Reviewerly/exHarmony huggingface dataset:
Research on Active Obstacle Avoidance of Intelligent Vehicles Based on Improved Artificial Potential Field Method

In the study of autonomous obstacle avoidance of intelligent vehicles, the traditional artificial potential field method has the problem that the vehicle may fall into the local minima and lead to obstacle avoidance failure. Therefore, this paper improves the traditional potential field function. Based on the vehicle dynamics model, a strategy of jumping out of local minima based on smaller steering angles is proposed. By finding a smaller steering angle and setting a suitable jump out step length, the intelligent vehicle is enabled to jump out of the local minima. Simulation experiments by MATLAB show that the improved method can jump out of the local minima. By comparing the planned trajectories of the traditional method and the improved method in static and dynamic obstacles situations, the trajectory planned by the improved method is smooth and the curvature is smaller. The planned trajectory is tracked by the Carsim platform, and the test results show that the improved method reduces the front steering wheel angle while the intelligent vehicle satisfies the vehicle dynamics constraints during active obstacle avoidance, which verifies the stability and rationality of the improved method.
Work in progress: Educational Technology and Knowledge Tracing Models

Nowadays, the rapid development of computing technology and its contribution to the creation and widespread availability of information, have changed the data in the school environment in all areas. However, having a lot of information available is not the same as learning from it. Cognitive psychology, artificial intelligence, and informatics have advanced to the level where it is achievable to create computer systems for intelligent tutoring. Also, educational technology can strengthen the learning process both inside and outside the classroom. In this paper, a specific discussion is conducted on how computer science and specifically Knowledge Tracing Models can support learning with the use of educational technology.
Optimal Deep Transfer Learning-Based Human-Centric Biomedical Diagnosis for Acute Lymphoblastic Leukemia Detection

Human-centric biomedical diagnosis (HCBD) becomes a hot research topic in the healthcare sector, which assists physicians in the disease diagnosis and decision-making process. Leukemia is a pathology that affects younger people and adults, instigating early death and a number of other symptoms. Computer-aided detection models are found to be useful for reducing the probability of recommending unsuitable treatments and helping physicians in the disease detection process. Besides, the rapid development of deep learning (DL) models assists in the detection and classification of medical-imaging-related problems. Since the training of DL models necessitates massive datasets, transfer learning models can be employed for image feature extraction. In this view, this study develops an optimal deep transfer learning-based human-centric biomedical diagnosis model for acute lymphoblastic detection (ODLHBD-ALLD). The presented ODLHBD-ALLD model mainly intends to detect and classify acute lymphoblastic leukemia using blood smear images. To accomplish this, the ODLHBD-ALLD model involves the Gabor filtering (GF) technique as a noise removal step. In addition, it makes use of a modified fuzzy c-means (MFCM) based segmentation approach for segmenting the images. Besides, the competitive swarm optimization (CSO) algorithm with the EfficientNetB0 model is utilized as a feature extractor. Lastly, the attention-based long-short term memory (ABiLSTM) model is employed for the proper identification of class labels. For investigating the enhanced performance of the ODLHBD-ALLD approach, a wide range of simulations were executed on open access dataset. The comparative analysis reported the betterment of the ODLHBD-ALLD model over the other existing approaches.
CMA-ES with margin

This study targets the mixed-integer black-box optimization (MI-BBO) problem where continuous and integer variables should be optimized simultaneously. The CMA-ES, our focus in this study, is a population-based stochastic search method that samples solution candidates from a multivariate Gaussian distribution (MGD), which shows excellent performance in continuous BBO. The parameters of MGD, mean and (co)variance, are updated based on the evaluation value of candidate solutions in the CMA-ES. If the CMA-ES is applied to the MI-BBO with straightforward discretization, however, the variance corresponding to the integer variables becomes much smaller than the granularity of the discretization before reaching the optimal solution, which leads to the stagnation of the optimization. In particular, when binary variables are included in the problem, this stagnation more likely occurs because the granularity of the discretization becomes wider, and the existing modification to the CMA-ES does not address this stagnation. To overcome these limitations, we propose a simple modification of the CMA-ES based on lower-bounding the marginal probabilities associated with the generation of integer variables in the MGD. The numerical experiments on the MI-BBO benchmark problems demonstrate the efficiency and robustness of the proposed method.
Human Action Recognition Based on Improved Two-Stream Convolution Network

Two-stream convolution network (2SCN) is a classical method of action recognition. It is capable of extracting action information from two dimensions: spatial and temporal streams. However, the method of extracting motion features from a spatial stream is single-frame recognition, and there is still room for improvement in the perception ability of appearance coherence features. The classical two-stream convolution network structure is modified in this paper by utilizing the strong mining capabilities of the bidirectional gated recurrent unit (BiGRU) to allow the neural network to extract the appearance coherence features of actions. In addition, this paper introduces an attention mechanism (SimAM) based on neuroscience theory, which improves the accuracy and stability of neural networks. Experiments show that the method proposed in this paper (BS-2SCN, BiGRU-SimAM Two-stream convolution network) has high accuracy. The accuracy is improved by 2.6% on the UCF101 data set and 11.7% on the HMDB51 data set.
Chatbot for construction firms using scalable blockchain network

Information and Communication Technologies (ICT), including multimedia tools, email services, voice-based tools, and handheld computing tools, have been extensively used for automating and digitalizing different construction processes and activities. However, these technologies are subjected to single-point attacks or failures, manipulation, and lack of privacy and traceability. This study introduces a novel information exchange and management system for construction firms based on blockchain technology and chatbots. The system leverages the characteristics of blockchain technology in terms of peer-to-peer operation mode, data integrity, structuring, and privacy, and the chatbots' merits regarding ease of use and degree of automation. The system is developed for tracking work progress in construction projects as a generic use-case using a four-step approach. First, a private blockchain network is configured for data distribution and storage. Second, a smart contract is coded for regulating data writing/reading operations. Third, a chatbot is developed for data collection and retrieval through textual conversations. Fourth, serverless cloud function and cloudant database are configured to allow the linkage between the blockchain network and the chatbot. A prototype of the system is built and applied to a case study of non-residential construction project to test and verify its capabilities. Further, the system's performance is assessed in terms of the writing and reading latencies and the storage size. The system features can be extended by embedding mathematical algorithms to simultaneously analyze data and employing Inter-Planetary File System (IPFS) to maintain visuals and large-size data.
Real-time dense-view imaging for three-dimensional light-field display based on image color calibration and self-supervised view synthesis

Three-Dimensional (3D) light-field display has achieved promising improvement in recent years. However, since the dense-view images cannot be collected fast in real-world 3D scenes, the real-time 3D light-field display is still challenging to achieve in real scenes, especially at the high-resolution 3D display. Here, a real-time 3D light-field display method with dense-view is proposed based on image color correction and self-supervised optical flow estimation, and a high-quality and high frame rate of 3D light-field display can be realized simultaneously. A sparse camera array is firstly used to capture sparse-view images in the proposed method. To eliminate the color deviation of the sparse views, the imaging process of the camera is analyzed, and a practical multi-layer perception (MLP) network is proposed to perform color calibration. Given sparse views with consistent color, the optical flow can be estimated by a lightweight convolutional neural network (CNN) at high speed, which uses the input image pairs to learn the optical flow in a self-supervised manner. With inverse warp operation, dense-view images can be synthesized in the end. Quantitative and qualitative experiments are performed to evaluate the feasibility of the proposed method. Experimental results show that over 60 dense-view images at a resolution of 1024 × 512 can be generated with 11 input views at a frame rate over 20 fps, which is 4× faster than previous optical flow estimation methods PWC-Net and LiteFlowNet3. Finally, large viewing angles and high-quality 3D light-field display at 3840 × 2160 resolution can be achieved in real-time.
A Game-Theoretically Optimal Defense Paradigm against Traffic Analysis Attacks using Multipath Routing and Deception

While encryption can protect network traffic against simple on-path eavesdropping attacks, it cannot prevent sophisticated traffic analysis (TA) attacks from inferring sensitive information. TA attackers utilize machine learning algorithms to learn the traffic patterns of a communication (e.g., a website visit) and then use these learned patterns to accurately identify similar communications (which website is being visited by a targeted user), even though packets are encrypted. In this paper, we propose a novel and effective defense approach to protect users' privacy against TA attacks. The proposed approach is based on two proactive defense paradigms: multipath routing and deception. The route randomization strategy distributes packets of a flow on multiple paths between a source and destination to restrict the amount of traffic that a TA adversary can collect from a flow. The deception strategy augments the randomization strategy by injecting fake packets among the real packets of a flow on different paths. Our focal research problem is to identify the optimal strategies for how real and fake packets must be distributed on multiple paths with different capacities to achieve maximum effectiveness against TA attacks. We formalize the problem as a zero-sum game and show that the water-filling distribution of real and fake packets provides an optimal defense solution. Through theoretical and experimental studies, we demonstrate that the proposed approach can significantly degrade the accuracy of the TA attacks. Unlike other defensive approaches in the literature, our approach works without manipulating the production traffic (e.g., delaying packets or padding), or requiring any real-time information about the protected traffic flows.
A Novel Multi-Objective Hybrid Election Algorithm for Higher-Order Random Satisfiability in Discrete Hopfield Neural Network

Hybridized algorithms are commonly employed to improve the performance of any existing method. However, an optimal learning algorithm composed of evolutionary and swarm intelligence can radically improve the quality of the final neuron states and has not received creative attention yet. Considering this issue, this paper presents a novel metaheuristics algorithm combined with several objectives—introduced as the Hybrid Election Algorithm (HEA)—with great results in solving optimization and combinatorial problems over a binary search space. The core and underpinning ideas of this proposed HEA are inspired by socio-political phenomena, consisting of creative and powerful mechanisms to achieve the optimal result. A non-systematic logical structure can find a better phenomenon in the study of logic programming. In this regard, a non-systematic structure known as Random k Satisfiability (RANkSAT) with higher-order is hosted here to overcome the interpretability and dissimilarity compared to a systematic, logical structure in a Discrete Hopfield Neural Network (DHNN). The novelty of this study is to introduce a new multi-objective Hybrid Election Algorithm that achieves the highest fitness value and can boost the storage capacity of DHNN along with a diversified logical structure embedded with RANkSAT representation. To attain such goals, the proposed algorithm tested four different types of algorithms, such as evolutionary types (Genetic Algorithm (GA)), swarm intelligence types (Artificial Bee Colony algorithm), population-based (traditional Election Algorithm (EA)) and the Exhaustive Search (ES) model. To check the performance of the proposed HEA model, several performance metrics, such as training–testing, energy, similarity analysis and statistical analysis, such as the Friedman test with convergence analysis, have been examined and analyzed. Based on the experimental and statistical results, the proposed HEA model outperformed all the mentioned four models in this research.
Impact of extended reality on architectural education and the design process

This paper presents a systematic review of how extended reality technologies (XR-technologies) affect the learning outcomes and students' performance in architectural education. Based on a modified PICO-strategy and the framework analysis of the findings of previous researches on immersive environments, this paper elucidates the impact of XR-technologies, including virtual reality (VR), augmented reality (AR), and mixed reality (MR), on Bryan Lawson's five phases of the design process. Furthermore, this study investigates the applications of XR-technologies in different courses of architectural education. The results are categorized into four different course types that were derived from the reviewed article. The results show that XR-technologies, including VR, AR, and MR are capable of enhancing various stages of the design process and improving learning outcomes in educational courses among architecture students. Similarly, the application of such environments is also suitable for professional architectural design and inclusion of end-users in the design process.
Image semantic segmentation with hierarchical feature fusion based on deep neural network

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YouTube Dataset on Mobile Streaming for Internet Traffic Modeling and Streaming Analysis

Abstract Around 4.9 billion Internet users worldwide watch billions of hours of online video every day. As a result, streaming is by far the predominant type of traffic in communication networks. According to Google statistics, three out of five video views come from mobile devices. Thus, in view of the continuous technological advances in end devices and increasing mobile use, datasets for mobile streaming are indispensable in research but only sparsely dealt with in literature so far. With this public dataset, we provide 1,081 hours of time-synchronous video measurements at network, transport, and application layer with the native YouTube streaming client on mobile devices. The dataset includes 80 network scenarios with 171 different individual bandwidth settings measured in 5,181 runs with limited bandwidth, 1,939 runs with emulated 3 G/4 G traces, and 4,022 runs with pre-defined bandwidth changes. This corresponds to 332 GB video payload. We present the most relevant quality indicators for scientific use, i.e., initial playback delay, streaming video quality, adaptive video quality changes, video rebuffering events, and streaming phases.
The Use of Responsible Artificial Intelligence Techniques in the Context of Loan Approval Processes

Despite the existing skepticism about the use of automatic systems in contexts where human knowledge and experience are considered indispensable (e.g., the granting of a mortgage, the prediction of stock prices, or the detection of cancers), our work aims to show how the use of explainability and fairness techniques can lead to the growth of a domain expert's trust and reliance on an artificial intelligence (AI) system. This article presents a system, applied to the context of loan approval processes, focusing on the two aforementioned ethical principles out of the four defined by the High-Level Expert Group on AI in the document "Ethics Guidelines for Trustworthy AI," published in April 2019, in which the key requirements that AI systems should meet to be considered trustworthy are identified. The presented case study is realized within a proprietary framework composed of several components for supporting the user throughout the management of the whole life cycle of a machine learning model. The main approaches, consisting of providing an interpretation of the model's outputs and monitoring the model's decisions to detect and react to unfair behaviors, are described in more detail to compare our system within state-of-the-art related frameworks. Finally, a novel Trust & Reliance Scale is proposed for evaluating the system, and a usability test is performed to measure the user satisfaction with the effectiveness of the developed user interface; results are obtained, respectively, by the submission of the mentioned novel scale to bank domain experts and the usability questionnaire to a heterogeneous group composed of loan officers, data scientists, and researchers.
SLMS-SSD: Improving the balance of semantic and spatial information in object detection

With the development of deep learning technology, the research on convolutional neural network-based object detection is becoming more and more mature. However, most methods are unsatisfactory in dealing with the issue of semantic and spatial information imbalance. In this article, we extend the single-shot multibox detector SSD and propose a self-learning multi-scale object detection network by balancing the semantic information and spatial information, named SLMS-SSD. We first construct a shallow feature enhancement module to enhance the representation of small objects by extracting richer context information. Second, in terms of feature connectivity, we design a multi-scale feature selection module for intermediate layer features with a combination of top-down and direct up-samplings. Finally, in terms of feature strength, we design a self-learning feature fusion module for measuring the feature importance. We validate our model on the PASCAL VOC and MS COCO datasets, and the results demonstrate that it can effectively improve the accuracy of object detection, especially the accuracy of small object detection. • Designing feature connectivity to balance semantic and spatial information. • Self-learning feature strength to measure the importance of different features. • Extracting shallow feature contextual information that facilitates detection. • Improving accuracy compared to SSD series methods, especially for small objects.
Edunudge

Over the past years, there has been an increasing prevalence of 'nudge thinking' in the educational field. While (critical) research on the rising adoption of nudging has increasingly proliferated in the broader social sciences over the past decades, nudging in education – what we designate here as Edunudge – is, however, only starting to be given substantial academic attention and still requires more nuanced conceptual and empirical investigation. As we argue in this contribution, such investigations should not only carefully disentangle the enormous complexity of nudging in education, including the role of digital and automated technologies. Equally, they should take more systematically into account the traditional closeness of the fields of pedagogy and behavioral insights. In line with that argumentation, the goal of this article is to develop foundations of such an Edunudge research program, focusing on four different dimensions: the technomodalities, the political economy, the spacetimes, and the pedagogies of Edunudge.
Protecting location privacy in cloud services

AbstractAbstractIn GPS system enabled hand-held communication devices which works as a beacon that transmits our location. Also, the location-based services that emerged in recent years make use of location data. The location data provided to these location-based service providers have enough sensitive information to bother about. The location privacy became an area of concern in recent years. Developed and developing countries are now in process to make laws against the use of location information without the consent of the user or without legal warrant. Most of the research strategies proposed in recent years have one thing in common that we provide a region instead of exact coordinates of the location of the user. These attacks are called location dependent attacks or velocity linkage attacks in literature. We have in our effort proposed a solution to the problem of location privacy protection taking care of the location dependent attacks. We also keep in mind the fact that user is more concerned about his locations at certain places. We tested the proposed solution against requests issued by 5000 and 15000 users moving in a field of 10000x10000 units' area.Subject Classification: Primary 93A30Secondary 49K15Keywords: High-performance GPS securityCloud security organizationsComputing security parametersSecure managementLocation privacyCloud security simulation