Reviewerly/exHarmony huggingface dataset:
Easy-Auth: Graphical Password Authentication using a Randomization Method

Graphical authentication methods have established themselves as a viable alternative to more established methods of authentication. The analysis of existing graphical authentication systems reveals that several of them compromise security by simplifying the method, making it possible to execute a variety of attacks such as hidden camera, guessing, shoulder surfing, and smudge. Additionally, some of them make performance sacrifices for security. Nonetheless, this article proposes a new graphical password method, Easy-Auth, that is immune to the aforementioned attacks. Easy-Auth dynamically repositions the images and limits the number of login attempts, thereby increasing its security. Easy-Auth is evaluated in terms of security by comparing with its counterparts.
SPLinux: An Information Flow Secure Linux

Enforcement of information flow control (IFC) policies for OS to realize a sufficiently secure OS has been a challenging area of research. In this paper, our primary objective has been to present a fully information flow (IF) secure Linux that is usable with a minimal overhead without losing any of the existing functionalities. Towards such a goal, we describe the design, implementation and evaluation of a fully information flow secure Linux operating system called $'{\mathcal{S}}{\mathcal{P}}Linux'$ through complete mediation. Our approach first derives a labeled system (with initial inputs from the user in terms of the given Linux DAC policy) and manages further the labels automatically without users’ intervention. It realizes complete mediation by interception of system calls and enforces IFC policy by implementing a recent decentralized security model that supports dynamic labelling and robust declassification. One of the distinct characteristics of the work is that the user has at his disposal all the features of Linux. We describe our experimental evaluation of $'{\mathcal{S}}{\mathcal{P}}Linux'$, its assessment of usability and performance evaluation with respect to other secure OS efforts. Results are quite encouraging in terms of performance, expressiveness, and usability.
The application of image processing for monitoring student’s attention level during online class

Abstract With the COVID-19 pandemic still causing the world to be quarantined in their house to prevent the spread of the virus, this means online classes are still the main method of conducting classes. This project aims to help lecturers monitor the students during class as they are having problems checking whether the students are paying attention or not. This project uses the student’s facial features to determine their attention level using two different coding algorithm Viola-Jones and Sobel edge. These two algorithms help to determine what kind of facial expression that the students are making. The Viola-Jones algorithm detects and captures the student’s facial features such as eyes and mouth while the Sobel edge algorithm detects the edges of the facial features to determine whether the eyes and mouth are open or closed. With the data collected it will run through the database to determine the student’s attention level and inform the lecturer.
The Impact of Semi-Supervised Learning on the Performance of Intelligent Chatbot System

Artificial intelligent based dialog systems are getting attention from both business and academic communities. The key parts for such intelligent chatbot systems are domain classification, intent detection, and named entity recognition. Various supervised, unsupervised, and hybrid approaches are used to detect each field. Such intelligent systems, also called natural language understanding systems analyze user requests in sequential order: domain classification, intent, and entity recognition based on the semantic rules of the classified domain. This sequential approach propagates the downstream error; i.e., if the domain classification model fails to classify the domain, intent and entity recognition fail. Furthermore, training such intelligent system necessitates a large number of user-annotated datasets for each domain. This study proposes a single joint predictive deep neural network framework based on long short-term memory using only a small user-annotated dataset to address these issues. It investigates value added by incorporating unlabeled data from user chatting logs into multi-domain spoken language understanding systems. Systematic experimental analysis of the proposed joint frameworks, along with the semi-supervised multi-domain model, using open-source annotated and unannotated utterances shows robust improvement in the predictive performance of the proposed multi-domain intelligent chatbot over a base joint model and joint model based on adversarial learning.
Copy-move image forged information detection and localisation in digital images using deep convolutional network

Image tempering is one of the significant issues in the modern era. The use of powerful tools for image editing with advanced technology and its widespread on social media raised questions on data integrity. Currently, the protection of images is uncertain and a severe concern, mainly when it transfers over the Internet. Thus, it is essential to detect an anomaly in images through artificial intelligence techniques. The simple way of image forgery is called copy-move, where a part of an image is replicated in the same image to hide unwanted content of the image. However, image processing through handcrafted features usually looks for pattern concerns with duplicate content, limiting their employment for huge data classification. On the other side, deep learning approaches achieve promising results, but their performance depends on training data with fine-tuning of hyperparameters. Thus, we proposed a custom convolutional neural network (CNN) architecture with a pre-trained model ResNet101 through a transfer learning approach. For this purpose, both models are trained on five different datasets. In both cases, the impact of the model is evaluated through accuracy, precision, recall, F-score and achieved the highest 98.4% accuracy using the Coverage dataset.
Complex Multivalued Hierarchical Logic with One Real and Two Imaginary Logical Structures

A complex multivalued hierarchical logic is proposed in which three propositional logics are integrated – one real and two imaginary. The first imaginary logic has “truer” states than the real logic, and the second imaginary logic has even more “truer” states than the first. This leads to hierarchy in the resulting complex multivalued logical structure.It is proved that in the obtained complex logic all axioms of the Boolean algebra and the distribution lattice are fulfilled, without leading to contradictions.The possibilities for applications of the proposed complex logic are stated.
Satellite Network Traffic Scheduling Algorithm Based on Multi-Agent Reinforcement Learning

Traditional network traffic scheduling algorithms are difficult to accurately analyze and model. In the face of satellite networks with complex network structures, diverse transmission resources, and high dynamics, routing strategies lack flexibility. In order to reduce the possibility of network congestion in the multi-layer satellite network, a satellite network traffic scheduling algorithm based on multi-agent reinforcement learning is designed. By improving the experience pool and combining the priority sampling mechanism, the training speed and generalization ability of the model are improved. By comparing with the other four classic algorithms, we verified that this algorithm has a faster convergence speed and a better flow balance effect.
Automatically Predict Question Difficulty for Reading Comprehension Exercises

Question difficulty is a critical indicator for educational examination and personalized learning resources recommendation. Its evaluation mainly based on experts’ experience, which is both subjective and labor intensive. Recently, many studies pay more attention to using neural network for question difficulty prediction (QDP). Though these methods have improved efficiency of difficulty prediction, they only regarded the difficulty prediction task as a simple classification or prediction task, which ignored the influence of the input text relation on it, such as confusion relation of multiple choice for English reading comprehension items. Therefore, in this paper, we proposed a Convolutional Neural Network with Multi-view attention (MACNN) to extract different relation from multiple-parts text in reading comprehension exercises with multiple-choice for automatically predicting question difficulty. Our experimental results demonstrate the effectiveness of the proposed framework on a real-world dataset. Besides, we give interpretable insights to analyze the effect of three module we designed with attention mechanism by attention weights visualization experiment.
A Haze Removal Method Based on Depth Information Fusion

In foggy weather, color distortion and image fading will occur in the captured images. Single image dehazing is a challenging and uncomfortable problem. In recent years, due to the continuous development of deep neural networks, it is gradually used in image dehazing problems as well, which solves the drawbacks of designing haze related features manually. In this paper, we propose a dehazing algorithm based on depth information fusion. We use the KITTI dataset, which contains real haze-free images of different scenes and some additional information. The additional information includes the LiDAR point cloud information and the original depth image, which can get the rough depth information through image fusion. The rough depth information is trained using backpropagation algorithm to complete the missing depth information, which further enriches the scene depth information. The completed accurate scene depth information is used for dehazing in combination with the atmospheric scattering model. Experiments show that our recovered haze-free images retain more image details than some classical research methods and can be detected with more information during target detection.
The Developer Nation

As the software engineering community, we continue to struggle to deliver better, faster, cheaper software to serve businesses. However, we don’t seem to reflect and ask frequently enough whether we are serving the ever-increasing number of developers and their needs to enable them to do their job better to develop better, faster, cheaper software. This article discusses the challenge of increasing demand for developers and argues that without a focus on how to better serve developers, recruiting more of them into the profession will not alone solve the demand problem.
SST-GCN: Structure aware Spatial-Temporal GCN for 3D Hand Pose Estimation

Human hand gesture is an efficient way of communication for Human-computer interaction (HCI) applications. To this end, one of the main requirements is an automatic hand pose estimation. Existing methods usually explore spatial relationships among hand joints in a single image to estimate the 3D hand pose. By doing so, the temporal constraints among hand poses are under-investigated. In this paper, we propose SST-GCN (Structure aware Spatial-Temporal Graphic Convolutional Network) that incorporates both spatial dependencies and temporal consistencies to improve 3D hand pose estimation results. Our method bases on an existing spatial-temporal GCN for 3D pose estimation. In addition, we introduce a new loss function that takes geometric constraints of hand structure into account. Our proposed method takes a 2D hand pose as an input to estimates the 3D hand pose. Finally, we evaluate our method on the First-Person Hand Action Benchmark (FPHAB) dataset. The experimental results show that the proposed method gives promising results in comparison with the original ST-GCN network.
Comparison of the Observation Errors of Augmented and Spatial Reality Systems

Using 3D technologies such as virtual reality (VR) and augmented reality (AR), has intensified nowadays. The mainstream AR devices in use today are head-mounted displays (HMDs), which, due to specification limitations, may not perform to their full potential within a distance of 1.0 m. The spatial reality display (SRD) is another system that facilitates stereoscopic vision by the naked eye. The recommended working distance is 30.0~75.0 cm. It is crucial to evaluate the observation accuracy within 1.0 m for each device in the medical context. Here, 3D-CG models were created from dental models, and the observation errors of 3D-CG models displayed within 1.0 m by HMD and SRD were verified. The measurement error results showed that the HMD model yielded more significant results than the control model (Model) under some conditions, while the SRD model had the same measurement accuracy as the Model. The measured errors were 0.29~1.92 mm for HMD and 0.02~0.59 mm for SRD. The visual analog scale scores for distinctness were significantly higher for SRD than for HMD. Three-dimensionality did not show any relationship with measurement error. In conclusion, there is a specification limitation for using HMDs within 1.0 m, as shown by the measured values. In the future, it will be essential to consider the characteristics of each device in selecting the use of AR devices. Here, we evaluated the accuracies of 3D-CG models displayed in space using two different systems of AR devices.
Notarization and Anti-Plagiarism: A New Blockchain Approach

In traditional notarization processes, the correctness of the activities between the parties is guaranteed by a central authority or guaranteeing institution. In this case, the authority is not able to quickly establish the originality of the content to be notarized, or at least to have a large degree of certainty without the use of automated systems. This paper presents a new notarization platform that uses blockchain technology and integrates advanced anti-plagiarism approaches able to effectively detect copyright violations of documents that users want to notarize. In addition, our proposal includes the use of models, methods, and techniques, through which a very high level of privacy and information security can be guaranteed.
A Real-Time Deep Learning OFDM Receiver

Machine learning in the physical layer of communication systems holds the potential to improve performance and simplify design methodology. Many algorithms have been proposed; however, the model complexity is often unfeasible for real-time deployment. The real-time processing capability of these systems has not been proven yet. In this work, we propose a novel, less complex, fully connected neural network to perform channel estimation and signal detection in an orthogonal frequency division multiplexing system. The memory requirement, which is often the bottleneck for fully connected neural networks, is reduced by ≈ 27 times by applying known compression techniques in a three-step training process. Extensive experiments were performed for pruning and quantizing the weights of the neural network detector. Additionally, Huffman encoding was used on the weights to further reduce memory requirements. Based on this approach, we propose the first field-programmable gate array based, real-time capable neural network accelerator, specifically designed to accelerate the orthogonal frequency division multiplexing detector workload. The accelerator is synthesized for a Xilinx RFSoC field-programmable gate array, uses small-batch processing to increase throughput, efficiently supports branching neural networks, and implements superscalar Huffman decoders.
Polarization image sensor-based laser scanner for reflective metals: architecture and implementation

Laser scanners are essential for many industrial applications such as inspection, metrology, assembly, and welding. Laser scanners are especially interesting in robotic welding to determine the shape and position of the welding groove. There is a wide range of commercial laser scanners available in the market, still, they exhibit some limitations when scanning reflective metals like aluminium, which poses a challenge when laser scanners are used for robotic welding of aluminium. In this paper, a novel laser scanning solution is presented based on a polarization image sensor, which minimizes unwanted reflections when scanning metallic surfaces. The hardware and software architectures are detailed in the paper. The proposed system is validated in experiments with the aluminium alloy 6082 where the system is able to detect the shape of a laser profile with higher precision than a standard image sensor-based laser scanner.
Evaluating Transitions for Streaming Big Data

Visualizations for Streaming Big Data convey high volumes of information in real-time, making it challenging for people to grasp significant data changes. One solution could be having visualizations that change themselves according to the incoming data. However, these changes would need to be effectively conveyed. In this work, we propose a set of transitions between different pairs of visual idioms, aiming to aid users in keeping track of the information in real-time and notice relevant changes. We target transitions between Line charts, Heat maps, and Stream graphs. We conceived seven transitions that modify different properties of the visual elements for each pair of visual idioms, following a novel taxonomy for their conceptualization. To assess the performance of the transitions, we conducted an online user study with 100 participants. Results suggest that animations are indeed better to change between different visualization idioms than abrupt transitions. We also suggest transition techniques for each visualization pair, between those proposed, according to participants' preferences. Lastly, we identify which concepts of our taxonomy were more present in our suggested transitions.
Image-Based Regulation of Mobile Robots Without Pose Measurements

This letter presents an image-based regulation control of nonholonomic mobile robots using a single onboard camera. State-of-the-art approaches depend on pose reconstruction or decomposition of homography for the position-based visual servo. A new image-based switching control scheme with a hierarchical structure is developed. The proposed approach does not depend on pose estimations and works on both planar and non-planar scenes. The dynamics is newly formulated in terms of invariant features to rotation, and a specific invariant feature introduced by Tahri <i>et al.,</i> is implemented in the practice. The applied features can be easily calculated according to at least three feature points. The closed-loop system is proved to be exponentially stable in the Lyapunov sense. The performance is validated through 90 experimental trials in three different environments without artificial markers. Results show the ability of regulation using direct image measurements for auto-charging, picking up goods, and home service. All of the approaches are performed onboard the vehicle.
Librarianship in the Era of Social Media

The chapter presents the symbiotic relationship between library services and social media and its possible implications for the 21st century librarian. The 21st century librarian has witnessed huge changes in the field of library and information science. These changes in the library and information science domain have altered the forms of information and the nature of services but the basic role of the libraries – to cater for the information needs and demands of the users remains. As the popularity of social media is growing exponentially, librarians cannot stand aloof but ensure they exploit the benefits attached to using social media in libraries. With social media, libraries can attract their users and provide improved services while keeping them abreast on latest developments in the libraries. The chapter discussed how social media can be exploited for the benefit of the library clients. The chapter concluded by providing suggestions that will benefit the librarians and libraries to ensure improved symbiotic relationship between library services and social media.
Bilateral Res-Unet for Image Colorization with Limited Data via GANs

Recently the method based on deep learning has made progress in automatic colorization using rich training data, but the colorization model for limited training data still performs unsatisfactorily, resulting in mottled colors and coloring errors in some regions of interest. To solve the above problems, we design a novel Bilateral Res-Unet based on GAN, which is used in generator to transfer color features on both sides of the encoder. The residual connections are then used to improve the ability of color feature transmission and image semantics accuracy. In addition, we redefine a multi-feature semantic perception loss, which constrains the image by assigning different weights to the feature distance after multiple convolution layers of the pretrained network to enhance semantic similarity and make the colored image more vivid. We have implemented comprehensive experiments compared with the recent coloring models, using the PSNR, SSIM, and LPIPS metric. Our method has achieved SOTA results on three limited image datasets.
Mining high utility pattern with negative items in dynamic databases

High utility pattern mining with negative items (HUPMN) has more practical applications because it can process data with negative utility values. But the existing HUPMN algorithms assume that the database is static and it would be very expensive to use these algorithms directly to deal with dynamic databases. To cope with this challenge, an high utility pattern algorithm for mining negative items from an incremental database is proposed for the first time, and an incremental index list structure is designed, which uses index values to quickly access and update the information stored in the list. In addition, A memory reuse strategy is also applied to reduce memory usage. Finally, a HUPMN algorithm based on sliding window was proposed, which can quickly update item information when the window was sliding. Extensive experimental evaluations have been carried out on a variety of data sets, and the results show that the proposed algorithm exhibits excellent performance in terms of running time and memory usage.
Machine-Learning Based Prediction of Next HTTP Request Arrival Time in Adaptive Video Streaming

Continuously monitoring the network activity to proactively recognise possible problems and prevent users QoE degradation is a major concern for network operators, for both mobile radio and home networks. Considering video streaming applications, which generate the majority of overall Internet traffic, monitoring the chunk requests from the video client to the video server is of particular interest, as they not only indicate that a download burst is imminent, but their type (e.g., request of an audio or video chunk) and frequency also allow to estimate which and how much data will be downloaded to the client. In this work, we propose a machine-learning based video streaming traffic monitoring architecture able to i) predict when next uplink request will be issued by the video client and ii) classify the type of next uplink request. We evaluate the system performance on a dataset of more than 900 HTTP adaptive streaming sessions and 15,000 request-response exchanges, where both the predictor of the next request arrival and the request type classifier are fed with lightweight features extracted from encrypted traffic in an online fashion, both in the uplink and downlink directions of the traffic. Results show that i) the system is able to classify the type of a HAS uplink requests with an accuracy greater than 95 % and ii) pipe-lining request type classification and prediction of next request arrival time improves the final prediction performance.