected to be constructed for every fall and nonfall actions. However, in the context of IoT, it would be challenging, if not impossible, to cover all scenarios, because it is necessary to keep the computational cost low. Thus, we propose a hidden Markov model that aggregates the actions measured with a single Doppler sensor (based on the likelihood) in each fall and nonfall events. To validate the proposed method, we conducted an experiment with 20 subjects who performed three fall actions and four nonfall actions each. An accuracy of 0.95 was achieved, and the number of generated fall and nonfall detection models was 1:2 and 1:3, respectively. The experiment’s results show that the proposed method has successfully reduced the number of classification models, which is crucial for IoT applications.
An Augmented Reality Platform with Hand Gestures-Based Navigation for Applications in Image-Guided Surgery: Prospective Concept Evaluation by Surgeons

(2022). An Augmented Reality Platform with Hand Gestures-Based Navigation for Applications in Image-Guided Surgery: Prospective Concept Evaluation by Surgeons. International Journal of Human–Computer Interaction: Vol. 38, No. 2, pp. 131-143.
Multi-feature fusion tracking algorithm based on generative compression network

In order to solve the problem of inaccurate positioning in the process of target tracking due to illumination, scale change and occlusion, a correlation filtering tracking algorithm based on generate compression network is proposed. Firstly, the encoder is used to compress the high-dimensional depth features extracted from VGG16 network, and the soft quantizer reduces floating-point operation so that improves the operation speed. Secondly, the discriminator of generative adversarial network guides the encoder and generator to better compress and recover the original depth features by generating a discriminator against the network, so as to enhance the ability of the encoder to extract the key features of the target, then, the compressed depth features, gray features and hog features are combined to improve the ability of target representation. Thirdly, the correlation filter and PCA feature dimensionality reduction are used to complete the target precise location and discriminant scale estimation. The experimental results show that the tracking accuracy of the proposed algorithm is 87.1%, and the tracking rate is up to 70fps.
Tackling the Credit Assignment Problem in Reinforcement Learning-Induced Pedagogical Policies with Neural Networks

Intelligent Tutoring Systems (ITS) provide a powerful tool for students to learn in an adaptive, personalized, and goal-oriented manner. In recent years, Reinforcement Learning (RL) has shown to be capable of leveraging previous student data to induce effective pedagogical policies for future students. One of the most desirable goals of these policies is to maximize student learning gains while minimizing the training time. However, this metric is often not available until a student has completed the entire tutor. For this reason, the reinforcement signal of the effectiveness of the tutor is delayed. Assigning credit for each intermediate action based on a delayed reward is a challenging problem denoted the temporal Credit Assignment Problem (CAP). The CAP makes it difficult for most RL algorithms to assign credit to each action. In this work, we develop a general Neural Network-based algorithm that tackles the CAP by inferring immediate rewards from delayed rewards. We perform two empirical classroom studies, and the results show that this algorithm, in combination with a Deep RL agent, can improve student learning performance while reducing training time.
Efficient Traffic Classification Using Hybrid Deep Learning

Network traffic classification provides an essential contribution in network administration functions and network management such as QoS, security, and billing. Those functions need a timely and accurate detection of specific traffics. Current network traffic classification methods offer supervised and unsupervised learning capabilities for network traffic prediction or classification. Classical machine learning classifiers that use a single classification model suffer from low prediction and classification accuracy, especially for high dimensional datasets with a high sparsity level. These challenges in individual-based learning models have created a need for hybrid learning. Recently, hybriddeep learning has shown a significant role in traffic forecasting and classification due to its efficiency. However, a tradeoff between the aggregate models and the classification accuracy presents a substantial challenge in network traffic classification problems. In this paper, we have suggested two hybrid models that combine the Convolutional Neural Network (CNN) along with the Recurrent Neural Network (RNN) models, inclusive of the Gated recurrent unit (GRU) and Long Short-Term Memory (LSTM), to improve traffic classification accuracy. The efficiency of the suggested models has been evaluated by comparing them with various individual-based models using real network traffic traces. The hybrid CNN-LSTM and CNN-GRU have achieved an accuracy of up to 99.23% and 93.92%, respectively, for binary classification and 67.16% for multiclass classification.
Three-dimensional reconstruction and measurement of fuel assemblies for sodium-cooled fast reactor using linear structured light

Three-dimensional reconstruction and measurement of sodium-cooled fast reactor fuel assemblies has played an important role in evaluating the deformation and mechanical damage of sodium-cooled fast reactor fuel assemblies and judging whether they can continue to be used. In this paper, a three-dimensional reconstruction and measurement method for the fuel assemblies of sodium cooled fast reactor based on linear structured light with radiation resistance is proposed.Then, the measurement error is corrected by the refractive correction model, which is introduced by radiation resistant glass. What's more, a contour splicing method to unify the data obtained by four linear structured light measuring instruments into a world coordinate system is put forward.The effect of radiation resistant glass on measurement accuracy was tested, and the measurement results are corrected with a correction error of ±0.05 mm.The three-dimensional reconstruction and measurement of a standard part and a simulation component of sodium-cooled fast reactor fuel assembly shows that the method can achieve high-precision 3D reconstruction, and the measurement error of the opposite margin and the diagonal distance of the standard part is less than ± 0.05 mm.
Interpretable visual reasoning: A survey

Visual reasoning refers to the process of solving questions about visual information. At present, most visual reasoning models are mainly based on deep learning and end-to-end architecture. Although these models have achieved good performance, they are usually black boxes for users, and it is difficult to understand the basic rationales of the reasoning process. In recent years, the academic community has realized the importance of interpretability in visual reasoning and has developed a series of Interpretable Visual Reasoning (IVR) models. In this paper, we review these models. First, we have established a taxonomy based on four explanation forms of vision, text, graph and symbol used in current visual reasoning. Secondly, we explore the typical IVR models of each category and analyze their pros and cons. Thirdly, we elaborate on the current mainstream datasets about visual reasoning and VQA, and analyze how these datasets promote IVR research from different perspectives. Finally, we summarize the challenges for IVR and point out potential research directions.
TSDM: Tracking by SiamRPN++ with a Depth-refiner and a Mask-generator

In a generic object tracking, depth (D) information provides informative cues for foreground-background separation and target bounding box regression. However, so far, few trackers have used depth information to play the important role aforementioned due to the lack of a suitable model. In this paper, a RGB-D tracker named TSDM is proposed, which is composed of a Mask-generator (M-g), SiamRPN++ and a Depth-refiner (D-r). The M-g generates the background masks, and updates them as the target 3D position changes. The D-r optimizes the target bounding box estimated by SiamRPN++, based on the spatial depth distribution difference between the target and the surrounding background. Extensive evaluation on the Princeton Tracking Benchmark and the Visual Object Tracking challenge shows that our tracker outperforms the state-of-the-art by a large margin while achieving 23 FPS. In addition, a light-weight variant can run at 31 FPS and thus it is practical for real world applications. Code and models of TSDM are available at https://github.com/lql-team/TSDM.
Discovering features for detecting malicious websites: An empirical study

Website features and characteristics have shown the ability to detect various web threats – phishing, drive-by downloads, and command and control (C2). Prior research has thoroughly explored the practice of choosing features ahead of time (a priori) and building detection models. However, there is an opportunity to investigate new techniques and features for detection. We perform a comprehensive evaluation of discovering features for malicious website detection versus selecting features a priori. We gather 46,580 features derived from a response to a web request and, through a series of feature selection techniques, discover features for detection and compare their performance to those used in prior research. We build several detection models using unsupervised and supervised learning algorithms over various sampling and feature transformation scenarios. Our approach is evaluated on a diverse dataset composed of common threats on the internet. Overall, we find that discovered features can achieve more efficient and comparable detection performance to a priori features with 66% fewer features and can achieve a Matthews Correlation Coefficient (MCC) of up to 0.9008.
Object-Based Video Forgery Detection via Dual-Stream Networks

The object-based video forgery detection aims to expose tampered regions from video sequences without any codec information. However, existing methods mainly focus on manually selected features and models for a specific task, either splicing or copy-move, while the general representation ability of deep learning models and the correlation of different forensic features have not been fully explored. In this paper, we propose a dual-stream framework to jointly discover and integrate effective features for object-based video forgery detection. First, two different types of branches are employed to extract discriminative features. Then, after the dual-stream feature fusion, a Conditional Random Field (CRF) layer is utilized to further refine segmentation results. Finally, we consider temporal consistency by incorporating the video tracking strategy. Extensive experiments on four datasets show that the proposed method achieves competitive performance against the state-of-the-art methods.
A Model Compression Based Framework for Electrical Equipment Intelligent Inspection on Edge Computing Environment

The edge services including electrical equipment intelligent inspection on power IoTs often adopt deep neural networks (DNNs) to accurately recognize abnormal equipment by image classification and object detection for reducing work workload on the power cloud. However, the high computation complexity based on neural network models poses great challenges to real-world edge services and applications due to the limited computational ability and storage space on edge devices. In this paper, we propose a framework for electrical equipment intelligent inspection based on deep neural network model compression where a combination approach is used to prune and quantize the DNNs automatically without using any hyperparameters to manually set the compression rate for each layer, which is applied in the edge services to handle and analyze the real-time massive data acquired by a number of power devices for reducing the computational complexity and the workload of edge computing services. The related experiments were made, and the results show that the electrical equipment intelligent inspection based on the proposed framework has superior classification accuracy, in particularly maintaining a competitive compression rate in comparison with the popular deep compression approach.
Ontology-driven evolution of software security

Ontologies as a means to formally specify the knowledge of a domain of interest have made their way into information and communication technology. Most often, such knowledge is subject to continuous change, which demands for consistent evolution of ontologies and dependent artifacts. In this article, we study ontology evolution in the context of software security, where ontologies may be used to formalize the security context knowledge which is needed to properly implement security requirements. In this application scenario, techniques for detecting ontology changes and determining their semantic impact are required to maintain the security of a software-intensive system in response to changing security context knowledge. Our solution is capable of detecting semantic editing patterns, which may be customly defined using graph transformation rules, but it does not depend on information about editing processes such as persistently managed changelogs. We leverage semantic editing patterns for (i) generating system co-evolution proposals, (ii) adapting the configuration of standard security checks, and (iii) performing incremental security compliance analyses between co-evolved system models and the implementation. We demonstrate the feasibility of the approach using a realistic medical information system known as iTrust.
A security type verifier for smart contracts

• We introduce a formal calculus for Solidity, including syntax and semantics. • We propose a Solidity Type Checker ( STC ) to prevent both explicit and implicit flows that violate integrity policies, whose soundness is proved w.r.t. non-interference. • We present a type verifier based on the proposed STC . The type verifier can help users to find an optimal secure type assignment T such that the contract is still well-typed. We also prove that finding the optimal secure type assignment is theoretically a NP-complete problem. • We develop a prototype implementation of our type verifier STV based on K-framework. We evaluate our prototype with over 100 smart contracts. Evaluations show that our approach is effective to detect vulnerabilities such as the Parity Multisig Wallet attack. The widespread adoption of smart contracts demands strong security guarantees. Our work is motivated by the problem of statically checking potential information tampering in smart contracts. This paper presents a security type verification framework for smart contracts based on type systems. We introduce a formal calculus for reasoning smart contract operations and interactions and design a lightweight type system for checking secure information flow in Solidity (a popular high-level programming language for writing smart contracts). The soundness of our type system is proved w.r.t. non-interference. In addition, a type verifier based on our type system is proposed to assist users to automatically find an optimal secure type assignment for state variables, which makes contracts well-typed. We also prove that finding the optimal secure type assignment is theoretically a NP-complete problem. We develop a prototype implementation of the S olidity T ype V erifier ( STV ) including the S olidity T ype C hecker ( STC ) based on the K-framework, and demonstrate its effectiveness on real-world smart contracts.
Data science training for official statistics: A new scientific paradigm of information and knowledge development in national statistical systems

The ability to incorporate new and Big Data sources and to benefit from emerging technologies such as Web Technologies, Remote Data Collection methods, User Experience Platforms, and Trusted Smart Statistics will become increasingly important in producing and disseminating official statistics. The skills and competencies required to automate, analyse, and optimize such complex systems are often not part of the traditional skill set of most National Statistical Offices. The adoption of these technologies requires new knowledge, methodologies and the upgrading of the quality assurance framework, technology, security, privacy, and legal matters. However, there are methodological challenges and discussions among scholars about the diverse methodical confinement and the wide array of skills and competencies considered relevant for those working with big data at NSOs. This paper develops a Data Science Model for Official Statistics (DSMOS), graphically summarizing the role of data science in statistical business processes. The model combines data science, existing scientific paradigms, and trusted smart statistics, and develops around a restricted number of constructs. We considered a combination of statistical engineering, data engineering, data analysis, software engineering and soft skills such as statistical thinking, statistical literacy and specific knowledge of official statistics and dissemination of official statistics products as key requirements of data science in official statistics. We then analyse and discuss the educational requirements of the proposed model, clarifying their contribution, interactions, and current and future importance in official statistics. The DSMOS was validated through a quantitative method, using a survey addressed to experts working at the European statistical systems. The empirical results show that the core competencies considered relevant for the DSMOS include acquisition and processing capabilities related to Statistics, high-frequency data, spatial data, Big Data, and microdata/nano-data, in addition to problem-solving skills, Spatio-temporal modelling, machine learning, programming with R and SAS software, Data visualisation using novel technologies, Data and statistical literacy, Ethics in Official Statistics, New data methodologies, New data quality tools, standards and frameworks for official statistics. Some disadvantages and vulnerabilities are also addressed in the paper.
A Semantic-Enhanced Method Based On Deep SVDD for Pixel-Wise Anomaly Detection

Detecting the anomalous information in multimedia is valuable to many computer vision applications. Recently, many pixel-wise methods modeling by deep learning model have been presented, which can be divided in reconstruction-based and distance-based methods. However, reconstruction-based methods suffer from the low precision of pixel reconstructions. Distance-based methods extract the hierarchical features by a pre-trained model, in order to estimate the anomalies by distances between normal and anomalous features. Nevertheless, multi-level features are ignored in these methods, and semantic information is not considered which is important to enhance the description of anomalies. To over-come the problems, we propose a novel semantic-enhanced anomaly detection method based on deep Support Vector Data Description (SVDD). A new semantic correlation module (SCB) is introduced to enhance the semantic information of the feature representations by cosine similarity. Mean-while, the multi-level architecture is utilized to estimate the final pixel-wise anomaly score. Experimental results demonstrate the proposed method outperforms state-of-the-art methods on MVTec and STC dataset.